The transmission and distribution systems are essential in facilitating power flow from the source multiple loads over large distances with high magnitudes of voltages and currents. Hence, the monitoring and control of various components of these structures are crucial. Traditionally, this was implemented by sensing only the grid current and grid voltage parameters through coils, clamps, or instrument transformers. However, these have bulky structures that restrict them to the substation and have installation and maintenance issues due to their direct contact with high voltage conductors. Currently, the power grid is undergoing various developments e.g., penetration of renewable energy sources, remote control, and automation, bidirectional power flow, etc. These developments call for compact and energy-efficient sensors to sense multiple grid parameters such as the magnetic field data, temperature, humidity, acoustics, etc., to enable real time, wide area monitoring and the predictive maintenance of the power grid. The goal of this paper is to summarize the advancements in sensing technologies on transmission and distribution systems over a decade and to explain their role in the forthcoming expansion of the power grids. This paper aims to outline the current state-of-affairs of sensor technology as well as to fill research gaps by exploring their limitations.

Wind turbine drive trains consist of mechanical components such as the gearbox, the bearings and the rotating shaft, and an electrical component, the electric generator. These components are electromagnetically and electromechanically coupled with each other and have continuously rotating parts, making them more susceptible to faults. Unfortunately, these components are individually modeled while performing finite element analysis (FEA), and the coupling effects are neglected, yielding approximations that are not acceptable for, e.g., modal analysis. This paper addresses the issue and presents a FEA model of the complete wind turbine drive train subsystem. Here, a unique co-simulation platform was developed to integrate and analyze the mechanical and electrical components of the drive train. The case studies performed here report the existence of electromagnetic coupling among the components of the drive train by simulating various faults such as broken gearbox tooth and demagnetization of permanent magnets and studying their influence on the magnetic flux of the electric generator. The paper offers new insights on motivation, applications as well as the importance of FEA modeling of the entire wind turbine drive train, and how it could be critical to non-destructive evaluations and fault detections on the wind turbines.

The magnetic-flux-based condition monitoring techniques are becoming increasingly popular due to their advantages, such as non-invasiveness, low costs, and ease of sensor installation. However, developing diagnostics and prognostics-based condition monitoring systems becomes challenging as magnetic signals-based wind time series exhibit non-linear and non-stationary characteristics due to being subjected to diverse combination of dynamic system behavior. The present data-driven feature extraction techniques, as well as knowledge-based and parameter estimator models, fall short in effectively quantifying these characteristics due to increased signal complexity and computation cost. Additionally, methods like the short-time Fourier transform that assumes stationarity of signals and has a fixed window size and discrete wavelet transform that has pre-defined wavelet bases have proven suboptimal for analyzing non-linear and non-stationary magnetic flux signals due time-frequency resolution tradeoffs. This research bridges the gap by investigating a new methodology of feature extraction to adeptly process the non-linear and non-stationary behavior in magnetic signals-based wind time series. This methodology is unique in nature since it utilizes magnetic signature-based fault condition indicators derived from the wind generator of a drive train model that accounts for the electromagnetic coupling. Additionally, the signal processing technique employed here considers non-linearity and non-stationarity that arises from wind characteristics, gearbox dynamics, and grid conditions and its effects on the magnetic flux density of the wind generator. Notably, this methodology offers new insights on motivation, applications, and significance of magnetic-flux-based condition monitoring techniques and highlights its potential as a critical tool for non-invasive fault detection on multiple components of a wind turbine.