scholarly journals Analysis of Methods for Determining the Optimal Location of a Static Var Compensator for Power Systems

2018 ◽  
Vol 5 (1) ◽  
pp. 22-27
Keyword(s):  
Power Systems ◽  
Optimal Location ◽  
Static Var Compensator

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Hybrid adaptive robust control of static var compensator in power systems

2013 ◽  
Vol 24 (12) ◽  
pp. 1707-1723 ◽  
Author(s):  
Lei Zhang ◽  
Aimin Zhang ◽  
Zhigang Ren ◽  
Guoqi Li ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Robust Control ◽  
Power Systems ◽  
Adaptive Robust Control ◽  
Static Var Compensator

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.

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