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Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner
Ain Shams Engineering Journal xxx (2017) xxx–xxx
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Electrical Engineering
Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner V. Veera Nagireddy a,⇑, Venkata Reddy Kota b, D.V. Ashok Kumar c a
Dept of Electrical Power Engineering, IbriCT, Ibri, Oman Dept of EEE, JNTU, Kakinada, A.P, India c Dept of EEE, RGMCET, Nandyal, Kurnool (Dt.), A.P, India b
a r t i c l e
i n f o
Article history: Received 20 May 2016 Revised 27 July 2017 Accepted 4 September 2017 Available online xxxx Keywords: Hybrid fuzzy-back propagation control Power quality Voltage sag mitigation Total harmonic distortion Cascaded H-bridge inverter UPQC Load balancing
a b s t r a c t To increase the lifetime and performance of equipment in utility distribution systems, the enhancement of power quality is essential. In this paper, to enhance the power quality, we present the design of a hybrid fuzzy back-propagation control scheme for a unified power quality Conditioner. The gating pulses of the UPQC are generated using hybrid fuzzy-back propagation controllers. The reference currents for the controller are estimated using back-propagation algorithms with source currents and load currents as input control parameters. The reference voltages for the controllers are estimated using fuzzy logic controllers for the dc voltage regulator with a terminal voltage, and a dc voltage for the input control parameters. We perform this analysis in zero-voltage regulation mode and power factor correction mode in a multilevel UPQC-connected distribution system. We analyze the results of the total harmonic distortion, dynamic performance, load balancing and mitigation of the voltage sag using MATLAB/Simulink. Ó 2017 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction A small improvement in the standard power quality significantly affects the lifetime and performance of utility equipment. Most distribution centers have established quality control centres to provide reliable, flexible, and intelligent electrical power supply to customers, even under abnormal circumstances. Intelligent control schemes that utilize artificial intelligence (AI) techniques such as artificial neural networks (ANNs), fuzzy logic, and the artificial bee colony algorithm are good substitutes for conventional control schemes to realize power quality enhancement in existing scenarios. In Ref. [1], the authors proposed artificial intelligence technique in defining optimal operating cost of smart grid. Realizing power quality involves maintaining the quality of voltage, eliminating harmonics that are due to nonlinear loads, and performing load balancing as a result of load imperfections and constant frequency [2]. In Ref. [3], the authors proposed a basic backpropagation algorithm, which proved that the weights in the training process were bounded in that they lead to good convergence.
Peer review under responsibility of Ain Shams University. ⇑ Corresponding author. E-mail addresses: [email protected] (V.V. Nagireddy), kvr.jntu@gmail. com (V.R. Kota), [email protected] (D.V. Ashok Kumar).
This algorithm was modified in [4] by changing the total error of the performance function of incremental conductance algorithms such that the learning process is accelerated and can be predicted with better accuracy. In [5], the mathematical analysis of complex versions of the back-propagation algorithm network was proposed using the sinusoidal error representation of the transformation. The power quality of distribution systems has improved owing to the introduction of power electronic devices, called custom power devices, such as distribution static compensators (DSTATCOMs), dynamic voltage restorers (DVRs), and unified power quality conditioners (UPQC). UPQCs combine the functions of a DVR and DSTATCOM [6], and eliminates harmonics, voltage sag, and load balancing. The estimation and elimination of harmonics is an area of interest for many researchers. In three-phase distribution systems, the harmonics can be estimated and eliminated using the variable step size least mean squares approach, as proposed in [7], for shunt active-power filters with various simulation results. The DSTATCOM is an effective device for the elimination of harmonics using advanced control techniques, as in [8–10]. In [8], a DSTATCOM was modelled using a self-tuning filter-based instantaneous reactive power theory control algorithms, and it was validated using digital signal processors. The method proposed in [9], symmetrical component theory and improved instantaneous active and reactive current component theory control strategies,
https://doi.org/10.1016/j.asej.2017.09.004 2090-4479/Ó 2017 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Nagireddy VV et al. Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner. Ain Shams Eng J (2017), https://doi.org/10.1016/j.asej.2017.09.004
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Nomenclature MI-UPQC multilevel inverter unified power quality conditioner VSC voltage source converter PCC point of common coupling v cabc compensator three phase voltage v sabc source three phase voltage isabc source three phase current load three phase current iLabc icabc compensator three phase current Vdc DC link voltage netyp net in-phase current input to hidden layer I quadrature net current input to hidden layer I netyq YLp in-phase ouput of hidden layer I YLq quadrature ouput of hidden layer I wo initial weight k learning rate net zp net in-phase current input to hidden layer II
and compared these two under unbalanced conditions. The intelligent control scheme using back-propagation algorithms for DSTATCOM was proposed in [10]. In short, existing studies have attempted to eliminate harmonics by estimating harmonics. To extend this attempt to voltage sags and load balancing, the UPQC was proposed under unbalanced load and non-ideal voltage mains conditions, as in [11]. In Ref. [12], the authors proposed exponential control algorithm based UPQC to mitigate the power quality. Synchronous reference frame-based control schemes have been proposed to three-phase four wire with UPQC, and the computational delay was reduced and analyzed using MATLAB / Simulink [13]. In [14], A fuzzy based multiobjective optimization technique was proposed in hybrid distributed generation system. An active power conditioners were used in industrial distribution systems for voltage harmonics, current harmonics, voltage sag, and load-balancing evaluations. Fuzzy logic controllers (FLCs) and ANN are the two branches of AI. This work combines the advantages of ANNs and FLCs to design a new control scheme [15]. An adaptive neural fuzzy inference system (ANFIS-) based fault-classification control schemes for neutral grounded distribution systems was proposed [16]. In Ref. [17], An ANN-based DSTATCOM with one cycle control strategy was proposed to mitigate harmonics. In Ref. [18], the authors proposed comparison of Genetic algorithms (GAs), particle-swarm optimization (PSO), and the artificial bee colony (ABC) algorithm which are recently developed AI techniques in the field of electrical engineering, with integer order and fractional order techniques. In recent years, owing to an increase in the use of nonlinear power electronic equipment, power quality has become a major issue, and hybrid AI techniques are currently being researched for UPQCs. An efficient multilayered ANN-based active power filter was proposed the shift method to reduce harmonics [19]. The PSO technique has been evaluated based on error convergence and compared with backpropagation neural networks [20]. Power quality enhancement using AI-controlled schemes is the latest area of research in the field of custom power devices in electrical distribution systems. The fuzzy control strategy was developed for UPQC with sudden switching loads [21]. The performance of compensating devices can be determined by measuring the degree of extraction of harmonic components and voltage sag. In this paper, we present a hybrid fuzzy back-propagationbased control scheme for three-phase shunt and series connected custom power devices, called a UPQC, for the estimation of a reference-weighted value for current and voltage components.
Zp Zq net p Op Oq v, w FLC GP Gq THD FP Fq ep eq
in-phase ouput of hidden layer II quadrature ouput of hidden layer II net in-phase current input to hidden layer II in-phase ouput of neural network quadrature ouput of neural network weight vectors fuzzy logic controller in-phase reference source current amplitude quadrature reference source current amplitude total harmonic distortion in-phase output of fuzzy controller quadrature output of fuzzy controller in-phase voltage vector quadrature voltage vector
The proposed control scheme for UPQC can suppress harmonics, as well as mitigate voltage sag, and load balancing. 2. System configuration We tested the proposed control scheme on a three-phase distribution system that consists of three-phase sources and threephase linear/nonlinear loads. A series multilevel voltage source converter (VSC) is connected to the three-phase distribution system through a three-phase injection transformer, and a shunt multilevel VSC is connected through an interfacing inductor at the point of common coupling (PCC), as shown in Fig. 2. In Section 3, we discuss the generation gating pulses to the cascaded H-bridge inverter [22] using a hybrid fuzzy back-propagation network controller. A UPQC is a custom power device that is a combination of a dynamic voltage restorer (DVR) and distribution STATCOM (DSTACOM). The multilevel inverter UPQC (MI-UPQC) three-phase voltages (vcabc) are injected to compensate for the voltage sag, and three-phase currents (icabc) are injected to cancel the reactive power components of the load currents on the distribution system. The number of voltage levels is 2n + 1, and Vdc/2n is the voltage step for the two single phase cascaded H-bridge inverters, as shown in Fig. 1, and the switching modes of operation are presented in Table 1. The number of capacitors required for a H bridge five level UPQC are six, where each phase is shared by two H-bridge invert-
Fig. 1. Single-phase five-level cascaded H-bridge inverter.
