- Email: [email protected]
Multispectral detection of partial discharge in SF6 gas with silicon photomultiplier-based sensor array
Accepted Manuscript Title: Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-based Sensor Array Authors: Ming Ren, Siyun Wang, Jierui Zhou, Tianxin Zhuang, Shujing Yang PII: DOI: Reference:
S0924-4247(18)31176-2 https://doi.org/10.1016/j.sna.2018.09.036 SNA 11011
To appear in:
Sensors and Actuators A
Received date: Revised date: Accepted date:
15-7-2018 22-8-2018 13-9-2018
Please cite this article as: Ren M, Wang S, Zhou J, Zhuang T, Yang S, Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-based Sensor Array, Sensors and amp; Actuators: A. Physical (2018), https://doi.org/10.1016/j.sna.2018.09.036 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-
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based Sensor Array
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Ming Ren*, Jierui Zhou, Tianxin Zhuang, Shujing Yang
Correspondence information: Ming Ren, State Key Laboratory of Electrical Insulation for Power
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Equipment, Xi'an Jiaotong University, 28 Xianning West Road, Beilin District, Xi'an 710049, Shaanxi
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Province, China. Email address: [email protected]
Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-based Sensor Array
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Ming Ren*, Jierui Zhou, Tianxin Zhuang, Shujing Yang
State Key Laboratory of Electrical Insulation for Power Equipment, Xi'an Jiaotong University, Xi'an
author: Email: [email protected]
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*Corresponding
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710049, Shaanxi Province, China.
Highlights
Based on the fact that PDs in SF6 are accompanied with photon emissions, a micro SiPM-based multispectral PD sensor (SMPDS) that combines the PD low light detection and spectral analysis is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed.
By using SMPDS, stochastic PD patterns can be obtained for PD recognition in higher dimensions. The proposed triangle stochastic clustering approach can distinguish the different natures of PDs with a satisfactory discrimination even without conventional phase-resolved statistics.
The superiorities rendered by the sensor in size, voltage bias, sensitivity and interference immunity provide a way for the promotion of the refined optical detection from laboratory scale to in-situ monitoring.
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Abstract
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The upgrading demand on the fault prevention necessitates highly reliable and precise approaches for monitoring partial discharge (PD) which is the essential potential hazards existing in SF6-insulated power system under electric field. In this paper, a micro SiPM-based multispectral PD sensor (SMPDS) is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed. Experiments indicates that SMPDS can efficiently response to various types of PD including point corona, gas spark and surface creepage with significant dissimilarities in the three
spectral bands. The triangle clustering approach based on SMPDS stochastic detection can distinguish the different natures of PD with a satisfactory discrimination even without conventional phase-resolved statistics. This proposed solution provides a way for optical PD in-situ monitoring, circumventing the necessities of anti-interference measure and complex diagnosis algorithm.
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Keywords: Sulfur hexafluoride (SF6), silicon photomultiplier, partial discharge, multispectral detection, optical sensor.
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1. Introduction
Sulfur hexafluoride (SF6) gas is widely used as the dielectric medium of HV power equipment, such as gas-insulated switchgear (GIS), gas-insulated transmission line (GIL) and gas-insulated transformer
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(GIL), because of its strong electronegativity and good arc-quenching property. However, SF6-insulation
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is more fragile to the inhomogeneous electric field comparing to air or other non-electronegative gases.
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Partial discharge (PD) caused by the conductive protrusion or particle that is usually formed during processes of manufacture and assembly, can easily develop into complete surface flashover or breakdown,
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resulting in serious electrical insulation failures [1]. Over the years, a variety of PD sensors were
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developed for PD monitoring based on the electromagnetic radiation and acoustic vibration of PD, such as ultra-high frequency (UHF) sensor [2] and acoustic emission (AE) sensor [3], but the complex and indeterminate noises in field application has always posed a significant challenge to the availability of
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PD signal coupling [4]. Based on the fact that PD in SF6 can produce the steady decomposition resultants
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such as SOF2, SO2F2, H2S and SO2 [5] which are formed by the reactions between transient products [6] during PD and small amounts of water and oxygen in SF6, researchers turned to the trace detections on the decomposition products. Thus, a range of approaches that includes Fourier transform infrared spectroscopy (FTIR) [7], optical time domain reflectometry (OTDR) [8] and carbon nanotube-based
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sensors [9] are employed for off-line PD diagnosis. However, PD in gas insulation is a kind of nonequilibrium plasma maintained by local ionization, normally active in a limited region in gas atmosphere and thus the concentrations of the decomposition products are very small, normally in ppm level. The above laboratory instruments can hardly keep the sensitivity and timeliness as they are developed for insitu monitoring. A prolonged duration, normally hours or even days, for the amount of decomposition products to reach a detectable level is needed.
It should be noted that PD propagation involves a range of photon interactions, including photon excitation, de-excitation, photon-ionization and radiative recombination [10], resulting in the specific spectra depending on electron energy distribution. For example, the spectral lines in the range from 670.8 nm to 679.5 nm as well as the range from 687.0 nm to 712.8 nm (2s22p4(3P)3s→2s22p4(3P)3p) demonstrate that the excitation energy level of Fluorine is in the range of 12.7 eV to 14.8 eV [11, 12].
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The spectral components in the specific bands can be deemed as the evidences of transition from streamer discharge to leader discharge because of the different temperatures in their channels [13]. Therefore, the
integral light intensities over the different spectral bands can intrinsically present the status of PD. With
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this concept in mind, we realized a multispectral optical PD detection by employing vacuum
photomultipliers in our previous work [14] and proved its great potential in recognizing multi-source discharge.
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Due to the short duration time and low light intensity of PD in SF6 gas, a high photoelectric
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efficiency and a high time resolution of the photon coupling device are essential for optical PD detection.
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In laboratory study, vacuum PMT and high-speed intensified charge-coupled device (ICCD) are usually employed [15, 16]. However, because of their large size, complicated structure, high driving voltage
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(~kV) and high cost, they are inapplicable for actual PD monitoring. To facilitate the practical use of
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multispectral detection in SF6-insulated power system, we turned to the solid-state photosensitive semiconductor which has experienced a fast development in recent year [17]. The silicon photomultiplier (SiPM) is such a good alternative to vacuum PMT and was proven to have an excellent performance in
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PD detection [14]. Unlike vacuum PMT, SiPM integrates sufficient density of microcells of SPAD unit
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which is developed based on solid-state semiconductor. Each SPAD unit could be activated by a received photon and generate a self-sustained avalanche ionization, which is called Geiger discharge [18]. By integrating the ionization current of SPADs, a SiPM can realize the cumulative photon counting for photon flux as the conventional PMT performs. SiPM is superior in many aspects, such as the high
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quantum efficiency (up to 50%), the broad spectral response (from ultraviolet to near-infrared), the small size of packaging (up to several 1000 SPAD/mm2), the low driving voltage bias (several volts) and the excellent magnetic interference immunity [17], and thus it is deemed as an ideal built-in optical sensor for in-situ PD monitoring. Based on the concept of multispectral PD detection, in this paper a micro SiPM-based multispectral sensor (SMPDS) is devised to promote multispectral PD diagnosis from laboratory scale to practical
application for SF6-insulated system. This paper begins with the physical basis of multispectral diagnosis of PD and the principle of SMPDS; the multispectral pulses and the statistical intensities of different PDs in SF6 coupled by SMPDS are presented. Then a new fault clustering approach is tailored for multispectral stochastic PD detection in order to realize a PD recognition, even in the absence of phase information. The impacts of gas pressure, applied voltage level and discharge polarity on the clustering
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patterns are also discussed. The findings in this work verify the availability of the multispectral PD detection based on SMPDS and unleash a new strategy for the more refined PD diagnosis with a high reliability.
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2. The principle of multispectral PD diagnosis 2.1 The physical basis of multispectral PD detection
PD in gas is a kind of non-equilibrium plasmas dominated by local ionization phenomena and normally
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active in very limited regions in gas atmosphere with the notably high time gradients of electron
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multiplication. Photons are produced and absorbed in a range of collision processes mainly including
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photon excitation, de-excitation, photon-ionization and radiative recombination [10], some of which dominates in the propagations of PD. In the presence of solid dielectric, luminescence is also involved.
