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Author: search.filters.author.Kunakorn, AnantawatSubject: search.filters.subject.Artificial neural network

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    Fast and Effective Technique in Evaluation of Lightning Impulse Voltage Parameters
    (2021-01-01)
    Yutthagowith, Peerawut
    ;
    Kitwattana, Krit
    ;
    Kunakorn, Anantawat
    This paper presents an approach for the waveform parameter evaluation of lightning impulse voltage in high voltage tests according to the IEC standards. Such waveform parameters are composed of peak voltage (U<inf>p</inf>), front time (T<inf>1</inf>), time to half (T<inf>2</inf>), and the overshoot rate (B<inf>e</inf>). An artificial neural network with a back-propagation learning algorithm was applied to determine a base curve and its parameters from 14 points along the recorded waveform between 20% of the peak voltage on the wave front part to 40% of the peak voltage on the wave tail part. The 29 waveforms recommended by the standard were used in the training process of the development of the network model, and some experimental cases were also utilized for verification of the proposed method. It is found that the waveform parameters evaluated by the proposed approach are in the tolerances of the standard requirements. Maximum absolute deviations of U<inf>p</inf>, T<inf>1</inf>, T<inf>2</inf>, and B<inf>e</inf> are 0.06%, 2.00%, 0.12%, and 0.79%, respectively. Due to that no iteration process in the proposed approach is required, the efficiency in calculation process is significantly faster than the standard recommended approach.
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    An algorithm for circuit parameter identification in lightning impulse voltage generation for low-inductance loads
    (2020-08-01)
    Tuethong, Piyapon
    ;
    Kitwattana, Krit
    ;
    Yutthagowith, Peerawut
    ;
    Kunakorn, Anantawat
    This paper presents an effective technique based on an artificial neural network algorithm utilized for circuit parameter identification in lightning impulse generation for low inductance loads such as low voltage windings of a power transformer, a large distribution transformer and an air core reactor. The limitation of the combination between Glaninger’s circuit and the circuit parameter selection from Feser’s suggestions in term of producing an impulse waveform to be compliant with standard requirements when working with a low inductance load is discussed. In Feser’s approach, the circuit parameters of the generation circuit need to be further adjusted to obtain the waveform compliant with the standard requirement. In this process, trial and error approaches based on test engineers’ experience are employed in the circuit parameter selection. To avoid the unintentional damage from electrical field stress during the voltage waveform adjustment process, circuit simulators, such as Pspice and EMTP/ATP, are very useful to examine the generated voltage waveform before the experiments on the test object are carried out. In this paper, a system parameter identification based on an artificial neural network algorithm is applied to determine the appropriate circuit parameters in the test circuit. This impulse voltage generation with the selected circuit parameters was verified by simulations and an experiment. It was found that the generation circuit gives satisfactory impulse voltage waveforms in accordance with the standard requirement for the maximum charging capacitance of 10 µF and the load inductance from 400 µH to 4 mH. From the simulation and experimental results of all cases, the approach proposed in this paper is useful for test engineers in selection of appropriate circuit components for impulse voltage tests with low inductance loads instead of employing conventional trial and error in circuit component selection.