The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment.
In recent days, an increased number of sensitive loads have been integrated into the electrical power system. Consequently, both electric utilities and end users are becoming increasingly concerned about the quality and the reliability of electric power. Power quality disturbances have become more serious due to the widespread application of power electronics in the industrial, commercial, and residential sectors. Voltage sag and harmonic distortion are common power quality events that can cause significant destruction to the industrial customers with sensitive loads, resulting in equipment damage, loss of production, and heavy financial loss. Voltage sag is defined as a decrease in the utility supply voltage from 90% to 10% of its nominal value at the power frequency for periods ranging from a half-cycle to a minute. Voltage sag can be symmetrical or unsymmetrical depending on the causes of the sag. Voltage and current waveforms that deviate massively from a sinusoidal form is usually called harmonic distortion. The harmonic distortion of the voltage and the current results in varied problems like greater power losses in distribution systems, malfunctioning of protective devices, electrical, electronic equipment, and problems of electromagnetic interference in communication systems. These disturbances are caused by the use of nonlinear load, energization of large loads, load variations, system faults, and poorly designed systems. According to IEEE 519 standard, THD of voltage should not exceed 5% for distribution systems as keeping low THD values on the system will ensure accurate operation of the equipment and increased life expectancy. There are various conventional and VSC-based compensators available in combating the consequences of voltage disturbances in the distribution systems. Dynamic Voltage Restorer (DVR) is a most cost effective and efficient approach to improve the voltage quality at load side. DVR is a power electronic converter based Distributed-Static Synchronous Series Compensator (DSSSC), designed to protect the sensitive load from supply-side short-term voltage disturbances other than outages. It is connected in series with distribution feeder, at the point of common coupling. By injecting a voltage of desired magnitude and phase angle through a booster transformer, it restores the load-side voltage to be balanced and sinusoidal, even when the source voltage is unbalanced and distorted [
The inverter is the core component of the DVR, and its control directly affects the performance of the DVR. Over the last few years, major research works have been carried out on control operation of the voltage source inverter in DVR with the objective of obtaining reliable control and fast response procedures to obtain the switch control. Control system based on fast repetitive control of DVR was applied in [
The DVR configuration of the proposed system, mainly, consists of three-phase voltage source inverter, energy storage device, and passive filter and three-phase injection transformer and control circuit to regulate the output voltage of the inverter.
Inverter is the main component of DVR. It employs six IGBT power electronic switches with self-commutation by shunt diodes. IGBT combines the fast switching times of the MOSFET with the high voltage capabilities of the GTO. It results in the combination of a medium speed controllable switch capable of supporting the medium power range. Sinusoidal pulse width modulation technique manages the switching operation and through which it can generate a three-phase sinusoidal voltage with any required magnitude, frequency, and phase angle.
Battery energy storage system (BESS) has been considered for inverter side input due to its high operating flexibility and very short response time. These functions provide dynamic power benefits ensuring improved quality of the energy delivered.
The inverter side filtering is preferred and, using this filtering scheme, the high-order harmonic currents are prevented from penetrating into the series transformer, thus reducing the voltage stress on the transformer.
Its main function is amplifying the injected voltage and creating an electrical isolation between the voltage source inverter (VSI) and the network. The primary winding of the injection transformer is connected to the inverter side filter while its secondary winding is connected to the distribution network and sensitive load.
The significant role of the controller is to detect the voltage sag and inject voltage deviation by providing appropriate switching strategies for the inverter. The controller input is an actuating signal which is the difference between the reference voltage and the sensitive load voltage. Switching frequency is in the range of few kHz. Pulse width modulation (PWM) control system is applied for IGBT inverter switching so as to generate a three-phase 50 Hz sinusoidal signals in order to maintain 1 pu voltage at the sensitive load terminal.
The DVR has two modes of operation, namely, standby mode and boost mode. In standby mode voltage injection is low, so it compensates the voltage sag caused by transformer reactance losses. Most of the time, the DVR will be in this mode. In boost mode, in case of voltage sag caused by any fault conditions or nonlinear load in the distribution system, DVR injects voltage to sensitive load to compensate it.
