Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

Lei Shi; Zongyu Zhang; Yongrui Yu; Chun Xie; Tongbin Yang

doi:10.4108/ew.7325

Authors

Lei Shi Guizhou Power Grid Co.
Zongyu Zhang Guizhou Power Grid Co.
Yongrui Yu Guizhou Power Grid Co.
Chun Xie Guizhou Power Grid Co.
Tongbin Yang Guizhou Power Grid Co.

DOI:

https://doi.org/10.4108/ew.7325

Keywords:

PV Systems, MPPT, Irradiation, Power output, Ant-colony integrated bald eagle search optimization (AC-BESO)

Abstract

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task.

OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems.

METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance.

RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses.

CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

Downloads

Download data is not yet available.

References

[1] He, B., & Bhatti, U. A. (2024). Smart Cities and Smart Networks: AI Applications in Urban Geography and Industrial Communication. International Journal of High Speed Electronics and Systems, 2440122.

[2] Tang, H., Zhang, Z., Zhang, Y., Xu, B., & Bhatti, U. A. (2024, June). Plug-and-Work edge collaborator design for customised manufacturing. In 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 63-68). IEEE.

[3] Tang, H., Chen, D., Zhang, Y., Xu, B., Bhatti, U. A., & Yu, M. (2024, June). Bidirectional Interaction Techniques Based on Device Digital Twin Model. In 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 132-138). IEEE.

[4] Didi, F., Chaouche, M.S., Amari, M., Guezmir, A., Belhenniche, K. and Chellali, A., 2023. Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking MPPT Based on Artificial Intelligence. Tobacco Regulatory Science (TRS), pp.1074-1098.

[5] Hai, T., Zain, J.M. and Nakamura, H., 2023. Maximum power point tracking technique based on variable step size with sliding mode controller in photovoltaic system. Soft Computing, 27(7), pp.3829-3845.

[6] Azam, M.S., Bhattacharjee, A., Hassan, M., Rahaman, M., Aziz, S., Shaikh, M.A.A. and Islam, M.S., 2024. Performance enhancement of solar PV system introducing semi-continuous tracking algorithm based solar tracker. Energy, 289, p.129989.

[7] Guanghua, L., Siddiqui, F.A., Aman, M.M., Shah, S.H.H., Ali, A., Soomar, A.M. and Shaikh, S., 2024. Improved maximum power point tracking algorithms by using numerical analysis techniques for photovoltaic systems. Results in Engineering, 21, p.101740.

[8] Ahmed, S., Mekhilef, S., Mubin, M.B. and Tey, K.S., 2022. Performances of the adaptive conventional maximum power point tracking algorithms for solar photovoltaic system. Sustainable Energy Technologies and Assessments, 53, p.102390.

[9] Xu, S., Gao, Y., Zhou, G. and Mao, G., 2020. A global maximum power point tracking algorithm for photovoltaic systems under partially shaded conditions using modified maximum power trapezium method. IEEE Transactions on industrial electronics, 68(1), pp.370-380.

[10] Verma, P., Alam, A., Sarwar, A., Tariq, M., Vahedi, H., Gupta, D., Ahmad, S. and Shah Noor Mohamed, A., 2021. Meta-heuristic optimization techniques used for maximum power point tracking in solar pv system. Electronics, 10(19), p.2419.

[11] Chou, K.Y., Yang, S.T. and Chen, Y.P., 2019. Maximum power point tracking of photovoltaic system based on reinforcement learning. Sensors, 19(22), p.5054.

[12] Rafeeq Ahmed, K., Sayeed, F., Logavani, K., Catherine, T.J., Ralhan, S., Singh, M., Prabu, R.T., Subramanian, B.B. and Kassa, A., 2022. Maximum power point tracking of PV grids using deep learning. International Journal of Photoenergy, 2022(1), p.1123251.

[13] Zhang, X., Li, S., He, T., Yang, B., Yu, T., Li, H., Jiang, L. and Sun, L., 2019. Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition. Energy, 174, pp.1079-1090.

