A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Renewable and Sustainable Energy Reviews 51 (2015) 1650–1663

Contents lists available at ScienceDirect

Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances Suhail Khokhar a,b,n, Abdullah Asuhaimi B. Mohd Zin a, Ahmad Safawi B. Mokhtar a, Mahmoud Pesaran a a b

Faculty of Electrical Engineering, Universiti Teknologi Malaysis (UTM), Malaysia Department of Electrical Engineering, Quaid e Awam University of Engineering, Science and Technology (QUEST) Nawabshah Pakistan

art ic l e i nf o

a b s t r a c t

Article history: Received 11 February 2015 Received in revised form 14 May 2015 Accepted 15 July 2015

The increasing trend towards renewable energy sources requires higher power quality (PQ) at the generation, transmission and distribution systems. The PQ disturbances are produced due to the nonlinear loads, power electronic converters, system faults and switching events. The utilities and consumers of electric power are expected to acquire ideal voltage and current waveforms at rated power frequency. The development of new techniques for the automatic classification of PQ events is at present a major concern. This paper presents a comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances. Various signal processing techniques used for the feature extraction such as Fourier transform, wavelet transform, S-transform, Hilbert transform, Gabor transform and their hybrids have been reviewed. The artificial intelligent techniques used for the pattern recognition such as artificial neural network, fuzzy logic, support vector machine are reviewed in detail. The optimization techniques used for the optimal feature selection such as genetic algorithm, particle swarm optimization and ant colony optimization are also reviewed. A comparison of various classification systems is presented in tabular form which highlights the important techniques used in the field of PQ disturbance monitoring. The comparison of research works carried out on the classification of PQ disturbances points out that many researchers have focussed on the feature extraction and classification techniques. Only few authors have used the feature selection techniques for selecting the best suitable features. This review may be considered a valuable source for researchers as a reference point to explore the opportunities for further improvement in the field of PQ classification. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Power quality disturbances Signal processing Artificial intelligence Optimization techniques Feature extraction

Contents 1. 2. 3. 4.

n

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Power quality standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feature extraction techniques in power quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Fourier transform based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Kalman filter based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Wavelet transform based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1. Wavelet transform for disturbances detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2. Continuous and discrete wavelet transforms for feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3. Wavelet packet transform for feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4. Miscellaneous wavelet transforms for feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Corresponding author at: Faculty of Electrical Engineering, Universiti Teknologi Malaysis, Malaysia. E-mail address: [email protected] (S. Khokhar).

http://dx.doi.org/10.1016/j.rser.2015.07.068 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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4.3.5. Wavelet transforms for data compression and de-noising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Stockwell-transform based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Hilbert–Huang transform based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Gabor transform based feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7. Miscellaneous feature extraction techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Artificial intelligence classification techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Artificial neural network based classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Support Vector Machine based classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Fuzzy expert system based classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4. Neuro-fuzzy system based classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5. Miscellaneous classification systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Feature selection and parameter optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Comparative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Future scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Nowadays, the power quality monitoring has become an important issue for modern power industry to protect the electrical and electronic equipment and to identify the cause of the disturbance [1]. The integration of renewable energy sources and distributed generation in a conventional power system is one of the major sources of PQ disturbances [2]. The increasing application of solid-state switching devices, non-linear loads, rectifiers and inverters, lighting controls, computer and data processing equipment, protection and relaying equipments are also the causes of the PQ disturbances. The PQ disturbances, if not mitigated properly, may cause the overall interruption of the power transmission and distribution networks. In literature, several methodologies consisting of signal processing based feature extraction, artificial intelligence based classifiers and heuristic optimization techniques based optimal feature selection have been proposed for the identification and classification of PQ

Fig. 1. Block diagram of PQ disturbances classification system.

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disturbances. The major steps usually involved in the automatic classification of PQ disturbances are illustrated in Fig. 1. The PQ disturbances are broadly classified into three categories namely, the magnitude variations, sudden transients and steadystate harmonics. The voltage/current magnitude variations such as sag, swell and interruption have been observed in power system networks due to inception of line faults and penetration of heavy or light loads. The sudden changes in the operating conditions such as switching of capacitor banks and lightning may cause spikes or impulsive and oscillatory transients. The steady-state PQ disturbances such as harmonics, flickers, notches are created due to the applications of nonlinear loads and power electronic converters [3]. These PQ disturbances may cause huge cost due to equipment malfunction. Therefore, PQ disturbances need to be monitored and mitigated continuously to restore the normal power supply without any interruption. In complex and large power systems, a huge amount of PQ disturbances data gathered is difficult for analysis and monitoring. Thus, the intelligent and automatic methodologies are required for the detection and classification of these PQ disturbances in order to take preventive actions by utilities and their customers about the load requirements under sudden changes of operating conditions. Usually, many researchers in this area have applied one of the renowned signal processing techniques for feature extraction and complete the classification process by using an artificial intelligence technique as a classifier. The signal processing techniques provide some redundant features which affect the efficiency of the classifiers. Besides, there is no discussion on how to set the best parameters for the classifiers. Only few researchers have attempted on optimization techniques for selecting the suitable feature subset and parameter selection. In this regard, signal processing techniques for feature extraction and artificial intelligent techniques for the classification are the most important parts of the pattern recognition of PQ disturbances. The feature extraction stage provides a set of statistical data to make analysis more effective. The set of feature extraction is then used as inputs for the classification systems. In spite of technical advancement in signal processing techniques, the proper selection of feature extraction is still a challenge. Thus the optimal feature selection techniques have been proposed to retain the useful features and discard the redundant features. The existing reviews [4–8] of the PQ disturbances classification methods in technical literature have not much focused on the most recent signal processing and artificial intelligence techniques as well as new optimal feature selection

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methods. In this paper, a comprehensive and critical literature review is presented for the detection and classification of power quality disturbances. The classification of PQ disturbances is based on the applications of signal processing, artificial intelligence and optimization techniques. The state-of-art techniques used for the analysis of the PQ disturbances are discussed. An extensive review on these techniques provides a clear perspective to the researchers and engineers working on the classification methods of PQ disturbances. The paper is organized as follows. The introduction is presented in Section 1. The power quality and its standards are briefly discussed in Sections 2 and 3 respectively. The reviews of feature extraction techniques, artificial intelligence technique and optimization tools are presented in Sections 4–6. In Section 7, a table of comparison of the classification techniques is described. Finally, the future scope and conclusion are covered in Sections 8 and 9 respectively.

