• 전기학회논문지
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• 제63권7호
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• pp.867-874
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• 2014

The pumped storage power plants have excellent load following characteristics. It can also be committed quickly for synchronous reserve when it is in the generating mode because it can readily increase its generating power and, consequently, increases the overall system reliability. There are strong incentives for standing the system reliability. Additionally, $CO_2$ emission can be typically impacted due to operation of pumped generators. The increase or decrease of $CO_2$ depends on the generation mix. This paper proposes evaluation of reliability, economy and environment of power system considering pumped generator. This paper describes three case studies of the reliability and economy and environment according to capacity factor and storage capacity of pumped generators. The probabilistic production simulation model is used in this paper. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model on the $5^{th}$ power plan in Korea.

• 산업경영시스템학회지
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• 제46권2호
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.