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North American Power Symposium (NAPS)

Quantum Federated Learning Based Power System Stability Assessment: A Noise Robust Approach

Sijia Yu, Yifan Zhou

Published Oct 26, 2025• 0 citations
Publisher

Abstract

The integration of distributed energy resources requires effective distributed transient stability assessments (TSA) that can capture complex stability dynamics through localized data processing. In this paper, we propose a Quantum Vertical Federated Learning (QVFL)-enabled TSA framework. Our core innovation is a quantum average pooling-based distributed TSA method (QdTSA-AP), which enables data-driven transient stability prediction in a resource-efficient manner while being robustness-by-design against quantum noise, thus ensuring practical applicability in the current Noisy Intermediate-Scale Quantum (NISQ) era. Key contributions include: 1) A quantum average pooling method for effective information extraction from local TSA data while minimizing the communication overhead of transferring quantum information; 2) A lightweight noise-robustness evaluation method, which, for the first time, applies the Signal-to-Noise Ratio (SNR) concept from signal processing to assess the noise robustness of QdTSA-AP, which can be generalized to a wide range of quantum machine learning (QML) tasks; 3) A theoretical proof, based on SNR, showing that average pooling enhances the noise resilience of QVFL through its architectural design rather than requiring additional computational overhead. Extensive numerical experiments validate the accuracy and noise robustness of QdTSA-AP and the effectiveness of the proposed average pooling strategy.

DOI
10.1109/naps66256.2025.11272268

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Transient stability assessment (TSA) is an indis-pensable routine in power system operation and control. The increasing integration of distributed energy resources highlights the necessity of distributed transient stability assessment which can effectively capture the complicated stability characteristics of the entire power system without compromising the data privacy of individual local subsystems. This paper devises a quantum-enabled distributed transient stability assessment (Q-dTSA) method to enable data-driven transient stability prediction of power grids in a distributed, expressive and privacy-preserving manner. Our contributions include: 1) A quantum federated learning (QFL) architecture, which enables local power grids to jointly realize the data-driven TSA for the entire system using shallow-depth quantum circuits; 2) A distributed quantum gradient descent (d-QGD) algorithm, which supports effective coordination between local subsystems to perform distributed training of the QNNs without leaking local power system in-formation. 3) Extensive experiments in real-scale power grids obtained from both noise-free simulators and noisy IBM quantum computers, which validate the accuracy, fidelity, and noise-resilience of Q-dTSA, as well as its superiority over centralized quantum computing algorithms.
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Transient stability assessment (TSA) is a cornerstone for resilient operations of today’s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth quantum circuit (HELD) is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD training and 3) A systematical analysis on QTSA’s performance under various quantum factors is performed. QTSA underpins a foundation of quantum machine learning-enabled power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.
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Transient stability assessment (TSA) in power systems evaluates the system’s ability to withstand and recover from disturbances, which can be formulated as a classification problem. This paper presents a quantum machine learning-based TSA approach that leverages quantum embedded kernels (QEKs) to map nonlinear power system features into a high-dimensional Hilbert space, where the data becomes linearly separable. By employing a quantum kernel function and Kernel Target Alignment (KTA), we optimize a variational quantum circuit for the stability classification of power systems. Through extensive experiments on both noise-free quantum simulators and noisy quantum environments, we demonstrated the accuracy and noise resilience of the developed algorithm.
Quantum machine learning (QML) methods possess the potential to address transient stability assessment (TSA) in power systems characterized by high computational complexity. In this paper, we introduce a QML-based transient stability assessment method, designed to assess the stability of modern large-scale power systems. Initially, quantum principal component analysis (QPCA) is employed to extract the most crucial features of power systems with an exponentially speedup than traditional PCA. Subsequently, to project original data into a lower-dimensional space to utilize state-of-the-art quantum computing resources, an inner product computation method is developed in a quantum manner, requiring significantly fewer measurements. The inner product results serve as inputs of a viable variational quantum algorithm (VQA) for conducting TSA. Preliminary results have shown that the QML-based TSA method can successfully determine system stability, with more details provided in the full paper. The reduced computational complexity provided by QML enables faster, potentially online analysis for TSA, thereby taking proactive action to prevent system failure.