**1. Introduction**

Since wind energy is renewable and pollution-free, many governments cite wind energy as a primary future energy source [1]. However, the high maintenance costs of Wind Turbines (WTs) seriously restrict the development of the wind energy industry [2]. The most effective way to reduce maintenance costs is to monitor the working state of WTs to sound alarms when failures occur. Thus, model-based WT monitoring has attracted widespread attention, and different methods have been proposed. Existing model-based WT monitoring methods can be roughly divided into two categories [3]: theoretical and data-driven. The advantage of theoretical methods is that fewer data are required. Reference [4] focused on the main physical mechanisms responsible for temperature changes. Reference [5] proposed an advanced annual energy computation model.

However, WTs are complex electromechanical systems, and the relationships between the various parameters are primarily nonlinear; thus, the construction of a theoretical model is difficult and often inaccurate [6]. Reference [7] proposed an approach that identifies turbines with weakened power generation performance through assessing the wind power curve profiles. Reference [8] discussed the short-horizon prediction of wind speed and power. Reference [9] used sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision-making. Reference [10] proposed a robust data-driven fault detection approach. For data-driven methods, model accuracy relies on the quality and quantity of the data. A Supervisory Control and Data Acquisition (SCADA) system, which can record electrical parameters (active power, phase current, etc.), temperature parameters (main bearing temperature, generator rotor temperature, etc.), and operating parameters (motor speed, etc.), has been widely applied in wind farms [11–13].With hardware improvements, the abilities of SCADA are increasing, making the data-driven methods more suitable for WT monitoring [14].

**Citation:** Hou, Z.; Lv, X.; Zhuang, S. Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions' Effects. *Energies* **2021**, *14*, 7529. https://doi.org/ 10.3390/en14227529

Academic Editors: Davide Astolfi and Paweł Lig ˛eza

Received: 26 September 2021 Accepted: 4 November 2021 Published: 11 November 2021

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A typical data-driven WT monitoring method analyzes real-time data using a model with one parameter as the output and other parameters as inputs [15]. When building data-driven models, intelligent algorithms are applied. By using Nonlinear State Estimate Technology (NSET), Reference [16] and Reference [17] built a tower vibration model and gearbox oil temperature model, respectively. Based on logistic regression (LR), Reference [18] and Reference [19] analyzed the direct-drive wind power generation set and the bearing performance condition, respectively. With Support Vector Machine (SVM), Reference [19] classified and diagnosed the possible fault of the bearing of WT. Reference [20] optimized SVM for Wind Turbine fault diagnosis based on a diagonal spectrum and clustering binary tree. Reference [21] presented a multi-sensory system for fault diagnosis using SVM. Reference [22] presented a two-stage fault detection and classification scheme for electric motor drives in Wind Turbine pitch systems using SVM. In addition to the above methods, Neural Network (NN) is widely applied due to its high accuracy and short training time. Reference [23] achieved a robust simultaneous estimate of actuator faults and system states using NN. Reference [24] applied an artificial NN approach to the gearbox bearings. Reference [25] improved NN by mapping the original samples into feature vectors in an embedding space. With NN, Reference [26] identified the wavelettransformed power components' open-circuit faults accurately. Reference [27] captured dynamic equations modeling wind power output, vibration of the drive train, and vibration of the tower. Reference [28] attempted to assess the prediction intervals of time-series predictions with NN and Extreme Learning Machine (ELM).

Since SCADA of wind farms can obtain very large datasets, NN would entail high computational costs [29]. In contrast to NN, the input weights and hidden layer biases in ELM are assigned randomly, and the output weights of the hidden layer are directly calculated by a Moore–Penrose (MP) generalized inverse operation [30–32]. Consequently, ELM is more computationally efficient than NN [33]. However, the ELM model is based on one set of initial weights and biases, which are mostly random. With the randomness of the initial coefficients and large quantity of datasets, the model may easily fall into local minima.GA is a global random search optimization algorithm based on the genetic mechanism and evolution. Through genetic operations, the individuals with good fitness are selected. With the strong global search capability, the initial coefficients can be optimized by GA and the solution can avoid local minima [34]. Reference [35] improved the accuracy and efficiency of a prediction algorithm by adopting the Genetic Algorithm.

