Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Adaptive Variational Mode Decomposition for Bearing Fault Detection

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.

An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

The detection of seizure onset and events using electroencephalogram(EEG) signals are important tasks in epilepsy research.The literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the cloud.However,seizure monitoring application needs near sensor processing due to privacy and latency issues.In this paper,a real time seizure detection system has been implemented using an embedded system.The proposed system is based on ensemble empirical mode decomposition(EEMD) and tunable-Q wavelet transform(TQWT) algorithms.The analysis and classification of non-stationary EEG signals require the wavelet transform with high Q-factor.However,direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG signals.In this paper,the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions(IMFs).The Kraskov(KraEn),sample(SampEn),and permutation(PermEn) entropy features of IMFs are extracted and based on optimum values,and 4 IMFs are decomposed using TQWT.Secondly,centered correntropy(CenCorrEn) features of the 1^(st)and 16^(th) sub-band of TQWT have been used as classifier inputs.The performance of multilayer perceptron neural networks(MLPNN),least squares support vector machine(LSSVM),and random forest(RF) classifiers has been tested on the multichannel EEG data recorded from a local hospital.The RF classifier has produced the highest accuracy of 96.2% in classifying the signals.The proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.

Anchoring Bolt Detection Based on Morphological Filtering and Variational Modal Decomposition

The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.

Patterns of cross-correlation in time series:A case study of gait trails

A complex system contains generally many elements that are networked by their couplings.The time series of output records of the system's dynamical process is subsequently a cooperative result of the couplings.Discovering the coupling structure stored in the time series is an essential task in time series analysis.However,in the currently used methods for time series analysis the structural information is merged completely by the procedure of statistical average.We propose a concept called mode network to preserve the structural information.Firstly,a time series is decomposed into intrinsic mode functions and residue by means of the empirical mode decomposition solution.The mode functions are employed to represent the contributions from different elements of the system.Each mode function is regarded as a mono-variate time series.All the mode functions form a multivariate time series.Secondly,the co-occurrences between all the mode functions are then used to construct a threshold network(mode network)to display the coupling structure.This method is illustrated by investigating gait time series.It is found that a walk trial can be separated into three stages.In the beginning stage,the residue component dominates the series,which is replaced by the mode function numbered M14 with peaks covering^680 strides(~12 min)in the second stage.In the final stage more and more mode functions join into the backbone.The changes of coupling structure are mainly induced by the co-occurrent strengths of the mode functions numbered as M11,M12,M13,and M14,with peaks covering 200-700 strides.Hence,the mode network can display the rich and dynamical patterns of the coupling structure.This approach can be extended to investigate other complex systems such as the oil price and the stock market price series.

The Modified Ensemble Empirical Mode Decomposition Method and Extraction of Oceanic Internal Wave from Synthetic Aperture Radar Image

In this paper a modified ensemble empirical mode decomposition(EEMD) method is presented, which is named winning-EEMD(W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions(IMFs), are discussed in this method. The signal-to-noise ratio(SNR) is used to measure the amplitude of added noise and the winning number of IMFs(which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.

Application of BEMD in Extraction of Magnetic Anomaly Components Associated with Sn-W Polymetallic Mineralization in SE Yunnan,SW China

The tin(Sn)-tungsten(W)polymetallic ore concentrated district in SE Yunnan is distributed at the junction region of the Yangtze Block,the Cathaysian Block and the Indosinian Block,where there are several giant deposits of tin,tungsten,copper,silver,lead,zinc and indium closely associated with a large scale Late Cretaceous magmatism.Bi-dimensional empirical mode decomposition(BEMD)is used to extract aeromagnetic anomalous components at the survey scale of 1:200000 from the original aeromagnetic data of SE Yunnan.Four intrinsic mode functions(IMFs)and a residues component are obtained,which may reflect the geological structures and geological bodies at different spatial scales from high frequency to low frequency.The results are shown as follows:(1)Two different types of Precambrian basement in the study area were recognized:one is the Yangtze Block basement characterized by a strong positive magnetic anomaly,the other is the Cathaysian Block basement with a weak negative magnetic anomaly.The former consists of high grade metamorphic rocks including metamorphosed basic igneous rocks,while the latter consists of low grade metamorphosed sedimentary rocks.(2)The aeromagnetic anomalies associated with Sn-W polymetallic mineralization and related to granites in the study area illustrate a pattern of a skarnized alteration-mineralization zone with a positive ring magnetic anomaly enclosing a granitic intrusion with negative magnetic anomaly;(3)The ring positive magnetic anomaly zones enclosing the negative magnetic anomaly are defined as the SnW polymetallic ore-searching targets in the study area.