Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
This work addresses the problem of grid stability for power system operators during deep decarbonization, but it is incremental as it builds on existing ML methods with a novel integration approach.
The paper tackles the challenge of building trustworthy machine learning models for power systems by proposing a framework that links sequential modules in the ML pipeline with feedback loops, demonstrating its effectiveness on a North Sea Wind Power Hub system to learn N-1 small-signal stability margins.
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes. With its ability to learn from complex datasets and provide predictive solutions on fast timescales, machine learning (ML) is well-posed to help overcome these challenges as power systems transform in the coming decades. In this work, we outline five key challenges (dataset generation, data pre-processing, model training, model assessment, and model embedding) associated with building trustworthy ML models which learn from physics-based simulation data. We then demonstrate how linking together individual modules, each of which overcomes a respective challenge, at sequential stages in the machine learning pipeline can help enhance the overall performance of the training process. In particular, we implement methods that connect different elements of the learning pipeline through feedback, thus "closing the loop" between model training, performance assessments, and re-training. We demonstrate the effectiveness of this framework, its constituent modules, and its feedback connections by learning the N-1 small-signal stability margin associated with a detailed model of a proposed North Sea Wind Power Hub system.