FPGA-Based Degradation and Reliability Monitor for Underground Cables

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

A Diagnostics Framework for Underground Power Cables Lifetime Estimation Under Uncertainty

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 physicsof-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

LOCAL POSITIONING SYSTEMS VERSUS STRUCTURAL MONITORING: A REVIEW

SUMMARY Structural monitoring and structural health monitoring could take advantage from different devices to record the static or dynamic response of a structure. A positioning system provides displacement information on the location of moving objects, which is assumed to be the basic support to calibrate any structural mechanics model. The global positioning system could provide satisfactory accuracy in absolute displacement measurements. But the requirements of an open area position for the antennas and a roofed room for its data storage and power supply limit its flexibility and its applications. Several efforts are done to extend its field of application. The alternative is local positioning system. Non-contact sensors can be easily installed on existing infrastructure in different locations without changing their properties: several technological approaches have been exploited: laser-based, radar-based, vision-based, etc. In this paper, a number of existing options, together with their performances, are reviewed. Copyright © 2014 John Wiley & Sons, Ltd

Concepts and Methods to Assess the Dynamic Thermal Rating of Underground Power Cables

With the increase in the electrical load and the progressive introduction of power generation from intermittent renewable energy sources, the power line operating conditions are approaching the thermal limits. The definition of thermal limits variable in time has been addressed under the concept of dynamic thermal rating (DTR), with which it is possible to provide a more detailed assessment of the line rating and exploit the electrical system more flexibly. Most of the literature on DTR has addressed overhead lines exposed to different weather conditions. The interest in the dynamic thermal rating of power cables is increasing, considering the evolution of computational methods and advanced systems for cable monitoring. This paper contains an overview of the concepts and methods referring to dynamic cable rating (DCR). Starting from the analytical formulations developed many years ago for determining the power cable rating in steady-state conditions, also reported in International Standards, this paper considers the improvements of these formulations proposed during the years. These improvements are leading to include more specific details in the models used for DCR analysis and the computational methods used to assess the power cable’s thermal conditions buried in soil. This paper is focused on highlighting the path from the initial theories and models to the latest literature contributions. Attention is paid to thermal modelling with different levels of detail, applications of 2D and 3D solvers and simplified models, and their validation based on experimental measurements. A salient point of the overview is considering the DCR impact on reliability aspects, risk estimation, real-time calculations, forecasting, and planning with different time horizons

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Proceedings of the 2017 Coal Operators\u27 Conference

Proceedings of the 2017 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978174128261