TLBO-trained ANN-based Shunt Active Power Filter for Mitigation of Current Harmonics

  • Venkata Anjani kumar G Department of E.E.E, S.V. University College of Engineering, S.V. University, Tirupathi-517502, Andhra Pradesh, India https://orcid.org/0009-0004-3111-0525
  • Damodar Reddy M Department of E.E.E, S.V. University College of Engineering, S.V. University, Tirupathi-517502, Andhra Pradesh, India https://orcid.org/0000-0002-7113-5805
Keywords: Particle Swarm Optimization Algorithm(PSO), Teaching learning-based optimization (TLBO), Power Quality, Total Harmonic Distortion, Shunt Active Power Filter, ANN-Controller Tuning

Abstract

The increased utilization of nonlinear devices is resulting in damage to power distribution infrastructure by introducing harmonics into power system networks, which in turn causes distortion in voltage and current signals. A novel solution called Shunt Active Power Filter (SAPF) has been developed to address this issue using power electronics. This study aims to provide a method that is efficient and cost-effective for lowering harmonics and improving power quality in distribution infrastructure. The proposed method combines the Teaching learning-based optimization (TLBO) technique with an Artificial Neural Network Controller (TLBO-ANN) in conjunction with SAPF. The primary objective of the TLBO-ANN algorithms in SAPF is to minimise total harmonic distortion (THD) for maximum system efficiency. Initially, Gain values (Ki, Kp) for a regular Proportional-Integral controller are optimised with the Particle Swarm Optimisation (PSO) technique. Those optimized parameters obtained from the PSO-tuned PI controller serve as input and target datasets for training the ANN controller. Subsequently, the TLBO algorithm is utilized to further refine the ANN controller by finding the optimal weight and bias values. Using MATLAB/SIMULINK software, we compare the performance of the proposed algorithm to that of the PSO-tuned PI controller and traditional PI controller. The findings from the simulation suggest that a SAPF utilizing a TLBO-trained ANN controller could improve THD in the supplying current while maintaining harmonics within IEEE-519 accepting levels.

References

Akagi, H., Watanabe, E. H., & M.I. (2017). Aredes, instantaneous power theory and applications to Akagi, H., Watanabe, E. H., & M.I. (2017). Aredes, instantaneous power theory and applications to power conditioning (2nd ed). John Wiley & Sons, Inc.

Asadi, Y., Eskandari, M., Mansouri, M., Chaharmahali, S., Moradi, M. H., &Tahriri, M. S. (2022). Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids. Applied Sciences, 12(16), 8060. https://doi.org/10.3390/app12168060

Babu, B. M., Kumar, N. U., Kumar, K. S., Amarendra, A., & Bindhu, B. (2020). SAPF for power quality improvement based on PSODE optimization algorithm. International Journal of Engineering and Advanced Technology, 9(3), 3454–3460. https://doi.org/10.35940/ijeat.B2517.029320

Chelli, Z., Toufouti, R., Omeiri, A., & Saad, S. (2015). Hysteresis control for shunt active power filter under unbalanced three-phase load conditions. Journal of Electrical and Computer Engineering, 2015, 1–9. https://doi.org/10.1155/2015/391040

Diab, M., El-Habrouk, M., Abdelhamid, T. H., & Deghedie, S. (2018). 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA), pp. 1–14. https://doi.org/10.1109/ICSCAN.2018.8541225

Diab, M., El-Habrouk, M., Abdelhamid, T. H., & Deghedie, S. (2019). Switched capacitor active power filter optimization using nature-inspired techniques 21st International Middle East. Power Systems Conference (MEPCON), Cairo, Egypt, 2019, pp. 556–561. https://doi.org/10.1109/MEPCON47431.2019.9008148

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. http://doi.org/10.1162/106454699568728

Gowtham, N., & Shankar, S. (2016). PI tuning of shunt active filter using GA and PSO algorithm. IEEE Publications, Informatics (AEEICB), 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio., pp. 207–213. https://doi.org/10.1109/AEEICB.2016.7538274

Huaisheng, W., &Huifeng, X. (2012). A novel double hysteresis current control method for active power filter. Physics Procedia, 24, 572–579. http://doi.org/10.1016/j.ph pro.2012.02.084

