At high penetration level of photovoltaic (PV) generators, their abrupt disturbances (caused by moving clouds) cause voltage and frequency perturbations and increase system losses. Meanwhile, the daily irradiation profile increases the slope in the net-load profile, for example, California duck curve, which imposes the challenge of quickly bringing on-line conventional generators in the early evening hours. Accordingly, this paper presents an approach to achieve two objectives: (1) address abrupt disturbances caused by PV generators, and (2) shape the net load profile. The approach is based on employing battery energy storage (BES) systems coupled with PV generators and equipped with proper controls. The proposed BES addresses these two issues by realizing flexible power ramp-up and ramp-down rates by the combined PV and BES. This paper presents the principles, modeling and control design aspects of the proposed system. A hybrid dc/ac study system is simulated and the effectiveness of the proposed BES in reducing the impacts of disturbances on both the dc and ac subsystems is verified. It is then shown that the proposed PV-BES modifies the daily load profile to mitigate the required challenge for quickly bringing on-line synchronous generators.
Long-time non-contact water level measurement with a 5.8-GHz DC-coupled interferometry radar
Flooding caused by tropical cyclones, tsunami, and many other phenomena is one type of natural disaster that occurs all around the world. While these disasters cannot be prevented, the communities can be made more resilient and damages caused by them to lives and infrastructure can be minimized by developing early warning systems. Microwave-based systems provide a non-contact measurement setup to monitor water level, thus requiring low maintenance and operation costs. In this paper, a DC-coupled 5.8-GHz interferometry radar was designed and tested by observing water level in a barrel, which had water poured in and drained out over a long-time period. By adding more gains to the RF chain and removing the gain in the baseband, the proposed water-level monitoring radar system eliminates the requirement of complex DC tuning structure in the previous works. The experiment demonstrated that the proposed water-level monitoring radar system was able to accurately measure the relative position of water with mm-accuracy.
- Award ID(s):
- 1760497
- Publication Date:
- NSF-PAR ID:
- 10080349
- Journal Name:
- 2018 IEEE International Instrumentation and Measurement Technology Conference
- Page Range or eLocation-ID:
- 1 to 5
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Application performance improvements in emerging systems with hybrid memory components, such as DRAM and Intel’s Optane DC persistent memory, are possible via periodic data movements, that maximize the DRAM use and system resource efficiency. Similarly, predominantly used NUMA DRAM-only systems benefit from data balancing solutions, such as AutoNUMA, which periodically remap an application and its data on the same NUMA node. Although there has been a significant body of research focused on the clever selection of the data to be moved periodically, there is little insight as to how to select the frequency of the data movements, i.e., the duration of the monitoring period. Our experimental analysis shows that fine-tuning the period frequency can boost application performance on average by 70% for systems with locally attached memory units and 5x when accessing remote memory via interconnection networks. Thus, there is potential for significant performance improvements just by cleverly selecting the frequency of the data movements apart from choosing the data itself. While existing solutions empirically set the duration of the period, our work provides insights into the application-level properties that influence the choice of the period. More specifically, we show that there is a correlation between the application-level data reusemore »
-
Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their heterogeneous memory components. This breaks many assumptions and heuristics designed for traditional DRAM-only platforms. High application performance is feasible via dynamic data movement across memory units, which maximizes the capacity use of DRAM while ensuring efficient use of the aggregate system resources. Newly proposed solutions use performance models and machine intelligence to optimize which and how much data to move dynamically. However, the decision of when to move this data is based on empirical selection of time intervals, or left to the applications. Our experimental evaluation shows that failure to properly conFigure the data movement frequency can lead to 10%-100% performance degradation for a given data movement policy; yet, there is no established methodology on how to properly conFigure this value for a given workload, platform and policy. We propose Cori, a system-level tuning solution that identifies and extracts the necessary application-level data reuse information, and guides the selection of data movement frequency to deliver gains in application performance and system resource efficiency. Experimental evaluation shows that Cori configures data movement frequencies that provide applicationmore »
-
Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.
-
This paper presents a water intake monitoring system for animal agriculture that tracks individual animal watering behavior, water quality, and water consumption. The system is deployed in an outdoor environment to reach remote areas. The proposed system integrates motion detectors, cameras, water level sensors, flow meters, Radio-Frequency Identification (RFID) systems, and water temperature sensors. The data collection and control are performed using Arduino microcontrollers with custom-designed circuit boards. The data associated with each drinking event are water consumption, water temperature, drinking duration, animal identification, and pictures. The data and pictures are automatically stored on Secure Digital (SD) cards. The prototypes are deployed in a remote grazing site located in Tucumcari, New Mexico, USA. The system can be used to perform water consumption and watering behavior studies of both domestic animals and wild animals. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/I2MTC.2018.8409735