A controller is required to control or to operate a DVR under boost mode during the fault condition. In [
The characterisation of the fitness function of the particle is of paramount importance for the effective performance of an optimization function. The optimization problem is modelled as a minimization problem with three individual indices linearly combined to describe the overall fitness function of a particle.
The first parameter in the fitness function description is the ability of the current solution in reducing the Total Harmonic Distortion in the system:
The second parameter under the fitness function description is the sag magnitude of each phase during the fault period. The load terminal containing the voltage sensitive load must operate precisely at 1.0 pu and hence the objective function is designed to minimize the deviation from the operating voltage. If the fault timing ranges from
The third parameter describing the fitness of a particle is the root mean square value of the voltage phasor at the load terminal containing the voltage sensitive load. The objective may be mathematically modelled as shown below if the fault timing ranges from
The overall objective function of the optimization problem
Swarm intelligence is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking, and herd phenomena in vertebrates. The Particle Swarm Optimization (PSO) was proposed by Eberhart and Kennedy in [
This was the basic method of PSO as proposed by Eberhart and Kennedy in [
The Autonomous Group Particle Swarm Optimization was proposed in [
In conventional PSO, all particles behave in the same way in terms of local and global search, so particles can be considered as a group with one strategy. However, using diverse autonomous groups with a common goal in any population-based optimization algorithm could result in more randomized and directed search simultaneously. Autonomous groups have different strategies to update
Create and initialize a Divide particles randomly into autonomous groups Repeat Calculate particles’ fitness, Gbest, and Pbest For each particle: Extract the particle’s group Use its group strategy to update Use Use new velocities to define new positions End for Until stopping condition is satisfied
To verify the effectiveness and applicability of the proposed DVR, the distribution system was tested by considering two cases of voltage disturbances and was evaluated using MATLAB/Simulink. The first simulation was carried out in order to see the performance of DVR in compensating 50% balanced voltage sag due to three-phase to ground fault. The second simulation showed the DVR performance by considering nonlinear load. The total period of simulation for each case was 0.12 s. It was assumed that the voltage magnitude of sensitive load was maintained at 1 pu during the voltage sag condition. The system parameters are listed in the Appendix.
The efficient operation of a DVR from the perspective of power quality may be considered based on the performance of the system on certain vital parameters during the occurrence of the fault. The proposed work identifies the following indices regarding the goodness of the system quality.
The Total Harmonic Distortion (THD) is an important indication used for the power quality analysis. The fundamental definition of THD is given by
The THDV measured can be written as follows:
In the event of a fault occurring in one feeder, for reasons such as a short circuit, a high current flows through it along with the supply current. During such a happening, voltage in another feeder will be decreased due to the increased voltage drop across the source reactance. In another event, the nonlinear load connected in one feeder affects the other. During the fault, the voltage magnitude of the corresponding phase drastically drops from the nominal value, and hence operating voltage during the fault period is taken into account.
This is the first voltage sag index that is used in contracts between utilities and consumers. The Detroit Edison sag score (SS) is defined as follows:
With the increased disruption of the signal owing to the presence of harmonic content, the RMS value of the signal is also considered as a parameter for analysis of the performance of the DVR. The computation of the RMS value will help in better characterisation of the signal.
The test system was employed to carry out the simulation of DVR as shown in Figure
The
This showed an improved performance of DVR as harmonic compensator. Autonomous Group Particle Swarm Optimization variants showed better improvement in reduction of THD.
Table
Harmonic mitigation of three-phase balanced fault.
Tuning method/parameter 1 | THD measured at PCC in % | THD measured at sensitive load in % | THD improvement in % | ||||||
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(compensated) | |||||||||
THD |
THD |
THD |
THDV | THD |
THD |
THD |
THDV | ||
ZN | 5.279 | 5.330 | 6.507 | 5.705 | 0.6629 | 1.057 | 1.132 | 0.9393 | — |
SPSO | 5.254 | 5.309 | 6.472 | 5.678 | 0.4310 | 0.9306 | 1.028 | 0.7965 | 15.20 |
AGPSO1 | 5.253 | 5.303 | 6.465 | 5.673 | 0.3660 | 0.8967 | 1.015 | 0.7592 | 19.17 |
AGPSO2 | 5.134 | 5.390 | 6.450 | 5.658 | 0.3265 | 0.8704 | 0.9913 | 0.7294 | 22.36 |
AGPSO3 | 5.142 | 5.390 | 6.457 | 5.663 | 0.3376 | 0.8797 | 0.9954 | 0.7375 | 21.48 |
Voltage sag compensation of three-phase balanced fault.
