Field PMU Test and Calibration Method——PartⅡ:Test Signal Identification Methods and Field Test Applications

Synchrophasor measurement units(PMUs)provide synchronized measurement data for wide-area applications.To improve the effectiveness of synchrophasor-based applications,field PMUs must be tested to ensure their performance and data quality.In the companion paper(Part I),we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center.Part I presents the development and test of the PMU calibrator.This paper focuses on the analysis center and field test applications.First,the critical component of the analysis center is the signal identification module,for which the step and oscillation signal identification methods are proposed.Here,the performance evaluation criteria of PMU in these two cases are different from others.The methods include a step signal detection method based on singular value decomposition(SVD),which has the capability of weak step detection to account for energy leakage of the signal during the step process,and an oscillation signal identification method based on SVD and fast Fourier transform,which can accurately extract oscillation components that benefit from the adaptive threshold setting method.Second,the analysis center software is implemented based on identification results.By integrating the PMU calibrator in Part I with the analysis center in Part II,we can examine in depth the field PMU test applications in three test scenarios,including standard,playback,and field signal test.Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

Virtual electromagnetic environment modeling based data augmentation for drone signal identification

Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF data.In such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying drones.In reality,signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight version.Without losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about 10-fold.For performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.

Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance

Aiming at the problems of low accuracy of image signal identification and poor anti-noise signal interference ability under strong noise environment,a signal identification method of correlated noisy image based on adaptive array stochastic resonance(SR)is proposed in this paper.Firstly,the two-dimensional grayscale image is transformed to a one-dimensional binary pulse amplitude modulation(BPAM)signal with periodicity by the row or column scanning method,encoding and modulation.Then,the one-dimensional low signal-to-noise ratio BPAM signal can be applied to the saturating nonlinearity array SR module for image signal identification processing and part of the noise energy is converted into signal energy.Finally,the one-dimensional image signal processed by the nonlinearities is demodulated,decoded and reverse scanned to get the restored grayscale image.The simulation results show that the image signal identification method proposed in this paper is highly efficient and accurate for the identification of noisy image signals of different sizes,and the bit error rate(BER)is also significantly reduced.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

FOLMS-AMDCNet:an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.

On measured-error pretreatment of bionic polarization navigation

A signal pre-treatment algorithm based on combination of 3-dimension system identification and Kalman filtering estimation（3DSKE）is proposed.The aim of designing the 3DSKE algorithm is to reduce errors caused by random noise,but leave the systematical errors caused by signal source remained to be solved by a special method.The 3DSKE algorithm is especially suitable for time series of pure measured data without dynamic equation and on-line real-time execution.The simulated result shows that the 3DSKE algorithm can help the basic theoretic calculation to realize feasible,stable,fast,high accurate and auto-executing computing process for the navigation applications.

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

To cope with the problem of emitter identification caused by the radar words＇ uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed translation schema（SSDTS）. This method, which is deduced from the syntactic modeling of multi-function radars, considers the probabilities of radar phrases appearance in different radar modes as well as the probabilities of radar word errors occurrence in different radar phrases. It concludes that the proposed method can not only correct the defective radar words by using the stochastic translation schema, but also identify the real radar phrases and working modes of measured emitters concurrently. Furthermore, a number of simulations are presented to demonstrate the identification capability and adaptability of the SSDTS algorithm.The results show that even under the condition of the defective radar words distorted by noise,the proposed algorithm can infer the phrases, work modes and types of measured emitters correctly.