Title :
Price prediction based congestion management using growing RBF neural network
Author :
Pandey, Seema N. ; Tapaswi, Shashikala ; Srivastava, Laxmi
Author_Institution :
Inf. Technol. Dept., ABV-IIITM, Gwalior
Abstract :
This paper proposes a growing radial basis function (GRBF) neural network based methodology for nodal congestion price (NCP) prediction for congestion management in emerging restructured power system. An unsupervised learning vector quantization (VQ) clustering has been employed as feature selection technique for GRBF neural network as well as for partitioning the power system into different congestion zones. This ensures faster training for proposed neural network and furnishes instant and accurate NCP values, useful for congestion management under real time power market environment. A case study of RTS 24-bus system is presented for demonstrating the computational efficiency and feasibility of this approach.
Keywords :
power engineering computing; power markets; power systems; pricing; radial basis function networks; unsupervised learning; vector quantisation; RBF neural network; RTS 24-bus system; congestion management; feature selection technique; growing radial basis function neural network; nodal congestion price prediction; power system; real time power market; restructured power system; unsupervised learning vector quantization clustering; Computer network management; Energy management; Environmental management; Management training; Neural networks; Power markets; Power system management; Power systems; Unsupervised learning; Vector quantization; Congestion management; Congestion zones; Growing radial basis function neural network; Nodal congestion prices; Optimal power flow; Vector quantization clustering;
Conference_Titel :
India Conference, 2008. INDICON 2008. Annual IEEE
Conference_Location :
Kanpur
Print_ISBN :
978-1-4244-3825-9
Electronic_ISBN :
978-1-4244-2747-5
DOI :
10.1109/INDCON.2008.4768771
