A Model of Voltage Distribution and Change of Current in the Network Containing Unaccounted Electricity Consumption
DOI:
https://doi.org/10.24160/1993-6982-2021-6-91-99Keywords:
commercial losses, unaccounted electricity consumption, electricity losses, control of electricity losses, electricity imbalance, electrical network section, automated fiscal electricity metering systemAbstract
The aim of the study is control of commercial losses in electrical grids, especially in low voltage grids, which is one of the priority lines of activities conducted by electric network companies. The complexity of solving this problem is stemming from the difficulty of exactly locating the commercial loss occurrence place under the conditions of extensively branched low and medium voltage electrical networks. Various methods are currently used to determine the commercial loss occurrence places. However, no effective methods have been created for determining the fact and place of unaccounted electricity consumption in networks under the conditions of performing remote analysis of networks based on the data from modern electricity meters used in the automated fiscal electricity metering system. These difficulties can be overcome by developing a model of voltage distribution and change of current in distribution networks of the 0.4--35 kV nominal voltage levels.
A model of voltage distribution and changes of current for a network containing unaccounted electricity consumption is proposed. The effectiveness of using the proposed model has been theoretically substantiated; its applicability limits are defined, and the accuracy of the obtained results is estimated.
Graphical representation of the proposed model, which is one of the electrical network digital imaging forms, can be used to analyze electrical networks for revealing if there is unaccounted electricity consumption in them.
By using the proposed model of voltage distribution and change of current in the network, it is possible to represent the electrical network as a set of electrical parameters to analyze electrical networks for the presence of commercial losses.
References
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2. Messinis G.M., Hatziargyriou N.D. Unsupervised Classification for Non-technical Loss Detection // Proc. 20th Power Systems Computation Conf. 2018. Pp. 1—7.
3. Rodrigues A.C., Costa A.S., Issicaba D. Identification of Non-technical losses in Distribution Systems Via State Estimation and Geometric Tests // Proc. 7th Brazilian Electrical Systems Symp. 2018. Pp. 1—6.
4. Bezerra U.H., Soares T.M., Vieira J.P.A., Manito A.R.R., Paye J.C.H. Equivalent Operational Impedance: a New Approach to Calculate Technical and Non-technical Losses in Electric Distribution Systems // Ibid. Pp. 1—6.
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6. ГОСТ 32144—2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения.
7. Henriques H.O., Correa M.R.L.S. Use of Smart Grids to Monitor Technical Losses to Improve Non-technical Losses Estimation // 7th Brazilian Electrical Systems Symp. 2018. Pp. 1—6.
8. Rossoni A., Braunstein S.H., Trevizan R.D., Bretas A.S., Bretas N.G. Contribution to Distribution Systems Technical and Non-technical Losses Estimation Using WLS State Estimator // IEEE Power and Energy Soc. General Meeting. 2018. Pp. 1—5.
9. Ibrahim K.A., Au M.T., Gan C.K., Tang J.H. System Wide MV distribution Network Technical Losses Estimation Based on Reference Feeder and Energy Flow Model // Intern. J. Electrical Power and Energy Syst. 2017. V. 93. Pp. 440—450.
10. Chatterjee S., Archana V., Suresh K., Gupta R., Doshi F. Detection of Non-technical Losses Using Advanced Metering Infrastructure and Deep Recurrent Neural Networks // Proc. 17th IEEE Intern. Conf. Environment and Electrical Eng. 2017. Pp. 1—6.
11. Белый В.Б. Модель процессов потребления электроэнергии коммунально-бытовым сектором в сельских электрических сетях // Энерго- и ресурсосбережение — XXI век: Материалы XVII Междунар. науч.-практ. конф. 2019. С. 42—45.
12. Цыгулев Н.И. и др. Цифровизация электрических сетей АПК на платформе интернета энергии // Актуальные проблемы науки и техники: Материалы Национальной науч.-практ. конф. 2019. С. 327—328.
13. Kazymov I, Kompaneets B. Definition of Fact and Place of Losses in low Voltage Electric Networks // Proc. 2019 International Conf. Industrial Eng., Appl. and Manufacturing. 2019. Pp. 1—5.
14. Иванов А.Г., Соколова В.Н. Финансовые потери потребителей в связи с низким качеством электроэнергии // Sci. Time. 2014. № 11. С. 113—115.
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16. Glauner P., Boechat A., Dolberg L., Rangoni Y., Duarte D. Large-scale Detection of Non-technical Losses in Imbalanced Data Sets // Proc. IEEE Power and Energy Soc. Innovative Smart Grid Technologies Conf. 2016. Pp. 1—5.