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ers in cascade and two capacitors to provide dc link between series and shunt converters. The selection of Vdc plays major role in efficient functioning of UPQC. Under dynamic conditions as shown in Fig. 8, the power capacities of series compensator and shunt compensator are assumed to be 3S KVA and 0.7S KVA respectively. The voltage across DC capacitor decreases with increase in KVA rating. Let change in Vdc across capacitor be 30%, then the change in energy is given by
1 DW dc ¼ C dc fð1:130V dc Þ2 þ ð0:7V dc Þ2 g 2
ð1Þ
Table 1 Switching table for single phase 5-level Cascaded H-bridge inverter. Switches ON
Voltage level
S1, S1, S1, S1, S3, S3, S3, S1,
Vdc/2 Vdc Vdc/2 0 Vdc/2 Vdc Vdc/2 0
S2, S5 and D7 S2, S6 and S5 S2, S6 and D8 D3, S6 and D8 S4, S6 and D8 S4, S7 and S8 S4, D6 and S8 S3, D6 and S8
Fig. 2. Schematic diagram of hybrid fuzzy back-propagation multilevel UPQC.
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If the load on the decreases from 3S KVA to 0.7S KVA in m number of cycles, then the decrease in energy of the network is
DW N ¼ ð3S 0:7SÞmT
ð2Þ
where T is the time period. From Eqs. (1) and (2), the DC capacitor is
C dc ¼
2ð3S 0:7SÞmT
ð3Þ
fð1:130V dc Þ2 þ ð0:7V dc Þ2 g
Let k be the ratio of DC link capacitor voltage to the system maximum voltage (Vp). It is observed for various values of ‘k’, out of that total harmonic distortion is minimum for k = 1.3. Hence, The THD depends on the selection of dc capacitor voltage. Therefore, the dc link capacitor voltage is
V dc ¼ 1:3V p
ð4Þ
3. Hybrid fuzzy back-propagation control scheme 3.1. Back-propagation network The design and implementation of hybrid fuzzy backpropagation controllers is presented in three stages. In the first stage, we estimate the reference in-phase and quadrature components of currents. Then, in the second stage, we estimate the reference voltages. The third stage involves combining the two references and estimating the final reference unit values for the pulse-width modulation process. In the first stage, load currents (iLa, iLb, and iLc) act as inputs to the back-propagation network, as shown in Fig. 3. The extracted in-phase and quadrature values (netyap , netybp , netycp , netyaq , netybq , and netycq ) are applied through
the activation of the sigmoid function. The real and reactive output signals (YLap, YLbp, YLcp, YLaq, YLbq, and YLcq) of the feed-forward section of hidden layer I for three phases are expressed by Eqs. (A1)–(A12). The output signals of hidden layer I (YLap, YLbp, YLcp, YLaq, YLbq, and YLcq) are fed as inputs for the hidden layer II of the backpropagation network. This input vector is propagated through the multi layer neural network, as shown in Fig. 3. The extracted in-phase and quadrature values are multiplied by respective weights (netzap , netzbp , netzcp , netzaq , netzbq , and netzcq ), and are applied by activating the sigmoid function. The output signals (Zap, Zbp, Zcp, ZLaq, Zbq, and Zcq) of the feed-forward section of hidden layer II for three phases are expressed by Eqs. (A13)–(A24). The weights between hidden layer II and output layer are in the ratio of 1:3. The outputs of the network for the real and reactive components are expressed by Eqs. (A25)–(A28). The real and reactive weights (v) between hidden layer I and hidden layer II, the network shown in Fig. 3 for three phases (a, b and c) can be adjusted, using back-propagation training algorithm. The weights are updated in the direction that reduces the harmonic distortion, voltage sag, voltage swell, and load unbalancing and these are expressed by Eqs. (A29)–(A40). 3.2. Fuzzy logic controller design for MI-UPQC An FLC involves fuzzification, fuzzy rules, a fuzzy inference system, and defuzzification. The FLC is used as terminal voltage regulator and a DC voltage regulator, as shown in Fig. 6. In the DC voltage regulator, the FLC has as its two inputs the error (ei = V⁄dc – Vidc; i = 1, 2, 3, 4, 5, 6) and the rate of change of error De (A44), and Fp as the fuzzy output. The number of DC common voltage regulators for series and shunt converters, that are used
Fig. 3. Back-propagation multilayer (3-6-6-2) neural network.
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in this H-bridge inverter UPQC are (n + 1), where ‘n’ is the number of levels. Similarly, for the terminal voltage regulator, the FLC has as its two inputs the error (e = V⁄t – Vt) and the rate of change of error De (A45). The triangular membership functions are used as the inputs of the FLC, as shown in Figs. 4 and 5. The linguistic variables for the error, e, and rate of change of error, De, along with the fuzzy controller output are given as ‘‘PH” - Positive High, ‘‘PM” - Positive Medium, ‘‘PL” - Positive Low, ‘‘Z” – Zero, ‘‘NL” - Negative Low, ‘‘NM” –‘‘Negative Medium, and ‘‘NH” - Negative High. One terminal voltage regulator is used in this H-bridge inverter UPQC and it can be calculated from Eq. (A41). From a practical perspective, fuzzy rules are developed using linguistic variables that are formulated in the form of ‘‘IF THEN” rules. The fuzzy rules for the terminal voltage regulator and DC voltage regulator are given in Table 2. 3.3. Fuzzy back-propagation control scheme The in-phase and quadrature unit voltages can be used as reference vectors to generate gating pulses by gating pulse
5
generator as shown in Fig. 6, so as to improve the power quality (A42) and (A43). The in-phase component of the reference source current amplitude (GP) is the sum of the output of the dc bus fuzzy controller (FP) and the in-phase output of the back-propagation network as represented in (A46). The component of the quadrature reference source current amplitude (Gq) is the sum of the output of the terminal bus fuzzy controller (Fq) and the quadrature output of the back-propagation network (A47). The three-phase reference in-phase source current and voltage components can be obtained using the amplitude of the three-phase load’s inphase current components and the PCC’s in-phase unit voltages as in (A48)–(A51). We compared the source currents (isa, isb, isc) with the reference source currents (A52), and three error signals (54) are fed to a PWM technique in order to generate the switching signals for insulated-gate bipolar transistors (IGBTs) S1 to S12 of the shunt multilevel VSC. We compared the source voltages (vsa, vsb, vsc) with the reference source voltages (A53), and three voltage error signals (A55) are fed to a PWM in order to generate the switching signals for IGBTs S0 1 to S0 12 of the
Fig. 4. Membership functions for input error.
Fig. 5. Membership functions for rate of change of error.
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Fig. 6. Back-propagation-based fuzzy logic control scheme for multilevel UPQC.
Table 2 Fuzzy rules. e
PH PM PL Z NL NM NH
De NH
NM
NL
Z
PL
PM
PH
Z NL NM NH NH NH NH
PL Z NL NM NH NH NH
PM PL Z NL NM NH NH
PH PM PL Z NL NM NH
PH PH PM PL Z NL NM
PH PH PH PM PL Z NL
PH PH PH PH PM PL Z
series VSC. The block diagram of the hybrid fuzzy backpropagation controller is shown in Fig. 6 and the flow chart of fuzzy back-propagation control scheme for multi level inverter UPQC is shown in Fig. 7.
3.4. Test system AC supply source: Three-phase, V f = 50 Hz.
L-L
= 415 V, frequency,
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Fig. 7. Flow chart of back-propagation-based fuzzy logic control scheme.
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Fig. 8. Dynamic performance of multilevel UPQC.
Source impedance: Rs = 0.8 X and Ls = 0.175 mH. Nonlinear: Three-phase full bridge uncontrolled rectifier, R = 20 X and L = 40 H. Rating of VSC = 10 kVA (approximately 35% higher than the rated value). Ripple filter: Rf = 3 X and Cf = 1F. Switching frequency of inverter = 10 kHz. Reference DC bus voltage: 700 V. Interfacing inductor (Lf) = 1 mH. Selected initial weights: wo = 1 and wo1 = 1. Learning rate (k) = 0.5. Cutoff frequency of low-pass filter used in dc bus voltage = 15 Hz.
Cutoff frequency of low-pass filter used in AC bus voltage = 10 Hz.
4. Results and discussion 4.1. Dynamic performance of fuzzy back-propagation controlled multilevel UPQC We studies the dynamic performance of a fuzzy backpropagation multi level UPQC in terms of the power factor correction (PFC) and ZVR modes for nonlinear loads. The dynamic performance results for phase voltages at PCC (vcabc), compensator
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Fig. 8 (continued)
Fig. 9. PCC phase ‘‘a” voltage in ZVR mode.
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Fig. 10. Phase ‘‘a” source voltage in ZVR mode.
Fig. 11. Phase ‘‘a” line current in ZVR mode.
