Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

Abstract

In artificial intelligence (AI) based-complex power system management and control technology, one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution. However, there is, currently, nearly no standard technical framework for objective and quantitative intelligence evaluation. In this article, based on a parallel system framework, a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems, by resorting to human intelligence evaluation theories. On this basis, this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning (AutoRL) systems. A parallel system based quantitative assessment and self-evolution (PLASE) system for power grid corrective control AI is thereby constructed, taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results. Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent, and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results, effectively, as well as intuitively improving its intelligence level through self-evolution.

Keywords

• AI quantitative intelligence assessment and self-evolution
• automated reinforcement learning
• Bayesian optimization
• corrective control
• parallel system