Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices
This work offers an incremental method for modeling smart grid device behavior and guiding them away from faults, which could benefit grid operators in improving resilience.
The paper explores using Probabilistic Boolean Networks (PBNs) to model the dynamics of smart grid devices, demonstrating their equivalence to the standard Reinforcement Learning (RL) cycle. By designing various reward structures, the authors show that PBNs can be guided to avoid fault conditions and failures in smart grids.
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.