Comparison of two three-phase pll systems for more electric aircraft

The More Electric Aircraft power system is characterized by variable supply frequency, in general between 360Hz and 900Hz. All equipment on board the aircraft have to operate delivering high performance under this variable frequency condition. In particular, power electronic converters need accurate control algorithms able to track the fundamental phase and frequency in real time, both in normal and unusual conditions. Phase Locked Loop (PLL) based algorithms are commonly used in traditional single and three phase power systems to provide phase and frequency estimations of the supply. Despite the simplicity of those algorithms, large estimation errors can arise when power supply voltage has variable frequency or amplitude, presents unbalances or is polluted with harmonics. To improve the quality of the phase and frequency real-time estimations, a robust PLL algorithm, based on a prediction-correction filter, is presented in this paper and compared with a Discrete Fourier Transform (DFT) based procedure. The performances of the two algorithms, implemented in a floating-point DSP, have been compared through an experimental validation obtained on a laboratory power converter prototype

A novel control technique for active shunt power filters for aircraft applications

The More Electric Aircraft is a technological trend in modern aerospace industry to increasingly use electrical power on board the aircraft in place of mechanical, hydraulic and pneumatic power to drive aircraft subsystems. This brings major changes to the aircraft electrical system, increasing the complexity of the network topology together with stability and power quality issues. Shunt active power filters are a viable solution for power quality enhancement, in order to comply with the standard recommendations. The aircraft electrical system is characterized by variable supply frequency in the range 360-900Hz, hence the harmonic components occur at high and variable frequencies, compared to the terrestrial 50/60Hz systems. In this kind of system, fast and accurate algorithms for the detection of the reference signal for the active filter control and robust high-bandwidth control techniques are needed, in order for the active filter to perform the harmonic elimination successfully. In this thesis, two novel algorithms are proposed. The first algorithm is a frequency and harmonic detection technique, particularly suitable for tracking the variable supply frequency and the harmonic components of voltages and currents in the aircraft electrical system. Complete identication of the reference signal for the active filter control is possible when applying this technique. The second algorithm is a control technique based on the use of multiple rotating reference frames. Only the measurement of the voltage at the Point of Common Coupling and the active filter output current are needed, hence no current sensors are required on the distorting loads. Both the techniques have been validated by means of simulation and experimental analysis. The results show that the proposed methods are effective for a successful harmonic compensation by means of active shunt filters, in the More Electric Aircraft environment

A Novel Repetitive Controller Assisted Phase-Locked Loop with Self-Learning Disturbance Rejection Capability for Three-Phase Grids

The synchronization between the power grid and distributed power sources is a crucial issue in the concept of smart grids. For tracking the real-time frequency and phase of three-phase grids, phase-locked loop (PLL) technology is commonly used. Many existing PLLs with enhanced disturbance/harmonic rejection capabilities, either fail to maintain fast response or are not adaptive to grid frequency variations or have high computational complexity. This article, therefore, proposes a low computational burden repetitive controller (RC) assisted PLL (RCA-PLL) that is not only effective on harmonic rejection but also has remarkable steady-state performance while maintaining fast dynamic. Moreover, the proposed PLL is adaptive to variable frequency conditions and can self-learn the harmonics to be canceled. The disturbance/harmonic rejection capabilities together with dynamic and steady-state performances of the RCA-PLL have been highlighted in this article. The proposed approach is also experimentally compared to the synchronous rotation frame PLL (SRF-PLL) and the steady-state linear Kalman filter PLL (SSLKF-PLL), considering the effect of harmonics from the grid-connected converters, unbalances, sensor scaling errors, dc offsets, grid frequency variations, and phase jumps. The computational burden of the RCA-PLL is also minimized, achieving an experimental execution time of only 12 μs