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The above photon interactions results in the formation of neutral and ionized excited atomic fragments
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with significant probabilities depending on photon energy. According to the distribution of kinetic energy (or electric field) in space, the PD region can be divided into three ones: ionization-dominated region (where electric field exceeds the critical effective ionization value, i.e. the ratio of ionization coefficient
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to attachment coefficient is above 1, α/η(E/N)>1), attachment-dominated region (where α/η(E/N)<1) and
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drift-diffusion region (where α(E/N)=0). Photons generate in the ionization-dominated and attachmentdominated regions and drive the discharge propagation as well. The cross sections involving photon excitation and photon absorption at a specific wavelength (λ) can be defined as σe,λ and σl,λ respectively,
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thus the net photon generation can be defined in Equation 1.
σe, λ' σe, λ σl , λ
(1)
The probability for photon generation with a certain λ in the PD active region (V) can be derived by
the definition of mean free path as Equation (2) in relation to the corresponding cross section (σe,λ’).
P ( λ) V
kB σ e, λ' nKσ e, λ' n
(2)
where kB is Boltzmann constant, n is the number density of the gas, K is a dimensionless function on defect geometry, gas surface combination and applied field strength. The photons derived from the PD active region will experience absorption and scattering processes in drift-diffusion region before being coupled. A total cross section can be related to light intensity absorbance through the Beer–Lambert law in which the absorbance is proportional to particle concentration, as expressed in
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Aλ Clσ a, λ
(3)
where Aλ is the absorbance at a given wavelength λ, C is the particle concentration as a number density,
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and ℓ is the path length. As the absorbance of the radiation is the logarithm of the reciprocal of the transmittance, so the coupled light intensity of PD at wavelength of λ has the form as
(4)
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I λ nKσe, λ' exp(σ a, λ nl )
The intensity of spectrum line of λ is determined by the gas pressure, photon path length, involved
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cross section and defect structure. Therefore, a PD event excited by a specific defect presents a distinctive
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spectral distribution. Because that PD in SF6 gas is strongly non-equilibrium and stochastic, the
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integrated spectrum of PD over a relatively long time instead of the transient spectrum of a single PD event is more meaningful in statistics. Figure 1 shows the integral spectra of PD excited by a floating
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particle in 0.35 MPa SF6 (Details of the defect models see Sec. 3). It implicates that in addition to light intensity, the spectral distribution can be an accessible intrinsic feature in characterizing the type of PD.
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By using the spectral information a refined PD diagnosis can be approached. For practical use, acquiring the integral light intensities in several wide bands is sufficiently applicable. In this case, the varying ratios
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among the different spectral bands can be deemed as a general indicators independent of intensity quantities and can be used for PD recognition in the absence of phase information of applied voltage. With the concept of multispectral detection and the availability of the SiPM sensor, in this present
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work a micro-sized SiPM array is developed for multispectral optical PD detection. In general, PD is active in a very limited regions in space so it can be considered as a point light source. The emitted photons from the source can be coupled by the different spectrum-sensitive areas in the SiPM array simultaneously. Beyond the conventional PD stochastic analysis (e.g. Phase-resolved PD pattern, PRPD), the multispectral information provide an additional physical dimensionality for PD diagnosis.
2.2 The principle of SMPDS As aforementioned, different PD types have the different spectral distributions over wide ranges. For instance, floating discharge leads to more components in the band below 350 nm and above 610 nm comparing point discharge and surface discharge; the point discharge prevails over other types of discharge in the bands of 460 nm ~ 520 nm and 570 nm ~ 600 nm; and surface discharge has two
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characteristic bands nearby the peaks of 665 nm and 770 nm. According to the discrepancies in the integral spectral distributions, three ion beam sputtering (IBS) optical filter sheets with different spectral responses are chosen for multispectral detection, i.e.
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Band1 filter: 210 nm~410 nm UV shortpass; upper cutoff wavelength of 400 nm; Absolute
transmission (Tabs) > 64%, Average Transmission (Tavg) > 73%; optical depth of 10-3 (OD3). In Band 1, the photons with energy range of 3.02 eV~5.90 eV can be coupled.
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photons with energy range of 1.96 eV~3.1 eV can be coupled.
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Band2 filter: 400 nm~633 nm VIS bandpass; Tabs > 85%, Tavg > 90%; OD3 level. In Band 2, the
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Band3 filter: a 550 nm~1980 nm NIR longpass; lower cutoff wavelength of 550 nm; Tabs > 85%, Tavg > 90%; OD3 level. In Band 3, the photons with energy range of 0.63 eV~2.26 eV can be coupled. Figure
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of the in the three spectral bands.
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2 shows the transmissions of three optical filters and the photon defection efficiencies (PDEs) of SMPDS
With the three optical filter sheets installed in front of three independent SiPM sensors in the array, three regions of different sensitive spectrum are built, as shown in Figure 3a and 3b. The transmissions
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of the three optical filters and photon defection efficiencies (PDEs) of SMPDS of in the three spectral
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bands are shown in Figure 2. Though the transmittances of the filters are different for the three bands but their normalized ratio is a constant. The output data of the preamplifier are fully recorded for further signal processing, as shown in Figure 3b. A power/readout board and a multi-channel preamplifier module are connected to the sensor array for
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driving and signal processing. The constitution of the processing circuit of the sensor is shown in Figure 3d, which is composed of a DC power module, a SiPM array and a preamplifier module. The circuit parameters and the applied overvoltage bias are in accordance with our previous work for a single SiPMbased PD sensor [14]. Because that the size of the reception area of SiPM is much smaller than the detection length, the optical path difference for the three areas of the sensor can be ignored and PD can be seen as a point light
source. Due to the stochastic characteristic of PD, the emission direction and energy distribution of the photons are not consistent for each PD event and thus a stochastic detection in a relatively long time is needed for acquiring the statistical characteristic of PD. Therefore, the occurrence time and the corresponding light intensity of each light pulse read out from the signal channel are recorded as (tk, Ik). As shown in Figure 4, to ensure an high pulse resolution of the stochastic detection, the threshold line
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for each channel is set just above the background device noise and the time window length (T) is set between the maximum pulse duration time (tw) and the minimum time interval between successive PDs
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(tl).
3. Experiment method
To perform the multispectral PD detection, a built-in SMPDS is installed through an epoxy flange
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aiming to the PD source in the test chamber. A high frequency current transformer (HFCT; Pearson 6585;
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400 Hz–250 MHz) is used as a supplementary for optical detection and is installed around the grounding
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wire of the defect model, and a 1500 pF coupling capacitor is connected in parallel with the test chamber. A 150 kV/200 kVA no-corona 50 Hz AC transformer is used as the high voltage power supply. The
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outputs of HFCT and SMPDS are recorded by a 6-channel PD recorder simultaneously. A high-speed
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digital oscilloscope (DSO) (Lecroy; 600 MHz; 10 GS/s) are used to observe the real-time current and light pulses. The arrangement of the PD measurement is shown in Figure 5a. According to the distributions and involved dielectric interfaces of the insulation defects in SF 6 gas-
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insulated system, three typical types of PD are employed as the test objects, i.e., the point discharge
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caused by a fixed metal protrusion on a conductor, the floating discharge caused by a floating metal particle near the electrode and the creeping discharge caused by a triple joint point of gas, conductor and insulator. For ease of description, the three types of PD are named “point discharge”, “floating discharge” and “surface discharge” for short, respectively. The corresponding artificial defect models are
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manufactured as shown in Figure 5b. Each defect model can be placed in a stainless-steel test chamber which can withstand a voltage up to 150 kV (peak) and a gas pressure up to 1.0 MPa.
