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Purpose: This paper aims to introduce a new numerical model that predicts the flashover voltage (FOV) value in the presence of polluted air surrounding a high-voltage insulator. The model focuses on simulating the propagation of arcs and aims to improve the accuracy and reliability of FOV predictions under these specific conditions. Design/methodology/approach: This arc propagation method connecting the high voltage fitting and the grounded insulator cap involves a two-step process. First, the electric field distribution in the vicinity of the insulator is obtained using finite element method analysis software. Subsequently, critical areas with intense electric field strength are identified. Random points within these critical areas are then selected as initial points for simulating the growth of electric arcs. Findings: by increasing the electric voltage applied to the insulator fittings, the arc path is, step by step, generated until a breakdown occurs on the polluted air surrounding the insulator surface, and thus a prediction of the FOV value. Practical implications: The proposed model for the FOV prediction can be a very interesting alternative to dangerous and costly experimental tests requiring an investment in time and materials. Originality/value: Some works were done trying to reproduce discharge propagation but it was always with simplified models such as propagation in one direction from a point to a plane. The difficulty and the originality of the present work is the geometry complexity of the insulator with arc propagation in three distinct directions that will require several proliferation conditions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process. Design/methodology/approach: The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated. Findings: It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time. Originality/value: The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach: Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings: The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value: The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the ionization and dispersion effects in combination with the inhomogeneity of soil simultaneously on the effective lengths of counterpoise wires. Design/methodology/approach: Improved multi-conductor transmission line model is used for computing effective length of counterpoise wires considering all aspects of soils. Findings: The simulation results show that the ionization and dispersion effects simultaneously results in placing the effective length between situations where only one effect is considered. Also, predicting formulae for effective length of counterpoise wires considering all effects are proposed. Originality/value: A sensitivity analysis on the effective lengths of counterpoise wires considering all aspects of soils is carried out. [ABSTRACT FROM AUTHOR]