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Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
Wind power is a significant alternate energy in times of energy crisis. In virtue of its intermittency and fluctuation, it poses several operational challenges to grid interfaced wind energy systems. This paper introduced autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) to forecast the hourly wind speed one to four hours ahead. The models are applied to wind...
Following the growing share of wind energy in electric power systems, several wind power forecasting techniques have been reported in the literature in recent years. In this paper, a wind power forecasting strategy composed of a feature selection component and a forecasting engine is proposed. The feature selection component applies an irrelevancy filter and a redundancy filter to the set of candidate...
This paper presents a comparison of data mining techniques for wind power forecasting in a time frame out to 15 minutes ahead. The forecasting is focused on the power generated by the wind farms and the power changes are predicted by using multivariate time series models ARMA, focus time-delay neural network (FTDNN) and a phenomenological model of the turbines. All these models are tested with real...
In this paper, we propose a high order neural network (HONN) trained with an extended Kalman filter based algorithm to predict wind speed. Due to the chaotic behavior of the wind time series, it is not possible satisfactorily to apply the traditional forecasting techniques for time series; however, the results presented in this paper confirm that HONNs can very well capture the complexity underlying...
In this paper, a novel microgrid energy trading model (METM) is proposed to determine an optimal schedule of all available units over a planning horizon so as to meet all system, plant and unit constraints, as well as meet the load and ancillary service demands. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather,...
Wind power generation increases rapidly. The available wind energy depends on the wind speed, which is a random variable. For the wind-farm operator, this poses difficulty in the system scheduling and energy dispatching, as the schedule of the wind-power availability is not known in advance. In this paper, we propose an intelligent technique for forecasting wind speed of wind turbine. This technique...
Wind energy is rapidly emerging as a substantial contributor to the electricity generation capacity of utilities around the world. While the use of wind power both adds to the electricity supply and offers significant environmental benefits as a renewable source of energy, the stochastic nature of forces that produce wind energy prevents relying on it to meet base load requirements. Intermittent availability...
This paper presents a methodology of short term generation scheduling (unit commitment) for thermal units integrated with wind energy system considering the unexpected deviation on load demand. The deviation in load demand occurs mainly due to variation in temperature which in turns yields error in load forecasting. Since the usual unit commitment (UC) scheduling as well as economic power dispatch...
This paper reports new results in adopting entropy concepts to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. It also addresses the differences relevant to power system operation between off-line and on-line training of neural networks. Real case examples are...
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