Xiangde Mao

The increasing proportion of wind power integration in the power systems and the dynamic interaction of power electronic equipment lead to frequent subsynchronous oscillation, which seriously threatens the safety and stability of the system. Therefore, it is urgent to develop a high-precision and robust identification method. Traditional standalone identification methods are vulnerable to wind speed fluctuations and noise, resulting in unsatisfactory accuracy and robustness. To accurately extract the oscillation parameters from the active power signal, this paper proposes a hybrid method for identifying subsynchronous oscillation in power systems. First, the active power signal is preprocessed using the wavelet threshold denoising strategy, which effectively filters out noise through multi-scale decomposition and signal reconstruction. Second, VMD is applied to the preprocessed signal to decompose it into intrinsic mode functions, thereby achieving effective separation of different oscillatory characteristics. Finally, the fast Fourier transform is used to perform spectral analysis on each IMF to accurately capture the dominant frequency and amplitude of each oscillating component. On the four-machine two-area system with DFIG, the verification is carried out under the wind speeds of 9 m/s, 10.5 m/s and 12 m/s and the no noise, 60 dB and 40 dB noise conditions. SSO is excited by inserting 20 Hz, 30.5 Hz or 40 Hz subsynchronous oscillation components into the external part of the mechanical power input of the generator. The results show that the relative error of frequency identification of the proposed method is less than 0.0426% under all test conditions. The relative error of amplitude identification is less than 1.4155%. Compared with different methods, the proposed method exhibits excellent performance under noise conditions and has robustness to wind change and noise interference.

In the electric locomotive traction transmission system, a four-quadrant rectifier has a high fault rate owing to the complicated control and bad operating conditions, and the fault directly affects the system’s safety and stability. To address such an issue, a rectifier fault diagnosis approach regarding a local tangent space alignment (LTSA) dimensionality reduction to optimize the high-dimensional energy entropy feature is proposed. Firstly, the fault signal is analyzed by using different wavelet functions through wavelet packet multi-resolution decomposition technology so as to extract the frequency band information of the signal. Each wavelet function corresponds to a specific frequency band; the energy–information entropy ratio of each frequency band coefficient is calculated, and then, the wavelet function and optimal frequency band, which are appropriate for the fault signal, are determined. Secondly, the energy entropy of each coefficient in the optimal frequency band is calculated to form the high-dimensional energy entropy feature. The LTSA algorithm is adopted to optimize the high-dimensional feature, through the fault sample number and clustering results, solve the difficulty of selecting the inherent dimension and nearest neighbor number in high-dimensional data, and obtain the simple and effective low-dimensional feature vector to describe the fault features, which reduces the conflict and redundancy between features. Finally, the optimized fault features are used as an input to the classifier support vector machine (SVM), and the fault types are obtained through training and testing. To validate the efficacy of the presented approach, it is tested from the aspects of noise environment, sample proportion and algorithm complexity, and compared with advanced methods. The results indicate that the proposed technique attains an average accuracy of 99.0625% in four-quadrant rectifier fault diagnosis. Under a different signal-to-noise ratio (SNR) and different training and test ratios, the average value after 30 diagnoses is better. Compared with other methods, this method shows a high diagnostic rate and strong robustness in terms of output voltage, noise, training and test ratio.

With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control and harsh environment, it can easily fail. Faults can cause current and voltage distortion, harmonic increases and other problems, which can threaten the safety of the power system and the train. In order to ensure the stable operation of the rectifier, incidences of faults should be reduced. A fault diagnosis technique based on Euclidean norm fusion multi-frequency bands and multi-scale permutation entropy is proposed. Firstly, by the optimal wavelet function, information on the optimal multi-frequency bands of the fault signal is selected after wavelet packet decomposition. Secondly, the multi-scale permutation entropy of each frequency band is calculated, and multiple fault feature vectors are obtained for each frequency band. To reduce the classifier’s computational cost, the Euclidean norm is used to fuse the multi-scale permutation entropy into an entropy value, so that each frequency band uses an entropy value to characterize the fault information features. Finally, the optimal multi-frequency bands and multi-scale permutation entropy after fusion are used as the fault feature vector. In the simulation system, it is shown that the method’s average accuracy is 78.46%, 97.07%, and 99.45% when the SNR is 5 dB, 10 dB, and 15 dB, respectively. And the fusion of multi-scale permutation entropy can improve the accuracy, recall rate, precision, and F1 score and reduce the False Alarm Rate (FAR) and the Missing Alarm Rate (MAR). The results show that the fault diagnosis method has high diagnosis accuracy, is a simple feature fusion method, and has good robustness to working conditions and noise.

Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of the system, and to detect and recognize the broadband oscillation information timely and accurately, this paper presents a multi-mode recognition method of broadband oscillation based on compressed sensing (CS) and the adaptive Variational Mode Decomposition (VMD) algorithm. Firstly, the high-dimensional oscillation signal data collected by the Phasor Measurement Unit (PMU) is compressed and sampled by a Gaussian random matrix, and the obtained low-dimensional data are uploaded to the main station. Secondly, the orthogonal matching pursuit (OMP) algorithm of the master station is used to reconstruct the low-dimension signal, and the original high-dimension signal data are recovered without losing the main features of the signal. Finally, an adaptive VMD algorithm with energy loss minimization as a threshold is used to decompose the reconstructed signal, and the Intrinsic Mode Function (IMF) components with broadband oscillation information are obtained. By constructing oscillating signals with different frequencies, Gaussian white noise with a signal-to-noise ratio of 10 dB to 30 dB is added successively. After the signal is compressed and reconstructed by the proposed method, the signal-to-noise ratio can reach 18.8221 dB to 40.0794 dB, etc., and the oscillation frequency and amplitude under each signal-to-noise ratio can be accurately identified. The results show that the proposed method not only has good robustness to noise, but also has good denoising effect to noise. By using the simulation measurement model, the original oscillation signal is compressed and reconstructed, and the reconstruction error is 0.1263. The basic characteristics of the signal are restored, and the frequency and amplitude of the oscillation mode are accurately identified, which proves that the method is feasible and accurate.