KR-102963070-B1 - METHOD FOR PREDICTING THE REMAINING LIFE OF A WIND TURBINE SUPPORT STRUCTURE TOWER

Abstract

The present invention relates to a method for predicting the remaining life of a support structure tower that supports wind power generation equipment, wherein each step is carried out sequentially by: a) installing three or more inclinometers in the vertical direction on the support structure tower; b) converting data measured by the inclinometers into strain data; c) processing the converted strain data; d) determining the strain range and the number of repetition cycles using the processed data; e) converting the strain range and the number of repetition cycles into the fatigue stress range and the number of repetition cycles; f) evaluating fatigue damage; and g) calculating the remaining life. This allows the remaining life of the support structure tower to be determined using only three inclinometers, thereby enabling low cost and ease of judgment work.

Inventors

• 김범식
• 김문옥
• 이규석

Assignees

• 주식회사 스펙엔지니어링와이엔피

Dates

Publication Date

20260511

Application Date

20260212

Claims (5)

1. For predicting the remaining lifespan of the support structure tower (1) of a wind turbine, a) A step of installing three or more inclinometers (01, 02, 03) in the vertical direction on a support structure tower (1); b) a step of converting data measured by the inclinometers (01, 02, 03) into strain data; c) A step of processing the converted strain data; d) A step of determining the strain range and the number of iteration cycles using processed data; e) a step of converting the strain range and number of repetition cycles into the fatigue stress range and number of repetition cycles; f) Step for evaluating fatigue damage; g) A method for predicting the remaining life of a wind turbine support structure tower, characterized in that each step of the step of calculating the remaining life proceeds sequentially.
2. In paragraph 1, A method for predicting the remaining lifespan of a wind turbine support structure tower, characterized in that the inclinometers (01, 02, 03) of step a) above are each installed at the lower (B), middle (M), and upper (T) parts of the support structure tower (1), and each installation point where the inclinometers (01, 02, 03) are located is set in a straight vertical direction facing the vertical centerline of the support structure tower (1).
3. In paragraph 1, A method for predicting the remaining lifespan of a wind turbine support structure tower, characterized in that the inclinometer (01, 02, 03) in step a) above is a two-axis inclinometer.
4. In paragraph 1, A method for predicting the remaining lifespan of a wind turbine support structure tower, characterized by the fact that between steps a) and b) above, a step of reviewing the suitability for using measured inclination data is performed.
5. In paragraph 2, In step b) above, Deflection angle at an arbitrary point (Z) that is predicted to be a weak point of the support structure tower (1) ( ) is derived as a quadratic function passing through each point where the inclinometers (01, 02, 03) are installed, and through this, the x-direction strain at an arbitrary point (Z) and strain in the y-direction A method for predicting the remaining lifespan of a wind turbine support structure tower, characterized by the calculation of

Description

Method for Predicting the Remaining Life of a Wind Turbine Support Structure Tower The present invention relates to a method for predicting the remaining lifespan of a support structure tower that supports wind power generation equipment, and more specifically, to a technology that can predict the remaining lifespan by identifying the degree of damage to the support structure tower in an installed and operating onshore wind turbine. To achieve the 2050 carbon neutrality goal, Korea is continuously supporting and expanding new and renewable energy facilities, such as wind and solar power generation, to ensure that the share of renewable energy in power generation exceeds 20% by 2030. In particular, wind power generation is capable of generating power with higher efficiency relative to the installation area compared to solar power generation. Since the construction of the first wind power complex began in 1998, continuous construction has resulted in the formation of 127 wind power complexes with a total capacity of 2.27 GW in Korea as of the end of 2024. However, the design life of a wind turbine is typically estimated to be 20 to 25 years. In some wind farms, the supporting towers have already exceeded their design lifespan and are in operation. Furthermore, it is projected that 146 wind turbines in 23 wind farms will reach their design lifespan within the next five years. This signifies that the aging of onshore wind turbines in Korea has begun in earnest, making it urgent to predict the lifespan of wind power systems and to develop strategies for repowering and operation. Support towers, where wind power generation equipment such as turbines is installed, accumulate operational fatigue over time, leading to a gradually increasing probability of failure during long-term operation. Therefore, measuring the actual behavior of wind turbines in operation and estimating fatigue damage based on this data to predict their remaining lifespan is essential for ensuring the safety of support towers and establishing operational management strategies. Accordingly, registered patent publication No. 10-1576799 proposed a method to evaluate the fatigue life of a support structure by installing an accelerometer, a strain gauge, and an inclinometer on the support structure and using them. The fatigue life evaluation in Registration No. 10-1576799 is conducted by constructing a numerical analysis model using the design drawings of the support structure, installing accelerometers, strain gauges, and inclinometers at arbitrary multiple points of the support structure to measure the acceleration, strain, and inclination at each point, improving the numerical analysis model using these measured values, deriving a relationship between the response value at the measurement point and the strain at the target point using the improved numerical analysis model, obtaining a stress value using the strain at the target point calculated using the above relationship, and determining the remaining fatigue life through this. However, since this fatigue life evaluation method fundamentally requires the installation of various and numerous measurement sensors, it not only increases the time and cost for installation and management and raises the possibility of errors, but also presents a problem in that the remaining life evaluation process becomes cumbersome because the numerical analysis model must be modified according to the changing loads acting on the support structure based on the wind direction acting on the wind turbine during operation. FIG. 1 is a flowchart of a method for predicting the remaining lifespan of a support structure tower according to the present invention. FIG. 2 is an explanatory diagram regarding the installation location of an inclinometer for the above-mentioned remaining life prediction method. Figure 3 is a diagram explaining the correlation between the deflection angle of a supporting structure tower and the deflection angle of a cantilever beam. Figure 4 is an explanatory diagram regarding the installation locations of strain gauges and inclinometers for conducting a test. Figure 5 is a graph of strain data obtained by a strain gauge. Figure 6 is a graph of slope data by an inclinometer. Figure 7 shows the maximum strain value and time series graph of measurement data by strain gauge and inclinometer. Figure 8 shows the maximum strain value and occurrence angle distribution of measurement data by strain gauges and inclinometers. Figure 9 is a graph of fatigue damage results according to sensitivity analysis. Figure 10 is a graph of the number of valid data after data processing. Figure 11 is a comparison graph of raw data and Butterworth frequency filtered data. Figure 12 is a comparison graph of raw data and peak value extraction data. Figure 13 is the Rainflow Counting result. FIG. 14 is a graph of cumulative damage calculated using one month of data for each of the strain gauge and the inclinometer. Fig. 15