Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems. Issue 17 (November 2018)

• Record Type: Journal Article
• Authors: Chen, Min-Rong; Zeng, Guo-Qiang; Xie, Xiao-Qing
• Is Part Of: Journal of the Franklin Institute. Volume 355:Issue 17(2018)
• Page Start: 8266
• Page End: 8295
• Publication Date: 2018-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2018.08.020
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.