# Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance
## 3 Case Study, Results and Discussion
This is in agreement with [37] who states that once a unit has reached _DP_ values lower than 250, it is considered that it has entered the end-of-life stage. The _II_ assessment results for the PT fleet show a certain degree of uniformity in the final results, with _II_ values between 0.3 and 0.7. It can be noted that unit 10 which presents a high HI value has a relatively low strategic importance. Meanwhile, unit 18, which has the best technical condition in the fleet, also presents a higher _II_ than unit 10. This can be explained by the independence in the calculation of indices, as well as the location and operative voltage of the units. PT number 10 has a voltage of 132/33 kV and operates in a rural environment. In contrast, PT number 18 works in a more demanding urban setting and has an operative voltage of 220/132 kV. Therefore, the critical condition of the asset in the system is higher. Units 4 and 13 have the highest _II_ value operating at extra-high voltages (EHV) of 500/132 kV. The risk of the fleet can be assessed with the HI and _II_ calculated. As stated before, three groups or clusters were defined using the k-means clustering technique. Out of the 19 units, 8 were placed in cluster number one, 6 in cluster number two and the remaining 5 PT in cluster number three. The units in the risk matrix are presented in Figure 12a, while the cluster arrangements are displayed in Figure 12b. The placement of the units on the risk matrix in Figure 12a allows a better understanding of the HI and _II_ when combined. Also, it can be discern  
Figure 12: (**a**) Transformers fleet in the risk matrix. ; (**b**) Transformers fleet in the risk matrix with k-means clustering technique applied. the units are concentrated in the lower left quadrant. An objective categorization of the units in groups using traditional methods might be challenging. Therefore, to solve this obstacle the proposed k-means technique is applied. Figure 12b shows that the k-means technique defined three clusters, in which the PT belonging to cluster number three was the easiest to disaggregate from the rest of the units. Furthermore, cluster three represents the units with the highest risk. Hence the units where the most thorough and immediate maintenance measures will be needed. As for clusters number one and two, the units were closer to each other and the manual allocation could be difficult. However, with the k-means technique as a non-supervised ML method, the allocation of units in a particular cluster is done automatically by comparing Euclidean distances and the minimization of the MSE. This allocation of units is mainly observed in units 1 and 5 which were close to both centroids number one and number two. Nevertheless, unit 5 was allocated in cluster number one and unit 1 was allocated in cluster number two. The RI using the HI*\(II\) approach was plotted in Figure 13. The units with higher risk were PT 4, 7, 8 and 16. The method failed to categorize PT 10 as a unit with high risk, moreover, the classification of the remaining units becomes fuzzy and subjective. The main advantage of the proposed methodology is its capacity to categorize the PT according to their condition into groups. This is particularly useful when dealing with large fleets of PT. In case an individual assessment is required, the use of the traditional RI calculation technique is advised. In view of these findings, it can be assumed that the proposed method has the capabilities of estimating the risk in PT fleets that can be embraced by utility expects for asset management.