Salp Swarm Optimized Hybrid Elman Recurrent Neural Network (SSO-ERNN) based MPPT Controller for Solar PV
Article Sidebar
Main Article Content
Abstract
Renewable energy technologies provide clean and abundant energy that can be self-renewed from natural sources; more support from the public to replace fossil fuels with various renewable energy sources to protect the environment. Although solar energy has less impact on the environment than other renewable sources, the output efficiency is lower due to the different weather conditions. So to overcome that, the MPPT controller is used for tracking peak power and better efficiency. Some conventional methods in MPPT controllers provide less tracking efficiency, and steady-state oscillations occur in maximum power tracking due to the sudden variations in solar irradiance. Thus, in this work salp swarm optimized (SSO) based Elman recurrent neural network (ERNN) controller is proposed to track the maximum power form PV with high efficiency. The weight parameter of ERNN layer is optimized with the help of SSO, which solve the complex problems and give maximum efficiency. The proposed method is performed in MATLAB/Simulink environment, which differs from existing plans and gives a better output efficiency. Using this proposed controller, the system can achieve high tracking efficiency of 99.74% compared to conventional processes.
Article Details
References
S. Messalti, A. Harrag, A. Loukriz, “A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation.” Renewable and Sustainable Energy Reviews pp. 221-33, vol. 68, 2017,
C.H. Hussaian Basha, C. Rani, “Performance analysis of MPPT techniques for dynamic irradiation condition of solar PV.” International Journal of Fuzzy Systems pp. 2577-98, vol. 22, no. 8, 2020,
D. Haji, N. Genc, “Fuzzy and P&O based MPPT controllers under different conditions.”In2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) pp. 649-655, 2018, IEEE.
S.R. Pendem, S. Mikkili, P.K. Bonthagorla, “PV distributed-MPP tracking: Total-cross-tied configuration of string-integrated-converters to extract the maximum power under various PSCs.’ IEEE Systems Journal pp. 1046-57, vol. 14, no. 1, 2019,
C.R. Algarín, R.L. Fuentes, A.O. Castro, “Implementation of a cost-effective fuzzy MPPT controller on the Arduino board.” International journal on smart sensing and intelligent systems pp. 1-0, vol. 11, no. 1, 2018,
S. Ozdemir, N. Altin, I. Sefa, “Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter.” International Journal of Hydrogen Energy pp. 17748-59, vol. 42, no. 28, 2017,
M.A. Zainuri, M.A. Radzi, A. Che Soh, N.A. Rahim, “Development of adaptive perturb and observe?fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter.” IET Renewable Power Generation pp. 183-94, vol. 8, no. 2, 2014,
T. Radjai, L. Rahmani, S. Mekhilef, J.P. Gaubert, “Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE.” Solar Energy pp. 325-37, vol. 110, 2014,
B.N. Alajmi, K.H. Ahmed, S.J. Finney, B.W. Williams, “Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system.” IEEE transactions on power electronics pp. 1022-30, vol. 26, no. 4, 2010,
X. Li, H. Wen, Y. Hu, L. Jiang, “A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application.” Renewable energy pp. 416-27, vol. 130, 2019,
M. Castelli, L. Manzoni, L. Mariot, M.S. Nobile, A. “Tangherloni, Salp Swarm Optimization: A critical review.” Expert Systems with Applications pp. 116029, vol. 189, 2022,
R.A. Ibrahim, A.A. Ewees, D. Oliva, M. Abd Elaziz, S. Lu, “Improved salp swarm algorithm based on particle swarm optimization for feature selection.” Journal of Ambient Intelligence and Humanized Computing pp. 3155-69, vol. 10, no. 8, 2019,
O.I. Abiodun, A. Jantan, A.E. Omolara, K.V. Dada, N.A. Mohamed, H. Arshad, “State-of-the-art in artificial neural network applications: A survey.” Heliyon pp. e00938, vol. 4, no. 11, 2018,
A.W. Minns, M.J. Hall, “Artificial neural networks as rainfall-runoff models.” Hydrological sciences journal pp. 399-417, vol. 41, no. 3, 1996,
W. Zaremba, I. Sutskever, O. Vinyals, “Recurrent neural network regularization.” arXiv preprint arXiv: 1409.2329. 2014,
T. Mikolov, M. Karafiát, L. Burget, J. Cernocký, S. Khudanpur, “Recurrent neural network based language model.” InInterspeech pp. 1045-1048, vol. 2, no. 3, 2010,
A.N. Sharkawy, “Principle of neural network and its main types.” Journal of Advances in Applied & Computational Mathematics pp. 8-19, no. 7, 2020,
J. Wang, Y. Gao, X. Chen, “A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting.” Energies pp. 1561, vol. 11, no. 6, 2018,
R. Çelikel, A. Gündo?du, “ANN-based MPPT algorithm for photovoltaic systems.” Turkish Journal of Science and Technology pp. 101-10, vol. 15, no. 2, 2020,
