A Cyber-secure Framework for Power Grids Based on Federated Learning

Abstract

Cyber security is important of power grids to ensure secure and reliable power supply. This paper presented a cyber- secure framework for power grids based on federated learning. In this framework, each entity, which may be a distribution/transmission/generation service provider or even a customer, can contribute to the overall system immunity and robustness to cyber-attacks, while not required to share local data, which may have privacy, legal and property concerns. The main idea is to use the federated learning framework to share the knowledge learned from local data instead of sharing power grid data itself. With complete knowledge learned from all data from the power grid, each entity is better positioned to defend the cyber-attacks and improve power grid resiliency. Future work on applying this federated learning based framework in power systems is also discussed.

Towards Convergence in Federated Learning via Non-IID Analysis in a Distributed Solar Energy Grid

“…FL is a diversely implemented architecture for optimizing the power grid system for pragmatic energy usage [7,8] via collaboration with geographically dispersed AI models. Diverse studies were suggested to design a cost-effective structure in such an energy-grid system, analyzing the challenges and issues from multiple perspectives, such as resource allocation, convergence analysis [3,4], and network cost [9].…”

“…Depending on the environmental characteristic, D ∪ ∀j y (∃i,j) may have unique distribution, such as y i ∼ N µ(y ∀i ), σ 2 (y ∀i ) , y i ∼ Gamma(α, β), etc., and when σ ∪ ∀i D ∪ ∀j y (∃i,j) ≈ 0, the most frequent D ∪ ∀j y (∃i,j) becomes the ground truth. The left term in Case C shows the time-dependent distribution difference δ(y i , y i ) in (7). On the other hand, Case B does not consider the time-series attribute, which may incur the risk of unstable convergence while training the local models.…”

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

“…Al-Saffar and Musilek [84,85] used deep reinforcement learning to solve the optimal power flow (OPF) problem while considering the microgrids as agents. Federated learning has also been used for increasing cybersecurity [86] and customer privacy [87] in non-intrusive load monitoring (NILM). A summary of the discussed distributed learning applications in power systems is given in Table 2.…”