POWER QUALITY ASSESSMENT AND ENHANCEMENT IN A GRID CONNECTED RENEWABLE ENERGY SYSTEM USING DYNAMIC VOLTAGE RESTORER

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1 Applied Mechanics and Materials Online: ISSN: , Vol. 573, pp doi: / Trans Tech Publications, Switzerland POWER QUALITY ASSESSMENT AND ENHANCEMENT IN A GRID CONNECTED RENEWABLE ENERGY SYSTEM USING DYNAMIC VOLTAGE RESTORER S.Rajeshbabu 1,a*, B.V.Manikandan 2,b 1* Assistant Professor, EEE Dept, Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, India. 2 Professor, EEE Dept, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. a sbaburajesh@gmail.com b bvmani73@yahoo.com Keywords: Neural network, Point of common coupling, Renewable energy source, Power quality, Dynamic voltage restorer, electric grid. Abstract: Renewable energy sources provide the additional/satisfy the power to the consumer through power electronics interfaces and integrated with the grid. In grid integration power quality is one of the important parameter that need to be paying more attention. This proposed work focuses on power quality issues in a grid connected renewable energy system. Power quality issues will arises due to many factors here with the by introducing a fault condition in a grid connected renewable energy system the measurements were made at the point of common coupling and the mitigation is done with the help of a dynamic voltage restorer. The dynamic voltage restorer is a device which offers series compensation activated by neural network based controller. The sag improvement and the total harmonic assessment were made at the point of common coupling. INTRODUCTION The objective of introducing distributed generation is to provide electricity to a customer; however the challenge for the utilities is to maintain the quality in the supply. Power quality has become a real problem over the last decade due to the ever increasing of power electronics and sensitive load equipments. [1] Power quality worsening is due to voltage sags, swells and steady state disturbances.in this paper assessment on harmonics and the unbalance is focused. The presence of harmonics in electrical networks poses many problems to power system experts. The impacts of harmonics on power system devices may reduce the operating life of rotating machines, malfunctioning of power system protection devices, errors in power measurements etc. unbalance conditions will also affect the machine, under unbalance conditions the machines will draw a current with a degree of unbalance several times than that of the supply voltages. As a result the three phase current might differ considerably and a temperature rise would take place in the machine.[2] Moreover the passive filters do not provide any solution for the unbalance and reactive power compensation. [3] Voltage source convertors (VSC) based custom power devices are increasingly being used in custom power devices applications to mitigate power quality problems in power distribution system. A series converter can also known as dynamic voltage restorer that provides series compensation can be controlled with the help of a controller. Fast detection of the disturbance signal with high accuracy, rapid processing of the reference signals and high dynamic response of the controller are the prime requirements for the desired compensation.[4] instantaneous reactive power theory in a rotating frame is used to suppress the harmonics and to correct the power factor which is discussed in. [5] To generate reference signals for the series converter dq transform.[6]short time window sampling technique[7] positive sequence calculation methods [8] are used. The other sag/swell detection methods for the series converter are the half-cycle average detector.[10] Root mean square method[5] and supply voltage peak calculator[11]this work focuses on assessment of power quality issues and mitigation of power quality problems with a neural network controlled dynamic voltage restorer. This paper is structured as follows section (II) describes the power quality issues. Section (III) describes the system description. While the artificial neural network design is discussed in section (IV).simulations and results are discussed in the section (V). All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-17/05/16,14:31:51)

