Unai Garro

The increasing penetration of renewable energy sources (RESs) has a direct impact on the efficiency and reliability of the power grid. In this context, maintaining voltage stability and power quality has become more important than ever. Trans5 formers are crucial components in the grid and are generally equipped with on-load tap changer (OLTC) mechanisms. With the OLTC, transformers can automatically adapt to different voltage levels depending on the dynamics of the grid. Thereby, they assist in the penetration of RESs and improve system operation efficiency. However, the continuous grid fluctuations require increased activity from OLTCs, which in turn contributes to elevated stress on equipment and accelerated ageing. Existing OLTC monitoring solutions are generally invasive and time-consuming. Accordingly, this research work presents a new cost-effective OLTC monitoring approach based on audio signals. The approach is inspired by self-paced learning strategies. Initially, different monitoring solutions are evaluated and tested with respect to accuracy, computational cost, and robustness to noise. After the best architecture is selected, the audio signal processing approach is further refined through signal processing and Convolutional Neural Networks (CNNs). The proposed approach is benchmarked against AutoML, which shows a competitive performance, but with increased computational cost. The approach has been validated using real data from a transformer in operation.

Power cables are critical assets for the reliable operation of the grid. The cable lifetime is generally estimated from the conductor temperature, and associated lifetime reduction. However, these tasks are intricate due to the complex physics-of-failure (PoF) degradation mechanism of the cable. This is further complicated with the different sources of uncertainty that affect the cable lifetime estimation. Generally, simplified or deterministic PoF models are adopted resulting in non-accurate decision-making under uncertainty. In contrast, the integration of uncertainties leads to a probabilistic decision-making process impacting directly on the flexibility to adopt decisions. Accordingly, this paper presents a novel cable lifetime estimation framework that connects data-driven probabilistic uncertainty models with PoF-based operation, and degradation models through Bayesian state-estimation techniques. The framework estimates the cable health state, and infers confidence intervals to aid decision-making under uncertainty. The proposed approach is validated with a case study with different configuration parameters, and the effect of measurement errors on cable lifetime are evaluated with a sensitivity analysis. Results demonstrate that ambient temperature measurement errors influence more than load measurement errors, and the greater the cable conductor temperature the greater the influence of uncertainties on the lifetime estimate.

The online Remaining Useful Life (RUL) estimation of underground cables and their reliability analysis requires obtaining the cable failure time probability distribution. Monte Carlo (MC) simulations of complex thermal heating and electro-thermal degradation models can be employed for this analysis, but uncertainties need to be considered in the simulations, to produce accurate RUL expectation values and confidence margins for the results. The process requires performing large simulation sets, based on past temperature or load measurements and future load predictions. Field Programmable Gate Arrays (FPGAs) permit accelerating simulations for live analysis, but the thermal models involved are complex to be directly implemented in hardware logic. A new standalone FPGA architecture has been proposed for the fast and on-site degradation and reliability analysis of underground cables, based on MC simulation, and the effect of load uncertainties on the predicted cable End Of Life (EOL) has been analyzed from the results.