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Maximum Power Point Tracking in Photovoltaic Arrays under Partial Shading Conditions Using Evolutionary Algorithms.
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Esmaeil Bahmani   , Mohsen Ahmadnia * , Hossein Sharifzadeh |
| Hakim Sabzevari University , ahmadniamohsen85@gmail.com |
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Abstract: (1467 Views) |
Extracting maximum power, especially with partial shading conditions, is one of the most critical issues in using a photovoltaic system. Under partial shading conditions, the power-voltage characteristic of photovoltaic arrays has several local maximum points. A maximum power point tracking method for photovoltaic systems should enable fast and accurate tracking of the global maximum during partial shading conditions to minimize power losses and steady-state fluctuations. This research presents an algorithm for tracking the maximum power point in a photovoltaic system under partial shading conditions using the gray wolf optimization technique. The gray wolf algorithm is a new optimization method that overcomes limitations such as poor tracking, steady-state fluctuations, and undesirable transients in perturb and observe and particle swarm optimization techniques. The proposed algorithm based on the gray wolf optimization algorithm is implemented on a photovoltaic system in MATLAB software to prove its efficiency. The performance of the proposed design is compared with two maximum power point tracking techniques based on cuckoo search and particle swarm optimization. The simulation results show that the performance of the proposed maximum power point tracking technique is superior to the compared designs in terms of speed and steady-state stability of the response, so that it reduces the values of maximum overshoot, settling time, and sustained fluctuations up to 40.91%, 66.67% and 59.1% respectively.
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Article number: 5 |
| Keywords: Grey wolf optimization (GWO), maximum power point tracking (MPPT), partial shading conditions (PSC), photovoltaic (PV). |
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Full-Text [PDF 9862 kb]
(605 Downloads)
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Type of Study: Research |
Subject:
Optimization
Received: 2022/10/25 | Accepted: 2023/05/26 | Published: 2023/07/22
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