2. Power quality The attempt of PQ definition might be absolutely different in the views of utilities, consumers and equipment suppliers. It is actually a consumer-driven problem, therefore, it can be defined as, “any sudden change in the normal operation of voltage, current or frequency which causes malfunction or failure of the consumer equipment” [9]. Any sudden change or deviation manifested in the voltage, current or frequency from the normal rating is known as a PQ disturbance which results in failure or malfunctioning of the power equipment. The main aspects of the PQ research involve basic concepts and definitions, simulations and analysis, instrumentation and measurement, causes, effects and solutions of PQ disturbances [10]. The ultimate consequences of the PQ disturbances and the resulting issues are the huge economic loss of equipment failure.

Number of Papers published

600

554

500

475

400

499

355 362 315

300 222

484

326

245 255

200 136

100 10 14 11 17

33

115

129

54 61 63 61 60 56

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

Year of Publication

Fig. 2. Number of published papers each year in the field of Power Quality (indexed in Scopus).

The increasing research interest in the field of power quality can be observed immediately from Fig. 2 which shows the statistics of articles published per year indexed by the Scopus database [11] by using the exact search phrase power quality in the title of each article. The interest in the field of PQ has been increased since the year 2001. The integration of renewable energy sources and distributed generation into the power grids utilize power electronic technology which may cause numerous PQ disturbances in the electric power systems. Therefore, further research trend in the area of PQ analysis will be increased in future due to the more applications of the power electronic converters used in distributed generation and renewable energy sources [12].

3. Power quality standards The PQ standards have established the consistent description and electromagnetic phenomena of the PQ disturbances used in the monitoring data. The nominal operating conditions of the voltage/current supply and their parameters variation within the power supply and the load equipment are described. Moreover, the selection of the appropriate monitoring instruments, their limitations, application techniques and the interpretation of results have also been illustrated. The IEEE 1159 standard [13] and the European EN 50160 standard [14] classify the PQ disturbances according to thresholds of the root mean square (rms) values of voltage and current deviations with respect to nominal operating conditions during the time of disturbance. The IEC 61000-4-30 standard [15] has established the reliable methods for measurement and interpretation of electrical parameters in 50/ 60 Hz power systems.

4. Feature extraction techniques in power quality The feature extraction process is the most important part of the pattern recognition system applied to find the distinctive features from the obtained transform coefficients of the original signals. The PQ disturbances can be detected and classified by using a feature extraction technique. The extracted features subsequently can be used for the classification of PQ disturbances. Features can either be extracted from the original signals or form some time– frequency transformation techniques. Various signal processing techniques have been used for the feature extraction such as Fourier transform, Wavelet transforms, Stockwell transform, Hilbert transform, Kalman filter, Gabor transform, and their hybrids. A state-of-art taxonomy of the signal processing techniques used for the feature extraction of the PQ disturbances is shown in Fig. 3. These transformations are used to obtain information in time and frequency domains. The selection of the most suitable features of the PQ events is of extreme importance in order to achieve the

Signal Processing Techniques

Fourier transform

DFT FFT STFT

Kalman Filter

EKF UKF

Wavelet transform

S-transform

HilbertHuang transforms

Gabor Transforms

CWT DWT DWPT

DST DOST FDST

HHT

GT

Fig. 3. Taxonomy of feature extraction techniques.

Miscellaneous transforms

TFR MM PC

CT SLT SK

SP TEO AFD

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highest accuracy of the classification [16]. However, the performance of a classifier depends upon the extracted feature vector [17]. Therefore, instead of designing a complicated classifier, the distinctive features of the patterns are the main focus of the pattern recognition systems. The statistical parameters of the transformed coefficients of the PQ disturbances can be calculated in order to reduce the data size and to obtain distinctive features of the PQ disturbances. The most widely used statistical parameters for the classification of PQ disturbances are energy, entropy, minimum, maximum, standard deviation, mean, rms value, etc. and their combination. 4.1. Fourier transform based feature extraction The Fourier Transform (FT) is the most widely used computation algorithm for the steady-state analysis of the stationary signals by extracting spectrum at specific frequencies. The signal to be analysed can be represented as a sum of constituent sinusoids of different frequencies [18]. The FT only detects the existence of certain frequency component in a signal without any information of time at which this frequency component appears. Thus the time information is ignored in transformation of the signal to the frequency domain. The FT of a continuous-time signal xðt Þ is defined as [19] Z 1 X ðf Þ ¼ xðt Þe  jωt dt ð1Þ 1

The Discrete Fourier Transform (DFT) that is used for computer analysis can be expressed as X½k ¼

N 1 X

x½ne  j2π kn=N

ð2Þ

n¼0

The PQ disturbances signals are usually non-stationary and transitory in nature. Therefore, the DFT is inappropriate to detect the abrupt changes in PQ events such as their starting and end points. The DFT can only be suitable for the stationary PQ disturbances. The DFT represents the discrete signals that repeat themselves periodically with infinite length. The Fast Fourier Transform (FFT) algorithm gives exactly the same result as the DFT in much less time. The FFT is widely used for harmonic analysis of a PQ events [20]. In [21], authors used windowed FFT for power quality assessment. The windowed FFT [22] is a time version of the discrete time FT. However, the signal parameters (frequencies, amplitudes, phases) cannot be obtained accurately due to leakage, picket fence, and aliasing effects produced by FFT [23]. The Short-Time Fourier-Transform (STFT), an alternative of FFT, analyses the time–frequency decomposition of non-stationary signals by splitting the signals into small sections where each section is assumed stationary. Unlike DFT, the discrete STFT [19] is multiplied by a window function w½n  m whose position is translated in time by m: X STFT½k; m ¼ x½nw½n  me  j2π kn=N ð3Þ n

where w½n  m in its simplest form is the rectangular window function  1 if 0 r n m r N  1 w½n ¼ ð4Þ 0 otherwise Thus the STFT determines the sinusoidal frequency and phase contents of local sections of signals as they change over time. Also, it extracts several stationary and rotating frames of signals with a window moving with time. The discrete STFT has been applied in [24] for time–frequency analysis of non-stationary PQ disturbances by decomposing the time-varying signals into time–frequency domain components. The STFT can detect transient

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positions in disturbance data by choosing a small window size. Although the STFT has a fixed resolution for all frequencies, once the size of the window is chosen, it enables an easier analysis for harmonics signals. Hence, it is difficult to analyse non-stationary signals with STFT operating in a fixed window size. The selection of the size of the window is the deficiency of the STFT. Applying large window size results in a good frequency resolution and a bad time resolution and as the window size reduces, the frequency resolution decreases and the time resolution increases. Therefore, the STFT cannot provide accurate time–frequency information simultaneously to classify the PQ disturbances according to IEEE standard 1159. In [25], authors presented unique features that characterize PQ events and methodologies to extract them from recorded voltage and/or current waveforms using Fourier and Wavelet Transforms (WT). 4.2. Kalman filter based feature extraction The Kalman Filter (KF) is a well-known signal processing tool used as a state-space model to estimate accurate amplitude, phase-angle and frequency of the noise riding harmonic signal by breaking it into constant elements and fluctuating elements in its envelope [26]. Mathematically, for a given observation data, KF is described by a set of state equations and a set of observation equations as follows [27]: State equations : xðnÞ ¼ Aðn  1Þxðn  1Þ þ wðnÞ Observation equations : zðnÞ ¼ C ðnÞxðn  1Þ þ vðnÞ

