Hybrid Analytical and Data-Driven Model Based Instance-Transfer Method for Power System Online Transient Stability Assessment

Abstract

Data-driven methods are widely recognized and generate conducive results for online transient stability assessment. However, the tedious and time-consuming process of sample collection is often overlooked. The functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode, thereby highlighting the importance of collection efficiency. As a means to achieve high sample collection efficiency following the operation mode change, we propose a novel instance-transfer method based on compression and matching strategy, which facilitates the direct acquisition of useful previous samples, used for creating the new sample base. Additionally, we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection, where features of analytical modeling with special significance are introduced into data-driven methods. At the same time, a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models, enabling fast and accurate post-disturbance transient stability assessment. As a paradigm, we consider a scheme for online critical clearing time estimation, where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part, respectively. Derived results validate the credible efficacy of the proposed method.

Keywords

• Critical clearing time
• extreme learning machine
• instance-transfer method
• transient stability assessment