With the rapid development of wind power, a lot of wind turbines have faults in the operation because most of them are installed in remote areas, and their loads are unstable. The operation and maintenance cost of up to 25–30% seriously restrict the development of wind power industry [1
]. Therefore, how to reduce the risk of wind turbine operation and decrease the cost of wind power generation has become a research topic for many scholars. The fault isolation of wind turbine is the bottleneck to solve the above problems; undoubtedly, it is the key to ensuring long-term stable operation of the wind turbine, and it is the necessary means to realize the condition-based maintenance of the wind turbines [3
Variable speed constant frequency doubly fed wind turbine is the main type in wind farms [4
], and its simplified model is shown in Figure 1
. The fault rate of gearbox and generator is relatively high [5
]. There are many reasons for the faults of gearbox and generator, and the misalignment between them is one of the most important [6
]. This is mostly because:
The precise alignment in the wind turbine is very difficult;
The wind speed fluctuation can cause the start-stop of wind turbine frequently, and, as time goes on, it will cause the shift of generator or the deformation of some parts, resulting in the misalignment of generator and gearbox.
When the misalignment occurs, it is easy to cause damage to gearbox gears, bearings and generator bearings and so on. Therefore, the study and fault isolation of the transmission system in the wind turbine is the key to ensuring its long stable operation.
The misalignment belongs to a latent fault. When the fault is accumulated to a certain extent, the equipment is damaged seriously, leading to the outage of the wind turbine, affecting the power generation seriously, resulting in huge economic losses. For many years, how to isolate the latent fault accurately has been a difficult problem. This is mainly due to the fault feature of latent faults not being obvious, and researchers lack the awareness of the developing rules for latent fault. In order to identify and isolate the existence of the misalignment fault accurately and to overcome the difficulty of obtaining a large number of misalignment fault samples in reality, in this paper, Solidworks and Adams models are established according to the 1.5 MW wind turbine. The simulation of the transmission system is carried out in Adams under the normal operation, the parallel misalignment, the angle misalignment and the comprehensive misalignment. The vibration signal (angular acceleration) of a high speed shaft is extracted as the information (the established model and its effectiveness are shown in [7
The methods of extracting fault features from vibration signals are mainly based on time-domain analysis, frequency-domain analysis and time-frequency domain analysis:
The time-domain analysis is one of the most simple and direct analysis methods, and it is effective in isolating the fault in a certain extent. Except for the poor anti-interference of serious faults, the numerical values in time-domain are close to that in normal state, so it is easy to make misjudgments only using time-domain analysis.
The frequency-domain analysis of signal is the most commonly used method of mechanical equipment fault analysis. It can be used to get more intuitive fault information than by the time-domain analysis. However, the theoretical basis of frequency-domain analysis is the method of Fourier analysis, so it has sidedness, and cannot extract the features of vibration signal comprehensively.
The vibration signal is expressed in the time-domain and frequency-domain at the same time for the time-frequency domain analysis, and it has very prominent advantages. The main methods of feature extraction based on time-frequency analysis are as follows: short time Fourier Transform, Wigner-Ville Distribution, Wavelet Transform, Blind Source Separation, Empirical Mode Decomposition (EMD) and so on.
Due to the complexity of the fault mechanism of the wind turbine transmission system, the state information is different with different feature indexes, the sensitivity and regularity of the running state, as well as the clustering and separability in the model space not being the same. Single features or single domain features are difficult to fully reflect the different conditions of wind turbines with different degrees and types of faults. Therefore, it is necessary to construct a mixed feature library with time-domain, frequency-domain and time-frequency domain, which becomes one of the development trends of fault feature extraction.
