I. J. of Electrical & Electronics Research
Research Article | 
Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load
Author(s) : Spandan Srivastava1, Charu Lata2, Prateek Lohan3 Rinchin W. Mosobi4
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 3, Special Issue on Recent Advancements in the Electrical & Electronics Engineering
Publisher : FOREX Publication
Published : 10 August 2022
e-ISSN : 2347-470X
Page(s) : 460-465
Abstract
The escalating demands and increasing awareness for the environment, resulted in deployment of Photovoltaic (PV) system as a viable option. PV system are widely installed for numerous applications. However, the challenges in tracking the maximum power with intermittent atmospheric condition and varying load is significant. Maximum Power Point Tracking (MPPT) algorithms are employed and based on their convergence speed, control of external variations and oscillation, the output power efficiency, and other significant factors viz. the algorithm complexity and implementation cost, novel MPPT approach are preferable than the conventional approach. This paper presents an artificial intelligence-based optimization controller for MPPT in a PV system under varying load and irradiance conditions. Comparative analysis of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) based MPPT is simulated and analysed. The PV system consisting of PV array and boost converter with MPPT controller feeds the DC load. The power conversion and panel efficiency are the significant factors to determine the effectiveness of tracking maximum power point. The simulation results show the performance of these controllers on the PV panel output power and the load side output power under changing loads and irradiance. In addition, the comparison of PV panel efficiency of ANN and PSO based MPPT techniques w.r.t changing loads is carried out. Based on the above analysis, PSO based MPPT algorithm marginally outperforms the ANN based MPPT algorithm. Further, the implementation of hybrid MPPT (ANN &PSO) for higher accuracy and tracking capability can be carried out as future work.
Keywords: Maximum Power Point Tracking (MPPT), Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Photovoltaic (PV) System
Spandan Srivastava, Electrical Engineering Department, Delhi Technological University (DTU), New Delhi, India; Email: spandansrivastava_2k18ee208@dtu.ac.in
Charu Lata, Electrical Engineering Department, Delhi Technological University (DTU), New Delhi, India; Email: charulata_2k18ee056@dtu.ac.in
Prateek Lohan, Electrical Engineering Department, Delhi Technological University (DTU), New Delhi, India; Email: prateeklohan_2k18ee143@dtu.ac.in
Rinchin W. Mosobi, Electrical Engineering Department, Delhi Technological University (DTU), New Delhi, India; Email: wangzom@dtu.ac.in
-
[1] Li, X., Wen, H., Hu, Y., Du, Y., & Yang, Y., “A comparative study on photovoltaic MPPT algorithms under EN50530 dynamic test procedure". IEEE Transactions on Power Electronics, 36(4), 4153-4168, 2020. [Cross Ref]
-
[2] Chitransh, A., Kumar, S., “The Different Type of MPPT Techniques for Photovoltaic System”, Indian Journal of Engineering and Materials Sciences, 2021[Cross Ref]
-
[3] Haq IU, Khan Q, Ullah S, Khan SA, Akmeliawati R, Khan MA, et al., “Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems”, PLOS ONE 17(1): e0260480, 2022[Cross Ref]
-
[4] Podder, A. K., Roy, N. K., & Pota, H. R., “MPPT methods for solar PV systems: a critical review based on tracking nature”, IET Renewable Power Generation, 13(10), 1615-1632, 2019 [Cross Ref]
-
[5] W.Hayder, A.Abid, M. Hamed and L. Sbita, “Improved PSO Algorithms in PV System Optimisation”,European Journal of Electrical Engineering and Computer Science, 2020.[Cross Ref]
-
[6] Abdellatif, W., Mohamed, M., Barakat, S., Brisha, Ayman., “A Fuzzy Logic Controller Based MPPT Technique for Photovoltaic Generation System”, International Journal on Electrical Engineering and Informatics, 13, 394 – 417, 2021[Cross Ref]
-
[7] S. A. Nasirudeen, M. S. Haruna, M. A. Aminu and O. Osanaiye, "Mathematical Modelling, Simulation and Analysis of Solar PV module in Simulink," 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 2021, pp. 1-5, 2021[Cross Ref]
-
[8] Abo-Sennah M.A., El-Dabah M.A., Mansour A.E.-B., “Maximum power point tracking techniques for photovoltaic systems: A comparative study”, International Journal of Electrical and Computer Engineering, vol. 11, no. 1, p. 57-73, 2021[Cross Ref]
-
[9] Prabhu, U., Ramesh Babu, Muthu., “Performance Study of MPPT Algorithms of DC-DC Boost Converters For PV Cell Applications”, 7th International Conference on Electrical Energy Systems (ICEES), 201-205, 2021[Cross Ref]
-
[10] Raj, A., Arya, S. R., Gupta, J., “Solar PV array-based DC–DC converter with MPPT for low power applications”, Renewable Energy Focus, 2020[Cross Ref]
-
[11] Mishra, S., Mohanty, D., Sahu, J., Mohanty, S., “Artificial Neural Network Based MPPT Controller for Stand-alone Solar PV System”, International Conference on System, Computation, Automation and Networking, 2021[Cross Ref]
-
[12] Thangavel, S., “Neural Network Based MPPT Controller for Solar PV Connected Induction Motor”, IAES International Journal of Robotics and Automation (IJRA), 2018[Cross Ref]
-
[13] Almajidi,S., Abbod,M., Al-Raweshidy, H., “A particle swarm optimisation-trained feedforward neural network for predicting the maximum power point of a photovoltaic array”, Engineering Applications of Artificial Intelligence, 2020.[Cross Ref]
-
[14] Himabindu Eluri, M. Gopichand Naik (2022), Energy Management System and Enhancement of Power Quality with Grid Integrated Micro-Grid using Fuzzy Logic Controller. IJEER 10(2), 256-263. DOI: 10.37391/IJEER.100234.[Cross Ref]
Spandan Srivastava, Charu Lata, Prateek Lohan, Rinchin W. Mosobi (2022), Comparative Analysis of Particle Swarm Optimization and Artificial Neural Network Based MPPT with Variable Irradiance and Load. IJEER 10(3), 460-465. DOI: 10.37391/IJEER.100309.