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Computers
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Computing, Electrical and Industrial Systems 2022
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Computing, Electrical and Industrial Systems 2022

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 45490

Special Issue Editors

Prof. Dr. Pedro Pereira

E-Mail Website
Guest Editor
Centre of Technology and Systems, UNINOVA Instituto Desenvolvimento de Novas Tecnologias, 2829-517 Caparica, Portugal
Interests: smart grids; energy efficiency; grid resilience; evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Gomes

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Faculty of Sciences and Technology, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal
Interests: petri nets; embedded systems; hardware/software co-design; reconfigurable computing platforms; model-based development; design automation; cyber-physical systems; Globally Asynchronous Locally Synchronous (GALS) systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João Goes

E-Mail Website
Guest Editor
Centre of Technology and Systems, UNINOVA Instituto Desenvolvimento de Novas Tecnologias, 2829-517 Caparica, Portugal
Interests: electronics; CMOS; analog circuits; cad; data converters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 13th Advanced Doctoral Conference on Computing, Electrical, and Industrial Systems will take place in Caparica, Portugal, on 29 June–1 July 2022. For more information about the conference, please follow this link: https://doceis.dee.fct.unl.pt/.

Selected papers that are presented at the conference are invited to be submitted as extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with a minimum 50% extension of new results. All submitted papers will undergo a standard peer-review procedure. Accepted papers will be published in open access format in Computers and will be collected on this Special Issue website. There is no page limit.

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Prof. Dr. Pedro Pereira
Prof. Dr. Luis Gomes
Prof. Dr. João Goes
Guest Editors

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Published Papers (18 papers)

