Hilbert Based Testing of ADC Differential Non-linearity Using Wavelet Transform Algorithms

In testing Mixed Signal Devices such as Analog to Digital and Digital to Analog Converters, some dynamic parameters, such as Differential Non-Linearity and Integral Non-linearity, are very critical to evaluating devises performance. However, such analysis has been notorious for complexity and massive compiling process. Therefore, this research will focus on testing dynamic parameters such as Differential Non-Linearity by simulating numerous numbers of bits Analog to Digital Converters and test the output signals base on new testing algorithms of Wavelet transform based on Hilbert process. Such a new testing algorithm should enhance the testing process by using less compiling data samples and prompt testing results. In addition, new testing results will be compared with the conventional testing process of Histogram algorithms for accuracy and enactment

Impact of the noise on the emulated grid voltage signal in hardware-in-the-loop used in power converters

This work evaluates the impact of the input voltage noise on a Hardware-In-the-Loop (HIL) system used in the emulation of power converters. A poor signal-to-noise ratio (SNR) can compromise the accuracy and precision of the model, and even make certain techniques for building mathematical models unfeasible. The case study presents the noise effects on a digitally controlled totem-pole converter emulated with a low-cost HIL system using an FPGA. The effects on the model outputs, and the cost and influence of different hardware implementations, are evaluated. The noise of the input signals may limit the benefits of increasing the resolution of the model.This research was funded by the Spanish Ministry of Science and Innovation under Project PID2021-128941OB-I00 TRENTI–Efficient Energy Transformation in Industrial Environment

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Frequency-domain characterization of random demodulation analog-to-information converters

The paper aims at proposing test methods for Analog-to-Information Converters (AICs).In particular, the objective of this work is to verify if figures of merit and test methods, currently defined in standards for traditional Analog-to-Digital Converters, can be applied to AICs based on the random demodulation architecture.For this purpose, an AIC prototype has been designed, starting from commercially available integrated circuits. A simulation analysis and an experimental investigation have been carried out to study the additional influencing factors such as the parameters of the reconstruction algorithm. Results show that standard figures of merit are in general capable of describing the performance of AICs, provided that they are slightly modified according to the proposals reported in the paper. In addition, test methods have to be modified in order to take into account the statistical behavior of AICs.</p

A novel electric power quality monitoring system for transient analysis

Electricity is vital for our daily life in modern cites. In order to ensure its reliability and supply, an electric power monitoring system is indispensable in an electric power system. Currently, most electric power monitoring systems are designed for steady-state monitoring only. They may not be able to monitor instantaneous power disturbances, such as voltage surge, happened in electric power systems. In fact, instantaneous power disturbances are frequently found in electric power systems, which result in equipment failures and cause financial losses. Therefore, a novel electric power monitoring system is proposed in this thesis. Besides traditional functions, the proposed system is capable of monitoring and analyzing instantaneous power disturbances in electric power systems. Novelties of the proposed monitoring system are in the following three major aspects. Firstly, the proposed system is capable of monitoring instantaneous power disturbances. Unlike traditional monitoring systems, the proposed system captures not only statistical power quantities (e.g. kW, kWh), but also voltage and current waveforms. Since a considerable communication network bandwidth is required to transmit electric waveforms in a remote monitoring system, a novel waveform compression algorithm is proposed to realize real-time electric power waveform monitoring on low-speed communication networks (e.g. Zigbee). Secondly, the proposed system is capable of identifying various kinds of power disturbances automatically. It relieves electrical engineers from manned disturbance identification on preserved waveforms. Unlike traditional disturbance identification algorithms, the proposed system can identify not only voltage disturbances, but also current disturbances. Hence, it can provide a better chance in identifying more problems and disturbances in electric power systems. Thirdly, a novel time-frequency analysis method is proposed to analyze preserved waveforms. The proposed method is an improvement to the well-known Discrete Wavelet Packet Transform (DWPT). DWPT has been used by researchers and engineers to analyze disturbances and harmonics in electric power systems. However, DWPT is subjected to a non-uniform leakage problem, which has been discussed intensively in many studies. In order to tackle this issue, a frequency shifting scheme is introduced in the proposed method. A prototype has been implemented to demonstrate the feasibility of the proposed electric power monitoring system. There are two major components – a prototype meter and a central monitoring system. The performance of the prototype has been evaluated by conducting experiments and field tests. The capability of the proposed system for realtime remote monitoring has been verified on Zigbee network, which is a low-power, low speed wireless communication network

