Dissertation
ENHANCED SINGLE AND MULTIPLE BAD DATA PROCESSING IN POWER SYSTEM STATIC STATE ESTIMATION
Doctor of Philosophy (PhD), Washington State University
01/2019
Handle:
https://hdl.handle.net/2376/16824
Abstract
Although the state estimator has been a regular application running in many utility control centers for over four decades, detection and identification of bad data (outliers) among the input measurements continues to be a difficult task. The widely adopted Weighted Least Square formulation of the power system static state estimation is known to be vulnerable to presence of bad data and even a single bad data can significantly impact the solution quality. Since the estimate of system state obtained from state estimation serves as starting point for many security and market related downstream applications that run within a control center, the problem of detection and identification of bad data is important.
It has been shown that the traditionally used methods of detection and/or identification of bad data in power system static state estimation suffer from drawbacks like failing to detect mild to medium bad data in leverage measurements and excessive false detection rates. The traditional approach to process multiple bad data has been successive elimination (of single bad data) and re-estimation. This approach is highly computationally intensive and time consuming. The problem of computationally intensive multiple bad data processing has an even greater bearing on the Linear State Estimator, which is expected to run every second or potentially at sub-second intervals in the control centers.
The work presented here focuses on the problem of multiple bad data processing in power system static state estimation in two ways. Firstly, use of Custom Thresholds is proposed for detection of bad data. The Custom Thresholds are shown to exhibit better false detection performance while being sensitive to mild bad data, even in leverage measurements. Secondly, a new algorithm is proposed to identify the culprit bad measurements. The proposed algorithm utilizes the very nature of bad data in different types of measurements, to accomplish the processing within few cycles of successive elimination and re-estimation. The efficiency and accuracy of the proposed algorithm is validated through thousands of simulations on various standard test systems. The proposed algorithm can be easily integrated in any commercial Weighted Least Square power system static state estimation – linear or iterative.
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Details
- Title
- ENHANCED SINGLE AND MULTIPLE BAD DATA PROCESSING IN POWER SYSTEM STATIC STATE ESTIMATION
- Creators
- Saurabh Sahasrabuddhe
- Contributors
- Anjan Bose (Advisor)Mani Venkatasubramanian (Committee Member)Anurag Srivastava (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- School of Electrical Engineering and Computer Science
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 108
- Identifiers
- 99900581615601842
- Language
- English
- Resource Type
- Dissertation