An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method

Abstract

High voltage direct current (HVDC) is expected to bring forth large capacity, long transmission distance, and asynchronous grid interconnection. To quantitatively analyze the protection systems of HVDC, an evaluation system is proposed with a novel indicator framework and an innovative weighting method for the assessment of HVDC operating status. The novel indicator framework includes 31 indicators from the perspectives of reliability, fault monitoring, operational maintenance, control efficiency, and system redundancy. A self-learning interval analytic hierarchical process is used to decide the weights of the indicators based on the maximum entropy method. The optimal subjective weights of the indicators can be obtained by the self-learning process, considering not only the fuzziness of single expert scoring but also the difference between experts' weights. A real HVDC project in Hubei province, China, was studied to verify the effectiveness of the proposed evaluation system.