scholarly journals Optimization techniques for economic load dispatch problem in power plant

2021 ◽  
pp. 10-17
Keyword(s):  
Computation Time ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Hybrid Techniques ◽  
Power Stations ◽  
Programming Techniques ◽  
Single Method ◽  
Parameter Robustness

This paper presents survey of optimization techniques used to solve the problem of economic load dispatch in power stations. Since there is no single method available for solving all economic dispatch problems efficiently, thus a number of different optimization methods have been developed to solve this problem. These techniques were divided into three types depending on the efficiency of the solution: stochastic process techniques, statistical methods, and mathematical programming techniques which divided to local optimization, and global optimization. It is found that is better to use hybrid techniques to overcome load dispatch problems so as to achieve significant improvements in computation time, convergence properties, solution quality, or parameter robustness.

scholarly journals Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas

2015 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Risk Assessment ◽  
Path Planning ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions

2016 ◽  
pp. 201-226
Author(s):  
Provas Kumar Roy ◽  
Moumita Pradhan ◽  
Tandra Pal
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Thermal Systems ◽  
Generation Scheduling ◽  
Valve Point Effect ◽  
Real Coded Genetic Algorithm ◽  
Ramp Rate Limits

This chapter presents various novel evolutionary algorithms, namely Real Coded Genetic Algorithm (RGA), two variants of Biogeography-Based Optimization (BBO), and three variants of Particle Swarm Optimization (PSO) in order to find the optimal power generation scheduling to simultaneously optimize fuel cost and power loss for solving constrained economic load dispatch problems of all thermal systems, considering multiple fuel operation and valve point effect. The effectiveness of the proposed algorithms is demonstrated in five different ELD problems, considering different constraints such as transmission losses, ramp rate limits, multi-fuel options and valve point loading. Comparative studies are carried out to examine the effectiveness and superiority of the proposed approaches. A comparison of simulation results reveals optimization usefulness of the proposed BBO scheme over other well established population based optimization techniques. It is also found that the convergence characteristics of the BBO algorithm are better than other optimization methods.

scholarly journals Optimization for Software Implementation of Fractional Calculus Numerical Methods in an Embedded System

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 566
Author(s):  
Mariusz Matusiak
Keyword(s):  
Embedded System ◽  
Fractional Order ◽  
Numerical Algorithms ◽  
Computation Time ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Software Implementation ◽  
Software Optimization ◽  
Fractional Order Differential Equations ◽  
Non Locality

In this article, some practical software optimization methods for implementations of fractional order backward difference, sum, and differintegral operator based on Grünwald–Letnikov definition are presented. These numerical algorithms are of great interest in the context of the evaluation of fractional-order differential equations in embedded systems, due to their more convenient form compared to Caputo and Riemann–Liouville definitions or Laplace transforms, based on the discrete convolution operation. A well-known difficulty relates to the non-locality of the operator, implying continually increasing numbers of processed samples, which may reach the limits of available memory or lead to exceeding the desired computation time. In the study presented here, several promising software optimization techniques were analyzed and tested in the evaluation of the variable fractional-order backward difference and derivative on two different Arm® Cortex®-M architectures. Reductions in computation times of up to 75% and 87% were achieved compared to the initial implementation, depending on the type of Arm® core.

Optimal Balancing of High-Speed Linkages Using Multiobjective Programming Techniques

1986 ◽  
Vol 108 (4) ◽  
pp. 454-460 ◽  
Author(s):  
S. S. Rao ◽  
R. L. Kaplan
Keyword(s):  
High Speed ◽  
Goal Attainment ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
External Loading ◽  
Planar Mechanisms ◽  
Joint Friction ◽  
Lexicographic Method ◽  
Programming Techniques ◽  
Four Bar Linkage

The application of multiobjective optimization techniques to minimize the dynamic reactions of planar mechanisms is studied. A systems-oriented procedure, which can easily incorporate variable angular velocity input as well as joint friction and external loading, is used for the dynamic analysis of the mechanisms. Goal programming, goal attainment, and a combined bounded objective function/lexicographic method are outlined as solution procedures. These optimization methods are implemented using a computer program based on an exterior penalty function approach. An example four-bar linkage is considered for illustration and the results obtained with different methods of optimization are reportred. It is observed that the methods presented in this paper offer greater flexibility and wider application in the optimal balancing of high-speed linkages.

Risk-Based Path Planning Optimization Methods for Unmanned Aerial Vehicles Over Inhabited Areas1

2016 ◽  
Vol 16 (2) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality ◽  
Aerial Vehicles

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches that can find low-risk flight paths between the mission's start and finish points. Because the flight paths with the lowest risk could be excessively long and indirect, UAV operators are concerned about the tradeoff between risk and flight time. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

scholarly journals Emission Constrained Economic Dispatch with PV Energy Penetration

2019 ◽  
Vol 8 (12) ◽  
pp. 3021-3026
Keyword(s):  
Renewable Energy ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Optimization Strategy ◽  
Renewable Energy Resources ◽  
Pv System ◽  
Power Stations

Power system planners are forced to consider the alarming rate of environmental pollution and rapiddepletion of fossil fuels andutilize renewable energy resources to mitigate the environmental effects of thermal power stations. Combined Economic Emission Dispatch(CEED)offers an effectivesolution to reducefossil fuel emissions as well ascost.Since 1985, CEED is considered to be a common optimization strategy. Literature contains lot of optimization methods for the strategy.In the recent times, using PV energy has proved to be a feasible and dependable alternative for electricity generation systems based on fossil fuels. In the developing countries, the dependency on fossil fuels has been seen as inevitable. At present,the use of renewable energy sources is rapidly increasing in inconventional power generation systems. The present paper puts forwardan approach of combining PVenergy-based grid integrated PV system with fossil fuel based thermal power plant using evolutionary programmingbased optimization technique.Among the various optimization techniques, the Particle Swarm Optimization (PSO) is considered to be the most suitable technique for the problem is explained in detailed manner.The proposed method is to combine CEED with the PV energy and thereby reduces the use of conventional energy resources.It also permits an effective utilizationof abundantlyavailable PV energy.It is tested on standard IEEE 30 bus system with the real time ratings of proposed PV plant situated in Tamilnadu.

Determination of the Minimum Distance Between Moving Objects Including Velocity Information

1993 ◽  
Author(s):  
Meyer Nahon
Keyword(s):  
Minimum Distance ◽  
Moving Objects ◽  
Rapid Determination ◽  
Computation Time ◽  
Optimization Techniques ◽  
Real Time Control ◽  
Time Step ◽  
Time Control ◽  
Velocity Information

Abstract The rapid determination of the minimum distance between objects is of importance in collision avoidance for a robot maneuvering among obstacles. Currently, the fastest algorithms for the solution of this problem are based on the use of optimization techniques to minimize a distance function. Furthermore, to date this problem has been approached purely through the position kinematics of the two objects. However, although the minimum distance between two objects can be found quickly on state-of-the-art hardware, the modelling of realistic scenes entails the determination of the minimum distances between large numbers of pairs of objects, and the computation time to calculate the overall minimum distance between any two objects is significant, and introduces a delay which has serious repercussions on the real-time control of the robot. This paper presents a technique to modify the original optimization problem in order to include velocity information. In effect, the minimum distance calculation is performed at a future time step by projecting the effect of present velocity. This method has proven to give good results on a 6-dof robot maneuvering among obstacles, and has allowed a complete compensation of the lags incurred due to computational delays.

scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

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