Development of an Artificial Neural Network based Hardware Prototype for Fault Localization in Distribution Grids
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Date
2015-01-14
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Abstract
In modern distribution grids, the task of fault
localization using conventional techniques is increasingly
becoming a challenge due to the rising domination of inverterbased,
volatile, distributed power generation. Since approved
methods from high voltage level such as reactance-based methods
lack accuracy in distribution level topology, alternative
approaches for accurate fault localization are required. Within
the scope of this work, an artificial neural network (ANN) based
solution for the localization of electric faults at distribution level
has been developed, evaluated and implemented on standard
hardware from industrial automation technology i.e. a
programmable logic controller (PLC). A reduced yet
representative model of a distribution grid incorporating a
variety of aspects influencing the accuracy of fault localization
such as distributed generation, ring network topology with open
or closed loop as well as variable fault resistance has been
developed. Current and voltage measurements generated under
various fault conditions have been used for training of an ANN.
Different ANNs have been trained with various network
structures and training algorithms and after thorough analysis
and comparison of their performance, the most suitable networks
have been implemented on hardware and tested in hardware-in-the-
loop configuration. Thereby a real-time simulator suitable for
application testing and rapid prototyping provided process
values of the modeled distribution grid.
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Keywords
distributed generation, programmable logic controller, artificial neural network, fault localization