{"id":16347169,"url":"https://github.com/zenithclown/decompose","last_synced_at":"2025-03-23T00:32:50.664Z","repository":{"id":39451517,"uuid":"365761743","full_name":"ZenithClown/decompose","owner":"ZenithClown","description":"Principal Component Analysis (PCA) Algorithm was implemented to determine the Functional Age of the Power Transformer using Return Voltage Measurement (RVM). [submitted]","archived":false,"fork":false,"pushed_at":"2024-02-18T05:44:15.000Z","size":632,"stargazers_count":6,"open_issues_count":3,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-18T16:04:04.510Z","etag":null,"topics":["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"],"latest_commit_sha":null,"homepage":"https://ieeexplore.ieee.org/document/9972517","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ZenithClown.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":["ZenithClown"]}},"created_at":"2021-05-09T13:39:01.000Z","updated_at":"2024-02-13T18:28:09.000Z","dependencies_parsed_at":"2024-02-18T06:25:55.637Z","dependency_job_id":"2b31cd8d-60af-4fff-8c2b-e14100d81fe1","html_url":"https://github.com/ZenithClown/decompose","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZenithClown%2Fdecompose","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZenithClown%2Fdecompose/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZenithClown%2Fdecompose/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZenithClown%2Fdecompose/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZenithClown","download_url":"https://codeload.github.com/ZenithClown/decompose/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245040235,"owners_count":20551297,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-10-11T00:40:07.825Z","updated_at":"2025-03-23T00:32:50.332Z","avatar_url":"https://github.com/ZenithClown.png","language":"Python","funding_links":["https://github.com/sponsors/ZenithClown"],"categories":[],"sub_categories":[],"readme":"\u003ch1 align = \"center\"\u003e\n  decompose\u003cimg src = \"./assets/LogoMakr-6pNJd4.png\" height = \"108\" width = \"200\" align = \"right\" /\u003e\u003cbr\u003e\n  \u003csup\u003e(supplement code)\u003c/sup\u003e\u003cbr\u003e\n  \u003ctable align = \"center\"\u003e\n    \u003c!-- using TAB-SPACE = 2 - as long lines present --\u003e\n    \u003cthead\u003e\n      \u003ctr\u003e\n        \u003cth\u003e\u003csub\u003eDebmalya Pramanik\u003c/sub\u003e\u003c/th\u003e\n        \u003cth\u003e\u003csub\u003eDr. Arijit Baral\u003c/sub\u003e\u003c/th\u003e\n      \u003c/tr\u003e\n    \u003c/thead\u003e\n    \u003ctbody\u003e\n      \u003ctr\u003e\n        \u003ctd\u003e\n          \u003ca href = \"https://www.linkedin.com/in/dpramanik/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/linkedin.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://github.com/ZenithClown\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/github.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://gitlab.com/ZenithClown/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/gitlab.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://www.researchgate.net/profile/Debmalya_Pramanik2\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/researchgate.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://www.kaggle.com/dPramanik/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/kaggle.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://app.pluralsight.com/profile/Debmalya-Pramanik/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/pluralsight.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://stackoverflow.com/users/6623589/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/stackoverflow.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://scholar.google.com/citations?user=GPHPApYAAAAJ\u0026hl=en\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/googlescholar.svg\"/\u003e\u003c/a\u003e\n        \u003c/td\u003e\n        \u003ctd\u003e\n          \u003ca href = \"https://www.linkedin.com/in/arijit-baral-2a2b4819/\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/linkedin.svg\"/\u003e\u003c/a\u003e\n          \u003ca href = \"https://scholar.google.com/citations?user=PYdjFe8AAAAJ\u0026hl=en\"\u003e\u003cimg height=\"16\" width=\"16\" src=\"https://unpkg.com/simple-icons@v3/icons/googlescholar.svg\"/\u003e\u003c/a\u003e\n        \u003c/td\u003e\n      \u003c/tr\u003e\n    \u003c/tbody\u003e\n  \u003c/table\u003e\n\u003c/h1\u003e\n\n\u003cp align = \"justify\"\u003eA implementation of \u003ci\u003ePrincipal Component Analysis (PCA)\u003c/i\u003e Algorithm for determining the Functional Age of Power Transformer, for the Paper Titled \"\u003ci\u003eReliable Estimation of Dissipation Factor of In-service Power Transformer\u003c/i\u003e\", by Debmalya Pramanik (\u003ca href = \"https://orcid.org/0000-0002-3955-8170\"\u003eORCiD:0000-0002-3955-8170\u003c/a\u003e) and Dr. Arijit Baral (\u003ca href = \"https://orcid.org/0000-0002-1905-9059\"\u003eORCiD:0000-0002-1905-9059\u003c/a\u003e).\u003c/p\u003e\n\n## Abstract\n\n\u003cp align = \"justify\"\u003eInsulation 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).