The Real-Time Wind Turbine Fault Diagnosis Method Based on Safety Evaluation Model

2014 ◽  
Vol 953-954 ◽  
pp. 453-457
Author(s):  
Ming Li Yang ◽  
San Ming Liu ◽  
Yong Hai Lv ◽  
Yang Zou ◽  
Guo Dong Ding
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Real Time ◽  
Wind Turbines ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Mean Value ◽  
Safety Level ◽  
Significance Level ◽  
The Real

In order to determine the best maintenance time of wind turbines and identify the fault type when it is the best time to do the diagnosis work immediately. The establishment of 4-level safety status model for critical parts of wind turbines, based on wind turbine parts’ significance level, was proposed. According to the corresponding safety level of the wind turbines in real-time working status, you can decide whether the wind turbine needs diagnosis at the time or not. Therefore, we should take measures to monitor the real-time working conditions of the wind turbine’s critical parts, confirming whether the critical part need the fault diagnosis analysis or not according to its real-time working safety status. If it is the right time, then the corresponding fault diagnosis process will be initiated, through which the real online fault diagnosis can be achieved. The multi-scale wavelet decomposition and Hilbert transformation was employed to get the useful parameters such as amplitude, effective value, mean value, kurtosis value and so on of the corresponding waveform to confirm the concrete diagnosis type.

Remote Wireless Monitoring System for Wind Turbine

2013 ◽  
Vol 385-386 ◽  
pp. 614-617
Author(s):  
Lan Xin Hu ◽  
Hai Bo Feng ◽  
Hai Meng Yin
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Monitoring System ◽  
Wind Turbines ◽  
The Real ◽  
Wireless Monitoring ◽  
Real Time Information ◽  
Time Information

This paper probes into the design of the remote wireless monitoring system for wind turbine, based on STC89C54RD+ microprocessor. TC35 produced by Siemens is used to send and receive information. Through this we can obtain the Real-time information of the temperature and vibration of the wind turbines.

scholarly journals Impact of Typhoons on Floating Offshore Wind Turbines: A Case Study of Typhoon Mangkhut

2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Jiawen Li ◽  
Jingyu Bian ◽  
Yuxiang Ma ◽  
Yichen Jiang
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Safety Evaluation ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Motion Response ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Coupling Motion

A typhoon is a restrictive factor in the development of floating wind power in China. However, the influences of multistage typhoon wind and waves on offshore wind turbines have not yet been studied. Based on Typhoon Mangkhut, in this study, the characteristics of the motion response and structural loads of an offshore wind turbine are investigated during the travel process. For this purpose, a framework is established and verified for investigating the typhoon-induced effects of offshore wind turbines, including a multistage typhoon wave field and a coupled dynamic model of offshore wind turbines. On this basis, the motion response and structural loads of different stages are calculated and analyzed systematically. The results show that the maximum response does not exactly correspond to the maximum wave or wind stage. Considering only the maximum wave height or wind speed may underestimate the motion response during the traveling process of the typhoon, which has problems in guiding the anti-typhoon design of offshore wind turbines. In addition, the coupling motion between the floating foundation and turbine should be considered in the safety evaluation of the floating offshore wind turbine under typhoon conditions.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2184
Author(s):  
Andrea Mannelli ◽  
Francesco Papi ◽  
George Pechlivanoglou ◽  
Giovanni Ferrara ◽  
Alessandro Bianchini
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Power Output ◽  
Discrete Wavelet ◽  
Smoothing Technique ◽  
The Real ◽  
Power Smoothing ◽  
Li Ion

Energy Storage Systems (EES) are key to further increase the penetration in energy grids of intermittent renewable energy sources, such as wind, by smoothing out power fluctuations. In order this to be economically feasible; however, the ESS need to be sized correctly and managed efficiently. In the study, the use of discrete wavelet transform (Daubechies Db4) to decompose the power output of utility-scale wind turbines into high and low-frequency components, with the objective of smoothing wind turbine power output, is discussed and applied to four-year Supervisory Control And Data Acquisition (SCADA) real data from multi-MW, on-shore wind turbines provided by the industrial partner. Two main research requests were tackled: first, the effectiveness of the discrete wavelet transform for the correct sizing and management of the battery (Li-Ion type) storage was assessed in comparison to more traditional approaches such as a simple moving average and a direct use of the battery in response to excessive power fluctuations. The performance of different storage designs was compared, in terms of abatement of ramp rate violations, depending on the power smoothing technique applied. Results show that the wavelet transform leads to a more efficient battery use, characterized by lower variation of the averaged state-of-charge, and in turn to the need for a lower battery capacity, which can be translated into a cost reduction (up to −28%). The second research objective was to prove that the wavelet-based power smoothing technique has superior performance for the real-time control of a wind park. To this end, a simple procedure is proposed to generate a suitable moving window centered on the actual sample in which the wavelet transform can be applied. The power-smoothing performance of the method was tested on the same time series data, showing again that the discrete wavelet transform represents a superior solution in comparison to conventional approaches.

scholarly journals Real-Time Safety Evaluation for Slope during Construction Using Numerical Forecast and Sensor Monitoring Platform

