Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow (original) (raw)
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An Efficient Particle Swarm Optimization Algorithm for Optimal Power Flow Solution
Recent Patents on Electrical Engineeringe, 2010
h i g h l i g h t s • Optimal power flow (OPF) with FACTS devices on IEEE 30-bus system is scrutinized. • Different practical constraints are included into the OPF problem. • Particle Swarm Optimization (PSO) is implemented as basic search algorithm. • Several fortifications are proposed to enhance the PSO algorithm's performance. • Results are compared with most recent studies and improvement is clearly observed.
Particle Swarm Optimization Applied to Optimal Power Flow Solution
2009 Fifth International Conference on Natural Computation, 2009
This paper presents solution of optimal power flow (OPF) problem of a power system via a simple particle swarm optimization (PSO) algorithm. This method is dynamic in nature and it overcomes the shortcomings of other evolutionary computation techniques such as premature convergence and provides high quality solutions. The objective is to minimize the fuel cost and keep the power outputs of generators, bus voltages, shunt capacitors/reactors and transformers tapsetting in their secure limits. The effectiveness of PSO was compared to that of OPF by MATPOWER. The potential and superiority of PSO have been demonstrated through the results of IEEE 30-bus system.
Energies
Metaheuristic optimization techniques have successfully been used to solve the Optimal Power Flow (OPF) problem, addressing the shortcomings of mathematical optimization techniques. Two of the most popular metaheuristics are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The literature surrounding GA and PSO OPF is vast and not adequately organized. This work filled this gap by reviewing the most prominent works and analyzing the different traits of GA OPF works along seven axes, and of PSO OPF along four axes. Subsequently, cross-comparison between GA and PSO OPF works was undertaken, using the reported results of the reviewed works that use the IEEE 30-bus network to assess the performance and accuracy of each method. Where possible, the practices used in GA and PSO OPF were compared with literature suggestions from other domains. The cross-comparison aimed to act as a first step towards the standardization of GA and PSO OPF, as it can be used to draw preliminar...
Optimal power flow using particle swarm optimization
International Journal of Electrical Power & Energy Systems, 2002
This paper presents an ef®cient and reliable evolutionary-based approach to solve the optimal power¯ow (OPF) problem. The proposed approach employs particle swarm optimization (PSO) algorithm for optimal settings of OPF problem control variables. Incorporation of PSO as a derivative-free optimization technique in solving OPF problem signi®cantly relieves the assumptions imposed on the optimized objective functions. The proposed approach has been examined and tested on the standard IEEE 30-bus test system with different objectives that re¯ect fuel cost minimization, voltage pro®le improvement, and voltage stability enhancement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach. q
2018
The current trend of study is to hybridize two and more algorithms to gain the best solution in the area of optimization problems. In this paper presents the recently developed hybrid optimization technique named PSO-GWO combines the framework of particle swarm optimization (PSO) with grey wolves optimization (GWO) to solve the optimal power flow (OPF) problem. OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus test system with a single objective and multi-objective cases such as fuel cost minimization, Active power loss reduction, Voltage profile improvement and Voltage stability enhancement, and is compared to approaches available in the literature. The hybrid PSO-GWO provides better results compared to the original PSO, GWO, and other techniques mentioned in the literature as shown in the simulation re...
Journal of Electrical Engineering, 2015
This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered. © 2014 Springer-Verlag Berlin Heidelberg.
The study presents an improved particle swarm optimisation (IPSO) method for the multi-objective optimal power flow (OPF) problem. The proposed multi-objective OPF considers the cost, loss, voltage stability and emission impacts as the objective functions. A fuzzy decision-based mechanism is used to select the best compromise solution of Pareto set obtained by the proposed algorithm. Furthermore, to improve the quality of the solution, particularly to avoid being trapped in local optima, this study presents an IPSO that profits from chaos queues and self-adaptive concepts to adjust the particle swarm optimisation (PSO) parameters. Also, a new mutation is applied to increase the search ability of the proposed algorithm. The 30-bus IEEE test system is presented to illustrate the application of the proposed problem. The obtained results are compared with those in the literatures and the superiority of the proposed approach over other methods is demonstrated.
Improved Pseudo-Gradient Search Particle Swarm Optimization for Optimal Power Flow Problem
Sustaining Power Resources through Energy Optimization and Engineering
This paper proposes an improved pseudo-gradient search particle swarm optimization (IPG-PSO) for solving optimal power flow (OPF) with non-convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The proposed IPG-PSO method is an improved PSO by chaotic weight factor and guided by pseudogradient search for particle's movement in an appropriate direction. Test results on the IEEE 30-bus and 118-bus systems indicate that IPG-PSO method is superior to other methods in terms of lower generator fuel cost, smaller voltage deviation, and lower voltage stability index.
Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution
Discrete Dynamics in Nature and Society, 2010
This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization PSO . The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight CIW , a time-varying inertia weight TVIW , and global-local best inertia weight GLbestIW , are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.
International Journal of System Assurance Engineering and Management, 2014
This paper presents a hybrid genetic algorithm and particle swarm optimization (HGAPSO) for solving optimal power flow problem with non-smooth cost function and subjected to limits on generator real, reactive power outputs, bus voltages, transformer taps and power flow of transmission lines. In (HGAPSO), individuals in a new generation are created, not only by crossover and mutation operation as in (GA), but also by (PSO). The effectiveness of this algorithm is examined and tested for standard IEEE 30 bus system with six generating units. The results of the proposed technique are compared with that of PSO and other methods reported in the literature.