DOI: https://doi.org/10.14710/ijred.2024.57096

Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump

Department of Electrical and Electronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India

Received: 9 Aug 2023; Revised: 25 Oct 2023; Accepted: 11 Nov 2023; Available online: 21 Nov 2023; Published: 1 Jan 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To address this issue, this paper proposes the integration of Maximum Power Point Tracking (MPPT) controllers with efficient algorithms designed to identify the peak power during shading events. In this study, a hybrid approach combining Grey Wolf Optimization (GWO) and Incremental Conductance (INC) is employed to maximize the power output of SPWPs driven by an induction motor under partial shading conditions. In order to achieve faster convergence to the global peak, GWO handles the first stages of MPPT and then INC algorithm is employed at the end of the MPPT process.  This method reduces the computations of GWO and streamlines the search space. The paper evaluates the performance of the induction motor in terms of speed settling time and torque ripple. To validate the effectiveness of the GWO-INC hybrid approach, simulations are conducted using the MATLAB Simulink platform. The outcomes are then compared with results obtained from various well-known approaches, including Particle Swarm Optimization – Perturb and Observe (PSO-PO), PSO-INC, and GWO-PO, illustrating the superiority of the GWO-INC hybrid approach in enhancing the efficiency and performance of solar water pumps during shading. The GWO-INC excels with 99.6% accuracy in uniform shading and 99.8% in partial shading. It achieves convergence in a mere 0.55 seconds under uniform shading conditions and only 0.42 seconds when partial shading is present. Moreover, it significantly reduces torque oscillations, with a torque ripple of  8.26% in cases of uniform shading and 10.56% in partial shading.

Fulltext View|Download
Keywords: solar power; water pump; hybrid MPPT technique; partial shading; motor control