Janga Reddy, M., & Nagesh Kumar, D. (2020). Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: A state-of-the-art review. H2 Open Journal, 3(1), 135–188. https://doi.org/10.2166/h2oj.2020.128

Kazemzadeh, R., NajafiAghdam, E., Fallah, M., & Hashemi, Y. (July 2014). Performance scrutiny of two control schemes based on DSM and HB in active power filter. Journal of Operation and Automation in Power Engineering, 2(2), 103–112. https://doi.org/10.35940/ijeat.B2517.029320

Mikkili, S., & Panda, A. K. (2012). Real-time implementation of shunt active filter P-Q control strategy for mitigation of harmonics with different fuzzy M.F.s. Journal of Power Electronics, 12(5), 821–829. http://doi.org/10.6113/JPE.2012.12.5.821

Om, P., Mahela, O.P., & Shaik, A.G. (2016). Topological aspects of power quality improvement techniques: A comprehensive overview. Renewable and Sustainable Energy Reviews, 58, 1129–1142. https://doi.org/10.1016/j.rser.2015.12.251

Rajeshwari., & Bagwari, A. (2017). Voltage harmonic reduction using passive filter shunt passive-active filters for non-linear load 7th International Conference on Communication Systems and Network Technologies (CSNT), IEEE, 2017, pp. 131–136. https://doi.org/10.1109/CSNT.2017.8418524

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006

Ramesh, R., Sasi, C., & Manikandan, M. (2023). An Efficient PV - Integrated UPQC System for Power Quality Enhancement Using Improved Chicken Swarm Optimization. International Journal of Experimental Research and Review, 33, 57-70. https://doi.org/10.52756/ijerr.2023.v33spl.006

Sabarimuthu, M., Senthilnathan, N., Priyadharshini, N., Kumar, M. A., Telagam, N., & Sree, S. K. (2021). Comparison of current control methods for a three phase shunt active filter, IEEE Publications, 7th International Conference on Electrical Energy Systems (ICEES). https://doi.org/10.1109/ICEES51510.2021.9383754

Satapathy, S. C., & Naik, A. (2013). Improved teaching learning based optimization for global function power conditioning (2nd ed). John Wiley & Sons, Inc.

Asadi, Y., Eskandari, M., Mansouri, M., Chaharmahali, S., Moradi, M. H., &Tahriri, M. S. (2022). Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids. Applied Sciences, 12(16), 8060. https://doi.org/10.3390/app12168060

Babu, B. M., Kumar, N. U., Kumar, K. S., Amarendra, A., & Bindhu, B. (2020). SAPF for power quality improvement based on PSODE optimization algorithm. International Journal of Engineering and Advanced Technology, 9(3), 3454–3460. https://doi.org/10.35940/ijeat.B2517.029320

Chelli, Z., Toufouti, R., Omeiri, A., & Saad, S. (2015). Hysteresis control for shunt active power filter under unbalanced three-phase load conditions. Journal of Electrical and Computer Engineering, 2015, 1–9. https://doi.org/10.1155/2015/391040

Diab, M., El-Habrouk, M., Abdelhamid, T. H., & Deghedie, S. (2018). 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA), pp. 1–14. https://doi.org/10.1109/ICSCAN.2018.8541225

Diab, M., El-Habrouk, M., Abdelhamid, T. H., & Deghedie, S. (2019). Switched capacitor active power filter optimization using nature-inspired techniques 21st International Middle East. Power Systems Conference (MEPCON), Cairo, Egypt, 2019, pp. 556–561. https://doi.org/10.1109/MEPCON47431.2019.9008148

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. http://doi.org/10.1162/106454699568728

Gowtham, N., & Shankar, S. (2016). PI tuning of shunt active filter using GA and PSO algorithm. IEEE Publications, Informatics (AEEICB), 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio., pp. 207–213. https://doi.org/10.1109/AEEICB.2016.7538274

Huaisheng, W., &Huifeng, X. (2012). A novel double hysteresis current control method for active power filter. Physics Procedia, 24, 572–579. http://doi.org/10.1016/j.ph pro.2012.02.084

Janga Reddy, M., & Nagesh Kumar, D. (2020). Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: A state-of-the-art review. H2 Open Journal, 3(1), 135–188. https://doi.org/10.2166/h2oj.2020.128