Fig. 12. Phase ‘‘a” source current in ZVR mode.
currents (iCabc), compensator voltages (vCabc), source currents (isabc), load currents (iLabc), terminal voltage (vt), and DC bus voltage (vdc) are shown in Fig. 8 (for t = 3.7–3.8 s) under varying nonlinear load conditions. In the ZVR mode, the PCC voltage is regulated by injecting additional leading reactive power components. We observed that the terminal voltages (Vtabc) have equal magnitudes, and that they are at angles of 120° with respect to each other, as
shown in Fig. 8, and the multilevel UPQC can perform load balancing under abnormal conditions. 4.2. Total harmonic distortion The FFT analysis results for the phase ‘‘a” PCC voltage, source voltage, load current, source current, and compensator voltage in
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Fig. 13. Compensator phase ‘‘a” voltage in ZVR mode.
Fig. 14. Phase ‘‘a” source voltage in PFC mode.
Fig. 15. Phase ‘‘a” source current in PFC mode.
the ZVR mode are shown in Figs. 9–13 and in PFC mode shown in Figs. 14–18. We observe that the total harmonic distortion (THD) of phase ‘‘a” for the PCC voltage, source current, source voltage, load current, and compensator voltage in the ZVR mode are 1.07, 2.27, 0.91, 28.87, and 0.11, respectively, while in PFC mode they are
1.04, 2.24, 1.04, 25.74, and 1.40, respectively. These simulation results are summarized in Table 3. The results show that there is satisfactory performance of the hybrid fuzzy back-propagation multilevel UPQC because the THDs are within the IEEE-519 standard limit (<5%) [23].
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Fig. 16. Phase ‘‘a” line current in PFC mode.
Fig. 17. PCC phase ‘‘a” voltage in PFC mode.
Fig. 18. Compensator phase ‘‘a” voltage in PFC mode.
4.3. Mitigation of voltage sag The mitigation of voltage sags using a hybrid fuzzy backpropagation multilevel UPQC can be observed under nonlinear load
and faulty conditions. The source voltage of phase ‘‘a” having a sag of 150 V for a nominal voltage of 250 V are shown in Fig. 19. The terminal voltage sags have been mitigated using the proposed control scheme, as shown in Fig. 20.
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V.V. Nagireddy et al. / Ain Shams Engineering Journal xxx (2017) xxx–xxx Table 3 Performance of fuzzy-back propagation based UPQC under non-linear load. Operating mode
Parameter
Magnitude
Total Harmonic Distortion (%THD)
ZVR mode
PCC voltage Supply current Source voltage Load current Compensator voltage
240 V 33.39 A 240 V 27.8 A 50.22 V
1.07 2.27 0.91 28.87 0.11
PFC mode
PCC voltage Supply current Source voltage Load current Compensator voltage
237.5 V 26.69 A 237.4 V 29.46 A 27.93
1.04 2.24 1.04 25.74 1.40
Fig. 19. Phase ‘‘a” terminal voltage sag.
Fig. 20. Phase ‘‘a” terminal voltage with mitigated sag using proposed control scheme.
5. Conclusions UPQC can compensate the voltage and current levels under abnormal conditions such as those involving DVR and DSTATCOM, and reduce current and voltage harmonics. In ZVR mode, the load currents have a THD of 28.87%, which has been compensated to 2.27% at the source side. In PFC mode, the load currents have a THD of 25.74%, which has been compensated to 2.24% at the source side. In ZVR mode, the voltage harmonics at the load side has a THD of 1.07%, and it is 0.91% at the source end. In PFC mode, the voltage harmonics at the load side has a THD of 1.04%, and it is 1.04% at the source end. We conclude that the proposed five-level UPQC and its control scheme are suitable for nonlinear load compensation even under faulty conditions with respective to the THD, voltage sag, and load balancing using the back-propagation control scheme. The DC voltage of the UPQC was regulated using the fuzzy controller at the voltage without any overshoot or undershoot under abnormal conditions. The proposed hybrid
fuzzy back-propagation control scheme has been used to estimate gating signals for series and shunt VSCs of multilevel UPQCs. Appendix A. Mathematical formulation The in-phase net input vector to the hidden layer I of the neural network, as shown in Fig. 3, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netyap ¼ wo þ iLa wpa þ iLb wpb þ iLc wpc
ðA1Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘a” is given by
Y Lap ¼ f ðnetyap Þ ¼
1 1 þ eknetyap
ðA2Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘a” are expressed as
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netyaq ¼ wo þ iLa wqa þ iLb wqb þ iLc wqc Y Laq ¼ f ðnetyaq Þ ¼
1 1 þ eknetyaq
ðA3Þ ðA4Þ
The in-phase net input vector to the hidden layer I of the neural network, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netybp ¼ wo þ iLa w0pa þ iLb w0pb þ iLc w0pc
ðA5Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘b” is given by
Y Lbp ¼ f ðnetybp Þ ¼
1 1 þ eknetybp
ðA6Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘b” are expressed as
netybq ¼ wo þ iLa w0qa þ iLb w0qb þ iLc w0qc Y Lbq ¼ f ðnetybq Þ ¼
1 1 þ eknetybq
ðA7Þ ðA8Þ
The in-phase net input vector to the hidden layer I of the neural network, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netycp ¼ wo þ iLa w00pa þ iLb w00pb þ iLc w00pc
ðA9Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘c” is given by
Y Lcp
1 ¼ f ðnetycp Þ ¼ 1 þ eknetycp
ðA10Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘c” are expressed as
netycq ¼ wo þ iLa w00qa þ iLb w00qb þ iLc w00qc Y Lcq ¼ f ðnetycq Þ ¼
1 1 þ eknetycq
ðA11Þ ðA12Þ
where wo is the randomly chosen initial weight, wpa, wpb, and wpc are the in-phase three-phase weights and wqa, wqb, and wqc are the quadrature three-phase weights. The in-phase net input vector to the hidden layer II of the neural network, as shown in Fig. 3, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzap ¼ wo1 þ Y Lap v ap þ Y Lbp v bp þ Y Lcp v cp
ðA13Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘a” is given by
Z ap ¼ f ðnetzap Þ ¼
1 1 þ eknetzap
ðA14Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘a” are expressed as
netzaq ¼ wo1 þ Y Laq v aq þ Y Lbq v bq þ Y Lcq v cq Z aq
1 ¼ f ðnetzaq Þ ¼ 1 þ eknetzaq
ðA15Þ ðA16Þ
The in-phase net input vector to the hidden layer II of the neural network, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzbp ¼ wo1 þ Y Lap v 0ap þ Y Lbp v 0bp þ Y Lcp v 0cp
ðA17Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘b” is given by
Z bp ¼ f ðnetzbp Þ ¼
1 1 þ eknetzbp
ðA18Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘b” are expressed as
netzbq ¼ wo1 þ Y Laq v 0aq þ Y Lbq v 0bq þ Y Lcq v 0cq Z bq ¼ f ðnetzbq Þ ¼
1 1 þ eknetzbq
ðA19Þ ðA20Þ
The in-phase net input vector to the hidden layer II of the neural network, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzcp ¼ wo1 þ Y Lap v 00ap þ Y Lbp v 00bp þ Y Lcp v 00cp
ðA21Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘c” is given by
Z cp ¼ f ðnetzcp Þ ¼
1 1 þ eknetzcp
ðA22Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘c” are expressed as
netzcq ¼ wo1 þ Y Laq v 00aq þ Y Lbq v 00bq þ Y Lcq v 00cq Z cq ¼ f ðnetzcq Þ ¼
1 1 þ eknetzcq
ðA23Þ ðA24Þ
where wo1 is the randomly chosen initial weight, v ap , v bp , and v cp are the real initial weights between hidden layer I and hidden layer II, and v aq , v bq , and v cq are the three-phase reactive initial weights between hidden layer I and hidden layer II.The in-phase net input to the output layer of the neural network, as shown in Fig. 3, is given by
netp ¼
1 ðZ ap þ Z bp þ Z cp Þ 3
