The operation and maintenance of power equipment is a crucial aspect of the construction of new power systems. The artificial intelligence large language model (AI-LLM) presents significant opportunities for the digital intelligence of traditional power equipment operation and maintenance. This study aims to explore the enabling role of multimodal AI-LLM in health assessment, operational state prediction, fault diagnosis, life prediction, and maintenance strategy recommendation, among other specific scenarios of power equipment operation and maintenance. Additionally, this study analyzes the challenges faced by multimodal AI-LLM in enabling power equipment operation and maintenance, including the varying quality of multimodal data, the "black box" nature of algorithms leading to low transparency in decision-making processes, and model performance deterioration induced by environmental changes. To address these challenges, this study combines knowledge graph retrieval-augmented generation, multimodal alignment, fine-tuning and continuous learning, and other big model application optimization techniques to construct an AI-LLM power equipment maintenance system. It then sorts out the implementation process of multimodal AI-LLM in the operation and maintenance of power equipment, covering four stages: demand analysis, model training, application deployment, and operational management. Furthermore, strategies for continuously optimizing model performance are proposed, including the continuous monitoring and optimization of data quality, use of continuous learning algorithms, and establishment of a feedback loop mechanism for model performance. Finally, this study explores the future directions for multimodal AI-LLM in the field of power equipment operation and maintenance and provides a series of implementation safeguards to promote the intelligent transformation of power equipment operation and maintenance and support the construction of new power systems.