Improvement of reliability and wind power generation based on wind turbine real-time condition assessment

Zhu, Yongchao; Zhu, Caichao; Song, Chaosheng; Li, Yao; Chen, Xu; Yong, B.L.

doi:10.1016/j.ijepes.2019.05.027

Cited by 50 publications

(30 citation statements)

References 48 publications

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“…If the window width is set at 24 h and the window increment is set at 1 h, the change rule of wind turbine generator operating health index is obtained via Equation (16), as shown in Figure 7 The window data of the 30th and 50th hours with their running state model curves are shown in Figure 8. The wind turbine unit was shown to operate normally at the 30th hour, and the state model curve at this time was not very different from the standard model curve, showing fluctuations within the normal range.…”

Section: Resultsmentioning

confidence: 99%

“…Yan et al [15] selected characteristic parameters based on the BP neural network model and used the RSME method to identify generator bearing faults. Zhu et al [16] proposed a fuzzy synthetic method for real-time condition assessment of the wind turbine gearbox to reduce the Operation and Maintenance (O&M) cost with the optimized O&M strategy. In terms of artificial intelligence methods, Zaher et al [17] constructed a BP neural network model of gearbox temperature to identify faults.…”

Section: Introductionmentioning

confidence: 99%

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Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

et al. 2020

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This paper proposes an evaluation index of wind turbine generator operating health based on the relationships with SCADA (Supervisory Control and Data Acquisition) data. First, the relationship among the data from a wind turbine SCADA system is thoroughly analyzed. Then, a time based sliding window model is used to process the SCADA data by the bin method, and a running state model of the wind turbine is established by data fitting. Taking the normal operation state model of the wind turbine as the standard reference and based on the Euclidean distance between the state model curve and the standard model curve, the health index of the wind turbine operation state is proposed. Finally, using SCADA data from two 2 MW direct-drive wind turbines as examples for analysis and discussion, the results show that: (1) health indicators have good stability and sensitivity to wind turbine operating conditions; (2) the width of the data window in the sliding window model must cover all operating conditions of the wind turbine to ensure that the health index depicts the operating state of the wind turbine; (3) the data window width, window increment, and data fitting modeling all affect the health indicators, and thus, the selection of the sliding window model parameters and the data relationship modeling methods should consider the accuracy and real-time performance of the health indicators; and (4) the data acquisition cycle does not affect the health indicators. Once the basic characteristics of the data relations are known, direct data fitting modeling is more efficient than bin preprocessing modeling.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

et al. 2020

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“…In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78][79][80][81][82][83][84]. However, the ESA reported being able to reveal a large number of relations between the machine parameters [85][86][87][88]. Therefore, the ML techniques are highly suitable to support the processing of such extracted information.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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“…The PMSG is supplied to the electrical grid system by means of the grid-side converter (GSC), machine-side converter (MSC), and control systems [5,6]. The fault ride-through (FRT) capability is one of the important issues for the operation system of the wind power system [7][8][9][10]. The grid connection requirements (GCRs) involve the operational condition control of the distributed power system [11].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer

Long

Zhou

et al. 2019

Energies

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Recently, wind energy conversion systems (WECSs) have attracted attention due to their effective application in renewable energy sources. It is a complex system with multi-variables, strong coupling, non-linearity, and variable parameters; however, traditional control systems are inadequate in answering the demands of complex systems. In order to solve the complexity and improve the transient stability during grid faults and power fluctuations, this paper proposes a fuzzy logic system with the linear extended state observer (FLS-LESO) applied to WECSs based on a permanent magnet synchronous generator (PMSG). The FLS-LESO consists of a fuzzy logic controller, a conventional PD controller, and the linear extended state observer (LESO). This paper analyzes the mathematical model of a wind power system and combines it with LESO to improve the estimation accuracy of the observer and further improve the control performance. In the simulation study, the control performance of the FLS-LESO was also tested under various operating conditions using the MATLAB/Simulink simulation platform to verify the correctness and effectiveness of the control system.

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Improvement of reliability and wind power generation based on wind turbine real-time condition assessment

Cited by 50 publications

References 48 publications

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer

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