Forecasting-Aided Monitoring for the Distribution System State Estimation
In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4281219 |
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| _version_ | 1841524474570080256 |
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| author | S. Carcangiu A. Fanni P. A. Pegoraro G. Sias S. Sulis |
| author_facet | S. Carcangiu A. Fanni P. A. Pegoraro G. Sias S. Sulis |
| author_sort | S. Carcangiu |
| collection | DOAJ |
| description | In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid. |
| format | Article |
| id | doaj-art-9643c284a56f4c9bbad921d52a092bf3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9643c284a56f4c9bbad921d52a092bf32025-02-03T05:53:10ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/42812194281219Forecasting-Aided Monitoring for the Distribution System State EstimationS. Carcangiu0A. Fanni1P. A. Pegoraro2G. Sias3S. Sulis4DIEE, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyDIEE, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyDIEE, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyDIEE, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyDIEE, University of Cagliari, Via Marengo 2, 09123 Cagliari, ItalyIn this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid.http://dx.doi.org/10.1155/2020/4281219 |
| spellingShingle | S. Carcangiu A. Fanni P. A. Pegoraro G. Sias S. Sulis Forecasting-Aided Monitoring for the Distribution System State Estimation Complexity |
| title | Forecasting-Aided Monitoring for the Distribution System State Estimation |
| title_full | Forecasting-Aided Monitoring for the Distribution System State Estimation |
| title_fullStr | Forecasting-Aided Monitoring for the Distribution System State Estimation |
| title_full_unstemmed | Forecasting-Aided Monitoring for the Distribution System State Estimation |
| title_short | Forecasting-Aided Monitoring for the Distribution System State Estimation |
| title_sort | forecasting aided monitoring for the distribution system state estimation |
| url | http://dx.doi.org/10.1155/2020/4281219 |
| work_keys_str_mv | AT scarcangiu forecastingaidedmonitoringforthedistributionsystemstateestimation AT afanni forecastingaidedmonitoringforthedistributionsystemstateestimation AT papegoraro forecastingaidedmonitoringforthedistributionsystemstateestimation AT gsias forecastingaidedmonitoringforthedistributionsystemstateestimation AT ssulis forecastingaidedmonitoringforthedistributionsystemstateestimation |