Fig 1.
Drone-based magnetic field sensing for grounding grid fault diagnosis.
Fig 2.
System model of the proposed drone-based magnetic sensing approach for grounding grid fault diagnosis.
Fig 3.
Drone position while flying over the grounding grid.
Fig 4.
Grounding grid (source signal) magnetic field [12].
Fig 5.
Magnetic fields from the drone and transformer, along with other EMI. The transformer magnetic field decays with respect to the distance as the drone magnetic field is constant.
Fig 6.
SIR performance of the FastICA algorithm in case of quasi-static condition i.e., = 0, for all the source estimated signals.
Fig 7.
SIR performance of the FastICA algorithm in case of time-varying condition at = 0.05, for all the source estimated signals.
Fig 8.
SIR performance of the proposed technique in case of time-varying scenario at = 0.05, for all the separated source signals.
Fig 9.
SIR performance of the proposed technique in case of time-varying scenario at = 0.1, for all the source signals.
Fig 10.
SIR performance of the proposed technique in case of time-varying scenario at = 0.2, for all the separated source signals.
Table 1.
Comparison of the FastICA and the proposed technique in terms of SIR for quasi-static and time-varying conditions utilizing the grounding grid signals.
Fig 11.
Performance comparison of the FastICA, IVA, and the Proposed technique in time varying scenario at = 0.05, for all the separated source signals.