Abstract Fault section location (FSL) plays a critical role in shortening blackout time and restoring power supply for distribution networks. This paper converts the FSL task into a binary optimization problem using the feeder terminal unit (FTU) information. The discrepancy between the reported overcurrent alarms and the expected overcurrent states of the FTUs is adopted as the objective function. It is a typical 0–1 combinatorial optimization problem with many local optima. An improved binary gaining–sharing knowledge-based algorithm (IBGSK) with mutation is proposed to effectively solve this challenging binary optimization problem. Since the original GSK cannot be applied in binary search space directly, and it is easy to get stuck in local optima, IBGSK encodes the individuals as binary vectors instead of real vectors. Moreover, an improved junior gaining and sharing phase and an improved senior gaining and sharing phase are designed to update individuals directly in binary search space. Furthermore, a binary mutation operator is presented and integrated into IBGSK to enhance its global search ability. The proposed algorithm is applied to two test systems, i.e. the IEEE 33-bus distribution network and the USA PG&E 69-bus distribution network. Simulation results indicate that IBGSK outperforms the other 12 advanced algorithms and the original GSK in solution quality, robustness, convergence speed, and statistics. It equilibrates the global search ability and the local search ability effectively. It can diagnose different fault scenarios with 100% and 99% success rates for these two test systems, respectively. Besides, the effect of mutation probability on IBGSK is also investigated, and the result suggests a moderate value. Overall, simulation results demonstrate that IBGSK shows highly promising potential for the FSL problem of distribution networks.

中文翻译：

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改进的基于二元增益共享知识的变异配电网故障断面定位算法

摘要 故障段定位（FSL）在缩短配电网停电时间和恢复供电方面发挥着重要作用。本文使用馈线终端单元 (FTU) 信息将 FSL 任务转换为二元优化问题。将报告的过流警报与 FTU 的预期过流状态之间的差异作为目标函数。这是一个典型的 0-1 组合优化问题，具有许多局部最优。为了有效解决这一具有挑战性的二进制优化问题，提出了一种改进的具有变异的二进制增益共享知识算法（IBGSK）。由于原始 GSK 不能直接应用于二进制搜索空间，并且容易陷入局部最优，IBGSK 将个体编码为二进制向量而不是实向量。而且，改进的初级获取和共享阶段以及改进的高级获取和共享阶段旨在直接在二分搜索空间中更新个人。此外，提出了一个二元变异算子并将其集成到 IBGSK 中以增强其全局搜索能力。所提出的算法应用于两个测试系统，即IEEE 33-bus 配电网络和美国PG&E 69-bus 配电网络。仿真结果表明，IBGSK 在解决方案质量、鲁棒性、收敛速度和统计数据方面优于其他 12 种高级算法和原始 GSK。它有效地平衡了全局搜索能力和局部搜索能力。它可以对这两个测试系统分别以 100% 和 99% 的成功率诊断不同的故障场景。除了，还研究了突变概率对 IBGSK 的影响，结果表明值适中。总体而言，模拟结果表明，IBGSK 对配电网络的 FSL 问题显示出非常有前景的潜力。