• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.546-547
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• 2008

2003년부터 석유가격의 급격한 상승으로 인한 리비아 정부의 재정투자 증대 및 경제자유화 조치에 따른 내수증가 등으로 2003년 경제성장률이 9.1% 2004년도 7.9%, 2005년 8.45%의 눈부신 경제성장을 이룩하고 있는 리비아 전력수요를 예측하기 위한 프로그램을 개발하여 리비아 국영전력회사(GECOL) 직원들을 대상으로 수요예측에 관한 교육을 시행하고 향후 리비아 경제발전과 전력소비의 연관성에 에 관해 고찰하였다. 급증하는 오일달러를 이용한 사회간접 인프라구축에 집중하는 반면에 인접국가들과의 전력 계통연계도 함께 관찰하여 최적의 모델을 찾아 전력수요 예측에 활용할 수 있다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.5
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• pp.195-201
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• 2006

Tower footing resistance and fault current division factor are important design factors for evaluation of the lightning performance of the transmission line and/or design of the grounding electrode system. The periodic measurement of those factors are also important to verify that the grounding performance of the towers has been maintained good. However, the direct measurement of those factors in operating or energized condition is very difficult because of many practical reasons, such as the difficulty of disconnecting overhead groundwires from the tower under test. With supports by GECOL (General Electricitiy Company of Libya), we had a special chance to conduct Fall-Of-Potential (FOP) test on the energized 220 kV transmission towers before and after disconnecting the overhead groundwires from the towers under test. In this paper, the FOP test results on the towers and the fault current division factors estimated from the comparision of the FOP tests with and without overhead groundwires were presented. The computer models for the FOP test simulations were also constructed to find that the simulated results agreed very well with the measured ones.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.