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Aiming at solving the problem that the parameters of a fault detection model are difficult to be optimized, the paper proposes the fault detection of the wind turbine variable pitch system based on large margin distribution machine (LDM) which is optimized by the state transition algorithm (STA). By setting the three parameters of the LDM model as a three-dimensional vector which was searched by STA, by using the accuracy of fault detection model as the fitness function of STA, and by adopting the four state transformation operators of STA to carry out global search in the form of point, line, surface, and sphere in the search space, the global optimal parameters of LDM fault detection model are obtained and used to train the model. Compared with the grid search (GS) method, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA), the proposed model method has lower false positive rate (FPR) and false negative rate (FNR) in the fault detection of wind turbine variable pitch system in a real wind farm.

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.

Meta-heuristic algorithms are widely viewed as feasible techniques to solve continuous large-scale numerical optimization problems. Grey wolf optimizer (GWO) is a relatively new stochastic algorithm with only a few parameters to adjust that can be easily used for global optimization. This paper presents an efficient and robust GWO (ERGWO) variant to solve large-scale numerical optimization problems. Inspired by particle swarm optimization, a nonlinearly adjustment strategy for parameter control is designed to balance exploration and exploitation. Additionally, a modified position-updating equation is presented to improve convergence speed. The performance of ERGWO is verified on 18 benchmark large-scale numerical optimization problems with dimensions ranging from 30 to 10,000, 30 benchmarks from CEC 2014, 30 functions in CEC 2017, respectively. Numerical experiments are performed to compare ERGWO to the basic GWO algorithm, other GWO variants, and other well-known meta-heuristic search techniques. Simulations demonstrate that the proposed ERGWO algorithm can find high quality solutions with low computational cost and very fast convergence.

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate. Refereed/Peer-reviewed

Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.

Grey wolf optimizer algorithm was recently presented as a new heuristic search algorithm with satisfactory results in real-valued and binary encoded optimization problems that are categorized in swarm intelligence optimization techniques. This algorithm is more effective than some conventional population-based algorithms, such as particle swarm optimization, differential evolution and gravitational search algorithm. Some grey wolf optimizer variants were developed by researchers to improve the performance of the basic grey wolf optimizer algorithm. Inspired by particle swarm optimization algorithm, this study investigates the performance of a new algorithm called Inspired grey wolf optimizer which extends the original grey wolf optimizer by adding two features, namely, a nonlinear adjustment strategy of the control parameter, and a modified position-updating equation based on the personal historical best position and the global best position. Experiments are performed on four classical high-dimensional benchmark functions, four test functions proposed in the IEEE Congress on Evolutionary Computation 2005 special session, three well-known engineering design problems, and one real-world problem. The results show that the proposed algorithm can find more accurate solutions and has higher convergence rate and less number of fitness function evaluations than the other compared techniques.

A new self-adaptive time-frequency analysis method named local oscillatory-characteristic decomposition (LOD) is introduced. This method is based on local oscillatory characteristics of signal itself, and it uses kinds of mathematical operations such as differential, coordinate domain transform and piecewise linear transform to decompose the signal into a series of mono-oscillatory components (MOC) whose instantaneous frequency has physical meanings. Each of MOC represents a kind of oscillatory characteristic of the original signal, and thus reflects the intrinsic characteristics of the original signal, so the LOD method is particularly suitable for processing non-stationary signals. After illustrating the decomposition principle of LOD in detail, the LOD is compared with the empirical mode decomposition (EMD) and local mean decomposition (LMD) by analyzing simulation signals. The results show the superiority of LOD method. Meanwhile, aiming at the multi-component modulated characteristic of gear and roller bearing fault vibration signals, the envelope analysis based on LOD is applied to gear and roller bearing fault vibration signals analysis. Analytical results from experimental signal and real signal demonstrate that the new diagnosis approach based on LOD can identify gear and roller bearing work condition accurately and effectively.

Grey wolf optimizer (GWO) algorithm is a relatively novel population-based optimization technique that has the advantage of less control parameters, strong global optimization ability and easy of implementation. It has received significant interest from researchers in different fields. However, there is still an insufficiency in the GWO algorithm regarding its position-updated equation, which is good at exploitation but poor at exploration. In this work, we proposed an improved algorithm called the exploration-enhanced GWO (EEGWO) algorithm. In order to improve the exploration, a new position-updated equation is presented by applying a random individual in the population to guide the search of new candidate individuals. In addition, in order to make full use of and balance the exploration and exploitation of the GWO algorithm, we introduced a nonlinear control parameter strategy, i.e., the control parameter of a is nonlinearly increased over the course of iterations. The experimental result on a set of 23 benchmark functions and 4 engineering applications demonstrate the effectiveness and efficiency of the modified position-updated equation and the nonlinear control parameter strategy. The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO. Moreover, EEGWO offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all of the test functions.

Establishing accurate and reliable models based on the measured data for photo-voltaic (PV) modules are significant to design, control and evaluate the PV systems. Although many meta-heuristic algorithms have been proposed in the literature, achieving reliable, accurate and quick parameters identification for PV models is still a challenge. This paper develops a variant of butterfly optimization algorithm (called EABOA) to identify the unknown parameters of PV models. In EABOA, a new position search equation and good-point set are proposed to balance between exploration and exploitation. 12 classical benchmark test problems are firstly selected for verifying the effectiveness of EABOA, and the results indicate that EABOA provides better performance than other selected algorithms. Then, EABOA is applied to identify the unknown parameters of three benchmark test PV models, i.e., single diode (SD), double diode (DD) and PV module models. The comparison results with some other reported parameter identification methods from literature suggest that the proposed EABOA outperforms most approaches in terms of accuracy and reliability. The least SIAE value of EABOA is smaller than other compared algorithms about 56.6%, 5.84%, and 10.2% for SD, DD, and PV module models, respectively. Finally, EABOA is applied to solve parameter identification problem of practical module and obtains the satisfactory results.

Sample mean is generally the simplest feasible description of samples.Margin distribution can characterize the generalization performance.Double Distribution Support Vector Machine (DDSVM) maximizes the margin distribution of two classes sample means.DDSVM can obtain efficient and more balanced classification performance. This paper studies the role of the sample mean in binary classifier based on the margin theory. Support Vector Machine (SVM) with maximized minimum margin is widely used in pattern recognition, but it sometimes induces the weak margin distribution which is negative for the generalization performance. Therefore Double Distribution Support Vector Machine (DDSVM) is proposed to obtain strong generalization performance by maximizing the margin distribution of two classes sample means and the minimum margin. The sample mean is usually a good description of samples, and DDSVM can increase the margin distribution and improve the generalization performance. DDSVM is a general learning approach, and its superiority is verified both theoretically and experimentally.

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

Given the importance of the class-imbalanced data and misclassified unequal costs in large wind turbine datasets, this paper proposes a cost-sensitive large margin distribution machine (CLDM) for fault detection of wind turbines. The margin mean and margin variance are use to characterize the margin distribution. The objective function and constraints of the large margin distribution machine (LDM) are modified to be cost-sensitive. The class-imbalanced data and misclassified unequal costs are solved by selecting the appropriately cost-sensitive parameters. Then CLDM is designed to train and test data from wind turbines in a wind farm. In order to verify the effectiveness of CLDM, it is compared with support vector machine (SVM), cost-sensitive SVM, and LDM. Comprehensive experiments on 7 datasets from a benchmark model of wind turbines and 5 datasets from a real wind farm show that CLDM has better sensitivity, gMean and average misclassified cost than the other methods.

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response. Refereed/Peer-reviewed

Whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refraction-learning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.