Parallel Integrated Model-Driven and Data-Driven Online Transient Stability Assessment Method for Power System

More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming,while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation.Therefore,it is a future development trend of transient stability assessment methods to combine these two kinds of methods.In this paper,the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage,and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage.In order to quantify the credibility level of the data-driven methods,the credibility index of the output results is proposed.Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index.Thus,a newparallel integratedmodel-driven and datadriven online transient stability assessment method is proposed.The accuracy,efficiency,and adaptability of the proposed method are verified by numerical examples.

Comparisons of MFDFA, EMD and WT by Neural Network, Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

A method for gearbox fault diagnosis consists of feature extraction andfault identification. Many methods for feature extraction have beendevised for exposing nature of vibration data of a defective gearbox. Inaddition, features extracted from gearbox vibration data are identifiedby various classifiers. However, existing literatures leave much to bedesired in assessing performance of different combinatorial methods forgearbox fault diagnosis. To this end, this paper evaluated performance ofseveral typical combinatorial methods for gearbox fault diagnosis byassociating each of multifractal detrended fluctuation analysis (MFDFA),empirical mode decomposition (EMD) and wavelet transform (WT) witheach of neural network (NN), Mahalanobis distance decision rules(MDDR) and support vector machine (SVM). Following this,performance of different combinatorial methods was compared using agroup of gearbox vibration data containing slightly different faultpatterns. The results indicate that MFDFA performs better in featureextraction of gearbox vibration data and SVM does the same in faultidentification. Naturally, the method associating MFDFA with SVMshows huge potential for fault diagnosis of gearboxes. As a result, thispaper can provide some useful information on construction of a methodfor gearbox fault diagnosis.