Power and Voltage Estimation of Unobserved Bus on Distribution Network Using ANFIS Algorithm with the Modified PSO-GA Hybrid
(*) Corresponding author
DOI: https://doi.org/10.15866/iree.v15i6.18313
Abstract
Power and voltage estimation is a procedure for estimating the voltage of all buses in an electric power system based on measurements made on several buses. The voltage and the current on the bus are measured by a measuring device called the Phasor Measurement Unit (PMU). The Surabaya-Indonesia Bendul-Merisi distributed network has become the object of research for optimizing PMU placement by dividing into 3 clusters and placing 3 PMU on selected buses as observed buses. In this study, power and voltage estimation is done on buses that are not installed PMU (unobserved bus) using ANFIS algorithm with modified PSO-GA hybrid. This algorithm is used because of its ability to get predictive values with high accuracy and speed in achieving convergence. Input data for the estimation process are the voltage and current of all the buses. There are 10 data sets, divided into 7 training data and 3 test data. Estimation results compared with measurement results show a high degree of accuracy. The average accuracy of estimation results for power to 3 clusters is 99.968%, while the estimated accuracy for voltage is 99.758%.
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Kumar, K., Ramana, N., Kamakshaiah, S., Design and Development of Modified PSO Algorithm for Network Reconfiguration, (2013) International Review of Electrical Engineering (IREE), 8 (5), pp. 1586-1593.
Wang, Hui; Sun, Hui; Li, Changhe; Rahnamayan, Shahryar; Pan, Jeng-shyang, Diversity enhanced particle swarm optimization with neighborhood search, Information Sciences, Vol. 223, pp. 119–135, Elsevier, 2013.
https://doi.org/10.1016/j.ins.2012.10.012
Gang,Ma., Wei, Zhou., A novel particle swarm optimization algorithm based on particle migrasion, Applied Mathematics and Computation, Vol. 218, pp. 6620–6626, Elsevier, 2012.
https://doi.org/10.1016/j.amc.2011.12.032
Syahputra, R., Robandi, I., Ashari, M., Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 293-304.
https://doi.org/10.15866/iree.v10i2.5410
R. Sulistyowati, DC. Riawan, M . Ashari, PV Farm Placement and Sizing using GA for Area Development Plan of Distribution Network, International Seminar on Intelligent Technology and Its Applications (ISITIA), IEEE proceeding, 2016.
https://doi.org/10.1109/isitia.2016.7828712
Sulistyowati, Riny; Riawan, Dedet Candra; Ashari, Mochamad Clustering Based Optimal Sizing and Placement of PV-DG Using Neural Network, Advanced Science Letters, Vol 23, No. 3, pp. 2373-2375, American Scientific Publisher, March 2017.
https://doi.org/10.1166/asl.2017.8680
Jabri, M., Aloui, H., Genetic Lagrangian Relaxation Selection Method for the Solution of Unit Commitment Problem, (2019) International Journal on Engineering Applications (IREA), 7 (2), pp. 59-64.
https://doi.org/10.15866/irea.v7i2.17022
Indrawati, A., Girsang, A., Electricity Demand Forecasting Using Adaptive Neuro-Fuzzy Inference System and Particle Swarm Optimization, (2016) International Review of Automatic Control (IREACO), 9 (6), pp. 397-404.
https://doi.org/10.15866/ireaco.v9i6.10810
Sabzi, Hamed Zamani; Humbersona, Delbert; Abuduc, Shalamu; King, James Phillip, Optimization of adaptive fuzzy logic controller using novel combine devolutionary algorithms, and its application in Diez Lagos flood controlling system, Southern New Mexico, Expert Systems With Applications, Vol. 43, pp. 154–164, Elsevier , 2016.
https://doi.org/10.1016/j.eswa.2015.08.043
Dervis Karaboga, Ebubekir Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artificial Intelligence Review, Vol. 52, pp. 2263–2293, Springer, 2019.
https://doi.org/10.1007/s10462-017-9610-2
Mallikharjuna, K., Anuradha, K., An Efficient Method for Software Reliability Growth Model Selection Using Modified Particle Swarm Optimization Technique, (2015) International Review on Computers and Software (IRECOS), 10 (12), pp. 1169-1178.
https://doi.org/10.15866/irecos.v10i12.8089
Bassera, Hossein., Karami, Hojat, et al., Hybrid ANFIS–PSO Approach For Predicting Optimum Parameters Of A Protective Spur Dike, Applied Soft Computing, Vol. 30, pp. 642–649, Elsevier, 2015.
https://doi.org/10.1016/j.asoc.2015.02.011
Yue-Jiao Gong, Jing-Jing Li, Yicong Zhou, Yun Li, Henry Shu-Hung Chung, Yu-Hui Shi, Jun Zhang, Genetic Learning Particle Swarm Optimization, IEEE Transactions on Cybernetics, Vol 46, No. 10, pp. 2277 - 2290, October 2016.
