Practical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data
Instituto Superior Técnico（高等技术学院）；Enmova GmbH（Enmova有限公司）；Fraunhofer Institute for Solar Energy Systems ISE（弗劳恩霍夫太阳能系统研究所）；Pohlen Solar GmbH（波伦太阳能有限公司）
Eduardo Abdon Sarquis Filho, Björn Müller, Nicolas Holland, Christian Reise, Klaus Kiefer, Bernd Kollosch, Paulo J. Costa Branco
备注：
33 pages, 30 figures, preprint submitted to Elsevier Solar Energy
Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults in PV systems is based on the comparison between measured and simulated performance. Although this approach has been explored by many authors, due to the lack a common basis for evaluating their performance, it is still unclear what are the influencing aspects in the design of AFD algorithms. In this study, a series of AFD algorithms have been tested under real operating conditions, using monitoring data collected over 58 months on 80 rooftop-type PV systems installed in Germany. The results shown that this type of AFD algorithm have the potential to detect up to 82.8% of the energy losses with specificity above 90%. In general, the higher the simulation accuracy, the higher the specificity. The use of less accurate simulations can increase sensitivity at the cost of decreasing specificity. Analyzing the measurements individually makes the algorithm less sensitive to the simulation accuracy. The use of machine learning clustering algorithm for the statistical analysis showed exceptional ability to prevent false alerts, even in cases where the modeling accuracy is not high. If a slightly higher level of false alerts can be tolerated, the analysis of daily PR using a Shewhart chart provides the high sensitivity with an exceptionally simple solution with no need for more complex algorithms for modeling or clustering.
Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines
ETH Zurich（苏黎世联邦理工学院）
深度神经网络（DNN）通常与基于物理的模型或数据驱动的代理模型耦合，以在低数据状态下执行系统的故障检测和健康监测。这些模型作为数字孪生体，生成大量数据来训练DNN，否则很难从现实系统中获得。然而，这样的模型可以表现出传播到生成的数据的参数不确定性。此外，DNN在训练期间学习的参数中表现出不确定性。在这种情况下，DNN模型的性能将受到基于物理的模型中的不确定性以及DNN的参数的影响。在本文中，我们量化了这两种不确定性来源对DNN性能的影响。我们通过DNN的所有层执行输入数据中的不确定性的显式传播，以及输出不确定性的隐式预测以捕获前者。此外，我们采用Monte Carlo dropout来捕获DNN参数中的不确定性。我们展示了基于物理模型，两种类型的输入数据和三种不同的神经网络架构的电力线路故障检测的方法。我们比较这种不确定性感知的概率模型与确定性对应的性能。结果表明，概率模型提供了关于预测置信度的重要信息，同时也提供了比确定性模型更好的性能。
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
University of Bath（巴斯大学）；Loughborough University（拉夫堡大学）
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Department of Electrical and Computer Engineering, University of Victoria（电气与计算机工程系，维多利亚大学）；Department of Mechanical Engineering, University of Victoria（机械工程系，维多利亚大学）