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Research

38 pages, 9944 KiB  
Article
Modeling Collaborative Behaviors in Energy Ecosystems
by Kankam O. Adu-Kankam and Luis M. Camarinha-Matos
Computers 2023, 12(2), 39; https://doi.org/10.3390/computers12020039 - 13 Feb 2023
Cited by 6 | Viewed by 1953
Abstract
The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that [...] Read more.
The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that are designed to possess some cognitive capabilities, enabling them to make autonomous decisions on behalf of their human owners based on the value system of their physical twin. Due to their cognitive and decision-making capabilities, these agents can exhibit some behavioral attributes, such as engaging in diverse collaborative actions aimed at achieving some common goals. These behavioral attributes can be directed to the promotion of sustainable energy consumption in the ecosystem. Along this line, this work demonstrates various collaborative practices that include: (1) collaborative roles played by the CVPP manager such as (a) opportunity seeking and goal formulation, (b) goal proposition/invitation to form a coalition or virtual organization, and (c) formation and dissolution of coalitions; and (2) collaborative roles played by CHDTs which include (a) acceptance or decline of an invitation based on (i) delegation/non-delegation and (ii) value system compatibility/non-compatibility, and (b) the sharing of common resources. This study adopts a simulation technique that involves the integration of multiple simulation methods such as system dynamics, agent-based, and discrete event simulation techniques in a single simulation environment. The outcome of this study confirms the potential of adding cognitive capabilities to CHDTs and further shows that these agents could exhibit certain collaborative attributes, enabling them to become suitable as rational decision-making agents in households. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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21 pages, 11516 KiB  
Article
Estimation of 5G Core and RAN End-to-End Delay through Gaussian Mixture Models
by Diyar Fadhil and Rodolfo Oliveira
Computers 2022, 11(12), 184; https://doi.org/10.3390/computers11120184 - 12 Dec 2022
Cited by 2 | Viewed by 2538
Abstract
Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) [...] Read more.
Network analytics provide a comprehensive picture of the network’s Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the Core and the Radio Access Network (RAN) E2E delay of 5G networks with the Standalone (SA) and Non-Standalone (NSA) topologies when a single known Probability Density Function (PDF) is not suitable to model its distribution. To this end, multiple PDFs, denominated as components, are combined in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components and a number of samples. The results presented in the paper are based on a dataset of E2E delay values sampled from both SA and NSA 5G networks. Finally, we show that the GMM can be adopted to estimate a high diversity of E2E delay patterns found in 5G networks and its computation time can be adequate for a large range of applications. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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9 pages, 4155 KiB  
Article
On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals
by Filipa Esgalhado, Valentina Vassilenko, Arnaldo Batista and Manuel Ortigueira
Computers 2022, 11(12), 177; https://doi.org/10.3390/computers11120177 - 6 Dec 2022
Cited by 1 | Viewed by 1546
Abstract
Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® [...] Read more.
Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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14 pages, 2310 KiB  
Article
Improving ERPs Integration in Organization: An EOS-Based GreneOS Implementation
by Joseph Rahme, Bharat Masimukku, Nicolas Daclin and Gregory Zacharewicz
Computers 2022, 11(12), 171; https://doi.org/10.3390/computers11120171 - 28 Nov 2022
Viewed by 1761
Abstract
Current ERPs are still limited by cost, customization, implementation time, and interoperability with other systems. Even if cloud-based ERPs attempt to overcome these limits, they do not completely answer all of them. Based on that postulate about recent ERPs, a conceptual architecture, technical [...] Read more.
Current ERPs are still limited by cost, customization, implementation time, and interoperability with other systems. Even if cloud-based ERPs attempt to overcome these limits, they do not completely answer all of them. Based on that postulate about recent ERPs, a conceptual architecture, technical architecture, and implementation architecture of an Enterprise Operating System (EOS) have been designed and proposed to address the services and functionality needed by Enterprise 4.0. This conceptual architecture describes the essential functions required in the EOS, while the technical architecture shows how these tasks cooperate to achieve the mission of the EOS. Among some implementation architectures proposed that benefited from the innovation and concept of the EOS, GreneOS has proposed a technical architecture motivated by EOS concepts. The purpose of this paper is to discuss the current interest, complementarity, and limitation of both the EOS conceptual architecture and its implementation into GreneOS to propose perspectives for the future developments of the EOS. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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15 pages, 3083 KiB  
Article
Digital Twin in the Provision of Power Wheelchairs Context: Support for Technical Phases and Conceptual Model
by Carolina Lagartinho-Oliveira, Filipe Moutinho and Luís Gomes
Computers 2022, 11(11), 166; https://doi.org/10.3390/computers11110166 - 19 Nov 2022
Cited by 3 | Viewed by 2113
Abstract
