scholarly journals A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow

2019 ◽  
Vol 11 (14) ◽  
pp. 3862 ◽  
Author(s):  
Imene Cherki ◽  
Abdelkader Chaker ◽  
Zohra Djidar ◽  
Naima Khalfallah ◽  
Fadela Benzergua
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Transformation Ratio ◽  
Reactive Power Flow ◽  
Optimal Reactive Power Flow

In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA.

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.

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.

Power Management System of a Particle Swarm Optimization Controlled Grid Integrated Hybrid PV/WIND/FC/Battery Distributed Generation System

2021 ◽  
Author(s):  
T. Praveen Kumar ◽  
N. Subrahmanyam ◽  
Maheswarapu Sydulu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Management ◽  
Distributed Generation ◽  
Power Flow ◽  
Reactive Power ◽  
Particle Swarm ◽  
System Control ◽  
Swarm Optimization ◽  
Power System Control

In this manuscript, the Power management of grid integrated hybrid distributed generation (DG) system with Particle swarm optimization (PSO) algorithm is proposed. The hybrid DG system combines with photovoltaic, wind turbine, fuel cell, battery. Depending on the use of hybrid sources and the changes of power production the variation of power can occurs in the DG system. The major purpose of the proposed method restrains the power flow (PF) on active with reactive power between the source and grid side. In the power system control the proposed PSO method is utilized to maximize the active with reactive PF and the controllers. The proposed method interact the load requirement energy and maintain the load sensitivity due to charging and discharging battery control. In the DG system, the proposed PSO method allows maximum power flow. To assess the PF, the constraints of equality and inequality have been evaluated and they are utilized to determine the accessibility of renewable energy source (RES), electricity demand, and the storage elements of charge level. The protection of the power system is enhanced based on the proposed PSO method. Additionally, for retaining a stable output the renewable power system and battery is used. The proposed method is activated in MATLAB/Simulink working platform and the efficiency is likened with other existing methods.

scholarly journals Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 151
Author(s):  
David Lionel Bernal-Romero ◽  
Oscar Danilo Montoya ◽  
Andres Arias-Londoño
Keyword(s):  
Power Systems ◽  
Programming Language ◽  
Power Flow ◽  
Reactive Power ◽  
Flow Problem ◽  
Computational Effort ◽  
Power Losses ◽  
Reactive Power Flow ◽  
Power Flow Problem ◽  
Optimal Reactive Power Flow

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were 32.33 s, 49.45 s, and 138.88 s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment.

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