Residual Generator Fuzzy Identification for Wind Farm Fault Diagnosis

2014 ◽  
Vol 47 (3) ◽  
pp. 4310-4315 ◽  
Author(s):  
S. Simani ◽  
S. Farsoni ◽  
P. Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Farm ◽  
Fuzzy Identification ◽  
Residual Generator

scholarly journals Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

Machines ◽  
2014 ◽  
Vol 2 (4) ◽  
pp. 275-298 ◽  
Author(s):  
Silvio Simani ◽  
Saverio Farsoni ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Identification ◽  
Residual Generator

scholarly journals Residual generator fuzzy identification for automotive diesel engine fault diagnosis

2013 ◽  
Vol 23 (2) ◽  
pp. 419-438 ◽  
Author(s):  
Silvio Simani
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Control Strategies ◽  
Direct Consequence ◽  
Fault Detection And Isolation ◽  
Fuzzy Identification ◽  
Overall Performance ◽  
Residual Generator ◽  
Residual Generation

Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

scholarly journals Data-Driven Adaptive Observer for Fault Diagnosis

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Shen Yin ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Fault Diagnosis ◽  
Process Model ◽  
Large Scale ◽  
Process Models ◽  
Physical Models ◽  
Adaptive Observer ◽  
Data Driven ◽  
Process Data ◽  
Tank System ◽  
Residual Generator

This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

scholarly journals Wind Turbine Anomaly Identification Based on Improved Deep Belief Network with SCADA Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiafei Long ◽  
Shengqing Li ◽  
Xiwen Wu ◽  
Zhao Jin
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Domain Knowledge ◽  
Wind Farm ◽  
Prediction Models ◽  
Moving Average ◽  
Pearson Correlation ◽  
Whale Optimization

This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.

scholarly journals Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Lu Wei ◽  
Zheng Qian ◽  
Yan Pei ◽  
Jingyue Wang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Euclidean Distance ◽  
Wind Farm ◽  
Hamming Distance ◽  
Fault Identification ◽  
Farm Operators ◽  
One Year ◽  
The Right ◽  
Better Than

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.

Research of Single Component Shock Methods in Wind Turbine Fault Diagnosis

2013 ◽  
Vol 805-806 ◽  
pp. 303-311
Author(s):  
Ning Jia ◽  
Tian Xia Zhang ◽  
Yuan Sheng Li ◽  
Tao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Farm ◽  
Vibration Signal ◽  
Single Component ◽  
Vibration Monitoring ◽  
Generator System ◽  
Diagnosis Method ◽  
Demodulation Method ◽  
Fault Characteristic

The structure of the wind turbine generator system is complex and it is difficult to identify the fault signals because of fault frequency aliasing on the vibration characteristics. The wind turbine fault diagnosis method is raised on single component shock to solve the vibration signal feature extraction during the wind turbines operating. Based on the principle of Hilbert envelope demodulation, this envelope demodulation method is presented for the single IMF component which contains shock fault characteristic frequency to solve the possible problem which fault Frequency is difficult to identify when the original signal is directly asked to envelope. This method has been applied and verified when a wind farm CSC-855W wind turbine vibration monitoring device was presented. The results show that compared with the traditional envelope demodulation method, by this method wind turbine fault characteristic can be more effectively and directly extracted and the accuracy of fault diagnosis can be improved. It is of great practical value.

Fault Diagnosis With a Model-Based Recurrent Neural Network

2000 ◽  
Author(s):  
Chengyu Gan ◽  
Kourosh Danai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
State Space Model ◽  
Black Box ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Starting Point ◽  
Residual Generator ◽  
Structure Assignment

Abstract The utility of a model-based recurrent neural network (MBRNN) is demonstrated in fault diagnosis. The MBRNN can be formatted according to a state-space model. Therefore, it can use model-based fault detection and isolation (FDI) solutions as a starting point, and improve them via training by adapting them to plant nonlinearities. In this paper, the application of MBRNN to the IFAC Benchmark Problem is explored and its performance is compared with ‘black box’ neural network solutions. For this problem, the MBRNN is formulated according to the Eigen-Structure Assignment (ESA) residual generator developed by Jorgensen et al. [1]. The results indicate that the MBRNN provides better results than ‘black box’ neural networks, and that with training it can perform better than the ESA residual generator.

scholarly journals LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

2018 ◽  
Vol 8 (10) ◽  
pp. 1816 ◽  
Author(s):  
Zhimin Yang ◽  
Yi Chai ◽  
Hongpeng Yin ◽  
Songbing Tao
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Synchronous Generator ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Permanent Magnet Synchronous Generator ◽  
Multiple Sensor ◽  
Residual Generator

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.

scholarly journals A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 829
Author(s):  
Quanqing Yu ◽  
Changjiang Wan ◽  
Junfu Li ◽  
Rui Xiong ◽  
Zeyu Chen
Keyword(s):  
Fault Diagnosis ◽  
Open Circuit Voltage ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Open Circuit ◽  
Sensor Data ◽  
Battery Management System ◽  
Sensor Fault ◽  
Model Based ◽  
Residual Generator

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

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