Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150490
R. Sethi, J. Kleissl
This paper presents a comparative analysis of the different forecasting techniques (Statistical method (ARIMA)), Machine Learning method (Multivariate Linear Regression) and Deep Learning method (LSTM)) for short term load forecasting. The forecasting model for each of these techniques takes into account the historical load (annual), site temperature data and US calendar data as input features. The model is trained on the first nine months of data and then tested for accuracy on the remaining three months. A case study using the load profile of a commercial building (amusement park) in San Diego, California is presented, where the performance of the above forecasting techniques is compared. The error metric used for comparison is the Root Mean Squared Error (RMSE) value. The results indicate that LSTM model offers the best performance in terms of forecasting accuracy. The designed LSTM model can be deployed as part of Energy Management System (EMS) for smart grids. The robustness of the LSTM model is further explored by comparing the above LSTM encoder-decoder architecture with a standard LSTM architecture. In addition to the above dataset, both architectures were tested on two more datasets- one for an office building and another for an operations center building located in San Diego. Both architectures were trained on first 9 months of load data and tested on the remaining 3 months. The encoder-decoder architecture performed better than the standard architecture across all the datasets.
{"title":"Comparison of Short-Term Load Forecasting Techniques","authors":"R. Sethi, J. Kleissl","doi":"10.1109/SusTech47890.2020.9150490","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150490","url":null,"abstract":"This paper presents a comparative analysis of the different forecasting techniques (Statistical method (ARIMA)), Machine Learning method (Multivariate Linear Regression) and Deep Learning method (LSTM)) for short term load forecasting. The forecasting model for each of these techniques takes into account the historical load (annual), site temperature data and US calendar data as input features. The model is trained on the first nine months of data and then tested for accuracy on the remaining three months. A case study using the load profile of a commercial building (amusement park) in San Diego, California is presented, where the performance of the above forecasting techniques is compared. The error metric used for comparison is the Root Mean Squared Error (RMSE) value. The results indicate that LSTM model offers the best performance in terms of forecasting accuracy. The designed LSTM model can be deployed as part of Energy Management System (EMS) for smart grids. The robustness of the LSTM model is further explored by comparing the above LSTM encoder-decoder architecture with a standard LSTM architecture. In addition to the above dataset, both architectures were tested on two more datasets- one for an office building and another for an operations center building located in San Diego. Both architectures were trained on first 9 months of load data and tested on the remaining 3 months. The encoder-decoder architecture performed better than the standard architecture across all the datasets.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114204815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150515
B. Jensen, Ryan Uyehara
The U.S. power grid is undergoing a transformation. Operational focus is shifting away from utilities with large centralized generators at the transmission level and moving toward individual customers who contribute generation from distributed energy resources (DERs) at the distribution level. Utility-scale renewable generation, meanwhile, continues to span across both transmission and distribution networks. The current interconnection process for small- to grid-scale DERs is well-defined, time-consuming and complex. It involves studying every DER application within a network model to identify its impacts on the network, both locally and systemwide. New technologies and applications are expanding the use cases for the capacity analysis approach, which is radically changing how interconnections are evaluated. Rather than focusing on individual DER connections, capacity analysis can allow you to study nearly every possible interconnection scenario. By identifying and understanding the ideal locations and usage times for DERs-and optimizing operating conditions-capacity analysis is making faster approvals and interconnections, as well as higher DER penetration, possible. This presentation will cover recent implementations of capacity analysis performed for Southern California Edison.
{"title":"Hosting Capacity: A Tool for Modernizing the Grid","authors":"B. Jensen, Ryan Uyehara","doi":"10.1109/SusTech47890.2020.9150515","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150515","url":null,"abstract":"The U.S. power grid is undergoing a transformation. Operational focus is shifting away from utilities with large centralized generators at the transmission level and moving toward individual customers who contribute generation from distributed energy resources (DERs) at the distribution level. Utility-scale renewable generation, meanwhile, continues to span across both transmission and distribution networks. The current interconnection process for small- to grid-scale DERs is well-defined, time-consuming and complex. It involves studying every DER application within a network model to identify its impacts on the network, both locally and systemwide. New technologies and applications are expanding the use cases for the capacity analysis approach, which is radically changing how interconnections are evaluated. Rather than focusing on individual DER connections, capacity analysis can allow you to study nearly every possible interconnection scenario. By identifying and understanding the ideal locations and usage times for DERs-and optimizing operating conditions-capacity analysis is making faster approvals and interconnections, as well as higher DER penetration, possible. This presentation will cover recent implementations of capacity analysis performed for Southern California Edison.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/sustech47890.2020.9150514
Diego A. Aponte-Roa, Gerardo David Guerrero Cabarcas, W. Weaver
This work presents the analysis, simulation and implementation of a Hybrid Micro-Grid based on wind and solar power to source both an AC and DC load. The goal is to observe the availability of wind and solar energy at UAGM Gurabo campus, and perform studies between AC vs DC electrification by the use of commercial off-the-shelf electrical and electronics components. The efficiency of the system to deliver the required the same amount of power to the loads is presented as well. Results demonstrate that in small scale power consumption applications such as lighting, a DC Micro-Grid shown to be more energy efficient and reliable than using an AC Micro-Grid, especially due to the additional conversion stages that the AC requires.
