Maximum Power Point Tracking of a Partially Shaded Solar PV Generation System Using Coyote Optimization Algorithm (COA)

Pervez, Imran; Sarwar, Adil; Pervez, Arsalan; Tariq, Mohd; Zaid, Mohammad

doi:10.1007/978-981-15-5463-6_44

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ACO has been shown to be effective for MPPT, especially for PV systems with changing environmental conditions and non-linear characteristics [22]. Similarly, other various nature-inspired algorithms have been used in the literature to obtain the maximum power out of a solar PV array, such as the Jaya algorithm [23], gravitational search algorithm (GSA) [24], teaching learning-based optimization (TLBO) algorithm [25], coyote optimization algorithm (COA) [26], a very commonly used PSO algorithm with various modifications [27,28], adaptive radial movement optimization (ARMO) algorithm [29], etc. These algorithms, due to their search space exploration capability that is exploited to find the optimal solution, were found very useful for MPPT applications.…”

Section: Applicationsmentioning

confidence: 99%

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

Hussain,

Tariq,

Sarwar

et al. 2023

Processes

View full text Add to dashboard Cite

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization.

show abstract