Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

Yuan Zhang,Li Wang,Yun-Tao Shi,De-Hui Sun,Xiang Xiang

doi:10.3390/en11010193

Yuan Zhang, Li Wang + Show 3 more

Open Access

https://doi.org/10.3390/en11010193

Copy DOI

Journal: Energies	Publication Date: Jan 12, 2018
Citations: 7	License type: CC BY 4.0

Affiliation: North China University of Technology

Abstract

Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC) fault-tolerant controller with the Conditional Value at Risk (CVaR) objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC) problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

Highlights

Wind energy is drawing considerable attention as an important kind of green energy [1].Wind turbines with variable speed and pitch angle have become the main Wind Energy ConversionSystem (WECS) since they have the advantages of maximizing wind energy capture and providing stable output power
To solve the fault tolerance control problem of wind energy conversion systems (WECSs) with actuator probability faults, this paper proposes a scenario-based stochastic model predictive control (SMPC) controller with the
The Markov jump linear model of the wind turbine can be formulated with the help of the probability information of the wind

Summary

Introduction

Wind energy is drawing considerable attention as an important kind of green energy [1]. The main limitation of the current research on WECS modeling and fault-tolerant control is that it does not provide a thorough description of the stochastic and nonlinear switching dynamic characteristics of wind turbines at random wind speeds. Model Predictive Control (MPC) is an advanced fault-tolerant control method since it has many benefits arising from its features, such as prediction model [21,22,23], constraints [24,25,26], objective function [23,27], etc Based on these features, the MPC controller can effectively deal with the influence on a WECS suffering from a fault. To improve the fault-tolerant control performance, the CVaR objective function is adopted in the scenario-based SMPC controller.

Markov Jump Linear Model of Wind Energy Conversion System

Markov Transition Matrix of the Wind Speed

Aerodynamics Model

Drive Train Model

The Dynamics of the Wind Energy Conversion System

Discrete Markov Jump Linear Model of Wind Energy Conversion System

Scenario Tree Design

Control Problem Formulation

Design of Stochastic Model Predictive Fault Tolerant Controller

Actuator Failure of Wind Energy Conversion System

Design of CVaR Fault Tolerant Controller

Calculate corresponding controllers Ci