A PSOI based MPPT Technique for PV System under Dynamically Changing PSC
Santhan Kumar Ch1, Sukanth T2, Ramji Tiwari3, Y V Prasanth4
1Santhan Kumar Ch, Associate Professor, Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
2Sukanth T, Assistant Professor, Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
3Ramji Tiwari, Assistant Professor, Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
4Y V Prasanth, Assistant Professor, Department of Electrical and Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 19 August 2019 | Revised Manuscript received on 10 September 2019 | Manuscript Published on 17 September 2019 | PP: 1111-1117 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10220882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1022.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As conventional techniques fail to track global MPP under partial shaded condition; maximum power point tracking algorithms based on optimization algorithms are an attractive alternative to track the global maximum power point under partial shaded condition. Due to several advantages particle swarm optimization algorithm is most implemented and most suitable for MPP tracking under PSC. Though in most of the cases PSO guarantees global MPP under PSC, it suffers from certain disadvantages like local maxima trapping due to random initialization of population, increased tracking time, larger exploration of search space, output power oscillations and larger settling time. To overcome the limitations of PSO, a novel improved PSO algorithm is proposed, which includes opposition based learning and worst population elimination methods. The performance of the proposed algorithm is examined on 8S and 4S2P PV configurations subjected to dynamically changing partial shaded condition irradiation patterns and results are presented. The results are compared with the conventional PSO algorithm under similar conditions. From results it is noticed that proposed algorithm has very less tracking time, less exploration of search space, do not suffer from local maxima trapping and reduces the output power oscillations. The proposed algorithm shows superior performance compared to conventional PSO algorithm.
Keywords: Maximum Power Point Tracking, Partial Shaded Condition, Photovoltaic System, Single Diode Model, Improved PSO Algorithm.
Scope of the Article: System Integration