Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

Kadhem, Athraa Ali; Wahab, Noor Izzri Abdul; Aris, Ishak; Jasni, Jasronita; Abdalla, Ahmed N.

doi:10.3390/en10030343

Cited by 14 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generation units could be "in service (up)" or "out of service (down)" states when needed due to planned or unplanned outages [17]. The generation system reliability is usually calculated and quantified using the reliability index which reveals the system reliability strength [18].…”

Section: Power System Reliability Assessmentmentioning

confidence: 99%

Predictive Reliability Assessment of Generation System

et al. 2020

View full text Add to dashboard Cite

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

show abstract

Section: Power System Reliability Assessmentmentioning

confidence: 99%

Predictive Reliability Assessment of Generation System

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The test systems "IEEE-RTS-79 and RTS -96" have 32 and 96 generating units respectively, with the capability to generate power ranging approximately from (12-400 MW), with a total power supply of 3405 and 10215 MW respectively, at approximately (2850 MW) and (9000 MW) peak load [24], [25]. Figure 5 showing the diagram of the single area of the system RTS-79, and Figure 6 showing the outline of the RTS-96 of threeareas interconnected through merging three single areas from the RTS-79 system.…”

Section: Cases Running the Algorithmmentioning

confidence: 99%

The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm

Kadhem¹,

Wahab²,

Abdalla³

2020

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

Currently, increasing penetration of the Wind Energy Conversion System (WECs) in Power generation systems has influenced the supply of electrical power reliability for the consumer in comparison with other traditional sources. In this paper, the performance and efficiency of a new optimization approach referred to as the "Differential Evolution Optimization Algorithm" (DEOA) to measure the reliability of power adequacy systems (RPAs). The proposed intelligent algorithm which relies on the Population-based Intelligent Search (PBIs) technique is viewed as a feasible alternative for the Monte Carlo simulation (MCS) method in the assessment of the non-chronological system. The benefit of utilizing this algorithm is apparent in the manner it expedites the calculation and achieves greater precision with less calculation effort. Additionally, there is a deeper understanding of the effect of the increasing levels of wind energy on generation adequacy from the WECs sources to satisfy future power electricity demands. In addition, the effectiveness of the suggested algorithm in assessing the RPAs was compared to the analytical and MCS method.

show abstract

“…These are normally regular observations (hours, days, months, years), but the sampling generated may be irregular or not, and as such, there is a need to predict the wind speed for the next hour or day. Therefore, the Weibull model is a suitable representation of the wind speed data distribution but is inadequate in simulating wind speed during the wind power potential models or for reliability assessment models [18], etc. The most obvious disadvantage of the Weibull probabilistic model is that the chronological characteristics of wind speed and its impact on wind power output are not reflected, thus the Weibull model is unable to consider both diurnal and seasonal wind speed variations [19].…”

Section: Weibull Distributionmentioning

confidence: 99%

Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

show abstract

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

Cited by 14 publications

References 24 publications

Predictive Reliability Assessment of Generation System

Predictive Reliability Assessment of Generation System

The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm

Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Contact Info

Product

Resources

About