High-voltage infrastructure condition monitoring and diagnostics is essential for the continuous and uninterrupted supply of electricity to the grid. A common metric for establishing HV plant equipment condition is the identification and subsequent monitoring of insulation faults known as partial discharge. Traditional techniques for this require physical connection of sensors to plant equipment to be monitored, leading to potentially high cost and system complexity. A non-invasive and significantly less costly approach is the use of wireless receivers to measure the electromagnetic waves produced by partial discharge faults. Hence, a large-scale monitoring network can be realized to monitor equipment within a substation compound at relatively low cost.
An issue with the use of commercially available wireless sensor technology, based around high-speed data conversion, is ensuring each node is low cost and low-power, whilst still being capable of detecting and measuring partial discharge faults within a reasonable distance. In order to reduce power consumption two signal processing techniques are proposed, a transistor-reset integrator and a gated high-speed analogue-to-digital converter, which provide a low power method of radiometric partial discharge measurement by avoiding continuous high-speed sampling.
The measured results show that the transistor-reset integrator based system is capable of measuring partial discharge over a distance of 10 m with an error within 1 m, and performing location estimation to within 0.1 m as compared to estimation performed using high-speed sampling, at fraction of the power consumption. The folding ADC based system is able to sample a PD like signal, but requires additional development to improve performance and fully integrate it into a system. The overall results prove the operating principle of the partial discharge monitoring system, which has the potential to be developed into a viable solution.
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (6MB) | Preview
Downloads
Downloads per month over past year