{"title":"AC Vs DC Power Efficiency Comparison of a Hybrid Wind/Solar Microgrid","authors":"Diego A. Aponte-Roa, Gerardo David Guerrero Cabarcas, W. Weaver","doi":"10.1109/sustech47890.2020.9150514","DOIUrl":"https://doi.org/10.1109/sustech47890.2020.9150514","url":null,"abstract":"This work presents the analysis, simulation and implementation of a Hybrid Micro-Grid based on wind and solar power to source both an AC and DC load. The goal is to observe the availability of wind and solar energy at UAGM Gurabo campus, and perform studies between AC vs DC electrification by the use of commercial off-the-shelf electrical and electronics components. The efficiency of the system to deliver the required the same amount of power to the loads is presented as well. Results demonstrate that in small scale power consumption applications such as lighting, a DC Micro-Grid shown to be more energy efficient and reliable than using an AC Micro-Grid, especially due to the additional conversion stages that the AC requires.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122431608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150496
Sean Monemi, John Paul Dulay, David Kelble
Across the United States, utility companies rely on power generated from long distances to provide power for cities and customers. System stability is a tremendous effort that requires strong physical infrastructure and requires ways to monitor and protect the power systems over long distances and varying terrain. This article researches power system protection and models a typical power system using Real Time Digital Simulator, RSCAD, and SEL 311-L relays.
{"title":"Power System Protection in RTDS","authors":"Sean Monemi, John Paul Dulay, David Kelble","doi":"10.1109/SusTech47890.2020.9150496","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150496","url":null,"abstract":"Across the United States, utility companies rely on power generated from long distances to provide power for cities and customers. System stability is a tremendous effort that requires strong physical infrastructure and requires ways to monitor and protect the power systems over long distances and varying terrain. This article researches power system protection and models a typical power system using Real Time Digital Simulator, RSCAD, and SEL 311-L relays.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126856294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150530
E. Elwakil, M. Hegab
This paper presents a qualitative and quantitative research to explore and understand the agents that determine the level of productiveness and the importance of qualitative factors. A questionnaire was conducted to estimate the outcome of the qualitative agents or factors in improving the productivity of microtunneling projects. Information was collected from 26 competent professionals in microtunneling projects for almost 20years through an expert's opinion, using a semi-structured questionnaire. The Analytical Hierarchy Process and the clustering analysis technique have been used to account for the twenty qualitative agents or factors concerned in developing a proposed assessment model. The present research has developed an assessment model that stems from the result of qualitative factors on microtunneling to investigate the effect induced by the qualitative agents on productiveness. The assessment model is thereafter confirmed, showing an analytical outcome of 79.85% moderately justified percent. This study proved that the qualitative factors affecting the productivity of microtunneling projects can be assessed through the productivity index concept. This paper helps the industry parties to understand the factors influencing the success of microtunneling projects as well as, development of an assessment model to calculate productivity. Most of the past research on productivity models are based on quantitative factors only. This paper presents qualitative research to explore and understand the factors that affect productivity and the importance of qualitative factors. The developed assessment model presented is the first in connecting theory, analytical methods, and experts' opinions to help practitioners to understand and assess the productivity of microtunneling projects.
