Next Article in Journal
Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation
Next Article in Special Issue
A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter
Previous Article in Journal
Determination of Cost-Effective Energy Efficiency Measures in Buildings with the Aid of Multiple Indices
Previous Article in Special Issue
Design and Experimental Verification of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2018, 11(1), 193; https://doi.org/10.3390/en11010193

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

Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China
*
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 5 January 2018 / Accepted: 10 January 2018 / Published: 12 January 2018
View Full-Text   |   Download PDF [4147 KB, uploaded 18 January 2018]   |  

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. View Full-Text
Keywords: wind energy conversion system; scenario tree; stochastic model predictive control; fault-tolerant control; conditional value at risk; Min-Max wind energy conversion system; scenario tree; stochastic model predictive control; fault-tolerant control; conditional value at risk; Min-Max
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Shi, Y.-T.; Xiang, X.; Wang, L.; Zhang, Y.; Sun, D.-H. Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System. Energies 2018, 11, 193.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top