ð5Þ

where xðnÞ, zðnÞ are state vector and observation vector respectively. Aðn  1Þ is the state transition matrix, wðnÞ is a white noise. C ðnÞ connects the measurement zðnÞ with the state vector xðnÞ, vðnÞ is a vector of observation noise. In [28] authors proposed a combination of Discrete Wavelet Transform (DWT) and KF with Fuzzy Expert System (FES) for the identification and classification of the PQ disturbances. The DWT was used to identify the noise in the captured PQ signal and KF was used to speed up its rate of convergence. The outputs of the KF were applied to FES for the classification. A hybrid approach of ST and Extended Kalman Filter (EKF) was proposed in [29] for the classification of the short duration PQ disturbances. The Stockwell Transform (ST) was proposed to detect and localize the signals whereas the EKF technique was used to estimate the changes in amplitude, frequency, phase and harmonic contents of the distorted signal. In [30] a hybrid methodology using Unscented Kalman filter (UKF) and modified Particle Swarm Optimization (PSO) algorithms was proposed for tracking the amplitude, phase, frequency and harmonic contents of PQ disturbances corrupted with a low signal-tonoise ratio value. 4.3. Wavelet transform based feature extraction The wavelet transform is an advanced signal processing tool which performs a significant role of feature extraction for the pattern recognition of PQ disturbances [31]. It has been proven as a powerful feature extraction technique for PQ disturbances data by using Multi-resolution Analysis (MRA) techniques [32]. The signal to be analysed is decomposed into various scales of a short term waveform called the “mother wavelet”. Unlike FT, the WT simultaneously provides time–frequency information of a signal which makes it suitable for analysing time–frequency resolution of signals. The WT coefficients hold the characteristics of the PQ signals in the different frequency bands. The statistical parameters such as the amplitude, the mean value, the standard deviation, the energy distribution pattern and the entropy can be extracted from the detail and approximation coefficients (cD and cA) of the WT

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[33]. Thus the application of WT is more suitable for the classification of PQ disturbances. In literature, the WT has been used for feature extraction, data compression and de-noising of the PQ disturbances. The wavelet transform approaches offer continuous WT (CWT), discrete WT (DWT) and wavelet packet transform (WPT) methods for the feature extraction of signals. The CWT of a time-continuous signal xðt Þ is defined as [34]   Z 1 1 t b CWT ða; bÞ ¼ pffiffiffiffiffiffi dt ð6Þ xðt Þ ψ a jaj  1 where ψ ðtÞis "mother wavelet", a and b are the scale and translation parameters, respectively. In realistic applications, the CWT can be transferred to discrete form via a sampling way. The DWT of the discrete signal x(k) is employed to replace the CWT in (6)   n  kb0 am 1 X 0 ð7Þ xðkÞ ψ DWT ðm; nÞ ¼ pffiffiffiffiffiffi am am 0 0 k where a and b in (6) are replaced to be the functions of integers m and n respectively. In (7), a0 and b0 are the discrete scale and translation factor, respectively. 4.3.1. Wavelet transform for disturbances detection The WT has been applied for the first time by Robertson et al. [19], Santoso et al. [34] and Pillay et al. [35] for the detection of nonstationary PQ signals. In [34], authors proposed WT approach for the detection and localization of the PQ disturbances where Multiresolution Signal Decomposition (MSD) technique was proposed to decompose the signals into various scales and Squared WT Coefficients (SWTC) were extracted for each type of PQ disturbances. A proper classification tool was suggested to classify the signals by using unique features extracted. In [19] the WT was proposed to analyze the electromagnetic power system transients. The MRA property was proposed for the detection of transient disturbances and identification of their sources. In [35] the basic theory of WT was applied to reconstruct non-stationary PQ disturbances. It was proposed that various types of faults can be classified by using the less number of wavelet coefficients. Recently, a hybrid DWT based voltage sag/swell detection algorithm was proposed in [36] consisting of db2 and db8 mother wavelets to detect sag and swell with and without phase jumps. Refs. [19,34–36] proposed the WT only for the detection of PQ disturbance signals. However, none of them have used WT for the feature extraction from PQ signals. 4.3.2. Continuous and discrete wavelet transforms for feature extraction Gaouda et al. proposed the WT based MSD technique to extract the standard deviation [37] and root mean square [38] features of the decomposed signals for the detection and classification of PQ transient events. A WT based MSD technique was proposed in [39] for the classification of Short Duration Variation (SDV) in the power distribution systems. In [40] authors proposed the CWT with its modulus local maxima properties as well as the DWT based MSD and reconstruction properties for the detection of the PQ disturbances and their classification even in noisy environment. In [41], authors proposed DWT to determine the magnitude of voltage sag only and span of the sag. In [42] authors proposed the Wavelet Energy Entropy (WEE) and Wavelet Entropy Weight (WEW) features for the classification of transients disturbances created by line faults and switching conditions. In [43] DWT–FFT based PQ disturbances classification approach was proposed where DWT based MSD technique with percentage Energy Entropy of Squared Detailed Coefficient (EESDC) was used for feature extraction and FFT was used for the classification. Another hybrid approach of DWT–FFT based feature extraction technique

