DEVELOPMENTS IN THE THEORY OF DISTRIBUTION SYSTEM RELIABILITY FOR DEVELOPING POWER SYSTEMS

1988 ◽  
Vol 7 (3) ◽  
pp. 103-106
Author(s):  
A.O. EKWUE
Keyword(s):  
Power Systems ◽  
System Reliability ◽  
Distribution System ◽  
Distribution System Reliability

scholarly journals Reliability Enhancement of Electric Distribution Network Using Optimal Placement of Distributed Generation

2021 ◽  
Vol 13 (20) ◽  
pp. 11407
Author(s):  
Sanaullah Ahmad ◽  
Azzam ul Asar
Keyword(s):  
Power Systems ◽  
Distributed Generation ◽  
System Reliability ◽  
Distribution System ◽  
Energy Demand ◽  
Distribution Network ◽  
Optimal Location ◽  
Computational Time ◽  
Actual Distribution ◽  
Distribution System Reliability

As energy demand is increasing, power systems’ complexities are also increasing. With growing energy demand, new ways and techniques are formulated by researchers to increase the efficiency and reliability of power systems. A distribution system, which is one of the most important entities in a power system, contributes up to 90% of reliability problems. For a sustainable supply of power to customers, the distribution system reliability must be enhanced. Distributed generation (DG) is a new way to improve distribution system reliability by bringing generation nearer to the load centers. Artificial intelligence (AI) is an area in which much innovation and research is going on. Different scientific areas are utilizing AI techniques to enhance system performance and reliability. This work aims to apply DG as a distributed source in a distribution system to evaluate its impacts on reliability. The location of the DG is a design criteria problem that has a relevant effect on the reliability of the distribution system. As the distance of load centers from the feeder increases, outage durations also increase. The reliability was enhanced, as the SAIFI value was reduced by almost 40%, the SAIDI value by 25%, and the EENS value by 25% after injecting DG into the distribution network. The artificial neural network (ANN) technique was utilized to find the optimal location of the DG; the results were validated by installing DG at prescribed localities. The results showed that the injection of DG at proper locations enhances the reliability of a distribution system. The proposed approach was applied to thte Roy Billinton Test System (RBTS). The implementation of the ANN technique is a unique approach to the selection of a location for a DG unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could also decrease the computational complexities and computational time. This research can assist distribution companies in determining the reliability of an actual distribution system for planning and expansion purposes, as well as in injecting a DG at the most optimal location in order to enhance the distribution system reliability.

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

scholarly journals Distributed Energy Resources Impact on Distribution System Reliability Under Load Transfer Restrictions

2012 ◽  
Vol 3 (4) ◽  
pp. 2048-2055 ◽  
Author(s):  
Armando M. Leite da Silva ◽  
Luiz C. Nascimento ◽  
Mauro Augusto da Rosa ◽  
Diego Issicaba ◽  
João A. Peças Lopes
Keyword(s):  
System Reliability ◽  
Distribution System ◽  
Load Transfer ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy ◽  
Distribution System Reliability
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