Transformer health index (THI) is a sophisticated tool for ranking the transformers in a fleet according to risks and intervention priority. THI is a numerical score that shows the overall status of a transformer based on its condition-based monitoring. The term “status” is synonymous to reliability, risk, life cycle etcetera; by assuming it as a part of transformer assessment and indexing strategies. Using THI for fleet management, is like having an alarm bulb on your control panel.
How to obtain a transformer health index?
Typically, a fleet contains many transformers. Anyone managing these fleets is bound to collect a lot of information. However, indentifying and removing redundancy from this information to decide which transformer needs immediate attention is very difficult. Therefore, transformers need to be ranked according to their intervention priority.
CIGRE A2.761 reports that transformer asset indexing (TAI) can help an asset manager in making prompt and quick decisions. Presently, there are six types of acceptable indexing; health, replacement, repair, refurbishment, composite, and mitigation index.
The transformer health index (THI) is particularly based on condition monitoring data, visual inspection, operational and historical records. It is a tool that can answer two important questions; is my asset reliable, and if not, how long do I have to respond?
Among the various TAI calculation methods available, THI calculated uses a typical weighed-average or score-weight matrix method. A summary of all TAI calculation methods is available below.
How THI helps in fleet management?
A health index can rank the transformers based on its failure probability. Simply put, it will evaluate the extent of reliability of your transformers. The ranking is based on the priority of technical intervention such as increasing testing frequency, proposing additional tests etc. In short, a transformer health index can have a significant impact on the cost of transformer testing and management.
What are the risks and limitations of using THI?
One of the prime limitations of using THI is its inability towards elaborate fault description. It is designed to generalize the impact of each deciding factor and may not classify fault mechanisms/modes. Additionally, it can’t pin-point the fault location. Hence, asset managers should use root cause analysis (RCA) tools after primary transformer health indexing. This will reduce the cost and improve the quality of service by several manifolds.
Latest developments and the future of THI
One of the key developments in the field of THI is due to artificial intelligence (AI) and machine learning (ML). AI/ML can easily manage, mine, pre-process, and analyse big data resulting from fleet management. The role of these AI/ML tools is not to replace humans, but improve their decision making capacities. In this context, we have developed RONIN, an online tool for quick and accurate prediction of transformer health indices. It works best on large transformer fleets containing up to 500 assets. It allows very simple bulk data uploading and producing results in mere seconds!