4. Experiment result and analysis 4.1 PD light detection in multispectral bands Point discharge (See Figure 6a) presents a significant polarity effect. The PD charge and light intensity
of positive point discharge are considerably greater than those of negative discharge (e.g. as applied voltage is about 1.7PDIV, the light intensity (PD charge) of the positive discharge will exceed 3.0 a.u. (33 pC), while it is only 0.2 a.u. (3.1 pC) for negative discharge). Meanwhile the PD occurrence frequency shows the opposite result (e.g. 1.7 PDIV, 21/cyc. for positive discharge, while only 0.35/cyc. for negative discharge). The statistical result (See Figure 6b) clearly indicates that the relationship of size
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between the integral light intensity in Band 1 and that in Band 2 is opposite for the different polarity discharges due to their different driving mechanisms. Floating discharges occur in rising and falling edges of AC voltage, featured as relatively great intensity (13.5 a.u.) and relatively great time interval
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(several hundreds of μs), as shown in Figure 6c. The polarity effect is not obvious, but the ratio among the integral intensities in the three bands present some differences for positive discharge and negative
discharge, as shown in Figure 6d. With regards to the surface discharges, they tend to occur at the rising
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edges of the AC voltage with a wide dynamic range of intensity. The light emission in Band 1 is dominant
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is different for the different polarities of discharge.
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for both polarities of the surface discharge, but the ratio among the integral intensities of the three bands
The spectral features presented by the different types of PD are essentially relative to the proportions
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of the activated collision processes involving generation and absorption of photons, which could be
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described by the different discharge mechanisms. For example, point discharge is normally driven by Townsend discharge or streamer discharge in relatively low degrees of ionization [19]; Surface discharge is driven not only by the ionization in gas but also the secondary electron emission from the insulator
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surface [20]; Floating discharge is a kind of spark discharge which is fully developed with the complete
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discharge channels, thus yielding the intensive light emissions [21]. These different inherent processes are also inhibited by the multispectral features of PD. For example, the light emission in Band 2 is dominant for the case of floating discharge, while that in Band 1 is dominant for surface discharge; For point discharge, the primary component of the light emission even is determined by the polarity of the
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applied voltage. With this validated concept, a new PD diagnosis strategy emerges, i.e., the ratio among the light intensities in the multispectral bands can be used as the monitoring object for an advanced diagnosis of PD in real time. The number distributions of the light pulses in the three bands with light intensity are shown in Figure 7. It indicates that for all the cases of defects, the light pulses coupled in Band 3 concentratively distribute in a relatively low range of light intensity, while the pulses in Band 1 and Band 2 dispersedly distribute
in a relatively high range of light intensity. For floating discharge, the light pulses coupled in the three bands distributes in three range of light intensity without any overlaps, as shown in Figure 7b. The results of log-normal distribution fitting validates that the mean values of the pulses in the three bands have significant differences not only in the type of defect but also in the polarity of discharge. These differentiated features exist and almost remain unchanged with variation of the waveform of applied
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voltage, which can be uses as the basis for multispectral diagnosis.
4.2 The ratio among the multispectral stochastic PD pulses
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Although PD occurs with the significant randomicity in magnitude as well as in time interval, its statistical characteristic can be revealed by some stochastic PD analysis. Phase-resolved PD (PRPD) pattern is a commonly used approach for PD diagnosis of AC voltage power system. The signal
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processing procedure is briefly described as follows.
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i. Convert the occurrence time of each PD (ti) into the phase angle degree (φi) in the same cycle of AC
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applied voltage by using
where T is the period time of a AC cycle.
(5)
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i 2 (ti T 1 ti T 1 )
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ii. Determine the length of window (l) according to the PD intensity in unit time, and count the number
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of PDs in the i-th window (Ni) and calculate the average magnitude in the i-th window (Ii) by using
Ii
1 Nj
Ni
I j 1
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(6)
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Then the PRPD pattern can be drawn by plotting each point (φi, Ii, Ni) recorded over a certain time in a fixed cycle of applied voltage. However, with regards to HVDC system or the situation that the synchronous phase signal of the applied voltage is unachievable in practice, this PRPD clustering is no
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longer applicable. Therefore, in this paper, a new clustering analysis approach is proposed based on the multispectral stochastic detection which is independent of phase information and is more intrinsic in characterizing the behaviors of PD. Firstly, the average magnitudes of the light pulses in the three bands in the i-th time window is recorded as IB1,i IB2,i and IB3,i, by using Equa. (6). Then, the point of each window (i, IB1,i, IB2,i, IB3,i) is replotted as (i, xi, yi) in a triangular map by normalizing the ratio among the IB1,i IB2,i and IB3,i in a X-Y plane. The
triangle transformation can be implemented by using
xi I B1,i ( I B1,i I B 2,i I B 3,i )1 3 I B 2,i ( I B1,i I B 2,i I B 3,i )1 yi 2
(7)
Figure 8 shows the Pseudo-color ternary map of the different types of PD with the information of the distribution density. The positive and negative PD are drawn separately in the same ternary map.
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As shown in Figure 8a, at gas pressure of 0.1 MPa, the positive point discharges distribute in the relatively wide region close to Band 2 and the negative discharges are highly clustered in a relatively
and negative discharges shift to the region close to Band 2 slightly.
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narrow region close to Band 1. At gas pressure is improved to 0.3 MPa, both the distributions of positive
Comparing to point discharge, floating discharges distribute more intensively in the triangular map,
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as shown in Figure 8b. At gas pressure of 0.1MPa, the distributions of the negative PD and positive PD overlap to a certain extent and both of them incline to regions of Band 1 and Band 2, as shown in Figure
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8c; At 0.3 MPa, the negative PD and the positive almost completely overlap and both of them shift
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slightly to region of Band 3, as shown in Figure 8d. In general, the component of Band 3 in total light
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emissions of floating discharge is comparatively great.
For surface discharge, the positive discharge distribution is completely overlapped by negative
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distribution at 0.1 MPa. Both of the positive discharges and the negatives distribute in a concentrated area that significantly close to region of Band 1, as shown in Figure 8e. As gas pressure increases to 0.3
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MPa, both the distributions of negative and positive discharges experience an obvious shifting to the region of Band 2, as shown in Figure 8f. It means that light over Band 1 dominates in the total component
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at relatively low gas pressure, while at high pressure, light over Band 2 dominates.
4.3 General distribution of PD in multispectral triangle map
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The proportions of the integrated light intensities of three spectral bands can provide valuable
reference for the refined PD diagnosis, especially for PD recognition and risk assessment. To implement this idea, the results of all the types of PD are processed by the same approach as aforementioned and are drawn in the same triangular coordinate, as shown in Figure 9. It is found that at gas pressure of 0.1 MPa the results of three types of PD separately distribute in different regions in the triangular map and their statistical mean values locate in distinctly different areas. As gas pressure increases to 0.3 MPa, all
the distributions shift, but are still highly desirable to present a high degree of recognition. By comparing the results drawn in Figure 9a and 9b, it demonstrates that the triangular distribution of surface discharge is more sensitive to the change of gas pressure, e.g. the mean values of surface discharge in the three bands shift from (0.6571, 0.2983, 0.0452) to (0.1473, 0.8245, 0.0292). For details, Table I summarizes the boundaries of 95% confidence intervals and mean values (log-normal distribution) of the distributions
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for the three types of PD in the triangle map. Compared to conventional phase-resolved PD diagnosis, this method can achieve the essential characteristics of PD more directly and is independent of the phase information of applied voltage, so it
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can be effectively applied to DC power system. Moreover, multispectral diagnosis can directly distinguish PD from two or more types of defect at the same time without any additional time-domain or frequency-domain separation signal processing that is necessary for conventional PD diagnosis.
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Due to the small size and of SiPM, the SMPDS can be developed with more sensitive spectral bands
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to achieve more intrinsic features of PD emissions. It is believed that this new strategy can unleash the
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great potential of optical PD detection for a range of gas-insulated system and promote the multispectral
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analysis from the laboratory to the engineering practice at a high feasibility and a low cost.
