This study examines the application of big data clustering algorithms in the field of electrical engineering automation. Electrical engineering automation is a key area that covers power system management, smart grids, and industrial automation, and requires effective data processing and analysis methods to optimise operations and decision-making. Big data clustering algorithms have been introduced to cope with the huge datasets generated in electrical engineering, as well as the diversity and complexity in the data. Big data clustering can be used for power load forecasting to help utilities better plan resources and reduce energy waste. It can also be used for equipment fault detection in smart grids to improve grid reliability and stability. In addition, the study highlights challenges such as data quality and privacy protection, which are important issues to consider when adopting big data clustering algorithms in electrical engineering automation. Finally, the study summarises the potential value of big data clustering algorithms in electrical engineering automation and suggests directions for future research to further advance the field. This study provides insights and guidance to professionals in the field of electrical engineering automation on how big data clustering algorithms can be utilised to improve productivity and decision making.