![]() ![]() The joint assessment between the equipment critical state index and its risk analysis, which is based on the history of maintenance procedures, allows for a more assertive decision-making process. Continued operation under Condition 4 may result in a total fault. In condition 4 there is an indication of excessive cellulose and/or mineral oil decomposition. When the historical data are not available, the IEEE method can be more efficient, since it uses four conditions to identify failure states:Ĭondition 1 refers to a normal operating condition, that is, if the dissolved gases are at levels below those presented in Table 3, then the transformer is considered to be operating correctly.Ĭondition 2 indicates a possible failure and many mineral oil samples should be taken to determine the tendency of gas growth.Ĭondition 3 indicates a high level of cellulose and/or mineral oil decomposition, and there is probably a transformer failure. Thus, this methodology has an appropriate way to evaluate the condition of a transformer based on the historical data about its chromatographic assays. To confirm the existence of a fault, one of the gases shown in Table 3 must have a concentration equal to or higher than L1 and more significant than the value indicated by G2. The purpose of this chapter is to present a case study involving the processing of asset databases of an electrical transmission system in order to create subsidies for the development of an efficient asset management system. For example, machine learning applied to data mining is capable of performing specific tasks such as:įeature selection (selection of the most critical variables in a process, system, or database) The computational tools and techniques based on machine learning help to solve problems related to the extraction of information and knowledge from data. These algorithms and techniques are vast and are also used in non-database applications-for example such methods can directly interact with the environment to accomplish the tasks discussed above. With a large amount of information currently available from assets, it is critical that standalone or semi-autonomous machine learning techniques should be able to extract knowledge and increase the performance or robustness of a system or process. Requirement level to be met or minimum performance requirement Type of operation requests (existence of redundancies or equipment in standby) Still, the first parameters to be considered in the asset management of electrical systems can be listed as follows:Īdditionally, directly or indirectly, operational context aspects must also be considered, such as: The big challenge involving asset management in electric systems is to seek a solution that enables the electric sector to reconcile interests with the environmental, economic, market, technological, regulatory and corporate constraints. ![]()
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