8 research outputs found

    On Deep Machine Learning Based Techniques for Electric Power Systems

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    This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. The the delays originate from software (response time delay) and hardware (reaction time delay). To reduce the response time delays of APFs, this thesis propose and investigate several different techniques. First a technique based on multiple synchronous reference frame (MSRF) and order-optimized exponential smoothing (ES) to decrease the settling time delay of lowpass filtering steps. To reduce the computational time, this method is implemented in a parallel processing using a graphics processing unit (GPU) to estimate the time-varying harmonics and interharmonics of currents. Furthermore, the MSRF and three machine learning-based solutions are developed to predict future values of voltage and current in electric power systems which can mitigate the effects of the response and reaction time delays of the APFs. In the first and second solutions, a Butterworth filter is used to lowpass filter the\ua0 dq\ua0 components, and linear prediction and long short-term memory (LSTM) are used to predict the filtered\ua0 dq\ua0 components. The third solution is an end-to-end ML-based method developed based on a combination of convolutional neural networks (CNN) and LSTM. The Simulink implementation of the proposed ML-based APF is carried out to compensate for the current waveform harmonics, voltage dips, and flicker in Simulink environment embedded AI computing system Jetson TX2.\ua0In another study, we propose Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) method to replace the controller loops and estimation blocks such as PID, MSRF, and lowpass filters in grid-forming inverters. In a conventional approach it is well recognized that the controller tuning in the differen loops are difficult as the tuning of one loop influence the performance in other parts due to interdependencies.In DDPG the control policy is derived by optimizing a reward function which measure the performance in a data-driven fashion based on extensive experiments of the inverter in a simulation environment.\ua0Compared to a PID-based control architecture, the DDPG derived control policy leads to a solution where the response and reaction time delays are decreased by a factor of five in the investigated example.\ua0Classification of voltage dips originating from cable faults is another topic addressed in this thesis work. The Root Mean Square (RMS) of the voltage dips is proposed as preprocessing step to ease the feature learning for the developed\ua0 LSTM based classifier. Once a cable faults occur, it need to be located and repaired/replaced in order to restore the grid operation. Due to the high importance of stability in the power generation of renewable energy sources, we aim to locate high impedance cable faults in DC microgrid clusters which is a challenging case among different types of faults. The developed Support Vector Machine (SVM) algorithm process the maximum amplitude and\ua0 di/dt\ua0 of the current waveform of the fault as features, and the localization task is carried out with\ua0 95 %\ua0 accuracy.\ua0Two ML-based solutions together with a two-step feature engineering method are proposed to classify Partial Discharges (PD) originating from pulse width modulation (PWM) excitation in high voltage power electronic devices. As a first step, maximum amplitude, time of occurrence, area under PD curve, and time distance of each PD are extracted as features of interest. The extracted features are concatenated to form patterns for the ML algorithms as a second step. The suggested feature classification using the proposed ML algorithms resulted in\ua0 95.5 %\ua0 and\ua0 98.3 %\ua0\ua0 accuracy on a test data set using ensemble bagged decision trees and LSTM networks

    Distributed Control and Advanced Modulation of Cascaded Photovoltaic-Battery Converter Systems

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    DFT-based Synchrophasor Estimation Algorithms and their Integration in Advanced Phasor Measurement Units for the Real-time Monitoring of Active Distribution Networks

