Yan-Bo Wang

Bio: Yan-Bo Wang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Partial discharge & Acoustic emission. The author has an hindex of 7, co-authored 20 publications receiving 165 citations.

Acoustic localization of partial discharge sources in power transformers using a particle-swarm-optimization-route-searching algorithm

Abstract: Methods to infer the location of partial discharge (PD) in high-power transformers using acoustic emission (AE) data have been extensively studied. The inner complex structure of the transformers is one of the most critical points in localization with AE method. Windings and cores affect acoustic wave propagation by changing the arrival time because of inhomogeneous propagation. A transformer physical model has been established herein by taking these complex factors into consideration. Each node in the model is a potential PD position, and an acoustic wave route comprise a series of nodes. The velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics. A propagation-time estimation algorithm is proposed to calculate the propagation-time. Based on the transformer physical model, a particle-swarm-optimization route-searching (PSORS) algorithm is employed for searching the position of the PD source. By comparing time differences of measured AE signals and the ones estimated by the PSORS algorithm, the velocities and positions of particles are continually adjusted, which can ensure their convergence to the PD source position. Localization experiments were performed in 35 and 110 kV transformers, respectively, to verify the applicability of the proposed algorithm. A protrusion defect is used to trigger PD pulses, and four AE sensors with two different arrangements are employed. The results confirm that the accuracy of proposed localization method is insensitive to the presence of metal structures blocking acoustic wave routes.

Localization of multiple partial discharge sources in air-insulated substation using probability-based algorithm

Abstract: Ultra-high-frequency (UHF) sensing technique has been introduced to detect and localize partial discharge (PD) sources in air-insulated substation (AIS). This paper presents a probability-based algorithm to localize multiple PD sources which may occur simultaneously in different power equipment. Assuming that the time difference of arrival (TDOA) between all pairs of antennas in a array are normally distributed, the probability density function (PDF) of PD source coordinates can be obtained by substituting the linearized form of time difference equations into PDFs of TDOAs. When large number of PD signals are recorded, the joint PDF (JPDF) can be calculated from the product of PDF of each TDOA. Then the PD coordinates to be solved are regarded as with highest probability, and can be solved by taking the derivative of JPDF. In the case of multiple PD sources, mixed UHF signals are separated by clustering the TDOA vectors with K Means clustering method. PD experiments are performed to test the presented algorithm, and the localization accuracy of proposed algorithm is compared with other typical methods such as Newton-Raphson, Particle Swarm Optimization and plane intersection method. The results indicate that the probability-based localization algorithm reasonably integrates the TDOAs of continuous signal sequence, which can effectively reduce the influence of TDOA estimation errors and improve the localization accuracy.

Separating Multi-Source Partial Discharge Signals Using Linear Prediction Analysis and Isolation Forest Algorithm

Abstract: Partial discharge (PD) detection is an effective way to find defects and diagnose the insulation condition of power equipment. During manufacturing and operating, there may exist multi-source PD signals in the equipment, which could seriously affect the accuracy of subsequent defect analyses. In this paper, a universal separation methodology using linear prediction analysis (LPA) and the isolation forest algorithm (IFA) is introduced. By approximating the present waveform point by a linear combination of several points in the past, a 12-D linear prediction cepstrum coefficient (LPCC) feature space can be established which can accurately characterize PD waveforms. In this paper, we applied principal component analysis (PCA) to reduce the feature space to 2-D space. The IFA was adopted to separate multi-source PD signals which quantified the degree of clustering and added one more parameter based on the original features. Thus, the proposed method can separate multiple PD sources effectively even if the difference in the features of different types of PDs is small. In addition, the proposed algorithm easily removed the noise points during the separation process. The algorithm was applied to different types of PD signals on 35-kV transformers and a three-source PD data set on a 252-kV gas-insulated switchgear (GIS) platform. The separation results confirm that the proposed algorithm can effectively separate and distinguish various PD signals.

UHF antenna array arrangement optimization for partial discharge direction finding in air-insulted substation based on phased array theory

Abstract: Ultra-high-frequency (UHF) antenna array system is applied to detect and locate partial discharge (PD) sources in air-insulated substation (AIS). Time-difference localization algorithm is generally adopted in such system in which the antenna array is of large scale. In this paper, the phased array theory from radar field is introduced to estimate the direction-of-arrival (DOA) of PD source to reduce the array size. The DOA estimation results are calculated by two-sided correlation transformation (TCT) focusing algorithm and multiple signal classification (MUSIC) algorithm. Based on that, the optimum arrangement of antenna array is investigated considering both high directional accuracy and small array size. The DOA estimation performances of three array geometries namely uniform linear array (ULA), uniform circular array (UCA) and uniform Y-shaped array are compared through Cramer-Rao lower bound (CRLB), numerical simulation and experimental testing. Moreover, the number of elements and element spacing are optimized. The results indicate that 4-elements UCA with 0.2m element spacing is the optimum array arrangement for PD location in AIS.

Partial discharge localisation methodology for power transformers based on improved acoustic propagation route search algorithm

Abstract: The localisation of partial discharge (PD) sources using the acoustic emission (AE) technique has attracted increasing research attention. The complicated propagation routes and wave-type conversion in power transformer can induce considerable localisation error. In this paper, the catadioptric phenomenon of AE wave propagation is explained in detail. With the incident angle varying, the wave type of the direct wave in tank wall could convert and the velocity will change accordingly. A transformer model is established in which each node refers to a suspected PD position and a shortest route search algorithm is proposed to calculate the shortest path between two nodes in this model. However, the fastest route is most significant to PD localisation rather than the shortest route when TDOA method is used. As a result, an improved propagation route search (IPRS) algorithm, which can recreate the propagation process and calculate the fastest AE routes, is proposed to localise the PD origin. To verify the feasibility of the IPRS algorithm, localisation experiments were performed in 35 and 110 kV transformers, respectively. Compared with other present localisation methods, such as the Chan algorithm, the genetic algorithm and the imperial competitive algorithm, the proposed algorithm can effectively reduce localisation error.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning

Abstract: Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 40 However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision–making about the GIS status

Maritime Moving Target Localization Using Passive GNSS-Based Multistatic Radar

Abstract: This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author’s knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general.

Applications of the Acoustic Method in Partial Discharge Measurement: A Review

Abstract: This paper reviews the main features of acoustic emission technique. First, the characteristics of acoustic signals and their physics are addressed. Then, the structure of acoustic sensors employed for capturing signals is studied. In the next step, the acoustic method of PD measurement is compared with the standard electrical method. Afterward, the applications of AET in PD measurement in different equipment are summarized. All acoustic method and combined acoustic-electrical method for PD localization in transformers are discussed. Moreover, the applications of AET in the monitoring of GIS systems are explained, along with the acoustic behaviors of moving particles in these systems. Finally, the general considerations in the signal processing of the acoustic signals are reviewed.