# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 3 Transformer Health Assessment Techniques
### Hybrid Artificial Intelligence Approaches  
In recent years, the searching algorithms have been interested in by researchers to find the best subset of features in the feature selection problems [32]. As mentioned above, the hybrid artificial intelligence approaches that use optimization algorithms to support leaning algorithms can overcome the weakness of single learning algorithms. Because the selection parameters of learning algorithms has a significant impact on its own usefulness and classification performance. Therefore, it is necessary to find the optimal value of these parameters to improve the accuracy of the prediction. The optimization algorithm can support the health index computation methods and fuzzy logic as well to optimize weighted parameters so the results for transformer health assessment will be more accurate. Figure 5 presented a general structure of hybrid artificial intelligence model. Figure 5: A general structure of the hybrid artificial intelligence model. The genetic algorithm (GA) is a good computational tool in seeking optimum and supporting for multi-objective functions. It was the most advanced algorithm of artificial intelligence techniques and was widely used to solve optimization problems. In [32], a hybrid algorithm of transformer health index computation and GA is proposed. The health index of the transformer was identified based on weighted parameters. The genetic algorithm was used to support the conventional health index computation. It optimized these weighted parameters and then provided a better weight approach. A smart system is utilized to online supervise distribution transformers. The system integrates intelligent electronic devices to collect data from sensors that are installed on transformers. With the inclusion of a communication network, the results of transformer health assessment is transferred to the control room and cell phone of the transformer operator. In articles [52; 53], the combination of genetic algorithm (GA) and support vector machine (SVM) was also presented. Particle swarm optimization (PSO) is another optimization algorithm which is commonly applied in coordination with an artificial neural network. PSO can be utilized to problems which GA can be utilized to. The combination of PSO and SVM is presented in article [42]. In order to obtain the best classification model, the PSO was used to provide optimizing parameters for the SVM module. The transformer statement was classified by groups: excellent, good, normal, attention and fault, the fault statement was divided into five levels based on the temperature failure of overheating. The results proved that the proposed method can improve the accuracy of transformer health evaluation. The results showed the proposed method had the highest accuracy. Similar approaches can be found in the articles [54; 55]. A comparison between transformer health assessment methodologies based on the dependency on the transformer monitoring parameters, number of samples, the accuracy, the complexity, implement cost and computation effort is presented in Table 3.  
Table 4 summarizes the above analysis highlighting the advantages and disadvantages of the well-known approaches present in literature.