The presence of cold load pickup (CLPU) creates a challenge in service restoration owing to an increased load in power systems. To mitigate the effects of CLPU, the system is divided into sections to restore them sequentially. Another approach to solving the CLPU problem is by reducing the total power consumption, with conservation voltage reduction (CVR) as the popular approach to achieve this. This article develops a new restoration algorithm with discrete particle swarm optimization (DPSO) using CVR with CLPU in a power system. DPSO is a global search algorithm that is able to avoid a locally optimal solution. By combining the merits of DPSO and CVR, the restoration time will be improved while maintaining an acceptable operating condition. To verify the algorithm, two simulations are performed. The first simulation compares the proposed algorithm with the tabu search (TS), which shows the improvement in the total restoration time of the system. Then, the second simulation shows an improvement in the restoration time and the ability to maintain the operating voltage during the restoration process by including the CVR in the service restoration.

中文翻译：

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利用守恒降压和粒子群算法的冷负荷情况下服务恢复算法的研制。

由于电源系统中负载的增加，冷负载代步（CLPU）的存在给服务恢复带来了挑战。为了减轻CLPU的影响，系统分为多个部分以按顺序恢复它们。解决CLPU问题的另一种方法是降低总功耗，而节省电压降低（CVR）是实现这一目标的常用方法。本文使用CVR和CLPU在电力系统中开发了一种具有离散粒子群优化（DPSO）的新恢复算法。DPSO是一种全局搜索算法，能够避免局部最优解。通过结合DPSO和CVR的优点，可以在保持可接受的工作条件的同时缩短恢复时间。为了验证该算法，执行了两个仿真。第一次仿真将提出的算法与禁忌搜索（TS）进行了比较，这表明系统的总恢复时间有所缩短。然后，第二模拟显示了通过将CVR包括在服务恢复中，恢复时间得到了改善，并在恢复过程中保持了工作电压。