2 Applied Mechanics and Materials Vol POWER QUALITY ISSUES Power quality is the combination of voltage and current quality. It is the concept of powering and grounding sensitive electronic equipment in a manner suitable for the equipment. [12] Voltage distortions Due to variation in the wind velocity and solar radiation the renewable energy sources such as wind turbine and solar PV system are unable to produce a constant power. The intermittent nature distorts the voltage.the voltage distortions are commonly classified as Voltage sag/dips, Voltage swells, Short term interruptions, Long term interruptions The above power quality issues will exist in all the renewable energy sources when they are connected with electric grid [13]. Harmonics The harmonic occurs due to the function of power electronics converters. The harmonic voltage and current need to be limited to the satisfactory level at the point of renewable energy source connection to the network [14]. The total harmonic voltage distortion of a voltage is given as (1) = Where V n is the harmonic voltage and V 1 is the fundamental frequency (50) Hz. SYSTEM DESCRIPTION The proposed test system comprises of renewable energy source connected with a dc link of a grid interfacing inverter integrated with the grid through an injection transformer along with a dynamic voltage restorer as shown in fig.1. (1) Fig 1.Renewable energy source connected with a grid with DVR The renewable energy source connected to the grid requires an intermediate element to regulate the power extracted from the source. Generally the power produced from the wind turbine generator are at variable ac voltage,while the solar PV source and fuel cells produces a low dc voltage therefore the power from a wind power, solar PV source needs conditioning of power(ac/dc or dc/dc ). It can be accomplished using a dc link connected inverter [15].The grid interfacing inverter is controlled with the help of a PWM controller and the grid interfacing inverter is connected with the grid through an injection transformer along with the dynamic voltage restorer since the measurements were made at the point of common coupling. Wind energy generating system In this configuration wind generations are made on constant speed topologies with different wind speed. The asynchronous machine used in the proposed scheme and do not require any separate field circuit. It supports constant and variable loads and has natural protection against short circuit. The available power of wind energy systems is given by Eq. (2) = (2) Where ρ is the air density in (kg/m 3 ), A (m 3 ) is the swept area out by turbine blade, and U is the wind speed in m/s. Dynamic voltage restorer It has two operating approach Stand by approach-the injected voltage magnitude is equal to zero.

3 718 Advancements in Automation and Control Technologies Boost approach-dvr injects the required voltage of appropriate magnitude and phase to restore the pre fault load bus voltage. [16] It consists of the following parts like voltage source converter, Boost or injection transformer, passive filters, energy storage system DVR Controller The DVR control consists of reference current generated by the ANN and the sag/swell detection estimations. Figure 2. Shows the control algorithm if a DVR for phase A. this control algorithm is similar for the remaining phases. Figure 2 Control algorithm of a DVR for phase A The proposed sag/swell detection scheme is a conventional method, three phases currents I a I b and I c are transformed in to dq plane and as given in (3).the sag/swell depth is obtained by (4) [9]. cos 2 /3 cos +2 /3 = cos sin sin 2 /3 sin +2 /3 (3) = 1 + (4) The three phase currents is transformed in to d and q components. The square root of the sum of squares of these components is obtained the obtained value is subtracted from 1 (reference value) and the resulting value is filtered out with a low pass filter to extract the positive sequence component of current. The filtered output is transformed to abc plane and subjected to a hysteresis comparator generates the sag/swell detection signal. The detection signal is high when the sag/swell occurs and is low otherwise. ARTIFICIAL NEURAL NETWORK DESIGN (ANN) Neural network based controllers provide fast dynamic response while maintaining stability of the converter system over wide operating range. [17] The ANN is made up of interconnecting artificial neurons. In this DVR the reference signal generated by the multilayer feed forward type ANN.this network is designed with three layers input layer with 1, the hidden layer with 10 and the output layer with 3 neurons respectively. Fig.3 Network topology of the Reference signal generator The large data are stored in MATLAB workspace. The data are used for training the neural network the activation function chosen are tan sigmoid for input and hidden layer and pure linear in the output layer respectively. The multilayer feed forward type neural network works as a reference signal generator the network topology is as shown in figure (3).The training algorithm used is levenberg-marquardt back propagation algorithm.

4 Applied Mechanics and Materials Vol In this neural network scheme 100 epoch are considered for training totally 130 samples are given as a training input and 40 for validation and 30 for testing process the best validation is appeared at epoch 15 as shown in Figure (4). Fig.4.Best validation performance Fig.5.a. Training state diagram Fig.5.b.Error histogram The training state and the error histogram plot was as shown in figure (5). (a) and (b). SIMULATION AND RESULTS In this work wind energy source is considered for the test system it was integrated with the grid through a 20Km transmission line and the fault was introduced at 0.1s. the dynamic voltage restorer injects power when fault was introduced in the system and the DVR is connected at the point of common coupling (PCC).the total harmonic distortion was measured at the PCC simultaneously.

