Evaluation And Concept Design Of Remaining Life Determination System For Power Asset In Utilities Electrical Substations

• Main Author: Nur Ilinadiah Binti Ismail
• Language: English
• Published: 2023
• Subjects: Degree Of Polymerization

In the energy system networks, the health of transmission energy transformers is critical to the reliability of electricity supply. Knowing the transmission power transformer's accurate life expectancy is of essential significance as it allows for optimized replacement of assets. Electrical power systems play a main role in both industrial and commercial manufacturing and services and have a significant impact on citizens ' personal life. The transformer is one of the main assets of any energy distribution scheme. Transformers constitute comprehensive investment in any energy delivery system, and due to the significant impact on scheme reliability of a transformer outage, careful management of this type of asset is critical. This project provides a technique for estimating and predicting the probability of failure in the energy scheme associated with aging transformer units. Statistically, if used in a situation where there is a large population, the sample mean, or average age technique is appropriate. Obviously, this technique is not appropriate for parts of the energy scheme with very few end-of-life samples. The sample's vital weakness means that it utilizes only died component data. This study suggested a strategy by using information from TNB Research to evaluate and predict an energy transformer's lifetime. Information with both killed and surviving transformers will help estimate the mean life of the power transformer. Manual calculation technique and graph of updated DP value are two methods. While the two methods have different assessment approaches and solution techniques, they are interrelated and use the same raw format of data. Of this research as well as the shape of distributions, the mean life and standard deviation for ordinary and DP-value assessment graph should be quite close. Thus, statistical reliability analysis can provide predictions, such a percentage of transformers that will fail at a time before a particular age, and how many transformers will fail in the next year using the failure rate model. This prediction can also define the capital expenditure forecast (the cost of substitute and consequential cost of failure). It will thus prevent the harvesting of assets and the chance of unforeseen expenses.