Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this paper to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.

Artificial intelligence is the general trend in the field of power equipment fault diagnosis. However, limited by operation characteristics and data defects, the application of the intelligent diagnosis method in power transformers is still in the initial stage. To fill this research gap, in this article, a novel double-stacked autoencoder (DSAE) is proposed for a fast and accurate judgment of power transformer health conditions with an imbalanced data structure. Three problems affecting the diagnosis effectiveness are overcome by a DSAE framework, an aging-tolerance criterion, and an advanced sparse deep clustering network. The proposed DSAE method is validated by two case studies based on an actual power transformer dataset. The results indicate that the proposed DSAE method can achieve a fairly reliable diagnosis with a higher accuracy and less time than the other methods, which demonstrates the superiority and effectiveness of the proposed approach.

Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy Internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system, namely, the Spearman rank correlation-based CNNs (SR-CNNs). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time-series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with many new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy Internet fault diagnosis over conventional methods.

In industrial applications, rolling bearings operate under high-speed and high-precision conditions, and their physical and mechanical properties change with prolonged operation. Traditional diagnostic methods rely on fixed models trained offline for online diagnosis, making it difficult to adapt to the changing characteristics of bearings over time. This paper proposes a dynamically updated dual-boundary Support Vector Data Description (DDSVDD) anomaly detection method. A dual-boundary SVDD based on slack thresholds is introduced to select more effective support vectors, constructing a classification hypersphere and enhancing the accuracy of bearing anomaly detection. Furthermore, a fusion strategy combining spatial and temporal weights is presented to achieve real-time dynamic updates of SVDD, improving the timeliness of bearing anomaly detection. Additionally, a lifespan online prediction method based on equipment characteristic evolution and center offset is proposed to define the model’s endpoint update boundary, enabling precise lifespan prediction of bearing failure models. To validate the effectiveness of the proposed method, experiments were conducted using the Case Western Reserve University (CWRU) bearing dataset and the University of Cincinnati bearing dataset. The experimental results demonstrate the effectiveness and superiority of the DDSVDD in practical applications.

In this paper, robust finite-time H _∞ filtering for uncertain switched systems with state constraints and multiple time delays is investigated. In this studied plant, there are nonlinear functions and exogenous disturbances. First, a filter is described for the switched systems. A closed-loop system is earned to acquire filtering error. Secondly, piecewise Lyapunov function is designed to gain sufficient stability conditions for the closed-loop system. Not only finite-time boundedness (FTB) but also input-output finite-time stability (IO-FTS) is studied. The attempt is the first time to research. In the end, an example is shown to demonstrate the availability of this method suggested in this study.