Kazemzadeh, R., NajafiAghdam, E., Fallah, M., & Hashemi, Y. (July 2014). Performance scrutiny of two control schemes based on DSM and HB in active power filter. Journal of Operation and Automation in Power Engineering, 2(2), 103–112. https://doi.org/10.35940/ijeat.B2517.029320

Mikkili, S., & Panda, A. K. (2012). Real-time implementation of shunt active filter P-Q control strategy for mitigation of harmonics with different fuzzy M.F.s. Journal of Power Electronics, 12(5), 821–829. http://doi.org/10.6113/JPE.2012.12.5.821

Om, P., Mahela, O.P., & Shaik, A.G. (2016). Topological aspects of power quality improvement techniques: A comprehensive overview. Renewable and Sustainable Energy Reviews, 58, 1129–1142. https://doi.org/10.1016/j.rser.2015.12.251

Rajeshwari., & Bagwari, A. (2017). Voltage harmonic reduction using passive filter shunt passive-active filters for non-linear load 7th International Conference on Communication Systems and Network Technologies (CSNT), IEEE, 2017, pp. 131–136. https://doi.org/10.1109/CSNT.2017.8418524

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006

Ramesh, R., Sasi, C., & Manikandan, M. (2023). An Efficient PV - Integrated UPQC System for Power Quality Enhancement Using Improved Chicken Swarm Optimization. International Journal of Experimental Research and Review, 33, 57-70. https://doi.org/10.52756/ijerr.2023.v33spl.006

Sabarimuthu, M., Senthilnathan, N., Priyadharshini, N., Kumar, M. A., Telagam, N., & Sree, S. K. (2021). Comparison of current control methods for a three phase shunt active filter, IEEE Publications, 7th International Conference on Electrical Energy Systems (ICEES). https://doi.org/10.1109/ICEES51510.2021.9383754

Satapathy, S. C., & Naik, A. (2013). Improved teaching learning based optimization for global function optimization. Decision Science Letters, 2(1), 23–34. http://doi.org/10.5267/j.dsl.2012.10.005

Soliman, A. M. A., El-Sayed, S. K., & Mehanna, M. A. (2017). Assessment of control strategies for conventional and multi-functional inverter interfacing power grid with renewable energy sources (RES). International Journal of Scientific and Engineering Research, 12(4),1727-1734. http://doi.org/10.20508/ijrer.v12i4.13413.g8597

Tekwani, P. N., Chandwani, A., Sankar, S., Gandhi, N., & Chauhan, S. K. (2020). Artificial neural network-based power quality compensator. International Journal of Power Electronics, 11(2), 256–282.

Vadi, S., Gurbuz, F. B., Sagiroglu, S., & Bayindir, R. (2021). Optimization of PI based buck-boost converter by particle swarm optimization algorithm 9th International Conference on Smart Grid (ic Smart Grid), Setubal, Portugal. 2021, 295–301. https://doi.org/10.1109/icSmartGrid52357.2021.9551229

Venkata, A.K.G., & Reddy, M.D. (2023). Fuzzy and PSO tuned PI controller based SAPF for Harmonic Mitigation. International Journal of Electrical and Electronics Research, 11(1). 119–125. https://doi.org/10.37391/IJEER.110116

Vishwakarma, A. P., & Milan Singh, K. M. (2020). Comparative analysis of adaptive PI controller for current harmonic mitigation International Conference on Computational Performance Evaluation (Com PE), Shillong, India. 2020, 643–648. https://doi.org/10.1109/ComPE49325.2020.9200057

Wen-guan Wang, W.-G., Ying-biao Ling, Y.-B., Zhang, J., & Wang, Y. (2012). Ant colony optimization algorithm for design of analog filters. IEEE Publications Congress on Evolutionary Computation, 2012, pp. 1–6. https://doi.org/10.1109/CEC.2012.6252942

Wilamowski, B. M., & Yu, H. (June 2010). Improved computation for Levenberg–Marquardt training. In IEEE Transactions on Neural Networks, 21(6), 930–937. https://doi.org/10.1109/TNN.2010.2045657

Published
2023-10-30
How to Cite
G, V., & M, D. (2023). TLBO-trained ANN-based Shunt Active Power Filter for Mitigation of Current Harmonics. International Journal of Experimental Research and Review, 34(Special Vo), 11-21. https://doi.org/10.52756/ijerr.2023.v34spl.002