Abstract: The interpretability of machine learning is one of the key points and important foundation for the safe and reliable application in power systems. In this paper, the interpretable machine learning methods applied in power system intelligent analysis are studied preliminarily. First, the basic concept, mathematical description, main theories and evaluative dimension of the interpretable methods are elaborated. Then, the general methodology and technology framework are analyzed, in which the interpretable methods of machine learning are categorized into ante-hoc, post-hoc and self-explained approaches. The application areas of the machine learning interpretability, including model diagnosis, security evaluation, data rectification and knowledge discovery, are studied. Finally, the current research challenges of interpretability are discussed for the machine learning application in power systems intelligent analysis.