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IEEE Computer Society Annual Symposium on VLSI

Extended Abstract: Quantum-Accelerated Transient Stability Assessment for Power Systems

Jianing Chen, Yan Li

Published Jul 1, 2024• 2 citations
Publisher

Abstract

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.

DOI
10.1109/isvlsi61997.2024.00112

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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.
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.
Transient stability assessment (TSA), a cornerstone for resilient operations of today's interconnected power grids, is a grand challenge yet to be addressed since the genesis of electric power systems. This paper is a confluence of quantum computing, data science and machine learning to potentially resolve the aforementioned challenge caused by high dimensionality, non-linearity and uncertainty. We devise a quantum TSA (qTSA) method, a low-depth, high expressibility quantum neural network, to enable scalable and efficient data-driven transient stability prediction for bulk power systems. qTSA renders the intractable TSA straightforward and effortless in the Hilbert space, and provides rich information that enables unprecedentedly resilient and secure power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA. qTSA underpins a solid foundation of a quantum-enabled, ultra-resilient power grid which will benefit the people as well as various commercial and industrial sectors.
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.
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.
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.
This study investigates the use of Quantum Machine Learning (QML) for metamodeling the techno-economic stability of an islanded smart microgrid, which can also be applied to larger and more extensive grid systems. Using a physics-accurate simulator, we generated a small, many-shot dataset for the proposed microgrid architecture that is powered by a wave energy generator. To address the scalability challenges of both classical simulation and quantum computation, we propose a Quantum Many-shot Out-of-Context Learning (QM-OCL) pipeline. This pipeline uses Principal Component Analysis (PCA) for data dimensionality reduction, which was also shown to mitigate the barren plateau phenomenon in the optimization landscape of the Quantum Neural Network (QNN). The QNN was trained in a context-free manner, and its performance was benchmarked against classical models, including Shallow Neural Networks (NNs), Deep Neural Networks (DNNs), and Support Vector Classifiers (SVCs). QNN achieves an accuracy of 0.75 on this limited data, slightly outperforming most state-of-the-art classical models that achieve accuracies in the range of0.70−0.770.70-0.770.70−0.77. While the approach currently shows some limitations in generalization, this is expected given that the QNN algorithm is still in its early stages of development. This work highlights the potential of QML for metamodeling complex, data-scarce systems like smart grids/microgrids and shows that dimensionality reduction can significantly improve the trainability of QNNs. Future work will focus on larger datasets and a quantitative analysis of barren plateau mitigation.