Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges

2025-01-27

AI Summary

To address the scarcity of instability samples and degraded stability prediction accuracy under adversarial attacks in smart grids, this paper proposes a novel instability detection paradigm relying solely on stable operational data. Methodologically, we design a generative adversarial network (GAN) enhanced with adversarial robustness: it synthesizes high-fidelity unstable samples without access to real instability data, while an embedded adversarial training layer jointly performs instability prediction and attack identification. Furthermore, the model is optimized for lightweight deployment on edge devices, enabling real-time inference on single-board computers. Experiments demonstrate 97.5% accuracy in stability prediction, 98.9% adversarial attack detection rate, and an end-to-end latency below 7 ms. To the best of our knowledge, this work establishes the first grid instability detection framework that is purely stable-data-driven and intrinsically resilient to adversarial attacks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Smart grids are critical for addressing the growing energy demand due to global population growth and urbanization. They enhance efficiency, reliability, and sustainability by integrating renewable energy. Ensuring their availability and safety requires advanced operational control and safety measures. Researchers employ AI and machine learning to assess grid stability, but challenges like the lack of datasets and cybersecurity threats, including adversarial attacks, persist. In particular, data scarcity is a key issue: obtaining grid instability instances is tough due to the need for significant expertise, resources, and time. However, they are essential to test novel research advancements and security mitigations. In this paper, we introduce a novel framework to detect instability in smart grids by employing only stable data. It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator. Moreover, we include a new adversarial training layer to improve robustness against adversarial attacks. Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5% in predicting grid stability and up to 98.9% in detecting adversarial attacks. Moreover, we implemented our model in a single-board computer demonstrating efficient real-time decision-making with an average response time of less than 7ms. Our solution improves prediction accuracy and resilience while addressing data scarcity in smart grid management.

Problem

• Smart Grid
• Data Scarcity
• Cybersecurity Threats

Innovation

• Generative Adversarial Networks
• Power System Stability Prediction
• Security Enhancement