A Novel Evolutionary Optimization Technique for Solving Optimal Reactive Power Dispatch Problems

2016 ◽  
pp. 244-275 ◽  
Author(s):  
Provas Kumar Roy
Keyword(s):  
Reactive Power ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Search Space ◽  
Continuous Space ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Local Optimal Solution ◽  
Reactive Power Dispatch

Biogeography based optimization (BBO) is an efficient and powerful stochastic search technique for solving optimization problems over continuous space. Due to excellent exploration and exploitation property, BBO has become a popular optimization technique to solve the complex multi-modal optimization problem. However, in some cases, the basic BBO algorithm shows slow convergence rate and may stick to local optimal solution. To overcome this, quasi-oppositional biogeography based-optimization (QOBBO) for optimal reactive power dispatch (ORPD) is presented in this study. In the proposed QOBBO algorithm, oppositional based learning (OBL) concept is integrated with BBO algorithm to improve the search space of the algorithm. For validation purpose, the results obtained by the proposed QOBBO approach are compared with those obtained by BBO and other algorithms available in the literature. The simulation results show that the proposed QOBBO approach outperforms the other listed algorithms.

scholarly journals Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2968 ◽  
Author(s):  
Zelan Li ◽  
Yijia Cao ◽  
Le Van Dai ◽  
Xiaoliang Yang ◽  
Thang Trung Nguyen
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Optimal Solution ◽  
System Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch ◽  
Antlion Optimization

In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

scholarly journals Solving Optimal Reactive Power Dispatch Problem by Chaotic Based Brain Storm Optimization Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 145-150
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Reactive Power ◽  
Optimal Solution ◽  
Test System ◽  
Predator Prey ◽  
Brain Storm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Local Optimal Solution ◽  
Reactive Power Dispatch

In this work Chaotic Predator-Prey Brain Storm Optimization (CPS) algorithm is proposed to solve optimal reactive power dispatch problem. Predator–Prey Brain Storm Optimization position cluster centers to execute as predators, accordingly it will progress towards enhanced positions, although the left over thoughts do as preys; consequently they move far from their neighboring predators. In the projected algorithm chaotic theory has been applied to enhance the quality of the exploration.  Ergodicity and indiscretion are utilized in the CPS algorithm, such that projected algorithm will not get trapped in the local optimal solution.  Chaotic predator-prey brain storm optimization (CPS) algorithm has been tested in standard IEEE 30 bus test system and results show the projected algorithm reduced the real power loss effectively.

scholarly journals BAT algorithm and Ant Colony Optimization based Optimal Reactive Power Dispatch to Improve Voltage Stability

2017 ◽  
Vol 2 (6) ◽  
pp. 27 ◽  
Author(s):  
Rayudu Katuri ◽  
Guduri Yesuratnam ◽  
Askani Jayalaxmi
Keyword(s):  
Ant Colony Optimization ◽  
Voltage Stability ◽  
Reactive Power ◽  
Optimization Technique ◽  
Bat Algorithm ◽  
Ant Colony ◽  
Voltage Profile ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

One of the important tasks of a power system engineer is to run the system in safe and reliable mode for secure operation with increase in loading. So, it is significant to perform voltage stability analysis by optimal reactive power dispatch with Artificial Intelligence (AI) techniques. This paper presents the application of Ant Colony Optimization (ACO) and BAT algorithms for Optimal Reactive Power Dispatch (ORPD) to enhance voltage stability. The proposed ACO and BAT algorithms are used to find the optimal settings of On-load Tap changing Transformers (OLTC), Generator excitation and Static Var Compensators (SVC) to minimize the sum of the squares of the voltage stability L– indices of all the load buses. By calculating system parameters like L-Index, voltage error/deviation and real power loss for the practical Equivalent of Extra High Voltage (EHV) Southern Region Indian 24 bus system, voltage profile is improved and voltage stability is enhanced. A comparative analysis is done with the conventional optimization technique like Linear Programming (LP) for the given objective function to demonstrate the effectiveness of proposed ACO and BAT algorithms. 

New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

2014 ◽  
pp. 321-339
Author(s):  
Provas Kumar Roy
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Transmission Loss ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Opposition Based Learning ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Equality And Inequality Constraints ◽  
Reactive Power Dispatch

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm's performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.

Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch

2016 ◽  
Vol 5 (3) ◽  
pp. 43-62 ◽  
Author(s):  
Susanta Dutta ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Chemical Reaction ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Optimal Solution ◽  
Chemical Reaction Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reaction Optimization ◽  
Reactive Power Dispatch ◽  
Static Synchronous Series Compensator

Static synchronous series compensator (SSSC) is one of the most effective flexible AC transmission systems (FACTS) devices used for enhancing power system security. In this paper, optimal location and sizing of SSSC are investigated for solving the optimal reactive power dispatch (ORPD) problem in order to minimize the active power loss in the transmission networks. A new and efficient chemical reaction optimization (CRO) is proposed to find the feasible optimal solution of the SSSC based optimal reactive power dispatch (ORPD) problem. The proposed approach is carried out on the standard IEEE 30 bus and IEEE 57 bus test systems. The optimization results obtained by the proposed CRO are analyzed and compared with the same obtained from genetic algorithm (GA), teaching learning based optimization (TLBO), quasi-oppositional TLBO (QOTLBO) and strength pareto evolutionary algorithm (SPEA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solutions.

scholarly journals Optimal reactive power dispatch by chaotic biogeography based optimization

2015 ◽  
Vol 18 (3) ◽  
pp. 55-64
Author(s):  
Quy Xuan Truong ◽  
Dieu Ngoc Vo
Keyword(s):  
Reactive Power ◽  
Optimization Problems ◽  
Capacitor Bank ◽  
Stability Index ◽  
Total Power ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Voltage Deviation ◽  
Tap Changer ◽  
Reactive Power Dispatch

This paper proposes a chaotic biogeography based optimization (CBBO) for solving optimal reactive power dispatch (ORPD) problem. Based on biogeography based optimization (BBO) theory proposed by Dan Simon in 2008, a new artificial intelligence with full models and equations have been used to achieve the best solution for objective function of ORPD such as total power loss, voltage deviation and voltage stability index while satisying various constraints of power balance, voltage limits, transformers tap changer limits and switchable capacitor bank limits. The BBO has been enhanced its search ability by adding chaotic theory. Therefore, the proposed CBBO can obtain better solutiong quality than BBO for optimization problems. The proposed method has been tested on the IEEE-30 and IEEE-118 bus systems and the obtained results have been verified with other methods. The result comparison has indicated that the CBBO can be a promise method for dealing the ORPD problem

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