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Investigation of wind veer characteristics on complex terrain using ground-based lidar Site suitability analysis of wind energy resources in different regions of Algeria’s southwestern highland Enhancing microbial fuel cell performance with carbon powder electrode modifications for low-power sensors modules Policies and measures for energy efficiency improvement at households: A bibliometric analysis More recent articles
Most cited articles
The Energy Processing by Power Electronics and its Impact on Power Quality The Performance of A Diesel Engine Fueled With Diesel Oil, Biodiesel and Preheated Coconut Oil Tax Incentive Policy for Geothermal Development: A Comparative Analysis in ASEAN Distributed Generation: A Critical Review of Technologies, Grid Integration Issues, Growth Drivers and Potential Benefits Microgrids for rural schools: An energy-education accord to curb societal challenges for sustainable rural developments More cited articles
  1. Abdulkadir, M., & Yatim, A. H. M. (2014). Hybrid maximum power point tracking technique based on PSO and incremental conductance. 2014 IEEE Conference on Energy Conversion, CENCON 2014, 271–276. https://doi.org/10.1109/CENCON.2014.6967514
  2. Aguila-Leon, J., Vargas-Salgado, C., Chiñas-Palacios, C., & Díaz-Bello, D. (2023). Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms. Expert Systems with Applications, 211(August 2022). https://doi.org/10.1016/j.eswa.2022.118700
  3. Ahmad, R., Murtaza, A. F., Ahmed Sher, H., Tabrez Shami, U., & Olalekan, S. (2017). An analytical approach to study partial shading effects on PV array supported by literature. Renewable and Sustainable Energy Reviews, 74(December 2016), 721–732. https://doi.org/10.1016/j.rser.2017.02.078
  4. Aliyu, M., Hassan, G., Said, S. A., Siddiqui, M. U., Alawami, A. T., & Elamin, I. M. (2018). A review of solar-powered water pumping systems. Renewable and Sustainable Energy Reviews, 87(February), 61–76. https://doi.org/10.1016/j.rser.2018.02.010
  5. Ammar, A., Hamraoui, K., Belguellaoui, M., & Kheldoun, A. (2022). Performance Enhancement of Photovoltaic Water Pumping System Based on BLDC Motor under Partial Shading Condition. 22. https://doi.org/10.3390/engproc2022014022
  6. Angadi, S. (2021). Comprehensive Review on Solar, Wind and Hybrid Wind-PV Water Pumping Systems-An Electrical Engineering Perspective. CPSS Transactions on Power Electronics and Applications, 6(1), 1–19. https://doi.org/10.24295/cpsstpea.2021.00001
  7. Antonello, R., Costabeber, Alessandror, C., Tinazzi, F., & Zigliotto, M. (2017). Energy-Efficient Autonomous Solar Synchronous Motors. IEEE Transactions on Industrial Electronics, 64(1), 43–51
  8. Arfaoui, J., Rezk, H., Al-Dhaifallah, M., Elyes, F., & Abdelkader, M. (2019). Numerical performance evaluation of solar photovoltaic water pumping system under partial shading condition using modern optimization. Mathematics, 7(11). https://doi.org/10.3390/math7111123
  9. Ayop, R., & Tan, C. W. (2018). Design of boost converter based on maximum power point resistance for photovoltaic applications. Solar Energy, 160(August 2017), 322–335. https://doi.org/10.1016/j.solener.2017.12.016
  10. Baba, A. O., Liu, G., & Chen, X. (2020). Classification and Evaluation Review of Maximum Power Point Tracking Methods. Sustainable Futures, 2(February). https://doi.org/10.1016/j.sftr.2020.100020
  11. Belhachat, F., & Larbes, C. (2018). A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions. Renewable and Sustainable Energy Reviews, 92(January), 513–553. https://doi.org/10.1016/j.rser.2018.04.094
  12. Bollipo, R. B., Mikkili, S., & Bonthagorla, P. K. (2021). Hybrid, optimal, intelligent and classical PV MPPT techniques: A review. CSEE Journal of Power and Energy Systems, 7(1), 9–33. https://doi.org/10.17775/CSEEJPES.2019.02720
  13. Chandel, S. S., Nagaraju Naik, M., & Chandel, R. (2015). Review of solar photovoltaic water pumping system technology for irrigation and community drinking water supplies. Renewable and Sustainable Energy Reviews, 49, 1084–1099. https://doi.org/10.1016/j.rser.2015.04.083
