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New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Genetic Algorithm Optimization


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DOI: https://doi.org/10.15866/irecon.v10i3.22156

Abstract


Solar energy is highly dependent on climatic conditions and swings sporadically, making photovoltaic integration into power systems difficult. In order to improve the design of power infrastructure and the successful deployment of decentralized and renewable energy sources, a new paradigm for the energy supply chain is established, leading to the creation of microgrids. Intelligence should be introduced at all levels of the grid, and it should act over a long period. This article introduces new research tools and methodologies to help utility engineers better understand the grid's potential impact from these new power sources. Therefore, this research proposes a novel PV solar system control method. The strategy is an innovative Maximum Power Point Tracking (MPPT) technology for solar systems. Designing an effective controller capable of lowering Power Stress inside the PV System is critical in this respect. In this research, the Proportional Integral (PI) controller has been utilized in conjunction with a heuristic technique termed Genetic Algorithm (GA) in order to regulate and stabilize the MPP of the power supplied by the PV system. Then the GA technique has been utilized to identify the ideal settings, based on the four performance indices of Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time Absolute Error (ITAE), and Integrated Time Squared Error (ITSE) (Kp, Ki). The simulation results show that the PV system can successfully monitor the required MPP.
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Keywords


Solar Energy; MPPT Technique; PI Controller; GA Optimization

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References


R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization, Renewable and Sustainable Energy Reviews, vol. 124, p. 109792, May 2020.
https://doi.org/10.1016/j.rser.2020.109792

E. Mocanu, P. H. Nguyen, M. Gibescu, and W. L. Kling, Deep learning for estimating building energy consumption, Sustainable Energy, Grids and Networks, vol. 6, pp. 91-99, Jun. 2016.
https://doi.org/10.1016/j.segan.2016.02.005

I. Hanif, Impact of fossil fuels energy consumption, energy policies, and urban sprawl on carbon emissions in East Asia and the Pacific: A panel investigation, Energy Strategy Reviews, vol. 21, pp. 16-24, Aug. 2018.
https://doi.org/10.1016/j.esr.2018.04.006

M. G. Simoes et al., A Comparison of Smart Grid Technologies and Progresses in Europe and the U.S., IEEE Trans. on Ind. Applicat., vol. 48, no. 4, pp. 1154-1162, Jul. 2012.
https://doi.org/10.1109/TIA.2012.2199730

K. Yung Yap, C. R. Sarimuthu, and J. Mun-Yee Lim, Artificial Intelligence Based MPPT Techniques for Solar Power System: A review, Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1043-1059, 2020.
https://doi.org/10.35833/MPCE.2020.000159

E. K. Anto, J. A. Asumadu, and P. Y. Okyere, PID control for improving P&O-MPPT performance of a grid-connected solar PV system with Ziegler-Nichols tuning method, in 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, Jun. 2016, pp. 1847-1852.
https://doi.org/10.1109/ICIEA.2016.7603888

B. Yang et al., Energy reshaping based passive fractional-order PID control design and implementation of a grid-connected PV inverter for MPPT using grouped grey wolf optimizer, Solar Energy, vol. 170, pp. 31-46, Aug. 2018.
https://doi.org/10.1016/j.solener.2018.05.034

R. Hotchi, H. Chibana, T. Iwai, and R. Kubo, Active Queue Management Supporting TCP Flows Using Disturbance Observer and Smith Predictor, IEEE Access, vol. 8, pp. 173401-173413, 2020.
https://doi.org/10.1109/ACCESS.2020.3025680

M. Arsalan, R. Iftikhar, I. Ahmad, A. Hasan, K. Sabahat, and A. Javeria, MPPT for photovoltaic system using nonlinear backstepping controller with integral action, Solar Energy, vol. 170, pp. 192-200, Aug. 2018.
https://doi.org/10.1016/j.solener.2018.04.061

H. Gudimindla, M. S. Krishnamurthy, and S. Sandhya, Performance analysis of automated QFT robust controller for long-term grid tied PV simulations, in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, Oct. 2020, pp. 412-417.
https://doi.org/10.1109/ICSTCEE49637.2020.9277021

