Optimal Capacitor Allocation in Distribution System Using Particle Swarm Optimization

2014 ◽  
Vol 699 ◽  
pp. 770-775
Author(s):  
Ihsan Jabbar Hasan ◽  
Chin Kim Gan ◽  
Meysam Shamshiri ◽  
Mohd Ruddin Ab Ghani ◽  
Ismadi bin Bugis
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
Standard Case ◽  
Capacitor Placement ◽  
Penalty Factor ◽  
Ieee Standard

Capacitor installation is one of the most commonly used methods for reactive power compensation in the distribution networks. In this paper, the optimum capacitor placement and its sizing has been applied in the distribution network in terms of power losses minimization and voltage profile improvement. The maximum and minimum bus voltage and the maximum possible capacitor size are the constraints of optimum capacitor placement and sizing problem. There are considered as the penalty factor in the objective function. In order to evaluate the obtained objective function, the Particle Swarm Optimization (PSO) is utilized to find the best possible capacitor placement and capacity. The OpenDSS software has then been utilized to solve the power flow through Matlab coding interface. To validate the functionality of the proposed method, the IEEE 13-bus test system is implemented and the obtained results have been compared with the IEEE standard case without capacitor compensation. The results show that the proposed algorithm is more cost effective and has lower power losses as compared to the IEEE standard case. In addition, the voltage profile has been improved, accordingly.

scholarly journals Distributed Expansion Planning By Using Particle Swarm Optimization Method

2019 ◽  
Vol 8 (11) ◽  
pp. 510-514
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Planning Horizon ◽  
Optimization Method ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
Expansion Planning ◽  
Nodal System

This paper exhibits a methodology for distribution expansion planning utilizing multi objective Particle Swarm Optimization (PSO). The Optimization objectives are power losses, Investment & Operating costs, Improve voltage profile. The PSO method has been verified by 30 real time nodal system. While planning the expansion and operation of distribution network, utilities have a complex combination of technical constraints, which must be considered together with the investment decisions on the behavior of the distribution system along the planning horizon.

scholarly journals Distribution System Reconfiguration for Loss Minimization and Voltage Profile Enhancement by using Discrete – Improved Binary Particle Swarm Optimization Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 900-906
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
System Reconfiguration ◽  
Distribution System Reconfiguration ◽  
Bus System

Distribution system reconfiguration is done by altering the open / close position of two kinds of switches: usually open tie switches and sectionalizing switches usually closed. Its main purpose is restoration of supply via other route to improve reliability, sometimes for load balancing by relieving overloads. Feeder reconfiguration is very good alternative to reduce power losses and improve voltage profile to improve overall performance. Distribution system reconfiguration is a very cost effective way to reduce the distribution system power losses, enhance voltage profile and system reliability. This paper presents application of novel Discrete - improved binary particle swarm optimization (D-IBPSO) algorithm for distribution system reconfiguration for minimization of real power loss and improvement of voltage profile. The algorithm is implemented to a 16-bus, 33-bus system and a 69-bus system considering different loading conditions. The simulation results indicate that the suggested technique can accomplish optimal reconfiguration and significantly reduce power losses on the supply scheme and enhance the voltage profile.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

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.

scholarly journals A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2851 ◽  
Author(s):  
Valeriya Tuzikova ◽  
Josef Tlusty ◽  
Zdenek Muller
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Particle Swarm ◽  
Electric Power Industry ◽  
Main Function ◽  
Power Losses ◽  
Swarm Optimization ◽  
Flexible Ac Transmission ◽  
Definition Of ◽  
Ac Transmission

In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.

scholarly journals Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

2020 ◽  
Vol 14 (4) ◽  
pp. 285-311
Author(s):  
Bernd Bassimir ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Upper Bounds ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Stopping Criteria ◽  
Swarm Optimization ◽  
Self Adaptive

Abstract We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

2014 ◽  
Vol 699 ◽  
pp. 809-815 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
Mohd Hafiz Jali ◽  
Wan Mohd Bukhari ◽  
M.N.M. Nasir ◽  
Hazriq Izzuan Jaafar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Time ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Swarm Optimization

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

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