Title :
Study on detecting and identifying and correcting bad and wrong data in power system
Author :
Zhi-ping, Shi ; Jun-ji, Wu ; Hu, Wang ; Ling-jun, Shi ; Li-fang, Fan
Author_Institution :
NUST, Nanjing, China
Abstract :
Aiming at the distinction of the number, type and correlation, respectively, of bad data, three methods based on land-voltage, node power balance and BP neural network, respectively, were proposed to correct bad data, which are applicable in different situations. The simulation results show that the three methods can correct bad data effectively with a high precision in their own application scope.
Keywords :
backpropagation; neural nets; power system analysis computing; BP neural network; bad data correction; bad data detecting; bad data identification; land-voltage; node power balance; power system; wrong data; Load flow; Load forecasting; Neural networks; Power measurement; Power system dynamics; Power system measurements; Power systems; Power transmission lines; State estimation; Voltage; Bad data; correction; land-voltage; neural network; node power balance;
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
DOI :
10.1109/SUPERGEN.2009.5348342
