scholarly journals Locating Pd in Transformers through Detailed Model and Neural Networks

2014 ◽  
Vol 65 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Hamed Nafisi ◽  
Mehrdad Abedi ◽  
Gevorg B. Gharehpetian
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Power Transformer ◽  
Present Report ◽  
Fuzzy Artmap ◽  
Correct Identification ◽  
Power Networks ◽  
Detailed Model ◽  
Electric Power Networks ◽  
Transformer Winding

Abstract In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.

A Solid-State On-Load Tap Changer for the Power Transformers of 10—0.4 kV Distribution Networks

Vestnik MEI ◽  
2020 ◽  
Vol 6 (6) ◽  
pp. 82-90
Author(s):  
Dmitriy I. Panfilov ◽  
◽  
Mikhail G. Astashev ◽  
Aleksandr V. Gorchakov ◽  
◽  
...  
Keyword(s):  
Solid State ◽  
Physical Model ◽  
High Speed ◽  
Power Transformer ◽  
Voltage Control ◽  
Power Transformers ◽  
Control Algorithms ◽  
Load Voltage ◽  
Tap Changer ◽  
Load Tap Changer

The specific features relating to voltage control of power transformers at distribution network transformer substations are considered. An approach to implementing high-speed on-load voltage control of serially produced 10/0.4 kV power transformers by using a solid-state on-load tap changer (SOLTC) is presented. An example of the SOLTC circuit solution on the basis of thyristor switches is given. On-load voltage control algorithms for power transformers equipped with SOLTC that ensure high reliability and high-speed operation are proposed. The SOLTC performance and the operability of the suggested voltage control algorithms were studied by simulation in the Matlab/Simulink environment and by experiments on the SOLTC physical model. The structure and peculiarities of the used simulation Matlab model are described. The SOLTC physical model design and its parameters are presented. The results obtained from the simulating the SOLTC operation on the Matlab model and from the experiments on the SOLTS physical model jointly with a power transformer under different loads and with using different control algorithms are given. An analysis of the experimental study results has shown the soundness of the adopted technical solutions. It has been demonstrated that the use of an SOLTC ensures high-speed voltage control, high efficiency and reliability of its operation, and arcless switching of the power transformer regulating taps without load voltage and current interruption. By using the SOLTC operation algorithms it is possible to perform individual phase voltage regulation in a three-phase 0.4 kV distribution network. The possibility of integrating SOLTC control and diagnostic facilities into the structure of modern digital substations based on the digital interface according to the IEC 61850 standard is noted.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4149-4162 ◽  
Author(s):  
Bruno Romeira ◽  
José M. L. Figueiredo ◽  
Julien Javaloyes
Keyword(s):  
Neural Networks ◽  
Energy Efficient ◽  
High Speed ◽  
Dynamic Properties ◽  
Optical Computing ◽  
Differential Conductance ◽  
Rate Equations ◽  
Dynamical Equations ◽  
Electrical Modulation ◽  
High Bandwidth

AbstractEvent-activated biological-inspired subwavelength (sub-λ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub-λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub-λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.

scholarly journals Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4097-4108 ◽  
Author(s):  
Moustafa Ahmed ◽  
Yas Al-Hadeethi ◽  
Ahmed Bakry ◽  
Hamed Dalir ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Graphics Processing Units ◽  
High Speed ◽  
Fourier Transforms ◽  
Optoelectronic Device ◽  
Small Sample ◽  
Sample Number ◽  
Chip Area ◽  
Domain Specific ◽  
Graphics Processing

AbstractThe technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.

scholarly journals Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Computation ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 35
Author(s):  
Hind R. Mohammed ◽  
Zahir M. Hussain
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Speed ◽  
Moving Objects ◽  
Real Life ◽  
Fuzzy Rules ◽  
High Rate ◽  
Moving Images ◽  
Animal Images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

Determination of Damping Factors Based on the Measured Frequency Responses of Power Transformer Windings. Part 2. Analysis of Measurement Results

2021 ◽  
Vol 2 (2) ◽  
pp. 22-28
Author(s):  
Vasily S. LARIN ◽  
◽  
Daniil A. MATVEEV ◽  
Keyword(s):  
Active Component ◽  
Power Transformer ◽  
Neutral Current ◽  
Response Analysis ◽  
Reactive Component ◽  
Input Admittance ◽  
Equivalent Scheme ◽  
Frequency Responses ◽  
Measurement Results ◽  
Transformer Winding

In the first part of the article, based on the results of theoretical studies performed for a simplified transformer winding equivalent scheme, it was shown that the damping factors can be estimated from the width of the resonant peaks of the frequency responses of the module and the reactive component of the voltage at the midpoint of the equivalent scheme, as well as the active component of the input admittance and neutral current of the considered resonant scheme. In this part of the article, the practical possibility of applying the obtained theoretical relations between the damping factors and the width of resonant peaks in relation to the frequency responses of power transformer windings is considered. The results of calculations of the damping factors at the two power transformers made by using the fitting of the free component of transient voltage and by determining the width of the resonance peaks of the active component of winding neutral current and the voltage transfer function, corresponding to intermediate points of the winding. It is shown that the evaluation of the values of the winding damping factors can be performed as a byproduct of transformer condition assessment by frequency response analysis (FRA).

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