scholarly journals Combined Diagnosis of PD Based on the Multidimensional Parameters

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Heidari
Keyword(s):  
Final Diagnosis ◽  
Semisupervised Learning ◽  
Adaptive Mutation ◽  
Characteristic Parameters ◽  
Ultrasonic Signals ◽  
Different Types ◽  
Diagnosis Method ◽  
Characteristic Values ◽  
Types Of Faults ◽  
Frequency Current

This paper presents a comprehensive multiparameter diagnosis method based on multiple partial discharge (PD) signals which include high-frequency current (HFC), ultrasound, and ultrahigh frequency (UHF). The HFC, ultrasound, and UHF PD are calculated under different types of faults. Therefor the characteristic values, as nine basic characteristic parameters, eight phase characteristic parameters, and the like are calculated. Diagnose signals are found with the method based on information fusion and semisupervised learning for HFC PD, adaptive mutation parameters of particle entropy for ultrasonic signals, and IIA-ART2A neural network for UHF signals. In addition, integrate the diagnostic results, which are the probability of fault of various defects and matrix, of different PD diagnosis signals, and analysis with Sugeno fuzzy integral to get the final diagnosis.

scholarly journals Combined diagnosis of partial discharge based on the multi-dimensional characteristic parameters

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1535-1546
Author(s):  
Weiqiang Qi ◽  
Yuan Gui ◽  
Dapeng Duan ◽  
Songlin Zhou ◽  
Jianpeng Dong
Keyword(s):  
High Frequency ◽  
Partial Discharge ◽  
Final Diagnosis ◽  
Adaptive Mutation ◽  
Characteristic Parameters ◽  
Discharge Data ◽  
Ultrasonic Signals ◽  
High Frequency Current ◽  
Multiple Classifier ◽  
Frequency Current

This paper presents a comprehensive multi-parameter diagnosis method based on multiple partial discharge signals include high-frequency current, ultrasound, ultrahigh frequency (UHF) etc. First, acquire the high-frequency current, ultrasound, UHF partial discharge data under various types of defects, and extract the characteristic values, including nine basic characteristic parameters, eight phase characteristic parameters and the like. Diagnose signals respectively, with the method based on information fusion and semi-supervised learning for high-frequency current PD data, the method based on adaptive mutation parameters of particle entropy for ultrasonic signals, the method based on IIA-ART2A neural network for UHF signals. Then integrate the diagnostic results, which is the probability of fault of various defects and matrix, of different PD diagnosis signals, and analysis with the multiple classifier based on multi-parameter fuzzy integral to get the final diagnosis.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

scholarly journals On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3079 ◽  
Author(s):  
Leopoldo Angrisani ◽  
Francesco Bonavolontà ◽  
Annalisa Liccardo ◽  
Rosario Schiano Lo Moriello
Keyword(s):  
Cost Effectiveness ◽  
Long Range ◽  
Distribution Networks ◽  
Fault Isolation ◽  
Optimized Design ◽  
Medium Voltage ◽  
Different Types ◽  
The Times ◽  
The Cost ◽  
Types Of Faults

In this paper, a logic selectivity system based on Long Range (LoRa) technology for the protection of medium-voltage (MV) networks is proposed. The development of relays that communicate with each other using LoRa allows for the combination of the cost-effectiveness and ease of installation of wireless networks with long-range coverage and reliability. The realized demonstrator to assess the proposed system is also presented in the paper; based on different types of faults and different locations, the times needed for clearing a fault and restoring the network were estimated from repeated experiments. The obtained results confirm that, with an optimized design of transmitted packets and of protocol characteristics, LoRa communication grants fault management that meets the criteria of logic selectivity, with fault isolation occurring within the maximum allowed time.

scholarly journals Design and construction of a prototype for the analysis of vibrations in an induction motor for the detection of faults

2020 ◽  
pp. 27-33
Author(s):  
Javier Garrido ◽  
Beatris Escobedo-Trujillo ◽  
Guillermo Miguel Martínez-Rodríguez ◽  
Oscar Fernando Silva-Aguilar
Keyword(s):  
Fourier Transform ◽  
Induction Motor ◽  
Wavelet Transforms ◽  
Data Acquisition System ◽  
Time Frequency ◽  
Different Types ◽  
Control Devices ◽  
The Fourier Transform ◽  
And Control ◽  
Types Of Faults

The contribution of this work is to present the design of a prototype integrated by an induction motor, a data acquisition system, accelerometers and control devices for stop and start, to generate and identify different types of faults by means of vibration analysis. in the domain: time, frequency or frequency-time, through the use of the Fourier Transform, Fast Fourier Transform or Wavelet Transforms (wavelet transform). In this prototype, failures can be generated in the induction motor such as: unbalance, different types of misalignment, mechanical looseness, and electrical failures such as broken bars or short-circuited rings, an example of a misalignment failure is presented to show the process of analysis and detection.