Detection of oscillatory actuator failures in passenger airliners

Thesis (MEng)--Stellenbosch University, 2019.ENGLISH ABSTRACT: This project investigates and develops techniques to detect oscillatory failure cases (OFCs) in aircraft control surface actuators. Oscillatory failures induce additional loads on the structure of the aircraft, requiring additional structural support to withstand these loads, increasing the overall mass of the aircraft. If oscillatory failures can be detected and pacified quickly, then the additional structural support would not be required, and the mass of the aircraft can be reduced, resulting in improved fuel efficiency and aircraft performance. Oscillatory failure case (OFC) detection is performed by evaluating the difference (residual) between the measured behaviour of the real actuator and the simulated behaviour of a fault-free analytically redundant actuator model running in parallel with the real actuator. An OFC detection system must generate a residual signal using the analytically redundant actuator model, and evaluate the residual signal to determine whether an oscillatory failure is present. The challenge for the residual evaluation stage is to distinguish between the components of the residual signal resulting from modelling uncertainty and sensor noise, and the components resulting from an actual oscillatory failure case. The OFC detection system must detect oscillatory failures within a maximum allowable detection time, but must not produce false alarms. Five different oscillatory failure detection techniques are investigated and developed, namely oscillation counting, integrated absolute error (IAE), discrete Fourier transform (DFT), multi-window Fourier transform (MWFT), and phase-locked loop (PLL) detection. Oscillation counting is an existing OFC detection technique that was developed by Goupil [1] and is currently in service on the Airbus A380 passenger airliner. The other four techniques are new OFC detection techniques that are developed in this project. A simulation framework is created to serve as a testbed for the training and testing of the different OFC detection techniques. The simulation framework contains models for the physical actuator, the analytically redundant actuator, the oscillatory failures (both liquid and solid failures), the flight control system, and the aircraft longitudinal dynamics. The simulation models the aircraft’s response to an oscillatory failure, since it affects the performance of the OFC detection. The five OFC detection techniques are trained and rigorously tested using training and testing data generated with the simulation framework. The detection thresholds for each technique are “trained” on fault-free data to determine the lowest detection thresholds that do not produce false alarms. The detection techniques are then tested using testing data to determine the smallest amplitude oscillatory failure that each technique can detect within the specified maximum allowable detection time. The number of false alarms for each technique is also determined. The results show that DFT, MWFT, and the PLL outperform oscillation counting and IAE by detecting smaller amplitude oscillatory failures and with shorter detection times, with MWFT providing the most promising results. However, oscillation counting and IAE are the most computationally efficient techniques, while DFT, MWFT, and PLL are more computationally expensive. Overall, the multi-window Fourier transform (MWFT) technique is the recommended approach for OFC detection, offering the best detection performance with only a small increase in computational complexity.AFRIKAANSE OPSOMMING: Hierdie projek ondersoek en ontwikkel tegnieke om ossillatoriese faling gevalle (OFGs) in vliegtuig beheeroppervlak aktueerders te bespeur. Ossillatoriese falings induseer bykomende ladings op die struktuur van die vliegtuig, en vereis dus bykomende strukturele ondersteuning om hierdie ladings te weerstaan, wat die algehele massa van die vliegtuig verhoog. Indien ossillatoriese falings bespeur en vinnig gepassifiseer kan word, dan sou die bykomende strukturele ondersteuning nie benodig word nie, en die massa van die vliegtuig sou verminder kon word, wat sou lei tot verbeterde brandstofverbruik en werkverrigting. Ossillatoriese faling geval (OFG) bespeuring word uitgevoer deur die verskil (residu) te evalueer tussen die gemete gedrag van die werklike aktueerder en die gesimuleerde gedrag van ’n foutvrye analities-oortollige aktueerder model wat in parallel met die werklike aktueerder uitvoer. ’n OFG bespeuringstelsel moet ’n residu sein genereer deur gebruik te maak van die analitiese-oortollige aktueerder model, en moet die residu evalueer om te bepaal of daar ’n ossillatoriese faling teenwoordig is. Die uitdaging vir die residu evaluasie stadium is om te onderskei tussen die komponente van die residu sein wat afkomstig is van model onsekerheid en sensor ruis, en die komponente wat afkomstig is van ’n werklike ossillatoriese faling geval. Die OFG bespeuringstelsel moet ossillatoriese falings bespeur binne ’n maksimum toelaatbare bespeuringstyd, en moet nie vals alarms gee nie. Vyf verskillende ossillatoriese faling bespeuringstegnieke word ondersoek en ontwikkel, naamlik ossillasie telling, geïntegreerde absolute fout (IAE), diskrete Fourier transform (DFT), multi-venster Fourier transform (MWFT), en fase-sluit lus (PLL) bespeuring. Ossillasie telling is ’n bestaande OFG bespeuring tegniek wat ontwikkel is deur Goupil [1] en word tans gebruik op die Airbus A380 passassiersvliegtuig. Die ander vier tegnieke is nuwe OFG bespeuringstegnieke wat ontwikkel is in hierdie projek. ’n Simulasie raamwerk is geskep om te dien as ’n toetsplatform vir die opleiding en toets van die verskillende OFG bespeuringstegnieke. Die simulasie raamwerk bevat modelle vir die fisiese aktueerder, die analities-oortollige aktueerder, die ossillatoriese falings (beide vloeibare en soliede falings), die vlugbeheerstelsel, en die vliegtuig se longitudinale vlugdinamika. Die simulasie modelleer die vliegtuig se reaksie op die ossillatoriese faling, aangesien dit die prestasie van die OFG bespeuring beïnvloed. Die vyf OFG bespeuringstegnieke is opgelei en volledig getoets deur gebruik te maak van opleiding en toets data wat genereer is met die simulasie raamwerk. Die bespeuring drempels vir elke tegniek is “opgelei” op foutvrye data om te bepaal wat die laagste bespeuringsdrempel is wat nie vals alarms gee nie. Die bespeuringstegnieke is dan getoets op toets data om te bepaal wat die kleinste amplitude ossillatoriese faling is wat elke tegniek kan bespeur binne die maksimum toelaatbare bespeuringstyd. Die aantal vals alarms vir elke tegniek is ook bepaal. Die resultate wys dat die DFT, MWFT, en PLL tegnieke oortref die ossillasie telling en IAE tegnieke deur kleiner amplitude ossillatoriese falings te bespeur in korter bespeuringstye, met die MWFT wat die mees belowende resultate lewer. Die ossillasie telling en IAE tegnieke bly egter die mees berekeningsdoeltreffende tegnieke, terwyl die DFT, NWFT, en PLL meer berekeningskoste dra. Algeheel, word die multi-venster Fourier transform (MWFT) tegniek aanbeveel as die voorkeurtegniek, omdat dit die beste bespeuringsprestasie bied met net ’n klein verhoging in berekeningskoste

