利用深度卷积神经网络和粒子群优化-极梯度提升技术进行风能系统故障分类和检测

IF 1.6 Q4 ENERGY & FUELS IET Energy Systems Integration Pub Date : 2024-03-12 DOI:10.1049/esi2.12144
Chun‐Yao Lee, Edu Daryl C. Maceren
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引用次数: 0

摘要

在全球向可持续能源系统转变的过程中,风能至关重要。因此,本研究创新性地解决了风能系统故障分类和检测方面的挑战,强调了鲁棒性机器学习方法的整合。我们的研究重点是通过监控和数据采集(SCADA)系统加强故障管理,解决不平衡数据表示问题和错误漏洞。一项关键的创新在于将粒子群优化调整的极梯度提升(XGBoost)应用于不平衡的 SCADA 数据集,将重新采样的 SCADA 数据与深度卷积神经网络生成的深度学习特征相结合。PSO-XGBoost 的新颖使用展示了在优化参数和确保模型稳健性方面的有效性。此外,我们的研究还有助于使用季节趋势分解(Seasonal-Trend decomposition)、局部估计散点图平滑(locally estimated scatterplot smoothing)和 PSO-XGBoost 建立有监督和无监督的异常检测模型,从而在故障分类和预测指标方面取得实质性进展。总之,该研究为故障管理提供了一个独特的集成框架,证明了预测性维护架构的可靠性得到了提高。最后,该研究强调了先进机器学习在提高高效清洁能源生产的可持续性方面的变革潜力。
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Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting
Wind energy is crucial in the global shift towards a sustainable energy system. Thus, this research innovatively addresses the challenges in wind energy system fault classification and detection, emphasising the integration of robust machine learning methodologies. Our study focuses on enhancing fault management through supervisory control and data acquisition (SCADA) systems, addressing imbalanced data representation issues and error vulnerabilities. A key innovation lies in applying particle swarm optimisation‐tuned extreme gradient boosting (XGBoost) on imbalanced SCADA datasets, combining resampled SCADA data with deep learning features produced by deep convolutional neural networks. The novel use of PSO‐XGBoost showcases effectiveness in optimising parameters and ensuring model robustness. Furthermore, our research contributes to supervised and unsupervised anomaly detection models using Seasonal‐Trend decomposition using locally estimated scatterplot smoothing and PSO‐XGBoost, presenting substantial advancements in fault classification and prediction metrics. Overall, the study offers a unique, integrated framework for fault management, demonstrating improved reliability in predictive maintenance architectures. Lastly, it highlights the transformative potential of advanced machine learning in enhancing sustainability within efficient and clean energy production.
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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
期刊最新文献
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