Change in Voltage Distortion Predictions at the PCC Due to Changing Nonlinear Load Current Profile Using Plant Startup Data

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics in a power distribution network. The interaction of the nonlinear load harmonics with the network impedances creates voltage distortions at the point of common coupling (PCC) which in turn affects other loads connected to the same PCC. When several nonlinear loads are connected to the PCC, it is difficult to predict mathematically how each nonlinear load is affecting the voltage distortion level at the PCC. Typically, customers with nonlinear loads apply harmonic filtering techniques to clean up their current and avoid penalties from the utility. When corrective action is taken by the customer, one important parameter of interest is the change in the voltage distortion level at the PCC due to the corrective action of the customer. This paper proposes a new method based on neural networks to predict the change in the distortion level of the voltage at the PCC if the customer were to draw only fundamental current and filter out its harmonics. The benefit of the proposed method is that it would indicate the impact of the customer\u27s front end filters on the voltage distortion at the PCC without actually having to install the filters. This paper presents the results of the proposed method applied to actual industrial sites

Echo State Networks for Determining Harmonic Contributions from Nonlinear Loads

This paper investigates the application of a new kind of recurrent neural network called Echo State Networks (ESNs) for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The interaction between loads connected to a point of common coupling (PCC) is a highly dynamic process. The determination of true harmonic current injection by individual loads is further complicated by the fact that the supply voltage waveform at the PCC is distorted by other loads at the PCC or further upstream and is therefore rarely a pure sinusoid. Harmonics in a power system are classified as either load harmonics or as supply harmonics. The principles of ESN are based on the use of a Recurrent Neural Network (RNN) as a dynamic reservoir. In order to compute the desired output dynamics, only the weights of connections from the reservoir to the output units are calculated. This is simply a linear regression problem. Experimental results presented in this paper confirm that attempting to predict the Total Harmonic Distortion (THD) of a load by simply measuring the load\u27\u27s current may not be accurate. The main advantage of this new method is that only waveforms of voltages and currents at the PCC have to be measured. This method is applicable for both single and three phase loads

Predicting Load Harmonics in Three Phase Systems Using Neural Networks

This paper proposes a artificial neural network (ANN) based method for the problem of measuring the actual harmonic current injected into a power system network by three phase nonlinear loads without disconnecting any loads from the network. The ANN directly estimates or identifies the nonlinear admittance (or impedance) of the load by using the measured values of voltage and current waveforms. The output of this ANN is a waveform of the current that the load would have injected into the network if the load had been supplied from a sinusoidal voltage source and is therefore a direct measure of load harmonics

Neural Network Based Method for Predicting Nonlinear Load Harmonics

Generation of harmonics and the existence of waveform pollution in power system networks are important problems facing the power utilities. The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. Interaction between loads and sources in a power distribution network is a complex process and often not possible to explain analytically without making assumptions. The determination of true harmonic current distortion of a load is further complicated by the fact that the supply voltage waveform at the point of common coupling (PCC) is rarely a pure sinusoid. This paper proposes a neural network based method to find a way of distinguishing between load contributed harmonics and supply harmonics, without disconnecting any load from the network. A neural network structure with memory is used to model the admittance of the nonlinear load. Once training is achieved, the neural network predicts the true harmonic current of the load if it could be supplied with a clean sine wave. The main advantage of this method is that only waveforms of voltages and currents have to be measured and is applicable for single phase as well as multiphase loads. This could be integrated into a commercially available power quality instrument or be fabricated as a standalone instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

A Novel Method for Predicting Harmonic Current Injection from Non-Linear Loads using Neural Networks

Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

Intelligent Tool for Determining the True Harmonic Current Contribution of a Customer in a Power Distribution Network

Customer loads connected to power distribution network may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence, any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on artificial neural networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this paper has a significant amount of triplen harmonics in the current. by processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion of a customer by simply measuring the customer\u27s current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single- and three-phase loads

Intelligent Tool for Determining the True Harmonic Current Contribution of a Customer in a Power Distribution Network

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on Artificial Neural Networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this study has a significant amount of triplen harmonics in the current. By processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion (THD) of a customer by simply measuring the customer\u27s current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single and three phase loads

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity

Background: Antivirals are needed to combat the COVID-19 pandemic, which is caused by SARS-CoV-2. The clinically-proven protease inhibitor Camostat mesylate inhibits SARS-CoV-2 infection by blocking the virus-activating host cell protease TMPRSS2. However, antiviral activity of Camostat mesylate metabolites and potential viral resistance have not been analyzed. Moreover, antiviral activity of Camostat mesylate in human lung tissue remains to be demonstrated. Methods: We used recombinant TMPRSS2, reporter particles bearing the spike protein of SARS-CoV-2 or authentic SARS-CoV-2 to assess inhibition of TMPRSS2 and viral entry, respectively, by Camostat mesylate and its metabolite GBPA. Findings: We show that several TMPRSS2-related proteases activate SARS-CoV-2 and that two, TMPRSS11D and TMPRSS13, are robustly expressed in the upper respiratory tract. However, entry mediated by these proteases was blocked by Camostat mesylate. The Camostat metabolite GBPA inhibited recombinant TMPRSS2 with reduced efficiency as compared to Camostat mesylate. In contrast, both inhibitors exhibited similar antiviral activity and this correlated with the rapid conversion of Camostat mesylate into GBPA in the presence of serum. Finally, Camostat mesylate and GBPA blocked SARS-CoV-2 spread in human lung tissue ex vivo and the related protease inhibitor Nafamostat mesylate exerted augmented antiviral activity. Interpretation: Our results suggest that SARS-CoV-2 can use TMPRSS2 and closely related proteases for spread in the upper respiratory tract and that spread in the human lung can be blocked by Camostat mesylate and its metabolite GBPA