Fuzzy based load shedding strategies for avoiding voltage collapse

The phenomenon of voltage collapse eclipses a potential hazard for the transmission systems. It is pertinent that energy management system is embodied with measures to avert the occurrence of voltage instability. The role of fuzzy as a tool augurs suitable on account of the innate nature of instability and the need for a rapid resurgent mechanism. Two fuzzy based load shedding algorithms that use a voltage stability indicator for averting voltage collapse are thus proposed in this paper. The first method identifies the most appropriate locations and uses an analytical procedure to compute the sheddable load, while the second directly predicts the amount of load to be shed at the critical buses. In spite of the fact that both the schemes facilitate to improve the bus voltage profile, in addition to enhancing voltage stability, still the second formulation offers on added weight in terms of its lower execution time and lesser extent of load shedding. The simulation results of the two methods on four test systems highlight its applicability on systems of any size.

[1]  S. P. Singh,et al.  Fuzzy neural network based voltage stability evaluation of power systems with SVC , 2008, Appl. Soft Comput..

[2]  D. Kottick,et al.  Neural-networks for predicting the operation of an under-frequency load shedding system , 1996 .

[3]  M. Z. El-Sadek,et al.  Optimum load shedding for avoiding steady-state voltage instability , 1999 .

[4]  Earl Cox,et al.  The Fuzzy Systems Handkbook with Cdrom , 1998 .

[5]  S. S. Venkata,et al.  An adaptive approach to load shedding and spinning reserve control during underfrequency conditions , 1996 .

[6]  Saikat Chakrabarti Voltage stability monitoring by artificial neural network using a regression-based feature selection method , 2008, Expert Syst. Appl..

[7]  Vijay Vittal,et al.  Self-healing in power systems: an approach using islanding and rate of frequency decline-based load shedding , 2002 .

[8]  James A. Momoh,et al.  Overview and literature survey of fuzzy set theory in power systems , 1995 .

[9]  C. S. Chen,et al.  Design of adaptive load shedding by artificial neural networks , 2005 .

[10]  A. M. Ranjbar,et al.  A global Particle Swarm-Based-Simulated Annealing Optimization technique for under-voltage load shedding problem , 2009, Appl. Soft Comput..

[11]  M. M Salama,et al.  Estimating the voltage collapse proximity indicator using artificial neural network , 2001 .

[12]  Luis Rouco,et al.  A corrective load shedding scheme to mitigate voltage collapse , 2006 .

[13]  Michio Sugeno,et al.  Fuzzy systems theory and its applications , 1991 .

[14]  C. W. Taylor Power System Voltage Stability , 1993 .

[15]  H. Glavitsch,et al.  Estimating the Voltage Stability of a Power System , 1986, IEEE Transactions on Power Delivery.

[16]  D. Kothari,et al.  A technique for load-shedding based on voltage stability consideration , 2005 .

[17]  M. Glavic,et al.  Distributed Undervoltage Load Shedding , 2007, IEEE Transactions on Power Systems.

[18]  Mohammad-Taghi Vakil-Baghmisheh,et al.  Dynamic voltage stability assessment of power transmission systems using neural networks , 2008 .

[19]  Earl Cox,et al.  The fuzzy systems handbook , 1994 .

[20]  Shyh-Jier Huang,et al.  An adaptive load shedding method with time-based design for isolated power systems , 2000 .

[21]  A. C. Zambroni de Souza Discussion on some voltage collapse indices , 2000 .

[22]  B. Jeyasurya Artificial neural networks for on-line voltage stability assessment , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[23]  Avinash Kumar Sinha,et al.  A comparative study of voltage stability indices in a power system , 2000 .

[24]  Chi-Kong Wong,et al.  Adaptive Under-Frequency Load Shedding , 2008 .