使用智能基于软计算策略从太阳能光伏系统中提取最大功率:批判性审查和全面的性能分析
Abhinav Saxena1, Rajat Kumar2, Mohammad Amir3,4
1Department of Electrical Engineering, JSS Academy of Technical Education, Noida, 201301, India.
Heliyon
|February 5, 2024
概括
本综述将太阳能光伏 (PV) 最大功率点跟踪 (MPPT) 技术进行比较. 人工神经网络 (ANN) 在电力提取方面表现出色,而增量导电性 (IC) 将偏差最小化,粒子群优化 (PSO) 减少损失.
科学领域:
- 可再生能源系统可再生能源系统
- 电力电子 电力电子 电力电子
- 控制系统 控制系统
背景情况:
- 太阳能光伏 (PV) 阵列需要高效的最大功率点跟踪 (MPPT) 来满足能源需求.
- 诸如阴影和辐射等可变的环境条件会影响光伏阵列的功率输出.
- 优化电力提取对于太阳能系统的经济可行性和性能至关重要.
研究的目的:
- 对太阳能光伏电池的各种MPPT技术进行全面的审查和比较分析.
- 在不同的环境条件下评估不同MPPT方法的性能.
- 确定每种技术的优点和弱点,以便在智能能源系统中切实实施.
主要方法:
- 文学综述和对著名的MPPT算法的比较分析.
- 包括诸如扰乱和观察 (P&O),模糊逻辑控制 (FLC),增量行为 (IC),波动校正控制 (RCC),人工神经网络 (ANN),粒子群集优化 (PSO),利亚普诺夫控制方案 (LCS) 和费舍尔歧视词典学习 (FDDL) 等技术.
- 性能评估考虑了诸如功率提取效率,偏离最大功率点,总波扭曲 (THD) 和开关损失等因素.
主要成果:
- 人工神经网络 (ANN) 在不同的辐射水平上展示了优越的最大功率提取.
- 增量导电 (IC) 实现了与最大功率点的最小偏差.
- 费雪区分词典学习 (FDDL) 在最小化总波扭曲 (THD) 中最有效,而粒子群优化 (PSO) 则导致最低的切换损失.
结论:
- 每种MPPT技术都有其独特的优势,使其适用于智能能源系统中的特定应用.
- 选择MPPT方法应基于太阳能系统的特定参数和要求.
- 本综述是研究人员选择适当的软计算方法来优化太阳能光伏功率的研究人员的宝贵资源.
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