17. Ramos C.C.O., Souza A.N., Papa J.P., Falcão A.X. Fast Non-technical Losses Identification Through Optimum-path Forest // Proc. 15th Intern. Conf. Intelligent System Appl. Power Systems. 2009. Pp. 1—5.
18. Queiroz L.M.O., Lyra C. Adaptive Hybrid Genetic Algorithm for Technical Loss Reduction in Distribution Networks Under Variable Demands // IEEE Trans. Power Systems. 2009. V. 24(1). Pp. 445—453.
19. Казымов И.М., Компанеец Б.С. Методика определения требуемого числа устройств сбора и передачи информации для создания цифрового представления распределительной электрической сети низкого и среднего уровня напряжений // Вестник НГИЭИ. 2021. № 1(116). С. 41—53.
20. Ожегов А.Н. Системы АСКУЭ. Киров: Изд-во ВятГУ, 2006.
21. Тиньгаев А.В, Шевченко А.А. Оптимизация протяжённости линий электропередач при подключении сельскохозяйственных потребителей с использованием WEB-технологий // Вестник Алтайского гос. аграрного ун-та. 2018. № 4. С. 186—191
---
Для цитирования: Казымов И.М., Компанеец Б.С. Модель распределения электрического напряжения и изменения электрического тока в сети с наличием неучтённого потребления электрической энергии // Вестник МЭИ. 2021. № 6. С. 91—99. DOI: 10.24160/1993-6982-2021-6-91-99
#
1. Viegas J.L., Esteves P.R., Vieira S.M. Clustering-based Novelty Detection for Identification of Non-technical Losses. Intern. J. Electrical Power and Energy Systems. 2018;101:01—310.
2. Messinis G.M., Hatziargyriou N.D. Unsupervised Classification for Non-technical Loss Detection. Proc. 20th Power Systems Computation Conf. 2018:1—7.
3. Rodrigues A.C., Costa A.S., Issicaba D. Identification of Non-technical losses in Distribution Systems Via State Estimation and Geometric Tests. Proc. 7th Brazilian Electrical Systems Symp. 2018:1—6.
4. Bezerra U.H., Soares T.M., Vieira J.P.A., Manito A.R.R., Paye J.C.H. Equivalent Operational Impedance: a New Approach to Calculate Technical and Non-technical Losses in Electric Distribution Systems. Ibid:1—6.
5. Elektrichestvo Stali Vorovat' Chashche [Elektron. Resurs] www.iz.ru/674956/mariia-nediuk/elektrichestvo-stali-vorovat-chashche (Data Obrashcheniya 13.05.2021). (in Russian).
6. GOST 32144—2013. Elektricheskaya Energiya. Sovmestimost' Tekhnicheskikh Sredstv Elektromagnitnaya. Normy Kachestva Elektricheskoy Energii v Sistemakh Elektrosnabzheniya Obshchego Naznacheniya. (in Russian).
7. Henriques H.O., Correa M.R.L.S. Use of Smart Grids to Monitor Technical Losses to Improve Non-technical Losses Estimation. 7th Brazilian Electrical Systems Symp. 2018:1—6.
8. Rossoni A., Braunstein S.H., Trevizan R.D., Bretas A.S., Bretas N.G. Contribution to Distribution Systems Technical and Non-technical Losses Estimation Using WLS State Estimator. EEE Power and Energy Soc. General Meeting. 2018:1—5.
9. Ibrahim K.A., Au M.T., Gan C.K., Tang J.H. System Wide MV distribution Network Technical Losses Estimation Based on Reference Feeder and Energy Flow Model. Intern. J. Electrical Power and Energy Syst. 2017;93:440—450.
10. Chatterjee S., Archana V., Suresh K., Gupta R., Doshi F. Detection of Non-technical Losses Using Advanced Metering Infrastructure and Deep Recurrent Neural Networks. Proc. 17th IEEE Intern. Conf. Environment and Electrical Eng. 2017:1—6.
11. Belyy V.B. Model' Protsessov Potrebleniya Elektroenergii Kommunal'no-bytovym Sektorom v Sel'skikh Elektricheskikh Setyakh. Energo- i Resursosberezhenie — XXI Vek: Materialy XVII Mezhdunar. Nauch.-prakt. Konf. 2019:42—45. (in Russian).
12. Tsygulev N.I. i dr. Tsifrovizatsiya Elektricheskikh Setey APK na Platforme Interneta Energii. Aktual'nye Problemy Nauki i Tekhniki: Materialy Natsional'noy Nauch.-prakt. Konf. 2019:327—328. (in Russian).
