A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Chen, J. S.; Chen, W. G.; Li, J.; Sun, Peidong

doi:10.3808/jei.201800393

Cited by 9 publications

(8 citation statements)

References 25 publications

(33 reference statements)

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“…Additionally, in [12], a model that could be used for detecting incipient failure condition of a WT based on SCADA data was proposed. The prediction model was developed based on different data mining algorithms, such as back propagation neural network (BPNN) algorithm, the radial basis function neural network (RBFNN), and the least-square support vector (LSSVM) algorithm.…”

Section: Rq4: Which Ai Techniques Are Currently Under Research For Wt Cm?mentioning

confidence: 99%

“…In [10], it was mentioned that O&M costs represented approximately 20% to 25% of the total power generation costs for offshore WFs and 10% to 15% of the total generation costs for onshore ones. The authors in [11][12][13][14][15][16] broadly agreed that WF O&M costs could account for approximately 25% to 35% of the total cost of power generation. These O&M cost percentages could be increased by recurring failures in the different components of a WT.…”

Section: Introductionmentioning

confidence: 99%

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Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

et al. 2020

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Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

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Section: Rq4: Which Ai Techniques Are Currently Under Research For Wt Cm?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

et al. 2020

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“…As noted by Wang et al [19], the more system factors that can be considered, the higher the forecasting precision. Electricity data rules and characteristics can be easily obtained using decomposition or combination [20]; for example, Zhang et al [21] applied a decomposition approach to forecast short-term electricity demand, in which the data series were split into two new series and two different models trained to forecast these separately, Li et al [22] proved that a random forest technique based on ensemble empirical mode decomposition was able to improve the forecasting accuracy of daily enterprise electricity consumption, Laouafi et al [1] developed a combination methodology for electricity demand forecasting, for which six individual models were applied to the real-time load data, and the final load estimation obtained by adding each model's forecasting value multiplied by its weight, and Chen et al [23] proposed a generalized model for wind turbine faulty condition detection using a combination prediction approach and information entropy.…”

Section: Literature Reviewmentioning

confidence: 99%

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Yang

et al. 2019

Sustainability

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(1) Background: Electricity consumption data are often made up of complex, unstable series that have different fluctuation characteristics in different industries. However, electricity demand forecasting is a prerequisite for the control and scheduling of power systems. (2) Methods: As most previous research has focused on prediction accuracy rather than stability, this paper developed a decomposition-based combination forecasting model using dynamic adaptive entropy-based weighting for total electricity demand forecasting at the engineering level. (3) Results: To further illustrate the prediction accuracy and stationarity of the proposed method, a comparison analysis using an analysis of variance and an orthogonal approach to solve the least squares equations was conducted using classical individual models, a combination forecasting model, and a decomposition-based combination forecasting model. The proposed method had a very satisfactory overall performance with good verification and validation compared to autoregressive integrated moving average (ARIMA) and artificial neural-networks (ANN). (4) Conclusion: As the proposed method dynamically combines various forecast models and can decompose and adapt to various characteristic data sets, it was found to have an accurate, stable forecast performance. Therefore, it could be broadly applied to forecasting electricity demand and developing electricity generation plans and related energy policies.

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“…Due to its powerful ability to identify complex non-linear relationships between input and output, it has successfully solved many problems in the fields of pattern recognition, intelligent robots, automatic control, predictive estimation, biology, medicine, etc. Among various types of ANN, the back propagation neural network (BPNN) has been used widely because of its simple and easily implemented architecture [23]. In this paper, BPNN is used to forecast crop-price time series.…”

Section: Eemd-ann For Forecastingmentioning

confidence: 99%

A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water

Shan

Guo

et al. 2019

Sustainability

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Different from the traditional irrigation optimization model based only on the water production function, in this study, we explored the water–yield–quality–benefit relationship and established a general irrigation scheduling optimization framework. To establish the framework, (1) an artificial neural network coupled with ensemble empirical mode decomposition (EEMD-ANN) is used to decompose the original price time series into several subseries and then forecast each of them; (2) factor analysis and a technique for order of preference by similarity to ideal solution (FA-TOPSIS), as an integrated evaluation method, is used to comprehensively evaluate the fruit quality parameters; and (3) regression analysis is used to simulate water-yield and water-fruit quality relationships. The model is applied to a case study of greenhouse tomato irrigation schedule optimization. The results indicate that EEMD-ANN can improve the accuracy of price forecasting. Jensen and additive models are selected to simulate the relationships of tomato yield and quality with water deficit at various stages. Besides, the model can balance the contradiction between higher yields and better quality, and optimal irrigation scheduling is obtained under different market conditions. Comparison between the developed model and a traditional modeling approach indicates that the former can improve net benefits, fruit quality, and water use efficiency. This model considers the economic mechanism of market price changing with fruit quality. Forecasting and optimization results can provide reliable and useful advices for local farmers on planting and irrigation.

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A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Cited by 9 publications

References 25 publications

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water

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