DocumentCode :
3580992
Title :
Predicting available transfer capability for power system with large wind farms based on multivariable linear regression models
Author :
Xu Yuqin ; Nie Yang ; Liu Wenxia
Author_Institution :
Dept. of Electr. power Eng., North China Electr. Power Univ., Baoding, China
fYear :
2014
Firstpage :
1
Lastpage :
6
Abstract :
Available Transfer Capability (ATC) of power system with large farms is influenced by many factors. These factors include wind farms output, load and generators output that they are uncertain and have statistical characteristics. This paper studies the feasibility and effectiveness of prediction ATC through Multivariable Linear Regression Models (MLRM). And the system which considering transmission line thermal stability will be checked continuous flow N-l security constraints through the method of sequential Monte Carlo simulation, the Reactive Power Reserves (RPR) of power supply nodes influences on ATC will become explanatory variables as well as ATC of power system will being explained variables, then build MLRM equations to predict power system´s ATC by system reactive power reserves. The improved IEEE 30-bus system and IEEE 118-bus system are the simulating examples, the results show that MLRM method can effectively predict the system´s ATC and the speed of calculation is fast, also analysis and evaluates the influence of large scale wind farms to power system´s ATC.
Keywords :
Monte Carlo methods; load flow; reactive power; regression analysis; thermal stability; wind power plants; ATC; IEEE 118-bus system; IEEE 30-bus system; MLRM; MLRM equations; RPR; available transfer capability; multivariable linear regression models; power supply nodes; power system; reactive power reserves; sequential Monte Carlo simulation; transmission line thermal stability; wind farms; Equations; Generators; Linear regression; Load flow; Mathematical model; Power system stability; Wind farms; Available Transfer Capability; Continuous Power Flow; Monte Carlo simulation; Multivariable Linear Regression Models; wind power;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2014 IEEE PES Asia-Pacific
Type :
conf
DOI :
10.1109/APPEEC.2014.7066008
Filename :
7066008
Link To Document :
بازگشت