Another issue is that a WT is typically exposed to harsh field conditions year-round, including extreme weather (blizzards, cold waves, etc.). Different ambient conditions have different influences on the WT's temperature parameters. On one hand, the extreme weather causes the internal temperature to fluctuate, which can easily trigger a false alarm. On the other hand, the internal temperature shows a difference in the same normal working state due to the change in ambient temperature. For example, the ambient temperature difference can reach 50 ◦C between summer noon and winter midnight in the wind farm studied in this paper. Due to this huge ambient temperature difference, the main bearing temperature of a WT can differ by 20 ◦C in a normal working state. If the WT temperature model is constructed without considering the ambient temperature, the model can be inaccurate, which would easily cause false alarms in a normal working state, or fail to sound alarms when the internal temperature is abnormal.

Furthermore, many studies have confirmed that the WT performance is directly related to wind speed conditions [36], and thus WTs' internal temperature parameters [37]. Therefore, it is necessary to consider the wind speed conditions in WT monitoring. In existing studies, the wind speed condition division is primarily based on the absolute value of the wind speed. This type of division is effective for WT power monitoring but not accurate for WT temperature monitoring. Through research on real data, the internal temperature is found to exhibit certain differences when wind speeds increase and decrease. For example, the temperature difference in the same main bearing between a wind speed increase and decrease may be more than 5 ◦C under the same wind speed of 10m/s and

the same ambient temperature of 15 ◦C. If the wind speed condition only depends on the absolute value, the temperature monitoring result may be inaccurate.

To address these issues, a novel WT monitoring method is proposed in this paper. Considering the effects of ambient conditions on the WT's internal temperature, the ambient temperature and wind speed change are used as inputs in the optimized ELM model, along with other related parameters. The model is developed by training with real SCADA data. The WT monitoring is achieved by analyzing the real-time data with the model.

In general, the primary innovations of this paper are as follows:


The rest of this paper is organized as follows. Section 2 investigates the influence of ambient conditions on WT monitoring. In Section 3, the framework of the proposed method is presented in detail. In Section 4, the WT model is developed and verified. Section 5 presents the case study and the monitoring results of various analyses. Conclusions are summarized in Section 6.

#### **2. Effects of Ambient Conditions**

A WT is a complex electromechanical system with many subsystems and components (gearbox, generator and converter, etc. [27]), and every key component has a temperature sensor. However, a WT is exposed to harsh weather conditions year-round and the ambient conditions of a WT can be very different. This huge difference in the ambient conditions can cause the internal temperatures to perform quite differently, even under the same working state. Therefore, the effects of the ambient conditions on the internal temperatures must be investigated. This paper investigates wind speed changes and ambient temperature. The data in this paper come from the Damianshan Wind Farm in Wanyuan City, Sichuan Province, China. The wind farm has a total of 33 1.5MW-WTs, with an annual power of 90.489 million kWh. The SCADA in this wind farm records data every 1 min.

#### *2.1. Wind Speed Change*

Figure 1a shows the active power–wind speed curve under a normal working state, and Figure 1b shows the main bearing temperature–wind speed curve. To reduce the impact of the ambient temperature, the ambient temperature of the two datasets shown in the two curves is between 14.5 ◦C and 15.5 ◦C.

**Figure 1.** WT in normal state: (**a**) active power–wind speed; (**b**) main bearing temperature– wind speed.

Figure 1a shows that the active power is directly related to the wind speed. Therefore, WT temperature monitoring should consider wind speed. Most existing methods only divide the absolute value of wind speed into three regions, (0–3, 3–12, and ≥12) m/s, without considering wind speed change. However, the fact is that wind speed change also affects the internal temperatures.

