In islanded low-voltage microgrids, the parallel operation of inverters using traditional droop control strategies often results in imbalanced output impedances among inverters due to variations in line impedance. This imbalance prevents the equitable distribution of reactive power according to the designated droop coefficients. To address this challenge, this paper proposes an improved droop control strategy. First, the virtual impedance control method is added to the strategy of traditional droop control, and then an adaptive weight particle swarm optimization algorithm (APSO) is introduced in the voltage outer loop control module to optimize the parameter setting of the PID controller. Finally, a comparative analysis was conducted on the output results of the improved droop control before and after introducing the algorithm. This proposed strategy is systematically analyzed through modeling and simulation in the MATLAB/Simulink environment to assess its impact on output voltage stability, reactive power sharing, and circulating current magnitude among parallel inverters. The simulation results indicate that the improved droop control with the introduction of virtual impedance effectively achieves equal reactive power sharing among parallel inverters. Moreover, by employing the APSO algorithm, the stability of the inverter output voltage is significantly enhanced. The maximum voltage deviation between two inverters is reduced to below 0.03%, while the maximum reactive power deviation decreases from over 3.5% to less than 2.5%. These improvements effectively reduce the circulating currents between two paralleled inverters, ensuring that the maximum current remains below 1.5 A and the minimum stays above −1 A.

The precise estimation of the operational lifespan of insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring the efficient and uncompromised safety of industrial equipment. However, numerous methodologies and models currently employed for this purpose often fall short of delivering highly accurate predictions. The analytical approach that combines the Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD is employed as an unsupervised feature learning method to partition the data into intrinsic modal functions (IMFs), which are used to eliminate noise and preserve the essential signal. Secondly, the BiLSTM network is integrated for supervised learning purposes, enabling the prediction of the decomposed sequence. Additionally, the hyperparameters of BiLSTM and the penalty coefficients of SVMD are optimized utilizing the POA technique. Subsequently, the various modal functions are predicted utilizing the trained prediction model, and the individual mode predictions are subsequently aggregated to yield the model’s definitive final life prediction. Through case studies involving IGBT aging datasets, the optimal prediction model was formulated and its lifespan prediction capability was validated. The superiority of the proposed method is demonstrated by comparing it with benchmark models and other state-of-the-art methods.

In this paper, solving the problem of the noise covariance matrix parameters tuning of the extended Kalman filter (EKF) and unscented Kalman filter algorithms (UKF) is difficult. A speed estimation strategy for a permanent magnet synchronous motor (PMSM) based on particle swarm optimization (PSO) optimized Kalman filter (KF) series algorithms is proposed. By using MATLAB/Simulink, in this paper, 20 effective simulation experiments on the noise covariance matrix parameter optimization process are conducted to obtain the optimal covariance matrix parameters of the extended Kalman filter and unscented Kalman filter. Moreover, EKF, PSO-EKF, UKF, and PSO-UKF are also compared to verify the effectiveness of the particle swarm optimization algorithm in optimizing the systems using the extended Kalman filter and unscented Kalman filter. For the error of speed estimation, taking 4000 rpm as a reference, the system using PSO-EKF has improved by 2.125% compared to that using EKF, and the system applying PSO-UKF has improved by 0.55% compared to the system applying UKF. For the error of electrical angle estimation, taking the system errors of original algorithms as references, the system adopting PSO-EKF has decreased by 60% compared to that adopting EKF, and the system using PSO-UKF has decreased by 47% compared to the system using UKF.

As an important part of electronic equipment, the reliability of analog circuits directly affects the safe and stable operation of electronic equipment. The optimal measurement node set selection and intelligent fault diagnosis are combined in this paper, and an analog circuit fault diagnosis method incorporates a multi-objective selection of measurement nodes. Firstly, the fault differentiation is calculated by a classification algorithm to construct the fault information table of measurement nodes, and then the number of measurement nodes and the number of faults are set as the two objective functions of the multiple objective beluga whale optimization (MOBWO) algorithm to realize the optimal measurement node set selection. Secondly, the fault data set from multiple measurement nodes is inputted into the fault diagnosis model based on the adaptive particle swarm optimization algorithm and stacked denoising autoencoder (APSO-SDAE), in which the hyperparameter combination of the SDAE model is optimized by the APSO algorithm based on the fitness value in order to improve fault classification capability, and, finally, the analog circuit fault diagnosis task is completed. In order to verify the effectiveness and superiority of the above method, two standard circuits are selected for experimentation, and diagnostic accuracies of 97.87% and 98.41% are obtained. The results show that the method proposed in this paper can effectively improve the fault diagnosis accuracy, which is better than other comparative methods.

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

As a new multilevel converter structure, modular multilevel converter (MMC) has many outstanding advantages, such as high degree of modularization and easy cascade expansion, which greatly promotes the development and application of flexible DC transmission technology. In this paper, the modular multilevel inverter is the main research object, its topology and multilevel generation mechanism are analysed, and the capacitor voltage of sub modules is balanced based on the carrier phase shifted PWM strategy. The MMC simulation model is built on the MATLAB/Simulink software platform. According to the simulation results, the feasibility of the modulation and control strategy in reducing the harmonic component of output voltage and maintaining the stability of capacitor voltage is verified.