was proposed in [44] for making a decision based on the fuzzy expert system to classify the PQ disturbances. The ability of the WT of analysing nonstationary signals with the classification capability of ANN were combined to develop a Wavelet Network (WN) which made it possible for simultaneous and automated detection and classification of transient. A wavelet network based classification system of transients signals was proposed in [45]. The neurons of the first layer were substituted by wavelet nodes and the activation functions by wavelet functions. In [46], a combination of DWT and WN was proposed for the detection and classification of a large number of PQ disturbances. In [47] authors proposed Adaptive Wavelet Network (AWN) model for the detection of power system disturbances. An AWN consists of two subnetwork architectures; the wavelet layer network and adaptive probabilistic network. The application of AWN was found suitable in a dynamic environment to incorporate features with automatic target adjustment and parameter tuning. In [48] authors proposed Multi-Wavelet Transform (MWT) using MSD techniques working together with multiple neural networks for the classification of the transient disturbance type. In [49] authors proposed a Modified Frequency Slice WT (MFSWT) based PNN classifier for the detection and classification of non-stationary PQ disturbances. The WT based online methods of PQ disturbances detection were proposed in [50,51]. The voltage disturbances were experimentally created by applying various types of line faults as well as by shifting the load by the static transfer switch to an alternative supply. A wavelet MRA (WMRA) based Nearest Neighbours (NN) pattern recognition technique was proposed in [52] for online classification of PQ disturbances. The online method was not suitable for the classification of the dynamically created PQ disturbances. 4.3.3. Wavelet packet transform for feature extraction A hybrid classification system consisting of a wavelet packetbased HMM and a rule based classifier was proposed in [53] for the classification of power distribution line disturbances where time-characterized-features based disturbances (sag and interruption) were classified by the rule based method and the frequencycharacterized-features based PQ disturbances (transients and impulses) were classified by using Wavelet-Packet Transform based Hidden-Morkov Model (WPT-HMM). In [54], an algorithm based on the DWT and SVM was proposed to identify the voltage disturbances. In [55] authors proposed Wavelet Packet Energy (WPE) features for training the Multiclass Support Vector Machine (MSVM) to perform the classification. A combination of three methods wavelet packet, non-extensive entropy and singular value decomposition known as a Wavelet Packet Tsallis Entropy (WPTSE) was proposed in [56] for the detection of transient PQ disturbances. 4.3.4. Miscellaneous wavelet transforms for feature extraction A Two-Dimensional Discrete WT (2D-DWT) representation of PQ disturbances was proposed in [57] for the analysis and classification of transient signals. In [58], image processing techniques like gamma correction, edge detection, and peak and valley detection were proposed to PQ data represented as a transverse wave having compressions and rarefactions to identify the PQ disturbances. In [59], a 2D-DWT based image processing technique was proposed for classifying multiple PQ events. An efficient intelligent recognition system of least square SVM (LS-SVM) based on the k-means Apriori algorithm optimal feature selection method to identify the three-phase PQ events was presented in [17]. In [60] authors introduced a decimation-free WT or Undecimated WT (UWT) to determine the power quantities in time– frequency domain using complex wavelet coefficients incorporating time-variant and time-invariant PQ disturbances. An effective

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DWT based feature extraction technique was proposed in [61] for the training of Self-Organising Learning Array (SOLAR) for the classification of PQ disturbances. The energy values of the details and approximation coefficients were calculated at each decomposition level by using the MRA on the original PQ waveforms to construct the feature vector for training and testing the SOLAR system. The combination of the WT and the SOLAR system accomplished a good PQ classification performance. A variant of WT known as dual tree complex wavelet transform (DTCWT) was proposed in [62] for feature extraction with sparse representation based classification (SRC) system to recognize the power system transients signals. 4.3.5. Wavelet transforms for data compression and de-noising The continuously monitoring of PQ disturbances causes an enormous amount of data gathered in PQ monitoring equipment. The huge data gathered creates several troubles in the storage and communication of the data. Hence, the data compression has become an important issue in the monitoring of PQ disturbances [63–69]. The Spline WT and Radial Basis Function (RBF) neural network have been successfully integrated for PQ monitoring data compression [67]. In practical applications, the classification of PQ disturbances is complicated due to noise riding on the signals. The effect of noise has not been considered in many of the WT-based detection and classification of the PQ disturbances. The presence of noise in the PQ signals degrades the features extracted which reduce the recognition rate of the classifiers. In [70–73], authors proposed denoising schemes for enhancing WT-based PQ classification system. In [70], authors presented a denoising scheme integrated with DWT to suppress the noise riding on the WTCs of signals. To improve the recognition rate authors in [73] suggested a spatial-correlation-based noise suppression algorithm by determining the corresponding correlation coefficients from the WTCs of the signals at several adjacent scales. 4.4. Stockwell-transform based feature extraction The Stockwell Transform (ST) is an extension of either phase correction of WT or a variable window STFT that has some better characteristics to either of the transforms. Like WT, it can provide a better time–frequency representation of a signal. The fixed modulating sinusoids with respect to the time axis as well as the scalable and movable Gaussian window have been found to have the superior properties of the ST which provide significant improvement in the identification of PQ disturbances. Mathematically S-transform can be defined as [74] The PQ disturbance signal hðtÞ can be expressed in a discrete form as hðkTÞ, where k ¼ 0; 1; …; N  1 and T is the sampling time interval. The DFT of the continuous time signal hðtÞ can be defined as H

1 h n i 1 NX ¼ hðkTÞei2π nk=N NT Nk¼0

ð8Þ

The standard S-transform of a discrete time signal hðkTÞ is obtained as N 1 h h m þ ni X ni 2 2 2 H ¼ e  2π m =n :ei2π mj=N S jT; NT NT m¼0

ð9Þ

where j; m ¼ 0; 1; 2; …; N  1 and n ¼ 1; 2; …; N  1. The time and frequency features of the signals can be extracted using ST to be applied as inputs to the intelligent classifiers for the automatic pattern recognition. These characteristics of ST attract the researchers for the detection and classification PQ disturbance waveforms [74]. The first time application of ST in the area of PQ analysis was proposed in [74,75] using Gaussian window with only one scaling

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factor. In [76,77], authors proposed S-transform as a simple and effective method for classification and quantification of PQ disturbances and proved that ST has a high tolerance of noise and guaranteed satisfactory pattern recognition. In [78] authors proposed a hybrid methodology of ST and dynamics for real-time PQDs classification where ST extracted five distinctive features and dynamics applied to reduce the run time of ST significantly. The Decision Tree (DT) was integrated with the ST as a classifier for better compatibility. The PQ disturbances classification using Discrete Orthogonal ST (DOST) was proposed in [79]. The ST based MRA technique integrated with fuzzy recognition system [80] and rule based approach [81] were proposed for the classification of PQ events. The MRA was based on a variable window changing with frequency according to user defined function. A Fast variant of the Discrete ST (FDST) algorithm was proposed in [82] to accurately extract the time localized spectral characteristics of the non-stationary PQ disturbances. In [83] Hyperbolic S-Transform (HST) and GA based Fuzzy C-means (FCM) algorithm was proposed for automatic pattern recognition of non-stationary PQ signals. In [84], windowed DFT and ST techniques were proposed to extract the distinctive features of the PQ signals, and then binary feature matrix was used for decision making regarding the disturbance type. In [85] S-transform with Module Time–Frequency Matrix (MTFM) based on maximum similarity principle was proposed to extract short duration disturbances characteristics. In [86] authors proposed S-transform and hybrid PSO-FES for PQ time series data mining. In [87] authors proposed ST and logistic model tree (LMT) for the classification of PQ disturbances. A fast S-transform with modified Gaussian window was proposed in [88] to generate time–frequency contours for extracting relevant feature vectors for automatic PQ disturbances classification. A hybrid optimization algorithm Chemotactic differential evolution algorithm (CDEA) composed of DEA and bacterial foraging optimization algorithm (BFOA) integrated with fuzzy decision tree was applied to improve the classification accuracy. In [89] a combination of ST and HMM was proposed for PQ disturbances classification. In [90,91] authors proposed a Fast Dyadic ST (FDST) algorithm based Fuzzy Decision Tree (FDT) based classifier for the classification of PQ disturbances. In the classification process, the FDST was used for accurate time–frequency localization, DT for optimal feature selection and Fuzzy rules were used for pattern classification. In [12] authors proposed ST to extract the statistical features of the PQ disturbances created by wind energy system and PV system. The Modular PNN, SVM and LS-SVM techniques were used as classifiers. In [92] S-Transform with Extreme Learning Machine (ST-ELM) based pattern recognition approach was proposed for the automatic classification of PQ disturbances. In [93] S-transform with rule-based decision tree and ANN classifiers was proposed for the recognition of single and multiple PQ disturbances. 4.5. Hilbert–Huang transform based feature extraction The recent advances in signal processing have developed Hilbert–Huang Transform (HHT) technique for the analysis of non-stationary signals. The HHT is the combination of empirical mode decomposition (EMD) and Hilbert Transform (HT) [94]. EMD is a time–frequency analysis method [95] based on the local characteristic time scale of signal and decomposes the power signal into number of intrinsic mode functions (IMFs) which are then analysed by the HT for estimating instantaneous frequency, amplitude, and phase. The HT of a real valued time domain signal xðt Þ provides another real valued time domain signal x^ ðtÞ, such that zðt Þ ¼ xðt Þ þjx^ ðt Þ is an analytical signal. Z 1 xðτ Þ ð10Þ x^ ðt Þ ¼ H ½xðt Þ ¼  1 π ðt  τ Þ The HT of a signal effectively produces an orthogonal signal that is phase shifted by 901 from the original signal. The instantaneous

