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370 results on '"Samet, Haidar"'

62. Improving the Performance of Short-Term Load Forecast Using a Hybrid Artificial Neural Network and Artificial Bee Colony Algorithm

Author

Hassanpouri Baesmat, Kamran, Masoudipour, Iman, Samet, Haidar, and Electrical Energy Systems

Abstract

Tools such as short-term load forecast (STLF) play an ever-important role in the operation and planning of power systems. Improving STLF accuracy could reduce heavy penalties and missed financial opportunities. In this article, a new hybrid forecast method is proposed to improve STLF accuracy. The proposed hybrid STLF method is based on artificial neural network (ANN) and artificial bee colony (ABC) algorithms. The ABC algorithm is used to optimize the learning procedure of ANN. New load modeling is presented based on historical and weather data. Bad data elimination and calendar effects are considered in the STLF procedure. The proposed hybrid STLF method is verified by forecasting the Bushehr province demand. The results proved that the proposed method significantly improved STLF accuracy.

Published
2021

65. TS fuzzy prediction-based SVC compensation of wind farms flicker: A dual-UKF approach

Author

Ketabipour, Saeedeh, Samet, Haidar, Vafamand, Navid, and Electrical Energy Systems

Subjects
General Energy, SVC, wind farm, voltage flicker, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials
Abstract

Fast fluctuations in wind farm power produce voltage flicker in the network. One way to mitigate the flicker is to place a static VAr compensator (SVC). Due to the operating delay of SVCs, it is essential to predict the wind farm reactive power. Here, a novel fuzzy nonlinear modeling approach is suggested and used in the one-step-ahead prediction of the power characteristics. The base of the developed fuzzy modeling is the Takagi-Sugeno fuzzy representation and a dual-unscented Kalman filter (D-UKF). In other words, a nonlinear TS fuzzy system is trained online via the D-UKF. The forecasted value is used as the SVC's reference signal. A large number of the actual data gathered from a wind farm are used for the performance evaluation. These data are collected in winter and summer for different climate situations. Using the actual data a current source with changing amplitude and phase which are updated every half-cycle is used to model the wind farm. Numerical results, including the flicker indices, confirm the improvement in the SVC's performance.

Published
2021

67. Time‐varying frequency model for electric arc furnaces.

Author

Samet, Haidar, Sadeghi, Reza, and Ghanbari, Teymoor

Subjects
*TIME-varying systems, *ELECTRIC arc, *AUTOREGRESSIVE models, *STOCHASTIC processes
Abstract

Accurate modelling of the Electric arc furnace (EAF) is necessary to study its impact on the power systems. Here, a new model for the EAF is presented in which both non‐linearity and time‐varying characteristics are taken into account. The model consists of seven parallel current sources which represent the fundamental and harmonic currents. Magnitude and phase of the fundamental harmonic and magnitude of the second to seventh harmonics are considered as time‐varying which are updated every 0.01 s. To do so, the actual data recorded from eight EAFs in the Mobarakeh steel company (MSC), Isfahan, Iran are used. Autoregressive moving average (ARMA) models are employed to derive the parameters of the time‐varying model. Also, different orders and coefficients of the ARMA model are considered at each run of the model, which enables the proposed model to predict the non‐stationary and stochastic behaviours of the EAF over long‐time periods. It is shown that the proposed combined time‐varying‐harmonic model can even model the generated inter‐harmonics. To evaluate the accuracy of the proposed model, the derived power spectral density (PSD) of the EAF time‐varying parameters, the instantaneous flicker, and short‐term flicker by the proposed model are compared with the actual recorded data. [ABSTRACT FROM AUTHOR]

Published
2022
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73. DM: Dehghani Method for Modifying Optimization Algorithms

Author

Dehghani, Mohammad, primary, Montazeri, Zeinab, additional, Dehghani, Ali, additional, Samet, Haidar, additional, Sotelo, Carlos, additional, Sotelo, David, additional, Ehsanifar, Ali, additional, Malik, Om Parkash, additional, Guerrero, Josep M., additional, Dhiman, Gaurav, additional, and Ramirez-Mendoza, Ricardo A., additional

Published
2020
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88. Predictive method for improving SVC speed in electric arc furnace compensation

Author

Samet, Haidar and Parniani, Mostafa

Subjects
Electric power distribution -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The ability of static VAr compensators (SVC) to reduce the flicker caused by electric arc furnaces and other variable VAr loads depends on their speed, which is limited by delays in reactive power measurement and thyristor ignition. In this paper, a predictive method is proposed to compensate the delay time and, hence, to improve the SVC performance. Previous samples of the load reactive power are used to predict its future values. Predicted VAr is then applied as a reference for reactive power compensation. Index Terms--Electric arc furnace, flicker, prediction, static VAr compensator (SVC), variable reactive power.

Published
2007

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