Risk assessment of cascading failures in wind power DC transmission system based on improved black-winged kite algorithm

• Improved Black-winged Kite Algorithm: By incorporating particle swarm optimization and leader strategy, the Black-winged Kite Algorithm has been optimized, enhancing its performance and convergence speed. This algorithm combines subjective and objective weights to solve the deviation minimization objective function, aiming to obtain the optimal weights. • Cascading failure sequence prediction: By establishing identification indicators based on key parameters most directly related to the line itself, the failure sequence of the wind power DC transmission system can be effectively predicted. • System risk assessment: For the wind power DC transmission system, five risk assessment indicators are proposed, fully considering the characteristics of wind turbines and DC factors in the system, to accurately assess the impact of cascading failures on the system. The wind power DC transmission system has unparalleled advantages over traditional AC transmission. However, under complex operating conditions, local faults may trigger a series of cascading failures, threatening the safety and stability of the entire system. To comprehensively assess the cascading failure characteristics of the wind power DC transmission system, this paper establishes identification indicators based on the most intuitive and closely related parameters of the transmission line itself to predict fault sequences. Additionally, five system risk assessment indicators: covering load levels, power flow distribution, voltage characteristics, wind power stability, and the interaction effects of the DC system are proposed to evaluate the impact of faults on the system. These indicators require the determination of comprehensive weights, which are calculated using the correlation coefficient method and variation coefficient method for subjective and objective weights, respectively. A linear combination of both weights is then used to establish a deviation minimization objective function, which is solved using the improved Black-winged Kite Algorithm to obtain the comprehensive indicator. The comprehensive identification indicators are applied for line identification to obtain the cascading fault sequence. Subsequently, risk assessment indicators are used to evaluate the severity of the obtained cascading fault sequence. Finally, the improved IEEE39 wind power DC transmission system is used as a test case to perform cascading fault sequence search and risk assessment, validating the feasibility and correctness of the proposed method.