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https://github.com/zenithclown/decompose

Principal Component Analysis (PCA) Algorithm was implemented to determine the Functional Age of the Power Transformer using Return Voltage Measurement (RVM). [submitted]
https://github.com/zenithclown/decompose

central-time-constant conference-paper curve-fitting dissipation-factor ieee initial-rate machine-learning matplotlib mysurucon numpy pandas principal-component-analysis recovery-voltage regression research-paper return-voltage scipy seaborn tan-delta transformer

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Principal Component Analysis (PCA) Algorithm was implemented to determine the Functional Age of the Power Transformer using Return Voltage Measurement (RVM). [submitted]

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README 

        


  decompose


  (supplement code)


  


    

    

      

        Debmalya Pramanik

        Dr. Arijit Baral

      

    

    

      

        

          

          

          

          

          

          

          

          

        

        

          

          

        

      

    

  



A implementation of Principal Component Analysis (PCA) Algorithm for determining the Functional Age of Power Transformer, for the Paper Titled "Reliable Estimation of Dissipation Factor of In-service Power Transformer", by Debmalya Pramanik (ORCiD:0000-0002-3955-8170) and Dr. Arijit Baral (ORCiD:0000-0002-1905-9059).



## Abstract



Insulation failure is a severe threat to high voltage equipment, and its protection using a reliable and efficient diagnostic tool has always been the interest to power utilities. Many traditional and newer techniques are available. Due to the complex aging process of oil-paper insulation, experts generally perform assessments after carefully evaluating measurement data. The paper presents a methodology to analyze recovery voltage measurement data to estimate aging sensitive performance parameters (dissipation factor).



### Keywords



power transformer, dissipation factor, tan delta, return voltage, recovery voltage, central time constant, principal component analysis, regression, oil moisture, initial rate, machine learning, curve fitting






[![IEEE Conference Paper Link](https://img.shields.io/badge/📃-IEEE_Conference_Paper_Link-blue)](https://ieeexplore.ieee.org/document/9972517)






## Figures



Significant figures related to the paper is added here for reference. Images files are available here, and the overall flowchart of the proposed algorithm and PCA is created using draw.io founded by Gaudenz Alder.






  

    Significant Figures from Conference Paper

  

  

    

      

      

      

    

    

      
Fig.: RVM Spectrum of trf1


      

      
Fig.: The Scree Plot to determine Optimal Components


    

    

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  Figure 1 Two-Electrode Model for Capturing RV Data



  

  


  Figure 2 RVM Spectrum of trf1



  


  Figure 3 The Scree Plot representing the Percentage of Explained Variance of all the Individual Principal Components calculated from PCA considering all the Transformer Parameters



  


  Figure 4 First Principal Component (PC-1) vs tan 𝛿



  


  Figure 5 PC-1 against Dissipation Factor with Class Label based on User-Defined Boundaries



  


  Figure 6 Proposed Curve to Estimate tan 𝛿 w.r.t. PC-1



  


  Figure 7 Final Proposed Polynomial Equation to Determine tan 𝛿 considering an Error Band of 0.25 𝜎^2






## License & Citaitions



This is licensed to © Debmalya Pramanik, Arijit Baral [MIT License](LICENSE). If you find this document useful, please *cite the original paper* as (or refer to [citation](./CITATION.cff) file):



### Paper/Plain Text Citations



```

D. Pramanik and A. Baral, "Reliable Estimation of Dissipation Factor of In-service Power Transformer," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1-6, doi: 10.1109/MysuruCon55714.2022.9972517.

```



### BibTex



```latex

@INPROCEEDINGS{9972517,

  author={Pramanik, Debmalya and Baral, Arijit},

  booktitle={2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)}, 

  title={Reliable Estimation of Dissipation Factor of In-service Power Transformer}, 

  year={2022},

  volume={},

  number={},

  pages={1-6},

  keywords={Voltage measurement;Fitting;Estimation;High-voltage techniques;Aging;Oil insulation;Reliability;power transformer;dissipation factor;tan delta;return voltage;recovery voltage;central time constant;principal component analysis;regression;oil moisture;initial rate;machine learning;curve fitting},

  doi={10.1109/MysuruCon55714.2022.9972517}}

```



## Credits & Reference



Principal Component Analysis (PCA) tries to find the axes with the maximum variance [1]. The decomposition.PCA() function is written using the mathematical formulation and step-by-step guide provided by Sebastian Raschka.



[1] Raschka, S. (2015). _Python Machine Learning_. Packt Publishing Ltd.



## Additional Notes



Paper is still under review and modifications, thus the content may change significantly.