ðA25Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of the neural network is given as
Op ¼ f ðnetp Þ ¼
1 1 þ eknetp
ðA26Þ
The quadrature net input to the output layer of the neural network, is given by
netq ¼
1 ðZ aq þ Z bq þ Z cq Þ 3
ðA27Þ
The quadrature net input signals are applied through the activation of the unipolar sigmoid function. The output signal of the neural network is given as
Oq ¼ f ðnetq Þ ¼
1 1 þ eknetq
ðA28Þ
v ap ðnewÞ ¼ v ap ðoldÞ þ afðOp Z ap Þgf 0 ðnetzap ÞY Lap
ðA29Þ
v aq ðnewÞ ¼ v aq ðoldÞ þ afðOq Z aq Þgf 0 ðnetzaq ÞY Laq
ðA30Þ
v bp ðnewÞ ¼ v bp ðoldÞ þ afðOp Zbp Þgf 0 ðnetzbp ÞY Lbp
ðA31Þ
v bq ðnewÞ ¼ v bq ðoldÞ þ afðOq Z bq Þgf 0 ðnetzbq ÞY Lbq
ðA32Þ
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v cp ðnewÞ ¼ v cp ðoldÞ þ afðOp Zcp Þgf 0 ðnetzcp ÞY Lcp
ðA33Þ
v cq ðnewÞ ¼ v cq ðoldÞ þ afðOq Zcq Þgf 0 ðnetzcq ÞY Lcq
ðA34Þ
Similarly, the real and reactive weights (w) between input layer and hidden layer II, the network shown in Fig. 3 for three phases (a, b and c) can be adjusted, using back-propagation training algorithm. The updated real and reactive weights (w) for three phases are:
The three-phase reference source currents (a, b, and c) are the sum of the in-phase and quadrature current components are shown in Fig. 6, and is given as
The three-phase reference source voltages (a, b, and c) are the sum of in-phase and quadrature voltage components, and is given as
0
ðA35Þ
vsa ¼ vsap þ vsaq ; vsb ¼ vsbq þ vsbq ; vsc ¼ vscp þ vscq
0
0
ðA36Þ
The error signals of the shunt VSC for the three phases (a, b, and c) are
0
0
ðA37Þ
0
0
waq ðnewÞ ¼ waq ðoldÞ þ afðOq Z aq Þgf ðzaq Þf ðnetyaq ÞiLa
wbp ðnewÞ ¼ wbp ðoldÞ þ afðOp Z bp Þgf ðzbp Þf ðnetybp ÞiLb wbq ðnewÞ ¼ wbq ðoldÞ þ afðOq Z bq Þgf ðzbq Þf ðnetybq ÞiLb
ðA38Þ
0
0
ðA39Þ
0
0
ðA40Þ
wcp ðnewÞ ¼ wcp ðoldÞ þ afðOp Z cp Þgf ðzcp Þf ðnetycp ÞiLc wcq ðnewÞ ¼ wcq ðoldÞ þ afðOq Z cq Þgf ðzcq Þf ðnetycq ÞiLc
The amplitudes of the sensed PCC voltages are estimated using three-phase source voltages as:
rffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 v 2sa þ v 2sb þ v 2sc ¼ 3
ðA41Þ
These unit voltage vector is the ratio of source voltage to PCC voltage as
eap ¼
v sa ; vt
ebp ¼
v sb ; vt
ecp ¼
v sc vt
ðA42Þ
The quadrature unit voltages are estimated as eaq ¼
ðebp þ ecp Þ pffiffiffi ; 3
ebq ¼
ð3eap þ ebp ecp Þ pffiffiffi ; 2 3
ecq ¼
ð3eap þ ebp ecp Þ pffiffiffi 2 3
ðA43Þ The in-phase and quadrature components of the error inputs for the FLC are respectively given by
ep ðkÞ ¼ Vdc dcðkÞ Vdc ðkÞ
ðA44Þ
eq ðkÞ ¼ vt ðkÞ Vt ðkÞ
ðA45Þ
The average magnitude of the load’s in-phase component (OP), and is given as
GiP ¼ FiP þ OP ;
i ¼ 1; 2; 3
ðA46Þ
The average magnitude of the load’s quadrature component as shown in Fig. 6, and is given (Oq) as
Giq ¼ Fiq þ Oq ;
ðA52Þ
0
wap ðnewÞ ¼ wap ðoldÞ þ afðOp Z ap Þgf ðzap Þf ðnetyap ÞiLa
vt
isa ¼ isap þ isaq ; isb ¼ isbp þ isbq ; isc ¼ iscp þ iscq
i ¼ 1; 2; 3
ðA47Þ
The three-phase reference in-phase source current and the voltage components can be obtained using the amplitude of the threephase load’s quadrature current components and PCC quadrature unit voltages as:
isap ¼ G1P eap ; isbp ¼ G2P ebp ; iscp ¼ G3P ecp
ðA48Þ
isaq ¼ G1q eaq ; isbq ¼ G2q ebq ; iscq ¼ G3q ecq
ðA49Þ
vsap ¼
Gp Gp Gp eap ; vsbq ¼ ebp ; vscp ¼ ecp Gq Gq Gq
ðA50Þ
vsaq ¼
Gq Gq Gq eaq ; vsbq ¼ ebq ; vscq ¼ ecq Gp Gp Gp
ðA51Þ
ea ¼ isa isa ; eb ¼ isb isb ; ec ¼ isc isc
ðA53Þ
ðA54Þ
The error signals of the series VSC for the three phases (a, b, and c) are
e0a ¼ vsa vsa ; e0b ¼ vsb vsb ; e0c ¼ vsc vsc
ðA55Þ
References [1] Elkazaz Mahmoud H, Hoballah Ayman A, Azmy Ahmed M. Operation optimization of distributed generation using artificial intelligent techniques. Ain Shams Eng J 2016;7(2):855–66. [2] Kusko Alexander, D Sc, Thompson Marc T. Power quality in electrical systems. McGraw-Hill; 2007. [3] Zhang Huisheng, Wu Wei, Yao Mingchen. Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks. Neurocomputing 2012;89:141–6. [4] Punitha K, Devaraj D, Sakthivel S. Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 2013;62:330–40. [5] Nitta Tohru. An extension of the back-propagation algorithm to complex numbers. Neural Networks 1997;10(8):1391–415. [6] Hingorani. Introducing custom power IEEE spectrum June 1995; vol. 32, No. 6, p:41–8. [7] Panda Gayadhar, Ray Pravat Kumar, Puhan Pratap S, Dash Santanu K. Novel schemes used for estimation of power system harmonics and their elimination in a three-phase distribution system. Int J Electr Power Energy Syst 2013;53:842–56. [8] Singh Bhim, Dube Sunil Kumar, Arya Sabha Raj. An improved control algorithm of DSTATCOM for power quality improvement. Int J Electr Power Energy Syst 2015;64:493–504. [9] Zaveri Tejas, Bhalja Bhavesh, Zaveri Naimish. Comparison of control strategies for DSTATCOM in three-phase, four-wire distribution system for power quality improvement under various source voltage and load conditions. Int J Electr Power Energy Syst 2012;43(1):582–94. [10] Singh Bawa, Arya Sabha Raj. Back-propagation control algorithm for power quality improvement using DSTATCOM. IEEE Trans Indus Electron 2014;61 (3):1204–12. [11] Kesler Metin, Ozdemir Engin. A novel control method for unified power quality conditioner (UPQC) under non-ideal mains voltage and unbalanced load conditions. In: 25th Annual IEEE applied power electronics conference and exposition (APEC); 2010. p. 374–9. [12] Sindhu S, Sindhu MR, Nambiar TNP. An exponential composition algorithm based UPQC for power quality enhancement. Proc Technol 2015;21:415–22. [13] Pal Yash, Swarup A, Singh Bhim. A comparative analysis of different magnetics supported three-phase four-wire unified power quality conditioners – a simulation study. Int J Electr Power Energy Syst 2013;47:436–47. [14] Shayeghi H, Hashemi Y. Application of fuzzy decision-making based on INSGAII to designing PV–wind hybrid system. Eng Appl Artif Intell 2015;45:1–17. [15] Sivanandam SN, Deepa SN. Principle of soft computing. New Delhi, India: Wiley India Ltd.; 2010. [16] Zhang J, He ZY, Lin S, Zhang YB, Qian QQ. An ANFIS-based fault classification approach in power distribution system. Int J Electr Power Energy Syst 2013;49:243–52. [17] Jayachandran J, Murali Sachithanandam R. ANN based controller for three phase four leg shunt active filter for power quality improvement. Ain Shams Eng J 2016;7(1):275–92. [18] Rajasekhar Anguluri, Jatoth Ravi Kumar, Abraham Ajith. Design of intelligent PID/PI k D l speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm. Eng Appl Artif Intell 2014;29:13–32. [19] El-Mamlouk Wael M, Mostafa Hossam E, El-Sharkawy Metwally A. Active power filter controller for harmonic suppression in industrial distribution system. Ain Shams Eng J 2011;2(3):161–72. [20] Suresh Abhijit, Harish KV, Radhika N. Particle swarm optimization over back propagation neural network for length of stay prediction. Proc Comput Sci 2015;46:268–75.
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[21] Shankar S, Kumar A. Fuzzy based unified power quality conditioner. In: International conference on power signals control and computations (EPSCICON), IEEE; January 2014. p. 1–7. [22] Corzine KA, Familiant YL. A new cascaded multi-level H-bridge drive. IEEE Trans Power Electron 2002;17(1):125–31. [23] Emanuel Alexander Eigeles. Summary of IEEE standard 1459: definitions for the measurement of electric power quantities under sinusoidal, nonsinusoidal, balanced, or unbalanced conditions. IEEE Trans Indus Appl 2004;40 (3):869–76.
