Research on the longitudinal protection of a through-type cophase traction direct power supply system based on the empirical wavelet transform

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a distinctive boundary structure.This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals,resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line.During internal line faults,the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines.Conversely,for faults external to the line,the energy is lower compared to adjacent lines.EWT is employed to decompose the collected fault current signals,allowing access to the high-frequency transient energy.The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary.Moreover,simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles,distances to faults,and fault transition resistances.

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.

Empirical Wavelet Transform;Stationary and Nonstationary Signals

Signal decomposition into the frequency components is one of the oldest challenges in the digital signal processing.In early nineteenth century,Fourier transform(FT)showed that any applicable signal can be decomposed by unlimited sinusoids.However,the relationship between time and frequency is lost under using FT.According to many researches for appropriate time-frequency representation,in early twentieth century,wavelet transform(WT)was proposed.WT is a well-known method which developed in order to decompose a signal into frequency components.In contrast with original WT which is not adaptive according to the input signal,empirical wavelet transform(EWT)was proposed.In this paper,the performance of discrete WT(DWT)and EWT in terms of signal decomposing into basic components are compared.For this purpose,a stationary signal including five sinusoids and ECG as biomedical and nonstationary signal are used.Due to being non-adaptive,DWT may remove signal components but EWT because of being adaptive is appropriate.EWT can also extract the baseline of ECG signal easier than DWT.

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

An improved empirical wavelet transform method for rolling bearing fault diagnosis

Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can easily be separated,causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively.This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set.The frequency domain feature parameter set includes two characteristic parameters:mean and variance.After adaptively dividing the frequency band by the scale space method,the mean and variance of each band are calculated.Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis.An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode.The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes.The empirical mode with the largest margin factor is selected to envelope spectrum analysis.Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.

Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression(LR), auto-regressive integrated moving average(ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform(EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory(LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization(BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed method.From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Anomaly Detection and Pattern Differentiation in Monitoring Data from Power Transformers

Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interruption,and other factors,a method of anomaly recognition and differentiation for monitoring data was proposed.Firstly,the empirical wavelet transform(EWT)and the autoregressive integrated moving average(ARIMA)model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value,and then the isolation forest algorithm was used to identify the abnormal information,and the monitoring sequence was segmented according to the recognition results.Secondly,the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method,and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors,and the monitoring sequence correlation was further used to verify the assessment.Finally,the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Enhanced Adaptive Approach of Video Coding at Very Low Bit Rate Using MSPIHT Algorithm

Nowadays video coding approach is a major key in many applications for easy transmission and storage consumption. The process of transformation is based on the empirical wavelet transform (EWT). The encoding process of video data provides secure and less consumption of storage and the reconstruction process consists of the reverse process with the extraction. In this paper, the coding of video is carried out at a very low bit rate with the enhancement of performance by proposing an approach of modified Set Partitioning in Hierarchical Tree (MSPIHT). This method encodes the high frequency frames with the scheduling of wavelet transform for efficient performances of encoding and improves the ability of both the frequency and time. By applying empirical wavelet transform on each video frame, the component of video frequency is extracted and the low frequency frame is encoded by the H.264/AVC standard. The low coefficient values are ignored in applying the threshold and in the reconstruction process, HBLPCE method is used for imaging enhancement. The simulation of the proposed approach analysis shows better performance in reliable process and efficiency when compared to existing.

Fault DiagnosisMethod of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved Sparrow Search Algorithm(ISSA)optimized Backpropagation Neural Network(BPNN)is proposed to improve the operational safety of LVCB.Taking the 1.5kV/4000A/75kA LVCB as an example.According to the current operating characteristics of the energy storage motor,fault characteristics are extracted based on Empirical Wavelet Transform(EWT).Traditional BPNN has problems such as difficulty adjusting network weights and thresholds,being sensitive to initial weights,and quickly falling into local optimal solutions.The Sparrow Search Algorithm(SSA)with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters.The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction:motor voltage increase,motor voltage decrease,energy storage spring stuck,transmission gear stuck,regular state and energy storage spring not locked.It is suitable for fault diagnosis and detection of the energy storage part of LVCB.