Detecting high-impedance faults (HIF) in distribution networks poses a significant challenge for conventional relay devices due to low fault currents and various characteristics like weaker fault features, distortion offset, and background noise interference. This paper introduces a novel and streamlined method for HIF detection, which ingeniously integrates frequency-band energy curve (FBEC) analysis and Gaussian smoothing to extract trend changes, thus enhancing the precision and effectiveness of HIF detection. The proposed method utilizes continuous wavelet transform (CWT) to extract the time-frequency spectrum from the zero-sequence current. By analyzing the feature-band energy, the FBEC is computed. To mitigate noise interference and enhance the periodic change pattern, a Gaussian filter is applied for smoothing. Distinguishing between HIF and normal operations, including low impedance fault (LIF), capacitor switching (CS), inrush current (IC), and ferromagnetic resonance (FR), is achieved by analyzing the peak points of FBEC. The proposed method’s performance was extensively validated through simulations and field data. The performance of the proposed method was extensively validated through a series of simulations and detailed analysis of real-world field data. The results demonstrated an excellent detection performance on field data, with an impressive accuracy rate of 86.5% and F1-score of 0.87. Moreover, we examined the method’s resilience against noise using data with a Signal-to-Noise Ratio (SNR) of 20dB, resulting in a detection accuracy of 77.3% and F1-score of 0.79. These findings underscore the method’s clear physical meaning, strong interpretability, and versatility, establishing its effectiveness and practicality for real-world applications.

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM_2.5is the main particulate matter in air pollution. Therefore, how to predict PM_2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM_2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM_2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM_2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM_2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM_2.5prediction, and it is effective.

Most multi-objective particle swarm optimization algorithms, which have demonstrated their good performance on various practical problems involving two or three objectives, face significant challenges in complex problems. For overcoming this challenges, a multi-objective particle swarm optimization algorithm based on enhanced selection(ESMOPSO) is proposed. In order to increase the ability of exploration and exploitation, enhanced selection strategy is designed to update personal optimal particles, and objective function weighting is used to update global optimal particle adaptively. In addition, R2 indicator is incorporated into the achievement scalarizing function to layer particles in archive, which promotes the archive update. Besides, Gaussian mutation strategy is designed to avoid particles falling into local optimum, and polynomial mutation is applied in archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental results demonstrate that ESMOPSO algorithm shows very competitive performance when dealing with complex MOPs.

This paper proposes a finite-time sliding mode adaptive tracking control strategy with a neural network disturbance observer to address problems of inertia uncertainties, unknown external disturbances, singularity, and chattering. First, an accurate mathematical model of the Bernoulli-Euler beam system is constructed using an in-domain constraint strategy for surface-mounted sensors. Next, a novel finite-time disturbance observer based on the Radial Basis Function (RBF) neural network is designed to handle uncertainties, enabling fast response. In addition, a novel integral sliding surface is designed to resolve the singularity issue in terminal sliding mode. Finally, an adaptive terminal sliding mode controller is developed based on the finite-time disturbance observer and integral sliding surface to ensure finite-time convergence of tracking errors; its stability is rigorously analyzed. Simulation and experimental results demonstrate that the proposed sliding mode control strategy achieves superior tracking performance and finite-time stability compared to traditional methods in industrial beam systems.

The PM${}_{2.5}$ concentration model is the key to predict PM${}_{2.5}$ concentration. During the prediction of atmospheric PM${}_{2.5}$ concentration based on prediction model, the prediction model of PM${}_{2.5}$ concentration cannot be usually accurately described. For the PM${}_{2.5}$ concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM${}_{2.5}$ concentration may be different, and the single model cannot play the corresponding ability to predict PM${}_{2.5}$ concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM${}_{2.5}$ concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM${}_{2.5}$ concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM${}_{2.5}$ concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM${}_{2.5}$ concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM${}_{2.5}$ concentration prediction is improved in whole time period.