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A Trustable Data-Driven Framework for Composite System Reliability Evaluation
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DOI：10.1109/JSYST.2021.3131822.png)
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Abstract
En 中文
Composite system reliability (CSR) evaluations suffer from heavy computational burdens because an enormous number of sampled system states must be analyzed. Recently, data-driven approaches are of growing interest because they can accelerate such evaluations by preidentifying success/failure samples. However, the current approaches are limited by unsatisfactory accuracy. Essentially, the main reason is that the data-driven results are not credible. In this article, a trustable data-driven framework based on a deep neural network (DNN) is proposed to enable accurate and credible results for CSR evaluations by the following two aspects: First, a fine-grained identification method with an effective learning strategy is proposed to effectively extract the data features of CSR evaluations and improve the identification accuracy of failure samples. Second, a secure strategy including adaptability criteria and a correction method for the DNN is proposed to guarantee the accuracy of data-driven results. The strategy can determine whether the DNN is utilized to evaluate CSR and reasonably correct the DNN outputs. The simulation results demonstrate the effectiveness of the proposed methods.
Keywords:
Mathematical models
Transfer learning
Power system reliability
Adaptation models
Uncertainty
Training data
Training
Credibility
deep learning
deep neural network (DNN)
reliability evaluation
risk assessment
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4.5K
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