Projects

2 minute read

Belows are several projects that I did before. Some of them were for my research, and some of them were for my business tasks. Also, there are some projects just for my interesting. They are traces of my studies and growth, and every project leaved a precious gift to me, which called Experience.

So, please, just take a look and I hope it would be valuable to you too.


[SW] Constructing Asset Management Server for KEPCO System

Goal

To construct a demo test environment of KEPCO(Korea Electric Power Corporation) SEDA(Substation Equipment Diagnostic & Analysis) system using Oracle, Docker, Python, Flask, Gunicorn, and Nginx(HTML)
Oracle, Docker, Python, Flask, Gunicorn and Nginx(HTML)를 활용한 한국전력공사의 송변전 예방진단시스템 데모 테스트 환경 구축

Background

KEPCO(Korea’s Electric Power Corporation) is a principle agent of T&D(Transmission and Distribution) industry, selling electricity supplied by power genertion companies. The market of electric power in Korea is managed not by private enterpries but by Korean government, and KEPCO is ranked as 2nd on global electricity utility companies by monopolize T&D industry. Thus, KEPCO possesses huge amount of industrial assets of T&D and SEDA(Substation Equipment Diagnositc & Analysis) system is a running business that integrate and centralize the management subjects which were each substations previously. Especially, this system is very important in that the web page of SEDA is coninuously used until present by workers of KEPCO.
한국전력공사는 발전사로부터 전기를 공급받아 사용자들에게 판매하는 T&D(Transmission and Distribution) 산업의 주체입니다. 한국은 전력시장이 민간이 아닌 한국전력공사 주도의 공공시장으로 형성되어 있으며, 따라서 한전은 글로벌 전력회사 2위(아시아 1위)에 위치할 정도로 규모가 거대합니다. 그만큼 한전이 관리하는 T&D분야의 산업설비는 그 양이 상당한데요, 각 사업소별로 관리되고 있던 이 산업설비들을 한전 중앙 서버로 군집하여 통합 관리하고자 하는 사업이 바로 SEDA(Substation Equipment Diagnostic & Analysis) 시스템 구축사업입니다. 특히 SEDA는 실제로 한국전력공사의 임직원들이 계속해서 사용하는 화면이기 때문에 중요합니다.

My company is taking charge of the task that develop an AI based health diagnosis solution for assets on T&D industry with interlocking it to main SEDA system. Unfortunately, it is impossible to shate the contents of AI based health diagnosis solution since it is confidential. Neverthless, the process of interlocking the system was very interesting, and I hope to share this experience.
제가 속한 회사는 이 사업에서 특히 AI기반 변전설비의 상태예방진단 알고리즘 및 UI를 개발하고 이를 SEDA 메인 홈페이지에 연동하는 업무를 담당하게 되었습니다. 알고리즘 개발은 보안사안으로 본 포스트에서 다룰 수 없지만, 그 시스템 연동과정에서 제법 유용한 경험을 할 수 있었어서 이를 공유해보고자 합니다.

drawing

A configuration of demo system that would be contructed in this project.

Contents

  1. Project Overview
  2. Install Docker
  3. Install Oracle
  4. SQL Developer on M1 Chip
  5. Set-up Orcal DB
  6. Import Data from Oracle DB by Cx_Oracle
  7. Construct Web Framework with Flask
  8. Connect Flask to Web Server with Gunicorn
  9. Build Web Server using Nginx
  10. Build Server using Docker

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