Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Huang, Shoudao; Wu, Xuan; Xiao, Lan; Gao, Jian; He, Yunze

doi:10.1007/s11465-017-0442-1

Cited by 17 publications

(7 citation statements)

References 124 publications

(155 reference statements)

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“…Resilient control is a technique to minimize the effects from the faulty components or unexpected disruptions so that the wind turbine system can work with tolerant performance degradation under some abnormal conditions. During the past two decades, essential studies were carried out in the area of monitoring, fault diagnosis, prognosis, and resilient control for wind energy systems, which were well documented in the survey papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Table 1 presents some existing survey papers categorized by years and topics.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], machine learning based condition monitoring and diagnosis approaches were surveyed. In [17], an overview was presented for condition monitoring, fault diagnosis, and operation control (including online maintenance and fault tolerant control) on electric power conversion systems in direct-drive wind turbines. In [18], major failures in off-shore wind turbines such as grid failure, yaw system failure, electrical control failure, hydraulic failure, blade failure, and gearbox failure, were discussed and possible prognosis approaches for the failures were commented.…”

Section: Introductionmentioning

confidence: 99%

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An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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“…Distributed wind power generation has been widely adopted to meet the increasing demand for electrical power due to its clean and renewable characteristics. Due to the advantages of high efficiency, low failure rate and low maintenance cost, direct-drive wind power systems based on permanent magnet synchronous generators (PMSGs) have become popular in recent years [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of PI Plus Repetitive Control for Grid-Side Converters of Direct-Drive Wind Power Systems Considering the Effect of Hardware Sampling Circuits

Lyu

Hong

et al. 2020

IEEE Access

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In inverters based on a single proportional-integral (PI) or deadbeat (DB) controller, an inherent resonance peak may emerge near their current loop cutoff frequency, which results in harmonic amplification or even resonance. Additionally, inappropriate filter circuits implemented in sampling circuits may result in the expansion of the resonance peak. Thus, this paper further investigates the influence of the sampling circuits on a PI-or DB-based control loop. Then, the RC filter in the sampling circuit is designed to reduce the inherent resonance peak. Moreover, a compound control strategy based on an improved repetitive controller (IRC) plus a PI controller is adopted for the grid-side converter of a direct-drive wind system. This strategy enhances the harmonic and reactive compensation performance by reconstructing the internal model of the classic repetitive controller (CRC) and limiting the bandwidth of the PI-based loop to a low level. The parameters of the presented IRC-plus-PI control are designed for the purpose of resonance peak elimination and system stability. Furthermore, the non-integer delay problem is solved with an inserted fraction compensator (FC), which plays the role of a low-pass filter in the IRC. Finally, the feasibility and effectiveness of the presented control method is verified by the experimental results.

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“…Reference [2] mainly aims to survey the most recent condition and performance monitoring approaches of WTs with the primary focus on blade, gearbox, generator, braking system, and rotor. However, the more recent trend in this type of literature review is to focus on a specific WT sub-assembly: the bearings and planetary gearbox [3,4], the generator and power converter [5,6], the blades [7,8], etc. Most of these methods, which focus on a specific part of the WT, require the choice of the most appropriate sensors, their advisable position in the sub-assembly, and the most convenient strategy to extract as much information as possible from the obtained data.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

2018

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Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

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Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Cited by 17 publications

References 124 publications

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Analysis and Design of PI Plus Repetitive Control for Grid-Side Converters of Direct-Drive Wind Power Systems Considering the Effect of Hardware Sampling Circuits

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

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