# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 1 Introduction  
With the increase in peak demand and the need to improve grid infrastructure for the ease of operation and management with high reliability, the distribution smart grid is one of the most important elements to accelerate the modernization of the currently aging power system. The distribution grid is also the area that is affected significantly by smart technologies [1]. Automating and controlling the grid remotely will help reduce operating costs, increase information accuracy as well as quickly fix faulty areas in the electricity power system [2].  
The distribution transformer is the last component for voltage transformation in the power grid. It is used to convert the medium voltage to the low voltage level that is used for households or for commercial use. Distribution transformers are thus one of the most important components of the distribution power system [3]. Moreover, for each HV/MV primary substation, there are tens of secondary substations. As a result, in a medium-sized city with 40 HV/MV primary stations, there is around one thousand distribution transformers. Many of them are damaged every year due to various reasons. Accelerated degradation and failure of distribution transformers can occur because of several conditions such as oil leakage, overloading, unbalanced loading and harmonics. However, the majority of failures are caused by a combination of these electrical, mechanical and thermal stresses acting upon the power transformer components over time [4]. Although the manufacturer generally establishes design and operational limits, the impact on service life is non-binary and multi-dimensional in nature. For example, exceeding a thermal limit to a moderate extent for a short amount of time will not cause immediate failure, but more severe overloading for an extended period will likely cause irreversible damage. If these impacts can be more fully characterized or impendingissues can be detected in the field, it could help utilities reduce the rate of failures and degradation, increase the reliability of electric service and reduce the cost of utility operations and maintenance. Therefore, it is important to study and develop effective methods of monitoring the condition and health of distribution transformers.  
There are many papers available on these individual diagnostic techniques for distribution transformers. However, a review of all these techniques is not available in recent times. In this paper, a brief introduction of different monitoring methods for distribution transformers is outlined and the different schemes based on different assessment methods is discussed. The main contributions of this paper are described in the following:  
* The paper provides a special review of modern monitoring distribution transformers methods. A number of methodologies are presented, evaluated and discussed. The advantages and disadvantages of using individual methods are presented.
* The paper investigates different thoughts regarding the appropriate parameters for different online/real-time monitoring processes to evaluate the statement of distribution transformers.
* A number of failure modes are also discussed in this paper.  
This paper has a practical value since it is a good reference work to help new transformer operators to establish a strategy for transformers maintenance and replacement. The paper is organized into the following sections: Section 2 covers distribution transformer failure modes and investigates the components that are most critical to distribution transformer health. Section 3 reviews the development of health assessment techniques. This section summarizes the advantages and disadvantages of different evaluation methods including basic health index calculation methods, fuzzy logic, machine learning algorithms and hybrid artificial intelligence approaches. Section 4 presents the advanced technologies for real-time transformer health assessment in smart distribution grids and Section 5 presents our conclusions.