This article presents a modular decentralized control method for wind farms (WFs) to optimize power dispatch among wind turbines (WTs) without instant communication. By harnessing the nonlinear fitting capability of two data-driven modules, this method facilitates decentralized control of each WT with existing centralized control methods. Specifically, the local-to-global mapping module captures the intricate relationship between the historical local state variables of each WT and the current global state variables of the WF. The open-loop and closed-loop predictive modes enable the demanded power prediction module to locally predict the active power required by the transmission system operator (TSO) under various communication delays. Two classical multiobjective control modes are incorporated into the local controller depending on whether it requires data from the TSO. Testing on a WF with 32 WTs in MATLAB/Simulink validates that the proposed method has robustness and effectiveness closely aligned with centralized control methods.

The intricate multicomponent coupling dynamics inherent in complex electromechanical systems often result in the aliasing of motor health status signals, posing a significant challenge for fault diagnosis techniques. To tackle this issue, this article presents an interpretable leakage flux pattern method, which introduces the motor mechanism knowledge system into the image representation method and enables the data-driven fault diagnosis method to have common knowledge with humans. Initially, the relationship between the magnetic field inside the motor and the leakage flux outside the motor is analyzed. Subsequently, the leakage flux pattern method is proposed to reconstruct the distribution of the magnetic induction lines inside the motor with a noninvasive measurement method. Furthermore, a lossless data compression technique grounded in causality is proposed, alongside a semantic-aware matching method for causal features. Finally, an interpretable fault diagnosis framework is proposed. The experimental outcomes demonstrate the excellent efficiency and performance of the proposed method.

In industrial production, the speed of the motor is constantly changing due to production demands. However, traditional diagnosis methods cannot guarantee their performance under all circumstance, especially the sharp changing speed condition. To tackle this issue, this article proposes an adaptive method that can be used for multifaults diagnosis of motor under variety working condition, especially for the dramatic changing and unstable working states. First, a time–frequency (TF) parameter and resolution adaptive algorithm is proposed for signal preprocessing. Second, the well-processed signal is resampled by TF diagram and peak search method to eliminate the effect of sudden change in speed. A proposed perception matching algorithm based on the symmetrized dot pattern and convolutional neural networks is implemented for improving the accuracy. Finally, an adaptive model with unified diagnose process is developed to improve the accuracy, efficiency, and practicality of the diagnose model. Compared with other state-of-art methods, the results show the out-performance of the proposed method under both in steady and transient state, especially in the case that the speed is changing fast.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

In the realm of industrial production, the condition of motors tends to deteriorate gradually over time. Among the various types of motor faults, the selection of appropriate diagnostic techniques stands out as a pivotal step in the process of motor fault identification. In this article, we introduce a fault diagnosis framework aimed at enhancing the precision and efficiency of diagnostics by perceptually matching the time–frequency features of data with the sensory fields of convolutional neural networks (CNNs). First, the time–frequency swing door algorithm (TF-SDA) is proposed to determine the optimal signal compression ratio, guided by the time–frequency attributes of the original data. Subsequently, the data are transformed into a two-dimensional image using temporal image conversion methodologies, specifically the Gramian angular field and symmetrized dot pattern. Finally, the diagnostic framework is realized by matching the CNN receptive fields with TF-SDA data features. Comparative analysis with other state-of-the-art methods and compression algorithms reveals that the proposed approach effectively isolates the optimal time–frequency components of fault characteristics while eliminating extraneous elements, thus substantially enhancing the precision and efficiency of multifault diagnosis.

Generalization ability is a critical property for practical motor fault diagnosis (FD). By converting time-series to images, several studies have made certain achievements. However, they still have following limitations. First, multisensor information fusion is rarely considered. Second, it is time consuming. To deal with the abovementioned problems, a multisensor-driven FD method based on visual features is proposed. Specifically, a color symmetrized dot pattern method is newly designed to infuse three multisensor signals to image. Next, a coarse and refined diagnosis framework is designed. In the coarse part, the color histogram features and a support vector machine (SVM) are utilized, and a threshold is selected to decide the coarse diagnostic samples. In the refined part, the gist (GIST) descriptor and another SVM are used to diagnose remaining samples. The results on induction motor and permanent magnet synchronous motor show that the proposed method achieved reliable diagnosis with relatively efficiency, and can generalize to different working conditions and noise.