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2978 ◽  
Author(s):  
Sherong Zhang ◽  
Dejun Hou ◽  
Chao Wang ◽  
Xuexing Cao ◽  
Fenghua Zhang ◽  
...  
Keyword(s):  
Numerical Model ◽  
Real Time ◽  
Evaluation System ◽  
Large Scale ◽  
Safety Evaluation ◽  
The Real ◽  
Study Results ◽  
Data Feedback ◽  
Numerical Forecast ◽  
Slope Safety

Geology uncertainties and real-time construction modification induce an increase of construction risk for large-scale slope in hydraulic engineering. However, the real-time evaluation of slope safety during construction is still an unsettled issue for mapping large-scale slope hazards. In this study, the real-time safety evaluation method is proposed coupling a construction progress with numerical analysis of slope safety. New revealed geological information, excavation progress adjustment, and the support structures modification are updating into the slope safety information model-by-model restructuring. A dynamic connection mapping method between the slope restructuring model and the computable numerical model is illustrated. The numerical model can be generated rapidly and automatically in database. A real-time slope safety evaluation system is developed and its establishing method, prominent features, and application results are briefly introduced in this paper. In our system, the interpretation of potential slope risk is conducted coupling dynamic numerical forecast and monitoring data feedback. The real case study results in a comprehensive real-time safety evaluation application for large slope that illustrates the change of environmental factor and construction state over time.

scholarly journals Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Large Scale ◽  
Support Vector ◽  
Effective Response ◽  
Support Vector Regression Model ◽  
Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Research on Fault Diagnosis Monitoring System of Natural Gas Compressor

2014 ◽  
Vol 1048 ◽  
pp. 541-544
Author(s):  
Huan Huan Nie ◽  
Zhen Lu Wu ◽  
Guo Yan Yu
Keyword(s):  
Fault Diagnosis ◽  
Natural Gas ◽  
Real Time ◽  
Monitoring System ◽  
Register Data ◽  
Parameter Method ◽  
Time Requirement ◽  
The Real ◽  
Technical Framework

In this paper the structure of natural gas compressor monitoring system was designed. All the register data in PLC could be obtained to meet the real-time requirement by an instruction of PPI protocol. The technical framework of system was introduced simply, and the parameter method for measuring was put forward in fault diagnosis of natural gas compressor.

A Discussion about FBG Sensors Application of Crane SHM System

2013 ◽  
Vol 336-338 ◽  
pp. 185-191
Author(s):  
Xiao Peng Xie ◽  
Dong Hui Wang ◽  
Guo Jian Huang ◽  
Xin Hua Wang
Keyword(s):  
Finite Element ◽  
Real Time ◽  
Failure Modes ◽  
Safety Evaluation ◽  
Structural Condition ◽  
Sensor Data ◽  
Gantry Crane ◽  
The Real ◽  
Time Stress ◽  
Fbg Sensors

The arrangement positions and the quantities are different for different types of cranes. In order to make suitable decision, much investigate and survey was done at preliminary stage, and we know that the flange connected gate legs and turntables, the connections between load-bearing beam and rotary column under the engine room and the connections between jib and turntable are easy to lose efficient, and their mainly failure modes are cracks. By the method of finite element, 32 sensors (including 21 welding strain FBG sensors and 11 temperature FBG sensors) were used after doing much investigate and survey and finite element modeling analysis, which are arranged in different places of a gantry crane of MQ2533, for real-time structure health monitoring. This method makes the sensor data obtained more realistically reflects the crane structural condition, which provides reliable data support for crane safety monitoring and safety evaluation. Then a software platform is developed to monitor the real-time stress. If the real-time stress exceeds the allowable stress, it issues an alarm signal to the operator.

Fault Diagnosis of Wind Turbines Based on Improved Neural Network

2014 ◽  
Vol 978 ◽  
pp. 78-83
Author(s):  
Qiang Lan ◽  
Peng Da Zhao ◽  
Man Li Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Feature Vector ◽  
Wind Turbine Gearbox ◽  
Vibration Sensors

The gearbox is an important module of wind turbine. In order to diagnosis the fault of wind turbine gearbox, a method based on the improved neural network is proposed. According to the characteristics of the wind turbine gearbox, several vibration sensors are set in the gearbox, so as to acquire the feature vector of gearbox. After training, the improved neural network is verified with some test samples. The result proved that the method is suitable for fault diagnosis in gearbox of wind turbine.. Keywords: wind turbine gearbox, fault diagnosis, particle swam, neural network.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet and Improved BP Neural Network for Wind Turbines

2013 ◽  
Vol 347-350 ◽  
pp. 117-120
Author(s):  
Zhao Ran Hou
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Transmission System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Vibration signal was a carrier of fault features of the wind turbine transmission system, it can reflect most of the fault information of the wind turbine transmission system. According to the frequency domain features of the roller bearing fault, wavelet packet transform for feature extraction was proposed as the characteristics of wind turbines in the presence of a large number of transient and non-stationary signals. The characteristics of wavelet packet was analyzed, combined with the wind turbines in the rolling bearing fault characteristic vibration extraction methods, the rolling bearing fault diagnosis was realized through the wavelet packet decomposition and reconstruction, the procedure was given. The simulation result shows that this application can reflect relationship of the failure characteristics and frequency domain feature vectors, also the nonlinear mapping ability of neural networks was played and the fault diagnosis capability enhanced.

Sign in / Sign up

Export Citation Format

Share Document