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    The increasing penetration of Distributed Energy Resources (DERs) at the low and medium-voltage levels is determining major changes in the operational procedures of distribution networks (DNs) that are evolving from passive to active power grids. Such evolution is causing non-negligible problems to DN operators (DNOs) and calls for advanced monitoring infrastructures composed by distributed sensing devices capable of monitoring voltage and current variations in real-time. In this respect, Phasor Measurement Units (PMUs) definitely represent one of the most promising technologies. Their higher accuracy and reporting rates compared to standard monitoring devices, together with the possibility of reporting time-tagged measurements of voltage and current phasors, enable the possibility to obtain frequent and accurate snapshots of the status of the monitored grid. Nevertheless, the applicability of such technology to DNs has not been demonstrated yet since PMUs where originally conceived for transmission network applications. Within this context, this thesis first discusses and derives the requirements for PMUs expected to operate at power distribution level. This study is carried out by analyzing typical operating conditions of Active Distribution Networks (ADNs). Then, based on these considerations, an advanced synchrophasor estimation algorithm capable of matching the accuracy requirements of ADNs is formulated. The algorithm, called iterative-interpolated DFT (i-IpDFT) improves the performances of the Interpolated-DFT (IpDFT) method by iteratively compensating the effects of the spectral interference produced by the negative image of the spectrum and at the same time allows to reduce the window length up to two periods of a signal at the nominal frequency of the power system. In order to demonstrate the low computational complexity of such an approach, the developed algorithm has been subsequently optimized to be deployed into a dedicated FPGA-based PMU prototype. The influence of the PMU hardware components and particularly the effects of the stability and reliability of the adopted UTC-time synchronization technology have been verified. The PMU prototype has been metrologically characterized with respect to the previously defined operating conditions of ADNs using a dedicated PMU calibrator developed in collaboration with the Swiss Federal Institute of Metrology (METAS). The experimental validation has verified the PMU compliance with the class-P requirements defined in the IEEE Std. C37.118 and with most of the accuracy requirements defined for class-M PMUs with the exception of out of band interference tests

    Real-Time Detection of Interharmonics and Harmonics of AC Electric Arc Furnaces on GPU Framework

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    In this paper, a method based on the multiple synchronous reference frame (MSRF) analysis is recommended and implemented to detect time-varying harmonics and interharmonics of rapidly fluctuating asymmetrical industrial loads. The experimental work has been carried out on a typical three-phase alternating current arc furnace (AC EAF) installation. In the recommended method, the reference frame is rotated in both directions at speeds corresponding to the positive and negative sequences of all harmonics and all interharmonics 5-Hz apart. To extract the direct current (DC) components of the transformed d-q quantities, a low-pass filter is employed. In order to keep the delay of the filter at zero frequency less than a few ms, Kalman estimation technique has been used. Back transformation is then applied for each harmonic and interharmonic component to obtain their positive- and negative sequences of the associated harmonic and interharmonic in the actual line current waveforms. Parallel computing technique has been applied for the real-time detection of both the phase and the amplitude of all harmonics and interharmonics. This is achieved on NVDIA Jetson TX1 graphics processing unit (GPU) framework for a sample industrial plant. The developed system is shown to be useful for fast and accurate generation of reference signals for the controllers of the advanced technology power conditioning systems which successfully compensates interharmonics, harmonics, and flicker of the rapidly fluctuating nonlinear industrial loads

    Real-Time Detection of Interharmonics and Harmonics of AC Electric Arc Furnaces on GPU Framework

    No full text
    In this paper, a method based on the multiple synchronous reference frame analysis is recommended and implemented to detect time-varying harmonics and interharmonics of rapidly fluctuating asymmetrical industrial loads. The experimental work has been carried out on a typical three-phase alternating current arc furnace installation. In the recommended method, the reference frame is rotated in both directions at speeds corresponding to the positive and negative sequences of all harmonics and all interharmonics 5 Hz apart. To extract the direct current components of the transformed d-q quantities, a low-pass filter is employed. In order to keep the delay of the filter at zero frequency less than a few ms, Kalman estimation technique has been used. Back transformation is then applied for each harmonic and interharmonic component to obtain their positive- and negative-sequences of the associated harmonic and interharmonic in the actual line current waveforms. Parallel computing technique has been applied for the real-time detection of both the phase and the amplitude of all harmonics and interharmonics. This is achieved on NVDIA Jetson TX1 graphics processing unit framework for a sample industrial plant. The developed system is shown to be useful for fast and accurate generation of reference signals for the controllers of the advanced technology power conditioning systems which successfully compensates interharmonics, harmonics, and flicker of the rapidly fluctuating nonlinear industrial loads

    Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting

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    At the EEDAL'15 conference 128 papers dealing with energy consumption and energy efficiency improvements for the residential sector have been presented. Papers focused policies and programmes, technologies and consumer behaviour. Special focus was on standards and labels, demand response and smart meters. All the paper s have been peer reviewed by experts in the sector.JRC.F.7-Renewables and Energy Efficienc

    2012 Annual Progress Report: DOE Hydrogen and Fuel Cells Program

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