Improve Wind Turbine Reliability Research Articles

A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks.

Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances

An increasing number of deep autoencoder-based algorithms for intelligent condition monitoring and anomaly detection have been reported in recent years to improve wind turbine reliability. However, most existing studies have only focused on the precise modeling of normal data in an unsupervised manner; few studies have utilized the information of fault instances in the learning process, which results in suboptimal detection performance and low robustness. To this end, we first developed a deep autoencoder enhanced by fault instances, that is, a triplet-convolutional deep autoencoder (triplet-Conv DAE), jointly integrating a convolutional autoencoder and deep metric learning. Aided by fault instances, triplet-Conv DAE can not only capture normal operation data patterns but also acquire discriminative deep embedding features. Moreover, to overcome the difficulty of scarce fault instances, we adopted an improved generative adversarial network-based data augmentation method to generate high-quality synthetic fault instances. Finally, we validated the performance of the proposed anomaly detection method using a multitude of performance measures. The experimental results show that our method is superior to three other state-of-the-art methods. In addition, the proposed augmentation method can efficiently improve the performance of the triplet-Conv DAE when fault instances are insufficient.

In-Service Wind Turbine DFIG Diagnosis Using Current Signature Analysis

Operation and maintenance costs of wind power plants represent a significant share of the total expenditure of a wind farm project. It is therefore vital to optimize maintenance activities in order to reduce costs while improving wind turbine reliability and availability. The induction generator is a major contributor to failure rates and downtime of wind turbines, where doubly fed induction generators (DFIGs) are the dominant variable-speed technology employed. Actual data from operating wind turbines is seldom presented in the scientific literature. This paper analyzes two in-service DFIGs from wind turbines operating in a Spanish wind power plant. One of the generators under study reported excessive temperature on the rotor windings, while the other did not. In order to achieve a diagnosis for the reported excess of temperature, current signature analyses were performed on both machines. Fault-related frequency components were identified in the current spectra of the faulty machine, and were compared against the healthy one. A diagnosis was achieved for the faulty machine: Dynamic eccentricity caused by a rotor fault was identified as the cause of the excessive temperature in the rotor windings.

Research and Development of the Wind Turbine Reliability

With the rapid development of wind power industry, the wind turbine reliability has become a hotspot in wind power research. The failure modes and research progress of wind turbine reliability both at home and abroad were analyzed. The failure modes, failure causes and detection methods of some key components in the wind turbines were summarized. Also, the frequently used methods of reliability analysis and research status of wind turbine reliability were analyzed. Following this, research focuses, methods and measures to improve wind turbine reliability were presented. We also shed light on the condition monitoring and assessment process with condition monitoring system and supervisory control and data acquisition. It is of great significance to reduce the cost of operation and maintenance and to improve the safety of wind turbines.

Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data

The fast-growing wind power industry faces the challenge of reducing operation and maintenance (O&M) costs for wind power plants. Predictive maintenance is essential to improve wind turbine reliability and prolong operation time, thereby reducing the O&M cost for wind power plants. This study presents a solution for predictive maintenance of wind turbine generators. The proposed solution can: (1) predict the remaining useful life (RUL) of wind turbine generators before a fault occurs and (2) diagnose the state of the wind turbine generator when the fault occurs. Moreover, the proposed solution implies low-deployment costs because it relies solely on the information collected from the widely available supervisory control and data acquisition (SCADA) system. Extra sensing hardware is needless. The proposed solution has been deployed and evaluated in two real-world wind power plants located in China. The experimental study demonstrates that the RUL of the generators can be predicted 18 days ahead with about an 80% prediction accuracy. When faults occur, the specific type of generator fault can be diagnosed with an accuracy of 94%.

A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions

Although Permanent Magnet Synchronous Generator (PMSG) wind turbines (WTs) mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

Using Improved Power Electronics Modeling and Turbine Control to Improve Wind Turbine Reliability

Improving offshore wind turbine reliability is a key industry goal to improve the availability of this renewable energy generation source. The semiconductor devices in the wind turbine power converter are traditionally considered as the most sensitive and important components to achieve this and managing their thermomechanical stressing is vital, since this is one of their principal long-term aging mechanisms. Conventional deterministic reliability prediction methods used in industrial applications are not suitable for wind turbine applications, due to the stochastic nature of the wind speed. This paper develops an electrothermal model of the power devices, which is integrated with a wind turbine system model for the investigation of power converter thermal cycling under various operating conditions. The model has been developed to eliminate the problems of pulse width modulation switching, substantially reducing simulation time. The model is used to improve the current controller tuning method to reduce thermal stresses suffered by the converter during a grid fault. The model is finally used to design a control method to alleviate a key problem of the doubly fed induction generator—severe thermal cycling caused during operation near synchronous speed.

Current-Based Mechanical Fault Detection for Direct-Drive Wind Turbines via Synchronous Sampling and Impulse Detection

Online fault detection is an effective means to improve wind turbine reliability and performance and reduce wind turbine downtime and operating and maintenance costs. Current-based wind turbine fault detection techniques have received more and more attention in academia and industry due to their nonintrusive character and economic advantages. This paper presents a novel computationally efficient high-resolution wideband synchronous sampling algorithm for the mechanical fault detection of variable-speed direct-drive wind turbines (i.e., no gearbox) only using nonstationary generator stator current measurements. The proposed algorithm synchronously resamples the current signals such that the varying characteristic frequencies of the excitations generated by wind turbine faults in the current signals become constant values. An impulse detection algorithm is then proposed to detect the faults by identifying the excitations from the frequency spectra of the synchronously sampled stator current signals. Experimental studies are carried out to demonstrate the effectiveness of the proposed algorithms for the detection of rotor eccentricity and bearing faults of a direct-drive wind turbine operating in variable-speed conditions.

Wind turbine SCADA alarm analysis for improving reliability

ABSTRACTPrevious research for detecting incipient wind turbine failures, using condition monitoring algorithms, concentrated on wind turbine Supervisory Control and Data Acquisition (SCADA) signals, such as power output, wind speed and bearing temperatures, using power‐curve and temperature relationships. However, very little research effort has been made on wind turbine SCADA alarms. When wind turbines are operating in significantly sized wind farms, these alarm triggers are overwhelming for operators or maintainers alike because of large number occurring in a 10 min SCADA period. This paper considers these alarms originating in two large populations of modern onshore wind turbines over a period of 1–2 years. First, an analysis is made on where the alarms originate. Second, a methodology for prioritizing the alarms is adopted from an oil and gas industry standard to show the seriousness of the alarm data volume. Third, two methods of alarm analysis, time‐sequence and probability‐based, are proposed and demonstrated on the data from one of the wind turbine populations, considering pitch and converter systems with known faults. The results of this work show that alarm data require relatively little storage yet provide rich condition monitoring information. Both the time‐sequence and probability‐based analysis methods have the potential to rationalize and reduce alarm data, providing valuable fault detection, diagnosis and prognosis from the conditions under which the alarms are generated. These methods should be developed and integrated into an intelligent alarm handling system for wind farms, aimed at improving wind turbine reliability to reduce downtime, increase availability and leading to a well‐organized maintenance schedule. Copyright © 2011 John Wiley & Sons, Ltd.