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amplitude signal Aðt Þ and instantaneous phase angle θðtÞ and instantaneous frequency f 0 of a signal xðt Þ in terms of xðt Þ and x^ ðt Þ are given as 2

Aðt Þ ¼ ½x2 ðt Þ þ x^ ðt Þ1=2

θðt Þ ¼ tan  1 f0 ¼

  x^ ðt Þ xðt Þ

  x^ ðt Þ 1 tan  1 xðt Þ 2π t

ð11Þ ð12Þ ð13Þ

The IMFs are mono-component signals to give well behaved HT and thus help in obtaining instantaneous frequencies of nonstationary signals. The characteristic which differentiate EMD from other techniques namely FFT or wavelet lies in deriving its basis functions from the signal itself thereby making it adaptive in nature. In [96] an approach of PQ assessment based on expanding a distorted signal into its intrinsic mode oscillations was proposed. In [97] a frequency-shifting wavelet decomposition via HT algorithm was introduced to overcome the spectral leakage problem in the DWPT and to estimate the power quantities accurately and detect flickers. In [98] HHT based PNN algorithm was proposed for the classification of single-stage and multiple PQ disturbances. In [99] EMD combined with HT was employed for the detection of voltage sag cause. The PNN classier was constructed based on EMD which classified the extracted features to identify the type of voltage sag cause. In [100] authors presented a hybrid algorithm of ensemble EMD (EEMD) for the feature extraction and selection and SVM for the classification of PQ disturbances. In [101] HT and Fuzzy Product Aggregation Reasoning Rule (FPARR) based intelligent classifier were applied with EMD based soft threshold and signal-chopped de-noising techniques. The Hilbert Huang transform (HHT) with PNN and SVM classifiers to efficiently classify composite PQ events was proposed in [102]. 4.6. Gabor transform based feature extraction The Gabor transform [103] is an advanced signal processing tool used for accurate phasor estimation. Unlike the FT, the GT provides time–frequency information of a signal to be analysed. The GT of the signal xðt Þ can be defined as follows [104]: Z 1 Gðτ; ωÞ ¼ xðt Þgðt  τÞe  j2ωt dt ð14Þ 1

where τ is the amount of time shifting; and g(t) is a window function. The GT has been applied in power system applications as a measurement tool for the analysis of short duration transient disturbances. In [104] GT was applied for monitoring power system transient harmonics. In [105] a hybrid approach of GT and ANN was used for the identification of the arcing faults. The GT was utilized as an advanced signal processing technique for optimal features extraction. In [106] combination of GT with the Winger Distribution Function (WDF) known as Gabor–Winger Transform (GWT) was proposed for the time–frequency analysis of the PQ disturbances. 4.7. Miscellaneous feature extraction techniques There are some other signal processing techniques significantly applied for the feature extraction and classification of the PQ disturbances. Min et al. [107,108], in the two-paper series, proposed a Time–Frequency Representation (TFR) feature extraction technique using time–frequency ambiguity plane, with kernel technique, for the application of PQ classification problems. Currently in [109] authors presented sparse signal decomposition

(SSD) on over-complete hybrid dictionary (OHD) as a novel technique for detection of single and multiple PQ events. The sparse signal decomposition is a new signal processing tool for the analysis of PQ disturbance signals. In literature, numerous other signal processing techniques recently used for the feature extraction of the PQ disturbances are Chirp-Transform (CT) [110], SpacePhasors (SP) applied to 3-phase voltage signals [111], Morphology Method (MM) [112], Slant-Transform (SLT) [113], Time–Time Transform (TTT) [114],Teager Energy Operator (TEO) [115], Principle Curves (PC) [116], Spectral Kurtosis (SK) [117], Amplitude and Frequency Demodulation (AFD) with FPARR classifier [118].

5. Artificial intelligence classification techniques Artificial intelligence can be described as the automation of the activities associated with human thinking such as learning from examples, perceptions, reasoning, decision making and problem solving. Recently intelligent tools are required for pattern recognition and decision making. The AI tools used for the classification of PQ disturbances are artificial neural network, support vector machines, Fuzzy logic, expert systems and k-nearest neighbour. Fig. 4 shows the taxonomy of the AI tools used as classifiers for the pattern recognition of the PQ disturbances. 5.1. Artificial neural network based classifiers Artificial neural networks have been broadly used in power system applications including classification of different PQ disturbances. The ANN can perform tasks such as pattern recognition, classification, function approximation, optimization and data clustering [119]. The ANN based methodologies have been proved effective for solving real-time problems. The patterns for the classification are frequently employed based on learning from examples. The learning rules for each type of ANN are different yet they can identify pattern features from a set of training data and then classify new data on the basis of features. The salient features of ANN based classifiers which attract the researchers are its self-learning capability, no need to know data relationship, selftuning capability and applicability to model various systems. The classification and function approximation capabilities of ANN have been utilized in PQ studies, fault analysis, and harmonics sources classification. A hybrid approach of FFT and DWT based Multilayer Perceptron (MLP) neural network was proposed in [120] for the automatic classification of the PQ disturbances. The application of two paradigms of ANN, Feedforward Neural Network (FFNN) and Time-Delay Neural Network (TDNN), was proposed in [121] for the

Artificial Intelligence Techniques

ANN

SVM

FES

Miscellaneous Classifiers

MLP RBF PNN

OAO OAA

FCM FkNN FPARR FARTMAP

Neuro-fuzzy kNN HMM

Fig. 4. Taxonomy of AI classifiers.

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automatic classification of PQ waveforms. In [122] authors proposed a WT based MLP neural network with three hidden layers for the real-time classification of PQ disturbances where a great number of training pattern of the PQ waveforms were configured by Electrical Pattern Generator (EPG). In [123] authors proposed a Quantum Neural Network (QNN) classifier with Dempster–Shafer (DS) evidence theory to recognize the PQ disturbances. Based on the strong classification capabilities of ANN, a combination of Spectral Kurtosis and RBF neural network was proposed in [124] to recognize the transient PQ disturbances only. In [125] authors proposed a dual neural-network-based methodology consisting of Adaptive Linear Network (ADALINE) and FFNN to detect and classify single and multiple PQ disturbances even when they appear simultaneously. An adaptive linear network was used for harmonic and interharmonic estimation to determine the rms values and THD indices and a FFNN used for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform to classify spikes, notching, flicker, and oscillatory transients. In [126] authors proposed DWT based Univariate Randomly Optimized Neural Network (URONN) with FL as a simple classifier to identify the types of disturbances with a significant speed and performance. In literature, various methodologies based on the combination of WT and ANN have been proposed for the identification of PQ disturbances. In [127] ST and WT based input feature vectors were applied to Probabilistic Neural Network (PNN), feedforward neural networks, and RBF neural networks. A hybrid methodology consisting of WT with MLP and RBF neural networks was suggested in [128] for the classification of synthetic PQ data through Matlab Graphical User Interface (GUI). A theoretical foundation [129] and practical implementation [130] of DWT and a set of multiple learning vector quantization (LVQ) neural networks with Dampster–Shafer theory of evidence were proposed for an actual power disturbance classification system. In [131] authors employed DWT technique integrated with the PNN model to classify disturbance types according to the transient duration and the detailed energy distribution features. The energy and time duration features were applied as inputs to PNN classification system to classify the seven types of PQ disturbances. The S-transform based useful statistical features were extracted for applying as inputs to different types of neural network structures, such as back propagation neural network (BPNN) [132], PNN [133] and modular neural network [134], have been proposed to identify PQ disturbances. A three-module neural network structure followed by a rule based classifier was proposed in [135] by combining the specific PQ disturbances least likely occurring simultaneously. A combination of fully informed particle swarm (FIPS) and adaptive PNN approach known as PNN based feature selection (PFS) was proposed in [136] to identify the PQ disturbances where the statistical features were extracted by ST and time–time (TT) transform (extension of ST) for training the PNN classifier. An integrated approach of wavelet layer and adaptive PNN known as Dynamic Wavelet Network (DWN) was proposed in [137] particularly suitable for the classification of PQ signals in a dynamic environment with time varying nonstationary PQ signals. The DWN is the combination of the two sub-networks consisting of input DWT layer and adaptive PNN layers. The DWN has the capability of reducing error by automatic adjustment of learning cycles for different types of signals. 5.2. Support Vector Machine based classifiers Support vector machine introduced by Vapnik [138] is a supervised learning machine tool applied for pattern recognition and classification. The pattern recognition approach is based on the statistical learning theory. SVM has been applied in many pattern recognition and regression problems such as for

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dependency estimation, forecasting, and constructing intelligent machines. The SVM is used efficiently in large classification problems because the training of SVM can handle very large feature vector dimensions more effectively as compared to conventional classifiers. Also, SVM has better generalization properties than the conventional classifiers. In [139], authors proposed DWT based SVM for the classification of five types of PQ disturbances. The real-time captured PQ disturbances were classified in [140] by applying the DWT for feature extraction and one versus one multiclass SVM as a binary classifier. The events were trained by SVM individually, but multiple PQ events were also classified successfully. In [111] authors proposed SVM based classifiers for the classification of PQ disturbances. The standard techniques of SVM are One-Versus-One (OVO) and One-Versus-Rest (OVR) suitable for the recognition of the multiclass problems. The multiclass SVM (SVM) techniques suffer due to network size, heuristic solution scheme, complicated data preparation, etc. In [141] authors proposed Disturbances-Versus-Normal (DVN) approach for multiclass SVM. An automatic classification algorithm using wavelet-MRA (WMRA) based SVM was proposed in [142] for the identification of the three-phase PQ events. An effective single feature vector was represented for each three-phase signal. In [143] wavelet packet energy entropy and weighted features based SVM was proposed where WPT was used to extract the energy entropy and weight features and SVM for automatic classification of PQ disturbances. In [54,144] an effective classification algorithm based on WT and SVM was proposed for identifying power system disturbances. In [145], WT and wavelet-SVM approach was proposed for the recognition and classification of PQ disturbances. A PQ disturbances classification system based on wavelet packet energy and multiclass SVM was proposed in [55] to discriminate seven types of PQ disturbances. In [146] authors proposed TT-transform (TTT) with a modified Gaussian window and SVM clustering to the problem of power signal classification. In [17] authors proposed WT based feature extraction, k-means based Apriori feature selection algorithm and Least Square SVM (LS-SVM) classifier algorithm for classification of the PQ events. In [2] authors proposed S-transform and SVM for the classification of PQ disturbances. 5.3. Fuzzy expert system based classifiers Fuzzy logic system generalizes the classical binary logic for reasoning under uncertainty. It is inspired by observing human reasoning to utilize concepts and knowledge. A fuzzy set is a function that maps objects in the domain of concern (i.e. the universe of discourse) to their membership values in the set. Such a function is called the membership function. The two most widely used membership functions are triangular and trapezoidal functions. The particular application of AI used in the diagnostic module is called an expert system [147]. An expert system that uses a collection of two key elements fuzzy sets and fuzzy rule base, instead of Boolean sets for reasoning about data is called Fuzzy Expert System (FES). A fuzzy set can be fully defined by its membership functions and fuzzy rules offer human-like reasoning capabilities and provide transparent interface mechanism. In [80] authors proposed the design of a simple fuzzy-based classification scheme using the ST based features with high classification accuracy even in the presence of random noise. A fuzzy-expert system was proposed in [44] for decision making based on the features extracted from FT and WT. A Fourier Linear Combiner (FLC) to extract the amplitude and the phase of the fundamental signal, and a FES to recognize the type of the PQ disturbances were proposed in [147]. In [16], a wavelet-based extended fuzzy reasoning approach was proposed to PQ disturbance waveform