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

scholarly journals Estimation of Phase Angle Jump (PAJ) for Different Types of Faults and Unbalancing in Distribution System

2018 ◽  
Vol 7 (4.38) ◽  
pp. 23 ◽  
Author(s):  
Muhammad Fawad Shaikh ◽  
Madad Ali Shah ◽  
Sunny Katyara ◽  
Bhawani Shankar Chowdhry
Keyword(s):  
Phase Angle ◽  
Power Supply ◽  
Distribution System ◽  
Research Work ◽  
Voltage Sag ◽  
Voltage Unbalance ◽  
Voltage Drops ◽  
Different Types ◽  
Zero Sequence ◽  
Types Of Faults

Voltage sag caused by the faults in the power system has serious power quality issues and sometimes leads to interruption of power supply. The characteristics of voltage sag are its magnitude, time and phase angle jump (PAJ). This paper represents the estimation of phase angle jump (PAJ) when different types of faults are occurred in distribution system. Since the unbalancing is one of the major issues in distribution system that increases the zero sequence currents, over heats the distribution transformer, causes huge voltage drops in distributor etc. Therefore, the method used in this paper shows the PAJ when distributor is unbalance due to uneven loading or the line parameters of the distributor are unsymmetrical. Simple radial system is used to analyze the PAJ caused by the different types of faults and unbalancing. Different comparisons are made that are associated with PAJ such as PAJ vs fault impedance, zero sequence current and percentage of voltage unbalance. The research work is performed on MATLAB/SIMULINK to analyze the real time results.  

scholarly journals Different Roles of Related Currents in Fast and Slow Spiking of Model Neurons From Two Phyla

2008 ◽  
Vol 100 (4) ◽  
pp. 2048-2061 ◽  
Author(s):  
En Hong ◽  
Fatma Gurel Kazanci ◽  
Astrid A. Prinz
Keyword(s):  
Neuronal Activity ◽  
Voltage Dependence ◽  
Membrane Current ◽  
Spiking Neurons ◽  
Maximal Conductance ◽  
Different Types ◽  
Channel Type ◽  
Frequency Current ◽  
Spiking Model

Neuronal activity arises from the interplay of membrane and synaptic currents. Although many channel proteins conducting these currents are phylogenetically conserved, channels of the same type in different animals can have different voltage dependencies and dynamics. What does this mean for our ability to derive rules about the role of different types of ion channels in neuronal activity? Can results about the role of a particular channel type in a particular type of neuron be generalized to other neuron types? We compare spiking model neurons in two databases constructed by exploring the maximal conductance spaces of two models. The first is a model of crustacean stomatogastric neurons, and the second is a model of rodent thalamocortical neurons, but both models contain similar types of membrane currents. Spiking neurons in both databases show distinct fast and slow subpopulations, but our analysis reveals that related currents play different roles in fast and slow spiking in the stomatogastric versus thalamocortical neurons. This analysis involved conductance-space visualization and comparison of voltage traces, current traces, and frequency-current relationships from all spiker subpopulations. Our results are consistent with previous work indicating that the role a membrane current plays in shaping a neuron's behavior depends on the voltage dependence and dynamics of that current and may be different in different neuron types depending on the properties of other currents it is interacting with. Conclusions about the function of a type of membrane current based on experiments or simulations in one type of neuron may therefore not generalize to other neuron types.

Trapping Runtime Faults from Web Service Fault Ontology

2020 ◽  
Vol 17 (8) ◽  
pp. 3759-3764
Author(s):  
K. Jayashree
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Users ◽  
Intended Behavior ◽  
Proposed Model ◽  
Different Types ◽  
Service Application ◽  
Common Understanding ◽  
Types Of Faults ◽  
The Web

The ontology offers a clear considerate of the runtime faults in web services and helps to share this common understanding with users and applications. This paper presents Web Service Fault Ontology and to trap the runtime faults from the Web Services Fault Ontology. Web Service Fault Ontology has been developed to represent the different types of faults that can occur during the interactions between service users, service publishers and service registries: publishing, discovery, binding and execution of web services. Ontology has been proposed to define the intended behavior of web services from the service provider. A sample web service application was developed for testing the proposed model.

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