Gehao Sheng

Robust Time Delay Estimation Method for Locating UHF Signals of Partial Discharge in Substation

Partial-discharging (PD) test is one of the most effective methods for insulation diagnosis. Because of the advantages of ultra-high frequency (UHF) electromagnetic wave, UHF signals radiated by PD have been widely used to locate faults on power equipment. Time delay estimation is not only on the basis of the PD localization algorithm, but is also the key in determining the localization accuracy. Currently, common methods are the threshold value method, energy accumulation method, and the cross-correlation analysis method, etc. However, field signals always include a variety of unknown noises, so common methods cannot be effective enough. This paper proposes time delay estimation algorithms based on four-order cumulant and bispectrum, and gives their computational realization. These algorithms have a prominent advantage of being insensitive to Gaussian noises with unknown correlation properties. The time delay of simulated UHF signals with Gaussian noises and fixed-frequency interference is estimated by the given algorithms. Numerical robustness of the algorithms is verified. Finally, time delays of field UHF signals received by UHF antennas are estimated, and then substituting the sequence of time delay estimations into the localization equations based on the time difference and 3-D coordinate of the PD source can be calculated accurately. The effectiveness and practicability of this algorithm are verified.

Localization Algorithm for the PD Source in Substation Based on L-Shaped Antenna Array Signal Processing

A novel algorithm based on L-shaped antenna array signal processing is proposed in this paper for the localization of partial-discharge (PD) sources in substations. The principle of estimation of signal parameter via rotational invariance techniques has been used for finding the direction of arrival (DOA) of signals. Third-order cumulants of signals are used in this algorithm, by which Gaussian white noises and periodic narrowband interference mixed in observed signals can be efficiently suppressed. Planar location of PD sources can be obtained by solving the intersecting point of two lines in DOAs. Therefore, solving nonlinear equations can be avoided. Besides, it is convenient to replace the observed signals with their envelopes in this algorithm. The proposed algorithm is used to process mixed signals with simulated ultra-high frequency (UHF) signals by electromagnetic-wave simulation software, Gaussian white noises of different signal-to-noise ratios, and fixed-frequency noises. The planar location of PD sources is obtained approximately. UHF signals collected in substations and their envelopes have proven to be suitable to locate PD sources effectively by the proposed algorithm as well. Therefore, the accuracy and feasibility of the proposed algorithm are proved.

A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.

A Novel Algorithm for Separating Multiple PD Sources in a Substation Based on Spectrum Reconstruction of UHF Signals

Field noise interference suppression and effective extraction of signal characteristics are two keys to partial-discharge (PD) signal detection and analysis. In this paper, the autoregressive moving average (ARMA) process is utilized to model ultra-high-frequency (UHF) signals radiated by PDs. The estimation, which is based on high-order cumulants, of the ARMA orders and parameters is given theoretical analysis and implementation. The spectra of the detected signals are reconstructed with the model estimated. Then, characteristic frequencies are selected based on the reconstructed spectra and Fisher-like class separation measures. The radial basis function neural network is trained for the separation of the observed signals. Using the proposed method, UHF signals generated by electromagnetic simulation software are efficaciously modeled, reconstructed, and separated from mixing Gaussian white noises of varying signal-to-noise ratios and fixed-frequency signals. Finally, the step to obtain the number of PD sources in the assumed substation is proposed. UHF signals collected in a substation are processed by the proposed procedure, and PD sources are separated. This separation result is compared with PD sources localization results calculated by the time delay sequence, and the effectiveness of the method in the substation field interference circumstances is verified.

A Novel Partial Discharge Localization Method in Substation Based on a Wireless UHF Sensor Array

Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling rate system. This paper proposes a novel PD localization method based on a received signal strength indicator (RSSI) fingerprint to quickly locate the power equipment with potential insulation defects. The proposed method consists of two stages. In the offline stage, the RSSI fingerprint data of the detection area is measured by a wireless UHF sensor array and processed by a clustering algorithm to reduce the PD interference and abnormal RSSI values. In the online stage, when PD happens, the RSSI fingerprint of PD is measured via the input of pattern recognition for PD localization. To achieve an accurate localization, the pattern recognition process is divided into two steps: a preliminary localization is implemented by cluster recognition to reduce the localization region, and the compressed sensing algorithm is used for accurate PD localization. A field test in a substation indicates that the mean localization error of the proposed method is 1.25 m, and 89.6% localization errors are less than 3 m.

