Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data analysis is performed using an approach coined as the cyclic histogram. The cyclic histogram process will be integrated into the digital fault recorders themselves to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increase the reliability of the transmission system by providing near real time detection and identification of disturbances as well as prevention of problems before they occur.Comment: 18 page

Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) Mission: Enabling Time-Resolved Cloud and Precipitation Observations from 6U-Class Satellite Constellations

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) mission is to demonstrate the capability of 6U-Class satellite constellations to perform repeat-pass radiometry to measure clouds and precipitation with high temporal resolution on a global basis. The TEMPEST mission concept is to improve understanding of clouds and precipitation by providing critical information on their time evolution in different climatic regimes. Measuring at five frequencies from 89 to 182 GHz, TEMPEST-D millimeter-wave radiometers are capable of penetrating into the cloud to observe changes as precipitation begins or ice accumulates inside the storm. The TEMPEST-D flight model radiometer instrument has been completed, passed functional testing, vibration testing and self-compatibility testing with the XB1 spacecraft bus. The next steps for the TEMPEST-D millimeter-wave radiometer are thermal vacuum testing and antenna pattern measurements. The complete TEMPEST-D flight system will be delivered to NanoRacks for launch integration in the autumn of 2017, in preparation for launch to the ISS in the second quarter of 2018, with deployment shortly thereafter into a nominal orbit at 400-km altitude and 51.6° inclination

Measurement of chlorophyll fluorescence within leaves using a modified PAM Fluorometer with a fiber-optic microprobe

By using a fiber-optic microprobe in combination with a modified PAM Fluorometer, chlorophyll fluorescence yield was measured within leaves with spatial resolution of approximately 20 mu m. The new system employs a miniature photomultiplier for detection of the pulse-modulated fluorescence signal received by the 20 mu m fiber tip. The obtained signal/noise ratio qualifies for recordings of fluorescence induction kinetics (Kautsky effect), fluorescence quenching by the saturation pulse method and determination of quantum yield of energy conversion at Photosystem II at different sites within a leaf. Examples of the system performance and of practical applications are given. It is demonstrated that the fluorescence rise kinetics are distinctly faster when chloroplasts within the spongy mesophyll are illuminated as compared to palisade chloroplasts. Photoinhibition is shown to affect primarily the quantum yield of the palisade chloroplasts when excessive illumination is applied from the adaxial leaf side. The new system is envisaged to be used in combination with light measurements within leaves for an assessment of the specific contributions of different leaf regions to overall photosynthetic activity and for an integrative modelling of leaf photosynthesis

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure