Bad data detection and handling in distribution grid state estimation using artificial neural networks

Autor:	Philipp Goergens, Moritz Cramer, Armin Schnettler
Rok vydání:	2015
Předmět:	Estimation Engineering Observational error Artificial neural network Basis (linear algebra) business.industry media_common.quotation_subject computer.software_genre Public records Measurement uncertainty Quality (business) Data mining State (computer science) business computer media_common
Zdroj:	2015 IEEE Eindhoven PowerTech.
DOI:	10.1109/ptc.2015.7232655
Popis:	This research project addresses the problem of erroneous measurements for distribution grid state estimation (DGSE) by using bad data detection. A method based on artificial neural networks is developed and tested in combination with DGSE. The necessary steps in order to create the neural networks are presented and training parameters are investigated. The method replaces identified measurement errors with new estimates. The developed method is validated by simulation on the basis of an electric distribution grid. It is shown that the bad data correction method detects and correctly identifies single and multiple erroneous measurement values. Furthermore, the ability of the developed method to handle different types of measurement errors and the impact on the quality of the state estimation result are examined. The method improves the quality of the DGSE result and reduces the state estimation's probability to diverge.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::15aac625e685e68c1b45c5f83602fb1d https://doi.org/10.1109/ptc.2015.7232655 Zobrazit plný text záznamu