Mr. V. Veera Nagi Reddy, Research scholar, JNTUK, KAKINADA, A.P, India, is graduated in 2004. He obtained masters degree from JNTU, Anantapur, India. Currently, he is working in Electrical Power Engineering at Ibri College of Technology, Ibri, Oman. He is working towards his Ph.D. degree in JNTUK, KAKINADA. His research interests include soft computing techniques, smart grids, distributed generation systems and renewable energy sources.
Prof. D.V. Ashok Kumar, is graduated in 1996, Masters in 2000from J.N.T.U.C.E, Anantapur and Ph.D. in 2008 from the same university. He worked 12 years at R.G.M. College of Engineering Technology, Nandyal, and A.P. in the cadres of Assistant Professor, Professor and Head of Electrical and Electronics Engg. Department. Since Oct. 2008 to June 2015 he worked as Principal at Syamaladevi institute of Technology for women, Nandyal. July 2015 to till date he is working as Professor in EEE & Dean- Administration at R.G.M. College of Engineering Technology, Nandyal, A.P. He has published 33 research papers in national and international conferences and journals. He has attended 10 National & International workshops. His areas of interests are Electrical Machines, Power Systems & Solar Energy.
Dr. Venkata Reddy Kota is currently working as Assistant Professor in the Department of Electrical and Electronics Engineering at Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh. Dr. Reddy has received his Ph.D. (Electrical Engineering) JNTUK Kakinada, India in 2012. Dr. Reddy is a member of IEEE, the Institution of Engineers (India) and Indian Society for Technical Education. He is a recipient of the ‘‘IEI Young Engineers Award” from the Institution of Engineers (India) in 2013. He is a recipient of the ‘‘Tata Rao Prize” from the Institution of Engineers (India) in 2012. His areas of interest Include Special Electrical Machines, Electric Drives, FACTS and Custom Power Devises and Power Quality.
Please cite this article in press as: Nagireddy VV et al. Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner. Ain Shams Eng J (2017), https://doi.org/10.1016/j.asej.2017.09.004
Contents lists available at ScienceDirect
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Electrical Engineering
Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner V. Veera Nagireddy a,⇑, Venkata Reddy Kota b, D.V. Ashok Kumar c a
Dept of Electrical Power Engineering, IbriCT, Ibri, Oman Dept of EEE, JNTU, Kakinada, A.P, India c Dept of EEE, RGMCET, Nandyal, Kurnool (Dt.), A.P, India b
a r t i c l e
i n f o
Article history: Received 20 May 2016 Revised 27 July 2017 Accepted 4 September 2017 Available online xxxx Keywords: Hybrid fuzzy-back propagation control Power quality Voltage sag mitigation Total harmonic distortion Cascaded H-bridge inverter UPQC Load balancing
a b s t r a c t To increase the lifetime and performance of equipment in utility distribution systems, the enhancement of power quality is essential. In this paper, to enhance the power quality, we present the design of a hybrid fuzzy back-propagation control scheme for a unified power quality Conditioner. The gating pulses of the UPQC are generated using hybrid fuzzy-back propagation controllers. The reference currents for the controller are estimated using back-propagation algorithms with source currents and load currents as input control parameters. The reference voltages for the controllers are estimated using fuzzy logic controllers for the dc voltage regulator with a terminal voltage, and a dc voltage for the input control parameters. We perform this analysis in zero-voltage regulation mode and power factor correction mode in a multilevel UPQC-connected distribution system. We analyze the results of the total harmonic distortion, dynamic performance, load balancing and mitigation of the voltage sag using MATLAB/Simulink. Ó 2017 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction A small improvement in the standard power quality significantly affects the lifetime and performance of utility equipment. Most distribution centers have established quality control centres to provide reliable, flexible, and intelligent electrical power supply to customers, even under abnormal circumstances. Intelligent control schemes that utilize artificial intelligence (AI) techniques such as artificial neural networks (ANNs), fuzzy logic, and the artificial bee colony algorithm are good substitutes for conventional control schemes to realize power quality enhancement in existing scenarios. In Ref. [1], the authors proposed artificial intelligence technique in defining optimal operating cost of smart grid. Realizing power quality involves maintaining the quality of voltage, eliminating harmonics that are due to nonlinear loads, and performing load balancing as a result of load imperfections and constant frequency [2]. In Ref. [3], the authors proposed a basic backpropagation algorithm, which proved that the weights in the training process were bounded in that they lead to good convergence.
Peer review under responsibility of Ain Shams University. ⇑ Corresponding author. E-mail addresses: [email protected] (V.V. Nagireddy), kvr.jntu@gmail. com (V.R. Kota), [email protected] (D.V. Ashok Kumar).
This algorithm was modified in [4] by changing the total error of the performance function of incremental conductance algorithms such that the learning process is accelerated and can be predicted with better accuracy. In [5], the mathematical analysis of complex versions of the back-propagation algorithm network was proposed using the sinusoidal error representation of the transformation. The power quality of distribution systems has improved owing to the introduction of power electronic devices, called custom power devices, such as distribution static compensators (DSTATCOMs), dynamic voltage restorers (DVRs), and unified power quality conditioners (UPQC). UPQCs combine the functions of a DVR and DSTATCOM [6], and eliminates harmonics, voltage sag, and load balancing. The estimation and elimination of harmonics is an area of interest for many researchers. In three-phase distribution systems, the harmonics can be estimated and eliminated using the variable step size least mean squares approach, as proposed in [7], for shunt active-power filters with various simulation results. The DSTATCOM is an effective device for the elimination of harmonics using advanced control techniques, as in [8–10]. In [8], a DSTATCOM was modelled using a self-tuning filter-based instantaneous reactive power theory control algorithms, and it was validated using digital signal processors. The method proposed in [9], symmetrical component theory and improved instantaneous active and reactive current component theory control strategies,
https://doi.org/10.1016/j.asej.2017.09.004 2090-4479/Ó 2017 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Nomenclature MI-UPQC multilevel inverter unified power quality conditioner VSC voltage source converter PCC point of common coupling v cabc compensator three phase voltage v sabc source three phase voltage isabc source three phase current load three phase current iLabc icabc compensator three phase current Vdc DC link voltage netyp net in-phase current input to hidden layer I quadrature net current input to hidden layer I netyq YLp in-phase ouput of hidden layer I YLq quadrature ouput of hidden layer I wo initial weight k learning rate net zp net in-phase current input to hidden layer II
and compared these two under unbalanced conditions. The intelligent control scheme using back-propagation algorithms for DSTATCOM was proposed in [10]. In short, existing studies have attempted to eliminate harmonics by estimating harmonics. To extend this attempt to voltage sags and load balancing, the UPQC was proposed under unbalanced load and non-ideal voltage mains conditions, as in [11]. In Ref. [12], the authors proposed exponential control algorithm based UPQC to mitigate the power quality. Synchronous reference frame-based control schemes have been proposed to three-phase four wire with UPQC, and the computational delay was reduced and analyzed using MATLAB / Simulink [13]. In [14], A fuzzy based multiobjective optimization technique was proposed in hybrid distributed generation system. An active power conditioners were used in industrial distribution systems for voltage harmonics, current harmonics, voltage sag, and load-balancing evaluations. Fuzzy logic controllers (FLCs) and ANN are the two branches of AI. This work combines the advantages of ANNs and FLCs to design a new control scheme [15]. An adaptive neural fuzzy inference system (ANFIS-) based fault-classification control schemes for neutral grounded distribution systems was proposed [16]. In Ref. [17], An ANN-based DSTATCOM with one cycle control strategy was proposed to mitigate harmonics. In Ref. [18], the authors proposed comparison of Genetic algorithms (GAs), particle-swarm optimization (PSO), and the artificial bee colony (ABC) algorithm which are recently developed AI techniques in the field of electrical engineering, with integer order and fractional order techniques. In recent years, owing to an increase in the use of nonlinear power electronic equipment, power quality has become a major issue, and hybrid AI techniques are currently being researched for UPQCs. An efficient multilayered ANN-based active power filter was proposed the shift method to reduce harmonics [19]. The PSO technique has been evaluated based on error convergence and compared with backpropagation neural networks [20]. Power quality enhancement using AI-controlled schemes is the latest area of research in the field of custom power devices in electrical distribution systems. The fuzzy control strategy was developed for UPQC with sudden switching loads [21]. The performance of compensating devices can be determined by measuring the degree of extraction of harmonic components and voltage sag. In this paper, we present a hybrid fuzzy back-propagationbased control scheme for three-phase shunt and series connected custom power devices, called a UPQC, for the estimation of a reference-weighted value for current and voltage components.
