Estimation of power system inertia using particle swarm optimization

Dimitrios Zografos, M. Ghandhari, K. Paridari
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引用次数: 10

Abstract

Power system inertia is being globally reduced, due to the substitution of conventional synchronous power plants by intermittent generation. This threatens the frequency stability of the system and makes the estimation of power system inertia necessary, so that proactive measures can be imposed. A disturbance-based method is proposed in this paper, which estimates the total inertia constant of the power system. The method applies particle swarm optimization (PSO) to minimize a cost function, which is defined based on the swing equation. To do that, data available at the generator buses are employed. The proposed method is applied on the Nordic57 test system under twenty different scenarios, which include generator and load disconnections. Furthermore, a comparison with two methods presented in the literature is performed and demonstrates the higher performance of the proposed method, in the sense of the mean and the variance of the errors.
基于粒子群算法的电力系统惯性估计
由于间歇性发电取代了传统的同步电厂,电力系统的惯性正在全球范围内降低。这将威胁到系统的频率稳定性,因此有必要对电力系统惯性进行估计,以便采取主动措施。提出了一种基于扰动的电力系统总惯性常数估计方法。该方法利用粒子群优化(PSO)来最小化基于摆动方程定义的代价函数。要做到这一点,需要利用发电母线上可用的数据。该方法在Nordic57测试系统上进行了包括发电机和负载断开在内的20种不同场景的应用。此外,与文献中提出的两种方法进行了比较,并证明了所提出的方法在误差的均值和方差意义上具有更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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