https://doi.org/10.1109/tcyb.2015.2475174
Santoso, I., Gulo, R., Girsang, A., An Adaptive Cat Swarm Optimization Based on Particle Swarm Optimization Approach (ACPSO) for Clustering, (2016) International Review on Computers and Software (IRECOS), 11 (1), pp. 20-26.
https://doi.org/10.15866/irecos.v11i1.8008
Piotr Dziwinski, Łukasz Bartczuk, and Piotr Goet-zen, A new hybrid particle swarm optimization and evolutionary algorithm, International Conference on Artificial Intelligence and Soft Computing, pp. 432–444, Springer, 2019.
https://doi.org/10.1007/978-3-030-20912-4_40
Yanhui Xi, Xin Tang, Zewen Li, Yonglin Cui, Ting Zhao, Xiangjun Zeng, Jun Guo, Wei Duan, Harmonic estimation in power systems using an optimised adaptive Kalman filter based on PSO-GA, IET Generation, Transmission & Distribution, Vol. 13, No. 17, pp. 3968 – 3979, 2019.
https://doi.org/10.1049/iet-gtd.2018.6148
Sabzi, Hamed Zamani., Humbersona, Delbert., Abuduc, Shalamu., Kinga, , James Phillip., Optimization of adaptive fuzzy logic controller using novel combine devolutionary algorithms, and its application in Diez Lagos floodcontrolling system, Southern New Mexico, Expert Systems with Applications: An International Journal, Vol 43, pp.154–164., Elsevier, 2016.
https://doi.org/10.1016/j.eswa.2015.08.043
Sarkheyli, Arezoo., Zain, Azlan Mohd., Sarif, Safian., Robust Optimation of ANFIS based on a new Modified GA, Neuro Computing, Vol. 166, pp. 357-366, Elsevier, 2015
https://doi.org/10.1016/j.neucom.2015.03.060
Rini, Dian Palupi; Shamsuddin, Siti Mariyam; Yuhanis, Sophiayati Yuhanis, Particle Swarm Optimization for ANFIS Interpretability and Accuracy, Methodologies and Application: Soft Computing, Vol. 20, pp. 251–262, Springer, 2016.
https://doi.org/10.1007/s00500-014-1498-z
M. Pau, P.A. Pegoraro, S. Sulis, Efficient branch-current-based distribution system state estimation including synchronized measurements, IEEE Transactions on Instrumentation and Measurement, Vol. 62, No. 9, pp. 2419–2429, 2013.
https://doi.org/10.1109/tim.2013.2272397
Mutanen, A., Jarventausta, P., Repo, S., Smart Meter Data-Based Load Profiles and Their Effect on Distribution System State Estimation Accuracy, (2017) International Review of Electrical Engineering (IREE), 12 (6), pp. 460-469.
https://doi.org/10.15866/iree.v12i6.13419
E. Mantisas, R. Singh, B.C. Pal, & G. Strbac. Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modelling, IEEE Transactions on Power Systems, Vol. 27, No. 4, pp. 1888-1896, 2012.
https://doi.org/10.1109/tpwrs.2012.2187804
M. Biserica, Y. Besanger, R. Caire, et al., Neural networks to improve distribution state estimation–Volt Var control performances, IEEE Transactions on Smart Grid, Vol. 3, No. 3, pp. 1137–1144, 2012.
https://doi.org/10.1109/tsg.2012.2193673
Piotr Dziwińsk, Łukasz Bartczuk, A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic, IEEE Transactions on Fuzzy Systems, Vol. 28, No. 6, pp. 1140 – 1154, 2020.
https://doi.org/10.1109/tfuzz.2019.2957263
Ismagilov, F., Vavilov, V., Urazbakhtin, R., Optimization of Synchronous Electric Motors with Asynchronous Start by Genetic Algorithms, (2018) International Review of Aerospace Engineering (IREASE), 11 (2), pp. 66-75.
https://doi.org/10.15866/irease.v11i2.13365
Si Tayeb, M., Fizazi, H., A Dual-Level Hybrid Approach for Classification of Satellite Images, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 42-49.
https://doi.org/10.15866/irease.v10i1.11191
Dalla Vedova, M., Berri, P., Optimization Techniques for Prognostics of On-Board Electromechanical Servomechanisms Affected by Progressive Faults, (2019) International Review of Aerospace Engineering (IREASE), 12 (4), pp. 160-170.
https://doi.org/10.15866/irease.v12i4.17356
Ismagilov, F., Vavilov, V., Sayakhov, I., Unipolar Magnetic Bearing for High-Speed Electric Motor of Aircraft Air Conditioning System: Structure and Optimization, (2018) International Review of Aerospace Engineering (IREASE), 11 (2), pp. 84-95.
https://doi.org/10.15866/irease.v11i2.14098
Tran, K., Modified GA Tuning IPD Control for a Single Tilt Tri-Rotors UAV, (2018) International Review of Aerospace Engineering (IREASE), 11 (1), pp. 1-5.
https://doi.org/10.15866/irease.v11i1.12807
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