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<p>In recent years, networks of power systems have increasingly implemented renewable energy sources. Due to the fast advancement of civilization, the incidence of global warming and certain catastrophic climatic changes is increasing, along with cultural modernization. Variations in sunlight intensity, atmospheric temperature, and other variables have a big effect on the performance of photovoltaic (PV) systems. This paper presents a novel Walrus optimization Algorithm (WOA) for Maximum Power Point Tracking (MPPT) in solar PV systems connected to DC microgrids. These systems must function well at their Maximum Power Point (MPP) under a variety of weather situations in order to maximize energy output. Three well-known optimization algorithms—Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (CUSA), and Particle Swaran Optimization (PSO)—are used to assess the efficiency of proposed algorithm in different operating conditions and partial shadowing conditions (PSCS). Obtained results demonstrate that the WOA not only achieves higher power output but also responds faster than existing methods. Authors have examined five different scenarios with the developed technique and compare the outcome with existing methods. Notably the developed method has a higher range efficiency of 85.25%, 82.38%, 89.28%, 84.45% in the first four cases with is outstanding compare with the other three MPPT techniques. Additionally, it effectively manages bidirectional power flow in both stable and fluctuating weather conditions. This method guarantees a robust and sustainable design for low power generation scenarios when a DC microgrid is connected to an MPPT system based on WOA.</p>

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A Resilient Framework for Solar-Powered DC Microgrid Stations Using Walrus Algorithm

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Debabrata Mazumdar,
  • Josep M. Guerrero,
  • Enrique Baeyens,
  • Chiranjit Sain,
  • Pabitra Kumar Biswas

摘要

In recent years, networks of power systems have increasingly implemented renewable energy sources. Due to the fast advancement of civilization, the incidence of global warming and certain catastrophic climatic changes is increasing, along with cultural modernization. Variations in sunlight intensity, atmospheric temperature, and other variables have a big effect on the performance of photovoltaic (PV) systems. This paper presents a novel Walrus optimization Algorithm (WOA) for Maximum Power Point Tracking (MPPT) in solar PV systems connected to DC microgrids. These systems must function well at their Maximum Power Point (MPP) under a variety of weather situations in order to maximize energy output. Three well-known optimization algorithms—Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (CUSA), and Particle Swaran Optimization (PSO)—are used to assess the efficiency of proposed algorithm in different operating conditions and partial shadowing conditions (PSCS). Obtained results demonstrate that the WOA not only achieves higher power output but also responds faster than existing methods. Authors have examined five different scenarios with the developed technique and compare the outcome with existing methods. Notably the developed method has a higher range efficiency of 85.25%, 82.38%, 89.28%, 84.45% in the first four cases with is outstanding compare with the other three MPPT techniques. Additionally, it effectively manages bidirectional power flow in both stable and fluctuating weather conditions. This method guarantees a robust and sustainable design for low power generation scenarios when a DC microgrid is connected to an MPPT system based on WOA.