Worldwide, many wheelchair users find it difficult to use or acquire a wheelchair that is appropriate for them, either because they do not have the necessary financial support or because they do not have access to trained healthcare professionals (HCPs), but they are [...] Read more.
Worldwide, many wheelchair users find it difficult to use or acquire a wheelchair that is appropriate for them, either because they do not have the necessary financial support or because they do not have access to trained healthcare professionals (HCPs), but they are essential for the correct provision of assistive products and user training. Consequently, although wheelchairs are designed to promote the well-being of many users, in many cases, they end up being abandoned or do not provide any benefit, with the chance of causing harm and potentially putting people in danger. This article proposes the creation and use of a Digital Twin (DT) of a Power Wheelchair (PWC) to promote the health of wheelchair users, by facilitating and improving the delivery of remote services by HCPs, as well as to include monitoring services to support timely maintenance. Specifically, a DT is a virtual counterpart that is seamlessly linked to a physical asset, both relying on data and information exchange for mirroring each other. Currently, DT is emerging and being applied to different areas as a promising approach to gather insightful data, which are shared between the physical and virtual worlds and facilitate the means to design, monitor, analyze, optimize, predict, and control physical entities. This article gives an overview of the Digital Twin concept, namely its definition, types, and properties, and seeks to synthesize the technologies and tools frequently used to enable Digital Twins; we also explain how a DT can be used in the technical phases of the PWC provision process and propose a conceptual model highlighting the use of an MDD approach benefiting from a Petri net formalism, which is presented to systematize the development of a PWC DT. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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20 pages, 2001 KiB  
Article
ILP-Based and Heuristic Scheduling Techniques for Variable-Cycle Approximate Functional Units in High-Level Synthesis
by Koyu Ohata, Hiroki Nishikawa, Xiangbo Kong and Hiroyuki Tomiyama
Computers 2022, 11(10), 146; https://doi.org/10.3390/computers11100146 - 26 Sep 2022
Cited by 1 | Viewed by 2152
Abstract
Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some [...] Read more.
Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some of these approximate functional units can dynamically change the degree of computation accuracy. The greater their computational inaccuracy, the faster they are. This study examined the high-level synthesis of approximate circuits that take advantage of such accuracy-controllable functional units. Scheduling methods based on integer linear programming (ILP) and list scheduling were proposed. Under resource and time constraints, the proposed method tries to minimize the computation error of the output value by selectively multi-cycling operations. Operations that have a large impact on the output accuracy are multi-cycled to perform exact computing, whereas operations with a small impact on the accuracy are assigned a single cycle for approximate computing. In the experiments, we explored the trade-off between performance, hardware cost, and accuracy to demonstrate the effectiveness of this work. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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23 pages, 1626 KiB  
Article
A Framework for Knowledge Management System Adoption in Small and Medium Enterprises
by Werner Richardt van Zyl, Sanchen Henning and John Andrew van der Poll
Computers 2022, 11(9), 128; https://doi.org/10.3390/computers11090128 - 25 Aug 2022
Cited by 6 | Viewed by 4083
Abstract
Knowledge is a key competitive advantage for small and medium enterprises (SMEs) as a way of competing with other organisations. There is a need to investigate SME adoption of knowledge management systems (KMSs). Knowledge management systems can only assist in this task if [...] Read more.
Knowledge is a key competitive advantage for small and medium enterprises (SMEs) as a way of competing with other organisations. There is a need to investigate SME adoption of knowledge management systems (KMSs). Knowledge management systems can only assist in this task if they are sufficiently adopted. The purpose of this research was to develop a conceptual framework for KMS adoption within an SME context. The research aimed to explore the interdependencies between various contextual KMS adoption factors, namely the technology, organization, environmental and human behavioural contexts. Four mini-focus groups were conducted and included employees in SMEs. Thematic analysis identified nine themes that describe the dynamics that either promote or prevent KMS adoption. The findings provide deeper insights into the influencing factors in KMS adoption to enhance SME performance and competitiveness. The KMS adoption framework can be applied to improve the adoption of technology in SMEs. Future research could include SMEs in specific industries to compare adoption factors and could also include larger organisations. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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30 pages, 20558 KiB  
Article
Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree
by Venkataramana Veeramsetty, Modem Sai Pavan Kumar and Surender Reddy Salkuti
Computers 2022, 11(8), 119; https://doi.org/10.3390/computers11080119 - 29 Jul 2022
Cited by 6 | Viewed by 2247
Abstract
Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution [...] Read more.
Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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17 pages, 5625 KiB  
Article
Implementation of a Programmable Electronic Load for Equipment Testing
by León Felipe Serna-Motoya, José R. Ortiz-Castrillón, Paula Andrea Gil-Vargas, Nicolás Muñoz-Galeano, Juan Bernardo Cano-Quintero and Jesús M. López-Lezama