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recognition. A Fuzzy C Means (FCM) expert system is a standard clustering algorithm that groups the data points in multidimensional space into a specific number of clusters. Feature vectors (hundreds of each disturbance) can be applied as the input to the algorithm. In [86] authors proposed an approach for PQ time series data mining using S-transform based FES. In [148] authors proposed DWT feature extraction based FPARR for the recognition of PQ disturbances and classified event classes with minimum error. An efficient PQ event analysis and classification system have been proposed in [118] using amplitude demodulation, frequency demodulation and Multiple Signal Classification (MUSIC) harmonic analysis for making knowledge base for FPARR classifier. The FPARR classifier has the high classification rate due to its learning and generalization capabilities. In [149] authors proposed a FuzzyARTMAP Neural Network (FANN) by extracting the real time voltage waveform characteristics through the combined use of DWT based MRA and entropy norm (EN) features. An approach based on the DWT, Kalman filter and fuzzy-expert system was proposed in [28] for identifying and classifying the power system disturbances. A decision tree based on fuzzy clustering was presented in [88] to group the extracted features into clusters and thereby identifying the class of the PQ disturbance data. A Bacterial Foraging Optimization Algorithm (BFOA) with Reformulated FCM Algorithm was proposed to refine the cluster centre, thereby increasing the average classification accuracy of the power signal disturbance classification [88]. A hybrid time–frequency Stransform and FES was proposed in [150] for power island detection in distributed generation. A Fuzzy Rule-Based classifier was built to distinguish between an islanding and a non-islanding event. In [90] authors proposed a FDST feature extraction based fuzzy decision tree to detect and classify various single and multiple PQ disturbances simultaneously. 5.4. Neuro-fuzzy system based classifiers In [151] authors developed a neural-fuzzy classifier by exploiting the powerful capability of the learning vector quantization (LVQ) architecture in pattern recognition and the flexibility of the Fuzzy Associative Memory (FAM) mapping in handling uncertainties. In [152] authors demonstrated a PQ event recognition system, which integrated the noise-suppression algorithm, feature extraction based on the Parseval's Theorem, and the neuro-fuzzy classification system, was successfully developed and tested using both simulated noise-riding data of various disturbance events and actual field data. In [153] authors proposed an automatic classification of the PQ disturbances based on a four step algorithm combining a 3-D space referential representation, Principal Component Analysis (PCA), feature extraction and neuro-fuzzy classifier. 5.5. Miscellaneous classification systems In [154], an expert system was proposed for the classification and analysis of a number of power system events in terms of the underlying causes. The classification scheme was able to distinguish the different types of voltage dips as well as interruptions based on identifying and characterizing the different stages of voltage during an event. In [84] authors proposed a Fourier and Stransform based simple and computational efficient Binary Feature Matrix (BFM) classifier for making a decision regarding the disturbance type. Chung et al. presented a rule based method and a wavelet packet-based HMM [53]. A wavelet packet based Hidden Markov Model was used for identification of disturbances except voltage sag and interruption. A WT and Fourier based HMM approach for classification of PQ events was proposed in [155]. A wavelet based HMM was utilized in [156] for the classification of

fifteen types of PQ disturbances. In [157] an integrated and effective classification tool Wavelet Multi-class Relevance Vector Machines (WMRVMs) was proposed for classification of PQ disturbances.

6. Feature selection and parameter optimization techniques The performance of the classification tools as well as discovering the distinctive features are equally important in classifying the PQ disturbances. In recent studies, feature selection algorithms have been used to obtain the most suitable features and to discard the redundant features of the PQ disturbances. In the feature selection based classifier algorithms, a large feature set is extracted from the feature extraction stage, out of which a best suitable feature subset with a high recognition rate is selected [17]. The feature selection process is tackled by optimization tools such as Genetic Algorithms (GA), particle swarm optimization, Ant Colony Optimization (ACO) shown in Fig. 5. Genetic algorithm is an adaptive heuristic search method based on the evolutionary ideas of natural selection. It is a used for solving optimization and search problems to find optimum solutions. GA operates on string structures called chromosomes, typically a concatenated list of binary digits representing the encoding of the control parameters of a specific problem. It repeatedly modifies a population of individuals and takes the decisions based on probabilistic rules. At each step, GA selects individual at random from the current population to be parent and use them to produce children for next generation. A competitive selection is carried out at each iteration, that eliminates poor solutions using genetic parameters (crossover and mutation). Over successive generation, the population evolves towards an optimal solution. Generally obtaining the global optimal solution is the major advantage of the GA. In [158] a combination of WPT and Fuzzy k-Nearest Neighbour (FkNN) algorithm along with genetic algorithms for automatic classification of PQ disturbances. The GA was proposed for selecting the optimal feature set from the WPT coefficients to improve the classification accuracy of PQ disturbances and selected features were applied to FkNN classifier. The accuracy was enhanced by selecting 16 better features from all 96 features generated from the WPT coefficients and the remaining redundant features were removed which may reduce the performance of the classification. The GA-SVM was proposed for simultaneous feature selection from DWT coefficients [159] and DWPT coefficients [160] and parameter optimization for two types of SVM kernels namely the polynomial kernel and RBF kernel to classify PQ disturbances. The two critical issues namely the selection of the most suitable features and the estimation of the best SVM kernel parameters are addressed through a classification system by using GA and simulated annealing (SA) optimization techniques. A combination of the extension theory and genetic algorithm known as Extension GA (EGA) was proposed in [161]. The extension theory provides a means for distance measurement in the classification process whereas the GA has the ability to

Optimization techniques

GA

PSO

ACO

Fig. 5. Feature selection techniques.

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search for an optimal solution within a wide space. The EGA is a kind of supervised learning that finds the best classical domain and gets better accuracy without adjusting the weight. In [162] hyperbolic ST was proposed for extracting several features to classify the PQ disturbances created due to distributed generation (DG) system. The optimal features were selected from several features by using GA for training and testing SVM and DT classifiers. These optimal features were used for PQ disturbances classification by employing SVM and decision tree classifiers. The WT and Genetic k-means Algorithm (GKA)-based RBF neural network classification system was proposed in [163] for noisy PQ events. The effectiveness of the clusters from the RBF training patterns was improved by avoiding being trapped in a local minimum solution. Particle swarm optimization is an evolutionary computation algorithm introduced by Kennedy et al. [164] modelled after the social behaviour of a bird flock. Like GA, PSO is a population based optimization tool that exploits the concept of social sharing of information. Each individual (called particle) of a given population (called swarm) can gain from the previous experiences of all other individuals in the same population. During the iterative search process in the multi-dimensional solution space, each particle (i.e., candidate solution) will adjust its flying velocity and position according to its own flying experience as well as those of the other companion particles in the swarm. The objective of the PSO algorithm is to obtain the particle position that results in the best evaluation of an objective function (fitness function). PSO has proved promising in solving a number of engineering problems such as automatic control, antenna design, and inverse problems