Development and application of synchronized wide-area power grid measurement

Phasor measurement units (PMUs) provide an innovative technology for real-time monitoring of the operational state of entire power systems and significantly improve power grid dynamic observability. This dissertation focuses on development and application of synchronized power grid measurements. The contributions of this dissertation are as followed:First, a novel method for successive approximation register analog to digital converter control in PMUs is developed to compensate for the sampling time error caused by the division remainder between the desirable sampling rate and the oscillator frequency. A variable sampling interval control method is presented by interlacing two integers under a proposed criterion. The frequency of the onboard oscillator is monitored in using the PPS from GPS.Second, the prevalence of GPS signal loss (GSL) on PMUs is first investigated using real PMU data. The correlation between GSL and time, spatial location, solar activity are explored via comprehensive statistical analysis. Furthermore, the impact of GSL on phasor measurement accuracy has been studied via experiments. Several potential solutions to mitigate the impact of GSL on PMUs are discussed and compared.Third, PMU integrated the novel sensors are presented. First, two innovative designs for non-contact PMUs presented. Compared with conventional synchrophasors, non-contact PMUs are more flexible and have lower costs. Moreover, to address nonlinear issues in conventional CT and PT, an optical sensor is used for signal acquisition in PMU. This is the first time the utilization of an optical sensor in PMUs has ever been reported.Fourth, the development of power grid phasor measurement function on an Android based mobile device is developed. The proposed device has the advantages of flexibility, easy installation, lower cost, data visualization and built-in communication channels, compared with conventional PMUs.Fifth, an identification method combining a wavelet-based signature extraction and artificial neural network based machine learning, is presented to identify the location of unsourced measurements. Experiments at multiple geographic scales are performed to validate the effectiveness of the proposed method using ambient frequency measurements. Identification accuracy is presented and the factors that affect identification performance are discussed

Characterization of DC series arc faults in PV systems based on current low frequency spectral analysis

This work presents an experimental study focused on the characterization of series arc faults in direct current (DC) photovoltaic (PV) systems. The aim of the study is to identify some relevant characteristics of arcing current, which can be obtained by means of low frequency spectral analysis of current signal. On field tests have been carried out on a real PV system, in accordance with some tests requirements of UL 1699B Standard for protection devices against PV DC arc faults. Arcing and non-arcing current signals are acquired and compared and the behavior of a set of indicators proposed by authors is analyzed. Different measurement equipment have been used, in order to study the impact of both measurement transducers and data acquisition systems on proposed indicators effectiveness. Presented results show that the considered indicators are suitable for detecting the arc presence even with commercial devices normally used for smart metering applications

