In this paper, a new stochastic optimizer called slime mould algorithm (SMA) for solving the optimal coordination of directional overcurrent relays in meshed power networks is proposed. The proposed SMA-based method deals with the coordination problem using the most recent digital relays by selecting the optimal tripping standardized curve plus time dial and current pickup. Afterwards, the problem gets more sophisticated by bounding the relay tripping time inside a practical range to simulate the real network conditions. SMA’s performance is evaluated on two different test cases including highly penetrated distributed generators in the second one. The results of SMA are compared with other recent metaheuristic and conventional approaches reported in the literature under same conditions aimed at fair comparisons. It is declared that the SMA attains 29.9% reduction in the relays total operating time for the first test case compared to the well-matured water cycle algorithm. Even, if the tested network is a highly penetrated by distributed generators, more reduction is achieved by 33.7% with better convergence characteristics. It can be established that the SMA-based method demonstrates a competitive and reliable tool for minimizing the relays total operating time satisfying all the constraints even for the sophisticated scenarios.

本文提出了一种新的随机优化器，称为粘液模制算法（SMA），用于求解网状电网中方向性过电流继电器的最佳协调。所提出的基于SMA的方法通过选择最佳的跳闸标准化曲线以及时间刻度和电流传感器来解决使用最新数字继电器的协调问题。此后，通过将继电器的跳闸时间限制在一个实际范围内以模拟实际的网络状况，问题变得更加复杂。在两个不同的测试案例中评估了SMA的性能，其中第二个案例中是高度渗透的分布式发电机。在相同的条件下，将SMA的结果与文献中报道的其他最近的元启发式方法和常规方法进行了比较，旨在进行公平比较。宣布与成熟的水循环算法相比，SMA在第一个测试案例中的继电器总工作时间减少了29.9％。即使被测试的网络被分布式生成器高度渗透，也可以以更好的收敛特性将下降幅度降低33.7％。可以确定的是，基于SMA的方法展示了一种有竞争力且可靠的工具，即使在复杂的情况下，也可以使继电器的总工作时间最小化，从而满足所有约束条件。