Research on Partial Discharge Source Localization Based on an Ultrasonic Array and a Step-by-Step Over-Complete Dictionary

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

Fiber optic Fabry-Perot sensor that can amplify ultrasonic wave for an enhanced partial discharge detection

Ultrasonic wave is a powerful tool for many applications, such as structural health monitoring, medical diagnosis and partial discharges (PDs) detection. The fiber optic extrinsic Fabry–Perot interferometric (EFPI) sensor has become an ideal candidate for detecting weak ultrasonic signals due to its inherent advantages, and each time with a performance enhancement, it can bring great application potential in broadened fields. Herein, an EFPI ultrasonic sensor for PDs detection is proposed. The sensing diaphragm uses a 5-μm-thickness and beam-supported structure to improve the responsive sensitivity of the sensor at the resonant frequency. Furthermore, the ability of the sensor to detect characteristic ultrasonic signal of PDs is further enhanced by assembling a Fresnel-zone-plate (FZP)-based ultrasonic lens with the sensing probe to amplify the ultrasonic wave before it excites the sensing diaphragm. The final testing results show that the originally developed sensor owns the sensitivity of − 19.8 dB re. 1 V/Pa at resonant frequency. While, when the FZP is assembled with the probe, the sensitivity reaches to − 12.4 dB re. 1 V/Pa, and leads to a narrower frequency band, which indicates that the proposed method has a great potential to enhance the detection ability of sensor to characteristic ultrasonic wave of PDs.

Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

According to the statistics, 40% of unplanned disruptions in electricity distribution grids are caused by failure of equipment in high voltage (HV) transformer substations. These damages in most cases are caused by partial discharge (PD) phenomenon which progressively leads to false operation of equipment. The detection and localization of PD at early stage can significantly reduce repair and maintenance expenses of HV assets. In this paper, a non-invasive PD detection and localization solution has been proposed, which uses three ultrasonic sensors arranged in an L shape to detect, identify and localize PD source. The solution uses a fusion of ultrasonic signal processing, machine learning (ML) and deep learning (DL) methods to classify and process PD signals. The research revealed that the support vector machines classifier performed best among two other classifiers in terms of sensitivity and specificity while classifying discharge and surrounding noise signals. The proposed ultrasonic signal processing methods based on binaural principles allowed us to achieve an experimental lateral source positioning error of 0.1 m by using 0.2 m spacing between L shaped sensors. Finally, an approach based on DL was suggested, which allowed us to detect a single PD source in optical images and, in such a way, to provide visual representation of PD location.

Active Dielectric Window: A New Concept of Combined Acoustic Emission and Electromagnetic Partial Discharge Detector for Power Transformers

The detection and location of partial discharge (PD) is of great significance in evaluating the insulation condition of power transformers. This paper presents an active dielectric window (ADW), which is a new concept of combined acoustic emission (AE) and electromagnetic PD detector intended for assembly in a transformer’s inspection hatch. The novelty of this design lies in the fact that all structural components of an ultrasonic transducer, i.e., the matching and backing layer, an active piezoelectric element with electrodes, and electrical leads, were built into a dielectric window. Due to the fact that its construction was optimized for work in mineral oil, it is characterized by much higher sensitivity of PD detection than a general-purpose AE sensor mounted outside a transformer tank. Laboratory tests showed that the amplitude of the AE pulses generated by creeping discharges, which were registered by the ADW, was around five times higher on average than the pulses registered by a commonly used contact transducer. A possibility of simultaneous detection of acoustic and electromagnetic pulses (with an integrated ultra-high frequency (UHF) antenna) is an important advantage of the ADW. It allows for an increase in the reliability of PD detection, the accuracy of defect location, and the effectiveness of disturbance identification. This paper describes in detail the applied methods of designing and modeling the ADW components, the manufacturing process of the prototype construction, and the results of preliminary laboratory tests, in which the detector’s sensitivity as well as the efficiency of the PD source location were evaluated.

Directional Sensitivity of a MEMS-Based Fiber-Optic Extrinsic Fabry⁻Perot Ultrasonic Sensor for Partial Discharge Detection

Extrinsic Fabry–Perot (FP) interferometric sensors are being intensively applied for partial discharge (PD) detection and localization. Previous research work has mainly focused on novel structures and materials to improve the sensitivity and linear response of these sensors. However, the directional response behavior of an FP ultrasonic sensor is also of particular importance in localizing the PD source, which is rarely considered. Here, the directional sensitivity of a microelectromechanical system (MEMS)-based FP ultrasonic sensor with a 5-μm-thick micromechanical vibrating diaphragm is experimentally investigated. Ultrasonic signals from a discharge source with varying incident angles and linear distances are measured and analyzed. The results show that the sensor has a 5.90 dB amplitude fluctuation over a ±60° incident range and an exciting capability to detect weak PD signals from 3 m away due to its high signal–noise ratio. The findings are expected to optimize the configuration of a sensor array and accurately localize the PD source.

Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers

Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions.

Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors

Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level.