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A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

With the increasing sustainable energy and environmental demands, wind energy has become one of the world’s fastest growing renewable and green energy sources. Due to unstable and unpredictable wind speed characteristics and energy potentials, which are very sensitive to variations in topography and weather patterns, the cost ratios of the operation and maintenance (O&M) costs over the total energy costs per unit output electrical energy from wind turbine systems are considerately high, which is up to 20%∼25% [

Currently, the SCADA signal has received a lot of attention owing to its application in wind speed-power forecasting [

Building a model to predict the normal behavior of SCADA parameters is the first issue of the wind turbine CM system. By using advanced SCADA data mining methods, various normal condition prediction models (NCPMs) have been developed to detect the significant changes in wind turbine behavior prior to anomaly occurrences. Kusiak et al. [

For a given NCPM, the relationship between the input and output SCADA state variables of the wind turbine could be learned. Subsequently, the departure of the current turbine state from the NCPM could be measured online and yield a time series of residuals. The control chart from statistical process control is a time-honored tool to monitor the residuals [

Although the EWMA control chart can provide greater sensitivity to small shifts, it is not as effective as the Shewhart chart, where the shifts in the process mean level are relatively large due to the inertia problem [

In actual engineering, it is not only expected to alarm an abnormal state as early as possible, but determining the cause and location of the abnormal state is also expected. Since the SCADA system records condition parameters of the main components of wind turbines (e.g., the blade, gearbox, main bearing, and generator), the components with the abnormal state might be identified by modeling the control charts of these multivariate conditional parameters. Lately, Yang et al. [

A literature review indicated that only a few studies have used the multivariate control charts for the CM of wind turbines; this is particularly true for the abnormal state alarm of wind turbine using adaptive control charts. Moreover, there have been few attempts to comprehensively compare the performance and robustness of both EWMA and AEWMA control charts in monitoring the residuals from the NCPM of wind turbine SCADA data. Therefore, the novelty and contributions of this study can be summarized as follows:

The framework for the CM of wind turbines is introduced based on the adaptive multivariate control charts (AMCCs). Two AMCCs (AEWMA and MEWMA) are introduced for abnormal state alarm and anomaly location of wind turbines, respectively. An optimal design is conducted to ensure that the obtained control charts are in the optimal state.

Multivariate regression analysis (MRA) is adopted to obtain the NCPM of wind turbine with fault-free SCADA data. Several popular algorithms in MRA, including the RF, least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), are used for feature selection and regression prediction.

Various tests on a wind turbine with normal and abnormal states are conducted. The exact anomaly time and type are known from the alarm log; thus, the performance and robustness of various control charts could be compared comprehensively.

The remainder of this paper is organized as follows. Section

Two AMCCs (AEWMA and MEWMA) are introduced for abnormal state alarm and anomaly location of wind turbines, respectively. The structures and procedures for these two control charts are derived in this section.

Monitoring data that obey the same distribution are represented by

However, for the actual wind turbine monitoring data, the mean shift is usually fluctuated in a certain range. The designed value of

In the abnormal state alarm of wind turbines, the data monitored by the AEWMA control chart are univariate, i.e., the output power data of the wind turbine. In addition to the early warning of an abnormal state, we also expect this method to identify the cause and location of the anomaly state. Fortunately, the SCADA system records condition parameters of the main components of wind turbines (e.g., the blade, gearbox, main bearing, and generator). Thus, we introduce the MEWMA control charts to monitor these multivariate conditional parameters, and then the components with anomaly state might be identified.

From the univariate EWMA control chart, Lowry et al. [

The average run length (ARL), which refers to the average number of extracted samples from the beginning of the control to the emission of alarm, is used to measure the performance of various control charts. Here, the

Three parameters, including _{0} using SAA. Then, on the premise of given value of

Sample size

Typically, the range of parameter optimization is selected as

By setting

Flowchart for the optimal design of AEWMA control charts.

A small positive number

In the above steps, the calculation of ARL can be obtained using the Monte Carlo sampling method.

The MEWMA control chart has two parameters:

Based on the MCA [

For a fixed smoothing parameter

Once

If this task is carried out until the method covers a whole range of smoothing parameter (

Flowchart for the optimal design of MEWMA control charts.

