Reliability evaluation of bulk power systems with wind generation using small signal stability analysis

Amaral, Tiago S.; Gomes, Sérgio Bittencourt Varella; Borges, Carmen L. T.

doi:10.1016/j.ijepes.2021.106840

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Extensive researchers in the field of reliability of power system issues have focused on using some reliability methods such as the analytical method [40], without considering reliability variable factors that would influence the reliability performance of a micro-grid system. Also, Amaral et al [41] deployed a simulation method but still did not holistically consider reliability variables that could impact power system reliability, hence this study focused on identifying the fragmented reliability variable in literature and analyzing them.…”

Section: Figure 2 Reliability Variable Scree Plotmentioning

confidence: 99%

Identifying Key Reliability Factors in Micro-Grid Systems Using Principal Component Analysis

Elizabeth,

Oyaka,

Orovwode

et al. 2024

JESA

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One way to solve the shortage in power supply and the rapid load growth is by operating power systems that could improve power supply reliability. The study aims to carry out a holistic evaluation by identifying the several reliability variables that could influence the micro-grid power system's reliability which is vital in electricity generation. Thirty-three reliability variable factors that are commonly observed to influence power systems reliability were chosen for the micro-grid power systems study and examined using the principal component analysis (PCA). The system reliability key variables were evaluated using the StatistiXL software. A structured questionnaire was crafted considering thirtythree reliability variables, harvested from literature, and administered to respondents in the micro-grid power system industry. The respondent size was determined at a level of confidence of 95% and an error margin of 5% was deployed to corroborate an adequate population size representation which validated the study data. StatistiXL software was deployed to analyze the (mxn) data matrix, collated from the respondents' scores. The matrix was used as the input variable for the model deployed for the factor analysis. Nine factors with eigenvalues (λ˃1) were mined and labeled for the analysis, but all the trivial variables were discarded. The PCA result holistically pinpointed the key reliability variables that influence the micro-grid reliability, revealing that system availability represented by factor 1 (F1) loaded 24% of the total variables studied, with reliability cluster including Mean Time Between Failures (MTBF) = -0.844, Mean Time to Repair (MTTR) = -0.737, Demand Response (DR) technique = 0.752, Failure Rate = 0.647 among others. The failure rate and the frequency of outages in F1, were an indication that system availability would be influenced, thereby affecting the micro-grid performance. The study also extracted some weak factor loading, F8 and F9 indicting them as reliability variables whose influences do not impact negatively on the micro-grid reliability but should not be discarded in the study of the reliability of micro-grid power systems. Hence an attempt to improve the system's reliability, concentrating on the key variables factors, the weak variables should not be neglected.

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Section: Figure 2 Reliability Variable Scree Plotmentioning

confidence: 99%

Identifying Key Reliability Factors in Micro-Grid Systems Using Principal Component Analysis

Elizabeth,

Oyaka,

Orovwode

et al. 2024

JESA

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“…In this proposed power system plan, static and dynamic analysis simulations are performed. The specific analysis of one aspect of power system planning can also be performed as presented in [29], which focuses on the small-signal stability analysis of wind-penetrated power systems.…”

Section: Rotor Angle Stabilitymentioning

confidence: 99%

Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System

et al. 2022

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The planning of electrical power systems in remote island areas poses a few challenges, such as requiring many load centers, various energy sources, and certain geographical conditions, which leads to inefficiencies in energy production. For this reason, it is necessary to plan an electrical transmission system to efficiently transfer the power between load centers. Previous research has found that the completion of the most economical power system will be achieved in 2050 on the condition that the Ambon, Seram, Haruku, and Saparua (Ambon-SHS) systems are interconnected in 2025. Providing complementary support, this paper conducts a power system analysis to assess the proposed interconnection system in Maluku Island, which has several islands, small load centers, and local renewable energy resources. The power system analysis was performed using DIgSILENT PowerFactory and was carried out every 5 years of the system planning period until 2050. The results show that the design of the obtained transmission system meets the standard security criteria, which include thermal loading and voltage, being under both normal and N-1 contingency conditions, a short-circuit current, voltage stability, frequency stability, and transient stability. Finally, this paper demonstrates that the proposed plan is economically and technically feasible.

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Optimization to controllability measure of the POD in DFIG-integrated power systems to improve small-signal stability margin considering the impact of pre-fault slip

Li,

Tao

2024

Electr Eng

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Reliability evaluation of bulk power systems with wind generation using small signal stability analysis

Cited by 9 publications

References 17 publications

Identifying Key Reliability Factors in Micro-Grid Systems Using Principal Component Analysis

Identifying Key Reliability Factors in Micro-Grid Systems Using Principal Component Analysis

Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System

Optimization to controllability measure of the POD in DFIG-integrated power systems to improve small-signal stability margin considering the impact of pre-fault slip

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