Distributed Grid Analytics Platform (DGAP) for power grid monitoring at the distribution level

Phasor measurement units (PMUs) which measure electrical waves with real-time synchronization at widely spread points across the power grid over great benefits. While the PMU device concept is well known in the power industry, the field of power system analysis stands to benefit greatly from using different methods of designing and implementing an inexpensive PMU that can be widely and densely distributed on the grid. Traditional PMUs are mainly installed at the transmission level, where they are hard to install and maintain, and can be expensive due to the rating requirements of the components. Given their benefits and increasingly widespread installation, easier-to-maintain and less costly PMUs are desired. In 2000, frequency disturbance recorders (FDRs), which are single-phase PMUs that monitor the power grid at the 120 V distribution level, were operated for the Frequency monitoring Network (FNET) project by Virginia Tech and the University of Tennessee. While installing FDRs at the low-voltage distribution level of the power grid was a great step toward reducing the cost and limitations of PMU use, there are still drawbacks and significant room for improvement: the sampling frequency is low at 1440 samples per second (SPS), there is no auxiliary power supply to support the device during an atypical power grid event, and the USD 2000 price can be driven lower. This thesis introduces the Distributed Grid Analytics Platform (DGAP) which has a higher sampling rate (20k SPS), a backup power supply, smaller size, and much lower cost (USD 200) while keeping the functionality of the FDRs including accurate data acquisition, GPS time synchronization, internet connectivity, and open source data upload. The improvements were realized by a more succinct approach for the system design and more updated component selection, which will be explained in this thesis. The designed DGAPs were built into prototypes and tested in household power outlets, experimentally validating their functionality

Design and construction of an autonomous race robot for Natcar Contest

A Natcar is an autonomous robot that uses magnetic and optical sensors to follow a track as fast as possible. The track is marked with a 1-inch wide tape and the wire carries a 100mA, 75 KHz sinusoidal signal. This project combines many different aspects of engineering including systems integration, mechatronics, digital design and analog design. A different approach was utilized in solving the most common problems that every person or team building a Natcar has to face. Instead of creating a traditional Natcar, every aspect of this project focused on improvement and aimed to create new techniques to change the way Natcars are built in the future. To achieve that, new technologies such as 3D printing and computer vision were employed.Natcar es un robot autónomo que usa sensores magnéticos y ópticos para navegar lo más rápido posible a lo largo de una pista. Dicha pista está marcada con una cinta blanca de una pulgada de ancho y bajo ella se sitúa un cable por el que circula una corriente eléctrica alterna de 100 mA y 75 KHz. Este proyecto combina diferentes aspectos y áreas de ingeniería tales como integración de sistemas, mecatrónica y diseño tanto analógico como digital. Esta vez se ha intentado hacer una apuesta diferente a como resolver los problemas más comunes que cualquier persona o equipo que esté construyendo un Natcar se va a encontrar. En vez de crear un Natcar tradicional, cada detalle de su diseño ha sido objetivo de mejora que pretende desarrollar nuevas técnicas que cambiaran la forma en la que los Natcars serán construidos en el futuro. Para conseguir esto, nuevas tecnologías tales como impresión en 3D y visión artificial han sido utilizadas en el proyecto.Ingeniería Electrónica Industrial y Automátic

Monitorización de señales biomédicas en sistemas Android

El propósito fundamental del proyecto es conseguir la lectura de muestras en tiempo real con unos plazos de captura de las mismas muy estrictos, con el fin de no perder ninguna muestra. El proyecto parte de una aplicación en espacio de usuario que presentaba unas pérdidas en la recepción de muestras de entre un 1 y un 2 % respecto al conjunto total de muestras enviadas originalmente. Se observó que si además de ejecutar esta aplicación en espacio de usuario, se ejecutaba otra tarea en paralelo, el número de muestras perdidas se disparaba. Era necesario realizar una descarga de la CPU. Para conseguirlo, se ha utilizado la Unidad Programable en Tiempo-real de la BeagleBone Black, que es un hardware específicamente diseñado para este tipo de operaciones, es decir, para realizar tareas sencillas en tiempo real; principalmente se programa en ensamblador y facilita la interacción entre el código ensamblador y una aplicación en espacio de usuario. Una vez implementada la solución con éxito, se evita la pérdida muestras en el proceso. Además, se consigue descargar la CPU, de manera que es posible ejecutar otras tareas de forma paralela. Finalmente, se consigue que la BeagleBone Black se comunique con una aplicación Android mediante Bluetooth. Esta aplicación facilita a un paciente la recogida de información de su electrocardiograma, para que posteriormente, o incluso en tiempo real, sea un médico el que trate de analizarla e interpretarla. Para que la comunicación entre médico y paciente sea posible, es necesario que ambos dispongan de conexión a internet, ya que la interacción se realiza a través de un servidor, el cual recibe la información de un dispositivo y la retransmite al otro

