scholarly journals Economic Load Dispatch

2021 ◽  
Vol 9 (VI) ◽  
pp. 1646-1651
Author(s):  
Prof. Kanika Lamba
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

scholarly journals Balancing Control of Bicyrobo by Particle Swarm Optimization-Based Structure-Specified Mixed H2/H∞ Control

10.5772/6235 ◽  
2008 ◽  
Vol 5 (4) ◽  
pp. 39 ◽  
Author(s):  
Bui Trung Thanh ◽  
Manukid Parnichkun
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Controller Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Control Technique ◽  
Swarm Optimization ◽  
Conventional Optimization ◽  
Many Sources

In this paper, a structure-specified mixed H2/H∞ controller design using particle swarm optimization (PSO) is proposed for control balancing of Bicyrobo, which is an unstable system associated with many sources of uncertainties due to un-model dynamics, parameter variations, and external disturbances. The structure-specified mixed H2/H∞ control is a robust and optimal control technique. However, the design process normally comes up with a complex and non-convex optimization problem which is difficult to solve by the conventional optimization methods. PSO is a recently useful meta-heuristic search method used to solve multi-objective and non-convex optimization problems. In the method, PSO is used to search for parameters of a structure-specified controller which satisfies mixed H2/H∞ performance index. The simulation and experimental results show the robustness of the proposed controller in compared with the conventional proportional plus derivative (PD) controller, and the efficiency of the proposed algorithm in compared with the genetic algorithm (GA).

scholarly journals A hybrid PSO approach for solving non-convex optimization problems

2012 ◽  
Vol 22 (1) ◽  
pp. 87-105 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthy
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Convex Optimization Problems ◽  
Computational Performance ◽  
Engineering Design Problems

A hybrid PSO approach for solving non-convex optimization problemsThe aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.

scholarly journals Solving Economic Load Dispatch Problem using Grey Wolf Optimizer Method

2019 ◽  
Vol 8 (4) ◽  
pp. 684-688
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Grey Wolf Optimizer ◽  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Load Demand ◽  
Alpha Beta ◽  
Critical Phases

The aim of economic load dispatch (ELD) is to accomplish the load demand with less fuel cost by the generators. This research shows a new grey wolf-inspired algorithm called the Grey Wolf Optimizer (GWO) to achieve ELD. The GWO algorithm follows mainly the grey wolves hierarchy and hunting scheme. The controlling hierarchy is driven by four wolves, namely alpha, beta, delta, and omega. Three critical phases of hunting are implemented, looking for a target, surrounding a target, and attacking a target. Now, on 20 generating units, the algorithm is used and is equated with Particle Swarm Optimization (PSO). The findings show that, compared to PSO, the GWO algorithm is set to yield economic results.

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