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Accurate wind speed forecasting has become an indispensable part in the dispatching and management of wind power generation. However, the volatility and intermittency of wind speed impede the improvement of forecasting accuracy. To figure out the problem and strengthen prediction performance, a novel hybrid framework consisting of variational mode decomposition (VMD), phase space reconstruction (PSR), improved beetle antenna search (BAS) and Volterra series model is established for multi-step ahead short-term wind speed forecasting. To confirm the performance of the proposed compound model, three datasets from the Sotavento Galicia wind farm are used for multi-step short-term wind speed forecasting experiment. The prediction results indicate that: (a) the prediction precision of the proposed model is significantly improved by employing VMD based on central frequency observation approach, which can weaken the non-stationarity of the original series; (b) the proposed DEBAS algorithm based on hierarchical strategy achieves the best performance compared with other comparative algorithms on the benchmark functions, because DEBAS can expand population diversity in the later iteration; (c) the forecasting performance of the hybrid model can be further enhanced by the proposed synchronous optimization strategy based on the improved DEBAS. • A fusion algorithm based on differential evolution and beetle antenna search is proposed by hierarchical strategy. • The proposed DEBAS performs well in optimization experiments for several benchmark functions. • Parameters of PSR and kernel identification coefficients of Volterra series model are optimized simultaneously by DEBAS. • Wind speed forecasting experiments as well as contrastive analysis verify the superiority of the proposed VMD-PSR-DEBAS-Volterra model. [ABSTRACT FROM AUTHOR]

Precise wind speed forecasting contributes to wind power consumption and power grid schedule as well as promotes the implementation of global carbon neutrality policy. However, in existing research, the negative impact of outliers on forecasting models is ignored and the inherent shortcomings of the single predictors have not been taken seriously. Moreover, the intrinsic parameters of predictors are set by manual and empirical methods in some research, leading to difficulties in achieving optimal forecasting performance. To solve the shortcomings of existing research, a multi-step short-term wind speed forecasting framework is proposed by incorporating boxplot-medcouple (MC), variational mode decomposition (VMD), phase space reconstruction (PSR), weight-based stacked generalization with enhanced differential evolution slime mold algorithm (DESMA). Firstly, boxplot-MC is employed to achieve outlier detection and correction for preprocessing original wind speed data by analyzing values and trends. Then, the modified data is further adaptively decomposed into multiple subsequences by VMD, after which each subsequence is constructed into feature matrices through PSR. Subsequently, weight-based multi-model fusion strategy in layer-1 of stacked generalization is proposed to integrate the predicting values acquired by three primary learners, of which the weight coefficients are calculated with the error between actual values and predicting values. After that, kernel extreme learning machine (KELM) in layer-2 of stacked generalization is applied to predict the fusion result to obtain forecasting value corresponding to each subsequence. Meanwhile, an enhanced DESMA based on slime mold algorithm (SMA) and differential evolution (DE) is proposed to calibrate the parameters of KELM. Eventually, the final wind speed forecasting results are attained by summing the prediction values of all subsequences. Furthermore, comparative experiments from different aspects are undertaken on real datasets to ascertain the availability of the proposed framework. The experimental results are clarified as follows: (1) outlier detection and correction employing boxplot-MC is dedicated to analyzing values and trends effectively, with which the negative impact of outliers can be weakened while retaining valid data significantly; (2) VMD can prominently reduce the non-smoothness and volatility of wind speed data; (3) weight-based stacked generalization is conducive to exploiting the advantages of individual primary learners, contributing to compensating for instability; (4) DESMA enhances prediction accuracy by optimizing the parameters of KELM. Additionally, the code has been made available at https://github.com/fyc233/a-multi-step-short-term-wind-speed-forecasting-framework.git. [Display omitted] • Boxplot-MC is employed to achieve outlier detection and correction by analyzing values and trends of original data. • Weight-based stacked generalization is proposed to promote the availability of the overall framework. • An enhanced DESMA optimizer is proposed based on differential evolution and slime mold algorithm with hierarchy strategy. • The proposed DESMA is applied to calibrate the optimal parameters of KELM in layer-2 of stacked generalization. • Superiority of the proposed framework is verified through comparative experiments from different aspects. [ABSTRACT FROM AUTHOR]

Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability. [ABSTRACT FROM AUTHOR]

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method. [ABSTRACT FROM AUTHOR]

• An improved feature extraction method is proposed by aggregating time-shift multiscale technique and dispersion entropy. • A novel optimization algorithm called vibrational HHO is proposed to select optimal parameters of SVM adaptively. • Effectiveness of VHHO is verified by performance analysis with benchmark functions. • Superiority of the novel diagnosis model is ascertained by engineering experiment and comparative analysis. To fully mine the effective fault information and improve the fault diagnosis accuracy, a novel fault diagnosis approach for rolling bearings is proposed by integrating variational mode decomposition (VMD), time-shift multiscale dispersion entropy (TSMDE) and support vector machine (SVM) optimized by vibrational Harris hawks optimization algorithm (VHHO). Firstly, vibration signals with different fault types are decomposed into several intrinsic mode functions (IMFs) by VMD. Subsequently, the proposed TSMDE aggregating time-shift procedure and dispersion entropy is employed to extract multiscale fault features from IMFs. Afterwards, the proposed VHHO that adopts a periodic mutation mechanism to enhance the original Harris hawks optimization (HHO) is devoted to search the optimal parameters of SVM, with which different faults are recognized. Finally, simulations and applications are conducted to evaluate the proposed coordinated VMD-TSMDE-VHHO-SVM approach, and the results reveal that the proposed approach can achieve better diagnosis performance than other comparative ones. [ABSTRACT FROM AUTHOR]

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model. [ABSTRACT FROM AUTHOR]

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others. [ABSTRACT FROM AUTHOR]

Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can provide an effective modeling approach to capture the nonlinear characteristics of wind speed signal and thus providing more satisfactory forecasting results. [ABSTRACT FROM AUTHOR]