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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In this paper, by using an artificial neural network (ANN), a bad data detection, identification and then elimination preestimation filter is outlined.
📌 3 Key Takeaways
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Objective & Hypothesis:State estimators are vitally important in energy control centers.
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Key Findings & Data:The measurements that come from control system are generally analysed by a state estimator.
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Conclusion & Impact:In this paper, by using an artificial neural network (ANN), a bad data detection, identification and then elimination preestimation filter is outlined.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
State estimators are vitally important in energy control centers. The measurements that come from control system are generally analysed by a state estimator. Since there can always be bad measurements in the system, estimated value and the true value of the state estimator can be fer from each other. In this paper, by using an artificial neural network (ANN), a bad data detection, identification and then elimination preestimation filter is outlined.
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Cite This Article
UZUNOĞLU, M., KOCATEPE, C., YUMURTACI, R. (1996). An artificial neural network based preestimation filter for bad data detection identification and elimination in state estimation. Mathematical and Computational Applications, Cilt: 1 - Sayı: 1, 159–164. https://ajindex.com/articles/280272