Abstract
Since a smart distribution grid has a diversity of components and complicated topology; it is very hard to achieve fault early warning for each part. A fault early warning model for smart distribution grid combining a back propagation (BP) neural network with a gene sequence alignment algorithm is proposed. Firstly; the operational state of smart distribution grid is divided into four states; and a BP neural network is adopted to explore the operational state from the historical fault data of the smart distribution grid. This obtains the relationship between each state transition time sequence and corresponding fault, and is used to construct the fault gene table. Then; a state transition time sequence is obtained online periodically, which is matched with each gene in fault gene table by an improved Smith–Waterman algorithm. If the maximum match score exceeds the given threshold, the relevant fault will be detected early. Finally, plenty of time domain simulation is performed on the proposed fault early warning model to IEEE-14 bus. The simulation results show that the proposed model can achieve efficient early fault warning of smart distribution grids.
Highlights
In recent years, the smart grid has become a major concern of the international community [1,2,3,4,5,6,7].Smart distribution grids are the main connection between the main grid and power supply to users and are important parts of the smart grid [8]
In analogy to the human gene composed of the four canonical bases, the operational state of a smart distribution grid can be divided into four states: excellent, good, middle and bad labeled as E, G, M and B, which are shown as Table 1
The proposed model combined back propagation (BP) neural network and the Smith–Waterman gene sequence alignment algorithm, fully exploiting fault features of smart distribution grids, which provides a new thought in the solution of fault early warning for smart distribution grids
Summary
The smart grid has become a major concern of the international community [1,2,3,4,5,6,7]. In [13], a fault early warning method suitable for the active distribution grid based on harmonic current is proposed. In [21], an approach to efficiently identify the most probable failure modes in static load distribution for a given power network is developed This technique can help discover weak links which are saturated at the failure modes, providing predictive capability for improving the reliability of any power system. In analogy to the human gene bank, which aims to identify and map all human genes [25], the fault gene table is constructed to show the fault information of smart distribution grids, and the improved Smith–Waterman is adopted to realize the fault early warning for smart distribution grids so as to provide the monitoring of system operational state for power operation and management unit scientifically and effectively based on the constructed fault gene table
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