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The previous studies examined the relationship between traditional complex power (TCP) and extended complex power (ECP)-based direct power controls (DPCs). A novel complex power (NCP) is modeled, and we then study the relationship between the NCP and existing complex powers. The frameworks of TCP-, ECP-, and NCP-based model predictive DPC (MPDPC) are established and studied using mathematical models and tools. Under slightly unbalanced grid voltages, we perform a comparative analysis of the above three methods. The inherent equivalence or relationship between the three methods is described in terms of power variations. Under extremely unbalanced grids, the existing TCP- and ECP-based MPDPCs cannot work well, resulting in nonsinusoidal grid currents and larger power ripples. However, the NCP-MPDPC achieves the better steady-state performance. To reveal their inherent relationships, we conduct a comparative study from their output reference voltages. Finally, we are stimulated to design an NCP-based MPDPC. The MPDPC method is realized by selecting one extended active vector and a zero vector. The duty cycles of all voltage vectors are redesigned to achieve sinusoidal grid current and minimize total harmonic distortion (THD). Both the simulations and experiments validate the effectiveness of their inherent relationships of three methods.

The application of virtual synchronous generator (VSG) control in flywheel energy storage systems (FESS) is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids. Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches, this paper proposes an adaptive VSG control strategy for a flywheel energy storage array (FESA). First, by leveraging the FESA model, a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge (SOC) equalization across units. Furthermore, energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit. The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation. In addition, VSG technology is applied for the grid-connected control of the FESA, and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback. This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation. Subsequently, a parameter design method is developed through a small-signal stability analysis. Consequently, considering the SOC of the FESA, an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA. Finally, the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

Insulators are essential components in power transmission lines. Due to the harsh variations in bad environments, insulators may experience faults. Detecting these insulator faults promptly and effectively is an urgent issue. To rapidly and accurately locate insulators and their faulty regions in aerial images of insulators with complex backgrounds and varying fault sizes, this paper proposes an improved YOLOv8 algorithm for the detection of multiple insulator fault types (MFI-YOLO). This algorithm achieved target feature extraction in complex background images by replacing the C2F network constructed by fusing the GhostNet and multi-scale asymmetric convolution (MSA-GhostBlock). Furthermore, in the feature fusion stage, a multi-scale feature fusion structure called ResPANet, based on residual skip connections, was constructed to replace the PANet. This enhancement aims to improve the network detection accuracy in multi-target scenarios. Finally, to evaluate the algorithm's performance, this study constructed a target detection dataset containing four types of insulators: normal, self-explosive, damaged, and flashover. Experimental results indicate that, compared to the original model, the improved model has increased mean accuracy from 89.2% to 93.9%. The designed model exhibits high detection accuracy in the insulator and its three fault categories, especially for some hard-to-detect fitting.

Under severe unbalance of grid voltages, achieving sinusoidal grid currents while eliminating large power fluctuations is important, but the current methods are ineffective in selecting optimal voltages and duty cycle. Therefore, we propose a finite control set three-vector model predictive control based on the extended active power (FCSTV-MPC-EAP) to attain much better total harmonic distortion of grid currents and the lowest power fluctuations. The proposed FCSTV-MPC-EAP approach realizes the objective by selecting two extended active vectors and a zero vector, with their duty cycles reconfigured from fixed phase and magnitude to arbitrary values. Comparative analysis of the proposed approach on an ac/dc converter against other techniques validate the effectiveness of the FCSTV-MPC-EAP method.