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An augmented sliding mode observer is proposed to solve the actuator fault of an uncertain wind energy conversion system (WECS), which can estimate the system state and reconstruct the actuator faults. Firstly, the mathematical model of the WECS is established, and the non-linear term in the state equation is separated as the uncertain part of the system. Then, the states of the system are augmented, and the actuator fault is considered as part of the augmented state. The augmented sliding mode observer is designed to estimate the system state and actuator fault. A robust fault-tolerant controller is designed to ensure the reliable input of the WECS, maintain the stability of the fault system and maximize the acquisition of wind energy. The numerical simulation results verify the effectiveness of the control strategy.
Xu Wang; Yanxia Shen. Fault Tolerant Control of DFIG-Based Wind Energy Conversion System Using Augmented Observer. Energies 2019, 12, 580 .
AMA StyleXu Wang, Yanxia Shen. Fault Tolerant Control of DFIG-Based Wind Energy Conversion System Using Augmented Observer. Energies. 2019; 12 (4):580.
Chicago/Turabian StyleXu Wang; Yanxia Shen. 2019. "Fault Tolerant Control of DFIG-Based Wind Energy Conversion System Using Augmented Observer." Energies 12, no. 4: 580.
A robust sliding mode observer (SMO) is proposed to achieve multiple fault reconstruction for a wind energy conversion system (WECS) with simultaneous actuator and sensor faults. Firstly, the state equation of the WECS is established. The orthogonal transformation matrix and a post filter are introduced, and a new augmented system is constructed; then, the sensor fault is converted into an actuator fault to diagnose. The fault information is collected by the equivalent output control, and the simultaneous reconstruction algorithm of the sensor fault and the actuator fault is given. Through compensation control, the reliable control input of the WECS is guaranteed, and the function of active fault tolerant control for multiple faults is achieved. Simulation experiments show that the proposed method can accurately reconstruct the actuator and sensor faults, and maximum wind energy capture can be achieved by active fault-tolerant control.
Xu Wang; Yanxia Shen. Fault-Tolerant Control Strategy of a Wind Energy Conversion System Considering Multiple Fault Reconstruction. Applied Sciences 2018, 8, 794 .
AMA StyleXu Wang, Yanxia Shen. Fault-Tolerant Control Strategy of a Wind Energy Conversion System Considering Multiple Fault Reconstruction. Applied Sciences. 2018; 8 (5):794.
Chicago/Turabian StyleXu Wang; Yanxia Shen. 2018. "Fault-Tolerant Control Strategy of a Wind Energy Conversion System Considering Multiple Fault Reconstruction." Applied Sciences 8, no. 5: 794.
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.
Yanxia Shen; Xu Wang; Jie Chen. Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals. Applied Sciences 2018, 8, 185 .
AMA StyleYanxia Shen, Xu Wang, Jie Chen. Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals. Applied Sciences. 2018; 8 (2):185.
Chicago/Turabian StyleYanxia Shen; Xu Wang; Jie Chen. 2018. "Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals." Applied Sciences 8, no. 2: 185.
Constructing a reliable affinity matrix is crucial for spectral segmentation. In this paper, we define a technique to create a reliable affinity matrix for the application to spectral segmentation. We propose an affinity model based on the minimum barrier distance (MBD). First, the image is over-segmented into superpixels; then the subset of the pixels, located in the center of these superpixels, is used to compute the MBD-based affinities of the original image, with particular care taken to avoid a strong boundary, as described in the classical model. To deal with images with faint object and random or “clutter” background, we present gradient data that are integrated with the MBD data. To capture different perceptual grouping cues, the completed affinity model includes MBD, color, and spatial cues of the image. Finally, spectral segmentation is implemented at the superpixel level to provide an image segmentation result with pixel granularity. Experiments using the Berkeley image segmentation database validate the effectiveness of the proposed method. Covering, PRI, VOI, and the F-measure are used to evaluate the results relative to several state-of-the-art algorithms.
Jing Mao Zhang; Yan Xia Shen. Spectral segmentation via minimum barrier distance. Multimedia Tools and Applications 2017, 76, 25713 -25729.
AMA StyleJing Mao Zhang, Yan Xia Shen. Spectral segmentation via minimum barrier distance. Multimedia Tools and Applications. 2017; 76 (24):25713-25729.
Chicago/Turabian StyleJing Mao Zhang; Yan Xia Shen. 2017. "Spectral segmentation via minimum barrier distance." Multimedia Tools and Applications 76, no. 24: 25713-25729.