An improved variational mode decomposition (VMD) method is introduced into the mode recognition of low‐frequency oscillation of power system in this study. First, fast Fourier transform is used to determine the number of VMD components, and the approximate fitting formula of the best balancing parameter α is obtained by a large number of tests. The original signal is decomposed into several modes via improved VMD (IMVD) method. Then, Teager–Kaiser energy operator is applied on the fitting of each component to get the amplitude, frequency, and damping factor of it. By the constructed test signals, the proposed method is compared with the methods of non‐parametric VMD, empirical mode decomposition, total least‐squares estimation of signal parameters via rotational invariance techniques, and Prony on the performance of mode parameter identification. Results show that the IVMD method effectively overcomes the shortcomings of those methods mentioned above in dealing with mode mixing, noise sequence, and non‐stationary signals. Finally, the feasibility of the proposed method in extracting the low‐frequency oscillation mode parameters of power system is verified by the simulation signals of the IEEE two‐area four‐generator power system and the New England 39‐bus system.

Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed. The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC). The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD. For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively. The influence voltage level (vl), voltage type (vt), polarity/phases (pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline (d), wind velocity (wv), and rainfall (rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively.

The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

Converter transformers in HVDC system use oil-pressboard as the main insulation. The insulation structures of valve side need to withstand combined AC-DC voltage. Oil-pressboard insulation shows distinctive PD characteristics under combined AC-DC voltage which are the bases of insulation assessment and fault diagnosis. In this paper, PD characteristics are investigated with the established experimental platform of ballplate electrode under 1:1 combined AC-DC voltage. The behavior of experimental results is explained trough discharge mechanism. To reflect the characteristics of the whole PD development process without the influence of test conditions on PD magnitude, Φ-ΔT-N pattern is used to establish the 3D spectrum H _n (ΔT,Φ) based on time interval, phase and repetition rate of PD pulses. Barycenter C (x _c , y _c ) is calculated as a comprehensive feature parameter. In order to exclude the effect of oscillation components on real-time assessment, variational mode decomposition (VMD) is used to extract the primary development trend of the feature parameter. The difference value R (X, Y) between the value of each point in the trend and the reference target decreases in the whole PD development process. And R can assess the PD development degree continuously based on the historical trend without the normalization problem of PD magnitude.

In order to identify various kinds of combined power quality disturbances, the singular value decomposition (SVD) and the improved total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) are combined as the basis of disturbance identification in this paper. SVD is applied to identify the catastrophe points of disturbance intervals, based on which the disturbance intervals are segmented. Then the improved TLS-ESPRIT optimized by singular value norm method is used to analyze each data segment, and extract the amplitude, frequency, attenuation coefficient and initial phase of various kinds of disturbances. Multi-group combined disturbance test signals are constructed by MATLAB and the proposed method is also tested by the measured data of IEEE Power and Energy Society (PES) Database. The test results show that the new method proposed has a relatively higher accuracy than conventional TLS-ESPRIT, which could be used in the identification of measured data.

In the process of collecting the vibration signal of the circuit breaker, a certain degree of noise is mixed and the quality of the signal is reduced. In this paper, a method of denoising mechanical vibration signal of circuit breaker based on sparse decomposition is proposed. Firstly, the Gabor over complete dictionary is constructed. And then the matching pursuit (MP) algorithm based on fast fourier transform (FFT) is used to sparse decomposition of the signal containing noise. The useful information part of the signal is extracted and the residual component in the signal is discarded as the noise part. Then the signal is reconstructed using the linear combination of the best atoms, so that the denoising signal approximates the original signal to achieve the purpose of signal denoising. Through the analysis of the simulation signal and the measured signal, the method proposed in this paper realizes the ideal denoising effect and is helpful to improve the fault diagnosis precision of the circuit breaker.