What is it about?
This paper proposes a new method for detection of bad measurement areas and identification of individual bad measurements within measurement areas. The paper proposes newly developed decoupled Chi-squares to detect bad areas and bad individual phases within the area. The bad data identification detects multiple bad measurements using the newly developed whitened residuals calculated by applying of whitening (sphering) transformationon the measurement residuals.
Featured Image
Why is it important?
The proposed method is developed with consideration of characteristic of each distribution network, such as domination of pseudo measurements against real-time measurements and strong correlation of measurement residuals within single phase of single measurement area. For the first time, the research presented the gaps of traditional bad analysis methods for application in distribution networks, and proposed methodology to overcome these gaps.
Perspectives
The proposed methodology significantly improves the concept of monitoring of entire distribution network using distribution state estimation. The method clearly detects the phases and measurement areas where bad data exists, and this information improves situational awareness of distribution system operator and their decision making process, where operator is able to clearly differentiate the measurement areas where estimated state results are reliable as well as measurement areas where results cannot be trusted. In addition, proposed bad measurements identification algorithm identifies individual measurements, including pseudo, which improves the process of distribution network model maintenance.
Dr Vladan Krsman
Faculty of technical sciences, University of Novi Sad
Read the Original
This page is a summary of: Bad Area Detection and Whitening Transformation-based Identification in Three-Phase Distribution State Estimation , IET Generation Transmission & Distribution, April 2017, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-gtd.2016.1866.
You can read the full text:
Contributors
The following have contributed to this page
