Integrated Kalman-Independent Component Analysis Method for Harmonic Current Estimation on an Interconnected Four Bus Simulated and Laboratory Model
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P. Supriya
Abstract
Wide use of non-linear loads results in harmonic propagation throughout the entire power system. The harmonics generated in the power system by the harmonic injection buses need to be properly measured and quantified using minimal information about the power system network. Independent Component Analysis (ICA) provides several algorithms for harmonic state estimation, some of which are more accurate at specific harmonic frequencies. In this paper, the best ICA algorithm for steady state performance (i.e. the algorithm with the least error) is chosen and the resulting mixing matrix is processed by a Kalman Filter which functions as an optimal estimator. The harmonic state estimation is implemented on a simulated four bus system and a laboratory four bus model is also wired and the results of the work are presented.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- ANN - SQP Approach For NOx Emission Reduction In Coal Fired Boilers
- Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem
- A Transient Stability Constrained, Line-Flow-Based Fast Contingency Screening and Power Flow Evaluation
- Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms
- Economic Justification for a V2G Facility in a Radial Distribution Network
- Operation of Multi DC Smart Grids Based on Renewable Energy Sources and Protection of DC Transmission Line
- Novel Real-Time Stability Assessment Algorithm Based on Synchro-Phasors Measurement and Parallel Algorithms for Multi-Machine Networks.
- Integrated Kalman-Independent Component Analysis Method for Harmonic Current Estimation on an Interconnected Four Bus Simulated and Laboratory Model
- Identification of Fault Location in Distribution Networks Using Multi Class Support Vector Machines