{"title":"Toward Smart and Sustainable Infrastructure Solution: Assessment and Modelling of Qualitative Factors Affecting Productivity in Microtunneling Projects","authors":"E. Elwakil, M. Hegab","doi":"10.1109/SusTech47890.2020.9150530","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150530","url":null,"abstract":"This paper presents a qualitative and quantitative research to explore and understand the agents that determine the level of productiveness and the importance of qualitative factors. A questionnaire was conducted to estimate the outcome of the qualitative agents or factors in improving the productivity of microtunneling projects. Information was collected from 26 competent professionals in microtunneling projects for almost 20years through an expert's opinion, using a semi-structured questionnaire. The Analytical Hierarchy Process and the clustering analysis technique have been used to account for the twenty qualitative agents or factors concerned in developing a proposed assessment model. The present research has developed an assessment model that stems from the result of qualitative factors on microtunneling to investigate the effect induced by the qualitative agents on productiveness. The assessment model is thereafter confirmed, showing an analytical outcome of 79.85% moderately justified percent. This study proved that the qualitative factors affecting the productivity of microtunneling projects can be assessed through the productivity index concept. This paper helps the industry parties to understand the factors influencing the success of microtunneling projects as well as, development of an assessment model to calculate productivity. Most of the past research on productivity models are based on quantitative factors only. This paper presents qualitative research to explore and understand the factors that affect productivity and the importance of qualitative factors. The developed assessment model presented is the first in connecting theory, analytical methods, and experts' opinions to help practitioners to understand and assess the productivity of microtunneling projects.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150484
Emily Chang, S. Bahramirad, Daniel Kushner
As their population density grows, cities need to consider how to use technology to better serve the environmental, social, and economic well-being of its citizens. Many smart city action plans focus on the foundational and tech-enabled strategies that seek to find solutions to complex issues such as mobility, infrastructure and education. Utilities can play a pivotal role in deploying technologies, processes, and planning in the service of societal impacts to support smart city goals of finding green, sustainable, connected solutions for their residents and businesses. To illustrate, we highlight key projects and programs of an electric utility serving northern Illinois, including Chicago. These projects and programs utilize technology to further societal aims such as promoting STEM learning in the next generation; reinforcing community safety; and investigating micro-transit mobility options.
{"title":"Electric Utilities' Role in Promoting and Advancing Smart City Solutions","authors":"Emily Chang, S. Bahramirad, Daniel Kushner","doi":"10.1109/SusTech47890.2020.9150484","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150484","url":null,"abstract":"As their population density grows, cities need to consider how to use technology to better serve the environmental, social, and economic well-being of its citizens. Many smart city action plans focus on the foundational and tech-enabled strategies that seek to find solutions to complex issues such as mobility, infrastructure and education. Utilities can play a pivotal role in deploying technologies, processes, and planning in the service of societal impacts to support smart city goals of finding green, sustainable, connected solutions for their residents and businesses. To illustrate, we highlight key projects and programs of an electric utility serving northern Illinois, including Chicago. These projects and programs utilize technology to further societal aims such as promoting STEM learning in the next generation; reinforcing community safety; and investigating micro-transit mobility options.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132759819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150516
K. Morrissey, S. Kahrobaee, Andrew Ioan
Continued integration of distributed energy resources (DERs) into the grid, such as solar PVs, at a large-scale, contributes into the famous Duck Curve. New DER management algorithms are therefore deemed necessary to alleviate rapid variations within net load profiles of distribution systems. This paper proposes a process to determine the optimal energy storage schedules for leveling the distribution circuit feederhead net load. A series of sensitivity analyses shows how the proposed method can be used to determine the optimal energy storage schedules with different capacities, state of charge requirements, and net load ramp rate limitations.
