Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison
نویسندگان
چکیده
The use of wind energy for power electric systems attempts to reduce the dependence on fuel-based energy. With the aim of generating electrical power based on wind energy, it becomes necessary to model and predict wind speed. Wind speed observations are packed with outliers what makes it difficult to propose accurate predictors. This paper presents an experimental comparison of three different methods for making a Kalman filter robust to outliers in the context of one-step-ahead wind speed prediction. Two wind speed databases were used to test the predictive performance of the algorithms. The performance for all the methods is measured in terms of skewness and kurtosis for the predicted signal. The algorithms discussed worked efficiently in a sequential approach, and outperformed the standard Kalman filter. 2015 Elsevier Ltd. All rights reserved.
منابع مشابه
Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach
Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....
متن کاملRobust state estimation in power systems using pre-filtering measurement data
State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...
متن کاملA Hybrid Approach for Short-Term Forecasting of Wind Speed
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and a...
متن کاملOn-Line Nonlinear Dynamic Data Reconciliation Using Extended Kalman Filtering: Application to a Distillation Column and a CSTR
Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero m...
متن کاملImprovements in wind speed forecasts for wind power prediction purposes using Kalman filtering
This paper studies the application of Kalman filtering as a post-processing method in numerical predictions of wind speed. Two limited-area atmospheric models have been employed, with different options/capabilities of horizontal resolution, to provide wind speed forecasts. The application of Kalman filter to these data leads to the elimination of any possible systematic errors, even in the lowe...
متن کامل