Using Neural Networks to Estimate Wind Turbine Power Generation

This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach

Wind Turbine Power Estimation by Neural Networks with Kalman Filter Training on a SIMD Parallel Machine

We use a multi-layer perceptron (MLP) network to estimate wind turbine power generation. Wind power can be influenced by many factors such as wind speeds, wind directions, terrain, air density, vertical wind profile, time of a day, and seasons of a year. It is usually important to train a neural network with multiple influence factors and big training data set. We have parallelized the Extended Kalman Filter (EKF) training algorithm, which can provide fast training even for large training data sets. The MLP network is then trained with the consideration of various possible factors, which can cause influence on turbine power production. The performance of the trained network is studied from the point of view of information presented to the network through network inputs regarding to different affecting factors and large training data set covering all the seasons of a year

Extended Kalman Filter Taining of Neural Networks on a SIMD Parallel Mchine

The extended Kalman filter (EKF) algorithm has been shown to be advan- tageous for neural network trainings. However, unlike the backpropagation (BP), many matrix operations are needed for the EKF algorithm and therefore greatly increase the computational complexity. This paper presents a method to do the EKF training on a SIMD parallel machine. We use a multistream decoupled extended Kalman filter (DEKF) training algorithm which can provide efficient use of the parallel resource and more improved trained network weights. From the overall design consideration of the DEKF algorithm and the consideration of maximum usage of the parallel resource, the multistream DEKF training is realized on a MasPar SIMD parallel machine. The performance of the parallel DEKF training algorithm is studied. Comparisons are performed to investigate pattern and batch-form trainings for both EKF and BP training algorithms

Neural Network for Wind Power Generation with Compressing Function

The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to estimate this changing power. In this paper, the characteristics of wind power generation are studied and a neural network is used to estimate it. We use real wind farm data to demonstrate a neural network solution for this problem, and show that the network can estimate power even in changing wind conditions

Multi-Stream Extended Kalman Filter Training of Neural Networks on a SIMD Parallel Machine

The extended Kalman filter (EKF) algorithm has been shown to be advantageous for neural network trainings. This paper presents a method to do the EFK training on a SIMB parallel machine. We use multi-stream decoupled extended Kalman filter (DEKF) training algorithm which can provide more improved trained network weights and efficient use of the parallel resource. The performance of the parallel DEKF training algorithm is studied and simulation results for the estimation of the wind power using neural networks are provided

Comparative Analysis of Regression and Neural Network Models for Wind Power

This paper compares regression and neural network models for prediction of wind turbine power. The two techniques are first compared theoretically. Then, parameter estimates for the regression model and training of the neural network are completed and the performances of the two models are compared with wind farm data. The regression model is function dependent but the neural network model obtains its prediction through learning. For most cases, the neural network outperforms regression