Partial discharge classifications: Review of recent progress

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].

Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

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Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

A multi-source information fusion fault diagnosis for aviation hydraulic pump based on the new evidence similarity distance

Ladder-Wise calculation method for z-coordinate of transformer PD source based on planar layout UHF antenna sensors

Partial discharge (PD) is a key parameter which describes insulation condition of gas-insulated switchgear GIS, GIS internal defects can be discovered in time with the interferences such as corona discharge from the power system using Ultra-high frequency (UHF) method. Traditional method based on statistical characteristics is limited to the analysis of image features that describe the PD pattern plot, causing low recognition rate of some kinds of PD. In this paper, the acquisition of the PD characteristics caused by four typical insulation defects forms φ-v-n 3D PRPD pattern plots sample matrix. The largest Lyapunov exponent of each column of matrix is calculated. A 36-dimension vector is then obtained as the chaotic characteristics of the PD in different voltage phases. The experimental results show that the recognition method based on the chaotic characteristics performs well on all four kinds of insulation defects and can satisfy the recognition order. The method based on the chaotic characteristics has a strong recognition ability for the discharge physical models of gas gap, which is an advantage over the traditional method. The two recognition methods have a good complementary property. Combining the complementary chaotic and statistical characteristics in a decision-making level by using the Dempster-Shafer evidence theory results in an accuracy of above 98%.

Na Shu

Papers

GIS partial discharge pattern recognition based on the chaos theory