scholarly journals Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

2016 ◽  
Vol 08 (04) ◽  
pp. 77-91 ◽  
Author(s):  
Yousif Yahya ◽  
Ai Qian ◽  
Adel Yahya
Keyword(s):  
Fault Diagnosis ◽  
Power Transformer ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
Transformer Fault

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 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 Temporal Fuzzy Reasoning Spiking Neural P Systems with Real Numbers for Power System Fault Diagnosis

2016 ◽  
Vol 13 (6) ◽  
pp. 3804-3814 ◽  
Author(s):  
Kang Huang ◽  
Tao Wang ◽  
Yangyang He ◽  
Gexiang Zhang ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Real Numbers ◽  
Spiking Neural P Systems

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

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