Power Quality

Electrical power is becoming one of the most dominant factors in our society. Power generation, transmission, distribution and usage are undergoing signifi cant changes that will aff ect the electrical quality and performance needs of our 21st century industry. One major aspect of electrical power is its quality and stability – or so called Power Quality. The view on Power Quality did change over the past few years. It seems that Power Quality is becoming a more important term in the academic world dealing with electrical power, and it is becoming more visible in all areas of commerce and industry, because of the ever increasing industry automation using sensitive electrical equipment on one hand and due to the dramatic change of our global electrical infrastructure on the other. For the past century, grid stability was maintained with a limited amount of major generators that have a large amount of rotational inertia. And the rate of change of phase angle is slow. Unfortunately, this does not work anymore with renewable energy sources adding their share to the grid like wind turbines or PV modules. Although the basic idea to use renewable energies is great and will be our path into the next century, it comes with a curse for the power grid as power fl ow stability will suff er. It is not only the source side that is about to change. We have also seen signifi cant changes on the load side as well. Industry is using machines and electrical products such as AC drives or PLCs that are sensitive to the slightest change of power quality, and we at home use more and more electrical products with switching power supplies or starting to plug in our electric cars to charge batt eries. In addition, many of us have begun installing our own distributed generation systems on our rooft ops using the latest solar panels. So we did look for a way to address this severe impact on our distribution network. To match supply and demand, we are about to create a new, intelligent and self-healing electric power infrastructure. The Smart Grid. The basic idea is to maintain the necessary balance between generators and loads on a grid. In other words, to make sure we have a good grid balance at all times. But the key question that you should ask yourself is: Does it also improve Power Quality? Probably not! Further on, the way how Power Quality is measured is going to be changed. Traditionally, each country had its own Power Quality standards and defi ned its own power quality instrument requirements. But more and more international harmonization efforts can be seen. Such as IEC 61000-4-30, which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce of measurement instruments so that they can also be used in volume applications and even directly embedded into sensitive loads. But work still has to be done. We still use Power Quality standards that have been writt en decades ago and don’t match today’s technology any more, such as fl icker standards that use parameters that have been defi ned by the behavior of 60-watt incandescent light bulbs, which are becoming extinct. Almost all experts are in agreement - although we will see an improvement in metering and control of the power fl ow, Power Quality will suff er. This book will give an overview of how power quality might impact our lives today and tomorrow, introduce new ways to monitor power quality and inform us about interesting possibilities to mitigate power quality problems. Regardless of any enhancements of the power grid, “Power Quality is just compatibility” like my good old friend and teacher Alex McEachern used to say. Power Quality will always remain an economic compromise between supply and load. The power available on the grid must be suffi ciently clean for the loads to operate correctly, and the loads must be suffi ciently strong to tolerate normal disturbances on the grid

Digitally-Assisted Mixed-Signal Wideband Compressive Sensing

Digitizing wideband signals requires very demanding analog-to-digital conversion (ADC) speed and resolution specifications. In this dissertation, a mixed-signal parallel compressive sensing system is proposed to realize the sensing of wideband sparse signals at sub-Nqyuist rate by exploiting the signal sparsity. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) front-end, which not only can filter out the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. Moreover, the signal randomization in the system is able to spread the spurious energy due to ADC nonlinearity along the signal bandwidth rather than concentrate on a few frequencies as it is the case for a conventional ADC. This important new property relaxes the ADC SFDR requirement when sensing frequency-domain sparse signals. The mixed-signal compressive sensing system performance is greatly impacted by the accuracy of analog circuit components, especially with the scaling of CMOS technology. In this dissertation, the effect of the circuit imperfection in the mixed-signal compressive sensing system based on the PSCS front-end is investigated in detail, such as the finite settling time, the timing uncertainty and so on. An iterative background calibration algorithm based on LMS (Least Mean Square) is proposed, which is shown to be able to effectively calibrate the error due to the circuit nonideal factors. A low-speed prototype built with off-the-shelf components is presented. The prototype is able to sense sparse analog signals with up to 4 percent sparsity at 32 percent of the Nqyuist rate. Many practical constraints that arose during building the prototype such as circuit nonidealities are addressed in detail, which provides good insights for a future high-frequency integrated circuit implementation. Based on that, a high-frequency sub-Nyquist rate receiver exploiting the parallel compressive sensing is designed and fabricated with IBM90nm CMOS technology, and measurement results are presented to show the capability of wideband compressive sensing at sub-Nyquist rate. To the best of our knowledge, this prototype is the first reported integrated chip for wideband mixed-signal compressive sensing. The proposed prototype achieves 7 bits ENOB and 3 GS/s equivalent sampling rate in simulation assuming a 0.5 ps state-of-art jitter variance, whose FOM beats the FOM of the high speed state-of-the-art Nyquist ADCs by 2-3 times. The proposed mixed-signal compressive sensing system can be applied in various fields. In particular, its applications for wideband spectrum sensing for cognitive radios and spectrum analysis in RF tests are discussed in this work