Many scholars have applied one or two of the above domains to extract the fault isolation feature based on vibration signal. For example, Ref. [8
] extracted root mean square (RMS), peak value and kurtosis coefficient of the vibration signal, and combined with the time-domain waveform to judge whether there was fault in equipment. In Ref. [9
], the kurtosis and peak value of the vibration signal were selected as the time-domain indexes, and then the wavelet packet algorithm was used to extract the frequency band energy and the 2-norm as the time-frequency domain indexes, the PCA (Principal Component Analysis) was used to confirm the principal component, and the crack fault of the wind turbine gearbox was diagnosed effectively. The EMD method was used to judge the working states and fault types of the gear by calculating EMD energy entropy of different vibration signals as the input features of Support Vector Machine in [10
], but only the time-frequency domain feature was considered. Ref. [11
] used a testing system for a wind turbine vibration test to collect vibration signal, and the time-domain and frequency-domain analysis were carried out to get the fault features of the rotor misalignment through the analysis of wind turbine generator, but the time-frequency analysis was not considered. In Ref. [12
], the wavelet analysis was used to denoise the vibration signals of the wind turbine gearbox under normal, wear fault and broken tooth fault. Five feature parameters were extracted into the LVQ (Learning Vector Quantification) neural network, and the results showed that the method can identify the fault quickly and accurately. In Ref. [13
], the time-domain of the peak, RMS and kurtosis of the vibration signal were extracted as the time-domain features of the fault gearbox. Combining observation and analysis of the normal signal and fault signal frequency-domain feature, the possible location of the failure was determined. In Ref. [14
], the vibration signals of three typical states of normal conditions, tooth wear and tooth breakage of the gearbox in the wind turbine are analyzed, and the margin index, kurtosis index, peak index, pulse index, power spectrum entropy in the frequency-domain were extracted, the time-domain and frequency-domain features were the inputs in the fault isolation. In Refs. [15
] and [16
], the mixed-domain feature set is constructed to completely characterize the property of each fault by combining Empirical Mode Decomposition (EMD) with the Autoregression (AR) model coefficients.
A large amount of literature shows that there are few reports on the method of combining the time-domain, frequency-domain and time-frequency domain features together to construct a mixed-domain feature library when researching the fault of the wind turbine transmission system. In order to extract the feature parameters which reflect the vibration signal as much as possible, and to make the fault isolation more reliable and accurate, in this paper, firstly, the time-domain, frequency-domain and time-frequency domain of the wind turbine vibration signal are combined to construct the mixed-domain feature library to obtain more comprehensive and accurate fault isolation information. The information entropy of the intrinsic mode function (IMF) component decomposed by EMD is used as the time-frequency feature. Then, the support vector machine that is suitable for small samples is used as the isolation tool, and the PSO algorithm is used to optimize the relevant parameters of the support vector machine to obtain better classification performance with high diagnostic accuracy. The results show that the proposed method can identify the types of misalignment effectively compared with other methods.
4. PSO-SVM Fault Isolation Results Based on Mixed-Domain Features
The results of parameters optimizing of SVM using PSO and the mixed-domain features of the wind turbine are shown in Figure 3
, that is, the optimal fitness of PSO algorithm is 83.3882%, and the obtained optimal parameters are
. The classification results of the testing set using the optimized SVM classifier are shown in Figure 4
, where the category label "0" indicates the normal condition; “1” indicates the parallel misalignment; “2” indicates the angle misalignment; and “3” indicates the comprehensive misalignment.
The classification accuracy rate of training set obtained by using an optimized SVM classifier achieves 97.9441%, and the classification accuracy rate of testing set is 92.1053%, so the fault classification accuracy is very high. In order to better illustrate the superiority of the PSO-SVM algorithm proposed in this paper, the same fault features are adopted by SVM, GridSearch-SVM (the parameters of SVM are optimized by GridSearch) and GA-SVM (the parameters of SVM are optimized by Genetic Algorithm), and the testing results are shown in Figure 5
a–c and Table 5
From the simulation results, it can be seen that the fault isolation by SVM only has a poor recognition effect. When using GridSearch-SVM, the training accuracy and testing accuracy are better, but not higher than PSO-SVM. The training accuracy of GA-SVM is very high, but the promotion ability is less than PSO-SVM. Therefore, it is better to use the PSO-SVM algorithm to isolate the fault.
This paper is based on the time-domain, frequency-domain and time-frequency domain analysis of vibration signals of the wind turbine in four different operating conditions with four kinds of rotating speeds. The mixed-domain features are extracted in the paper, which are the inputs of SVM classifiers. The parameters of SVM are optimized using the PSO algorithm. The testing results show that the proposed model for misalignment fault isolation is effective compared with other commonly used algorithms.
When the wind turbine operates in the condition of misalignment, there will be some changes in the temperature and electrical signals in addition to the reflection of the mechanical aspects, so ANSYS, MATLAB, and other software should be included in the simulation further to get temperature and electrical parameters, so as to study the fault characteristics more comprehensively.
At the same time, it is necessary to point out that the method presented in this paper is a general method, and it can be used elsewhere.