Machine Learning in Operating of Low Voltage Future Grid
The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Conver...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/15/5388 |
| _version_ | 1797432926492688384 |
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| author | Bartłomiej Mroczek Paweł Pijarski |
| author_facet | Bartłomiej Mroczek Paweł Pijarski |
| author_sort | Bartłomiej Mroczek |
| collection | DOAJ |
| description | The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector. |
| first_indexed | 2024-03-09T10:08:47Z |
| format | Article |
| id | doaj.art-a9d554ac382945709eb68b0d939de7b2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T10:08:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a9d554ac382945709eb68b0d939de7b22023-12-01T22:54:35ZengMDPI AGEnergies1996-10732022-07-011515538810.3390/en15155388Machine Learning in Operating of Low Voltage Future GridBartłomiej Mroczek0Paweł Pijarski1Department of Power Engineering, Lublin University of Technology, 20-618 Lublin, PolandDepartment of Power Engineering, Lublin University of Technology, 20-618 Lublin, PolandThe article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector.https://www.mdpi.com/1996-1073/15/15/5388regression modelsartificial neural networksfeedforward neural networkBattery Energy Storage System (BESS)LV grid |
| spellingShingle | Bartłomiej Mroczek Paweł Pijarski Machine Learning in Operating of Low Voltage Future Grid Energies regression models artificial neural networks feedforward neural network Battery Energy Storage System (BESS) LV grid |
| title | Machine Learning in Operating of Low Voltage Future Grid |
| title_full | Machine Learning in Operating of Low Voltage Future Grid |
| title_fullStr | Machine Learning in Operating of Low Voltage Future Grid |
| title_full_unstemmed | Machine Learning in Operating of Low Voltage Future Grid |
| title_short | Machine Learning in Operating of Low Voltage Future Grid |
| title_sort | machine learning in operating of low voltage future grid |
| topic | regression models artificial neural networks feedforward neural network Battery Energy Storage System (BESS) LV grid |
| url | https://www.mdpi.com/1996-1073/15/15/5388 |
| work_keys_str_mv | AT bartłomiejmroczek machinelearninginoperatingoflowvoltagefuturegrid AT pawełpijarski machinelearninginoperatingoflowvoltagefuturegrid |