Multiarea Inertia Estimation Using Convolutional Neural Networks and Federated Learning

With the increase in penetration of renewable energy sources (RES), traditional inertia estimation techniques based purely on the number of online synchronous generators are increasingly unsuitable, ultimately leading towards suboptimal frequency control in the electric power grid. The stochastic nature of RES additionally makes the system inertia a time-varying quantity. Furthermore, the frequency and inertial response of power systems change drastically in multiarea power systems with interconnected tie-lines. Hence, it is important for state/parameter estimation (e.g., inertia) in multiarea systems, while ensuring communication between each of the areas. In this article, a client–server-based federated learning framework is used to estimate power system inertia in a multiarea system. Federated learning is a machine learning technique where multiple decentralized devices are trained with local data, and a global model is updated and redistributed by a central server by aggregating the trained weights of the decentralized devices, without exchanging the local data. Using local frequency measurements, obtained from the phase-locked loop of an energy storage system, the inertia at each of the areas can be estimated locally via offline training using convolutional neural networks (CNNs), whereas the CNN weights update in an online fashion. The framework, tested on a two-area power system, accurately estimated the inertia constant for both independent and identically distributed (IID) and non-IID data. Furthermore, the CNN-based method outperformed conventional neural network-based estimation techniques in terms of number of communication rounds and estimation accuracy.