A Novel Transient Fault Current Sensor Based on the PCB Rogowski Coil for Overhead Transmission Lines

The accurate detection of high-frequency transient fault currents in overhead transmission lines is the basis of malfunction detection and diagnosis. This paper proposes a novel differential winding printed circuit board (PCB) Rogowski coil for the detection of transient fault currents in overhead transmission lines. The interference mechanism of the sensor surrounding the overhead transmission line is analyzed and the guideline for the interference elimination is obtained, and then a differential winding printed circuit board (PCB) Rogowski coil is proposed, where the branch and return line of the PCB coil were designed to be strictly symmetrical by using a joining structure of two semi-rings and collinear twisted pair differential windings in each semi-ring. A serial test is conducted, including the frequency response, linearity, and anti-interference performance as well as a comparison with commercial sensors. Results show that a PCB Rogowski coil has good linearity and resistance to various external magnetic field interferences, thus enabling it to be widely applied in fault-current-collecting devices.

Secondary voltage regulation based on wide area network

With the development of wide area network, control information and data of secondary voltage regulation (SVR) systems can be gradually promoted to standard TCP/IP-based network transmission so that the flexibility and reliability of the control system can be enhanced. In terms of the basic principle of networked control system (NCS), this paper presents the networked SVR (NSVR) model with consideration of data transmission delays, packet dropout and time-sequence disorder induced by wide area network transmission. A novel control scheme is proposed based on multi-step prediction and delay compensation, and as a result, favorable control performance and stability of the control system under the network environment can be secured. The simulation results on the New England 39-bus system are presented to illustrate the validity and effectiveness of the proposed NSVR scheme.

Insulating property of polypropylene nanocomposites filled with nano-MgO of different concentration

Polypropylene (PP) has special advantages in replacing conventional crosslinked polyethylene (XLPE) to be insulation material of power cable. In order to reveal the effects of nano-filler addition on PP, five kinds of experiments, namely TEM, DSC, breakdown strengths (BDs), space charge and dielectric measurement were investigated to evaluate the insulating properties of PP and its nanocomposites with surface-treated nano-MgO of different concentration. It is revealed that, with the addition of nano-MgO, favorable dispersibility of nano-filler and significant increase of crystallinity of polymer are observed. BDs under dc voltage increase apparently with loading of nano MgO, but when the nano concentration reaches higher than 1 wt%, the BDs have a slight decline compared with 1 wt%. It is clarified that space charge and electric field distortion are well restricted with the addition of nano-MgO, while this effect is not obvious with the nano concentration reaches 6 wt%. Permittivity has trends of increase with the rise of temperature at first and then decrease when the temperature reaches about 333 K, and the addition of nano-filler MgO could also decrease permittivity. When the nano-filler concentration reaches high, dielectric loss increases to a high level. Low nano-filler concentration in MgO/PP shows better electrical insulation properties compared with PP and high nano-filler concentration composites.

Partial Discharge Detection and Recognition in Random Matrix Theory Paradigm

The detection and recognition of partial discharge (PD) is an important topic in insulation tests and diagnoses. Take advantage of the affluent results from random matrix theory (RMT), such as eigenvalue analysis, M-P law, the ring law, and so on, a novel methodology in RMT paradigm is proposed for fast PD pulse detection in this paper. Furthermore, a scheme of time series modeling as random matrix is also proposed to extend RMT for applications with non-Gaussian noise context. Based on that, the eigenvalue distribution property is used for PD pattern recognition, which is completely new compared with traditional phase resolved PD and time-resolved PD methods. The simulation and experimental results show that the proposed methods are efficient, reliable, and feasible for PD detection and recognition especially for online applications.

Identification of back flash and shielding failure on transmission line based on time domain characteristics of traveling wave

Identification of lightning type can provide directive guidance for the operation and maintenance of transmission lines. In this study, a corresponding relationship satisfied by the polarities of three phases of fault traveling wave current is built under the condition that a fault of transmission line is caused by lightning. On the basis of the lightning overvoltage theory and the boundary condition of transmission line, it is proposed that the back flash should be identified according to the differences and similarities in polarities of three phases of current traveling waves. Based on the modified energy ratio, a method is proposed for the judgment of the transmission line fault caused by lightning and shielding failure, in accordance with the energy variation feature of transient traveling wave. Finally, the identification of types of lightning on the transmission line is achieved. The method is proved to be correct and have good application value by performing simulations and field experiments.