Figure 1b shows that the main bearing temperature increases as the wind speed increases; this result occurs because wind speed has a direct positive correlation with rotor speed, which is directly related to the heat generated inside. Considering the progress of the heat conduction, there may be some delay between the wind speed change and internal temperature change. Due to this delay, at the same wind speed, the main bearing temperature during a wind speed increase is smaller than during a wind speed decrease. Figure 2 shows the main bearing temperature–wind speed curve of a WT during a wind speed increase and decrease. To accurately describe this phenomenon, despite the wind speed change, the working state and conditions are similar, which are an active power of 900–1000 kW, an ambient temperature of 14–16 ◦C, and a wind speed of 9–11 m/s.

**Figure 2.** Main bearing temperature during wind speed increase and decrease.

Figure 2 shows that, under the same wind speed, the main bearing temperature experiences a significant difference during a wind speed increase and decrease. The average difference is 4.6 ◦C, and the maximum difference can reach 5.4 ◦C. These results indicate that wind speed change affects the internal temperatures. Therefore, in order to improve the accuracy of WT monitoring, it is necessary to consider not only the absolute value of the wind speed but also the wind speed change as one of the ambient conditions.

#### *2.2. Ambient Temperature Change*

Besides wind speed change, ambient temperature may also directly affect the internal temperature. Figure 3 shows the main bearing temperature of a WT at midnight (2:00–3:00 a.m.) in January (winter) and during the afternoon (14:00–15:00 p.m.) in August (summer). It is worth mentioning that, to enable effective comparison, the wind speed during the two periods was maintained at around 15m/s.

As shown in Figure 3, the difference in ambient temperature between winter and summer can reach 50.6 ◦C. Due to this ambient temperature difference, the average difference in the main bearing temperature is 19.2 ◦C. The internal temperature of the WT may change with the ambient temperature.

Additionally, extreme weather may cause large fluctuations in internal temperature in a short time. Figure 4 shows the main bearing temperature changes in one hour during a cold wave in November. During this cold wave, the WT maintains normal full-load power generation.

**Figure 3.** Main bearing temperature within different ambient temperatures.

**Figure 4.** Main bearing temperature during cold wave.

Figure 4 shows that the ambient temperature undergoes a significant drop of 10.2 ◦C due to the cold wave and the main bearing temperature undergoes a drop of 4.4 ◦C. The internal temperature may fluctuate due to extreme weather. Therefore, temperature monitoring must consider the ambient temperature change.

#### **3. WT Monitoring Framework**

#### *3.1. Overview of the Proposed Framework*

To address the issues noted above, a novel WT monitoring method is proposed in this paper. The framework of the proposed method is shown in Figure 5. The monitoring process is composed of two parts: offline model construction and online data analysis. Offline model construction is to build a model that simulates normal WT behaviors, and online data analysis determines whether the WT is in a normal working state.

Compared with existing methods, the proposed method has two key advantages.

Ambient conditions are used as the input of the WT model. As mentioned earlier, both wind speed change and ambient temperature affect internal temperatures. Therefore, the proposed method uses wind speed change and ambient temperature as ambient conditions in the model input.

ELM is optimized for the randomness of initial weights and bias. Due to the randomness of initial coefficients, ELM may fall into local minima when constructing the WT model. To solve this problem, GA is applied to optimize the ELM to improve the model's accuracy.

**Figure 5.** Framework of the proposed method.

#### *3.2. Input Parameter Selection*

The output of the model should directly describe the WT's working state and have a strong impact on maintenance. Among the various WT failures, the main bearing failure costs the most [18]. Because the main bearing temperature is closely related to the health of the main bearing, the main bearing temperature is chosen as the output of the model in this paper.

The input should be directly related to the main bearing and WT, which are: (a) the production parameters, such as active/reactive power; (b) the parameters that are near the main bearing temperature, such as gearbox front/rear bearing temperature; and (c) ambient conditions. In this study, the input of the model contains 10 parameters: active power, rotor speed, gearbox front bearing temperature, gearbox rear bearing temperature, nacelle ambient temperature, tower vibration, ambient temperature, ambient temperature change, wind speed, and wind speed change.
