Abstract
The potential risk of cascading failure has been investigated by both industry and academia. With the introduction of new technologies, there is increasing uncertainty in power system operation which leads to greater needs for dynamic simulations to fully capture the behaviour and evolution of the system. This paper presents a new dynamic cascading failure simulation platform implemented in DIgSILENT PowerFactory via the Python Application Programming Interface (API). It automatically develops cascading mechanisms, simulates sets of failure scenarios and processes results, and also has good scalability such that it can be easily applied to any power system model. The proposed method overcomes the limitations of traditional manual simulation methods when performing a large number of repetitive modelling, simulation and data processing tasks, and greatly improves modelling efficiency. Case studies on 39-bus and 2000-bus systems are provided to illustrate the functions of various cascading mechanisms and to provide the probability distribution of blackout size based on N-2 contingency analysis.
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