On Deep Machine Learning Based Techniques for Electric Power Systems

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

Performance Improvement of Wide-Area-Monitoring-System (WAMS) and Applications Development

Wide area monitoring system (WAMS), as an application of situation awareness, provides essential information for power system monitoring, planning, operation, and control. To fully utilize WAMS in smart grid, it is important to investigate and improve its performance, and develop advanced applications based on the data from WAMS. In this dissertation, the work on improving the WAMS performance and developing advanced applications are introduced.To improve the performance of WAMS, the work includes investigation of the impacts of measurement error and the requirements of system based on WAMS, and the solutions. PMU is one of the main sensors for WAMS. The phasor and frequency estimation algorithms implemented highly influence the performance of PMUs, and therefore the WAMS. The algorithms of PMUs are reviewed in Chapter 2. To understand how the errors impact WAMS application, different applications are investigated in Chapter 3, and their requirements of accuracy are given. In chapter 4, the error model of PMUs are developed, regarding different parameters of input signals and PMU operation conditions. The factors influence of accuracy of PMUs are analyzed in Chapter 5, including both internal and external error sources. Specifically, the impacts of increase renewables are analyzed. Based on the analysis above, a novel PMU is developed in Chapter 6, including algorithm and realization. This PMU is able to provide high accurate and fast responding measurements during both steady and dynamic state. It is potential to improve the performance of WAMS. To improve the interoperability, the C37.118.2 based data communication protocol is curtailed and realized for single-phase distribution-level PMUs, which are presented in Chapter 7.WAMS-based applications are developed and introduced in Chapter 8-10. The first application is to use the spatial and temporal characterization of power system frequency for data authentication, location estimation and the detection of cyber-attack. The second application is to detect the GPS attack on the synchronized time interval. The third application is to detect the geomagnetically induced currents (GIC) resulted from GMD and EMP-E3. These applications, benefited from the novel PMU proposed in Chapter 6, can be used to enhance the security and robust of power system

A new method for analysis of signal harmonic distortion byevaluation of power quality

