# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 3 Transformer Health Assessment Techniques
### Machine Learning Algorithms  
Normally, the transformer HI can be computed from a parameter by the creation of a relationship rule and equation. To improve the accuracy and reliability, the HI needs to be determined from many parameters that might be not related to each other and hard to calculate. Several papers presented the method for predicting transformer health using artificial intelligence tools, such as machine learning (ML) [42; 43; 44]. ML is the algorithm that improves automatically through experience. ML can teach oneself and adapt non-linear mappings between input and output. Advanced ML techniques is used to build surrogate models that can be put in an application as low computational-cost approximations of more expensive calculations. All the ML-based HI methods need the database to learn the wanted correlations and make predictions or decisions without being explicitly programmed to implement the task. Transformer condition assessment programs using artificial intelligence algorithms may have the potential to apply to inexpensive sensor systems which helps to keep the overall system cost and complexity low. The basic structure of the machine learning method is shown in Figure 4.  
The most common algorithm of ML used for transformer diagnosis is Artificial Neural Network (ANN). In [45], a four-layer ANN is utilized to evaluate the distribution transformer's health status. The ANN includes one input layer, one output layer, and two hidden layers. The data of 84 different patterns of distribution transformers was collected to train an artificial neural network. The experimental test results of the proposed ANN are 97.62% matched to the results provided by the utility. In [46], an ANN approach has been made to classify the condition of the transformer based on the predicted HI value. The model was a feed-forward ANN with two hidden layers (four and two neurons respectively) that was trained on real measurements of 59 working transformers. Based on the testing outcomes, 97% of the testing samples were correctly classified based on a three-class condition problem. It can be observed that ANN can provide a reliable result but this method requires lots of data, especially for architectures with many layers. The same approach was also applied in other related works [47; 48]. The other powerful ML algorithms are random forest (RF) and support vector machine (SVM). RF is an ensemble learning method for classification and regression. In the training time, RF constructs a multitude of decision trees and then exports the class prediction [49]. The class prediction with the higher number of votes is the model's prediction of the individual trees. In [50], RF is used for fault diagnosis applied to transformers because of its strong model generalization ability. RF can produce a proximity matrix based on the similarity among patterns without preprocessing data. The model of the  
Figure 4: The basic structure of machine learning method. transformer's fault diagnosis was compared with some experiments on real transformers. The results show that the RF model has high accuracy and results are more stable than the other methods. Meanwhile, SVM is a supervised learning model with associated learning algorithms. It is more suitable for nonlinear problems with a small sampling. The SVM model represents the samples as points in space, the samples of the separate groups are divided by a wide clear gap. The new samples are mapped in the same space and its category will be predicted depending on the side of the gap where they fall. For example, in a set of training samples, each sample is marked as belonging to one or the other of two categories, the SVM model then assigns new samples to one category, making it a non-probabilistic binary linear classifier [51]. The ML algorithms have huge potential in practical applications with many outstanding advantages.