  14. Ding, K., Bian, X., Liu, H., & Peng, T. (2012). A MATLAB-simulink-based PV module model and its application under conditions of nonuniform irradiance. IEEE Transactions on Energy Conversion, 27(4), 864–872. https://doi.org/10.1109/TEC.2012.2216529
  15. Gadiraju, H. K. V. (2022). Improved Performance of PV Water Pumping System Using Dynamic Reconfiguration Algorithm Under Partial Shading Conditions. CPSS Transactions on Power Electronics and Applications, 7(2), 206–215. https://doi.org/10.24295/cpsstpea.2022.00019
  16. Ibrahim, M. N., Rezk, H., Al-Dhaifallah, M., & Sergeant, P. (2019). Solar Array Fed Synchronous Reluctance Motor Driven Water Pump: An Improved Performance under Partial Shading Conditions. IEEE Access, 7, 77100–77115. https://doi.org/10.1109/ACCESS.2019.2922358
  17. Jayabaskaran, G., Suresh, S., Gopinath, B., & Geetha, M. (2023). Solar PV Combined Efficient Torque Control of BLDC Motor Using Salp Swarm Optimization. Electric Power Components and Systems, 51(13), 1338–1354. https://doi.org/10.1080/15325008.2023.2196679
  18. Kashif, M., & Singh, B. (2023). Modified Active-Power MRAS Based Adaptive Control With Reduced Sensors for PMSM Operated Solar Water Pump. IEEE Transactions on Energy Conversion, 38(1), 38–52. https://doi.org/10.1109/TEC.2022.3197564
  19. Khadija, S., Ouadia, E. M., & Abdelmajid, F. (2023). Tracking the GMPP of a solar photovoltaic water pumping system with an SMC controller in partial shading conditions. 12(6), 3298–3310. https://doi.org/10.11591/eei.v12i6.5504
  20. Kumar, A., Bilal, M., Rizwan, M., & Nangia, U. (2022). Grey Wolf Optimization Inspired Maximum Power Extraction from SPV System for Water Pumping Application. 2022 International Conference for Advancement in Technology, ICONAT 2022, 1–6. https://doi.org/10.1109/ICONAT53423.2022.9726028
  21. Kumar, R., & Singh, B. (2019a). Grid Interactive Solar PV-Based Water Pumping Using BLDC Motor Drive. IEEE Transactions on Industry Applications, 55(5), 5153–5165. https://doi.org/10.1109/TIA.2019.2928286
  22. Kumar, R., & Singh, B. (2019b). Solar PV powered-sensorless BLDC motor driven water pump. IET Renewable Power Generation, 13(3), 389–398. https://doi.org/10.1049/iet-rpg.2018.5717
  23. Liu, L., Meng, X., & Liu, C. (2016). A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 53, 1500–1507. https://doi.org/10.1016/j.rser.2015.09.065
  24. Malla, S. G., Malla, P., Malla, J. M. R., Singla, R., Choudekar, P., Koilada, R., & Sahu, M. K. (2022). Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(4), 4832–4844. https://doi.org/10.1109/JESTPE.2022.3150008
  25. Mohanty, S., Subudhi, B., & Ray, P. K. (2016). A new MPPT design using grey Wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181–188. https://doi.org/10.1109/TSTE.2015.2482120
  26. Mohanty, S., Subudhi, B., & Ray, P. K. (2017). A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System. IEEE Transactions on Energy Conversion, 32(1), 340–347. https://doi.org/10.1109/TEC.2016.2633722
  27. Mohapatra, A., Nayak, B., Das, P., & Mohanty, K. B. (2017). A review on MPPT techniques of PV system under partial shading condition. Renewable and Sustainable Energy Reviews, 80(December 2016), 854–867. https://doi.org/10.1016/j.rser.2017.05.083
  28. Mudlapur, A., Ramana, V. V., Damodaran, R. V., Balasubramanian, V., & Mishra, S. (2019). Effect of Partial Shading on PV Fed Induction Motor Water Pumping Systems. IEEE Transactions on Energy Conversion, 34(1), 530–539. https://doi.org/10.1109/TEC.2018.2876132
  29. Muralidhar, K., & Rajasekar, N. (2021). A review of various components of solar water-pumping system: Configuration, characteristics, and performance. International Transactions on Electrical Energy Systems, 31(9), 1–33. https://doi.org/10.1002/2050-7038.13002
  30. Murshid, S., & Singh, B. (2019). Implementation of PMSM Drive for a Solar Water Pumping System. IEEE Transactions on Industry Applications, 55(5), 4956–4964. https://doi.org/10.1109/TIA.2019.2924401
