Electronics

Aiming at the chattering issue in speed observation for sensorless control, this paper proposes a sliding mode observer based on an improved double-power reaching law for high-performance speed estimation in five-phase induction motors. Traditional constant-rate reaching law observers exhibit significant chattering, while the double-power reaching law, though offering certain “variable-gain” adjustment effects, still has limited chattering suppression capability. To address this, the paper introduces a state variable related to the stator current into the conventional double-power observer, further enhancing the ability of the sliding mode gain to vary with the system state. This approach effectively suppresses chattering while maintaining convergence speed. The stability of the observer system employing the new reaching law is proven using Lyapunov stability theory, and the value ranges of key parameters are determined. Simulation results demonstrate that, compared to traditional constant-rate reaching law and conventional double-power reaching law observers, the proposed improved method significantly reduces speed observation chattering and effectively enhances the observation accuracy of the observer.

Threats that are caused by cyber-attacks on intelligent power networks promote the implementation of sophisticated intrusion detection devices, which can effectively detect advanced attacks. In this paper, a new model is introduced that combines the Modified Polar Fox Optimization Algorithm (MPFA) with an Inception-V4 deep neural network to enhance the effectiveness of the threat detection task. The MPFA optimizes inception-V4 hyperparameters and architecture to balance the exploration and exploitation processes of the courtship learning process and fitness-based scaling. The optimized model on the smart grid monitoring power is shown to perform well; it achieves over 99.5% accuracy, precision, recall, and F1-score on the detection of various attacks, including False Data Injection, Denial-of-Service, and Load Redistribution, and has a favorable computational overhead, thus it can be considered a formidable solution to protect critical smart grid infrastructure. The optimized model, evaluated on the Smart Grid Monitoring Power dataset, achieves state-of-the-art performance with an accuracy of 99.63%, a precision of 99.61%, a recall of 99.65%, and an F1-score of 99.63% for the detection of various cyber-physical attacks, including False Data Injection, Denial-of-Service, and Load Redistribution. It also maintains a favorable computational overhead, thus presenting a formidable solution for protecting critical smart grid infrastructure.

Modular multilevel converter (MMC) is widely employed in high-voltage direct current (HVDC) systems for the long-distance renewable energy transmission, where the larger submodule (SM) capacitors significantly increase its size, weight and cost. Conventional capacitor voltage fluctuation suppression methods, such as composite harmonic injection (CHI) strategies, can achieve lightweight MMC. However, these approaches often neglect the dynamic constraints between harmonic injection parameters and their coupled effect on modulation wave, which not only leads to suboptimal global solutions but also increases the risk of system overshoot. Therefore, this paper proposes a comprehensive CHI parameters optimization method to minimize capacitor voltage fluctuations, thereby allowing for a smaller SM capacitor. First, the analytical expression of SM average capacitor voltage is developed, incorporating the injected second-order harmonic circulating current and third-order harmonic voltage. On this basis, an objective function is defined to minimize the sum of the fundamental and second-order harmonic components of the average capacitor voltage, with the harmonic injection parameters and modulation index as optimization variables. Then, these parameters are optimized using a particle swarm optimization (PSO) algorithm, where their constraints are set to prevent modulation wave overshoot and additional power loss. Finally, the optimization method is validated through a ±500 kV, 1500 MW MMC-HVDC system under various power conditions in PSCAD/EMTDC (version 4.6.3). In addition, simulation results demonstrate that the proposed method can achieve a 13.33% greater reduction in SM capacitance value compared to conventional strategies.