Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices

TL;DR

This paper explores using Probabilistic Boolean Networks within reinforcement learning frameworks to model and improve the reliability and fault management of smart grid devices.

Contribution

It introduces a novel application of PBNs as models in reinforcement learning to understand and optimize smart grid device behavior.

Findings

PBNs can be integrated into reinforcement learning cycles.

Different reward structures influence device behavior.

PBN-based models help in fault avoidance strategies.

Abstract

The area of Smart Power Grids needs to constantly improve its efficiency and resilience, to pro-vide high quality electrical power, in a resistant grid, managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities, and novel methodologies to detect, classify, and isolate faults and failures, model and simulate processes with predictive algorithms and analytics (using data analysis and asset condition to plan and perform activities). We show-case the application of a complex-adaptive, self-organizing modeling method, Probabilistic Boolean Networks (PBN), as a way towards the understanding of the dynamics of smart grid devices, and to model and characterize their behavior. This work demonstrates that PBNs are is equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an inter-action with its environment and receives feedback from it in the form of a reward signal. Differ-ent reward structures were created in order to characterize preferred behavior. This information can be used to guide the PBN to avoid fault conditions and failures.