An improved fuzzy synthetic condition assessment of a wind turbine generator system

Li, H.; Yu, Hao; Yang, Chao; Chen, Zhe; Ji, Haiting; Zhao, Bin

doi:10.1016/j.ijepes.2012.09.014

Cited by 54 publications

(28 citation statements)

References 20 publications

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“…An overview of WTCM based on SCADA data analysis is presented in Figure 12. Several recent studies on SCADA data for WECS CM can be found in the literatures [132,[134][135][136][137]. A wind turbine condition monitoring system (WTCMS) based on SCADA using normal behavior models and fuzzy logic was presented in [135].…”

Section: Wtcm Based On Scada Data Analysismentioning

confidence: 99%

“…This CMS is designed to detect trends and patterns in SCADA data and predict possible failures. Another recent research study by Li et al [136] focused on improving the fuzzy synthetic condition assessment of a WT generator system. The results indicated that the evaluation of dynamic limits and deterioration degree functions for the characteristic variables for WECSs could be improved by analyzing SCADA data with the improved fuzzy synthetic model.…”

Section: Wtcm Based On Scada Data Analysismentioning

confidence: 99%

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Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

et al. 2014

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Abstract:As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today's WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.

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Section: Wtcm Based On Scada Data Analysismentioning

confidence: 99%

Section: Wtcm Based On Scada Data Analysismentioning

confidence: 99%

Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

et al. 2014

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“…Moreover, since the operational point of a WT change quite significantly with wind speed [11], the traditional fixed thresholds based condition assessment method [35,36] is not appropriate for WTs. Data preprocessing methods were proposed in [23,37] to mitigate the impacts of wind speed upon the real WT condition parameters. However, other factors such as the ambient temperature, yaw control and pitch angle control can also affect the performance of the WTs [18,38].…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

Sun¹,

Li²,

Wang³

et al. 2017

Journal of Electrical Engineering and Technology

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-This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

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“…Abou et al [9] solved fuzzy characteristics problem of the ship propulsion system condition monitoring parameters by using the fuzzy logic method and realized the ship propulsion system health state assessment. Li et al [10] proposed an improved fuzzy health evaluation method using the degradation degree function, dynamic threshold and variable weight, and evaluated the degradation status of the wind turbine with the actual monitoring data. The results show that the improved method is accurate and reliable.…”

Section: Introductionmentioning

confidence: 99%

A health state assessment method for ship propulsion system based on fuzzy theory and variable weight theory

Hu¹,

Zhou²

2017

Vib. proced.

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Abstract. It is hard to determine the equipment weight in the ship propulsion system health status evaluation. A device weight determination method based on fuzzy theory and variable weight theory is presented. In this method, expert knowledge is used to determine the initial weight of each device; variable weights theory is used to appropriately adjust devices weights combining with the actual health status of each device. Simulation analysis results show that the proposed propulsion system integrated status assessment method could reasonably reflect actual status, which proves it to be scientific and valid in engineering application.

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An improved fuzzy synthetic condition assessment of a wind turbine generator system

Cited by 54 publications

References 20 publications

Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

A health state assessment method for ship propulsion system based on fuzzy theory and variable weight theory

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