  31. Narendra, A., Venkataramana Naik, N., Panda, A. K., & Tiwary, N. (2020). A Comprehensive Review of PV Driven Electrical Motors. Solar Energy, 195(August 2019), 278–303. https://doi.org/10.1016/j.solener.2019.09.078
  32. Periasamy, P., Jain, N. K., & Singh, I. P. (2015). A review on development of photovoltaic water pumping system. Renewable and Sustainable Energy Reviews, 43, 918–925. https://doi.org/10.1016/j.rser.2014.11.019
  33. Poompavai, T., & Kowsalya, M. (2019). Control and energy management strategies applied for solar photovoltaic and wind energy fed water pumping system: A review. Renewable and Sustainable Energy Reviews, 107(February), 108–122. https://doi.org/10.1016/j.rser.2019.02.023
  34. Priyadarshi, N., Bhaskar, M. S., Padmanaban, S., Blaabjerg, F., & Azam, F. (2020). New CUK-SEPIC converter based photovoltaic power system with hybrid GSA-PSO algorithm employing MPPT for water pumping applications. IET Power Electronics, 13(13), 2824–2830. https://doi.org/10.1049/iet-pel.2019.1154
  35. Priyadarshi, N., Maroti, P. K., & Hussien, M. G. (2022). Extensive performance investigation of Luo converter-based modified firefly maximum point tracking algorithm for permanent magnet synchronous motor-driven photovoltaic pumping system. IET Renewable Power Generation. https://doi.org/10.1049/rpg2.12520
  36. Priyadarshi, N., Padmanaban, S., Bhaskar, M. S., Azam, F., Khan, B., & Hussien, M. G. (2022). A novel hybrid grey wolf optimized fuzzy logic control based photovoltaic water pumping system. IET Renewable Power Generation. https://doi.org/10.1049/rpg2.12638
  37. Rezk, H., AL-Oran, M., Gomaa, M. R., Tolba, M. A., Fathy, A., Abdelkareem, M. A., Olabi, A. G., & El-Sayed, A. H. M. (2019). A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system. Renewable and Sustainable Energy Reviews, 115(August), 109372. https://doi.org/10.1016/j.rser.2019.109372
  38. Seyedmahmoudian, M., Horan, B., Soon, T. K., Rahmani, R., Than Oo, A. M., Mekhilef, S., & Stojcevski, A. (2016). State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review. Renewable and Sustainable Energy Reviews, 64, 435–455. https://doi.org/10.1016/j.rser.2016.06.053
  39. Shukla, S., & Singh, B. (2018). Single-Stage PV Array Fed Speed Sensorless Vector Control of Induction Motor Drive for Water Pumping. IEEE Transactions on Industry Applications, 54(4), 3575–3585. https://doi.org/10.1109/TIA.2018.2810263
  40. Sundareswaran, K., Vignesh kumar, V., & Palani, S. (2015). Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renewable Energy, 75, 308–317. https://doi.org/10.1016/j.renene.2014.09.044
  41. Taibi, D., Amieur, T., Laamayad, T., & Sedraoui, M. (2023). Improvement of the Standard Perturb & Observe MPPT control strategy by the proposed Fuzzy Logic Mechanism for a Cascade Regulation of a PMSM-based PV pumping system. Arabian Journal for Science and Engineering, 48(5), 6631–6647. https://doi.org/10.1007/s13369-022-07496-9
  42. Vitorino, M. A., De Rossiter Corrêa, M. B., Jacobina, C. B., & Lima, A. M. N. (2011). An effective induction motor control for photovoltaic pumping. IEEE Transactions on Industrial Electronics, 58(4), 1162–1170. https://doi.org/10.1109/TIE.2010.2054053
  43. Yang, B., Zhu, T., Wang, J., Shu, H., Yu, T., Zhang, X., Yao, W., & Sun, L. (2020). Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition. Journal of Cleaner Production, 268, 121983. https://doi.org/10.1016/j.jclepro.2020.121983
  44. Yap, K. Y., Sarimuthu, C. R., & Lim, J. M. Y. (2020). Artificial Intelligence Based MPPT Techniques for Solar Power System: A review. Journal of Modern Power Systems and Clean Energy, 8(6), 1043–1059. https://doi.org/10.35833/MPCE.2020.000159

Last update:

  1. Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

    Van Giao Nguyen, Prabhu Paramasivam, Marek Dzida, Sameh M. Osman, Duc Trong Nguyen Le, Dao Nam Cao, Thanh Hai Truong, Viet Dung Tran. Case Studies in Thermal Engineering, 60 , 2024. doi: 10.1016/j.csite.2024.104743

Last update: 2024-07-01 07:24:08

No citation recorded.