A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

Abstract

Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

“…4, is applied as an example to describe how to use tFRSN P systems with MBFRA to solve a fault diagnosis problem. The system contains 34 system sections, including 14 A local part, which is composed of a transmission line L 1314 , its adjoining two buses, B 13 and B 14 , and its adjoining three transmission lines, L 1213 , L 0613 and L 0914 , of the protection system is given to describe its structure and symbols of protection devices. The local system is shown in Fig.…”

“…The bus relay BR 13 protects the bus B 13 and it will operate to trip the three CBs, i.e., CB 1312 1409 and CB 0914 , in the process of protecting the line L 1314 and the bus B 14 are similar and the protection systems for other sections in this 14-bus power system have the same protection rules, so it is not necessary to repeatedly describe Figure 5: A local part of the protection system of the 14-bus power system. their operation rules.…”

“…In [3], a fuzzy reasoning spiking neural P system with real numbers (rFRSN P system) was presented to perform diagnosis knowledge representation and reasoning. In [13], an rFRSN P system was used for fault diagnosis of power systems and three examples were used to verify its effectiveness. The studies in [3,13], dealing with the fault diagnosis problem, used certainty factors and truth degree values, which are described by real numbers obtained from the frequency of occurrences in historical data, It is known how difficult it is to obtain and process real-time statistical data from power network data and the knowledge of dispatchers and experts in electrical power systems as they usually contain linguistic terms with…”

Fuzzy Membrane Computing: Theory and Applications

“…These fuzzy production rules can be modeled by the following rFRSN P system Π 5 , as shown in Figure 8. (4) syn = {(1, 15), (2,15), (3,16), (4,16), (4,18), (5,16), (5,17), (5,18), (6,18), (7,17), (8,17), (9,17), (10,18), (15,11), (16,12), (17,13), (18,14)}.…”

An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

“…Except for theoretical results [10,13], SN P systems have been widely used to solve various application problems, such as combinatorial and engineering optimization problems [30,38], signal recognition [2], arithmetic operations [17,21,28] and fuzzy knowledge representation [23]. Of a particular interest is the combination of SN P systems with fuzzy set theory, called fuzzy membrane computing [36], to solve fault diagnosis problems with respect to transformers [16,42], transmission lines [8,24,25], traction power supply systems of high-speed railways [39] and metro 522 H. Rong, M. Ge, G. Zhang, M. Zhu traction systems [10], in electric power systems. In general, there are two kinds of fuzzy reasoning spiking neural P systems (FRSN P systems) [36]: fuzzy reasoning spiking neural P system with real numbers (rFRSN P systems) [16] and fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) [24].…”