13. Kazymov I, Kompaneets B. Definition of Fact and Place of Losses in low Voltage Electric Networks. Proc. 2019 International Conf. Industrial Eng., Appl. and Manufacturing. 2019:1—5.
14. Ivanov A.G., Sokolova V.N. Finansovye Poteri Potrebiteley v Svyazi s Nizkim Kachestvom Elektroenergii. Sci. Time. 2014;11:113—115. (in Russian).
15. Kochneva E., Sukalo A. Impact of Technical Losses Calculation Method on Bad Data Validation on the Basis of a Posteriori Analysis. Proc. IEEE Intern. Energy Conf. 2016:1—6.
16. Glauner P., Boechat A., Dolberg L., Rangoni Y., Duarte D. Large-scale Detection of Non-technical Losses in Imbalanced Data Sets. Proc. IEEE Power and Energy Soc. Innovative Smart Grid Technologies Conf. 2016:1—5.
17. Ramos C.C.O., Souza A.N., Papa J.P., Falcão A.X. Fast Non-technical Losses Identification Through Optimum-path Forest. Proc. 15th Intern. Conf. Intelligent System Appl. Power Systems. 2009:1—5.
18. Queiroz L.M.O., Lyra C. Adaptive Hybrid Genetic Algorithm for Technical Loss Reduction in Distribution Networks Under Variable Demands. IEEE Trans. Power Systems. 2009;24(1):445—453.
19. Kazymov I.M., Kompaneets B.S. Metodika Opredeleniya Trebuemogo Chisla Ustroystv Sbora i Peredachi Informatsii dlya Sozdaniya Tsifrovogo Predstavleniya Raspredelitel'noy Elektricheskoy Seti Nizkogo i Srednego Urovnya Napryazheniy. Vestnik NGIEI. 2021;1(116):41—53. (in Russian).
20. Ozhegov A.N. Sistemy ASKUE. Kirov: Izd-vo VyatGU, 2006. (in Russian).
21. Tin'gaev A.V, Shevchenko A.A. Optimizatsiya Protyazhennosti Liniy Elektroperedach pri Podklyuchenii Sel'skokhozyaystvennykh Potrebiteley s Ispol'zovaniem WEB-tekhnologiy. Vestnik Altayskogo Gos. Agrarnogo Un-ta. 2018;4:186—191. (in Russian).
---
For citation: Kazymov I.M., Kompaneets B.S. A Model of Voltage Distribution and Change of Current in the Network Containing Unaccounted Electricity Consumption. Bulletin of MPEI. 2021;6:91—99. (in Russian). DOI: 10.24160/1993-6982-2021-6-91-99
2. Messinis G.M., Hatziargyriou N.D. Unsupervised Classification for Non-technical Loss Detection // Proc. 20th Power Systems Computation Conf. 2018. Pp. 1—7.
3. Rodrigues A.C., Costa A.S., Issicaba D. Identification of Non-technical losses in Distribution Systems Via State Estimation and Geometric Tests // Proc. 7th Brazilian Electrical Systems Symp. 2018. Pp. 1—6.
4. Bezerra U.H., Soares T.M., Vieira J.P.A., Manito A.R.R., Paye J.C.H. Equivalent Operational Impedance: a New Approach to Calculate Technical and Non-technical Losses in Electric Distribution Systems // Ibid. Pp. 1—6.
5. Электричество стали воровать чаще [Электрон. ресурс] www.iz.ru/674956/mariia-nediuk/elektrichestvo-stali-vorovat-chashche (дата обращения 13.05.2021).
6. ГОСТ 32144—2013. Электрическая энергия. Совместимость технических средств электромагнитная. Нормы качества электрической энергии в системах электроснабжения общего назначения.
7. Henriques H.O., Correa M.R.L.S. Use of Smart Grids to Monitor Technical Losses to Improve Non-technical Losses Estimation // 7th Brazilian Electrical Systems Symp. 2018. Pp. 1—6.
8. Rossoni A., Braunstein S.H., Trevizan R.D., Bretas A.S., Bretas N.G. Contribution to Distribution Systems Technical and Non-technical Losses Estimation Using WLS State Estimator // IEEE Power and Energy Soc. General Meeting. 2018. Pp. 1—5.
9. Ibrahim K.A., Au M.T., Gan C.K., Tang J.H. System Wide MV distribution Network Technical Losses Estimation Based on Reference Feeder and Energy Flow Model // Intern. J. Electrical Power and Energy Syst. 2017. V. 93. Pp. 440—450.
