Title :
Artificial neural network approach for on-line ATC estimation in deregulated power system
Author :
Selvi, V. Agnes Idhaya ; Karuppasamypandiyan, M. ; Narmathabanu, R. ; Devaraj, Deepashree
Author_Institution :
Dept. of Electr. & Electron. Eng., Kalasalingam Univ., Krishnankoil, India
Abstract :
In the deregulated power systems, it is essential to know the value of Available Transfer Capability (ATC) for the smooth operation of the power system. ATC is generally calculated using repeated load-flow simulations of the interconnected transmission network. This paper presents an Artificial Neural Network based approach for online-ATC estimation for both bilateral and multilateral transactions. The proposed approach uses Feed forward neural network trained by Back Propagation Algorithm (BPA) for estimating ATC under normal and contingency condition. The proposed method is tested on IEEE 24 bus Reliability Test System (RTS) and results are compared with Repeated Power Flow (RPF) results. The experimental results show the suitability of proposed method for on-line ATC estimation.
Keywords :
backpropagation; electricity supply industry deregulation; load flow; neural nets; power engineering computing; power system interconnection; IEEE 24 bus reliability test system; artificial neural network; available transfer capability; backpropagation algorithm; deregulated power system; feed forward neural network; interconnected transmission network; on-line ATC estimation; power system smooth operation; repeated load flow simulations; Artificial neural networks; Biological neural networks; Estimation; Load flow; Neurons; Training; Training data; Artificial Neural Network; Available Transfer Capability; Bilateral transaction;
Conference_Titel :
Power Signals Control and Computations (EPSCICON), 2014 International Conference on
Conference_Location :
Thrissur
Print_ISBN :
978-1-4799-3611-3
DOI :
10.1109/EPSCICON.2014.6887500
