# Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance
###### Abstract  
In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (_II_). Finally, the analyzed units with similar HI and _II_ were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.  
risk assessment; health index; power transformers; fuzzy logic; importance index Article  
Risk Assessment Algorithm for Power Transformer  
1  
A.A.; [PERSON].  
In the literature, there are many methodologies to assess the risk of PTs [11; 12; 13; 14]. What differentiates one from the other is the technique used to process the data and the entry criteria. While most of the experts consider the HI to evaluate the risk, some authors also consider the consequences of failure [15], the probability of shutdown [16], the technical condition [17] or the strategic importance of the PT [18].  
It should be noted that the RI is an indicator that enables an influential prioritization/ranking of the assets to support maintenance and replacement decisions [19; 20]. However, when dealing with a set of transformers, more than a ranking, an asset manager needs to categorize the units in groups with similar characteristics to apply maintenance actions. Most of the current methodologies to assess the risk in PT analyze one unit at a time or use a ranking system. This paper proposes a practical application to determine the RI in fleets of PT and then categorize the units in groups of similar RI using the k-means clustering technique. This, in order to be a support tool for improving the efficiency of PT maintenance. The contributions of this work can be listed as:  
* An up-to-date approach to determine the degree of polymerization (_DP_) of the insulating paper and the HI.
* A standardized procedure to assess the strategic importance of the PT.
* A new method to categorize the risk in PT fleets employing a clustering technique  
The following sections introduce a description of the methodology developed to calculate the HI, the strategic importance of the asset or _II_, and the RI. Next, a fleet of 19 PTs is analyzed to validate the proposed method. Finally, the results, discussions and conclusions are presented.