10. Chatterjee S., Archana V., Suresh K., Gupta R., Doshi F. Detection of Non-technical Losses Using Advanced Metering Infrastructure and Deep Recurrent Neural Networks // Proc. 17th IEEE Intern. Conf. Environment and Electrical Eng. 2017. Pp. 1—6.
11. Белый В.Б. Модель процессов потребления электроэнергии коммунально-бытовым сектором в сельских электрических сетях // Энерго- и ресурсосбережение — XXI век: Материалы XVII Междунар. науч.-практ. конф. 2019. С. 42—45.
12. Цыгулев Н.И. и др. Цифровизация электрических сетей АПК на платформе интернета энергии // Актуальные проблемы науки и техники: Материалы Национальной науч.-практ. конф. 2019. С. 327—328.
13. Kazymov I, Kompaneets B. Definition of Fact and Place of Losses in low Voltage Electric Networks // Proc. 2019 International Conf. Industrial Eng., Appl. and Manufacturing. 2019. Pp. 1—5.
14. Иванов А.Г., Соколова В.Н. Финансовые потери потребителей в связи с низким качеством электроэнергии // Sci. Time. 2014. № 11. С. 113—115.
15. Kochneva E., Sukalo A. Impact of Technical Losses Calculation Method on Bad Data Validation on the Basis of a Posteriori Analysis // Proc. IEEE Intern. Energy Conf. 2016. Pp. 1—6.
16. Glauner P., Boechat A., Dolberg L., Rangoni Y., Duarte D. Large-scale Detection of Non-technical Losses in Imbalanced Data Sets // Proc. IEEE Power and Energy Soc. Innovative Smart Grid Technologies Conf. 2016. Pp. 1—5.
17. Ramos C.C.O., Souza A.N., Papa J.P., Falcão A.X. Fast Non-technical Losses Identification Through Optimum-path Forest // Proc. 15th Intern. Conf. Intelligent System Appl. Power Systems. 2009. Pp. 1—5.
18. Queiroz L.M.O., Lyra C. Adaptive Hybrid Genetic Algorithm for Technical Loss Reduction in Distribution Networks Under Variable Demands // IEEE Trans. Power Systems. 2009. V. 24(1). Pp. 445—453.
19. Казымов И.М., Компанеец Б.С. Методика определения требуемого числа устройств сбора и передачи информации для создания цифрового представления распределительной электрической сети низкого и среднего уровня напряжений // Вестник НГИЭИ. 2021. № 1(116). С. 41—53.
20. Ожегов А.Н. Системы АСКУЭ. Киров: Изд-во ВятГУ, 2006.
21. Тиньгаев А.В, Шевченко А.А. Оптимизация протяжённости линий электропередач при подключении сельскохозяйственных потребителей с использованием WEB-технологий // Вестник Алтайского гос. аграрного ун-та. 2018. № 4. С. 186—191
---
Для цитирования: Казымов И.М., Компанеец Б.С. Модель распределения электрического напряжения и изменения электрического тока в сети с наличием неучтённого потребления электрической энергии // Вестник МЭИ. 2021. № 6. С. 91—99. DOI: 10.24160/1993-6982-2021-6-91-99
#
1. Viegas J.L., Esteves P.R., Vieira S.M. Clustering-based Novelty Detection for Identification of Non-technical Losses. Intern. J. Electrical Power and Energy Systems. 2018;101:01—310.
2. Messinis G.M., Hatziargyriou N.D. Unsupervised Classification for Non-technical Loss Detection. Proc. 20th Power Systems Computation Conf. 2018:1—7.
3. Rodrigues A.C., Costa A.S., Issicaba D. Identification of Non-technical losses in Distribution Systems Via State Estimation and Geometric Tests. Proc. 7th Brazilian Electrical Systems Symp. 2018:1—6.
4. Bezerra U.H., Soares T.M., Vieira J.P.A., Manito A.R.R., Paye J.C.H. Equivalent Operational Impedance: a New Approach to Calculate Technical and Non-technical Losses in Electric Distribution Systems. Ibid:1—6.
5. Elektrichestvo Stali Vorovat' Chashche [Elektron. Resurs] www.iz.ru/674956/mariia-nediuk/elektrichestvo-stali-vorovat-chashche (Data Obrashcheniya 13.05.2021). (in Russian).
6. GOST 32144—2013. Elektricheskaya Energiya. Sovmestimost' Tekhnicheskikh Sredstv Elektromagnitnaya. Normy Kachestva Elektricheskoy Energii v Sistemakh Elektrosnabzheniya Obshchego Naznacheniya. (in Russian).