{"title":"Optimal Energy Storage Schedules for Load Leveling and Ramp Rate Control in Distribution Systems","authors":"K. Morrissey, S. Kahrobaee, Andrew Ioan","doi":"10.1109/SusTech47890.2020.9150516","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150516","url":null,"abstract":"Continued integration of distributed energy resources (DERs) into the grid, such as solar PVs, at a large-scale, contributes into the famous Duck Curve. New DER management algorithms are therefore deemed necessary to alleviate rapid variations within net load profiles of distribution systems. This paper proposes a process to determine the optimal energy storage schedules for leveling the distribution circuit feederhead net load. A series of sensitivity analyses shows how the proposed method can be used to determine the optimal energy storage schedules with different capacities, state of charge requirements, and net load ramp rate limitations.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114481872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150499
Timothy Kidd, Zhen Yu, S. Dobbs, K. Anderson, G. Oetting, Jaehyung Kim, Martin O'Connell
This paper discusses the recent progress of a multi-year project investigating the concept of an unmanned aerial vehicle (UAV) being partially powered by the natural environment the drone will encounter along its flight path. This UAV flight is achieved using power generation, management, and storage systems. The aircraft's improvement in sustainability, or endurance, is the main benefit of this design as it harvests energy from the environment available to it, and also using the potential of replacing some of the UAV structure with the structure of the power storage devices to reduce parasitic weight. These UAV enhancements could have many beneficial applications for a modern technological society. Applications in defense are numerous, as a highly sustainable aircraft can provide longterm surveillance over essential targets. Also, weather monitoring can benefit immensely from a highly sustainable drone, as these drones will be able to follow weather patterns as they develop over time and achieve real time updates. The power generation comes from three sources: flexible solar panels, thermoelectric generators, and vibration induced power generation motors (installed in power pods or inside the wing) that are excited by either atmospheric gusts or wing flutter. These forms of AC and DC current will be combined into usable energy for the UAV energy storage devices. Utilizing these forms of energy should in theory greatly extend the flight time of the UAV as it will continually cycle through these different forms of energy throughout the day and night. The flexible solar cells will be either attached onto the wing or replace the wing upper skin structure and take in the energy from the sun during the day, outputting straight DC current. The vibration induced power generation devices are comprised of DC motors attached to leaf springs and a gear/rack mechanism that oscillate from the vibrations of the wings to spin the motor shaft and generate AC power. The thermoelectric generators will generate energy from heat flux across the hot and cold plates of the power management system and the solar cells. All these sources are combined into a single power output via the power management system (PWMS). The PWMS is based off a hybrid power design, where the input sources are combined into a single stable DC output in which lithium polymer (LiPo) batteries or graphene supercapacitors can be charged and discharged into the UAV electric motor driven propeller load.
{"title":"UAV Power Management, Generation, and Storage System Principles and Design","authors":"Timothy Kidd, Zhen Yu, S. Dobbs, K. Anderson, G. Oetting, Jaehyung Kim, Martin O'Connell","doi":"10.1109/SusTech47890.2020.9150499","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150499","url":null,"abstract":"This paper discusses the recent progress of a multi-year project investigating the concept of an unmanned aerial vehicle (UAV) being partially powered by the natural environment the drone will encounter along its flight path. This UAV flight is achieved using power generation, management, and storage systems. The aircraft's improvement in sustainability, or endurance, is the main benefit of this design as it harvests energy from the environment available to it, and also using the potential of replacing some of the UAV structure with the structure of the power storage devices to reduce parasitic weight. These UAV enhancements could have many beneficial applications for a modern technological society. Applications in defense are numerous, as a highly sustainable aircraft can provide longterm surveillance over essential targets. Also, weather monitoring can benefit immensely from a highly sustainable drone, as these drones will be able to follow weather patterns as they develop over time and achieve real time updates. The power generation comes from three sources: flexible solar panels, thermoelectric generators, and vibration induced power generation motors (installed in power pods or inside the wing) that are excited by either atmospheric gusts or wing flutter. These forms of AC and DC current will be combined into usable energy for the UAV energy storage devices. Utilizing these forms of energy should in theory greatly extend the flight time of the UAV as it will continually cycle through these different forms of energy throughout the day and night. The flexible solar cells will be either attached onto the wing or replace the wing upper skin structure and take in the energy from the sun during the day, outputting straight DC current. The vibration induced power generation devices are comprised of DC motors attached to leaf springs and a gear/rack mechanism that oscillate from the vibrations of the wings to spin the motor shaft and generate AC power. The thermoelectric generators will generate energy from heat flux across the hot and cold plates of the power management system and the solar cells. All these sources are combined into a single power output via the power management system (PWMS). The PWMS is based off a hybrid power design, where the input sources are combined into a single stable DC output in which lithium polymer (LiPo) batteries or graphene supercapacitors can be charged and discharged into the UAV electric motor driven propeller load.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114086341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150495
T. Dawood, E. Elwakil, H. Novoa, J. Delgado
The deterioration and failure of water distribution networks bring about grave consequences to urban communities. These events may include undesired occurrences of pipe breaks, interrupting supply, water loss, contaminant intrusion, and reduced revenue. This accentuates the need for pipe break and risk of failure models, which is an intricate and challenging field of research. The objective of this paper is to develop a risk of failure model based on regression analysis approach. The model is developed using seven years of historical data collected from the city of El Pedregal in Peru. The regression analysis-based model is validated versus different statistical indicators, in which the results revealed the coherency of the model with an R2 of 98%. The proposed model is a decision support tool, expected to assist infrastructure managers and civil engineers in their plans and decision-making.
