This work attempts to clarify the potentials of hybrid model based on least‐squares support vector machine (LS‐SVM) and a novel meta‐heuristic algorithm called Grey Wolf optimiser (GWO) in high‐voltage applications, considering several kernel functions. The selection of the suitable kernel function and its parameters play an important role in the performance of LS‐SVM. For this purpose, GWO is proposed in this study as an efficient optimisation approach to adjust the parameters of various kernel functions such as linear kernel (Lin), radial basis function kernel, polynomial kernel (poly) and multi‐layer perceptron kernel. Afterwards, the LS‐SVM with the most appropriate kernel function is designed to model flashover voltage of polluted high‐voltage insulators. The performance of the developed model is compared with the previous works. The results confirm high capabilities of the proposed hybrid model for the prediction of the flashover voltage of polluted insulators.

This study proposes a three‐dimensional (3D) quasi‐static modelling of the electric field produced by high voltage (HV) overhead power lines using charge simulation method combined with intelligent optimisation algorithms, such as particle swarm optimisation, genetic algorithm and grey Wolf optimiser (GWO), to find the optimal values of parameters for an accurate calculation. Results show that the GWO works better than other algorithms. Several parameters affecting the electric field have been studied; it is observed that taking into account the effect of the exact catenary curve of the power line conductors is much more interesting particularly at the mid‐span level where the electric field becomes very significant. According to these values, the limits set by the International Commission on Non‐Ionising Radiation Protection guidelines for electric field strength are met for occupational and public exposure. The simulation results are compared with those obtained by the 3D Integration method, a fairly good agreement is found. To confirm the performance and effectiveness of the proposed method, the results obtained are also verified with measurement values available in the literature, a good similarity is achieved.

The popularity of asynchronous machines, particularly squirrel cage machines, stems from their inexpensive production costs, resilience, and low maintenance requirements. Unfortunately, potential flaws in these devices might have a negative impact on the facility's profitability and service...

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DP_V) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DP_V and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.

Purpose: The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates. Design/methodology/approach: First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs. Findings: The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach. Originality/value: The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).