Branching Case Study

Real-Time Estimation of Propagation of Cascade Failure Using Branching Process
Priyanka Dey∗, Madhavi Parimi, S.R Wagh Electrical Engineering Dept
Veermata Jijabai Technological Institute
Mumbai India Email:[e-mail], [e-mail]
Abstract—Blackouts are characterized by a sequence of line trips and load shed which have a large impact on society. One of the models to predict the effect of a blackout is the Branching Process. The Branching Process model is utilized to compute the probability distribution of number of outages for a given power system. The mean propagation, a Branching Process parameter, is estimated from the simulated data to predict the size of blackout. The simulated cascading data is generated by

One of the world’s most severe blackout occurred in the Northern Region of India in July 2012 that left more than 700 million people without power [1]. There have been other severe blackouts which have occurred in the US [2] and Canada [3]. These scenarios are an indicator of the vulnerability of power system and it is essential to preserve the security and reliability of complex power grids. In order to avoid major loss to the utilities, it is necessary to take some corrective steps at the initial stage of the failure. This would be possible if, given an initial number of failures, it would be possible to predict how the failures would grow or propagate. The scale of the blackout for any system is determined by energy supply shortage , restoration time and number of transmission line outages

However, this paper neglects the other causes for cascade e.g voltage instability, variations in frequency etc. The paper suggests that Branching Process gives an accurate result even for shorter number of simulations. The average propagation of simulated load shed is estimated in [11] and then the initial load shed is propagated with discrete and continuous Branching Process. The estimated load shed data is validated using the OPA and the TRELSS simulators. In [12], it has been proposed that Branching Process model matches the distribution of number of cascading line outages and load shed simulated by two enhanced versions of the OPA simulation on the IEEE 118-bus system and the North-east Power Grid of China (NPGC). In [13], a bulk statistical model generated from industrial data is used to describe the Branching Process, thereby predicting the probability distribution of the total number of line outages. A Galton-Watson Branching Process in which the failures are grouped into stages with respect to closeness in time is proposed in [14]. Taking this idea further, a novel way of generating the time instant of each failure using a real-time simulator -RT-Lab is proposed in this