Grounding Grid Fault Diagnosis With Emphasis on Substation Electromagnetic Interference

Abstract

Grounding grid fault diagnosis is essential for the safe operation of a substation. However, the substation vicinity is highly electromagnetic. Therefore, the electromagnetic based fault diagnosis is vulnerable to electromagnetic interference (EMI). This paper presents the gradient electromagnetic method for the grounding grid fault diagnosis but unlike the previous methods, fault diagnosis here includes the breakpoints and percentage corrosion simultaneously. The diagnosis is based on comparing the calculated resistivity with the designed resistivity of the grid. The resistivity is calculated from the grid's surface electric and magnetic fields. Furthermore, the existing literature, emphasis on EMI is negligible with the main focus on fault diagnosis only. Therefore, to cope with the EMI, we utilized the Independent Vector Analysis (IVA) to isolate the grounding grid signal from the interfered signal. The validity of IVA is examined by comparing with different known isolation algorithms considering various evaluation criteria. The mathematical reasoning, simulation results and experimental output illustrate that the gradient electromagnetic method along the IVA is feasible for grounding grid fault diagnosis under substation electromagnetic environment (EME).

Isolation of multiple electrocardiogram artifacts using independent vector analysis

“…The source data is represented by in D data sets. After the estimation of through the IVA algorithm, the resultant source signals as given in Qamar et al (2022) are expressed as:…”

Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification

“…Currently, deep learning [14] has been broadly used in different fields [15][16][17][18]. Specially, as one of classical deep learning technology, autoencoder (AE) has been prevailing applied in a wide area such as image processing, speech recognition and medicine [19,20].…”