scholarly journals Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
David Gilberto Gracia-Velásquez ◽  
Andrés Steven Morales-Rodríguez ◽  
Oscar Danilo Montoya

The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.


2021 ◽  
Vol 2135 (1) ◽  
pp. 012009
Author(s):  
O. D. Montoya ◽  
V.M. Garrido ◽  
L.F. Grisales-Noreña

Abstract The problem of the optimal power dispatch of dispersed generators in direct-current networks under the presence of nonlinear loads (constant power terminals) is addressed through a combinatorial optimization strategy by using a master-slave solution methodology. The optimal power generation in the dispersed is solved in the master optimization stage through the application of the vortex-search algorithm. Each combination of the power outputs at the dispersed generation sources is provided to a power flow methodology known as the hyperbolic power flow approach for direct current networks. The main advantage of the proposed optimization method corresponds to the possibility of solving a complex nonlinear programming problem via sequential quadratic programming, which can be easily implemented at any programming language with low computational effort and high-quality results. The computational tests of the master-slave optimization proposal are evaluated in a 21-bus system, and the numerical results are compared with the implementation of the exact nonlinear programming model in the General Algebraic Modeling System (i.e., GAMS). All the computational results are conducted through the MATLAB programming environment licensed by Universidad Tecnologica de Pereira for academic usage.


Author(s):  
Oscar Danilo Montoya ◽  
Carlos Alberto Ramírez-Vanegas ◽  
Luis Fernando Grisales-Noreña

<p>The problem of parametric estimation in photovoltaic (PV) modules considering manufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (R<sub>p</sub> and R<sub>s</sub>)), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 × 10<sup>−28</sup> and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.</p>


2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yinghui Wu ◽  
Yifan Zhu ◽  
Tianyu Cao

Bus timetabling is a subproblem of bus network planning, and it determines departure time of each trip of lines to make vehicles from different lines synchronously arrive at transfer stations. Due to the well-designed coordination of bus timetables, passengers can make a smooth transfer without waiting a long time for connecting buses. This paper addresses the planning level of resynchronizing of bus timetable problem allowing modifications to initial timetable. Timetable modifications consist of shifts in the departure times and headways. A single-objective mixed-integer programming model is proposed for this problem to maximize the number of total transferring passengers benefiting from smooth transfers. We analyze the mathematical properties of this model, and then a preprocessing method is designed to reduce the solution space of the proposed model. The numerical results show that the reduced model is effectively solved by branch and bound algorithm, and the preprocessing method has the potential to be applied for large-scale bus networks.


2021 ◽  
Vol 55 (2) ◽  
pp. 275-296
Author(s):  
Soovin Yoon ◽  
Laura A. Albert ◽  
Veronica M. White

Emergency Medical Service systems aim to respond to emergency calls in a timely manner and provide prehospital care to patients. This paper addresses the problem of locating multiple types of emergency vehicles to stations while taking into account that vehicles are dispatched to prioritized patients with different health needs. We propose a two-stage stochastic-programming model that determines how to locate two types of ambulances in the first stage and dispatch them to prioritized emergency patients in the second stage after call-arrival scenarios are disclosed. We demonstrate how the base model can be adapted to include nontransport vehicles. A model formulation generalizes the base model to consider probabilistic travel times and general utilities for dispatching ambulances to prioritized patients. We evaluate the benefit of the model using two case studies, a value of the stochastic solution approach, and a simulation analysis. The case study is extended to study how to locate vehicles in the model extension with nontransport vehicles. Stochastic-programming models are computationally challenging for large-scale problem instances, and, therefore, we propose a solution technique based on Benders cuts.


2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.


Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 124
Author(s):  
Camilo Andres Arenas-Acuña ◽  
Jonathan Andres Rodriguez-Contreras ◽  
Oscar Danilo Montoya ◽  
Edwin Rivas-Trujillo

The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization and genetic algorithms, among others. All the simulations were carried out in the MATLAB programming environment.


Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 633 ◽  
Author(s):  
Jinsheng Gao ◽  
Xiaomin Zhu ◽  
Anbang Liu ◽  
Qingyang Meng ◽  
Runtong Zhang

This paper shows the results of our study on the pick-and-place optimization problem. To solve this problem efficiently, an iterated hybrid local search algorithm (IHLS) which combines local search with integer programming is proposed. In the section of local search, the greedy algorithm with distance weight strategy and the convex-hull strategy is developed to determine the pick-and-place sequence; in the section of integer programming, an integer programming model is built to complete the feeder assignment problem. The experimental results show that the IHLS algorithm we proposed has high computational efficiency. Furthermore, compared with the genetic algorithm and the memetic algorithm, the IHLS is less time-consuming and more suitable in solving a large-scale problem.


2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jun Wang ◽  
Pengcheng Luo ◽  
Xinwu Hu ◽  
Xiaonan Zhang

We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through combining it with a local search algorithm (LSA), we obtain the HDGWO. Moreover, we also introduce specific domain knowledge into both the encoding method and the local search algorithm to compress the feasible solution space. Finally, we examine the feasibility of the HDGWO and the scalability of the HDGWO, respectively, by adopting it to solve a benchmark case and ten large-scale WTA problems. All of the running results are compared with those of a discrete particle swarm optimization (DPSO), a genetic algorithm with greedy eugenics (GAWGE), and an adaptive immune genetic algorithm (AIGA). The detailed analysis proves the feasibility of the HDGWO in solving the benchmark case and demonstrates its scalability in solving large-scale WTA problems.


Sign in / Sign up

Export Citation Format

Share Document