scholarly journals Black-Hole Optimization Applied to the Parametric Estimation in Distribution Transformers Considering Voltage and Current Measures

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 124
Author(s):  
Camilo Andres Arenas-Acuña ◽  
Jonathan Andres Rodriguez-Contreras ◽  
Oscar Danilo Montoya ◽  
Edwin Rivas-Trujillo
Keyword(s):  
Black Hole ◽  
Programming Model ◽  
Estimation Error ◽  
Optimization Technique ◽  
Optimization Method ◽  
Point Of View ◽  
Parametric Estimation ◽  
Metaheuristic Optimization ◽  
Distribution Transformers ◽  
Matlab Programming

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.

scholarly journals Coordinated planning in improving power quality considering the use of nonlinear load in radial distribution system

2021 ◽  
Vol 11 (6) ◽  
pp. 4610
Author(s):  
Muhira Dzar Faraby ◽  
Ontoseno Penangsang ◽  
Rony Seto Wibowo ◽  
Andi Fitriati
Keyword(s):  
Power Quality ◽  
Radial Distribution ◽  
Distribution System ◽  
Optimization Technique ◽  
Electrical Energy ◽  
Optimization Method ◽  
Test System ◽  
Nonlinear Load ◽  
Radial Distribution System ◽  
Matlab Programming

<span lang="EN-US">Power quality has an important role in the distribution of electrical energy. The use of non-linear load can generate harmonic spread which can reduce the power quality in the radial distribution system. This research is in form of coordinated planning by combining distributed generation placement, capacitor placement and network reconfiguration to simultaneously minimize active power losses, total harmonic distortion (THD), and voltage deviation as an objective function using the particle swarm optimization method. This optimization technique will be tested on two types of networks in the form 33-bus and 69-bus IEEE Standard Test System to show effectiveness of the proposed method. The use of MATLAB programming shows the result of simulation of increasing power quality achieved for all scenario of proposed method.</span>

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
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Large Scale ◽  
Single Phase ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Parametric Estimation ◽  
Solution Technique ◽  
Matlab Programming

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.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

scholarly journals Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ágota Bányai ◽  
Tamás Bányai ◽  
Béla Illés
Keyword(s):  
Black Hole ◽  
Supply Chain ◽  
Optimization Problems ◽  
Optimization Method ◽  
Chain Model ◽  
Supply Chain Optimization ◽  
Proposed Model ◽  
Supply Chain Model ◽  
Supply Model ◽  
Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

scholarly journals Farm-Boarding School Management: Linear Programming Contributions in the Search of Self-Sufficiency and Optimization

2017 ◽  
Vol 9 (3) ◽  
pp. 59
Author(s):  
Carina Simionato de Barros ◽  
Gabriela Geraldi Mendonça ◽  
Augusto Hauber Gameiro
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Programming Model ◽  
Safety Margin ◽  
Boarding School ◽  
Point Of View ◽  
Technical Programs ◽  
Modeling Process ◽  
Self Sufficiency ◽  
Education Of Students

Farm schools offer a learning environment for the education of students in Agricultural Technical Programs and offer this program adopting boarding systems (“farm-boarding schools” or “FBS”). The big challenge in FBS is balancing education and production, that is, provide resources for practical classes and at the same time provide food for farm residents from a pre-defined budget by the sponsoring institution. The aim of this paper is to present a linear programming model to plan and optimize FBS production and supply. The model was applied in two FBS in Brazil. The model developed could show the complexity of the FBS system, which features a variety of productions and the interactions among them. The modeling process presented positive results from a technical and managerial point of view, including people management. The formulated model showed an optimized scenario which extended the managers’ analysis horizon and allowed safer decision making. The system’s complexity hampers dialogue between the farm-boarding school team and managers. From the modeling process and the standardization of data and generated results, there was a greater safety margin to present investment proposals and analyzes, accelerating the decision-making process, which was a positive addition to the system.

An Interval-Index-Oriented Intelligent Optimization Method and its Industrial Application

2011 ◽  
Vol 219-220 ◽  
pp. 546-550
Author(s):  
Ming Shan Cai ◽  
Ling Shuang Kong
Keyword(s):  
Optimal Control ◽  
Industrial Application ◽  
Programming Model ◽  
Optimization Method ◽  
Quality Indices ◽  
Reasoning Strategy ◽  
Goal Programming Model ◽  
Interval Goal Programming ◽  
Expert Reasoning

Based on the strong coupling and interval requirement of multiple quality indices, the interval-index-oriented optimization method is proposed to effectively realize the optimal control of alumina blending process. Firstly, the lexicographic interval goal programming model is built to describe the process requirements for quality indices. Then, based on the characteristics of the programming model, a kind of classificatory knowledge base is constructed by using the empirical knowledge accumulated in long-term production and the expert reasoning strategy is proposed to realize the optimal control of quality indexes with interval constraints. The results of industrial application shows that the proposed method can realize the optimal control of quality indices. It provides a good optimization mode for other blending processes of nonferrous metal production.

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