A novel methodology for optimal location of reactive compensation through deep neural networks
The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022023854 |
| _version_ | 1811192553816457216 |
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| author | Manuel Jaramillo Luis Tipán Jorge Muñoz |
| author_facet | Manuel Jaramillo Luis Tipán Jorge Muñoz |
| author_sort | Manuel Jaramillo |
| collection | DOAJ |
| description | The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions).Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum.Then, with these values, power flows are generated by locating each one of the reactive compensators’ possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu. |
| first_indexed | 2024-04-11T23:55:39Z |
| format | Article |
| id | doaj.art-dacd6e0e29cc41029395457f76e04784 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T23:55:39Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-dacd6e0e29cc41029395457f76e047842022-12-22T03:56:23ZengElsevierHeliyon2405-84402022-10-01810e11097A novel methodology for optimal location of reactive compensation through deep neural networksManuel Jaramillo0Luis Tipán1Jorge Muñoz2Corresponding author.; Electrical Engineering Department, Universidad Politécnica Salesiana, Quito, EcuadorElectrical Engineering Department, Universidad Politécnica Salesiana, Quito, EcuadorElectrical Engineering Department, Universidad Politécnica Salesiana, Quito, EcuadorThe present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions).Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum.Then, with these values, power flows are generated by locating each one of the reactive compensators’ possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu.http://www.sciencedirect.com/science/article/pii/S2405844022023854Deep learningMulti-layer neural networkReactive compensationElectrical power system |
| spellingShingle | Manuel Jaramillo Luis Tipán Jorge Muñoz A novel methodology for optimal location of reactive compensation through deep neural networks Heliyon Deep learning Multi-layer neural network Reactive compensation Electrical power system |
| title | A novel methodology for optimal location of reactive compensation through deep neural networks |
| title_full | A novel methodology for optimal location of reactive compensation through deep neural networks |
| title_fullStr | A novel methodology for optimal location of reactive compensation through deep neural networks |
| title_full_unstemmed | A novel methodology for optimal location of reactive compensation through deep neural networks |
| title_short | A novel methodology for optimal location of reactive compensation through deep neural networks |
| title_sort | novel methodology for optimal location of reactive compensation through deep neural networks |
| topic | Deep learning Multi-layer neural network Reactive compensation Electrical power system |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022023854 |
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