5 720 Advancements in Automation and Control Technologies Fig.6. Voltage at the point of common coupling when fault was introduced without DVR Fig.7. Vabc across the PCC with DVR Fig.8. Comparison between the signals with and without DVR for a Phase-A From the observation made from the above figures it is clear that sag condition occurs at the time interval 0.1s, with the application of DVR the sag condition was compensated and the load profile was improved. The Fig.8 shows the difference between the Voltages for a Phase_A when a single phase fault was introduced in the test system the total harmonic distortion measurements at the PCC are measured with DVR as 0.004, 0.004, and respectively

6 Applied Mechanics and Materials Vol CONCLUSION In this work a renewable energy source is integrated with the grid and a single phase to ground fault was introduced at 0.1s in the system a series compensating device (DVR) with neural network controller elevates the fault and supplies the load. The observations were made at the point of common coupling and the total harmonic distortions were calculated at the three phases by connecting a parallel compensation device along with the energy storage system in DVR we can eliminate the power quality issues that will occur during the integration of other renewable energy sources. REFERENCES [1] Handbook of power quality john Wiley and sons ltd edited by Angelo Baggini, P.No , [2] New power-quality assessment criteria for supply systems under unbalanced and non sinusoidal conditions, Power Delivery, IEEE Transactions on Volume: 19 Issue: 3 P.No [3] Bhim Singh,, and Vishal Verma, Selective Compensation of Power-Quality Problems through Active Power Filter by Current Decomposition IEEE transactions on power delivery, vol. 23, no. 2, April 2008 [4] R. Rezaeipour and A. Kazemi, Review of novel control strategies for UPQC, Int. J Elect. Power Eng., vol. 2, pp , [5] F. Z. Peng, G. W. Ott, and D. J. Adams, Harmonic and reactive power compensation based on the generalized instantaneous reactive power theory for three-phase four-wire systems, IEEE Trans. Power Electron., vol. 13, no. 6, pp , Nov [6] H. Fujita and H. Akagi, The unified power quality conditioner: The integration of series and shunt-active filters, IEEE Trans. Power Electron., vol. 13, no. 2, pp , Mar [7] H. Karimi, M. K. Ghartemani, M. R. Iravani, and A. R. Bakhshai, An adaptive filter for synchronous extraction of harmonics and distortions, IEEE Trans. Power Del., vol. 18, no. 4, pp , Oct [8] N. G. Jayanti, M. Basu, M. F. Conlon, and K. Gaughan, Rating requirements of the unified power quality conditioner to integrate the fixed-speed induction generator-type wind generation to the grid, Inst. Eng. Technol. Renew. Power Gen., vol. 3, no. 2, pp , [9] A. Teke, K. Ç. Bayindir, and M. Tümay, Fast sag/swell detection method for fuzzylogic based dynamic voltage restorer, Inst. Eng. Technol. Gen., Transm. Distrib., vol.4, no. 1, pp. 1 12, [10] Y. Y. Kolhatkar and S. P. Das, Experimental investigation of a singlephase UPQC with minimumva loading, IEEE Trans. Power Del., vol. 22, no. 1, pp , Jan [11] M. Basu, S. P. Das, and G. K. Dubey, Investigation on the performance of UPQC-Q for voltage sag mitigation and power quality improvement at a critical load point, Inst. Eng. Technol. Gen. Transm. Distrib., vol. 2, pp , [12] Power quality by C.Sankaran, published by CRC press, [13] B. Renders, K. De Gusseme, W. R. Ryckaert, K. Stockman, L. Vandevelde, and M. H. J. Bollen, Distributed generation for mitigating voltage dips in low-voltage distribution grids, IEEE Trans. Power. Del., vol. 23, no. 3, pp , Jul [14] Tarak Salmi, Mounir Bouzguenda, Adel Gastli, Ahmed Masmoudi, MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell, international journal of renewable energy research, Vol.2, No.2, 2012 [15] B. Renders, K. De Gusseme, W. R. Ryckaert, K. Stockman, L. Vandevelde, and M. H. J. Bollen, Distributed generation for mitigating voltage dips in low-voltage distribution grids, IEEE Trans. Power. Del., vol. 23, no. 3, pp , Jul [16] FACTS controllers in power transmission and distribution by K.R.Padiyar, published by New age international (P)Ltd [17] Vadirajacharya G. Kinhal, Promod Agarwal, and Hari Oam Gupta, Performance Investigation of Neural-Network-Based Unified Power-Quality Conditioner, IEEE transactions on power delivery, vol. 26, no. 1, January 2011.

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