Abstract
The previously proposed algorithms for preventing cascading failures, which lead to a blackout event, involve specific load shedding schemes, which introduce incurring losses in the power system network. In this paper, a cooperative control based algorithm using a vehicle to grid (V2G) technology based on a fuzzy logic approach is proposed to prevent cascading failures without loss incurrence. The algorithm is implemented on a standard IEEE-30 bus system, and it uses mathematical combinations heuristically to identify the critical nodes through the use of a self-propagation graph to dispatch the optimum power from V2G. For the enhancement of computational speed, a network operator considers only those vulnerable nodes, which are identified by a self-propagating graph. Through this, a network operator can easily detect critical nodes by rooting straight to the vulnerable transmission lines in the IEEE-30 bus network. The probabilistic modeling in this paper is performed in such a way that network operators will mitigate cascading failures events (CFEs) after the occurrence of (N -1) and (N -1-1) contingencies/blackout events without performing load shedding. The detailed experimental analysis provides better visualization of the impact of CFEs on power grids to the power network operators and therefore significantly improves the accuracy of taking necessary actions to compensate these CFEs.
Highlights
The complexity of the power system network, the interconnection between different components, and the various timebased scale dynamics and interactions in smart grids have made the analysis and modeling of cascading failure events (CFEs) immensely complicated
The main idea behind this algorithm is to shed loads on these critical lines in an optimum way using a technique based on a fair load shedding scheme
To avoid tripping of these critical transmission lines in the IEEE-30 bus network, we provide an optimum solution in the form of V2G cooperative control based algorithms using a fuzzy logic approach, as mentioned in Algorithm 1
Summary
The complexity of the power system network, the interconnection between different components, and the various timebased scale dynamics and interactions in smart grids have made the analysis and modeling of cascading failure events (CFEs) immensely complicated. These CFEs are responsible for triggering an unpredictable form of chain reactions [1]. To prevent these chain reactions, is a challenging research issue, especially after an occurrence of a CFE in a power system network.
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