RELIABILITY CENTERED MAINTENANCE (RCM) FOR ASSET MANAGEMENT IN ELECTRIC POWER DISTRIBUTION SYSTEM

The purpose of Maintenance is to extend equipment life time or at least the mean time to the next failure. Asset Maintenance, which is part of asset management, incurs expenditure but could result in very costly consequences if not performed or performed too little. It may not even be economical to perform it too frequently. The decision therefore, to eliminate or minimize the risk of equipment failure must not be based on trial and error as it was done in the past. In this thesis, an enhanced Reliability-Centered Maintenance (RCM) methodology that is based on a quantitative relationship between preventive maintenance (PM) performed at system component level and the overall system reliability was applied to identify the distribution components that are critical to system reliability. Maintenance model relating probability of failure to maintenance activity was developed for maintainable distribution components. The Markov maintenance Model developed was then used to predict the remaining life of transformer insulation for a selected distribution system. This Model incorporates various levels of insulation deterioration and minor maintenance state. If current state of insulation ageing is assumed from diagnostic testing and inspection, the Model is capable of computing the average time before insulation failure occurs. The results obtained from both Model simulation and the computer program of the mathematical formulation of the expected remaining life verified the mathematical analysis of the developed model in this thesis. The conclusion from this study shows that it is beneficial to base asset management decisions on a model that is verified with processed, analysed and tested outage data such as the model developed in this thesis

Real-time Condition Monitoring and Asset Management of Oil- Immersed Power Transformers

This research pioneers a comprehensive asset management methodology utilizing solely online dissolved gas analysis. Integrating advanced AI algorithms, the model was trained and rigorously tested on real-world data, demonstrating its efficacy in optimizing asset performance and reliability

Signal Processing and Classification Tools for Intelligent Distributed Monitoring and Analysis of the Smart Grid

This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included

Condition-based hazard rate estimation and optimal maintenance scheduling for electrical transmission system

The effectiveness of expending maintenance resources can vary dramatically depending on the target and timing of the maintenance activities. The objective of the work to develop a method of allocating economic resources and scheduling maintenance tasks among bulk transmission system equipment, so as to optimize the effect of maintenance with respect to the mitigation of component failure consequences. Techniques including condition-based failure rate estimation of electric transmission system components, analysis of failure consequences in power system, probabilistic modeling and risk assessment, and optimization are integrated in the work. Hidden Markov model is a good tool to estimate instantaneous status for deteriorating components. The maintenance selection and scheduling approach for bulk transmission equipment is based on the cumulative long-term risk caused by failure of each piece of equipment;This approach not only accounts for equipment failure probability and equipment damage, but it also accounts for the outage consequence in term of system related security problems. Various types of maintenance activities are studied and their relationship to the failure modes and system security improvement are investigated. An optimizer is developed to select and schedule the maintenance for large networks with various types of resource constraints, together with methods of resource reallocation;Finally, a strategy of incorporating maintenance activities among different transmission owners is developed. The objective of our work is to allocate resources economically and strategically so as to provide best performance of maintenance for electrical transmission system. These strategies can also be applied to problems inherent to resource intensive asset management in many similar types of infrastructures such as gas pipelines, airlines, and telecommunications

A Review on the Classification of Partial Discharges in Medium-Voltage Cables : Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Toward cascading failure mitigation in high voltage power system capacitors

