Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. (21st November 2019)

• Record Type: Journal Article
• Authors: Mir, Mahdi; Dayyani, Mohammad; Sutikno, Tole; Mohammadi Zanjireh, Morteza; Razmjooy, Navid
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 225
• Page End: 258
• Publication Date: 2019-11-21
• Subjects: GPSO -- load frequency control -- MIMO controller -- multiagent system -- power flow management; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12257
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.