Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
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Citations
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References
Computing with Membranes
Spiking Neural P Systems
Artificial neural network and support vector Machine approach for locating faults in radial distribution systems
An optimization spiking neural p system for approximately solving combinatorial optimization problems.
Bayesian networks-based approach for power systems fault diagnosis
Related Papers (5)
Frequently Asked Questions (9)
Q2. What future works have the authors mentioned in the paper "Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural p systems" ?
Future work will focus on verifying the performance superiority of FDSNP, compared with other diagnosis methods, by using performance metrics including diagnosis time, fault section misinformation rate, fault section missing rate and computational complexity. The authors would like to thank the Editor-in-Chief, Prof. A. Conejo, the editors, and the anonymous reviewers for their insightful comments and suggestions which greatly helped in improving the quality of this paper.
Q3. What are the four methods used to perform comparative experiments?
Four diagnosis methods, fuzzy logic (FL) [3], fuzzy Petri nets (FPN) [4], genetic algorithm-tabu search (GATS) [15], and genetic algorithm (GA) [17], are used as benchmarks to perform comparative experiments.
Q4. Why do the authors use linguistic terms to describe certainty factors?
Due to the uncertainty of the knowledge of experts and senior dispatchers, the authors use linguistic terms to describe certainty factors.
Q5. Why do the authors suggest network topology analysis?
The authors suggest network topology analysis because it decreases the number of candidate diagnosing areas and reduce the subsequent computational workload [10].
Q6. How many scientific papers has he published?
He has published thirteen books in computer science and mathematics, and over 250 scientific papers in international journals (collaborating with researchers worldwide).
Q7. What is the definition of a FRSN P system with trapezoidal fuzzy numbers?
In an FRSN P system, the pulse value contained in each neuron is not the number of spikes represented by a real number, but a trapezoidal fuzzy number in [0, 1], which can be interpreted as the potential value of spikes contained in neuron .
Q8. What is the main reason why FDSNP can be used in large-scale power transmission networks?
this method can be used for large-scale power transmission networks because the complexity of the fault diagnosis models based on FRSN P systems does not increase sharply and quickly with the scale of networks.
Q9. what is the process of this rule type modeled by using one FRSN P system?
The process of this rule type modeled by using one FRSN P system is shown in Fig. 10, where (a), (b), and (c) represent spike being transmitted from input neuron to output neurons.