Computers 2022, 11(7), 106; https://doi.org/10.3390/computers11070106 - 28 Jun 2022
Cited by 2 | Viewed by 2775
Abstract
This paper presents the implementation of an AC three-phase programmable electronic load (PEL) that emulates load profiles and can be used for testing equipment in microgrids (MGs). The implemented PEL topology is built with a voltage source inverter (VSI) which works as a [...] Read more.
This paper presents the implementation of an AC three-phase programmable electronic load (PEL) that emulates load profiles and can be used for testing equipment in microgrids (MGs). The implemented PEL topology is built with a voltage source inverter (VSI) which works as a current controlled source and a Buck converter which permits the dissipation of active power excess. The PEL operation modes according to the interchange of active and reactive power and its operation in four quadrants were determined. The power and current limits which establish the control limitations were also obtained. Three control loops were implemented to independently regulate active and reactive power and ensure energy balance in the system. The main contribution of this paper is the presentation a detailed analysis regarding hardware limitations and the operation of the VSI and Buck converter working together. The PEL was implemented for a power of 1.8 kVA. Several experimental results were carried out with inductive, capacitive, and resistive scenarios to validate the proper operation of the PEL. Experimental tests showed the correct behavior of the AC three-phase currents, VSI input voltage, and Buck converter output voltage of the PEL for profile changes, including transient response. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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19 pages, 362 KiB  
Article
Selection and Location of Fixed-Step Capacitor Banks in Distribution Grids for Minimization of Annual Operating Costs: A Two-Stage Approach
by Oscar Danilo Montoya, Edwin Rivas-Trujillo and Diego Armando Giral-Ramírez
Computers 2022, 11(7), 105; https://doi.org/10.3390/computers11070105 - 27 Jun 2022
Cited by 3 | Viewed by 2118
Abstract
The problem regarding the optimal location and sizing of fixed-step capacitor banks in distribution networks with radial configuration is studied in this research by applying a two-stage optimization approach. The first stage consists of determining the nodes where the capacitor banks will be [...] Read more.
The problem regarding the optimal location and sizing of fixed-step capacitor banks in distribution networks with radial configuration is studied in this research by applying a two-stage optimization approach. The first stage consists of determining the nodes where the capacitor banks will be placed. In this stage, the exact mixed-integer nonlinear programming (MINLP) model that represents the studied problem is transformed into a mixed-integer quadratic convex (MIQC) model. The solution of the MIQC model ensures that the global optimum is reached given the convexity of the solution space for each combination of nodes where the capacitor banks will be installed. With the solution of the MIQC, the suitable nodes for the installation of the fixed-step capacitors are fixed, and their sizes are recursively evaluated in a power flow methodology that allows for determining the optimal sizes. In the second stage, the successive approximation power flow method is applied to determine the optimal sizes assigned to these compensation devices. Numerical results in three test feeders with 33, 69, and 85 buses demonstrate the effectiveness of the proposed two-stage solution method for two operation scenarios: (i) operation of the distribution system under peak load conditions throughout the year, and (ii) operation considering daily demand variations and renewable generation penetration. Comparative results with the GAMS software confirm the excellent results reached using the proposed optimization approach. All the simulations were carried out in the MATLAB programming environment, version 2021b, as well as using the Gurobi solver in the convex programming tool known as CVX. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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19 pages, 16212 KiB  
Article
Non-Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Logistic Regression Model
by Venkataramana Veeramsetty, Srividya Srinivasula and Surender Reddy Salkuti
Computers 2022, 11(6), 94; https://doi.org/10.3390/computers11060094 - 11 Jun 2022
Cited by 2 | Viewed by 2360
Abstract
Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based [...] Read more.
Non-Zero crossing point detection in a sinusoidal signal is essential in case of various power system and power electronics applications like power system protection and power converters controller design. In this paper 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, logistic regression model is used to predict the non-zero crossing point in a distorted signal based on input features like slope, intercept, correlation and RMSE. Logistic regression model is trained and tested in Google Colab environment. As per simulation results, it is observed that logistic regression model is able to predict all non-zero-crossing point in a distorted signal. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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34 pages, 7068 KiB  
Article
Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting
by Sarunyoo Boriratrit, Chitchai Srithapon, Pradit Fuangfoo and Rongrit Chatthaworn
Computers 2022, 11(5), 66; https://doi.org/10.3390/computers11050066 - 27 Apr 2022
Cited by 10 | Viewed by 2930
Abstract
Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system [...] Read more.
Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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19 pages, 373 KiB  
Article
