Object detection of overhead transmission lines is a solution for promoting inspection efficiency for power companies. However, aerial images contain many complex backgrounds and small objects, and traditional algorithms are incompetent in the identification of details of power transmission lines accurately. To address this problem, this paper develops an object detection method based on optimized You Only Look Once v5-small (YOLOv5s). This method is designed to be engineering-friendly, with the objective of maximal detection accuracy and computation simplicity. Firstly, to improve the detecting accuracy of small objects, a larger scale detection layer and jump connections are added to the network. Secondly, a self-attention mechanism is adopted to merge the feature relationships between spatial and channel dimensions, which could suppress the interference of complex backgrounds and boost the salience of objects. In addition, a small object enhanced Complete Intersection over Union (CIoU) is put forward as the loss function of the bounding box regression. This loss function could increase the derived loss for small objects automatically, thereby improving the detection of small objects. Furthermore, based on the scaling factors of batch-normalization layers, a pruning method is adopted to reduce the parameters and achieve a lightweight method. Finally, case studies are fulfilled by comparing the proposed method with classic YOLOv5s, which demonstrate that the detection accuracy is increased by 4%, the model size is reduced by 58%, and the detection speed is raised by 3.3%.

Doubly-fed induction generators (DFIGs) participate in the system frequency regulation using a fixed-coefficient droop control scheme. Nevertheless, the frequency-supporting capability of this control scheme with fixed gain is limited for different disturbances. This paper suggests an improved droop control scheme for a DFIG that can both alleviate the frequency nadir and maximum rate of change of frequency (ROCOF) during the frequency regulation. To achieve this, an adaptive droop control coefficient based on the ROCOF is suggested. The proposed droop control coefficient is a linear function of the ROCOF. Therefore, the proposed scheme can adjust the control coefficient according to the varying ROCOF. Simulation results clearly demonstrate that the proposed droop control scheme shows better effectiveness in improving the maximum ROCOF and frequency nadir under various sizes of disturbance, even in a varying wind speed.

For weak grid scenario with high new energy proportion, large fluctuations of load are prone to cause low‐voltage ride through. Moreover, stator transient magnetic flux will cause overvoltage and overcurrent problems in the rotor of doubly fed induction generator. Based on model predictive control, a control strategy for transient flux linkage and reactive power compensation is proposed. Firstly, regarding the issue of reactive power allocation of grid side converters (GSC) and rotor side converters (RSC), an allocation strategy is derived under minimizing winding energy loss on case of low‐voltage ride through, enabling the wind energy conversion system to provide reactive power support during grid voltage recovery process. Meanwhile, an improved mixed second‐ and third‐order generalized integrator (MSTOGI) phase‐locked loop (PLL) is used to extract the positive and negative sequence components of the power grid voltage, further for RSC control. Secondly, in response to power grid faults, considering the influence of stator DC transient flux and negative sequence flux components on rotor current, by injecting rotor transient compensation current and stator flux feedforward compensation into RSC, the rotor impulse voltage and loop current are reduced. Moreover, combined with model predictive control algorithm, a control strategy of rotor current is designed. Finally, a simulation platform is built to validate the effectiveness of the proposed method based on comparing with several traditional vector control low‐voltage ride through methods.

A power electronic transformer (PET) is applied to the high-speed train for lightweight demand. A 300 kW/5 kHz high-power medium-frequency transformer (HPMFT) using hollow conductors in a power unit of the PET is optimally designed in this paper. The target of the design is to balance the loss, leakage inductance, and weight of the HPMFT. For this purpose, the design parameters of the HPMFT are firstly confirmed according to the system structure and parameters of the PET. Secondly, the design process of HPMFT is developed. Finally, the results of 48 design schemes of core-type and shell-type structures are compared by the comprehensive evaluation standard, which equilibrates the three above objective parameters of the HPMFT. According to the optimal scheme, a prototype is manufactured, whose test results verify the correctness of the optimal design method.

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation.

This paper investigates one kind of high performance control methods for Micro-Electro-Mechanical-System (MEMS) gyroscopes using adaptive sliding mode control (ASMC) scheme with prescribed performance. Prescribed performance control (PPC) method is combined with conventional ASMC method to provide quantitative analysis of gyroscope tracking error performances in terms of specified tracking error bound and specified error convergence rate. The new derived adaptive prescribed performance sliding mode control (APPSMC) can maintain a satisfactory control performance which guarantees system tracking error, at any time, to be within a predefined error bound and the error convergences faster than the error bound. Besides, adaptive control (AC) technique is integrated with PPC to online tune controller parameters, which will converge to their true values at last. The stability of the control system is proved in the Lyapunov stability framework and simulation results on a Z-axis MEMS gyroscope is conducted to validate the effectiveness of the proposed control approach.