Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This study proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric machines under varying working conditions with limited data. The fault diagnosis under various working conditions is cast as a few-shot classification problem, which is solved using a model-agnostic meta-learning-based model. Specifically, a meta-learner is first trained using a series of interrelated fault-diagnosis tasks under various working conditions. During this stage, the gradient-by-gradient rule is utilized for parameter optimization to achieve an effective representation of these tasks. Then, the parameters of the meta-learner are refined on a new task. This technique can achieve fast adaptation to new tasks by utilizing only few-shot samples. The proposed method can obtain high fault-detection accuracy under various working conditions when only a limited amount of data is available. Comparative tests among various methods were carried out on the Case Western Reserve University (CWRU) Bearing Dataset and the Paderborn University Rolling Bearing Dataset. The results show that the proposed model performs better than other state-of-the-art methods under various working conditions; our method has stronger generalization ability and faster adaptation ability. The fault diagnosis accuracy for both datasets was at least 99%, which proves that the proposed strategy can be flexibly applied to various scenarios.

Nowadays, with the highly penetrated wind generations, the accurate Wind Farm (WF) model is required for power system stability analysis. Due to the complexity on detailed WF model, the aggregated model, with a reasonable reduction of the detailed model meanwhile retaining the required level of accuracy is essential to be developed. In this paper, an improved WF aggregated modeling method for large-scale power system stability studies is proposed. To overcome the limitations of the traditional methods, a Geometric Template Matching based time series Wind Turbines (WTs) clustering method is developed. Moreover, a multi-objective optimization algorithm, which fully considered the wind speed disturbance and system fault together, is designed to identify both the generator and control parameters. Additionally, to shrink the size of identified parameters and increase the modeling accuracy, a sensitivity and correlation analysis based key parameters selection scheme is also adopted. To verify the effectiveness of the proposed method, dynamic responses of the proposed aggregated model are compared against the responses of the traditional equivalent model for various wind scenarios through an actual case.

Except for the benefits of mimicking synchronous generators, virtual synchronous generators (VSGs) still face particular small- and large-disturbance stability problems in practical applications. Therein, the large-disturbance stability is a much more dramatic and intractable problem, such as a short circuit or line tripping. Accordingly, this article explores this challenging problem from the perspective of output impedance optimization for VSGs. First, focusing on the potential risk of overcurrent during the electromagnetic process, the dynamic performances of different virtual impedance control structures used in VSGs are comparatively analyzed, and the output impedance boundary for suppressing the transient current is derived. Then, the output impedance boundaries for maintaining electromechanical transient stability are discussed in depth by using Lyapunov stability theory, which provides novel virtual impedance optimal design guidelines exclusively for the large-disturbance stability. Furthermore, a large-disturbance stability enhancement strategy based on adaptive virtual impedance control is proposed, which can limit the electromagnetic transient current and improves the electromechanical transient stability simultaneously. Finally, experiments validate the accuracy of the theoretical analysis and the effectiveness of the proposed strategy.

Damping control plays an important role in suppressing the oscillation modes of virtual synchronous generators (VSGs). The most widely used damping control method through a frequency deviation feedback path is confronted with multiple challenging inherent contradictions among the different control objectives, resulting in seriously degraded control performance of VSGs. To resolve these contradictions fundamentally, this article proposes a novel phase feedforward damping (PFD) control method for VSG from the perspective of restructuring the damping controller. It replaces the traditional frequency deviation feedback path with a novel damping controller utilizing a phase feedforward path. Then, considering the grid-connected and islanding operation modes, respectively, a detailed comparative study of the traditional damping-based VSG (Tra-VSG) and the PFD-based VSG (PFD-VSG) is conducted. Both the theoretical and experimental results demonstrate convincingly that the proposed PFD control method can reconcile the different control objects of VSG, and thus, compared with Tra-VSG, PFD-VSG exhibits vastly superior response performance of the active power and the frequency in both grid-connected and islanding operation modes.

Grid-forming converters, taking droop-controlled converters and virtual synchronous generators (VSGs) as typical examples, have shown great promise as the interfaces between renewable energy sources and the future power-electronics-based power system. However, the coupling between active and reactive power will adversely affect the dynamic performance and stability of grid-forming converters. In order to facilitate quantitatively analyzing and assessing the power coupling characteristics of various types of grid-forming strategies, this article develops a unified dynamic power coupling model based on a first amplification coefficient array and a relative gain array in the frequency domain. Then, by using the established unified model, a comprehensive analysis of dynamic power coupling properties for typical VSG control and droop control is presented, which reveals that droop control is helpful in weakening power coupling while VSG control is prone to amplify the coupling. Moreover, the influences of the pivotal control parameters in droop and VSG controllers on power coupling characteristics are discussed in detail, which can provide novel supplementary guidelines for parameter design of droop and VSG controllers. Finally, the experimental results verify the correctness of theoretical modeling and analysis.