In previous sections, both control charts of AEWMA and MEWMA have been introduced for the abnormal state alarm and anomaly location of wind turbines. The optimal design procedures for these control charts have been presented. The residuals monitored by these control charts are yielded by the departure of real-time SCADA data from the predictions of NCPM. In this section, we utilize the MRA to construct the NCPM of wind turbines with fault-free SCADA data. Several popular algorithms in MRA, including the RF, LASSO, and RFE, are used for feature selection and regression prediction.

This study aims to monitor and diagnose doubly fed wind turbines with rated power of 2 MW. Typically, the SCADA data of the unit include output power, speed, torque, temperature, and pitch angle. The data record interval is 10 min. To correctly establish the NCPM of wind turbines, the anomaly data should be avoided as much as possible. By reading the record table of the SCADA system, it was found that no anomaly was reported in the time period from 12/26/2013 to 2/12/2014. The wind turbine unit was built and connected to the grid in early 2012. In this time period, the unit has passed the initial running stage and is in the stage of normal power generation. Therefore, the data segment is ideal for MRA to construct the NCPM of wind turbines. There are 45 variables recorded by the SCADA system. After excluding the lost data points and data points during the maintenance downtime, the total amount of data is 6135 points.

At the beginning of MRA on the fault-free SCADA data, to minimize the problem of model deviation due to the lack of important variables, we usually select as many argument variables as possible. In this study, we select the output power as the response variable and the remaining 44 variables as argument variables. However, in the process of actual modeling, it is necessary to select a variable subset (feature selection) which has the best ability to explain the response variable to improve the regression and prediction accuracy of the NCPM ([

The RF, LASSO, and RFE, which are popular algorithms in MRA, are utilized for feature selection and regression prediction. Basic ideas and characteristics for these algorithms are introduced.

The RF is an integrated machine learning method [

LASSO [

The main idea of RFE [

In this study, the core algorithm is implemented through the CARET package in R software. After a series of tests, the decision tree model (treebagFuncs) is selected as the iteration model.

For the fault-free wind power SCADA data, the above three algorithms are used for regression prediction and feature selection. Table

Comparisons of regression accuracy using various algorithms.

RMSE | MAPE | MAE | |
---|---|---|---|

RF | 0.002208326 | 0.01083009 | 0.001168414 |

LASSO | 0.006998046 | 0.05116372 | 0.005377479 |

RFE | 0.005929714 | 0.03291574 | 0.004997808 |

Feature selection result based on RF.

Feature no. | Feature ranking |
---|---|

1 | Rotation torque |

2 | Generator phase A current |

3 | Average wind speed in 10 min |

4 | Generator speed |

5 | Rotor speed |

6 | Blade yaw angle |

7 | Generator temperature |

8 | Gearbox bearing temperature |

9 | Nacelle angle |

10 | Generator phase A voltage |

11 | Nacelle revolution |

12 | Gearbox temperature |

13 | Ambient temperature |

14 | Nacelle temperature |

15 | Bearing temperature |

Comparison between the source SCADA data and regression prediction results: (a) rotation torque, (b) generator current, (c) average wind speed in 10 min, and (d) generator speed.

Some key contents, including the structures of AMCCs, optimal design procedures of these control charts, and construction of NCPM with fault-free SCADA data, have been introduced in the previous sections, respectively.

How to implement these core algorithms needs to be explained for engineering applications. Figure

MRA is utilized to construct the NCPM of wind turbines with fault-free SCADA data. In this study, the RF shows better performance in feature selection and regression prediction.

Time-variable residuals of output power are produced by measuring the difference between the real-time SCADA data and the predictions of NCPM.

For the goal of minimum out-of-control ARL (see Figure

The optimal MEWMA control chart is established (see Figure

Flowchart for the wind turbine CM system based on AMCCs.

In the following, the effectiveness of the proposed CM method is shown by several examples. The performance and robustness of various control charts are compared in detail.

Based on the feature selection and regression prediction results, CM practice on the wind turbine unit is carried out. During the period from 12/1/2015 to 6/1/2016, there were three anomalies, namely, the generator brush worn, gearbox running hot in low generator stage, and shaft bearing overtemperature. The specific time of alarm log is shown in Table

Anomaly information for the wind turbine unit during the period from 12/1/2015 to 6/1/2016.