As electrical power networks adapt to new challenges, advances in high voltage direct current interconnection offer one means to reinforce alternating current networks with flexibility and control, accordingly improving diversity to become a present-day, viable alternative to network flexibility and energy storage measures. High voltage capacitors support these links and offer simple means of voltage support, harmonic filtering, and are inherent to established and emerging converter designs. Where research literature predominantly explores use of modern dielectrics in efforts toward improved capacitor technologies, but reveals little about: existing capacitor designs; associated failure modes or statistics; or avenues in monitoring or maintenance, simulation modelling equips engineers with an approach to pre-emptively anticipate probable incipient fault locations toward improving designs for systems yet to be commissioned. This Dissertation presents a high-voltage capacitor simulation model, before exploring two questions about these hermetically sealed, highly modular assets: where are incipient faults most likely to arise; and how can internal faults be externally located? Nonlinear voltage distributions are found within each and among connected units, induced through parasitic effects with housings supported at rack potential. Consequent implications are considered on: stresses within unit dielectrics, susceptibility to cascading failure, and an ability to locate internal faults. Corroboration of fault detection and location is additionally found possible using unit housing temperatures. A model is presented, developed to be scalable, configurable, and extensible, and made available for posterity. Opportunities in asset design, modelling, manufacture, and monitoring are proffered toward improvements not only in operational longevity, but in understanding and early awareness of incipient faults as they develop.As electrical power networks adapt to new challenges, advances in high voltage direct current interconnection offer one means to reinforce alternating current networks with flexibility and control, accordingly improving diversity to become a present-day, viable alternative to network flexibility and energy storage measures. High voltage capacitors support these links and offer simple means of voltage support, harmonic filtering, and are inherent to established and emerging converter designs. Where research literature predominantly explores use of modern dielectrics in efforts toward improved capacitor technologies, but reveals little about: existing capacitor designs; associated failure modes or statistics; or avenues in monitoring or maintenance, simulation modelling equips engineers with an approach to pre-emptively anticipate probable incipient fault locations toward improving designs for systems yet to be commissioned. This Dissertation presents a high-voltage capacitor simulation model, before exploring two questions about these hermetically sealed, highly modular assets: where are incipient faults most likely to arise; and how can internal faults be externally located? Nonlinear voltage distributions are found within each and among connected units, induced through parasitic effects with housings supported at rack potential. Consequent implications are considered on: stresses within unit dielectrics, susceptibility to cascading failure, and an ability to locate internal faults. Corroboration of fault detection and location is additionally found possible using unit housing temperatures. A model is presented, developed to be scalable, configurable, and extensible, and made available for posterity. Opportunities in asset design, modelling, manufacture, and monitoring are proffered toward improvements not only in operational longevity, but in understanding and early awareness of incipient faults as they develop

Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data

The ultimate goal of this research is to develop an online, non-destructive, incipient fault detection system that is able to detect incipient faults in transformers and other electric equipment before the faults become catastrophic. With the condition assessment capability of the detection system, operators are equipped with better information during their decision-making process. Corrective actions are taken prior to transformer and equipment failures to prevent down-time and reduce operating and maintenance costs. Diagnosis of data associated with incipient failures is essential to develop an efficient, non-destructive, and online system. Field testing data were collected from controlled experiment and simulation data from mathematical models are studied. This thesis presents a data-mining approach to analyze field recorded and simulation data to characterize incipient fault data and study its properties. A supervised classifier using neural network (NN) toolbox in Matlab provides an efficient and accurate classification method to separate monitoring signal data into clusters base on their properties. However, raw data collected from the field and simulations will create too many dimensions and inputs to the neural network and make it a complex and over-generalized classification. Therefore, features are extracted from the data set, and these features are formed into feature clusters in order to identify patterns in signals as they are related to various physical behaviors of the system. The similarity between recognized patterns and patterns shown in future monitoring signals will trigger the warning of initializing or developing faults in transformers or equipment. This thesis demonstrates how different features were extracted from the raw data using various analysis techniques in both time domain and time-frequency domain, and the design and implementation of a neural network-based classification method. The classifier outputs are classes of data being separated into groups based on their characteristics and behaviors. Meaning of different classes is also explained in this thesis.Texas A&M University Honors and Academic Scholarships Office, Power System Automation Lab at Texas A&M Universit

Analysis of medium voltage vacuum switchgear through advanced condition monitoring, trending and diagnostic techniques

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering, 2015Electrical utilities are tasked with managing large numbers of assets that have long useful lives and are fairly expensive to replace. With emphasis on medium voltage vacuum circuit breakers, a key challenge is determining when circuit breakers are close to their end-of-life and what the appropriate action at that point in time should be. Condition-based maintenance, intended to “do only what is required, when it is required,” has been reported as the most effective maintenance strategy for circuit breakers. This dissertation provides an overview, together with laboratory measurements, on non-intrusive technologies and analytics that could reduce maintenance costs, unplanned outages, catastrophic failures and even enhance the reliability and lifetime of circuit breakers by means of a real-time condition monitoring and effective failure prevention maintenance approach. The key areas of research are the condition assessment of the mechanical mechanism based on coil current signature diagnosis, degradation detection of the main interrupting contacts through thermal monitoring and interrupter vacuum integrity assessment based on magnetron atmospheric condition (MAC) testing. The information from test results allows both immediate onsite analysis and trending of key parameters which enables informed asset management decisions to be taken.GS201