Risk assessment for power transformers in PEA substations using health index

Abstract

This paper presents a risk assessment tool for power transformers in central region substations of Provincial Electricity Authority (PEA) by using health index, which is computing from history and condition weighted scores. The maintenance data from 237 power transformers in central region are used to analyze the risk factors' ranking and scores. The weights of the risk factors are assigned by experienced maintenance crews. The risk assessment factors and scores are categorized with equal weight into two parts: the history of the transformer; and the current condition of the transformer. The weighted scores from both parts are calculated to obtain the health index (HI) for the power transformer. The HI is then plotted on a square matrix called the "health-index matrix", which interprets the risk condition for that transformer. The risk assessment method proposed in this paper is applied to three power transformers in different load areas in central region of PEA. The results show that the method successfully detects the risk condition of those transformers. Therefore the proposed method can create an efficient preventive maintenance plans for power transformers in central region of Provincial Electricity Authority.

A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

“…The transformer transition states for the year 2013/2014 and 2012/2013 can be seen on Tables 5 and 6. The transition probability matrices were determined based on the frequency of a transition technique that utilized the transformer transition states for the year 2013/2014 and 2012/2013 and can be seen in Equations (17) and 18 Next, the future states of transformers were calculated according to Equation (4). Figure 4 shows the state probabilities of transformers over the years predicted based on transformer transition states for the year 2013/2014.…”

“…Multiple condition data are normally considered to diagnose the condition of transformers, such as oil quality, dissolved gases, furanic compounds, power factors, winding resistances, winding ratios, temperature, partial discharges, physical conditions and load tap changer conditions [2,3]. This information is embedded in a single quantitative value known as Health Index (HI), which can give overall condition status of transformers [4][5][6][7][8]. The HI can be utilized to evaluate transformer deteriorations that require diagnosis, maintenance and approaching end of life [9,10].…”

Estimation of Transformers Health Index Based on the Markov Chain

“…Under CBM, a single quantitative assessment known as Health Index (HI) is normally formulated to provide the overall condition of transformers. HI normally consists of multiple input parameters such as oil condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11].…”

“…condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11]. HI provides a comprehensive condition assessment of transformers as compared to Dissolved Gases Analysis (DGA), which mainly focuses on the identification of faults [5].…”

“…Currently, there are still less studies that have been carried out to model the future condition states of transformers based on HI. Other studies, such as those in References [6,9,[15][16][17], mainly focused on the utilization of the HI to determine the future reliability of transformers and its impact on the power system network. The Markov Model (MM) is identified as one of the prediction methods that can be used to determine the future states of transformers based on HI.…”