[14] Patel, A., Swathika, O.G., Subramaniam, U., Babu, T.S., Tripathi, A., Nag, S., Karthick, A. and Muhibbullah, M., 2022. A practical approach for predicting power in a small‐scale off‐grid photovoltaic system using machine learning algorithms. International Journal of Photoenergy, 2022(1), p.9194537.

[15] Nkambule, M.S., Hasan, A.N., Ali, A., Hong, J. and Geem, Z.W., 2021. Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions. Journal of Electrical Engineering & Technology, 16, pp.411-427.

[16] Liu, Y., 2022. Short‐Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid. Mathematical Problems in Engineering, 2022(1), p.8478790.

[17] Kabilan, R., Chandran, V., Yogapriya, J., Karthick, A., Gandhi, P.P., Mohanavel, V., Rahim, R. and Manoharan, S., 2021. Short‐Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms. International Journal of Photoenergy, 2021(1), p.5582418.

[18] Hameed, W.I., Saleh, A.L., Sawadi, B.A., Al-Yasir, Y.I. and Abd-Alhameed, R.A., 2019. Maximum power point tracking for photovoltaic system by using fuzzy neural network. Inventions, 4(3), p.33.

[19] Mahmud, K., Azam, S., Karim, A., Zobaed, S., Shanmugam, B. and Mathur, D., 2021. Machine learning based PV power generation forecasting in alice springs. IEEE Access, 9, pp.46117-46128.

[20] Abokhalil, A., 2020. Maximum power point tracking for a PV system using tuned support vector regression by particle swarm optimization. Journal of Engineering Research, 8(4).

[21] Padmavathi, N., Chilambuchelvan, A. and Shanker, N.R., 2021. Maximum power point tracking during partial shading effect in PV system using machine learning regression controller. Journal of Electrical Engineering & Technology, 16, pp.737-748.

[22] Xie, Z. and Wu, Z., 2021. Maximum power point tracking algorithm of PV system based on irradiance estimation and multi-Kernel extreme learning machine. Sustainable Energy Technologies and Assessments, 44, p.101090.

[23] Mahesh, P.V., Meyyappan, S. and Alla, R., 2023. Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems. International journal of electrical and computer engineering systems, 14(1), pp.100-108.

[24] Guo, M., Ren, M., Chen, J., Cheng, L. and Yang, Z., 2023. Tracking Photovoltaic Power Output Schedule of the Energy Storage System Based on Reinforcement Learning. Energies, 16(15), p.5840.

[25] Artetxe, E., Uralde, J., Barambones, O., Calvo, I. and Martin, I., 2023. Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin. Mathematics, 11(9), p.2166.

[26] Bollipo, R.B., Mikkili, S. and Bonthagorla, P.K., 2023. Application of radial basis neural network in MPPT technique for stand-alone PV system under partial shading conditions. IETE Journal of Research, 69(9), pp.6409-6430.

[27] Bristi, S. D., Tatha, M. J., Ali, M. F., Bhatti, U. A., Sarker, S. K., Masud, M., ... & Saha, D. K. (2023). A Meta-Heuristic Sustainable Intelligent Internet of Things Framework for Bearing Fault Diagnosis of Electric Motor under Variable Load Conditions. Sustainability, 15(24), 16722.

[28] Ali, A., Li, J., Chen, H., Bhatti, U. A., & Khan, A. (2023). Real-Time Spammers Detection Based on Metadata Features with Machine Learning. Intelligent Automation & Soft Computing, 38(3).

[29] Cheng, M., Li, D., Zhou, N., Tang, H., Wang, G., Li, S., ... & Khan, M. K. (2023). Vision-motion codesign for low-level trajectory generation in visual servoing systems. IEEE Transactions on Instrumentation and Measurement.

[30] Chen, H., Zhang, Y., Bhatti, U. A., & Huang, M. (2023). Safe decision controller for autonomous drivingbased on deep reinforcement learning innondeterministic environment. Sensors, 23(3), 1198.