| 摘要: |
| 目的 针对优化算法在处理配电网故障定位问题时存在后期收敛速度慢、定位准确度低的缺点,提出了一种
改进的灰狼算法(IGWO)。 方法 首先构建能用于分布式电源(DG)接入的开关函数和适应度函数,在此基础上建
立一个 IEEE33 节点配电网模型,再结合含 DG 配电网的情况对灰狼算法做离散化处理;其次参考蝠鲼优化算法翻
筋斗觅食的思想,同时加入小波函数最优解扰动,从而增强跳出局部最优的能力,并加速收敛;另外,对收敛因子作
非线性处理,提高算法的局部搜索能力、快速性和准确性。 结果 通过将配电网连接到不同位置和不同数量的分布
式电源,进行单点故障、多点故障和信息畸变的仿真测试,将 IGWO 算法与二进制灰狼算法、原始灰狼算法以及粒
子群算法在定位准确率、平均收敛代数和迭代时间方面进行对比,其整体性能更为优越。 结论 IGWO 算法在故障
定位中对比另 3 种优化算法在收敛速度和定位效率方面都是最好的。 |
| 关键词: 改进灰狼算法 分布式电源 故障定位 配电网 容错性 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Distribution Network Fault Location Based on an Improved Gray Wolf Optimizer |
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YU Tingyao LI Hongyue
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School of Electrical and Information Engineering Anhui University of Science and Technology Huainan 232001 Anhui
China
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| Abstract: |
| Objective To address the limitations of existing optimization algorithms in distribution network fault location
specifically slow convergence speed during later iterations and low positioning accuracy an improved gray wolf optimizer
IGWO is proposed. Methods First a switching function and a fitness function suitable for distributed generation DG
integration were constructed. An IEEE 33-node distribution network model was then established based on these functions.
Subsequently the gray wolf optimizer was discretized to accommodate distribution networks with DG. Second inspired by
the somersault-foraging mechanism in the manta ray foraging optimizer a wavelet-function-based perturbation strategy was
incorporated into the optimal solution to enhance the ability to escape local optima and accelerate convergence.
Additionally a nonlinear processing scheme was applied to the convergence factor to improve local search capability
computational speed and solution accuracy. Results Simulation tests for single-point faults multi-point faults and
information distortion were conducted by connecting DGs at varying locations and quantities. Comparative analyses against
the binary gray wolf optimizer original gray wolf optimizer and particle swarm optimizer demonstrated that the IGWO
achieved superior performance in positioning accuracy average convergence generation and iteration time.
Conclusion The IGWO outperforms the other three optimization algorithms in terms of convergence speed and positioning
efficiency in fault location. |
| Key words: improved gray wolf optimizer distributed generation fault location distribution network fault tolerance |