Tuning method/parameter 2 | Voltage at PCC in pu | Voltage at sensitive load in pu (compensated) | ||||||
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Phase A | Phase B | Phase C | Detroit Edison sag score (SS) | Phase A | Phase B | Phase C | Detroit Edison sag score (SS) | |
ZN | 0.4944 | 0.4884 | 0.4884 | 0.5096 | 1.007 | 1.006 | 1.006 | 0.006633 |
SPSO | 0.4943 | 0.4882 | 0.4883 | 0.5097 | 1.001 | 0.9996 | 0.9993 | 0.000033 |
AGPSO1 | 0.4946 | 0.4889 | 0.4890 | 0.5091 | 0.9997 | 0.9999 | 1.000 | 0.000133 |
AGPSO2 | 0.4938 | 0.4876 | 0.4882 | 0.5101 | 1.000 | 1.000 | 0.9999 | 0.000033 |
AGPSO3 | 0.4943 | 0.4881 | 0.4889 | 0.5095 | 0.9996 | 1.000 | 0.9999 | 0.000166 |
RMS voltage variation of three-phase balanced fault.
Tuning method/parameter 3 | RMS voltage at PCC in volts | RMS voltage at sensitive load in volts | ||||
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(compensated) | ||||||
Phase A | Phase B | Phase C | Phase A | Phase B | Phase C | |
ZN | 0.3448 | 0.3398 | 0.3370 | 0.7046 | 0.7045 | 0.7043 |
SPSO | 0.3447 | 0.3398 | 0.3370 | 0.7042 | 0.7041 | 0.7042 |
AGPSO1 | 0.3448 | 0.3399 | 0.3370 | 0.7048 | 0.7047 | 0.7048 |
AGPSO2 | 0.3471 | 0.3406 | 0.3380 | 0.7051 | 0.7047 | 0.7047 |
AGPSO3 | 0.3475 | 0.3410 | 0.3383 | 0.7045 | 0.7046 | 0.7048 |
Operating points of PI controllers for three-phase balanced fault.
Tuning method |
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ZN | 40 | 154 | 25 | 260 |
SPSO | 32.95 | 153.77 | 27.29 | 140.1 |
AGPSO1 | 28.13 | 200 | 39.06 | 130.23 |
AGPSO2 | 26.09 | 200 | 16.85 | 0 |
AGPSO3 | 26.64 | 200 | 23.92 | 42.51 |
Test system with three-phase balanced fault.
Convergence plot of various AGPSO algorithms.
Figure
Performance of DVR with 50% three-phase balanced fault using AGPSO2 based PI controller.
In the second case, various loads such as Load A (nonsensitive load), a nonlinear load, and Load B (a sensitive load) were connected at the Point of Common Coupling (PCC), as shown in Figure
The AGPSO optimized PI controllers outperformed their respective counterparts in all aspects. Table
Harmonic mitigation of rectifier load.
Tuning method/parameter 1 | THD measured at PCC in % | THD measured at sensitive load in % | THD improvement in % | ||||||
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(compensated) | |||||||||
THD |
THD |
THD |
THDV | THD |
THD |
THD |
THDV | ||
ZN | 38.94 | 45.70 | 40.80 | 41.81 | 0.7076 | 0.9788 | 1.013 | 0.8998 | — |
SPSO | 38.92 | 45.68 | 40.79 | 41.79 | 0.5972 | 0.9194 | 0.9890 | 0.8352 | 7.17 |
AGPSO1 | 38.93 | 45.70 | 40.80 | 41.81 | 0.5781 | 0.8934 | 0.9630 | 0.8115 | 9.81 |
AGPSO2 | 38.93 | 45.69 | 40.79 | 41.80 | 0.5824 | 0.9069 | 0.9697 | 0.8196 | 8.91 |
AGPSO3 | 38.93 | 45.69 | 40.79 | 41.80 | 0.5786 | 0.9048 | 0.9775 | 0.8203 | 8.83 |
Voltage sag compensation of rectifier load.
