{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:55:45Z","timestamp":1781574945427,"version":"3.54.5"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T00:00:00Z","timestamp":1560902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,7,9]]},"abstract":"<jats:p>The Maximum Power Point Tracking (MPPT) controller plays a vital role in maximizing power output from the solar Photovoltaic (PV) sources. The tracking efficiency of the MPPT controller is affected by a rapidly varying environmental condition. This paper presents a novel MPPT controller for standalone PV system based on a Neural Network (NN) and Gain-scheduled Proportional Integral (GS-PI) controller to track the fast-changing Maximum Power Point (MPP).The NN model is trained to predict the operating parameters of the PV array at which maximum power is generated. The gain scheduled PI controller parameters are optimally tuned with Real-coded Genetic Algorithm (RGA) to improve the controller performance. The developed MPPT controller is used to control the power converter in the solar PV system. The PV array along with the control scheme is developed using LabVIEW and Multisim environment. Further, the performance of the developed control strategy is experimentally validated with solar PV emulator and DC-DC boost converter under the varying irradiation conditions. The tracking performance of the developed MPPT controller is compared with the modified Perturb and Observe and NN+PI controller based MPPT controller. The experimental results reveal that the tracking performance of the developed MPPT technique is much improved and more accurate in MPP tracking.<\/jats:p>","DOI":"10.3233\/jifs-182556","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:49:18Z","timestamp":1561117758000},"page":"1085-1098","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel MPPT controller using Neural Network and Gain-Scheduled PI for Solar PV system under rapidly varying environmental condition"],"prefix":"10.1177","volume":"37","author":[{"given":"M.S.","family":"Ulaganathan","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishankoil Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Devaraj","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishankoil Tamilnadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,6,19]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","article-title":"A novel optimum operating point tracker of the solar cell power supply system","author":"Kobayashi K.","year":"2004","unstructured":"K.Kobayashi, H.Matsuo and Y.Sekine, A novel optimum operating point tracker of the solar cell power supply system, IEEE 35th Annual Power Electronics specialist conference (2004), 1\u201312.","journal-title":"IEEE 35th Annual Power Electronics specialist conference"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2014.2300162"},{"key":"e_1_3_1_4_2","first-page":"17","article-title":"Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review","author":"El Telbany M.E.","year":"2014","unstructured":"M.E.El Telbany, A.Youssef and A.A.Zekry, Intelligent Techniques for MPPT Control in Photovoltaic Systems: A Comprehensive Review, Proceeding of 4th International Conference on Artificial Intelligence with Applied Energy Technology (2014), 17\u201322.","journal-title":"Proceeding of 4th International Conference on Artificial Intelligence with Applied Energy Technology"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.12.105"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2016.07.024"},{"key":"e_1_3_1_7_2","first-page":"406","article-title":"A new adaptive P&O MPPT algorithm based on FSCC method for photovoltaic system, Power and Computing Technologies","author":"Kollimalla S.K.","year":"2013","unstructured":"S.K.Kollimalla and M.K.Mishra, A new adaptive P&O MPPT algorithm based on FSCC method for photovoltaic system, Power and Computing Technologies, ICCPCT (2013), 406\u2013412.","journal-title":"ICCPCT"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gee.2016.11.001"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2016.11.165"},{"key":"e_1_3_1_10_2","first-page":"1851","article-title":"Research on the MPPT algorithms of photovoltaic system based on PV neural network","author":"Jie L.","year":"2011","unstructured":"L.Jie and C.Ziran, Research on the MPPT algorithms of photovoltaic system based on PV neural network, Proceeding of Chinese Control and Decision Conference - 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