Abstract
The reasons because power systems monitoring is a challenging task are the complexity and high degree of interconnection present in electrical power networks, the presence of dynamic load changes in normal operation mode, the presence of both continuous and discrete variables, as well as noisy information and lack or excess of data. Therefore, in order to increase the efficiency of diagnosis, the need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. This paper proposes a methodology based on the system’s history data. It combines two techniques in order to give a complete diagnosis. The proposal is composed by two phases. The first phase is in charge of the fault detection by using Multidimensional Scaling (MDS). MDS acts like a first filter that gives the most probably state of each system’s node. The second phase gives the final diagnosis using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) over the node(s) given by the first phase in order to look for the faulty line(s) and the time when the fault starts. This proposal can detect the presence of either symmetrical or asymmetrical faults. A set of simulations are carried out over an electrical power system proposed by the IEEE. To show the performance of the approach, a comparison is made against similar diagnostic systems.
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Nieto González, J.P., Villanueva, P.P. (2013). Complete Diagnosis of Electrical Power Systems Using MDS and ANFIS. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_13
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DOI: https://doi.org/10.1007/978-3-642-45111-9_13
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