Software Integration of Power System Measurement Devices with AI Capabilities
The latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows f...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/170 |
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| author | Victoria Arenas-Ramos Federico Cuesta Victor Pallares-Lopez Isabel Santiago |
| author_facet | Victoria Arenas-Ramos Federico Cuesta Victor Pallares-Lopez Isabel Santiago |
| author_sort | Victoria Arenas-Ramos |
| collection | DOAJ |
| description | The latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time. |
| format | Article |
| id | doaj-art-55b435ccf87e4f8cbe99b738c280e31a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-55b435ccf87e4f8cbe99b738c280e31a2025-01-10T13:14:40ZengMDPI AGApplied Sciences2076-34172024-12-0115117010.3390/app15010170Software Integration of Power System Measurement Devices with AI CapabilitiesVictoria Arenas-Ramos0Federico Cuesta1Victor Pallares-Lopez2Isabel Santiago3Departamento de Ingeniería Electrónica y de Computadores, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainEscuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino Descubrimientos, E-41092 Sevilla, SpainDepartamento de Ingeniería Electrónica y de Computadores, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainDepartamento de Ingeniería Electrónica y de Computadores, Campus de Rabanales, Universidad de Córdoba, 14071 Córdoba, SpainThe latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time.https://www.mdpi.com/2076-3417/15/1/170power quality monitorphasor measurement unitsmart meteropen-source softwaredistributed measurement systemphotovoltaic plant |
| spellingShingle | Victoria Arenas-Ramos Federico Cuesta Victor Pallares-Lopez Isabel Santiago Software Integration of Power System Measurement Devices with AI Capabilities Applied Sciences power quality monitor phasor measurement unit smart meter open-source software distributed measurement system photovoltaic plant |
| title | Software Integration of Power System Measurement Devices with AI Capabilities |
| title_full | Software Integration of Power System Measurement Devices with AI Capabilities |
| title_fullStr | Software Integration of Power System Measurement Devices with AI Capabilities |
| title_full_unstemmed | Software Integration of Power System Measurement Devices with AI Capabilities |
| title_short | Software Integration of Power System Measurement Devices with AI Capabilities |
| title_sort | software integration of power system measurement devices with ai capabilities |
| topic | power quality monitor phasor measurement unit smart meter open-source software distributed measurement system photovoltaic plant |
| url | https://www.mdpi.com/2076-3417/15/1/170 |
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