A. Lashab, D. Sera, J. M. Guerrero, L. Mathe, and A. Bouzid, Discrete Model-Predictive-Control-Based Maximum Power Point Tracking for PV Systems: Overview and Evaluation, IEEE Trans. Power Electron., vol. 33, no. 8, pp. 7273-7287, Aug. 2018.
https://doi.org/10.1109/TPEL.2017.2764321

J. Hui and X. Sun, MPPT Strategy of PV System Based on Adaptive Fuzzy PID Algorithm, in Life System Modeling and Intelligent Computing, vol. 97, K. Li, X. Li, S. Ma, and G. W. Irwin, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 220-228.
https://doi.org/10.1007/978-3-642-15853-7_28

Ahmed, M., Al-Khashman, O., Zakaria, A., The Optimal Use of Renewable Energy in Residential Buildings Under Jordanian Building Regulation: a Case Study, (2021) International Journal on Energy Conversion (IRECON), 9 (6), pp. 283-291.
https://doi.org/10.15866/irecon.v9i6.21413

Firman, F., Said, N., Djalal, M., Characteristic Analysis of Solar Panels on Clay and Ceramic Roof Tiles, (2022) International Review of Mechanical Engineering (IREME), 16 (3), pp. 130-138.
https://doi.org/10.15866/ireme.v16i3.20004

Sunarno, E., Assidiq, R., Nugraha, S., Sudiharto, I., Qudsi, O., Eviningsih, R., Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage, (2019) International Review of Automatic Control (IREACO), 12 (3), pp. 145-153.
https://doi.org/10.15866/ireaco.v12i3.16455

Meo S., Toscano L., Some new results on the averaging theory approach for the analysis of power electronic converters, (2018) IEEE Transactions on Industrial Electronics, 65 (12), art. no. 8328884, pp. 9367 - 9377.
https://doi.org/10.1109/TIE.2018.2821620

Tarateeraseth, V., Innovative Magnetic Circuits for Boost Converters' and EMI Filters' Integrated Inductor, (2022) International Review of Electrical Engineering (IREE), 17 (1), pp. 2-10.
https://doi.org/10.15866/iree.v17i1.16519

Rachmayani, R., Ningsih, N., Karyoto, J., Assessment of Potential Location for Ocean Wave Energy Converter Installation in Southern Waters of Lampung, Banten and Sukabumi, (2021) International Journal on Energy Conversion (IRECON), 9 (1), pp. 7-16.
https://doi.org/10.15866/irecon.v9i1.19127

Vinod, R. Kumar, and S. K. Singh, Solar photovoltaic modeling and simulation: As a renewable energy solution, Energy Reports, vol. 4, pp. 701-712, Nov. 2018.
https://doi.org/10.1016/j.egyr.2018.09.008

Rerhrhaye, F., Chebli, S., Elakkary, A., Sefiani, N., Bond Graph-Based Modelling for Parameter Identification of 5 kW Photovoltaic Group, (2019) International Review on Modelling and Simulations (IREMOS), 12 (6), pp. 390-397.
https://doi.org/10.15866/iremos.v12i6.17046

Ikawanty, B., Ashari, M., Suryoatmojo, H., Taufik, T., A Novel Multiphase Interleaved DC-DC Boost Converter with Reduced Input Current Ripple for Renewable Energy Application, (2022) International Journal on Engineering Applications (IREA), 10 (1), pp. 77-86.
https://doi.org/10.15866/irea.v10i1.20941

Teamah, H., A Review on Phase Change Materials Integration in Photovoltaic (PV) and Photovoltaic Thermal Systems (PVT): Scope and Challenges, (2021) International Review of Mechanical Engineering (IREME), 15 (9), pp. 475-485.
https://doi.org/10.15866/ireme.v15i9.21375

Essaghir, S., Benchagra, M., El Barbri, N., Performance Improvement of the Control Techniques of a Grid-Connected Photovoltaic System with Battery Storage Management, (2020) International Review on Modelling and Simulations (IREMOS), 13 (5), pp. 297-305.
https://doi.org/10.15866/iremos.v13i5.18846

