Multi-source-data-driven microgrids reliability analysis via power supply chain using deep learning

Yulu Zhang, Zhiwei Chen, Xinghui Dong, Hongyan Dui, Min Chang and Junqiang Bai

Reliability Engineering and System Safety, 2025, vol. 264, issue PA

Abstract: Microgrid reliability is the ability to maintain a stable energy supply in a variable environment. However, such an environment (wind direction, temperature, humidity, pressure, and wind speed) renders the power supply with randomness, intermittency, and volatility. To ensure power stability in variable environments, a data-driven microgrid (DDMG) reliability analysis method is proposed based on the power supply chain (PSC) model, which fully considers the data-dependent output power. Firstly, a convolutional neural network-support vector machine (CNN-SVM) model is developed to effectively fuse multi-source data features. Secondly, a temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) approach is introduced to capture temporal dependencies to predict equipment states. The above two deep learning models provide accurate input values for reliability assessment. Then, a reliability assessment model is established based on the PSC model, complemented by an importance-measure-based reliability improvement strategy. Finally, the feasibility of methodology is validated with a case. The results show that compared with the traditional methods, the classification accuracy of CNN-SVM is up to 98.9747 %, the R2 of TCN-BiGRU is up to 96.1962 %, and the recovered ranking based on the importance measure markedly and stably improves the reliability, which would guide microgrid reliability design.

Keywords: Reliability analysis; Microgrids; Deep learning; Multi-source data; Importance measure (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:264:y:2025:i:pa:s0951832025005770

DOI: 10.1016/j.ress.2025.111376

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