Q-Learning Power Flow Adjustment for Grid Loss and Convergence
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Solution Overview
Problem
Current power flow adjustment methods rely heavily on human experience, leading to inefficiencies and inaccuracies, as they are manual, random, and impose high theoretical and practical requirements on operators.
Innovation Solution
A method using a Q-learning algorithm to intelligently adjust power flow by converting variables, actions, and goals into states, actions, and rewards, selecting optimal unit combinations to minimize grid loss and achieve convergent power flow, while reducing reliance on human experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment based on operator experience is used, then flexibility in handling various power flow situations is maintained, but adjustment efficiency and accuracy deteriorate due to randomness and high operator requirements
Solution Approach 1:
The system implements self-service through the Q-learning algorithm that automatically learns and determines optimal adjustment strategies without requiring operator intervention. The algorithm independently processes power flow data, selects actions, and updates policies based on observed outcomes, eliminating dependence on operator experience while maintaining operational flexibility
Solution Approach 2:
The patent replaces the mechanical decision-making process of human operators with an intelligent algorithmic system. The Q-learning algorithm substitutes human cognitive functions with automated computational processes that evaluate states, select actions, and learn from feedback, thereby improving efficiency and accuracy while preserving adaptability
2Productivity
If automated Q-learning algorithm is used, then adjustment efficiency and accuracy are improved, but system complexity increases due to algorithm implementation requirements
Solution Approach 1:
The system manages complexity through parameterization of the Q-learning algorithm, where key parameters such as learning rate, discount factor, and exploration rate can be adjusted to balance performance and computational burden. This allows the system to adapt its complexity level based on specific operational requirements while maintaining improved efficiency and accuracy
3Reliability
If repeated manual adjustment is performed, then power flow can be adjusted toward convergence, but time consumption increases and adjustment process becomes inefficient
Solution Approach 1:
The Q-learning algorithm performs preliminary learning during off-peak periods or simulation environments, building a knowledge base of optimal adjustment strategies before actual power flow issues arise. This preliminary action enables the system to rapidly respond to convergence requirements without time-consuming trial-and-error adjustments in real-time operations
Solution Approach 2:
The system implements continuous feedback loops where the algorithm observes the results of each adjustment action and uses this information to update its policy. This feedback mechanism ensures reliable convergence by learning from past outcomes and improving future decisions, while significantly reducing the time required compared to repeated manual adjustments
Data Source
AI summary
A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.