Modular Autopilot Design and Development Featuring Bayesian Non-parametric Adaptive Control

Over the last few decades, Unmanned Aircraft Systems, or UAS, have become a critical part of the defense of our nation and the growth of the aerospace sector. UAS have a great potential for the agricultural industry, first response, and ecological monitoring. However, the wide range of applications require many mission-specific vehicle platforms. These platforms must operate reliably in a range of environments, and in presence of significant uncertainties. The accepted practice for enabling autonomously flying UAS today relies on extensive manual tuning of the UAS autopilot parameters, or time consuming approximate modeling of the dynamics of the UAS. These methods may lead to overly conservative controllers or excessive development times. A comprehensive approach to the development of an adaptive, airframe-independent controller is presented. The control algorithm leverages a nonparametric, Bayesian approach to adaptation, and is used as a cornerstone for the development of a new modular autopilot. Promising simulation results are presented for the adaptive controller, as well as, flight test results for the modular autopilot.Mechanical & Aerospace Engineerin

Methods and Tools for Battery-free Wireless Networks

Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges

SMART PARALLEL WAVELET TRANSFORMATIONS FOR EDGE AND FOG DETECTION OF BEARING DEFECTS

Rolling Element Bearings (REB) are critical components of a wide range of rotating machines. Identifying and preventing their faults is critical for safe and efficient equipment operation. A variety of condition monitoring techniques have been developed that gather large amounts of data using acoustic or vibration transducers. Further information about the health of an REB can be extracted via time domain trend analysis, and amplitude modulation technics. The frequency domain-specific peaks corresponding to the defects can also be identified directly from the spectrum. Such approaches either provide little insight into the type of defect, are sensitive to noise, and require substantial post-processing. Complicating current fault diagnostic approaches are the ever-increasing size of datasets from different types of sensors that yield non-homogeneous databases and more challenging to execute prognostics for large-scale condition-based maintenance. These difficulties are addressable via approaches that leverage recent developments on microprocessors and system on chip (SoC) enabling more processing power at the sensor level, unloading the cloud from non-used or low information density data. The proposed research addresses these limitations by presenting a new approach for bearing defect detection using a SoC network to perform a wavelet transform calculation. The wavelet transforms enable an improved time- frequency representation and is less sensitive to noise than other classical methods; however, its analysis requires more complex processing techniques that must be executed at the edge (sensor) to limit the need for cloud computing of the results and large-scale data transmission to the cloud. To enable near real-time processing of the data, the BeagleBone AI SoC is employed, the wavelet transforms, and the defect classification are achieved at the edge. The contributions of this work are as follows: first, the real-time data acquisition driver for the SoC is developed. Second, the machine learning algorithm for improving the wavelet transform and the defect identification is implemented. Third federated learning in a network of SoC is formulated and implemented. Finally, the new approach is benchmarked to current approaches in terms of detection accuracy, and sensitivity to defect and was proven to obtain between 80 and 90 percent accuracy depending on the dataset.Ph.D

A LiDAR Based Semi-Autonomous Collision Avoidance System and the Development of a Hardware-in-the-Loop Simulator to Aid in Algorithm Development and Human Studies

In this paper, the architecture and implementation of an embedded controller for a steering based semi-autonomous collision avoidance system on a 1/10th scale model is presented. In addition, the development of a 2D hardware-in-the-loop simulator with vehicle dynamics based on the bicycle model is described. The semi-autonomous collision avoidance software is fully contained onboard a single-board computer running embedded GNU/Linux. To eliminate any wired tethers that limit the system’s abilities, the driver operates the vehicle at a user-control-station through a wireless Bluetooth interface. The user-control-station is outfitted with a game-controller that provides standard steering wheel and pedal controls along with a television monitor equipped with a wireless video receiver in order to provide a real-time driver’s perspective video feed. The hardware-in-the-loop simulator was developed in order to aid in the evaluation and further development of the semi-autonomous collision avoidance algorithms. In addition, a post analysis tool was created to numerically and visually inspect the controller’s responses. The ultimate goal of this project was to create a wireless 1/10th scale collision avoidance research platform to facilitate human studies surrounding driver assistance and active safety systems in automobiles. This thesis is a continuation of work done by numerous Cal Poly undergraduate and graduate students

Development of a compact, low-cost wireless device for biopotential acquisition

A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided design (CAD) files. The design will be made available at the GitHub repository https://github.com/kellygs/eeg