I. Haseeb, A. Armghan, W. Khan, F. Alenezi, N. Alnaim, F. Ali, F. Muhammad, F.R. Albogamy, N. Ullah, “Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm.” Applied Sciences pp. 11332, vol. 11, no. 23, 2021,
S.R. Kiran, C.H. Basha, V.P. Singh, C. Dhanamjayulu, B.R. Prusty, B. Khan, “Reduced Simulative Performance Analysis of Variable Step Size ANN Based MPPT Techniques for Partially Shaded Solar PV Systems.” IEEE Access pp. 48875-89, vol. 10, 2022,
A. Subramanian, J. Raman, “Modified seagull optimization algorithm based MPPT for augmented performance of photovoltaic solar energy systems.” Automatika pp. 1-5, vol. 63, no. 1, 2022,
H. Rezk, A. Fathy, “Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System.” Energies pp. 4971, vol. 13, no. 18, 2020,
M. Latifi, R. Abbassi, H. Jerbi, K. Ohshima, “Improved krill herd algorithm based sliding mode MPPT controller for variable step size P&O method in PV system under simultaneous change of irradiance and temperature.” Journal of the Franklin Institute. pp. 3491-511, vol. 358, no. 7, 2021,
A.M. Eltamaly, “A novel musical chairs algorithm applied for MPPT of PV systems.” Renewable and Sustainable Energy Reviews pp. 111135, vol. 146, 2021,
W.S. Abdellatif, M.S. Mohamed, S. Barakat, A. Brisha, “A Fuzzy Logic Controller Based MPPT Technique for Photovoltaic Generation System.” International Journal on Electrical Engineering & Informatics pp. 2021, vol. 13, no. 2,
S.R. Revathy, V. Kirubakaran, M. Rajeshwaran, T. Balasundaram, V.S. Sekar, S. Alghamdi, B.S. Rajab, A.O. Babalghith, E.M. Anbese, “Design and Analysis of ANFIS–Based MPPT Method for Solar Photovoltaic Applications.” International Journal of Photoenergy 2022,
R. Divyasharon, R.N. Banu, D. Devaraj, “Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions.” In2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) pp. 1-6, 2019, IEEE.
R. Srinivasan, C. Ramalingam Balamurugan, “Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system.” International Journal of Electronics pp. 576-95, vol. 109, no. 4, 2022,
M.F. Ab Aziz, S.A. Mostafa, C.F. Foozy, M.A. Mohammed, M. Elhoseny, A.Z. Abualkishik, “Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets.” Expert Systems with Applications pp. 115441, vol. 183, 2021,
E. Krichene, Y. Masmoudi, A.M. Alimi, A. Abraham, H. Chabchoub, “Forecasting using Elman recurrent neural network.” International Conference on Intelligent Systems Design and Applications pp. 488-497, 2016, Springer, Cham.
M. Premkumar, C. Kumar, R. Sowmya, J. Pradeep, “A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems.” Automatika: ?asopis za automatiku, mjerenje, elektroniku, ra?unarstvo i komunikacije pp. 1-20, vol. 62, no. 1, 2021,
S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, SM. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems.” Advances in engineering software pp. 163-91, vol. 114, 2017,
A.S. Saidi, C.B. Salah, A. Errachdi, M.F. Azeem, J.K. Bhutto, V.T. Ijyas, “A novel approach in stand-alone photovoltaic system using MPPT controllers & NNE.”Ain Shams Engineering Journal pp. 1973-84, vol. 12, no. 2, 2021,
M.H. Zafar, T. Al-shahrani, N.M. Khan, A. Feroz Mirza, M. Mansoor, M.U. Qadir, M.I. Khan, R.A. Naqvi, “Group teaching optimization algorithm based MPPT control of PV systems under partial shading and complex partial shading.” Electronics pp. 1962, vol. 9, no. 11, 2020,
C. González-Castaño, C. Restrepo, S. Kouro, J. Rodriguez, “MPPT algorithm based on artificial bee colony for PV system.” IEEE Access pp. 43121-33, vol. 9, 2021,
S. Jana, N. Kumar, R. Mishra, D. Sen, T.K. Saha, “Development and implementation of modified MPPT algorithm for boost converter?based PV system under input and load deviation.” International Transactions on Electrical Energy Systems pp. e12190, vol. 30, no. 2, 2020,
L. Assiya, D. Aziz, H. Ahmed, “Comparative study of P&O and INC MPPT algorithms for DC-DC converter based PV system on MATLAB/SIMULINK.” In 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) pp. 1-5, 2020, IEEE.
A.I.M. Ali, H.R.A. Mohamed, “Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation.” International Journal of Electrical Power & Energy Systems pp. 107805, vol. 137, 2022,
A.V. Prathaban, D. Karthikeyan, “Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application.” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) pp. 629-638, vol. 26, no. 2, 2022,
M.O.H.S.E.N. Davoudi, A.K. Far, “Recurrent neural network based MPPT control of grid connected DFIG for wind turbine.” Wseas Transactions on Computer Research pp. 1-10, vol. 8, 2020,
M.S. Nkambule, A.N. Hasan, A. Ali, “Commensurate Evaluation of Support Vector Machine and Recurrent Neural Network MPPT Algorithm for a PV system under different weather conditions.” In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO) pp. 329-335, 2019, IEEE.