M. Adly and K. Strunz, Efficient Digital Control for MPP Tracking and Output Voltage Regulation of Partially Shaded PV Modules in DC Bus and DC Microgrid Systems, IEEE Trans. Power Electron., vol. 34, no. 7, pp. 6309-6319, Jul. 2019.
https://doi.org/10.1109/TPEL.2018.2873753

S. Kouro, J. I. Leon, D. Vinnikov, and L. G. Franquelo, Grid-Connected Photovoltaic Systems: An Overview of Recent Research and Emerging PV Converter Technology, IEEE Ind. Electron. Mag., vol. 9, no. 1, pp. 47-61, Mar. 2015.
https://doi.org/10.1109/MIE.2014.2376976

Srivastava, A., Bajpai, R., Model Predictive Control of Renewable Energy Sources in DC Microgrid for Power Flow Control, (2021) International Journal on Energy Conversion (IRECON), 9 (4), pp. 176-190.
https://doi.org/10.15866/irecon.v9i4.20152

Q. Mei, M. Shan, L. Liu, and J. M. Guerrero, A Novel Improved Variable Step-Size Incremental-Resistance MPPT Method for PV Systems, IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2427-2434, Jun. 2011.
https://doi.org/10.1109/TIE.2010.2064275

A. Aboelhassan, M. Abdelgeliel, E. E. Zakzouk, and M. Galea, Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications, Energies, vol. 13, no. 24, p. 6594, Dec. 2020.
https://doi.org/10.3390/en13246594

W. Batayneh and Y. AbuRmaileh, Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers, Sensors, vol. 20, no. 12, p. 3525, Jun. 2020.
https://doi.org/10.3390/s20123525

S. Wang, H. Liang, and J. Wang, GA PID control research in inverter motor speed governing system, JCM, vol. 19, no. 2, pp. 299-306, May 2019.
https://doi.org/10.3233/JCM-180869

S. M. H. Mousakazemi and N. Ayoobian, Robust tuned PID controller with PSO based on two-point kinetic model and adaptive disturbance rejection for a PWR-type reactor, Progress in Nuclear Energy, vol. 111, pp. 183-194, Mar. 2019.
https://doi.org/10.1016/j.pnucene.2018.11.003

Azazy, N., Helmy, W., Hasanien, H., Optimal Siting and Sizing of DGs on Distribution Networks Using Grey Wolf Algorithm, (2021) International Journal on Energy Conversion (IRECON), 9 (3), pp. 113-124.
https://doi.org/10.15866/irecon.v9i3.20365

Jaber, A., Mohammed, K., Shalash, N., Optimization of Electrical Power Systems Using Hybrid PSO-GA Computational Algorithm: a Review, (2020) International Review of Electrical Engineering (IREE), 15 (6), pp. 502-511.
https://doi.org/10.15866/iree.v15i6.18599

J. Huang et al., Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning, Science of The Total Environment, vol. 722, p. 137701, Jun. 2020.
https://doi.org/10.1016/j.scitotenv.2020.137701

O. Nurdiawan, F. A. Pratama, D. A. Kurnia, Kaslani, and N. Rahaningsih, Optimization of Traveling Salesman Problem on Scheduling Tour Packages using Genetic Algorithms, J. Phys.: Conf. Ser., vol. 1477, p. 052037, Mar. 2020.
https://doi.org/10.1088/1742-6596/1477/5/052037

M. Mosayebi and M. Sodhi, Tuning genetic algorithm parameters using design of experiments, in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Cancún Mexico, Jul. 2020, pp. 1937-1944.
https://doi.org/10.1145/3377929.3398136

M. Love, Optimizing Language-based Navigation with Genetic Algorithms, p. 13.

A. A. M. Zahir, S. S. N. Alhady, W. A. F. W. Othman, and M. F. Ahmad, Genetic Algorithm Optimization of PID Controller for Brushed DC Motor, in Intelligent Manufacturing & Mechatronics, M. H. A. Hassan, Ed. Singapore: Springer Singapore, 2018, pp. 427-437.
https://doi.org/10.1007/978-981-10-8788-2_38

S. Chauhan, B. Singh, and M. Singh, Modified ant colony optimization based PID controller design for coupled tank system, Eng. Res. Express, vol. 3, no. 4, p. 045005, Dec. 2021.
https://doi.org/10.1088/2631-8695/ac2bf3


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