Technika Wysokich Napiec Pdf
. 47 Downloads.Abstract.The genesis of the research work presented in this paperconstitutes the issue of the effective and efficient recognition ofsingle-source one-time partial discharge (PD) forms that can occur in insulation systems of power transformers.
The paper presents researchresults referring to the use of single-direction neural networks (SNN) for recognizing basic PD formsthat can occur in paper-oil insulation impaired by aging processes. Theresearch work results presented show the recognition effectiveness of basicPD forms depending on the descriptor of the analysis of the acousticemission (AE) signal analysis.
Electric model of the motor and the power source, where R internal resistance of sourcesCoefficients R S, L S, E m, φ es, α that are being sought represent many of the phenomena that occur in the motor and the system that is driven. For example, the inertia of the rotor and the system driven will affect e s, and the angular velocity will exert an influence on mutual inductances, which are described with L S. When searching for the parameters of the model, the fact is also important that these factors cannot be determined with the engine being stopped.
Technika Wysokich Napiec Pdf Gratis
This means that the R S does not reflect the winding resistance and L S does not reflect their inductance. The parameters of the model are defined for a constant load on the machine shaft and for constant rotations. When changing the load, the parameters of the model change, as well.In this situation, the parameters that are being determined cannot in any way be unified. They should be determined for a specific drive train (the motor and the machine driven). These parameters can vary considerably for the same engine with different mechanical properties of the system driven.The identification of the model consists in searching for E m, φ es, R S, and L S. These parameters can be determined on the receiver , – by making measurements in the steady state (in the case of an induction motor: during operation with a constant load and a constant speed) in the system as shown below (Fig. ).
11Equation is consistent with. The coefficients of the model of the receiver that are obtained from the above equations are not determinable for all the input parameters ( U V, I a, P W, Q W). There are those areas that result from measurement inaccuracies where the system of Eq. has no solutions.It was found that these coefficients cannot be determined using the Newton’s interpolation algorithm ,.
There are no functions that are inverse to Eq. , either.Process time constant 1/α that is being sought, and which is mainly related to the inertia of the rotor and the system driven, can be determined experimentally by observing the course of voltage versus time at the motor terminals immediately after commutation.In , the authors proved that amplitude E S can be equal to amplitude U. In this paper, it was also observed that frequency E S is similar to the frequency of the mains voltage. It was also noted that phase shift φ es is equal to 0.In this model, it is assumed that the frequencies of both sources are identical. This assumption does not substantially affect the results of further simulations. Construction of an artificial neural networkCoefficients E m, R S, and L S can be determined from Eq.
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using a neural network. The network input parameters x 1 = U v V, x 2 = I a A, x 3 = P w W, x 4 = Q w VAr contain measurement errors. Due to the nature of the adopted activation function –, the output neuron of the output layer must be within range. The initial values were as follows: y 1 = R s kΩ, y 2 = L s H, y 3 = E s kV.Training of the network must be for those learning vectors that do not contain any measurement errors. Learning vectors are constructed from Eqs.
or for random values y 1, y 2, y 3 that lie within the set of permissible changes, and which is limited with values a and b ,.The test vector is built according to Fig. For values y i that are not contained within the training set.
Sheet with the saved results of the simulationThe output values of the neurons (Fig. a) are determined by analyzing the neurons in layers starting from the input layer; the output layer comes last.For all the layers, the steepness factor β of activation function fx was assumed as equal to 0.1.The determination of value (Fig. b) shall be in accordance with Formula. This determination takes place starting from the output layer; the input layer comes last.Correction of the values of weights (Fig. c) is carried out according to Relation.Network learning factor η from Formula was adopted on the first stage of the simulation as being constant and equal to 0.1. After an analysis of ca. 70,000 epochs, the value of target function Q.( t), which was calculated in accordance with Formula , began to oscillate on the level of 1.42. A decrease in Q.( t) occurred only after a reduction in network learning rate η. The correct procedure for the network training should provide for an ability to change this ratio during the analysis (Fig. ).
ConclusionsThe large error value is shown for the input values that occur least frequently in the training set. An improved performance is possible by enlarging the training set or by reducing the range of acceptable changes of the values being sought.Owing to the method presented of the selection of the electrical model parameters from the values that are measured on the receiver, it is not required to build any complex physical and electrical dependences. The engineering method of voltage, current, and power measurement allows one to determine the parameters of the model for constant electrical and mechanical conditions in the engine. The method presented is particularly useful in situations where measurement errors make it impossible to solve Eq.Building of a network with the use of the VBA environment is relatively simple. It requires the knowledge of the language basics. An important advantage of this approach is the ability to build its own networks of any topology. The design loop iteration depends largely on how one defines those variables that describe the network.In the present solution, the individual variables occupy adjacent bytes of the memory.
A sample definition of the variable holding the weights of neurons is. Where Lweights is the number of weights of all neurons.This solution facilitates the construction of a loop program, but special attention is to be paid to assigning the weight number with the neuron number.An alternative is to build one’s own variable (using the opportunity to build one’s own type of variables) that represents the neuron, and then group all the parameters that describe the type of the neuron in this variable. This approach will make the program more transparent, but there are problems in the construction of iterative loops. This will make the source code longer and will require more CPU load.