Quantum computing approach to smart grid stability forecasting
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Dosyalar
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data"dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid's stability into "stable"and "unstable"categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general. © 2024 IEEE.
Açıklama
Power Electronics in Everything (PEiE); TMEiC
12th International Conference on Smart Grid, icSmartGrid 2024 -- 27 May 2024 through 29 May 2024 -- Hybrid, Setubal -- 200813
12th International Conference on Smart Grid, icSmartGrid 2024 -- 27 May 2024 through 29 May 2024 -- Hybrid, Setubal -- 200813
Anahtar Kelimeler
Classification, Machine Learning, Quantum Computing, Quantum Neural Network, Smart Grid, Stability, SVM, Variational Quantum Classifier
Kaynak
12th International Conference on Smart Grid, icSmartGrid 2024
WoS Q Değeri
Scopus Q Değeri
N/A
