Bad Data Detection Using Linear WLS and Sampled Values in Digital Substations
| Autor: | Yiming Wu, Wei Zhao, Yong Xiao, Lars Nordström, Fabian Hohn, Jianping Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Engineering
business.industry 020209 energy Real-time computing Energy Engineering and Power Technology 02 engineering and technology Current transformer Data sharing Data link Data quality 0202 electrical engineering electronic engineering information engineering State (computer science) Electrical and Electronic Engineering Latency (engineering) business Intelligent control Circuit breaker |
| Zdroj: | IEEE Transactions on Power Delivery. 33:150-157 |
| ISSN: | 1937-4208 0885-8977 |
| DOI: | 10.1109/tpwrd.2017.2669110 |
| Popis: | Smart Grids employ intelligent control applications that require high quality data: fast, secure, and error free. Several researchers have focused on providing techniques for low latency and secured data links for these applications. Bad data detection is however generally provided only at the central level due to limitations in legacy technologies employed in many substations. With the introduction of IEC61850 data sharing within the substation becomes more flexible and transparent allowing more sophisticated management of data quality. Hence, this paper proposes a substation level bad data detection algorithm to facilitate also these types of requirements from applications. The algorithm is based on automatically detecting the substation topology by parsing standard substation description files and online state of circuit breakers and disconnectors. By applying linear weighted least square based state estimation algorithm, bad data from failing current transformers (CT) can be detected. By conducting the verification of different types of bad data, the results show the output of bad data detection algorithm provides higher accuracy than output from both measurement and protective CT in both static and faulty situations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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