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5 Conclusions
In summary, the multispectral optical PD detection is implemented with a SiPM-based multispectral sensor which paves a way to an optical solution for a highly believable and interference immunity PD
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monitoring. The distinguishable features of PD emissions in three spectral bands have been obtained for
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three different types of PD including point discharge, floating discharge and surface discharge, as well as the different polarities of discharge. Bases on multispectral stochastic PD detection, a triangular clustering approach has been tailored for PD recognition which is applicable without any phase-resolved statistics. It demonstrated that the stochastic multispectral results of the three typical PDs distribute in
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three different regions in the triangular map. The distributions are sensitive to the change of gas pressure of SF6 and slightly impacted by the value of the applied voltage in our investigated range. Acknowledgement This work was supported by the National Key Research and Development Program of China (Grant Nos. 2018YFB0904400 and 2017YFB0902705) and the National Natural Science Foundation of China (Grant Nos.51507130 and 51877171).
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[16] Lemzadmi, Bonifaci, Denat, Nemamcha. Light emission from corona discharge in SF6 and SF6/N2 gas mixtures at high pressure. European Physical Journal Applied Physics, 2007, 33(3): 213-219. [17] Eckert P, Schultz-Coulon H C, Shen W, Stamen R, Tadday A. Characterisation studies of silicon photomultipliers. Nuclear Inst & Methods in Physics Research A, 2010, 620(2): 217-226. [18] Gamal A, Paul B, Michael C, Roland H, Johann M, Herbert O, Ken S. Application of Geiger-mode photosensors in Cherenkov
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detectors[J]. Nuclear Instruments & Methods in Physics Research, 2011, 639(1):107-110. [19] Bartnikas R, Novak J P. On the spark to pseudoglow and glow transition mechanism and discharge detectability. IEEE Transactions on Electrical Insulation, 1992, 27(1): 3-14.
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[20] Blackmore P, Birtwhistle D. Surface discharges on polymeric insulator shed surfaces. IEEE Transactions on Dielectrics & Electrical Insulation, 1997, 4(2): 210-217.
[21] Negara Y, Yaji K, Imasaka K, Hayashi N. AC particle-triggered corona discharge in low pressure SF6 gas. IEEE Transactions
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on Dielectrics & Electrical Insulation, 2007, 14(1): 91-100.
Author Biography
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Ming Ren was born in Shaanxi Province, China, in 1987. He received the B.S. and Ph.D. degrees in electrical engineering from Xi’an Jiaotong University in 2009 and 2013. Currently, he works as an Associated Professor in HV Technology Institute in Xi'an Jiaotong University. His present research works are application of optical sensor in plasma diagnosis, risk assessment of UHV/EHV power equipment, insulation status diagnosis and advanced fault detection for power system. He is the corresponding author of this paper. Siyun Wang wan born in Hubei Province, China, in 1996. He received a bachelor’s degree in electrical engineering from Xi’an Jiaotong University in 2018. She is currently a graduate student pursuing the Doctoral Degree in HV Technology Insititute of Xi’an Jiaotong University. Her major is HV engineering and his main research interests are solid-state sensors and advanced gas and solid dielectrics. Jierui Zhou was born in Shanxi Province, China, in 1993. He received the B.S. degree in electrical engineering from Xi’an Jiaotong University in 2016. Then he was admitted to Xi'an Jiaotong University as a graduate student majoring in power engineering. His present works are concerned with ultra-weak light detection, optical sensor and diagnosis on HV power equipment. Tianxin Zhuang wan born in Fujian Province, China, in 1995. He received a bachelor’s degree in electrical engineering from Xi’an Jiaotong University in 2017. He is currently a graduate student in HV Technology Insititute of Xi’an Jiaotong University. His major is HV engineering and his main research interests are the partial discharge mechanism of both gas and solid dielectrics and the insulation status assessment technology. Shujing Yang was born in Jiangxi Province, China, in 1996. She received the B.S degrees in electrical engineering from Xi’an Jiaotong University in 2017. And she is pursuing the M.S degree in electrical engineering in Xi’an Jiaotong University. Her interests are partial discharge diagnosis and optical applications in partial discharge measurement. The upgrading demand on the fault prevention necessitates highly reliable and precise approaches for monitoring partial discharge (PD) which is the essential potential hazards existing in SF6-insulated power
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system under electric field. In this paper, a micro SiPM-based multispectral PD sensor (SMPDS) is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed. Experiments indicates that SMPDS can efficiently response to various types
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of PD including point corona, gas spark and surface creepage with significant dissimilarities in the three spectral bands. The triangle clustering approach based on SMPDS stochastic detection can distinguish the different natures of PD with a satisfactory discrimination even without conventional phase-resolved statistics. This proposed solution provides a way for optical PD in-situ monitoring, circumventing the
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necessities of anti-interference measure and complex diagnosis algorithm.
Figure captions Fig. 1.
Spectral distributions of floating discharge in 0.35MPa SF6 and PDE of SiPM over the same spectral range.
Fig. 2
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The PDE line of the SiPM is drawn according to the datasheet of SensL-MicroFJ-30035-TSV.
Transmissions of the three optical filters and photon defection efficiencies (PDEs) of SMPDS of in the three
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spectral bands.
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The principles of SMPDS. (a) Picture of the sensor; (b) Configuration of the sensor; (c) Signal processing;
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Fig. 3.
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(d) Circuit of the sensor. The specific band ranges of B1, B2 and B3 in this figure are explained in Sec. 2.2.
Fig. 4
The stochastic pulse data record method.
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Fig. 5 Schematic diagram of PD detection. (a) PD measurement system. R is the current limit resistor (1MΩ), Cp is the coupling capacitor for HFCT measurement (1500pF) and T is a no-corona 50Hz 150kV/200kVA power supply;
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(b) Configurations of the artificial PD defect models. (i) Metal needle in a quasi-uniform background field; (ii)
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Floating-potential metal particle; (ii) Rod electrode on an epoxy insulator.
Fig. 6
Light pulses and integrated phase-resolved light intensities in the three bands detected by SMPDS. The
results presented were obtained at a gas pressure of 0.1MPa and at a medium applied voltage of about 1.5 PDIV. (a)
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and (b), point discharge; (c) and (d), floating discharge; (e) and (f), surface discharge.
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Fig. 7
Number distributions of the light pulses in the three bands with light intensity. The results presented were
obtained at a gas pressure of 0.1MPa and at a medium applied voltage of about 1.5 PDIV.
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Triangle distributions of the positive and negative PD points detected by SMPDS. (a) Point discharges, (b)
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Fig. 8
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Floating discharges, (c) Surface discharges.
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Fig. 9
General clustering distributions of the different types of PD in triangular coordinates. (a) Gas pressure of
0.1MPa, (b) Gas pressure of 0.3MPa.
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Tables TABLE I 95% CONFIDENCE INTERVALS AND MEAN VALUES (LOG-NORMAL DISTRIBUTION) OF THE DISTRIBUTIONS IN TRIANGLE MAP (MPa)
Confidence intervals (95%)
Mean values (μ)
Band 1: [0.4436, 0.4481]
Band 1:0.4462
Band 2: [0.4751, 0.4783]
Band 2:0.4772
Band 3: [0.0756, 0.0773]
Band 3:0.0763
Band 1: [0.4251, 0.4272]
Band 1:0.4261
Band 2: [0.5116, 0.5164]
Band 2:0.5144
Band 3: [0.0575, 0.0615]
Band 3:0.0612
Band 1: [0.3611, 0.3633]
Band 1:0.3621
0.1 Point 0.3
0.1
Band 2: [0.5591, 0.5606] Band 3: [0.0788, 0.0811]
Float
Band 1: [0.2391, 0.2404] Band 2: [0.5867, 0.5882]
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0.3
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Gas pressure
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PD type
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Band 2:0.5878
Band 1: [0.6558, 0.6582]
Band 1:0.6571
Band 2: [0.2966, 0.2992]
Band 2:0.2983
Band 3: [0.0441, 0.0454]
Band 3:0.0452
Band 1: [0.1455, 0.1481]
Band 1:0.1473
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0.3
Band 1:0.2402 Band 3:0.1720
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Surface
Band 3:0.0802
Band 3: [0.1721, 0.1734]
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0.1
Band 2:0.5601
Band 2: [0.8231, 0.8256]
Band 2:0.8245
Band 3: [0.0281, 0.0296]
Band 3:0.0292
S0924-4247(18)31176-2 https://doi.org/10.1016/j.sna.2018.09.036 SNA 11011
To appear in:
Sensors and Actuators A
Received date: Revised date: Accepted date:
15-7-2018 22-8-2018 13-9-2018
Please cite this article as: Ren M, Wang S, Zhou J, Zhuang T, Yang S, Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-based Sensor Array, Sensors and amp; Actuators: A. Physical (2018), https://doi.org/10.1016/j.sna.2018.09.036 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-
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based Sensor Array
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Ming Ren*, Jierui Zhou, Tianxin Zhuang, Shujing Yang
Correspondence information: Ming Ren, State Key Laboratory of Electrical Insulation for Power
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Equipment, Xi'an Jiaotong University, 28 Xianning West Road, Beilin District, Xi'an 710049, Shaanxi
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Province, China. Email address: [email protected]
Multispectral Detection of Partial Discharge in SF6 Gas with Silicon Photomultiplier-based Sensor Array
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Ming Ren*, Jierui Zhou, Tianxin Zhuang, Shujing Yang
State Key Laboratory of Electrical Insulation for Power Equipment, Xi'an Jiaotong University, Xi'an
author: Email: [email protected]
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*Corresponding
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710049, Shaanxi Province, China.
