Project Descriptions

2018 Project Descriptions

Synchronized Phasors and Big Data in Smart Grid Power System (Ali Abur)
Abstract: North American power grid includes multiple Regional Transmission Organizations (RTOs) or Independent System Operators (ISOs) which coordinate, control and monitor the operation of the entire grid. Coordination of these complex and large volume of power exchanges while maintaining the system security and reliability presents a new and major challenge to the operators of power grids. Traditionally, state estimators (SE) perform real-time monitoring of the entire system state based on the available measurements and network model to help monitor the safety of the grid using the supervisory control and data acquisition (SCADA) system. However, in the past two decades, new metering devices that are referred as the phasor measurement units (PMU) have been introduced. These devices can provide measurements at high data acquisition rate and more importantly, use global positioning system (GPS) satellites to synchronize the measurements. This research develops improved state estimation functions for the power grid, and also emphasizes the insertion of renewable energy generation such as solar and wind. Advanced power system simulation tools are used to verify the robustness and feasibility of the grid, particularly in contingency cases, such as when there is lost generation or bad data.

Learning Objectives: Students will learn to 1) manipulate and process big data; 2) run power system simulations on small (30-bus) and large (2917-bus) utility test systems; 3) run weighted least square (WLS) state estimation (SE) algorithms in MATLAB; and 4) master concepts in phasor dynamics, reactive power, and other power system theoretical tools.

Role of REU Student: The student will run state estimation of power systems using MATLAB and then evaluate the robustness of the estimator using statistical error analysis.

Modeling Energy Transitions and Air Pollution Impacts (Matthew Eckelman)
Abstract: Energy use is responsible for 85% of global greenhouse gas emissions and a majority of other air pollutants from human activities. Potential transitions in the energy sector – towards large-scale renewable energy, electrified transportation, or low-carbon fuels – will cause major shifts in the types and locations of emissions, which in turn will affect air quality around the country. REU students will work with large environmental datasets compiled by federal agencies and learn macro-scale modeling techniques for emissions and air quality in order to quantitatively understand the implications of technology shifts and national energy policy choices. The different energy sources can be analyzed along with their environmental impact, including solar energy, wind energy, nuclear energy, and fossil fuel generation.

Learning Objectives: Students will understand how to 1) analyze time-series data on electric efficiency and greenhouse gasses; 2) interpret the influence of energy policies on the environment; and 3) become familiar with renewable energy generation methods and their design.

Role of REU Student: 1) Modeling system-level energy savings and air quality benefits of energy efficiency measures in residential and commercial buildings; 2) Using spatial statistics and machine learning techniques to predict local air quality based on regional power plant dispatching. Here we use software tools such as MATLAB or open source machine learning software such as TensorFlow; 3) Conducting surveys of residential renewable energy owners and purchasers to examine changes in human behavior and patterns of energy demand.

A Hybrid Systems Framework for Human-in-the-Loop Control of HVAC Systems (Michael Kane)
Abstract: Building occupants, building physics, HVAC systems, and controls make up a complex dynamical system with continuous and discrete states and interactions, i.e. a hybrid dynamical system. Traditionally, occupant physics and behavior are abstracted from the building engineering through simple room temperature set-points. At most, the interaction between the two systems is captured by changing setpoints based on occupancy. This abstraction neglects the autonomy of users to change setpoints and create over-rides that often forfeit the savings that the control system aimed to achieve. As users increasingly interact with home energy IoT devices, this shared autonomy will grow in significance. This research aims to leverage these interactions to build classes of hybrid-dynamical models of user behavior. Such models will be implemented in model-based estimators of the noisy channel between user desire and the control system reference. This data driven approach will utilize datasets from thousands of occupants from Pecan Street and Ecobee.

Learning Objectives: Students will learn to 1) clean, manipulate, and process 100s of GB of data from remote sources using Python and MATLAB; 2) run MATLAB scripts that identify continuous, discrete, and hybrid behavior in data; 3) identify the dynamical interconnections between continuous and discrete processes.

Role of REU Student: The student will use MATLAB and Python to fit hybrid-dynamical models to users’ home HVAC data and evaluate model fit and uncertainty using statistical analysis.

Photosynthetic Efficiency for Biomass and Biofuel (Carolyn Lee Parsons)
Abstract: Depletion of fossil fuel reserves and climate change attributed to greenhouse gases are imminent challenges facing our planet. To address both of these global challenges, the research focuses on engineering microalgae and plants for increased biomass production that can be harnessed for biofuel applications. One approach involves improving the efficiency of photosynthesis by improving the enzymes (i.e. reducing photorespiration) in the Calvin Cycle to enhance carbon fixation and its conversion to sugars through photosynthesis. Instead of focusing at the enzymatic level, this research project focuses on altering plant development to yield more leaves by which light capture, carbon fixation, and conversion to sugars can occur. In particular, this project involves characterizing the effect of several transcription factors that control plant development and ultimately their ability to capture energy through photosynthesis.

Learning Objectives: 1) to explore how biomass production for future biofuel applications can be enhanced through altering plant development, 2) to design and conduct experiments to measure the efficiency of photosynthesis both at the molecular level (i.e. sugar content) as well as at the macroscopic level (i.e. chloroplast development, biomass or foliage weight).

