Optimum Setting Strategy for WTGS by Using an Adaptive Neuro-Fuzzy Inference System

Yang Hu,Jizhen Liu,Zongli Lin

doi:10.4236/epe.2013.54b078

Abstract

With the popularization of wind energy, the further reduction of power generation cost became the critical problem. As to improve the efficiency of control for variable speed Wind Turbine Generation System (WTGS), the data-driven Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to establish a sensorless wind speed estimator. Moreover, based on the Supervisory Control and Data Acquisition (SCADA) System, the optimum setting strategy for the maximum energy capture was proposed for the practical operation process. Finally, the simulation was executed which suggested the effectiveness of the approaches.

Highlights

IntroductionWind energy is growing rapidly [1, 2]. The crucial problem of popularizing wind energy has become the further reduction of power generation cost
Nowadays, wind energy is growing rapidly [1, 2]
Based on the Supervisory Control and Data Acquisition (SCADA) System, the optimum setting strategy for the maximum energy capture was proposed for the practical operation process

Summary

Introduction

Wind energy is growing rapidly [1, 2]. The crucial problem of popularizing wind energy has become the further reduction of power generation cost. The higher efficiency and more optimal operation for wind power generation are required. Many anemometers have to be used to measure the wind speed which can derive the optimum setting values of rotor speed and generator power. In order to improve the training efficiency and accuracy, the ANFIS was adopted which fully combined the advantages of T-S fuzzy system and neuro-network and is very useful for data-driven modeling. Using the wind speed estimator and the measured data source in the SCADA system for wind power generation process, the optimum setting strategy based on measured value is given.

System Analysis

Algorithms Application

Wind Speed Estimation

Optimum Setting Strategy

Conclusions