Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers  
[PERSON]  
1 Advanced Lightning, Power and Energy Research Centre (ALPER), Universit Putra Malaysia,  
Serdang 43400, Selangor, Malaysia; [EMAIL_ADDRESS] (N.S.S. ); [EMAIL_ADDRESS] (M.Z.A.A.K. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (M.S.Y);  
1  
[PERSON]  
1 Advanced Lightning, Power and Energy Research Centre (ALPER), Universit Putra Malaysia,  
Serdang 43400, Selangor, Malaysia; [EMAIL_ADDRESS] (N.S.S. ); [EMAIL_ADDRESS] (M.Z.A.A.K. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (M.S.Y);  
1  
[PERSON]  
1 Advanced Lightning, Power and Energy Research Centre (ALPER), Universit Putra Malaysia,  
Serdang 43400, Selangor, Malaysia; [EMAIL_ADDRESS] (N.S.S. ); [EMAIL_ADDRESS] (M.Z.A.A.K. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (M.S.Y);  
1  
[PERSON]  
1 Advanced Lightning, Power and Energy Research Centre (ALPER), Universit Putra Malaysia,  
Serdang 43400, Selangor, Malaysia; [EMAIL_ADDRESS] (N.S.S. ); [EMAIL_ADDRESS] (M.Z.A.A.K. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (M.S.Y);  
1  
[PERSON]  
1 Advanced Lightning, Power and Energy Research Centre (ALPER), Universit Putra Malaysia,  
Serdang 43400, Selangor, Malaysia; [EMAIL_ADDRESS] (N.S.S. ); [EMAIL_ADDRESS] (M.Z.A.A.K. ); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (M.S.Y);  
1  
[PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]. 1  
Footnote  
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11: email: {[EMAIL_ADDRESS]}  
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capital or maintenance schemes [1]. HI concept is based on scoring, rating, and ranking methods, which considers fundamental theory, technical guidelines, and expert decisions. Most utilities employ HI for the management of transformer assets, such as Kinetrics Inc., DNV GL, Hydro-Quebec, Terna, Electricity Generating Authority of Thailand, and Tenaga Nasional Berhad [2; 3; 4; 5; 6]. Common data driven approaches, such as statistical and artificial intelligence (AI) application based models, have been widely used as prediction models for condition deterioration of high voltage assets [7; 8; 9; 10]. Predicting the HI of the transformers based on these approaches is one of many other applications. This will substantially help in the financial strategy of the utilities in asset maintenance plans. Currently, there are limited studies on the modelling of future transformers' health degradation that is based on HI. Most of the studies focus on the prediction based on the individual condition parameters data [11; 12; 13; 14]. Statistical approaches that are based on Markov Model (MM) [15; 16] and Hidden Markov Model (HMM) [17] have been used to predict the condition states of transformer population. Previous work in [15] utilizes the transition probabilities of the transformer's condition states that are derived from HI for a specific year interval. The other study in [16] implements a similar approach, except that the transformer's condition states are derived from condition parameters data. A previous study in [17] utilizes HMM to predict the transformer's condition states in a different approach as compared to MM [15; 16]. Hidden state transition and emission probabilities derived from condition parameters data have been computed to predict the HI of transformers [17]. MM and HMM both do not rely heavily on historical condition parameters data. Hence, the uncertainty effect due to constraints of a long-term data record could be minimized. The methods predict either the final HI or individual condition parameters in terms of probabilities, which were later converted into HI and condition parameters data values. It is found that the accuracy levels of HI that were obtained based on these models have been satisfactory.