Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities
Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/4/958 |
| _version_ | 1797396868994433024 |
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| author | Marco Toledo-Orozco Carlos Arias-Marin Carlos Álvarez-Bel Diego Morales-Jadan Javier Rodríguez-García Eddy Bravo-Padilla |
| author_facet | Marco Toledo-Orozco Carlos Arias-Marin Carlos Álvarez-Bel Diego Morales-Jadan Javier Rodríguez-García Eddy Bravo-Padilla |
| author_sort | Marco Toledo-Orozco |
| collection | DOAJ |
| description | Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project. |
| first_indexed | 2024-03-09T00:57:43Z |
| format | Article |
| id | doaj.art-77295d71beae42158e0debbdf0249034 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:57:43Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-77295d71beae42158e0debbdf02490342023-12-11T16:47:19ZengMDPI AGEnergies1996-10732021-02-0114495810.3390/en14040958Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power UtilitiesMarco Toledo-Orozco0Carlos Arias-Marin1Carlos Álvarez-Bel2Diego Morales-Jadan3Javier Rodríguez-García4Eddy Bravo-Padilla5Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, SpainElectrical Engineering Career, Universidad Politécnica Salesiana, Sede Cuenca 010103, EcuadorInstitute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, SpainElectrical Engineering Career, Circular Economy Laboratory-CIITT, Universidad Católica de Cuenca, Sede Cuenca 010107, EcuadorInstitute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, SpainSchool of Electrical Engineering, Faculty of Engineering, Universidad de Cuenca, Cuenca 010109, EcuadorMany electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.https://www.mdpi.com/1996-1073/14/4/958electrical energy lossesoutlier detectiondata analyticsconsumption patternsmachine learningartificial intelligence |
| spellingShingle | Marco Toledo-Orozco Carlos Arias-Marin Carlos Álvarez-Bel Diego Morales-Jadan Javier Rodríguez-García Eddy Bravo-Padilla Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities Energies electrical energy losses outlier detection data analytics consumption patterns machine learning artificial intelligence |
| title | Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities |
| title_full | Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities |
| title_fullStr | Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities |
| title_full_unstemmed | Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities |
| title_short | Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities |
| title_sort | innovative methodology to identify errors in electric energy measurement systems in power utilities |
| topic | electrical energy losses outlier detection data analytics consumption patterns machine learning artificial intelligence |
| url | https://www.mdpi.com/1996-1073/14/4/958 |
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