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and so forth. In [165], a hybrid fuzzy C-means with PSO technique was proposed to cluster the features into distinct groups so as to classify the nature of the time-series data. In [166], the optimal parameters of MSVM, the spread parameters of the PNN and K factor of k-NN classifiers were optimized using PSO for the PQ events under noisy conditions. In [167], PSO algorithm was applied for solving the problem of choosing optimal parameters of SVM classifiers. The penalty parameter and Gaussian kernel parameters of SVM classifier were selected by PSO algorithm. A fuzzy logic with PSO algorithm was proposed in [168] for the detection and classification of single and combined PQ disturbances. The features of the disturbances extracted from FT and DWT were applied to Fuzzy classifier. The PSO algorithm was used to accurately determine the membership function parameters for the fuzzy systems. In [169] authors proposed PSO-based extreme learning machine classifier for the classification of PQ disturbances. PSO was used to obtain the best subset of features extracted by DWT as well as for optimizing model selection parameters of the ELM algorithm for better classification accuracy. Ant colony optimization is a population-based optimization technique inspired by the ethological studies on the foraging behaviour of ants. The ants trace the shortest path between their nest and food source according to pheromone trial. ACO is a multiagent algorithm in which the agents find the solution by heuristic information obtained from the problem structure as well as by the global information obtained from the pheromone. Each agent finds a solution path with heuristic information so that shorter paths accumulation of the pheromone recursively. By utilizing the heuristic and pheromone information as well as the scheme of

Table 1 Recognition accuracies of the PQ classification techniques. References

Liao (2004) [44] Oleskovicz (2009) [128] Masoum (2010) [46] Eristi (2010) [142] Moravej (2010) [145] Eristi (2012) [144] Eristi (2013) [17] Wang (2011) [161] Haibo (2006) [61] Panigrahi (2009) [158] Manimala (2012) [160] Hu (2008) [143] Zhang (2012) [55] Chung (2002) [53] Moravej (2011) [157] Hasheminejad (2012) [89] Lee (2003) [75] Bhende (2008) [134] Uyar (2009) [132] Mishra (2008) [77] Chilukuri (2004) [80] Behera (2010) [86] Moravej (2011) [87] Fengzhan (2007) [76] Shunfan (2013) [78] Biswal (2013) [82] Biswal (2009) [83] Biswal (2013) [90] Eristi (2014) [92] Ray (2014) [162] Biswal (2011) [170] Lee (2011) [136] Jashfar (2013) [114] Abdelsalam (2012) [28] Saxena (2014) [102]

Feature extraction

FT–WT DWT DWT DWT DWT DWT DWT DWT DWT WPT WPT WPT WPT WPT ST ST ST ST ST ST ST ST ST ST ST FDST HST FDST S HST TTT ST–TTT ST–TTT KF–DWT HHT

Classifiers

FES MLP-RBF WN SVM SVM SVM LS-SVM–kMA – SOLAR FkNN–GA SVM SVM MSVM HMM–RBC LMT HMM FFNN–PNN FFNN FFNN PNN FL FES LMT RBDT DT DT FCM FDT ELM SVM–DT FCM PNN FFNN FES PNN–SVM

Feature selection

– — – – – – – EGA – – GA-SA – – – – – – – – – – PSO – – – – GA – – GA ACO PSO – – –

Classification accuracy Without noise

With noise (30 db)

99 91.03-87.07 98.18 99.71 98.89 98.51 98.88 96 94.93 96.33 98.33–97.92 98.4 97.7 98.7 99.11 98.14 95.33 95.5 99.67 97.4 98 99.8 99.11 99.7 99.27 99.28 95.75 98.66 99.50 99.5 95.97 96.3 92.083 98.71 100

– – 97.81 96.86 97.00 – 98.14 – 92 93.52 – – 92.25 – 97.79 95.05 93.33–94 – 99.11 93.2 96.5 99.2 97.79 98.5 97.91 97.49 – 97.948 99.67 96.1 – 96.1 – 97 –

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the pheromone accumulation, ants try to find the optimal (shortest) path from their nest to food source. ACO was inspired due to this mechanism. In [170] a hybrid fuzzy C-means and ACO algorithm was proposed to cluster the features into distinct groups so as to classify the nature of the time series PQ data. 7. Comparative analysis A large number of the classification systems have been discussed in this paper. The literature review shows that the various signal processing and artificial intelligence techniques have been combined together to complete the automatic PQ disturbances classification system. The classification accuracies of some of the main PQ disturbances detection system to recognize the types of the PQ disturbance are given in Table 1. A lot of research has been focused on feature extraction and classification stages for the automatic classification system. In feature extraction process the redundant features affect the performance of the classifiers due to inadequate features. The performance of a classifier depends upon the feature extraction process. Therefore, the scope of future research is there for optimal feature selection of PQ disturbances. Also, most of the researchers have focused on the single PQ disturbances, only few of them have used multiple PQ disturbances for the classification. The analysis of multiple PQ disturbances needs much attention. 8. Future scope There are several challenging issues in the automatic classification of PQ disturbances such as detection of the underlying causes of disturbances, detection of the single and multiple disturbances. The most suitable feature extraction technique should be applied because the classification algorithms are highly dependent on the input feature vectors. A lot of research has been focussed on the synthetic data for training and testing the classifiers. The real-time data must be applied. De-noising is still a challenge for the classification of noise riding PQ signals researchers because the feature extraction and classification algorithms poorly perform for the noise riding PQ signals. The most of the PQ disturbances classification techniques are based on the single-phase data. In actual practice, power system is a three-phase system. Therefore, the more research must be focused on real-time three-phase power system disturbances. 9. Conclusion A comprehensive and detailed review on the applications of signal processing and artificial intelligence techniques is presented in this paper. The feature extraction techniques such as Fourier transform, wavelet transform, S-transform, Hilbert–Huang transform are discussed in detail. The artificial intelligence techniques which are applied as classifiers such as artificial neural network, support vector machines, Fuzzy logic, expert systems have been reviewed in detail. The meta-heuristic optimization algorithms such as genetic algorithm, particle swarm optimization, ant colony optimization techniques used for choosing the most suitable features have also been discussed. The future scope for research in the field of power quality disturbances classification is also presented. It can be concluded from the detailed literature review that the signal processing techniques can be used for the detection of the type of the disturbances as well as for the effective feature extraction. The extracted features can be used as inputs for the artificial intelligence techniques. The optimization techniques have been used for the selection of the most suitable features extracted

from the signal processing techniques. There is still a need of improvement in automatic classification of PQ disturbances in order to tackle the challenges. The new techniques should be used for the identification of underlying causes of PQ disturbances such as line faults, power electronics based nonlinear loads and sudden switching events. This study shows that many researchers have used classification methods only for single PQ disturbances, hence the new algorithms should be developed for multiple PQ disturbances created simultaneously. Also, there is a need of much improvement of noise tolerant techniques for noise-riding PQ signals. Moreover, the application of real-time data, three-phase system PQ signals, optimal feature selection are regarded as the future work in the field of the PQ disturbances classification.

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