| 改进小生境粒子群算法应用于电网故障诊断 |
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| 引用本文：胡年平1,徐芳敏1,谢 宁2,解 翔1,李维海3,刘 涌4,袁秋实4.改进小生境粒子群算法应用于电网故障诊断[J].电网与清洁能源,2018,34(2):9~16 |
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| 基金项目:国家自然科学基金项目（71403098） |
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| 中文摘要:通过对电网中断路器、保护等设备动作信息分析，建立适合智能算法优化的电网故障诊断分析模型。电网故障模型维数高、离散型、非线性、动态性等特点对智能算法寻优性能要求极高。粒子群优化算法在多维函数寻优、动态目标寻优等方面有着收敛速度快、求解质量高和鲁棒性好等优点。针对电网故障模型的特点，从基本粒子群优化算法的优化特性出发，引入小生境搜索的思想，提出了改进的小生境粒子群优化算法。算例结果表明，改进的优化算法大幅度提高了搜索速度和收敛精度，从根本上提高了电网故障定位精度和故障抢修的反映速度，具有很好的应用前景。 |
| 中文关键词:小生境 粒子群优化算法 故障诊断 适应度比率距离 |
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| Application of Improved Niching Particle Swarm Optimization Algorithm in Power System Fault Diagnosis |
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| Abstract:In this paper， through an analysis of the action information circuit breakers and protection equipment of power networks， a power system fault diagnosis model is established for intelligent algorithm optimization. Due to its characteristics of high dimension， discreteness， nonlinear and dynamic performance， the power system fault model has high requirements for the intelligent algorithm optimization. The particle swarm optimization algorithm has advantages of fast convergence， high quality， good robustness and other advantages in the multi-dimension function optimization and dynamic target optimization. According to the characteristics of the power system fault model， starting from the advantages of the particle swarm optimization algorithm， this paper introduces the idea of the niche search and proposes an improved niche particle swarm optimization algorithm. The example results show that the optimization algorithm has greatly improved the search speed and convergence precision， resulting in an substantial improvement of the power network fault positioning accuracy and fault response speed and thus it has a very good application prospect. |
| keywords:particle swarm optimization fault diagnosis fitness distance ratio |
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