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Due to the complex surrounding environments, unpredictable occupants' behaviors, and the photovoltaic (PV) module degradation after years of operation, the fast and accurate maximum power point tracking (MPPT) of building‐integrated photovoltaic (BIPV) system is very difficult. Herein, the MPPT of a huge BIPV system is modeled as a large‐scale global optimization (LSGO) problem, in which all the variables are proven to be separable. Then, a novel differential evolution (DE) algorithm with the "DE/Current‐to‐Tbest/Current‐to‐Gbest/1" mutation operation (DE‐TG1) is developed to optimize the aforementioned separable LSGO−MPPT model. In addition, the problem of PV module degradation and its effect on the LSGO‐MPPT model are analyzed in detail. To reduce the influence of PV degradation on MPPT accuracy, the dynamic LSGO−MPPT model is developed by introducing the annual drift rates into the proposed LSGO−MPPT model. Finally, the developed methodologies are comprehensively tested by simulation experiments. Experimental results show that the developed DE‐TG1 algorithm significantly outperforms the other state‐of‐the‐art algorithms on separable LSGO problems. In addition, result of the experiments also indicates that with the cooperation of the DE‐TG1 algorithm and the dynamic LSGO−MPPT model, the effective MPPT control of a large‐scale BIPV system can be obtained with accurate and robust performance. [ABSTRACT FROM AUTHOR]

Extracting bearing degradation curves with good smoothness and monotonicity as a health indicator lays a solid foundation for predicting the bearing's remaining useful life. Traditional bearing health indicator construction methods generally have the following problems: (1) they require manual experience, such as manual labeling of data is burdensome when the amount of collected data is large, for feature extraction, selection, and fusion with other indicators and models because the methods rely on substantial expert experience and signal-processing technology; (2) deep belief networks in deep learning require engineering experts with rich experience to label the data, and because the degradation state of a bearing is constantly changing, it is difficult to rely on manual experience to distinguish and label it accurately; (3) owing to the noise in the data collected during the study, the extracted health indicator curve shows obvious oscillation and poor smoothness. In response to the above problems, this study proposes a model based on an unsupervised deep belief network and a new sigmoid zero local minimum point to eliminate health indicator curve oscillation and improve monotonicity. The main idea is that a deep belief network without a label output layer is used to extract the preliminary health indicator curve directly from the original signal, whereas the sigmoid zero local minimum point uses the average value based on a sigmoid function to reduce the weight of the current health indicator value to eliminate concussion, and then it uses the zero and local minimum points to further improve the monotonicity of the extracted health indicator without parameters. Finally, the superiority of the model proposed in this study (deep belief network–sigmoid zero local minimum point) is verified through a comparison of multiple bearing datasets and other models. [ABSTRACT FROM AUTHOR]