Transient Earth Voltage Method Research Articles

Equivalence analysis of multiple monitoring methods for partial discharge

In order to study the relationship between the amplitude (mV) and the discharge (pC) of a typical fault of a tip discharge in a partial discharge. Therefore, three different groups of tip electrode models (tip-tip, tip-plate, tip-ball) were simulated. The relationship between magnitude and discharge volume and voltage was obtained by using amplitude detection method (Ultra High Frequency Method, Acoustic Emission Method and Transient Earth Voltage method) and Pulse Current Method respectively. Kendall correlation analysis found that the amplitude and the voltage of the AE Method in the three amplitude detection methods are closest to the variation trend of the discharge quantity and the voltage of the Pulse Current Method. Exploring the equivalence between the amplitude and the discharge of the most accurate detection means, the mathematical model with the best fit is the Sum of Sin Functions.

Investigation on Propagation Mechanism of Low Frequency Component of Partial Discharge Signal Measured with Transient Earth Voltage Method

Investigation on Propagation Mechanism of Low Frequency Component of Partial Discharge Signal Measured with Transient Earth Voltage Method

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.

Impulsive noise reduction for transient Earth voltage‐based partial discharge using Wavelet‐entropy

Partial discharge (PD) is caused by the localised electrical field intensification in insulating materials. Early detection and accurate measurement of PD are very important for preventing premature failure of the insulating material. Detection of PDs in metal-clad apparatus through the transient Earth voltage method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment remains the major barrier to improving its measurement accuracy. In this study, a wavelet-entropy-based PD de-noising method has been proposed. The unique features of PD are characterised by combining wavelet analysis that reveals the local features and entropy that measures the disorder. With such features, a feed-forward back-propagation artificial neural network is adopted to recognise the actual PDs from noisy background. Comparing with other methods such as the energy-based method and the similarity-comparing method, the proposed wavelet-entropy-based method is more effective in PD signal de-noising.