Anomaly no. | Anomaly description | Monitored period | Number of data points | Time of alarm log |
---|---|---|---|---|

A | Generator brush worn | 12/3/2015 19 : 10–12/7/2015 6 : 30 | 500 | 12/6/2015 20 : 30 (alarm log: point no. 440) |

B | Gearbox running hot in low generator stage | 2/1/2016 00 : 00–2/4/2016 11 : 10 | 500 | 2/3/2016 22 : 10 (alarm log: point no. 421) |

C | Shaft bearing overtemperature | 5/28/2016 15 : 50–6/1/2016 3 : 10 | 500 | 5/31/2016 15 : 20 (alarm log: point no. 429) |

By using the NCPM model obtained in the previous section, the output power of the unit before and after the fault (500 data points in Table

Optimization results of AEWMA and EWMA control charts for abnormal state alarm.

Mean shifts | |||||
---|---|---|---|---|---|

AEWMA | 500 | 0.1 | 5.4750 | 1.3531 | |

EWMA-1 | 500 | 0.1 | — | 6.8110 | |

EWMA-2 | 500 | 0.2 | — | 5.9430 | |

EWMA-3 | 500 | 0.4 | — | 6.9531 |

As mentioned before, the out-of-control ARL is an important index to evaluate the performance of control charts. Figure

Variation of

When the shift becomes large enough

The AEWMA control charts are established for the output power residuals with anomaly A, B, and C, as shown in Figure

Residuals monitored by AEWMA control chart with (a) anomaly A, (b) anomaly B, and (c) anomaly C.

Residuals monitored by various EWMA control charts with (a) anomaly A, (b) anomaly B, and (c) anomaly C.

Compared with the AEWMA control chart, the EWMA control charts behave less sensitively to fault and have poor robustness. For the EWMA-3 of anomaly A (see Figure

From the above CM examples, one can say that compared with the EWMA control charts, the AEWMA control chart behaves more sensitively to the abnormal state. Thus, it can effectively identify the abnormal state and has better robustness. This is of great application value to the CM of practical wind turbine units.

In the previous section, it is demonstrated that the AEWMA control chart can effectively identify the abnormal state. However, for complex electromechanical systems (i.e., the wind turbine), in addition to the early warning of abnormal state, it is also expected to identify the anomaly component, which is called the anomaly location. From the important features in Table

The input parameter for MEWMA control chart should be determined by

When the different dimension variables are excluded, the changes in

For anomaly A (see Figure

For anomaly B, as shown in Figure

When anomaly C is considered (see Figure

Multidimensional SCADA data monitored by the MEWMA control chart with (a) anomaly A, (b) anomaly B, and (c) anomaly C.

Out-of-limit number (OLN) variation of various dimensional data with (a) anomaly A, (b) anomaly B, and (c) anomaly C.

Through the accurate location of the three different anomalies, one can see that the MEWMA control chart combined with the OLN index can effectively locate and identify the abnormal component.

A novel CM method of wind turbines is introduced based on AMCCs and SCADA data. Two AMCCs (AEWMA and MEWMA) are proposed for abnormal state alarm and anomaly location of wind turbines, respectively. Optimization procedures for these control charts are implemented with the goal of minimum out-of-control ARL. MRA is utilized to obtain the NCPM of wind turbine with fault-free SCADA data. After conducting comparisons of the regression accuracy of several popular algorithms in the MRA, the RF is used for feature selection and regression prediction. Various tests on a wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with the EWMA control charts, the AEWMA control chart behaves more sensitively to the abnormal state and thus has a more effective anomaly identification ability and better robustness. By accurately locating three different anomalies, it is demonstrated that the MEWMA control chart combined with the OLN index can effectively locate and identify the abnormal component.

The wind turbine data used to support the findings of this study were supplied by a wind power plant under license and so cannot be made freely available. Requests for access to these data should be made to (Qinkai Han, Email:

The authors declare that they have no conflicts of interest.

This work was supported by the National Science Foundation of China under grant no. 11872222 and the State Key Laboratory of Tribology under grant no. SKLT2019B09. Tao Hu’s work was partly supported by the Beijing Talent Foundation Outstanding Young Individual Project, the Support Project of High-Level Teachers in Beijing Municipal Universities in the Period of 13th Five-Year Plan (grant CIT & TCD 201804078), Academy for Multidisciplinary Studies Academy for Multidisciplinary Studies of Capital Normal University.