Zp Zq net p Op Oq v, w FLC GP Gq THD FP Fq ep eq
in-phase ouput of hidden layer II quadrature ouput of hidden layer II net in-phase current input to hidden layer II in-phase ouput of neural network quadrature ouput of neural network weight vectors fuzzy logic controller in-phase reference source current amplitude quadrature reference source current amplitude total harmonic distortion in-phase output of fuzzy controller quadrature output of fuzzy controller in-phase voltage vector quadrature voltage vector
The proposed control scheme for UPQC can suppress harmonics, as well as mitigate voltage sag, and load balancing. 2. System configuration We tested the proposed control scheme on a three-phase distribution system that consists of three-phase sources and threephase linear/nonlinear loads. A series multilevel voltage source converter (VSC) is connected to the three-phase distribution system through a three-phase injection transformer, and a shunt multilevel VSC is connected through an interfacing inductor at the point of common coupling (PCC), as shown in Fig. 2. In Section 3, we discuss the generation gating pulses to the cascaded H-bridge inverter [22] using a hybrid fuzzy back-propagation network controller. A UPQC is a custom power device that is a combination of a dynamic voltage restorer (DVR) and distribution STATCOM (DSTACOM). The multilevel inverter UPQC (MI-UPQC) three-phase voltages (vcabc) are injected to compensate for the voltage sag, and three-phase currents (icabc) are injected to cancel the reactive power components of the load currents on the distribution system. The number of voltage levels is 2n + 1, and Vdc/2n is the voltage step for the two single phase cascaded H-bridge inverters, as shown in Fig. 1, and the switching modes of operation are presented in Table 1. The number of capacitors required for a H bridge five level UPQC are six, where each phase is shared by two H-bridge invert-
Fig. 1. Single-phase five-level cascaded H-bridge inverter.
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ers in cascade and two capacitors to provide dc link between series and shunt converters. The selection of Vdc plays major role in efficient functioning of UPQC. Under dynamic conditions as shown in Fig. 8, the power capacities of series compensator and shunt compensator are assumed to be 3S KVA and 0.7S KVA respectively. The voltage across DC capacitor decreases with increase in KVA rating. Let change in Vdc across capacitor be 30%, then the change in energy is given by
1 DW dc ¼ C dc fð1:130V dc Þ2 þ ð0:7V dc Þ2 g 2
ð1Þ
Table 1 Switching table for single phase 5-level Cascaded H-bridge inverter. Switches ON
Voltage level
S1, S1, S1, S1, S3, S3, S3, S1,
Vdc/2 Vdc Vdc/2 0 Vdc/2 Vdc Vdc/2 0
S2, S5 and D7 S2, S6 and S5 S2, S6 and D8 D3, S6 and D8 S4, S6 and D8 S4, S7 and S8 S4, D6 and S8 S3, D6 and S8
Fig. 2. Schematic diagram of hybrid fuzzy back-propagation multilevel UPQC.
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If the load on the decreases from 3S KVA to 0.7S KVA in m number of cycles, then the decrease in energy of the network is
DW N ¼ ð3S 0:7SÞmT
ð2Þ
where T is the time period. From Eqs. (1) and (2), the DC capacitor is
C dc ¼
2ð3S 0:7SÞmT
ð3Þ
fð1:130V dc Þ2 þ ð0:7V dc Þ2 g
Let k be the ratio of DC link capacitor voltage to the system maximum voltage (Vp). It is observed for various values of ‘k’, out of that total harmonic distortion is minimum for k = 1.3. Hence, The THD depends on the selection of dc capacitor voltage. Therefore, the dc link capacitor voltage is
V dc ¼ 1:3V p
ð4Þ
3. Hybrid fuzzy back-propagation control scheme 3.1. Back-propagation network The design and implementation of hybrid fuzzy backpropagation controllers is presented in three stages. In the first stage, we estimate the reference in-phase and quadrature components of currents. Then, in the second stage, we estimate the reference voltages. The third stage involves combining the two references and estimating the final reference unit values for the pulse-width modulation process. In the first stage, load currents (iLa, iLb, and iLc) act as inputs to the back-propagation network, as shown in Fig. 3. The extracted in-phase and quadrature values (netyap , netybp , netycp , netyaq , netybq , and netycq ) are applied through
the activation of the sigmoid function. The real and reactive output signals (YLap, YLbp, YLcp, YLaq, YLbq, and YLcq) of the feed-forward section of hidden layer I for three phases are expressed by Eqs. (A1)–(A12). The output signals of hidden layer I (YLap, YLbp, YLcp, YLaq, YLbq, and YLcq) are fed as inputs for the hidden layer II of the backpropagation network. This input vector is propagated through the multi layer neural network, as shown in Fig. 3. The extracted in-phase and quadrature values are multiplied by respective weights (netzap , netzbp , netzcp , netzaq , netzbq , and netzcq ), and are applied by activating the sigmoid function. The output signals (Zap, Zbp, Zcp, ZLaq, Zbq, and Zcq) of the feed-forward section of hidden layer II for three phases are expressed by Eqs. (A13)–(A24). The weights between hidden layer II and output layer are in the ratio of 1:3. The outputs of the network for the real and reactive components are expressed by Eqs. (A25)–(A28). The real and reactive weights (v) between hidden layer I and hidden layer II, the network shown in Fig. 3 for three phases (a, b and c) can be adjusted, using back-propagation training algorithm. The weights are updated in the direction that reduces the harmonic distortion, voltage sag, voltage swell, and load unbalancing and these are expressed by Eqs. (A29)–(A40). 3.2. Fuzzy logic controller design for MI-UPQC An FLC involves fuzzification, fuzzy rules, a fuzzy inference system, and defuzzification. The FLC is used as terminal voltage regulator and a DC voltage regulator, as shown in Fig. 6. In the DC voltage regulator, the FLC has as its two inputs the error (ei = V⁄dc – Vidc; i = 1, 2, 3, 4, 5, 6) and the rate of change of error De (A44), and Fp as the fuzzy output. The number of DC common voltage regulators for series and shunt converters, that are used
Fig. 3. Back-propagation multilayer (3-6-6-2) neural network.
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in this H-bridge inverter UPQC are (n + 1), where ‘n’ is the number of levels. Similarly, for the terminal voltage regulator, the FLC has as its two inputs the error (e = V⁄t – Vt) and the rate of change of error De (A45). The triangular membership functions are used as the inputs of the FLC, as shown in Figs. 4 and 5. The linguistic variables for the error, e, and rate of change of error, De, along with the fuzzy controller output are given as ‘‘PH” - Positive High, ‘‘PM” - Positive Medium, ‘‘PL” - Positive Low, ‘‘Z” – Zero, ‘‘NL” - Negative Low, ‘‘NM” –‘‘Negative Medium, and ‘‘NH” - Negative High. One terminal voltage regulator is used in this H-bridge inverter UPQC and it can be calculated from Eq. (A41). From a practical perspective, fuzzy rules are developed using linguistic variables that are formulated in the form of ‘‘IF THEN” rules. The fuzzy rules for the terminal voltage regulator and DC voltage regulator are given in Table 2. 3.3. Fuzzy back-propagation control scheme The in-phase and quadrature unit voltages can be used as reference vectors to generate gating pulses by gating pulse
5
generator as shown in Fig. 6, so as to improve the power quality (A42) and (A43). The in-phase component of the reference source current amplitude (GP) is the sum of the output of the dc bus fuzzy controller (FP) and the in-phase output of the back-propagation network as represented in (A46). The component of the quadrature reference source current amplitude (Gq) is the sum of the output of the terminal bus fuzzy controller (Fq) and the quadrature output of the back-propagation network (A47). The three-phase reference in-phase source current and voltage components can be obtained using the amplitude of the three-phase load’s inphase current components and the PCC’s in-phase unit voltages as in (A48)–(A51). We compared the source currents (isa, isb, isc) with the reference source currents (A52), and three error signals (54) are fed to a PWM technique in order to generate the switching signals for insulated-gate bipolar transistors (IGBTs) S1 to S12 of the shunt multilevel VSC. We compared the source voltages (vsa, vsb, vsc) with the reference source voltages (A53), and three voltage error signals (A55) are fed to a PWM in order to generate the switching signals for IGBTs S0 1 to S0 12 of the
Fig. 4. Membership functions for input error.
Fig. 5. Membership functions for rate of change of error.
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Fig. 6. Back-propagation-based fuzzy logic control scheme for multilevel UPQC.
Table 2 Fuzzy rules. e
PH PM PL Z NL NM NH
De NH
NM
NL
Z
PL
PM
PH
Z NL NM NH NH NH NH
PL Z NL NM NH NH NH
PM PL Z NL NM NH NH
PH PM PL Z NL NM NH
PH PH PM PL Z NL NM
PH PH PH PM PL Z NL
PH PH PH PH PM PL Z
series VSC. The block diagram of the hybrid fuzzy backpropagation controller is shown in Fig. 6 and the flow chart of fuzzy back-propagation control scheme for multi level inverter UPQC is shown in Fig. 7.
