Intelligent Operation and Maintenance in the Digital Transformation of Power Grid Production Project Management Using Deep Reinforcement Learning

With the rapid development of artificial intelligence technology, digital transformation has opened up a new path for intelligent operation and maintenance of power grid production project management. Aiming at the problem that traditional operation and maintenance mode is difficult to dynamically adapt to real-time changes of equipment status, complex task scheduling and emergency response, this study proposes a new intelligent operation and maintenance decision-making optimization model based on deep reinforcement learning. The core adopts deep double-Q network algorithm. Through reinforcement learning framework, the model takes equipment operating parameters (temperature, current, voltage, etc.) as inputs, combines experience playback and target network mechanism, and dynamically generates operation and maintenance policies. Experiments show that compared with CNN, LSTM and the original DQN algorithm, the deep double-Q network performs best on cumulative reward (1,643.2) and discounted cumulative reward (2,198.5), and the convergence speed is improved by about 25%. The actual deployment in the regional power grid verifies its effectiveness in minute-level fault response and resource optimization, with an operation and maintenance cost reduction rate of 21.3%. This study provides a reusable methodological framework for grid intelligent operation and maintenance, and helps to improve the efficiency of life cycle management.