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).

Detection of unauthorized drones is mandatory for defense organizations and also for human life protection. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field. This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. In this paper, a time-varying drone detection technique (TVDDT) is proposed that first stores a data set of the mixed signals in a time-varying scenario, where time variations occur within the processing data blocks. After estimating the mixing matrices, we developed a technique to track variations in the channel. This technique is based on variations in the mixing coefficients. The proposed channel tracking technique performs classification and detection based on minimum variation criteria in the channel. The proposed TVDDT technique is evaluated through simulations and its superior performance is observed.

Grounding grid is responsible for driving lightning and short circuit currents into ground. Faults in substation grounding grid can lead to significant rise in surface potential and ultimately loss to power system and operators. This paper proposes a novel technique based on derivative method to diagnose breakpoints in grounding grid. Derivative of surface magnetic flux density on circle results in peak at conductor's location. Once a conductor is broken the flow of current and surface magnetic field ceases, which is recognized by the absence of peak at corresponding conductor's location. The use of circle even enables this method for diagnosing diagonal branch. Furthermore, the method is analyzed for soil of different resistivities and monolayer and multilayer soils. Simulation results show that the proposed method is feasible for breakpoint diagnosis of grounding grid without excavation.

Optimising the orientation of the unknown grounding grid is effective to measure its accurate configuration. On the other hand, burial depth and conductors’ current are essential for the status diagnosis of grounding grid. This study employs the derivative method to determine the above‐grounding grid characteristics without excavation. Surface magnetic flux density after the current injection process is subjected to derivative on the circle. The orientation of the grounding grid is accurately measured from the location of gradient peaks. Moreover, the grounding grid depth and conductors current are calculated from peaks width and peak value. The problem of substation intense electromagnetic environment that readily distorts the results of the proposed method is also addressed. Blind source separation famous technique, the fast independent component analysis (FastICA), is utilised to separate the original signal from the mixed signal. Furthermore, the authors also probe the influence of circle radius for the alarming case of a diagonal conductor. Mathematical reasoning, simulation, and experimental results illustrate that the proposed method is feasible to find the inverse features of grounding grid. Furthermore, the signal recovered through FastICA delivers results with high accuracy.

Grounding grids are essential for ensuring the safety of power substations, but their performance can degrade due to corrosion, fractures, or other faults. Traditional fault diagnosis methods are time-consuming, labor-intensive, and require physical access to substations, posing safety risks. This paper introduces a drone-based approach for magnetic field sensing to diagnose grounding grid faults, significantly reducing operational risks and improving efficiency. However, the movement of the drone introduces time-varying electromagnetic interference (EMI) from substation equipment and the drone itself, complicating the isolation of grounding grid signals. To address this problem, we propose a time-varying un-mixing technique combined with the Fast Independent Component Analysis (FastICA) algorithm to effectively suppress the EMI and extract the grounding grid signals. Simulation results demonstrate the efficacy of the proposed technique in separating grounding grid signals under time-varying conditions, outperforming the FastICA algorithm by 96.36% and the Independent Vector Analysis (IVA) by 41.17% at a block length of 4000 and ΓΔk=0.05 . These results highlight the robustness and applicability of the proposed approach for real-world grounding grid fault diagnosis, ensuring accuracy and safety in EMI-rich environments. However, the performance of the proposed technique degrades at higher values of ΓΔk , which represents the speed of the flying drone.