3.4. Test system AC supply source: Three-phase, V f = 50 Hz.
L-L
= 415 V, frequency,
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Fig. 7. Flow chart of back-propagation-based fuzzy logic control scheme.
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Fig. 8. Dynamic performance of multilevel UPQC.
Source impedance: Rs = 0.8 X and Ls = 0.175 mH. Nonlinear: Three-phase full bridge uncontrolled rectifier, R = 20 X and L = 40 H. Rating of VSC = 10 kVA (approximately 35% higher than the rated value). Ripple filter: Rf = 3 X and Cf = 1F. Switching frequency of inverter = 10 kHz. Reference DC bus voltage: 700 V. Interfacing inductor (Lf) = 1 mH. Selected initial weights: wo = 1 and wo1 = 1. Learning rate (k) = 0.5. Cutoff frequency of low-pass filter used in dc bus voltage = 15 Hz.
Cutoff frequency of low-pass filter used in AC bus voltage = 10 Hz.
4. Results and discussion 4.1. Dynamic performance of fuzzy back-propagation controlled multilevel UPQC We studies the dynamic performance of a fuzzy backpropagation multi level UPQC in terms of the power factor correction (PFC) and ZVR modes for nonlinear loads. The dynamic performance results for phase voltages at PCC (vcabc), compensator
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Fig. 8 (continued)
Fig. 9. PCC phase ‘‘a” voltage in ZVR mode.
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Fig. 10. Phase ‘‘a” source voltage in ZVR mode.
Fig. 11. Phase ‘‘a” line current in ZVR mode.
Fig. 12. Phase ‘‘a” source current in ZVR mode.
currents (iCabc), compensator voltages (vCabc), source currents (isabc), load currents (iLabc), terminal voltage (vt), and DC bus voltage (vdc) are shown in Fig. 8 (for t = 3.7–3.8 s) under varying nonlinear load conditions. In the ZVR mode, the PCC voltage is regulated by injecting additional leading reactive power components. We observed that the terminal voltages (Vtabc) have equal magnitudes, and that they are at angles of 120° with respect to each other, as
shown in Fig. 8, and the multilevel UPQC can perform load balancing under abnormal conditions. 4.2. Total harmonic distortion The FFT analysis results for the phase ‘‘a” PCC voltage, source voltage, load current, source current, and compensator voltage in
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Fig. 13. Compensator phase ‘‘a” voltage in ZVR mode.
Fig. 14. Phase ‘‘a” source voltage in PFC mode.
Fig. 15. Phase ‘‘a” source current in PFC mode.
the ZVR mode are shown in Figs. 9–13 and in PFC mode shown in Figs. 14–18. We observe that the total harmonic distortion (THD) of phase ‘‘a” for the PCC voltage, source current, source voltage, load current, and compensator voltage in the ZVR mode are 1.07, 2.27, 0.91, 28.87, and 0.11, respectively, while in PFC mode they are
1.04, 2.24, 1.04, 25.74, and 1.40, respectively. These simulation results are summarized in Table 3. The results show that there is satisfactory performance of the hybrid fuzzy back-propagation multilevel UPQC because the THDs are within the IEEE-519 standard limit (<5%) [23].
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Fig. 16. Phase ‘‘a” line current in PFC mode.
Fig. 17. PCC phase ‘‘a” voltage in PFC mode.
Fig. 18. Compensator phase ‘‘a” voltage in PFC mode.
4.3. Mitigation of voltage sag The mitigation of voltage sags using a hybrid fuzzy backpropagation multilevel UPQC can be observed under nonlinear load
and faulty conditions. The source voltage of phase ‘‘a” having a sag of 150 V for a nominal voltage of 250 V are shown in Fig. 19. The terminal voltage sags have been mitigated using the proposed control scheme, as shown in Fig. 20.
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V.V. Nagireddy et al. / Ain Shams Engineering Journal xxx (2017) xxx–xxx Table 3 Performance of fuzzy-back propagation based UPQC under non-linear load. Operating mode
Parameter
Magnitude
Total Harmonic Distortion (%THD)
ZVR mode
PCC voltage Supply current Source voltage Load current Compensator voltage
240 V 33.39 A 240 V 27.8 A 50.22 V
1.07 2.27 0.91 28.87 0.11
PFC mode
PCC voltage Supply current Source voltage Load current Compensator voltage
237.5 V 26.69 A 237.4 V 29.46 A 27.93
1.04 2.24 1.04 25.74 1.40
Fig. 19. Phase ‘‘a” terminal voltage sag.
Fig. 20. Phase ‘‘a” terminal voltage with mitigated sag using proposed control scheme.
5. Conclusions UPQC can compensate the voltage and current levels under abnormal conditions such as those involving DVR and DSTATCOM, and reduce current and voltage harmonics. In ZVR mode, the load currents have a THD of 28.87%, which has been compensated to 2.27% at the source side. In PFC mode, the load currents have a THD of 25.74%, which has been compensated to 2.24% at the source side. In ZVR mode, the voltage harmonics at the load side has a THD of 1.07%, and it is 0.91% at the source end. In PFC mode, the voltage harmonics at the load side has a THD of 1.04%, and it is 1.04% at the source end. We conclude that the proposed five-level UPQC and its control scheme are suitable for nonlinear load compensation even under faulty conditions with respective to the THD, voltage sag, and load balancing using the back-propagation control scheme. The DC voltage of the UPQC was regulated using the fuzzy controller at the voltage without any overshoot or undershoot under abnormal conditions. The proposed hybrid
fuzzy back-propagation control scheme has been used to estimate gating signals for series and shunt VSCs of multilevel UPQCs. Appendix A. Mathematical formulation The in-phase net input vector to the hidden layer I of the neural network, as shown in Fig. 3, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netyap ¼ wo þ iLa wpa þ iLb wpb þ iLc wpc
ðA1Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘a” is given by
Y Lap ¼ f ðnetyap Þ ¼
1 1 þ eknetyap
ðA2Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘a” are expressed as
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netyaq ¼ wo þ iLa wqa þ iLb wqb þ iLc wqc Y Laq ¼ f ðnetyaq Þ ¼
1 1 þ eknetyaq
ðA3Þ ðA4Þ
The in-phase net input vector to the hidden layer I of the neural network, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netybp ¼ wo þ iLa w0pa þ iLb w0pb þ iLc w0pc
ðA5Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘b” is given by
Y Lbp ¼ f ðnetybp Þ ¼
1 1 þ eknetybp
ðA6Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘b” are expressed as
netybq ¼ wo þ iLa w0qa þ iLb w0qb þ iLc w0qc Y Lbq ¼ f ðnetybq Þ ¼
1 1 þ eknetybq
ðA7Þ ðA8Þ
The in-phase net input vector to the hidden layer I of the neural network, is the weighted sum of load currents with weight vector ‘‘w”, and it can be expressed as
netycp ¼ wo þ iLa w00pa þ iLb w00pb þ iLc w00pc
ðA9Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer I for Phase ‘‘c” is given by
Y Lcp
1 ¼ f ðnetycp Þ ¼ 1 þ eknetycp
ðA10Þ
Similarly, the quadrature net input vector and output signal of hidden layer I for phase ‘‘c” are expressed as
netycq ¼ wo þ iLa w00qa þ iLb w00qb þ iLc w00qc Y Lcq ¼ f ðnetycq Þ ¼
1 1 þ eknetycq
ðA11Þ ðA12Þ
where wo is the randomly chosen initial weight, wpa, wpb, and wpc are the in-phase three-phase weights and wqa, wqb, and wqc are the quadrature three-phase weights. The in-phase net input vector to the hidden layer II of the neural network, as shown in Fig. 3, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzap ¼ wo1 þ Y Lap v ap þ Y Lbp v bp þ Y Lcp v cp
ðA13Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘a” is given by
Z ap ¼ f ðnetzap Þ ¼
