A diagnosis method for operation and maintenance events in converter stations based on association-causal analysis

With the increasing demand for electricity and the widespread application of renewable energy, the operation and maintenance of converter stations face increasingly complex challenges. The Sequential Event Recorder (SER) data generated during the operation and maintenance of converter stations not only reflect abnormal conditions in equipment operation but may also lead to more severe failures. Traditional diagnostic methods for operation and maintenance often rely on experience and historical data, which can easily overlook potential associations and causal relationships. To address this challenge, this study proposes a diagnosis method for operation and maintenance events in converter stations based on Association-Causal analysis. By establishing a knowledge graph of operation and maintenance events in converter stations, identifying key events and their associations, and analyzing causal relationships between events, this method aims to reveal potential causal chains, improve the efficiency and accuracy of fault diagnosis, reduce maintenance costs, and enhance the overall operational efficiency of converter stations.