{"title":"Toward Sustainable Water System: Modeling Pipe Failure in Water Distribution Networks","authors":"T. Dawood, E. Elwakil, H. Novoa, J. Delgado","doi":"10.1109/SusTech47890.2020.9150495","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150495","url":null,"abstract":"The deterioration and failure of water distribution networks bring about grave consequences to urban communities. These events may include undesired occurrences of pipe breaks, interrupting supply, water loss, contaminant intrusion, and reduced revenue. This accentuates the need for pipe break and risk of failure models, which is an intricate and challenging field of research. The objective of this paper is to develop a risk of failure model based on regression analysis approach. The model is developed using seven years of historical data collected from the city of El Pedregal in Peru. The regression analysis-based model is validated versus different statistical indicators, in which the results revealed the coherency of the model with an R2 of 98%. The proposed model is a decision support tool, expected to assist infrastructure managers and civil engineers in their plans and decision-making.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114103242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150507
Jesús Águila-León, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, E. Hurtado-Perez, Edith X. M. García
The Grey Wolf Optimizer (GWO) algorithm is a metaheuristic optimization method based on the hunting made by wolves in nature. In this work, the GWO algorithm was proposed for tuning a Proportional-Integral-Derivative (PID) controller parameters for a DC-DC boost converter. DC-DC boost converters are electronic devices widely used for voltage regulation in renewable energies applications, these devices need a controller, commonly a PID controller which needs to be correctly tuned to reduce the error between the reference voltage and the system output voltage. Classical PID controller tuning methods require a mathematical formulation or an empirical system response analysis, bioinspired optimization algorithms are an alternative for system design. This paper presents a performance comparative analysis between the proposed GWO algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation was carried out using MATLAB/Simulink environment, then the tuned PID controller performance was evaluated using the system response analysis to variable load conditions and Root Mean Squared Error (RMSE) between reference and output voltage. Results showed that the proposed GWO algorithm has a lower RMSE compared to PSO and GA, and therefore, it could be an effective method for optimal PID controllers for power converters applications.
灰狼优化算法(Grey Wolf Optimizer, GWO)是一种基于狼在自然界中狩猎行为的元启发式优化方法。在这项工作中，提出了GWO算法用于调整DC-DC升压转换器的比例-积分-导数(PID)控制器参数。DC-DC升压变换器是广泛用于可再生能源应用中的电压调节的电子器件，这些器件需要一个控制器，通常是一个PID控制器，需要正确调谐以减少参考电压和系统输出电压之间的误差。经典的PID控制器整定方法需要数学公式或经验系统响应分析，仿生优化算法是系统设计的另一种选择。本文对所提出的GWO算法、粒子群算法(PSO)和遗传算法(GA)的性能进行了比较分析。在MATLAB/Simulink环境下进行了仿真，通过系统对变负载条件的响应分析和参考电压与输出电压之间的均方根误差(RMSE)，对整定PID控制器的性能进行了评价。结果表明，与粒子群算法和遗传算法相比，所提出的GWO算法具有较低的均方根误差，可以作为一种有效的功率变换器PID控制器优化方法。
{"title":"Optimal PID Parameters Tunning for a DC-DC Boost Converter: A Performance Comparative Using Grey Wolf Optimizer, Particle Swarm Optimization and Genetic Algorithms","authors":"Jesús Águila-León, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, E. Hurtado-Perez, Edith X. M. García","doi":"10.1109/SusTech47890.2020.9150507","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150507","url":null,"abstract":"The Grey Wolf Optimizer (GWO) algorithm is a metaheuristic optimization method based on the hunting made by wolves in nature. In this work, the GWO algorithm was proposed for tuning a Proportional-Integral-Derivative (PID) controller parameters for a DC-DC boost converter. DC-DC boost converters are electronic devices widely used for voltage regulation in renewable energies applications, these devices need a controller, commonly a PID controller which needs to be correctly tuned to reduce the error between the reference voltage and the system output voltage. Classical PID controller tuning methods require a mathematical formulation or an empirical system response analysis, bioinspired optimization algorithms are an alternative for system design. This paper presents a performance comparative analysis between the proposed GWO algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation was carried out using MATLAB/Simulink environment, then the tuned PID controller performance was evaluated using the system response analysis to variable load conditions and Root Mean Squared Error (RMSE) between reference and output voltage. Results showed that the proposed GWO algorithm has a lower RMSE compared to PSO and GA, and therefore, it could be an effective method for optimal PID controllers for power converters applications.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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