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Документ Improving metrological characteristics of measuring instruments by discrete wavelet noise filtering using the recursion method(Національний науковий центр "Інститут метрології", 2022) Onufrienko, D.; Taranenko, Yu.; Suchkov, G. M.The method of recursive discrete wavelet noise filtering for improving metrological characteristics of measuring instruments was investigated for the first time. Methods with a common threshold for all decomposition levels, methods without threshold with a simple zeroing of detail coefficients until the minimum mean square (RMS) error is reached, and methods with universal threshold for detail coefficients at each decomposition level were studied. Twenty different types of measurement signals from the popular PyWavelets library were analyzed. The functions of filtering methods with a common threshold were determined, for which the use of recursion reduces the filtering error from 10 to 50%. For methods without threshold and with universal threshold, the recursion does not reduces the error by multiple filtering of measurement signals. To apply the recursion to the method with a common threshold for all decomposition levels, a mathematical model based on the fundamental equations of wavelet filtering was constructed. The character of distribution of the filtering RMS error depending on the number of reversible cycles is investigated. It was summarized that for the measurement signal models under consideration, the maximum error reduction occurs between the zero cycle, in which the initial measurement signal is filtered, and the first level of recursion. Further reduction of the filtering error with increasing number of recursion cycles occurs according to the law close to hyperbolic.Документ Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks(Національний технічний університет "Харківський політехнічний інститут", 2022) Bengharbi, Abdelkader Azzeddine; Laribi, Saadi Souad; Allaoui, Tayeb; Mimouni, AminaIntroduction. This research work focuses on the design and experimental validation of fault detection techniques in grid-connected solar photovoltaic system operating under Maximum Power Point Tracking mode and subjected to various operating conditions. Purpose. Six fault scenarios are considered in this study including partial shading, open circuit in the photovoltaic array, complete failure of one of the six IGBTs of the inverter and some parametric faults that may appear in controller of the boost converter. Methods. The fault detection technique developed in this work is based on artificial neural networks and uses discrete wavelet transform to extract the features for the identification of the underlying faults. By applying discrete wavelet transform, the time domain inverter output current is decomposed into different frequency bands, and then the root mean square values at each frequency band are used to train the neural network. Results. The proposed fault diagnosis method has been extensively tested on the above faults scenarios and proved to be very effective and extremely accurate under large variations in the irradiance and temperature.