Highlights
Based on the fact that PDs in SF6 are accompanied with photon emissions, a micro SiPM-based multispectral PD sensor (SMPDS) that combines the PD low light detection and spectral analysis is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed.
By using SMPDS, stochastic PD patterns can be obtained for PD recognition in higher dimensions. The proposed triangle stochastic clustering approach can distinguish the different natures of PDs with a satisfactory discrimination even without conventional phase-resolved statistics.
The superiorities rendered by the sensor in size, voltage bias, sensitivity and interference immunity provide a way for the promotion of the refined optical detection from laboratory scale to in-situ monitoring.
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Abstract
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The upgrading demand on the fault prevention necessitates highly reliable and precise approaches for monitoring partial discharge (PD) which is the essential potential hazards existing in SF6-insulated power system under electric field. In this paper, a micro SiPM-based multispectral PD sensor (SMPDS) is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed. Experiments indicates that SMPDS can efficiently response to various types of PD including point corona, gas spark and surface creepage with significant dissimilarities in the three
spectral bands. The triangle clustering approach based on SMPDS stochastic detection can distinguish the different natures of PD with a satisfactory discrimination even without conventional phase-resolved statistics. This proposed solution provides a way for optical PD in-situ monitoring, circumventing the necessities of anti-interference measure and complex diagnosis algorithm.
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Keywords: Sulfur hexafluoride (SF6), silicon photomultiplier, partial discharge, multispectral detection, optical sensor.
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1. Introduction
Sulfur hexafluoride (SF6) gas is widely used as the dielectric medium of HV power equipment, such as gas-insulated switchgear (GIS), gas-insulated transmission line (GIL) and gas-insulated transformer
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(GIL), because of its strong electronegativity and good arc-quenching property. However, SF6-insulation
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is more fragile to the inhomogeneous electric field comparing to air or other non-electronegative gases.
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Partial discharge (PD) caused by the conductive protrusion or particle that is usually formed during processes of manufacture and assembly, can easily develop into complete surface flashover or breakdown,
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resulting in serious electrical insulation failures [1]. Over the years, a variety of PD sensors were
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developed for PD monitoring based on the electromagnetic radiation and acoustic vibration of PD, such as ultra-high frequency (UHF) sensor [2] and acoustic emission (AE) sensor [3], but the complex and indeterminate noises in field application has always posed a significant challenge to the availability of
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PD signal coupling [4]. Based on the fact that PD in SF6 can produce the steady decomposition resultants
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such as SOF2, SO2F2, H2S and SO2 [5] which are formed by the reactions between transient products [6] during PD and small amounts of water and oxygen in SF6, researchers turned to the trace detections on the decomposition products. Thus, a range of approaches that includes Fourier transform infrared spectroscopy (FTIR) [7], optical time domain reflectometry (OTDR) [8] and carbon nanotube-based
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sensors [9] are employed for off-line PD diagnosis. However, PD in gas insulation is a kind of nonequilibrium plasma maintained by local ionization, normally active in a limited region in gas atmosphere and thus the concentrations of the decomposition products are very small, normally in ppm level. The above laboratory instruments can hardly keep the sensitivity and timeliness as they are developed for insitu monitoring. A prolonged duration, normally hours or even days, for the amount of decomposition products to reach a detectable level is needed.
It should be noted that PD propagation involves a range of photon interactions, including photon excitation, de-excitation, photon-ionization and radiative recombination [10], resulting in the specific spectra depending on electron energy distribution. For example, the spectral lines in the range from 670.8 nm to 679.5 nm as well as the range from 687.0 nm to 712.8 nm (2s22p4(3P)3s→2s22p4(3P)3p) demonstrate that the excitation energy level of Fluorine is in the range of 12.7 eV to 14.8 eV [11, 12].
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The spectral components in the specific bands can be deemed as the evidences of transition from streamer discharge to leader discharge because of the different temperatures in their channels [13]. Therefore, the
integral light intensities over the different spectral bands can intrinsically present the status of PD. With
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this concept in mind, we realized a multispectral optical PD detection by employing vacuum
photomultipliers in our previous work [14] and proved its great potential in recognizing multi-source discharge.
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Due to the short duration time and low light intensity of PD in SF6 gas, a high photoelectric
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efficiency and a high time resolution of the photon coupling device are essential for optical PD detection.
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In laboratory study, vacuum PMT and high-speed intensified charge-coupled device (ICCD) are usually employed [15, 16]. However, because of their large size, complicated structure, high driving voltage
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(~kV) and high cost, they are inapplicable for actual PD monitoring. To facilitate the practical use of
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multispectral detection in SF6-insulated power system, we turned to the solid-state photosensitive semiconductor which has experienced a fast development in recent year [17]. The silicon photomultiplier (SiPM) is such a good alternative to vacuum PMT and was proven to have an excellent performance in
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PD detection [14]. Unlike vacuum PMT, SiPM integrates sufficient density of microcells of SPAD unit
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which is developed based on solid-state semiconductor. Each SPAD unit could be activated by a received photon and generate a self-sustained avalanche ionization, which is called Geiger discharge [18]. By integrating the ionization current of SPADs, a SiPM can realize the cumulative photon counting for photon flux as the conventional PMT performs. SiPM is superior in many aspects, such as the high
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quantum efficiency (up to 50%), the broad spectral response (from ultraviolet to near-infrared), the small size of packaging (up to several 1000 SPAD/mm2), the low driving voltage bias (several volts) and the excellent magnetic interference immunity [17], and thus it is deemed as an ideal built-in optical sensor for in-situ PD monitoring. Based on the concept of multispectral PD detection, in this paper a micro SiPM-based multispectral sensor (SMPDS) is devised to promote multispectral PD diagnosis from laboratory scale to practical
application for SF6-insulated system. This paper begins with the physical basis of multispectral diagnosis of PD and the principle of SMPDS; the multispectral pulses and the statistical intensities of different PDs in SF6 coupled by SMPDS are presented. Then a new fault clustering approach is tailored for multispectral stochastic PD detection in order to realize a PD recognition, even in the absence of phase information. The impacts of gas pressure, applied voltage level and discharge polarity on the clustering
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patterns are also discussed. The findings in this work verify the availability of the multispectral PD detection based on SMPDS and unleash a new strategy for the more refined PD diagnosis with a high reliability.
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2. The principle of multispectral PD diagnosis 2.1 The physical basis of multispectral PD detection
PD in gas is a kind of non-equilibrium plasmas dominated by local ionization phenomena and normally
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active in very limited regions in gas atmosphere with the notably high time gradients of electron
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multiplication. Photons are produced and absorbed in a range of collision processes mainly including
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photon excitation, de-excitation, photon-ionization and radiative recombination [10], some of which dominates in the propagations of PD. In the presence of solid dielectric, luminescence is also involved.