7. Henriques H.O., Correa M.R.L.S. Use of Smart Grids to Monitor Technical Losses to Improve Non-technical Losses Estimation. 7th Brazilian Electrical Systems Symp. 2018:1—6.
8. Rossoni A., Braunstein S.H., Trevizan R.D., Bretas A.S., Bretas N.G. Contribution to Distribution Systems Technical and Non-technical Losses Estimation Using WLS State Estimator. EEE Power and Energy Soc. General Meeting. 2018:1—5.
9. Ibrahim K.A., Au M.T., Gan C.K., Tang J.H. System Wide MV distribution Network Technical Losses Estimation Based on Reference Feeder and Energy Flow Model. Intern. J. Electrical Power and Energy Syst. 2017;93:440—450.
10. Chatterjee S., Archana V., Suresh K., Gupta R., Doshi F. Detection of Non-technical Losses Using Advanced Metering Infrastructure and Deep Recurrent Neural Networks. Proc. 17th IEEE Intern. Conf. Environment and Electrical Eng. 2017:1—6.
11. Belyy V.B. Model' Protsessov Potrebleniya Elektroenergii Kommunal'no-bytovym Sektorom v Sel'skikh Elektricheskikh Setyakh. Energo- i Resursosberezhenie — XXI Vek: Materialy XVII Mezhdunar. Nauch.-prakt. Konf. 2019:42—45. (in Russian).
12. Tsygulev N.I. i dr. Tsifrovizatsiya Elektricheskikh Setey APK na Platforme Interneta Energii. Aktual'nye Problemy Nauki i Tekhniki: Materialy Natsional'noy Nauch.-prakt. Konf. 2019:327—328. (in Russian).
13. Kazymov I, Kompaneets B. Definition of Fact and Place of Losses in low Voltage Electric Networks. Proc. 2019 International Conf. Industrial Eng., Appl. and Manufacturing. 2019:1—5.
14. Ivanov A.G., Sokolova V.N. Finansovye Poteri Potrebiteley v Svyazi s Nizkim Kachestvom Elektroenergii. Sci. Time. 2014;11:113—115. (in Russian).
15. Kochneva E., Sukalo A. Impact of Technical Losses Calculation Method on Bad Data Validation on the Basis of a Posteriori Analysis. Proc. IEEE Intern. Energy Conf. 2016:1—6.
16. Glauner P., Boechat A., Dolberg L., Rangoni Y., Duarte D. Large-scale Detection of Non-technical Losses in Imbalanced Data Sets. Proc. IEEE Power and Energy Soc. Innovative Smart Grid Technologies Conf. 2016:1—5.
17. Ramos C.C.O., Souza A.N., Papa J.P., Falcão A.X. Fast Non-technical Losses Identification Through Optimum-path Forest. Proc. 15th Intern. Conf. Intelligent System Appl. Power Systems. 2009:1—5.
18. Queiroz L.M.O., Lyra C. Adaptive Hybrid Genetic Algorithm for Technical Loss Reduction in Distribution Networks Under Variable Demands. IEEE Trans. Power Systems. 2009;24(1):445—453.
19. Kazymov I.M., Kompaneets B.S. Metodika Opredeleniya Trebuemogo Chisla Ustroystv Sbora i Peredachi Informatsii dlya Sozdaniya Tsifrovogo Predstavleniya Raspredelitel'noy Elektricheskoy Seti Nizkogo i Srednego Urovnya Napryazheniy. Vestnik NGIEI. 2021;1(116):41—53. (in Russian).
20. Ozhegov A.N. Sistemy ASKUE. Kirov: Izd-vo VyatGU, 2006. (in Russian).
21. Tin'gaev A.V, Shevchenko A.A. Optimizatsiya Protyazhennosti Liniy Elektroperedach pri Podklyuchenii Sel'skokhozyaystvennykh Potrebiteley s Ispol'zovaniem WEB-tekhnologiy. Vestnik Altayskogo Gos. Agrarnogo Un-ta. 2018;4:186—191. (in Russian).
---
For citation: Kazymov I.M., Kompaneets B.S. A Model of Voltage Distribution and Change of Current in the Network Containing Unaccounted Electricity Consumption. Bulletin of MPEI. 2021;6:91—99. (in Russian). DOI: 10.24160/1993-6982-2021-6-91-99
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Published
2021-05-17
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Section
Electrical Complex and Systems (05.09.03)
