Fast probability estimation of HVDC successive commutation failure caused by AC grid cascading failures. (February 2022)

• Record Type: Journal Article
• Authors: Zhu, Yuanzhen; Liu, Tianhao; Li, Changgang; Liu, Yutian
• Is Part Of: International journal of electrical power & energy systems. Volume 135(2022)
• Publication Date: 2022-02
• Subjects: Cascading failure -- HVDC -- Sequential importance sampling -- Stacked denoising autoencoder -- Successive commutation failure; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107618
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Fast probability estimation of successive commutation failure risk of HVDC system based on SIS and SDAE. Efficiently generating AC grid cascading failure samples by Sequential Importance Sampling method. Fast assess the impact of AC grid cascading failure on successive commutation failure based on Stacked Denoising AutoEncoder. Demonstrating the validity of the proposed method on a real-life hybrid AC/DC grid in east China. Abstract: For line-commutated converter (LCC) based HVDC system, successive commutation failure (SCF) can result in continual cessations of power transmission or even forced blocking of the DC system. AC grid cascading failure near inverters is an important reason for the SCF. The SCF probability is utilized to represent the impact of AC grid cascading failures on DC systems. This paper proposes a fast probability estimation method of SCF based on sequential importance sampling (SIS) and stacked denoising autoencoder (SDAE). Firstly, the probability estimation of SCF caused by cascading failures is transformed into a statistical analysis problem. Secondly, considering AC line failures have different impacts on DC system, a cascading failure sampling method based on SIS is presented to efficiently generate cascading failure samples and evaluate the SCF probability. Thirdly, SDAE based assessment network is established to quickly assess the impact of AC failures on SCF. Topology features related to AC grid structure and short-circuit fault locations are selected as inputs of the assessment network. Finally, real-life hybrid AC/DC grids are adopted to demonstrate the validity of the proposed method.