Application of the Hurricane Optimization Algorithm to Estimate Parameters in Single-Phase Transformers Considering Voltage and Current Measures
by Brandon Cortés-Caicedo, Oscar Danilo Montoya and Andrés Arias-Londoño
Computers 2022, 11(4), 55; https://doi.org/10.3390/computers11040055 - 11 Apr 2022
Cited by 6 | Viewed by 2473
Abstract
In this research paper, a combinatorial optimization approach is proposed for parameter estimation in single-phase transformers considering voltage and current measurements at the transformer terminals. This problem is represented through a nonlinear programming model (NLP), whose objective is to minimize the root mean [...] Read more.
In this research paper, a combinatorial optimization approach is proposed for parameter estimation in single-phase transformers considering voltage and current measurements at the transformer terminals. This problem is represented through a nonlinear programming model (NLP), whose objective is to minimize the root mean square error between the measured voltage and current values and the calculated values from the equivalent model of the single-phase transformer. These values of voltage and current can be determined by applying Kirchhoff’s Laws to the model T of the transformer, where its parameters, series resistance and reactance as well as the magnetization resistance and reactance, i.e., R1, R2′, X1, X2′, Rc y Xm, are provided by the Hurricane Optimization Algorithm (HOA). The numerical results in the 4 kVA, 10 kVA and 15 kVA single-phase test transformers demonstrate the applicability of the proposed method since it allows the reduction of the average error between the measured and calculated electrical variables by 1000% compared to the methods reported in the specialized literature. This ensures that the parameters estimated by the proposed methodology, in each test transformer, are close to the real value with an accuracy error of less than 6%. Additionally, the computation times required by the algorithm to find the optimal solution are less than 1 second, which makes the proposed HOA robust, reliable, and efficient. All simulations were performed in the MATLAB programming environment. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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22 pages, 422 KiB  
Article
Optimal Allocation and Sizing of PV Generation Units in Distribution Networks via the Generalized Normal Distribution Optimization Approach
by Oscar Danilo Montoya, Luis Fernando Grisales-Noreña and Carlos Andres Ramos-Paja
Computers 2022, 11(4), 53; https://doi.org/10.3390/computers11040053 - 31 Mar 2022
Cited by 9 | Viewed by 2919
Abstract
The problem of optimal siting and dimensioning of photovoltaic (PV) generators in medium-voltage distribution networks is addressed in this research from the perspective of combinatorial optimization. The exact mixed-integer programming (MINLP) model is solved using a master–slave (MS) optimization approach. In the master [...] Read more.
The problem of optimal siting and dimensioning of photovoltaic (PV) generators in medium-voltage distribution networks is addressed in this research from the perspective of combinatorial optimization. The exact mixed-integer programming (MINLP) model is solved using a master–slave (MS) optimization approach. In the master stage, the generalized normal distribution optimization (GNDO) with a discrete–continuous codification is used to represent the locations and sizes of the PV generators. In the slave stage, the generalization of the backward/forward power method, known as the successive approximation power flow method, is adopted. Numerical simulations in the IEEE 33-bus and 69-bus systems demonstrated that the GNDO approach is the most efficient method for solving the exact MINLP model, as it obtained better results than the genetic algorithm, vortex-search algorithm, Newton-metaheuristic optimizer, and exact solution using the General Algebraic Modeling System (GAMS) software with the BONMIN solver. Simulations showed that, on average, the proposed MS optimizer reduced the total annual operative costs by approximately 27% for both test feeders when compared with the reference case. In addition, variations in renewable generation availability showed that from 30% ahead, positive reductions with respect to the reference case were obtained. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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19 pages, 735 KiB  
Article
Multi-Period Optimal Reactive Power Dispatch Using a Mean-Variance Mapping Optimization Algorithm
by Daniel C. Londoño Tamayo, Walter M. Villa-Acevedo and Jesús M. López-Lezama
Computers 2022, 11(4), 48; https://doi.org/10.3390/computers11040048 - 22 Mar 2022
Cited by 3 | Viewed by 2566
Abstract
Optimal reactive power dispatch plays a key role in the safe operation of electric power systems. It consists of the optimal management of the reactive power sources within the system, usually with the aim of reducing system power losses. This paper presents both [...] Read more.
Optimal reactive power dispatch plays a key role in the safe operation of electric power systems. It consists of the optimal management of the reactive power sources within the system, usually with the aim of reducing system power losses. This paper presents both a new model and a solution approach for the multi-period version of the optimal reactive power dispatch. The main feature of a multi-period approach lies on the incorporation of inter-temporal constraints that allow the number of switching operations in transformer taps and capacitor banks to be limited in order to preserve their lifetime and avoid maintenance cost overruns. The main contribution of the paper is the constraint handling approach which consists of a multiplication of sub-functions which act as penalization and allow simultaneous consideration of both the feasibility and optimality of a given candidate solution. The multi-period optimal reactive power dispatch is an inherently nonconvex and nonlinear problem; therefore, it was solved using the metaheuristic mean-variance mapping optimization algorithm. The IEEE 30-bus and IEEE 57-bus test systems were used to validate the model and solution approach. The results allow concluding that the proposed model guarantees an adequate reactive power management that meets the objective of minimizing power losses and keeping the transformer taps and capacitor bank movements within limits that allow guaranteeing their useful life. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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17 pages, 1963 KiB  
