Abstract
When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB® is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.
Highlights
The increasing demand for energy at the global level has placed pressure on the power sector to provide enough electricity that can fulfill the growing requirements due to population growth, and increased deployment of electrical and electronic technology
The DC-DC boost converter was used as an interface between the PV array and the load to send the optimal power at the output
Under full insolation except for the three-module system the tracking time of the adaptive jaya (Ajaya) was much faster than the other two algorithms. It was observed, especially when the maximum power point (MPP) was at the left-most position, particle swarm optimization (PSO) and jaya took much longer to settle and produced larger fluctuations compared to their usual performance and in that case a clear percentage increase in the reduction in settling time of Ajaya was observed
Summary
The increasing demand for energy at the global level has placed pressure on the power sector to provide enough electricity that can fulfill the growing requirements due to population growth, and increased deployment of electrical and electronic technology. These algorithms successfully track the maximum power point (MPP) under full insolation conditions but can become stuck at local maxima under PS conditions To avoid such issues, AI based algorithms such as [4]–[6], which use their past experiences as the basis for search criterion, were employed for MPPT. The results illustrated a significant improvement in the performance of Ajaya compared to the simple jaya algorithm in terms of tracking time, lesser fluctuations and robustness. Owing to all these features, especially robustness and lesser burden on processor, the algorithm can be trusted for being employed in industrial, commercial, and residential use as described in the upcoming sections thereby making the overall MPP tracker an ideal one. Only one equation is involved in its updating step which is the reason for its simplicity
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