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The above photon interactions results in the formation of neutral and ionized excited atomic fragments
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with significant probabilities depending on photon energy. According to the distribution of kinetic energy (or electric field) in space, the PD region can be divided into three ones: ionization-dominated region (where electric field exceeds the critical effective ionization value, i.e. the ratio of ionization coefficient
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to attachment coefficient is above 1, α/η(E/N)>1), attachment-dominated region (where α/η(E/N)<1) and
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drift-diffusion region (where α(E/N)=0). Photons generate in the ionization-dominated and attachmentdominated regions and drive the discharge propagation as well. The cross sections involving photon excitation and photon absorption at a specific wavelength (λ) can be defined as σe,λ and σl,λ respectively,
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thus the net photon generation can be defined in Equation 1.
σe, λ' σe, λ σl , λ
(1)
The probability for photon generation with a certain λ in the PD active region (V) can be derived by
the definition of mean free path as Equation (2) in relation to the corresponding cross section (σe,λ’).
P ( λ) V
kB σ e, λ' nKσ e, λ' n
(2)
where kB is Boltzmann constant, n is the number density of the gas, K is a dimensionless function on defect geometry, gas surface combination and applied field strength. The photons derived from the PD active region will experience absorption and scattering processes in drift-diffusion region before being coupled. A total cross section can be related to light intensity absorbance through the Beer–Lambert law in which the absorbance is proportional to particle concentration, as expressed in
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Aλ Clσ a, λ
(3)
where Aλ is the absorbance at a given wavelength λ, C is the particle concentration as a number density,
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and ℓ is the path length. As the absorbance of the radiation is the logarithm of the reciprocal of the transmittance, so the coupled light intensity of PD at wavelength of λ has the form as
(4)
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I λ nKσe, λ' exp(σ a, λ nl )
The intensity of spectrum line of λ is determined by the gas pressure, photon path length, involved
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cross section and defect structure. Therefore, a PD event excited by a specific defect presents a distinctive
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spectral distribution. Because that PD in SF6 gas is strongly non-equilibrium and stochastic, the
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integrated spectrum of PD over a relatively long time instead of the transient spectrum of a single PD event is more meaningful in statistics. Figure 1 shows the integral spectra of PD excited by a floating
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particle in 0.35 MPa SF6 (Details of the defect models see Sec. 3). It implicates that in addition to light intensity, the spectral distribution can be an accessible intrinsic feature in characterizing the type of PD.
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By using the spectral information a refined PD diagnosis can be approached. For practical use, acquiring the integral light intensities in several wide bands is sufficiently applicable. In this case, the varying ratios
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among the different spectral bands can be deemed as a general indicators independent of intensity quantities and can be used for PD recognition in the absence of phase information of applied voltage. With the concept of multispectral detection and the availability of the SiPM sensor, in this present
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work a micro-sized SiPM array is developed for multispectral optical PD detection. In general, PD is active in a very limited regions in space so it can be considered as a point light source. The emitted photons from the source can be coupled by the different spectrum-sensitive areas in the SiPM array simultaneously. Beyond the conventional PD stochastic analysis (e.g. Phase-resolved PD pattern, PRPD), the multispectral information provide an additional physical dimensionality for PD diagnosis.
2.2 The principle of SMPDS As aforementioned, different PD types have the different spectral distributions over wide ranges. For instance, floating discharge leads to more components in the band below 350 nm and above 610 nm comparing point discharge and surface discharge; the point discharge prevails over other types of discharge in the bands of 460 nm ~ 520 nm and 570 nm ~ 600 nm; and surface discharge has two
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characteristic bands nearby the peaks of 665 nm and 770 nm. According to the discrepancies in the integral spectral distributions, three ion beam sputtering (IBS) optical filter sheets with different spectral responses are chosen for multispectral detection, i.e.
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Band1 filter: 210 nm~410 nm UV shortpass; upper cutoff wavelength of 400 nm; Absolute
transmission (Tabs) > 64%, Average Transmission (Tavg) > 73%; optical depth of 10-3 (OD3). In Band 1, the photons with energy range of 3.02 eV~5.90 eV can be coupled.
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photons with energy range of 1.96 eV~3.1 eV can be coupled.
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Band2 filter: 400 nm~633 nm VIS bandpass; Tabs > 85%, Tavg > 90%; OD3 level. In Band 2, the
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Band3 filter: a 550 nm~1980 nm NIR longpass; lower cutoff wavelength of 550 nm; Tabs > 85%, Tavg > 90%; OD3 level. In Band 3, the photons with energy range of 0.63 eV~2.26 eV can be coupled. Figure
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of the in the three spectral bands.
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2 shows the transmissions of three optical filters and the photon defection efficiencies (PDEs) of SMPDS
With the three optical filter sheets installed in front of three independent SiPM sensors in the array, three regions of different sensitive spectrum are built, as shown in Figure 3a and 3b. The transmissions
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of the three optical filters and photon defection efficiencies (PDEs) of SMPDS of in the three spectral
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bands are shown in Figure 2. Though the transmittances of the filters are different for the three bands but their normalized ratio is a constant. The output data of the preamplifier are fully recorded for further signal processing, as shown in Figure 3b. A power/readout board and a multi-channel preamplifier module are connected to the sensor array for
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driving and signal processing. The constitution of the processing circuit of the sensor is shown in Figure 3d, which is composed of a DC power module, a SiPM array and a preamplifier module. The circuit parameters and the applied overvoltage bias are in accordance with our previous work for a single SiPMbased PD sensor [14]. Because that the size of the reception area of SiPM is much smaller than the detection length, the optical path difference for the three areas of the sensor can be ignored and PD can be seen as a point light
source. Due to the stochastic characteristic of PD, the emission direction and energy distribution of the photons are not consistent for each PD event and thus a stochastic detection in a relatively long time is needed for acquiring the statistical characteristic of PD. Therefore, the occurrence time and the corresponding light intensity of each light pulse read out from the signal channel are recorded as (tk, Ik). As shown in Figure 4, to ensure an high pulse resolution of the stochastic detection, the threshold line
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for each channel is set just above the background device noise and the time window length (T) is set between the maximum pulse duration time (tw) and the minimum time interval between successive PDs
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(tl).
3. Experiment method
To perform the multispectral PD detection, a built-in SMPDS is installed through an epoxy flange
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aiming to the PD source in the test chamber. A high frequency current transformer (HFCT; Pearson 6585;
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400 Hz–250 MHz) is used as a supplementary for optical detection and is installed around the grounding
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wire of the defect model, and a 1500 pF coupling capacitor is connected in parallel with the test chamber. A 150 kV/200 kVA no-corona 50 Hz AC transformer is used as the high voltage power supply. The
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outputs of HFCT and SMPDS are recorded by a 6-channel PD recorder simultaneously. A high-speed
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digital oscilloscope (DSO) (Lecroy; 600 MHz; 10 GS/s) are used to observe the real-time current and light pulses. The arrangement of the PD measurement is shown in Figure 5a. According to the distributions and involved dielectric interfaces of the insulation defects in SF 6 gas-
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insulated system, three typical types of PD are employed as the test objects, i.e., the point discharge
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caused by a fixed metal protrusion on a conductor, the floating discharge caused by a floating metal particle near the electrode and the creeping discharge caused by a triple joint point of gas, conductor and insulator. For ease of description, the three types of PD are named “point discharge”, “floating discharge” and “surface discharge” for short, respectively. The corresponding artificial defect models are
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manufactured as shown in Figure 5b. Each defect model can be placed in a stainless-steel test chamber which can withstand a voltage up to 150 kV (peak) and a gas pressure up to 1.0 MPa.
4. Experiment result and analysis 4.1 PD light detection in multispectral bands Point discharge (See Figure 6a) presents a significant polarity effect. The PD charge and light intensity
of positive point discharge are considerably greater than those of negative discharge (e.g. as applied voltage is about 1.7PDIV, the light intensity (PD charge) of the positive discharge will exceed 3.0 a.u. (33 pC), while it is only 0.2 a.u. (3.1 pC) for negative discharge). Meanwhile the PD occurrence frequency shows the opposite result (e.g. 1.7 PDIV, 21/cyc. for positive discharge, while only 0.35/cyc. for negative discharge). The statistical result (See Figure 6b) clearly indicates that the relationship of size
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between the integral light intensity in Band 1 and that in Band 2 is opposite for the different polarity discharges due to their different driving mechanisms. Floating discharges occur in rising and falling edges of AC voltage, featured as relatively great intensity (13.5 a.u.) and relatively great time interval
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(several hundreds of μs), as shown in Figure 6c. The polarity effect is not obvious, but the ratio among the integral intensities in the three bands present some differences for positive discharge and negative
discharge, as shown in Figure 6d. With regards to the surface discharges, they tend to occur at the rising
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edges of the AC voltage with a wide dynamic range of intensity. The light emission in Band 1 is dominant
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is different for the different polarities of discharge.
