because it can withstand only a small amount of load increase before causing voltage collapse. This information is useful to planning or operation engineers in ensuring that any increment in the system will not exceed the maximum loadability, hence violating the voltage stability limit. 

The increase in load of a bus beyond a critical limit pushes the system to the verge of voltage collapse, if the system is not compensated adequately. This critical limit of the bus load is defined as the Voltage Stability Margin. The voltage stability margin estimates the criticality of a bus. Hence, the identification of the critical buses in a system is useful in determining the location of additional voltage support devices to prevent possible voltage instability. 

A Fuzzy Set theory based algorithm is used to identify the weak buses in a power system. Bus voltage and reactive power loss at that bus are represented by membership functions for voltage stability study [1]. Newton optimal power flow is used to identify the weakest bus / area, which is likely to cause voltage collapse. The complex power - voltage curve is examined through Newton optimal power flow. The indicator, which identifies the weakest bus, was obtained by integrating all the marginal costs via KuhnTucker theorem [2]. A Fast Voltage Stability Indicator (FVSI) is used to estimate the maximum loadability for identification of weak bus. The indicator is derived from the voltage quadratic equation at the receiving bus in a two bus system. The load of a bus, which is to be ranked is increased till maximum value of FVSI is reached and this load value is used as an indicator for ranking the bus [3]. A weak bus-oriented criterion is used to determine the candidate buses for installing new VAR sources in VAR planning problem. 

Two indices are used to identify weak buses based on power flow Jacobian matrix calculated at the current operating point of the system [4]. A neural network based method for the identification of voltage-weak buses/areas uses power flow analysis and singular value decomposition method. Kohonen neural network is trained to cluster/rank buses in terms of voltage stability [5]. 

Voltage Stability Margin Index (VSMI) is developed based on the relationship of voltage stability and angle difference between sending and receiving end buses. VSMI is used to estimate voltage stability margin and identify weak transmission lines and buses at any given operating condition [6]. The weakest bus, most loaded transmission path for that bus from voltage security point of view is identified using nodal voltage security assessment. Control actions are taken to alleviate power flows across that branch to enhance voltage security condition [7]. The Singular Value Decomposition method is used in identifying weak boundaries with respect to voltage instabilities well as in the assessment of the effects of possible disturbances and their corrective actions [8]. The existing techniques [1] - [8] are basically to identify weak buses for a pre contingency system. But for secured operation of the stressed power system, it is essential to know the criticality of a bus at the verge of voltage collapse. 

This paper presents a fuzzy approach to rank critical buses in a power system under and normal and network contingencies. Voltage Stability Margin expressed in terms of Static Voltage Collapse Proximity Indicator at critical load of a selected load bus accurately estimates the criticality of that bus from the voltage collapse point of view. Hence the Line Flow index is used as a 