Rule extraction based on extreme learning machine and an improved ant-miner algorithm for transient stability assessment

TL;DR

This paper introduces a new rule extraction approach combining extreme learning machine and an improved Ant-miner algorithm to enhance the interpretability of transient stability assessment in power systems.

Contribution

It proposes a novel method for extracting understandable rules from ELM-based models using an improved Ant-miner algorithm, improving transparency in power system stability assessment.

Findings

Effective rule extraction demonstrated on New England 39-bus system.

Validated applicability on Hebei province power system.

Improved interpretability of transient stability assessment models.

Abstract

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system - the southern power system of Hebei province.