Predictive Reliability Assessment of Generation System

Okoye, Martin Onyeka; Yang, Junyou; Lei, Zhenjiang; Yuan, Jiahui; Ji, Huichao; Wang, Haixin; Feng, Jiawei; Otitoju, Tunmise Ayode; Li, Weidong

doi:10.3390/en13174350

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…TBF is the duration of the mobile crane in an uptime state before entering a failed state. In contrast, TTR is the duration of the mobile crane in a downtime state before returning to an uptime state [24,25]. TBF and TTR data for the mobile crane system are shown in Table 1 and illustrated in Figure 3.…”

Section: Figure 2 Mobile Cranementioning

confidence: 99%

Availability Optimization of the Mobile Crane Using Approach Reliability Engineering at Oil and Gas Company

Rahman¹,

Sugiono²,

Sonief³

et al. 2022

MMEP

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Reliability engineering is needed for scheduling maintenance to improve system or equipment performance. The purpose of this paper is to provide recommendations for maintenance schedules based on reliability, availability, and maintainability (RAM) analysis that optimizes the availability of mobile cranes operating in Indonesian oil and gas companies. This paper begins with searching for a critical system using the Pareto principle. The critical system is then made a reliability block diagram to facilitate the analysis. However, before conducting the analysis, it is necessary to know the characteristics of the probability distribution of the data so that the analysis results are accurate and stable. Analysis of reliability, availability, and maintainability (RAM) is then carried out in the probability distribution equation for each system. The analysis results show that the implementation of maintenance at the time of reliability reaches the mean time between failure, and the maximum maintainability time is 240 minutes. The result is an increase in the availability of the lower structure (LS) system by 0.53% (98.78% to 99.31%), electrical and safety equipment (ESE) by 0.19% (99.02% to 99.21%), upper structure (US) was 0.07% (99.32% to 99.39), and overall system availability was 0.07% (99.31% to 99.38%).

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Section: Figure 2 Mobile Cranementioning

confidence: 99%

Availability Optimization of the Mobile Crane Using Approach Reliability Engineering at Oil and Gas Company

Rahman¹,

Sugiono²,

Sonief³

et al. 2022

MMEP

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“…The principal components are defined according to these eigenvalues. The loss of load expectation (LOLE) as a reliability index is predicted using a linear regression method in Okoye et al (2020). A sequential MCS assesses the LOLE.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the above studies, a few researchers have investigated the generation system reliability prediction (Okoye et al , 2020). These studies have focused on improving the accuracy of these predictions to enhance the reliability of power systems.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

Bolourchi,

Gholami

2023

COMPEL

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Purpose The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management. Design/methodology/approach This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile. Findings The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management. Originality/value What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

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“…The random behavior of the system can be properly incorporated into the simulation method. The HLI reliability of the IEEE 96 bus system is evaluated [7] using Monte Carlo which gives the necessary idea about generation adequacy. As the transmission line is exposed to the environment there is a huge chance of failure of the transmission lines [8].…”

Section: Introductionmentioning

confidence: 99%

Composite reliability evaluation of Integrated Nepal Power System

Babita Sharma,

Suman Timilsina,

Ram Sharan Timilsina

et al. 2023

Jnl Innov. Engin. Educ.

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the worth of reliability has been increasing with the advancement of technology. Based on the involvement of the various functional zones, the assessment of the reliability of the power system is divided into 3 Hierarchy levels (HL). The composite reliability (HL II) involves the availability of generation systems and transmission systems in evaluating the reliability of power systems. In this paper, the composite reliability of the Integrated Nepal Power System (INPS) is assessed using sequential Monte Carlo Simulation (MCS). Based on the trip data of the various Hydro Electric Power Plant (HEPP) units of Nepal and of transmission line, the artificial UP and DOWN sequence of the composite system is simulated. The power output of the Hydro-Electric Power Plant (HEPP) is varied based on the variation of seasonal flow. The system state is compared with the hourly load data of various load centers of INPS to determine the reliability indices like LOLP, LOLE, and EENS. As the eastern part of INPS doesn’t have a generating unit, it is found to have lower reliability. The reliability of INPS can be improved by improving the reliability of load centers that have a lower value of reliability indices.

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Predictive Reliability Assessment of Generation System

Cited by 10 publications

References 29 publications

Availability Optimization of the Mobile Crane Using Approach Reliability Engineering at Oil and Gas Company

Availability Optimization of the Mobile Crane Using Approach Reliability Engineering at Oil and Gas Company

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

Composite reliability evaluation of Integrated Nepal Power System

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