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for both polarities of the surface discharge, but the ratio among the integral intensities of the three bands
The spectral features presented by the different types of PD are essentially relative to the proportions
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of the activated collision processes involving generation and absorption of photons, which could be
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described by the different discharge mechanisms. For example, point discharge is normally driven by Townsend discharge or streamer discharge in relatively low degrees of ionization [19]; Surface discharge is driven not only by the ionization in gas but also the secondary electron emission from the insulator
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surface [20]; Floating discharge is a kind of spark discharge which is fully developed with the complete
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discharge channels, thus yielding the intensive light emissions [21]. These different inherent processes are also inhibited by the multispectral features of PD. For example, the light emission in Band 2 is dominant for the case of floating discharge, while that in Band 1 is dominant for surface discharge; For point discharge, the primary component of the light emission even is determined by the polarity of the
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applied voltage. With this validated concept, a new PD diagnosis strategy emerges, i.e., the ratio among the light intensities in the multispectral bands can be used as the monitoring object for an advanced diagnosis of PD in real time. The number distributions of the light pulses in the three bands with light intensity are shown in Figure 7. It indicates that for all the cases of defects, the light pulses coupled in Band 3 concentratively distribute in a relatively low range of light intensity, while the pulses in Band 1 and Band 2 dispersedly distribute
in a relatively high range of light intensity. For floating discharge, the light pulses coupled in the three bands distributes in three range of light intensity without any overlaps, as shown in Figure 7b. The results of log-normal distribution fitting validates that the mean values of the pulses in the three bands have significant differences not only in the type of defect but also in the polarity of discharge. These differentiated features exist and almost remain unchanged with variation of the waveform of applied
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voltage, which can be uses as the basis for multispectral diagnosis.
4.2 The ratio among the multispectral stochastic PD pulses
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Although PD occurs with the significant randomicity in magnitude as well as in time interval, its statistical characteristic can be revealed by some stochastic PD analysis. Phase-resolved PD (PRPD) pattern is a commonly used approach for PD diagnosis of AC voltage power system. The signal
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processing procedure is briefly described as follows.
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i. Convert the occurrence time of each PD (ti) into the phase angle degree (φi) in the same cycle of AC
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applied voltage by using
where T is the period time of a AC cycle.
(5)
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i 2 (ti T 1 ti T 1 )
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ii. Determine the length of window (l) according to the PD intensity in unit time, and count the number
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of PDs in the i-th window (Ni) and calculate the average magnitude in the i-th window (Ii) by using
Ii
1 Nj
Ni
I j 1
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(6)
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Then the PRPD pattern can be drawn by plotting each point (φi, Ii, Ni) recorded over a certain time in a fixed cycle of applied voltage. However, with regards to HVDC system or the situation that the synchronous phase signal of the applied voltage is unachievable in practice, this PRPD clustering is no
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longer applicable. Therefore, in this paper, a new clustering analysis approach is proposed based on the multispectral stochastic detection which is independent of phase information and is more intrinsic in characterizing the behaviors of PD. Firstly, the average magnitudes of the light pulses in the three bands in the i-th time window is recorded as IB1,i IB2,i and IB3,i, by using Equa. (6). Then, the point of each window (i, IB1,i, IB2,i, IB3,i) is replotted as (i, xi, yi) in a triangular map by normalizing the ratio among the IB1,i IB2,i and IB3,i in a X-Y plane. The
triangle transformation can be implemented by using
xi I B1,i ( I B1,i I B 2,i I B 3,i )1 3 I B 2,i ( I B1,i I B 2,i I B 3,i )1 yi 2
(7)
Figure 8 shows the Pseudo-color ternary map of the different types of PD with the information of the distribution density. The positive and negative PD are drawn separately in the same ternary map.
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As shown in Figure 8a, at gas pressure of 0.1 MPa, the positive point discharges distribute in the relatively wide region close to Band 2 and the negative discharges are highly clustered in a relatively
and negative discharges shift to the region close to Band 2 slightly.
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narrow region close to Band 1. At gas pressure is improved to 0.3 MPa, both the distributions of positive
Comparing to point discharge, floating discharges distribute more intensively in the triangular map,
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as shown in Figure 8b. At gas pressure of 0.1MPa, the distributions of the negative PD and positive PD overlap to a certain extent and both of them incline to regions of Band 1 and Band 2, as shown in Figure
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8c; At 0.3 MPa, the negative PD and the positive almost completely overlap and both of them shift
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slightly to region of Band 3, as shown in Figure 8d. In general, the component of Band 3 in total light
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emissions of floating discharge is comparatively great.
For surface discharge, the positive discharge distribution is completely overlapped by negative
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distribution at 0.1 MPa. Both of the positive discharges and the negatives distribute in a concentrated area that significantly close to region of Band 1, as shown in Figure 8e. As gas pressure increases to 0.3
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MPa, both the distributions of negative and positive discharges experience an obvious shifting to the region of Band 2, as shown in Figure 8f. It means that light over Band 1 dominates in the total component
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at relatively low gas pressure, while at high pressure, light over Band 2 dominates.
4.3 General distribution of PD in multispectral triangle map
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The proportions of the integrated light intensities of three spectral bands can provide valuable
reference for the refined PD diagnosis, especially for PD recognition and risk assessment. To implement this idea, the results of all the types of PD are processed by the same approach as aforementioned and are drawn in the same triangular coordinate, as shown in Figure 9. It is found that at gas pressure of 0.1 MPa the results of three types of PD separately distribute in different regions in the triangular map and their statistical mean values locate in distinctly different areas. As gas pressure increases to 0.3 MPa, all
the distributions shift, but are still highly desirable to present a high degree of recognition. By comparing the results drawn in Figure 9a and 9b, it demonstrates that the triangular distribution of surface discharge is more sensitive to the change of gas pressure, e.g. the mean values of surface discharge in the three bands shift from (0.6571, 0.2983, 0.0452) to (0.1473, 0.8245, 0.0292). For details, Table I summarizes the boundaries of 95% confidence intervals and mean values (log-normal distribution) of the distributions
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for the three types of PD in the triangle map. Compared to conventional phase-resolved PD diagnosis, this method can achieve the essential characteristics of PD more directly and is independent of the phase information of applied voltage, so it
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can be effectively applied to DC power system. Moreover, multispectral diagnosis can directly distinguish PD from two or more types of defect at the same time without any additional time-domain or frequency-domain separation signal processing that is necessary for conventional PD diagnosis.
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Due to the small size and of SiPM, the SMPDS can be developed with more sensitive spectral bands
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to achieve more intrinsic features of PD emissions. It is believed that this new strategy can unleash the
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great potential of optical PD detection for a range of gas-insulated system and promote the multispectral
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analysis from the laboratory to the engineering practice at a high feasibility and a low cost.
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5 Conclusions
In summary, the multispectral optical PD detection is implemented with a SiPM-based multispectral sensor which paves a way to an optical solution for a highly believable and interference immunity PD
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monitoring. The distinguishable features of PD emissions in three spectral bands have been obtained for
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three different types of PD including point discharge, floating discharge and surface discharge, as well as the different polarities of discharge. Bases on multispectral stochastic PD detection, a triangular clustering approach has been tailored for PD recognition which is applicable without any phase-resolved statistics. It demonstrated that the stochastic multispectral results of the three typical PDs distribute in
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three different regions in the triangular map. The distributions are sensitive to the change of gas pressure of SF6 and slightly impacted by the value of the applied voltage in our investigated range. Acknowledgement This work was supported by the National Key Research and Development Program of China (Grant Nos. 2018YFB0904400 and 2017YFB0902705) and the National Natural Science Foundation of China (Grant Nos.51507130 and 51877171).
