Fault Diagnosis of Power Systems Based on Triangular Fuzzy Spiking Neural P Systems

2016 ◽  
pp. 385-398 ◽  
Author(s):  
Chengyu Tao ◽  
Wenping Yu ◽  
Jun Wang ◽  
Hong Peng ◽  
Ke Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
P Systems ◽  
Spiking Neural P Systems

scholarly journals Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

2015 ◽  
Vol 30 (3) ◽  
pp. 1182-1194 ◽  
Author(s):  
Tao Wang ◽  
Gexiang Zhang ◽  
Junbo Zhao ◽  
Zhengyou He ◽  
Jun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Electric Power ◽  
Fuzzy Reasoning ◽  
Electric Power Systems ◽  
P Systems ◽  
Spiking Neural P Systems

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

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Guojiang Xiong ◽  
Dongyuan Shi ◽  
Lin Zhu ◽  
Xianzhong Duan
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power System ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Diagnostic Model ◽  
Graphical Modeling ◽  
Spiking Neural P Systems ◽  
Reasoning Algorithm ◽  
First Time

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.

scholarly journals Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Haina Rong ◽  
Kang Yi ◽  
Gexiang Zhang ◽  
Jianping Dong ◽  
Prithwineel Paul ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Large Scale ◽  
Membrane Computing ◽  
Good Description ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
P System ◽  
Spiking Neural P Systems ◽  
Complex Power

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

scholarly journals A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Wei Liu ◽  
Tao Wang ◽  
Tianlei Zang ◽  
Zhu Huang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power Transmission ◽  
Attribute Reduction ◽  
P Systems ◽  
P System ◽  
Transmission Networks ◽  
Spiking Neural P Systems ◽  
Diagnosis Method ◽  
Power Transmission Networks

Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.

scholarly journals A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms

2016 ◽  
Vol 25 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Jun Wang ◽  
J. Mario P´erez-Jim´enez ◽  
Hong Peng ◽  
Peng Shi ◽  
Min Tu
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
Adaptive Fuzzy ◽  
Diagnosis Method

scholarly journals Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems

2018 ◽  
Vol 9 (5) ◽  
pp. 4777-4784 ◽  
Author(s):  
Hong Peng ◽  
Jun Wang ◽  
Jun Ming ◽  
Peng Shi ◽  
Mario J. Perez-Jimenez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
Intuitionistic Fuzzy

scholarly journals Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

2014 ◽  
Vol 9 (6) ◽  
pp. 786 ◽  
Author(s):  
Tao Wang ◽  
Gexiang Zhang ◽  
Haina Rong ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Spiking Neural P Systems

scholarly journals A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhu Huang ◽  
Tao Wang ◽  
Wei Liu ◽  
Luis Valencia-Cabrera ◽  
Mario J. Pérez-Jiménez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Analysis ◽  
Fault Prediction ◽  
P Systems ◽  
Analysis Method ◽  
Spiking Neural P Systems ◽  
Three Phase ◽  
Fuzzy Production Rules ◽  
Reasoning Algorithm

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

scholarly journals On Applications of Spiking Neural P Systems

2020 ◽  
Vol 10 (20) ◽  
pp. 7011 ◽  
Author(s):  
Songhai Fan ◽  
Prithwineel Paul ◽  
Tianbao Wu ◽  
Haina Rong ◽  
Gexiang Zhang
Keyword(s):  
Power Systems ◽  
Membrane Computing ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Diverse Range ◽  
Spiking Neural P Systems ◽  
Advantages And Disadvantages ◽  
New Ideas

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

scholarly journals Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks

2019 ◽  
Vol 82 ◽  
pp. 102-109 ◽  
Author(s):  
Jun Wang ◽  
Hong Peng ◽  
Wenping Yu ◽  
Jun Ming ◽  
Mario J. Pérez-Jiménez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Transmission ◽  
P Systems ◽  
Transmission Networks ◽  
Spiking Neural P Systems ◽  
Interval Valued ◽  
Power Transmission Networks
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