Tuning method/parameter 2 | Voltage at PCC in pu | Voltage at sensitive load in pu (compensated) | ||||||
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Phase A | Phase B | Phase C | Detroit Edison sag score (SS) | Phase A | Phase B | Phase C | Detroit Edison sag score (SS) | |
ZN | 0.6827 | 0.6947 | 0.8023 | 0.2741 | 0.9984 | 0.9993 | 0.999 | 0.001100 |
SPSO | 0.6835 | 0.6947 | 0.8013 | 0.2735 | 0.9998 | 1.000 | 1.000 | 0.000066 |
AGPSO1 | 0.6833 | 0.6945 | 0.8032 | 0.2730 | 0.9999 | 1.000 | 1.000 | 0.000033 |
AGPSO2 | 0.6834 | 0.6943 | 0.8018 | 0.2735 | 0.9999 | 1.000 | 1.000 | 0.000033 |
AGPSO3 | 0.6833 | 0.6945 | 0.8030 | 0.2730 | 0.9999 | 1.000 | 0.9999 | 0.000066 |
RMS voltage variation of rectifier load.
Tuning method/parameter 3 | RMS voltage at PCC in volts | RMS voltage at sensitive load in volts (compensated) | ||||
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Phase A | Phase B | Phase C | Phase A | Phase B | Phase C | |
ZN | 0.3912 | 0.3820 | 0.3944 | 0.7026 | 0.7024 | 0.7029 |
SPSO | 0.3914 | 0.3821 | 0.3945 | 0.7030 | 0.7027 | 0.7031 |
AGPSO1 | 0.3912 | 0.3820 | 0.3944 | 0.7036 | 0.7033 | 0.7038 |
AGPSO2 | 0.3913 | 0.3821 | 0.3945 | 0.7036 | 0.7032 | 0.7037 |
AGPSO3 | 0.3913 | 0.3820 | 0.3820 | 0.7036 | 0.7031 | 0.7035 |
Operating points of PI controllers for rectifier load.
Tuning method |
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ZN | 40 | 154 | 25 | 260 |
PSO | 43.45 | 162.62 | 62.23 | 131.91 |
AGPSO1 | 43.11 | 193.65 | 37.24 | 156.41 |
AGPSO2 | 41.68 | 195.69 | 47.61 | 219.66 |
AGPSO3 | 39.25 | 200 | 49.53 | 78.63 |
Test system with nonlinear load.
Performance of DVR with nonlinear load using AGPSO1 based PI controller.
This study aimed at making improvement in the DVR technology by adopting new control approaches to the system. A comprehensive analysis on the performance of various soft computing based controllers for obtaining better power quality indices was also carried out. From the obtained results, it could be observed that all the proposed artificial intelligence methodologies yielded significant results in comparison with the conventional way of tuning the PI controller using Ziegler-Nichols method. The performance of the conventional controllers and optimized controllers in lieu of system parameters and controller settings was discussed. The application of three versions of Autonomous Group Particle Swarm optimization could substantially improve the performance of the controllers and could achieve the recommended IEEE Standard 512-1992. The inclusion of the controllers in the power circuit was proved to be imperative by the obtained results. The fact that the voltage sag compensation and voltage sag compensation and harmonic mitigation could be achieved by the proposed DVR indicated a significant improvement in the quality of voltage. The work may be extended to study the feasibility of application by optimizing fuzzy and ANFIS controller to the proposed objective functions for the DVR system.
See Table
Summary of design specifications for the test system.
Parameters description | Values with units |
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Source voltage/frequency | 22.5 (kV)/(50 Hz) |
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Load A parameters | Configuration: |
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Load B parameters | Configuration: |
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Distribution transformer | Nominal power: 32 (kVA) |
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Series injection transformer | Rated power: 5 kVA/50 Hz |
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PWM generator | Switching frequency = 10 kHz |
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DC voltage source | 200 V |
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Passive filter | Series filter impedance: |
The authors declare that there is no conflict of interests regarding the publication of this paper.