Title :
Monte Carlo Method-Based Clustering Analysis Applied for Robust State Estimation and Data Debugging of Power Systems
Author :
Lin, Jeu-Min ; Huang, Shyh-Jier
Author_Institution :
Far East Univ., Tainan
Abstract :
This paper presents a robust method for power system state estimation along with a statistical technique of data debugging. In the estimation process, an exponential function is utilized to modify the variances of measurements in anticipation of enhancing the estimation performance and improving the convergence characteristics. Besides, with the aid of Monte Carlo method (MCM)-based clustering analysis, those bad data can be effectively identified from the set of raw measurements. To validate the effectiveness of the proposed approach, this method has been tested under different scenarios. Test results help confirm the feasibility of the method for the applications considered.
Keywords :
Monte Carlo methods; power system state estimation; Monte Carlo methods; clustering analysis; data debugging; exponential function; power system state estimation; Convergence; Data analysis; Debugging; Monte Carlo methods; Power system analysis computing; Power system measurements; Power system reliability; Robustness; State estimation; Testing; Monte Carlo method-based clustering analysis; State estimation; data debugging;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference on
Conference_Location :
Toki Messe, Niigata
Print_ISBN :
978-986-01-2607-5
Electronic_ISBN :
978-986-01-2607-5
DOI :
10.1109/ISAP.2007.4441617