Naučna rasprava izložena u ovoj tezi bavi se analizom kvaliteta električne energije. Visok nivo električne energije podrazumeva da su napon napajanja i struja potrošača idealne sinusoide sa tačno određenom amplitudom i učestanošću. Bilo kakva odstupanja od idealnog nazivaju se izobličenja i najčešće se karakterišu sa harmonicima. Poslednjih godina dolazi do naglog razvoja poluprovodničkih komponenata. Takve komponente su uticale na ubrzan razvoj snažnih uređaja energetske elektronike. Ti uređaji su nelinearnog karaktera, što dovodi do pojave harmonika u signalima napona i struja elektroenergetskog sistema. Prvi problem kojim se bavi ova teza je analiza talasnih oblika struja ispravljača. Metode primenjene za analizu su wavelet transformacija (VT) i modulated overlapped transformacija (MLT). MLT nadoknađuje nedostatak VT da dekomponuje signal u odgovarajuće podopsege koji mogu sadržati i više harmonika i daje tačnu informaciju o svakom harmoniku. Obe metode su pogodne za offline analizu. Za online analizu predložen je hibridni metod baziran na diskretnoj Furijeovoj transformaciji (DFT) i adaptivnom pojasnom filteru (EPLL). Hibridni metod je zadržao dinamički odziv DFT-a, dok je EPLL obezbedio sinhronizaciju sa osnovnom učestanošću sistema. Hibridni metod daje dovoljno tačnu informaciju o osnovnom i višim harmonicima samo ako su njihove učestanosti ceolobrojni umnožak učestanosti osnovnog harmonika. U slučaju pojave interharmonika, odnosno kada taj odnos više ne važi, hibridni metod ne daje tačne rezultate. Za analizu takvih signala predložen je novi metod, koji je baziran na adaptivnom diskretnom pojasnom filteru (ANF) t.j. metod koristi diskretni pojasni filter za modelovanje harmonijskih komponenata u ulaznom signalu, dok se prošireni Kalmanov filter (EKF) koristi kao adaptivni mehanizam. Novi metod je preuzeo osobinu ANF-a da može adaptivno da prati promene učestanosti i osobinu EKF-a da ima bolji dinamički odziv. Metode su implementirane na digitalnom procesoru za obradu signala i upoređene sa postojećim metodama. Metode pokazuju prednosti u odnosu na druge metode.Scientific research in this thesis discusses power quality analysis. High power quality assumes that both the voltage power supply and the load current are ideal sinusoidal signals with a precisely defined amplitude and frequency. Any deviations from this ideal vaweform are considered as distortion and are characterised by harmonics. Over the last few decades, there has been a rapid development of semiconductor components. Such components made an impact on the fast development of power electronics devices. These devices are nonlinear, introducing harmonics in both voltage and current of the power grid. The first issue researched in this thesis is the analysis of the rectifier voltage and current waveforms. Methods used for the analysis are the wavelet transform (WT) and the modulated overlapped transform (MLT). The MLT overcomes the drawback of the WT, which decomposes the signal into subbands that can contain more harmonics, and gives accurate information about every harmonic. Both methods are suitable for offline analysis. For online analysis, a hybrid method is proposed, based on the discrete Fourier transform (DFT) and the adaptive notch filter (EPLL). The hybrid method retains a good dynamic response of the DFT whereas the EPLL provides a synchronisation with the fundamental system frequency. The hybrid method provides accurate information on the fundamental and the higher harmonics only if their frequencies are integer multiples of the fundamental frequency. In the case of interharmonics, i.e. when this integer ratio is not valid, the hybrid method does not provide accurate results. In order to analyse such signals, a new method is proposed. It is based on discrete adaptive notch filter (ANF), i.e. the method uses a discrete notch filter for modeling the harmonic components in the input signal, whereas an Extended Kalman Filter (EKF) is used as an adaptation algorithm. The adaptive notch Kalman filter inherited the property of the ANF to adaptively track changes in the frequency and the property of the EKF to have a faster dynamic response. Methods have been implemented in a digital signal processor and compared with the existing ones. The methods show advantages compared to other methods

Alternative Doppler Extraction for Indoor Communication Signals

Radar is a common detection system for detecting the speed and velocity of moving objects. Radar is performed by transmitting a radio signal in the direction of a moving object and then processing the received reflected radio waves. The radar system processes the received signal to extract the Doppler frequency of the reflected waves, which reveals information about the velocity of the object. In traditional radar, the system uses one antenna for both the transmitter and the receiver. In passive bistatic radar, the transmit antenna and receiver antenna are separated. In addition, the radar system has no control over the transmitter. The transmitted signal is instead a signal of opportunity. Signals of opportunity include FM and AM radio signals, satellite GPS signals, and WiFi signals. The receiver in the radar system receives a signal of opportunity and extracts the Doppler frequency from the signal. The Doppler frequency can then be used to get the velocity of the object, which can be used for tracking the object. In previous research, the methods of traditional radar were applied to passive bistatic radar using a WiFi signal in order to track a moving object within a building. Traditional radar methods are to compare the Doppler-shifted signal to the original signal. In passive radar, the original signal needs to be measured without being affected by the Doppler-shifted signal, which requires a second antenna to be placed by the transmitter. The research presented suggests a new method of retrieving the Doppler frequencies from a communication signal. This new method does not require a separate antenna to get the original signal but uses knowledge about the communication signal to successfully measure the Doppler frequency on the signal. The system was implemented using GNURadio, a software-defined radio library. Additional processing was done using MATLAB

Review of fundamental active current extraction techniques for SAPF

The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.Web of Science2220art. no. 798