Nonlinear model identification of wind turbine with a neural network

A nonlinear model of wind turbine based on a neural network (NN) is described for the estimation of wind turbine output power. The proposed nonlinear model uses the wind speed average, the standard deviation and the past output power as input data. An anemometer with a sampling rate of one second provides the wind speed data. The NN identification process uses a 10-min average speed with its standard deviation. The typical local data collected in September 2000 is used for the training, while those of October 2000 are used to validate the model. The optimal NN configuration is found to be 8-5-1 (8 inputs, 5 neurons on the hidden layer, one neuron on the output layer). The estimated mean square errors for the wind turbine output power are less than 1%. A comparison between the NN model and the stochastic model mostly used in the wind power prediction is done. This work is a basic tool to estimate wind turbine energy production from the average wind speed.

I. Introduction

WIND TURBINE (WT) and photovoltaic (PV) arrays are considered today as energy sources which allow electrical production with minimum environment perturbations. These energy sources are especially suitable for remote areas, which are not connected to the conventional electrical grid. Since these energy sources are intermittent, a suitable energy storage device is required for long-term storage. The Hydrogen Research Institute (HRI) has developed a stand-alone renewable energy system (RES) by using such a device [1]. In this system, when the RES's load power demand is less than the primary sources (WT and PV array) power production, the excess energy is used to produce hydrogen via an electrolyzer (Fig. 1). When the RES's load power exceeds the available power from primary sources, a fuel cell (FC) is used to produce electricity from the stored hydrogen. The WT is installed on a 30-m tower. All components of the system are connected through power conditioning devices on a dc bus. In this system, batteries are used as an energy buffer (i.e., short time storage). Fig. 1. Functional description of the energy production system.