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Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

机译:连续粒子群优化(CPSO)在考虑惩罚方法时对过流继电器的最佳协调

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摘要

In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA) is implanted into the original particle swarm algorithm (PSO) and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO) are compared with other evolutionary optimization algorithms (EA) and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.
机译:在电力系统中,过电流继电器的协调在通过提供初级和备用保护来保护电气系统的重要作用。为了降低停电,这些继电器之间的协调应保持在最佳值,以最小化总运行时间,并确保在故障情况下发生最小损坏。它也必须确保中继设置不会产生无意的操作和连续的同情跳闸。在电力系统保护协调问题中,优化的目标函数是所有主继电器的总操作时间的总和。在本文中,将环形馈电分配系统中的过电流继电器的协调配制成优化问题。使用所提出的连续粒子群优化进行协调。为了增强和提高该解决方案的质量,将本地搜索算法(LSA)植入原始粒子群算法(PSO),并且除了约束之外,这些解决方案除了限制之外,这些方法还通过惩罚方法分配到健身功能中。将连续粒子群优化算法(CPSO)实现的结果与其他进化优化算法(EA)进行了比较,并且该比较表明,该方案在处理相关问题方面具有能力。从进一步分析所得结果,发现连续粒子群方法提供最全球最佳溶液。

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