Abstract
One of the ways to improve the reliability of electrical grids is associated with the introduction of complex and resource-intensive algorithms in intelligent electronic devices (IED) that perform the functions of relay protection and automation at substations. Simulation modelling is used to study the features of the protected object functioning and its application makes it possible to take into account the variability of electrical grid states in the formation of IED algorithms, which are characteristic of the analyzed electrical grid section. In addition, this approach makes it possible to use for short circuits detection only those information features that have a high information value in a specific problem of states recognition. Machine learning methods are advisable to use for modern relay protection algorithms implementation. One of such methods is the k-nearest neighbours method. The article substantiates the effectiveness of the method application in comparison with the conventional algorithms on the example of protection of an electrical grid section with a distributed generation source. The reported study was funded by RFBR, project number 19-38-90144.
Highlights
Recognition and localization of short circuits (SC) in electrical grids by relay protection devices are essential components of trouble-free operation of power systems
As a rule [1, 2], the features are combined into a system of setting planes with operating zone formed by the simulation modeling and training results
It lies in assigning some feature vector x to one of the given classes Y1...Ym based on a training sample
Summary
Recognition and localization of short circuits (SC) in electrical grids by relay protection devices are essential components of trouble-free operation of power systems. A promising direction in relay protection is the informational approach [1, 2] It involves the use of the states simulation modeling results to form protection algorithms. The classification problem is identical to the informational approach problem in relay protection In this case, various electrical state parameters act as features and they are available for measurement at the substation. Many classes in turn are formed by the controlled states of the protected object (normal state, self-starting state, short circuit state, etc.), and the simulation modeling results become the training sample. This contributes thereby to more efficient use of available information about the current electrical grid state In this regard, it is important to study new ways of relay protection implementation using machine learning algorithms, as well as to assess their recognition ability. A further increase in the recognition ability of protection can be achieved as a result of using machine learning algorithms, in particular, the k-nearest neighbors method
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