\u003c/p\u003e\n\n### Keywords\n\n\u003cp align = \"justify\"\u003e\u003ci\u003epower transformer, dissipation factor, tan delta, return voltage, recovery voltage, central time constant, principal component analysis, regression, oil moisture, initial rate, machine learning, curve fitting\u003c/i\u003e\u003c/p\u003e\n\n\u003cdiv align = \"center\"\u003e\n\n[![IEEE Conference Paper Link](https://img.shields.io/badge/📃-IEEE_Conference_Paper_Link-blue)](https://ieeexplore.ieee.org/document/9972517)\n\n\u003c/div\u003e\n\n## Figures\n\n\u003cp align = \"justify\"\u003eSignificant figures related to the paper is added here for reference. Images files are available \u003ca href = \"./assets/figures/\"\u003ehere\u003c/a\u003e, and the overall flowchart of the proposed algorithm and PCA is created using \u003ca href = \"https://draw.io/\"\u003edraw.io\u003c/a\u003e founded by \u003ci\u003eGaudenz Alder\u003c/i\u003e.\u003c/p\u003e\n\n\u003cdiv align = \"center\"\u003e\n\n\u003ctable\u003e\n  \u003cthead\u003e\n    \u003ctr colspan = 3\u003e\u003cb\u003e\u003ci\u003eSignificant Figures from Conference Paper\u003c/i\u003e\u003c/b\u003e\u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cimg src = \"./assets/figures/Figure 3 RVM Spectrum of trf1.svg\" width = 512/\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cimg src = \"./assets/figures/Figure 4 The Scree Plot representing the Percentage of Explained Variance of all the Individual Principal Components calculated from PCA considering all the Transformer Parameters.svg\" width = 512/\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e RVM Spectrum of \u003ci\u003etrf1\u003c/i\u003e\u003c/p\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e The \u003ci\u003eScree Plot\u003c/i\u003e to determine Optimal Components\u003c/p\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003eimage\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003c/td\u003e\n      \u003ctd\u003e\u003cp align = \"center\"\u003e\u003cb\u003eFig.:\u003c/b\u003e \u003c/p\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003c/div\u003e\n\n\u003cp align = \"center\"\u003e\n  \u003cimg src = \"./assets/figures/Figure 1 Two-Electrode Model for Capturing RV Data.jpeg\" /\u003e\u003cbr\u003e\n  Figure 1 Two-Electrode Model for Capturing RV Data\u003cbr\u003e\u003cbr\u003e\n  \u003c!-- \u003cimg src = \"./assets/figures/Figure 2 Flowchart of RV Measurement using Two-Electrode Model.png\" /\u003e\u003cbr\u003e\n  Figure 2 Flowchart of RV Measurement using Two-Electrode Model\u003cbr\u003e\u003cbr\u003e --\u003e\n  \u003cbr\u003e\n  Figure 2 RVM Spectrum of trf1\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src = \"./assets/figures/Figure 4 The Scree Plot representing the Percentage of Explained Variance of all the Individual Principal Components calculated from PCA considering all the Transformer Parameters.svg\" /\u003e\u003cbr\u003e\n  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\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src = \"./assets/figures/Figure 5 First Principal Component (PC-1) vs tan 𝛿.svg\" /\u003e\u003cbr\u003e\n  Figure 4 First Principal Component (PC-1) vs tan 𝛿\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src = \"./assets/figures/Figure 6 PC-1 against Dissipation Factor with Class Label based on User-Defined Boundaries.svg\" /\u003e\u003cbr\u003e\n  Figure 5 PC-1 against Dissipation Factor with Class Label based on User-Defined Boundaries\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src = \"./assets/figures/Figure 7 Proposed Curve to Estimate tan 𝛿 w.r.t. PC-1.svg\" /\u003e\u003cbr\u003e\n  Figure 6 Proposed Curve to Estimate tan 𝛿 w.r.t. PC-1\u003cbr\u003e\u003cbr\u003e\n  \u003cimg src = \"./assets/figures/Figure 8 Final Proposed Polynomial Equation to Determine tan 𝛿 considering an Error Band of 0.25 𝜎^2.svg\" /\u003e\u003cbr\u003e\n  Figure 7 Final Proposed Polynomial Equation to Determine tan 𝛿 considering an Error Band of 0.25 𝜎^2\u003cbr\u003e\u003cbr\u003e\n\u003c/p\u003e\n\n## License \u0026 Citaitions\n\nThis is licensed to \u0026copy; 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):\n\n### Paper/Plain Text Citations\n\n```\nD. 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.\n```\n\n### BibTex\n\n```latex\n@INPROCEEDINGS{9972517,\n  author={Pramanik, Debmalya and Baral, Arijit},\n  booktitle={2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)}, \n  title={Reliable Estimation of Dissipation Factor of In-service Power Transformer}, \n  year={2022},\n  volume={},\n  number={},\n  pages={1-6},\n  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},\n  doi={10.1109/MysuruCon55714.2022.9972517}}\n```\n\n## Credits \u0026 Reference\n\n\u003cp align = \"justify\"\u003ePrincipal Component Analysis (PCA) tries to find the axes with the maximum variance \u003ca href = \"https://sebastianraschka.com/Articles/2014_pca_step_by_step.html\"\u003e[1]\u003c/a\u003e. The \u003ccode\u003edecomposition.PCA()\u003c/code\u003e function is written using the mathematical formulation and step-by-step guide provided by Sebastian Raschka.\u003c/p\u003e\n\n[1] Raschka, S. (2015). _Python Machine Learning_. Packt Publishing Ltd.\n\n## Additional Notes\n\n\u003cp align = \"justify\"\u003ePaper is still under review and modifications, thus the content may change significantly.\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenithclown%2Fdecompose","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzenithclown%2Fdecompose","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenithclown%2Fdecompose/lists"}