Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand ...Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.展开更多
Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a ...Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a fan system is con-cerned. Basically, a fan is mainly consisting of a single phase induction motor and therefore fan system parameters are essentially the electrical parameters e.g. resistances, reactances and some load parameters (fan blades).These parame-ters often change under varying operating conditions and the knowledge of these parameters is necessary to have opti-mum and efficient operation of the system. Therefore, fan system parameters are required to be estimated. Further, fan system parameters estimation is required to ensure the smooth system operation and to avoid any malfunctioning of the system during abnormal working conditions. In this paper, Artificial Neural Networks (ANN) approach has been used for parameter estimation of a fan system. The simulated and experimental results are compared.展开更多
文摘Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.
文摘Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a fan system is con-cerned. Basically, a fan is mainly consisting of a single phase induction motor and therefore fan system parameters are essentially the electrical parameters e.g. resistances, reactances and some load parameters (fan blades).These parame-ters often change under varying operating conditions and the knowledge of these parameters is necessary to have opti-mum and efficient operation of the system. Therefore, fan system parameters are required to be estimated. Further, fan system parameters estimation is required to ensure the smooth system operation and to avoid any malfunctioning of the system during abnormal working conditions. In this paper, Artificial Neural Networks (ANN) approach has been used for parameter estimation of a fan system. The simulated and experimental results are compared.
