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

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Relationship Modeling of the Wind Turbine Operating State

#### 2.1.1. Data Analysis

#### 2.1.2. Sliding Window Model

_{i}is the dataset recorded at time t

_{i}(i = 1, 2, 3, …). The SCADA sampling frequency τ is 1/(t

_{i}− t

_{i}

_{−1}), and:

_{i}

_{j}is the jth wind turbine state parameter in X

_{i}(j = 1, 2, 3, …), such as wind speed, rotation speed, and power. During wind turbine operation, the SCADA system creates and stores a stream of data for later analysis.

_{k}(k > h) is a data matrix recorded from time t

_{k−h}to t

_{k}(i.e., sliding window data D

_{k}):

_{k}, both ends of the window move q along the positive time direction simultaneously, and the data matrix to be processed changes to D

_{k+q}.

#### 2.1.3. Data Bin Processing

_{max}is the maximum wind speed in the window and v

_{min}is the minimum wind speed in the window.

_{i}is the number of data groups in the ith bin; v

_{i,j}is the wind speed value of the jth group in the ith bin; and P

_{i,j}is the power value of the jth group in the ith bin.

#### 2.1.4. Data Relationship Modeling

#### 2.2. Health Index of Wind Turbine Operating State

#### 2.2.1. Health Indicators Based on Data Relations

_{k}are used to process SCADA data in real time using the sliding window model described in Figure 1. The sliding window gradually moves along as time continues, and the state parameters to be analyzed in each window are processed using Equations (5) and (6) to obtain the functional relation between the wind speed and out power of the wind turbine at different times (i.e., the coefficients ${a}_{0},{a}_{1},\dots ,{a}_{n}$). Let the coefficient matrix at t

_{k}time be:

#### 2.2.2. Discussion on Health Indicators of Wind Turbine Operation

_{k}time data relation model be:

_{power}be defined based on the output power attenuation as:

_{eu}based on Euclidean distance is defined as:

_{2}> t

_{1}and J is the moment of inertia of the wind wheel. Figure 6 shows the wind power efficiency of wind turbines at different times. The wind turbine generator power coefficient began to drop after 24 h, which is different from the change rule of the first three health indicators.

#### 2.2.3. Proposed Health Indicators for Wind Turbine Operating State

_{d}(k) of the wind turbine operating state is proposed based on the data relation at t

_{k}time and is defined as:

_{w}is the rated output power of the wind turbine.

## 3. Results

#### 3.1. Effect of Window Width on Health Indicators

#### 3.2. Impact of Window Increment on Health Indicators

#### 3.3. Effect of Data Sampling Period on Health Indicators

#### 3.4. Impact of Data Relationship Modeling on Health Indicators

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Comparison of the health indicators of wind turbine operating conditions (total time step n = 73).

**Figure 9.**Health indicators of WT1 under different window widths: (

**a**) h = 12 h, total time step n = 85; (

**b**) h = 24 h, total time step n = 73; (

**c**) h = 36 h, total time step n = 61; (

**d**) h = 48 h, total time step n = 49.

**Figure 10.**Effect of time increment on the health indicators of the wind turbine: (

**a**) q = 0.5 h, total time step n = 145; (

**b**) q = 1 h, total time step n = 73; (

**c**) q = 1.5 h, total time step n = 49; (

**d**) q = 2 h, total time step n = 37.

**Figure 12.**Effect of sampling frequency on the health indicators of the wind turbine: (

**a**) sampling frequency is 0.2 Hz, total time step n = 73; (

**b**) sampling frequency is 0.1 Hz, total time step n = 73.

**Figure 13.**Comparison between the relationship of wind speed and power with different fitting orders.

No. | Time (hh:mm) | Wind Speed (m/s) | Rotor Speed (rpm) | … | Power (kW) |
---|---|---|---|---|---|

1 | 14:20 | 5.9 | 12.82 | … | 434 |

2 | 14:20 | 6.0 | 12.72 | … | 432 |

3 | 14:20 | 6.1 | 12.72 | … | 435 |

… | … | … | … | … | … |

Parameter Name | Value | Parameter Name | Value |
---|---|---|---|

Rated power (kW) | 2000 | Cut-in wind speed (m/s) | 3.5 |

Rotor diameter (m) | 82.6 | Rated wind speed (m/s) | 12 |

Tower height (m) | 80 | Cut-out wind speed (m/s) | 25 |

Rated rotor speed (rpm) | 17 | Maximum wind speed (m/s) | 70 |

Blade weight (kg) | 6750 | Blade length (m) | 40 |

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**MDPI and ACS Style**

Zhang, F.; Wen, Z.; Liu, D.; Jiao, J.; Wan, H.; Zeng, B.
Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data. *Appl. Sci.* **2020**, *10*, 410.
https://doi.org/10.3390/app10010410

**AMA Style**

Zhang F, Wen Z, Liu D, Jiao J, Wan H, Zeng B.
Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data. *Applied Sciences*. 2020; 10(1):410.
https://doi.org/10.3390/app10010410

**Chicago/Turabian Style**

Zhang, Fan, Zejun Wen, Deshun Liu, Jie Jiao, Hengzheng Wan, and Bing Zeng.
2020. "Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data" *Applied Sciences* 10, no. 1: 410.
https://doi.org/10.3390/app10010410