An Imperialist Competitive Algorithm-Based Multi-Objective Optimization for Voltage Source Converter High-Voltage Direct Current Stations Control in Multi-Terminal HVDC Grids. Issue 4 (16th March 2019) - Rouzbehi, Kumars; Baa Wafaa, Majid; Rakhshani, Elyas
Abstract: This article presents a multi-objective-based imperialist competitive algorithm (ICA) for optimal tuning of voltage source converter (VSC) stations control parameters in multi-terminal high-voltage direct current (MT-HVDC) grid. The key objective here is to enhance the control performance of the MT-HVDC grid VSC-HVDC stations under dynamic situations. As VSC-HVDC stations are non-linear apparatuses of the MT-HVDC grids, conventional methods for the tuning of the control parameters which are commonly based on approximated linear plant models do not reach to optimal results. Therefore, as the main contribution of this research, an effective ICA-based multi-objective optimization technique is adopted for optimal tuning of the VSC-HVDC stations control parameters. The proposed composite objective function in this algorithm consists of both inner and outer loop parameter optimization characteristics. A fuzzification technique is used in this study to normalize all objective functions and to find the optimal solution. To confirm the merits of the presented optimization method, a detailed model of a four-terminal MT-HVDC grid is developed and its transient and steady-state performance in case of classically and ICA tuned have been studied and compared.