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[13] Yoshida S, Kojima H, Hayakawa N, Endo F, Okubo H. Light emission spectrum depending on propagation of partial discharge in SF6. IEEE International Symposium on Electrical Insulation, Vancouver, BC, Canada, pp. 365-368, 2008.
[14] Ren M, Song B, Zhuang T, Yang S. Optical partial discharge diagnostic in SF6, gas insulated system via multi-spectral detection. ISA Transactions, 2018(75): 247-257. [15] Schwarz R, Muhr M. Modern technologies in optical partial discharge detection. Annual Report-Electrical Insulation and Dielectric Phenomena, Vancouver, BC, Canada, pp. 163-166, 2007.
[16] Lemzadmi, Bonifaci, Denat, Nemamcha. Light emission from corona discharge in SF6 and SF6/N2 gas mixtures at high pressure. European Physical Journal Applied Physics, 2007, 33(3): 213-219. [17] Eckert P, Schultz-Coulon H C, Shen W, Stamen R, Tadday A. Characterisation studies of silicon photomultipliers. Nuclear Inst & Methods in Physics Research A, 2010, 620(2): 217-226. [18] Gamal A, Paul B, Michael C, Roland H, Johann M, Herbert O, Ken S. Application of Geiger-mode photosensors in Cherenkov
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detectors[J]. Nuclear Instruments & Methods in Physics Research, 2011, 639(1):107-110. [19] Bartnikas R, Novak J P. On the spark to pseudoglow and glow transition mechanism and discharge detectability. IEEE Transactions on Electrical Insulation, 1992, 27(1): 3-14.
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[20] Blackmore P, Birtwhistle D. Surface discharges on polymeric insulator shed surfaces. IEEE Transactions on Dielectrics & Electrical Insulation, 1997, 4(2): 210-217.
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Author Biography
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Ming Ren was born in Shaanxi Province, China, in 1987. He received the B.S. and Ph.D. degrees in electrical engineering from Xi’an Jiaotong University in 2009 and 2013. Currently, he works as an Associated Professor in HV Technology Institute in Xi'an Jiaotong University. His present research works are application of optical sensor in plasma diagnosis, risk assessment of UHV/EHV power equipment, insulation status diagnosis and advanced fault detection for power system. He is the corresponding author of this paper. Siyun Wang wan born in Hubei Province, China, in 1996. He received a bachelor’s degree in electrical engineering from Xi’an Jiaotong University in 2018. She is currently a graduate student pursuing the Doctoral Degree in HV Technology Insititute of Xi’an Jiaotong University. Her major is HV engineering and his main research interests are solid-state sensors and advanced gas and solid dielectrics. Jierui Zhou was born in Shanxi Province, China, in 1993. He received the B.S. degree in electrical engineering from Xi’an Jiaotong University in 2016. Then he was admitted to Xi'an Jiaotong University as a graduate student majoring in power engineering. His present works are concerned with ultra-weak light detection, optical sensor and diagnosis on HV power equipment. Tianxin Zhuang wan born in Fujian Province, China, in 1995. He received a bachelor’s degree in electrical engineering from Xi’an Jiaotong University in 2017. He is currently a graduate student in HV Technology Insititute of Xi’an Jiaotong University. His major is HV engineering and his main research interests are the partial discharge mechanism of both gas and solid dielectrics and the insulation status assessment technology. Shujing Yang was born in Jiangxi Province, China, in 1996. She received the B.S degrees in electrical engineering from Xi’an Jiaotong University in 2017. And she is pursuing the M.S degree in electrical engineering in Xi’an Jiaotong University. Her interests are partial discharge diagnosis and optical applications in partial discharge measurement. The upgrading demand on the fault prevention necessitates highly reliable and precise approaches for monitoring partial discharge (PD) which is the essential potential hazards existing in SF6-insulated power
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system under electric field. In this paper, a micro SiPM-based multispectral PD sensor (SMPDS) is first developed for in-situ optical PD monitoring and the corresponding strategy for multispectral PD diagnosis is also proposed. Experiments indicates that SMPDS can efficiently response to various types
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of PD including point corona, gas spark and surface creepage with significant dissimilarities in the three spectral bands. The triangle clustering approach based on SMPDS stochastic detection can distinguish the different natures of PD with a satisfactory discrimination even without conventional phase-resolved statistics. This proposed solution provides a way for optical PD in-situ monitoring, circumventing the
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necessities of anti-interference measure and complex diagnosis algorithm.
Figure captions Fig. 1.
Spectral distributions of floating discharge in 0.35MPa SF6 and PDE of SiPM over the same spectral range.
Fig. 2
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The PDE line of the SiPM is drawn according to the datasheet of SensL-MicroFJ-30035-TSV.
Transmissions of the three optical filters and photon defection efficiencies (PDEs) of SMPDS of in the three
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spectral bands.
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The principles of SMPDS. (a) Picture of the sensor; (b) Configuration of the sensor; (c) Signal processing;
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Fig. 3.
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(d) Circuit of the sensor. The specific band ranges of B1, B2 and B3 in this figure are explained in Sec. 2.2.
Fig. 4
The stochastic pulse data record method.
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Fig. 5 Schematic diagram of PD detection. (a) PD measurement system. R is the current limit resistor (1MΩ), Cp is the coupling capacitor for HFCT measurement (1500pF) and T is a no-corona 50Hz 150kV/200kVA power supply;
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(b) Configurations of the artificial PD defect models. (i) Metal needle in a quasi-uniform background field; (ii)
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Floating-potential metal particle; (ii) Rod electrode on an epoxy insulator.
Fig. 6
Light pulses and integrated phase-resolved light intensities in the three bands detected by SMPDS. The
results presented were obtained at a gas pressure of 0.1MPa and at a medium applied voltage of about 1.5 PDIV. (a)
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and (b), point discharge; (c) and (d), floating discharge; (e) and (f), surface discharge.
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Fig. 7
Number distributions of the light pulses in the three bands with light intensity. The results presented were
obtained at a gas pressure of 0.1MPa and at a medium applied voltage of about 1.5 PDIV.
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Triangle distributions of the positive and negative PD points detected by SMPDS. (a) Point discharges, (b)
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Fig. 8
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Floating discharges, (c) Surface discharges.
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Fig. 9
General clustering distributions of the different types of PD in triangular coordinates. (a) Gas pressure of
0.1MPa, (b) Gas pressure of 0.3MPa.
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Tables TABLE I 95% CONFIDENCE INTERVALS AND MEAN VALUES (LOG-NORMAL DISTRIBUTION) OF THE DISTRIBUTIONS IN TRIANGLE MAP (MPa)
Confidence intervals (95%)
Mean values (μ)
Band 1: [0.4436, 0.4481]
Band 1:0.4462
Band 2: [0.4751, 0.4783]
Band 2:0.4772
Band 3: [0.0756, 0.0773]
Band 3:0.0763
Band 1: [0.4251, 0.4272]
Band 1:0.4261
Band 2: [0.5116, 0.5164]
Band 2:0.5144
Band 3: [0.0575, 0.0615]
Band 3:0.0612
Band 1: [0.3611, 0.3633]
Band 1:0.3621
0.1 Point 0.3
0.1
Band 2: [0.5591, 0.5606] Band 3: [0.0788, 0.0811]
Float
Band 1: [0.2391, 0.2404] Band 2: [0.5867, 0.5882]
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0.3
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Gas pressure
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PD type
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Band 2:0.5878
Band 1: [0.6558, 0.6582]
Band 1:0.6571
Band 2: [0.2966, 0.2992]
Band 2:0.2983
Band 3: [0.0441, 0.0454]
Band 3:0.0452
Band 1: [0.1455, 0.1481]
Band 1:0.1473
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Band 1:0.2402 Band 3:0.1720
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Surface
Band 3:0.0802
Band 3: [0.1721, 0.1734]
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0.1
Band 2:0.5601
Band 2: [0.8231, 0.8256]
Band 2:0.8245
Band 3: [0.0281, 0.0296]
Band 3:0.0292