Article
Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images
by Karen Vanessa Angulo, Danilo Gustavo Gil and Helbert Eduardo Espitia
Computers 2022, 11(3), 46; https://doi.org/10.3390/computers11030046 - 20 Mar 2022
Cited by 2 | Viewed by 2490
Abstract
Given the grid features of digital images, a direct relation with cellular automata can be established with transition rules based on information of the cells in the grid. This document presents the modeling of an algorithm based on cellular automata for digital images [...] Read more.
Given the grid features of digital images, a direct relation with cellular automata can be established with transition rules based on information of the cells in the grid. This document presents the modeling of an algorithm based on cellular automata for digital images processing. Using an adaptation mechanism, the algorithm allows the elimination of impulsive noise in digital images. Additionally, the comparison of the cellular automata algorithm and median and mean filters is carried out to observe that the adaptive process obtains suitable results for eliminating salt and pepper type-noise. Finally, by means of examples, the result of the algorithm are shown graphically. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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25 pages, 435 KiB  
Article
Application of the Hurricane-Based Optimization Algorithm to the Phase-Balancing Problem in Three-Phase Asymmetric Networks
by Jose Luis Cruz-Reyes, Sergio Steven Salcedo-Marcelo and Oscar Danilo Montoya
Computers 2022, 11(3), 43; https://doi.org/10.3390/computers11030043 - 14 Mar 2022
Cited by 5 | Viewed by 2662
Abstract
This article addresses the problem of optimal phase-swapping in asymmetric distribution grids through the application of hurricane-based optimization algorithm (HOA). The exact mixed-integer nonlinear programming (MINLP) model is solved by using a master–slave optimization procedure. The master stage is entrusted with the definition [...] Read more.
This article addresses the problem of optimal phase-swapping in asymmetric distribution grids through the application of hurricane-based optimization algorithm (HOA). The exact mixed-integer nonlinear programming (MINLP) model is solved by using a master–slave optimization procedure. The master stage is entrusted with the definition of load connection at each stage by using an integer codification that ensures that, per node, only one from the possible six-load connections is assigned. In the slave stage, the load connection set provided by the master stage is applied with the backward/forward power flow method in its matricial form to determine the amount of grid power losses. The computational performance of the HOA was tested in three literature test feeders composed of 8, 25, and 37 nodes. Numerical results show the effectiveness of the proposed master–slave optimization approach when compared with the classical Chu and Beasley genetic algorithm (CBGA) and the discrete vortex search algorithm (DVSA). The reductions reached with HOA were 24.34%, 4.16%, and 19.25% for the 8-, 28-, and 37-bus systems; this confirms the literature reports in the first two test feeders and improves the best current solution of the IEEE 37-bus grid. All simulations are carried out in the MATLAB programming environment. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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16 pages, 743 KiB  
Article
Hierarchical Control for DC Microgrids Using an Exact Feedback Controller with Integral Action
by Oscar Danilo Montoya, Federico Martin Serra and Alexander Molina-Cabrera
Computers 2022, 11(2), 22; https://doi.org/10.3390/computers11020022 - 6 Feb 2022
Viewed by 2394
Abstract
This paper addresses the problem of the optimal stabilization of DC microgrids using a hierarchical control design. A recursive optimal power flow formulation is proposed in the tertiary stage that ensures the global optimum finding due to the convexity of the proposed quadratic [...] Read more.
This paper addresses the problem of the optimal stabilization of DC microgrids using a hierarchical control design. A recursive optimal power flow formulation is proposed in the tertiary stage that ensures the global optimum finding due to the convexity of the proposed quadratic optimization model in determining the equilibrium operative point of the DC microgrid as a function of the demand and generation inputs. An exact feedback controller with integral action is applied in the primary and secondary controller layers, which ensures asymptotic stability in the sense of Lyapunov for the voltage variables. The dynamical model of the network is obtained in a set of reduced nodes that only includes constant power terminals interfaced through power electronic converters. This reduced model is obtained by applying Kron’s reduction to the linear loads and step nodes in the DC grid. Numerical simulations in a DC microgrid with radial structure demonstrate the effectiveness and robustness of the proposed hierarchical controller in maintaining the stability of all the voltage profiles in the DC microgrid, independent of the load and generation variations. Full article
(This article belongs to the Special Issue Computing, Electrical and Industrial Systems 2022)
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