1 1 þ eknetzap
ðA14Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘a” are expressed as
netzaq ¼ wo1 þ Y Laq v aq þ Y Lbq v bq þ Y Lcq v cq Z aq
1 ¼ f ðnetzaq Þ ¼ 1 þ eknetzaq
ðA15Þ ðA16Þ
The in-phase net input vector to the hidden layer II of the neural network, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzbp ¼ wo1 þ Y Lap v 0ap þ Y Lbp v 0bp þ Y Lcp v 0cp
ðA17Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘b” is given by
Z bp ¼ f ðnetzbp Þ ¼
1 1 þ eknetzbp
ðA18Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘b” are expressed as
netzbq ¼ wo1 þ Y Laq v 0aq þ Y Lbq v 0bq þ Y Lcq v 0cq Z bq ¼ f ðnetzbq Þ ¼
1 1 þ eknetzbq
ðA19Þ ðA20Þ
The in-phase net input vector to the hidden layer II of the neural network, is the weighted sum of load currents with weight vector ‘‘v”, and it can be expressed as
netzcp ¼ wo1 þ Y Lap v 00ap þ Y Lbp v 00bp þ Y Lcp v 00cp
ðA21Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of hidden layer II for Phase ‘‘c” is given by
Z cp ¼ f ðnetzcp Þ ¼
1 1 þ eknetzcp
ðA22Þ
Similarly, the quadrature net input vector and output signal of hidden layer II for phase ‘‘c” are expressed as
netzcq ¼ wo1 þ Y Laq v 00aq þ Y Lbq v 00bq þ Y Lcq v 00cq Z cq ¼ f ðnetzcq Þ ¼
1 1 þ eknetzcq
ðA23Þ ðA24Þ
where wo1 is the randomly chosen initial weight, v ap , v bp , and v cp are the real initial weights between hidden layer I and hidden layer II, and v aq , v bq , and v cq are the three-phase reactive initial weights between hidden layer I and hidden layer II.The in-phase net input to the output layer of the neural network, as shown in Fig. 3, is given by
netp ¼
1 ðZ ap þ Z bp þ Z cp Þ 3
ðA25Þ
The in-phase net input signals are applied through the activation of the unipolar sigmoid function. The output signal of the neural network is given as
Op ¼ f ðnetp Þ ¼
1 1 þ eknetp
ðA26Þ
The quadrature net input to the output layer of the neural network, is given by
netq ¼
1 ðZ aq þ Z bq þ Z cq Þ 3
ðA27Þ
The quadrature net input signals are applied through the activation of the unipolar sigmoid function. The output signal of the neural network is given as
Oq ¼ f ðnetq Þ ¼
1 1 þ eknetq
ðA28Þ
v ap ðnewÞ ¼ v ap ðoldÞ þ afðOp Z ap Þgf 0 ðnetzap ÞY Lap
ðA29Þ
v aq ðnewÞ ¼ v aq ðoldÞ þ afðOq Z aq Þgf 0 ðnetzaq ÞY Laq
ðA30Þ
v bp ðnewÞ ¼ v bp ðoldÞ þ afðOp Zbp Þgf 0 ðnetzbp ÞY Lbp
ðA31Þ
v bq ðnewÞ ¼ v bq ðoldÞ þ afðOq Z bq Þgf 0 ðnetzbq ÞY Lbq
ðA32Þ
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v cp ðnewÞ ¼ v cp ðoldÞ þ afðOp Zcp Þgf 0 ðnetzcp ÞY Lcp
ðA33Þ
v cq ðnewÞ ¼ v cq ðoldÞ þ afðOq Zcq Þgf 0 ðnetzcq ÞY Lcq
ðA34Þ
Similarly, the real and reactive weights (w) between input layer and hidden layer II, the network shown in Fig. 3 for three phases (a, b and c) can be adjusted, using back-propagation training algorithm. The updated real and reactive weights (w) for three phases are:
The three-phase reference source currents (a, b, and c) are the sum of the in-phase and quadrature current components are shown in Fig. 6, and is given as
The three-phase reference source voltages (a, b, and c) are the sum of in-phase and quadrature voltage components, and is given as
0
ðA35Þ
vsa ¼ vsap þ vsaq ; vsb ¼ vsbq þ vsbq ; vsc ¼ vscp þ vscq
0
0
ðA36Þ
The error signals of the shunt VSC for the three phases (a, b, and c) are
0
0
ðA37Þ
0
0
waq ðnewÞ ¼ waq ðoldÞ þ afðOq Z aq Þgf ðzaq Þf ðnetyaq ÞiLa
wbp ðnewÞ ¼ wbp ðoldÞ þ afðOp Z bp Þgf ðzbp Þf ðnetybp ÞiLb wbq ðnewÞ ¼ wbq ðoldÞ þ afðOq Z bq Þgf ðzbq Þf ðnetybq ÞiLb
ðA38Þ
0
0
ðA39Þ
0
0
ðA40Þ
wcp ðnewÞ ¼ wcp ðoldÞ þ afðOp Z cp Þgf ðzcp Þf ðnetycp ÞiLc wcq ðnewÞ ¼ wcq ðoldÞ þ afðOq Z cq Þgf ðzcq Þf ðnetycq ÞiLc
The amplitudes of the sensed PCC voltages are estimated using three-phase source voltages as:
rffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 v 2sa þ v 2sb þ v 2sc ¼ 3
ðA41Þ
These unit voltage vector is the ratio of source voltage to PCC voltage as
eap ¼
v sa ; vt
ebp ¼
v sb ; vt
ecp ¼
v sc vt
ðA42Þ
The quadrature unit voltages are estimated as eaq ¼
ðebp þ ecp Þ pffiffiffi ; 3
ebq ¼
ð3eap þ ebp ecp Þ pffiffiffi ; 2 3
ecq ¼
ð3eap þ ebp ecp Þ pffiffiffi 2 3
ðA43Þ The in-phase and quadrature components of the error inputs for the FLC are respectively given by
ep ðkÞ ¼ Vdc dcðkÞ Vdc ðkÞ
ðA44Þ
eq ðkÞ ¼ vt ðkÞ Vt ðkÞ
ðA45Þ
The average magnitude of the load’s in-phase component (OP), and is given as
GiP ¼ FiP þ OP ;
i ¼ 1; 2; 3
ðA46Þ
The average magnitude of the load’s quadrature component as shown in Fig. 6, and is given (Oq) as
Giq ¼ Fiq þ Oq ;
ðA52Þ
0
wap ðnewÞ ¼ wap ðoldÞ þ afðOp Z ap Þgf ðzap Þf ðnetyap ÞiLa
vt
isa ¼ isap þ isaq ; isb ¼ isbp þ isbq ; isc ¼ iscp þ iscq
i ¼ 1; 2; 3
ðA47Þ
The three-phase reference in-phase source current and the voltage components can be obtained using the amplitude of the threephase load’s quadrature current components and PCC quadrature unit voltages as:
isap ¼ G1P eap ; isbp ¼ G2P ebp ; iscp ¼ G3P ecp
ðA48Þ
isaq ¼ G1q eaq ; isbq ¼ G2q ebq ; iscq ¼ G3q ecq
ðA49Þ
vsap ¼
Gp Gp Gp eap ; vsbq ¼ ebp ; vscp ¼ ecp Gq Gq Gq
ðA50Þ
vsaq ¼
Gq Gq Gq eaq ; vsbq ¼ ebq ; vscq ¼ ecq Gp Gp Gp
ðA51Þ
ea ¼ isa isa ; eb ¼ isb isb ; ec ¼ isc isc
ðA53Þ
ðA54Þ
The error signals of the series VSC for the three phases (a, b, and c) are
e0a ¼ vsa vsa ; e0b ¼ vsb vsb ; e0c ¼ vsc vsc
ðA55Þ
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Mr. V. Veera Nagi Reddy, Research scholar, JNTUK, KAKINADA, A.P, India, is graduated in 2004. He obtained masters degree from JNTU, Anantapur, India. Currently, he is working in Electrical Power Engineering at Ibri College of Technology, Ibri, Oman. He is working towards his Ph.D. degree in JNTUK, KAKINADA. His research interests include soft computing techniques, smart grids, distributed generation systems and renewable energy sources.
Prof. D.V. Ashok Kumar, is graduated in 1996, Masters in 2000from J.N.T.U.C.E, Anantapur and Ph.D. in 2008 from the same university. He worked 12 years at R.G.M. College of Engineering Technology, Nandyal, and A.P. in the cadres of Assistant Professor, Professor and Head of Electrical and Electronics Engg. Department. Since Oct. 2008 to June 2015 he worked as Principal at Syamaladevi institute of Technology for women, Nandyal. July 2015 to till date he is working as Professor in EEE & Dean- Administration at R.G.M. College of Engineering Technology, Nandyal, A.P. He has published 33 research papers in national and international conferences and journals. He has attended 10 National & International workshops. His areas of interests are Electrical Machines, Power Systems & Solar Energy.
Dr. Venkata Reddy Kota is currently working as Assistant Professor in the Department of Electrical and Electronics Engineering at Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh. Dr. Reddy has received his Ph.D. (Electrical Engineering) JNTUK Kakinada, India in 2012. Dr. Reddy is a member of IEEE, the Institution of Engineers (India) and Indian Society for Technical Education. He is a recipient of the ‘‘IEI Young Engineers Award” from the Institution of Engineers (India) in 2013. He is a recipient of the ‘‘Tata Rao Prize” from the Institution of Engineers (India) in 2012. His areas of interest Include Special Electrical Machines, Electric Drives, FACTS and Custom Power Devises and Power Quality.
Please cite this article in press as: Nagireddy VV et al. Hybrid fuzzy back-propagation control scheme for multilevel unified power quality conditioner. Ain Shams Eng J (2017), https://doi.org/10.1016/j.asej.2017.09.004