Role of REU Student: Investigate several transcription factors on plant development and improving the photosynthetic capabilities of leaves. In particular, the participant will: 1) evaluate the effect of silencing specific targets on the extent of leaf development using a transient expression technique known as virus induced gene silencing (VIGS); after silencing the specific target, the participant will compare the extent of foliar development (through pictures and biomass weight) to that of control plants; 2) determine the best conditions for cultivating transgenic plants which overexpress these specific targets; the participant will evaluate the effect of overexpressing the specific targets on photosynthesis by measuring the chlorophyll levels (by spectrophotometry), chloroplast development (through microscopy), and foliage’s sugar content (by enzymatic assays). The goal is to determine the role of specific transcription factors in improving the accumulation of foliar biomass.

Smart Solar Energy (Brad Lehman | Mahshid Amirabadi)
Abstract: Solar photovoltaic (PV) installations traditionally are stand-alone systems without integrated computation. However, it is possible to utilize real-time processes to adaptively reconfigure solar PV installations while sensing and computing the environmental factors. This research will design and build new types of solar installations that can adapt their performance depending on their environmental conditions. For example, a smart PV installation will include power converters and microprocessors within each panel that enable the solar panels to self-heal and self-optimize to produce higher power. New types of inverters for photovoltaic applications are included that use intermediate voltages on capacitors to have controllable fluctuations on them so that the capacitances can be substantially reduced. Combined with fast switching pulse-width-modulators the goal is to reduce the cost, volume and weight of the entire PV system by up to 50%. Sensor information, weather patterns, and high-performance computational algorithms running machine learning algorithms can rely on statistical analysis with massive amounts of data to predict PV power in large geographical regions. The utility and Independent System Operators can then predict the power produced in regions to better match overall power generations with the needed loads. This information is vital for the future of smart grid operation.

Learning Objectives: Students will understand 1) how to build and connect solar PV installations; 2) basic principles of circuit design and simulation using software such as ORCAD; 3) machine learning; 4) processing data in MATLAB from measurements, and 5) smart grid operation.

Role of REU Student: The students will process sensory information in MATLAB and use machine learning and other adaptation methods to develop controllers for the smart solar panels. They will build and experimentally test smart solar panels and inverters. They will run outdoor solar experiments with the smart panels over the summer, taking and analyzing data sets.

Magnetic Materials for Energy Transformation (Laura Lewis)
Abstract: The interdisciplinary Nanomagnetism Research Group at Northeastern investigates magnetic and electronic materials to tailor their properties towards for a variety of potential applications. In particular, magnetic materials allow the interconversion of electrical to mechanical energy as well as enable transmission and distribution of electric power. REU students will be able to assist investigations into synthesis and processing strategies to promote and tailor the crystal structure, microstructure and magnetic structure of new types of magnetic materials that underlie operation of myriad devices and machines, including hybrid/electric vehicles, direct-drive wind turbines, motors and generators. In this manner they will learn about and apply fundamental principles structure-property relationships in magnetic materials as well as become skilled in new techniques that can prepare them for technical careers.

Learning Objectives: Students will 1) understand the influence of nanostructuring on the magnetostructural response of various alloys; 2) understand and predict the influence of elemental substitution of the magnetocaloric response; and 3) understand magnetostructural trends in chemically-modified systems.

Role of REU Student: The student will 1) use force microscopy and magnetic measurement to understand the effects of processing on magnetic domain configuration in novel magnetic materials; and 2) build new types of apparatuses to enable in-situ magnetic fields to be applied to scanning probes.

Synchronized Phasors and Big Data in Smart Grid Power System (Marilyn Minus)
Abstract: This research area is of relative interest in terms of its focus on lightweight materials formed from polymer-based nano-composite materials with well-controlled structural morphology, for applications such as wind turbine blades, lightweight airplanes, etc. Nano-materials have been studied quite extensively in composites due to their impressive properties and opportunity to provide information regarding nano-scale phenomena and control of materials processing at this level. Typical composites exhibit average properties associated with components A (i.e., matrix) and B (i.e., filler). For a nano-composite, B is able to affect A locally (at the nano-scale) and this can subsequently dictate macroscopic properties. REU research projects s research will contribute toward experimental pathways to build a designer material. In other words, identify the materials: (i) desired properties, (ii) necessary architecture/morphology to exhibit said properties, and (iii) specific processing methods to achieve the desired material morphology and properties.

Learning Objectives: Students will 1) develop both experimental and analytical skills as they pertain to polymer composite processing; 2) gain a general understanding regarding the field of nano-composite materials and their broad application across multiple fields as well as their role in various energy applications; and 3) learn about the fundamental connections between material structure and morphology and its direct relation to resultant properties and function.

Role of REU Student: The student will 1) learn fabrication approaches for lightweight nano-composite materials (i.e., fiber spinning, non-woven filtration, and film casting); 2) characterize composites using various approaches including X-ray scattering/diffraction, thermo-mechanical analysis, and spectroscopy studies; and 3) perform theoretical analysis to support experimental data.

Assessing Risk to Offshore Wind Energy Structures (Andy Myers)
Abstract: The overarching goal of this research project is to reduce the cost of offshore wind energy through research on the spatio-temporal interaction of multiple offshore hazards and the calculation of novel system-level performance metrics and the advancement of shallow water wave modeling. The project involves direct collaboration with industry and requires fundamental advancements to metamodels (aka surrogate models), so that, for the first time, such models can consider (1) the spatio-temporal variability of multiple hazards coupled with structural loads with high-dimensional input and output vectors, (2) the effects of model adequacy assessed by comparing model predictions with physical observations categorized in terms of inter- and intra-event uncertainty and (3) more accurate shallow water wave models suitable for design and including important nonlinear features such as skewed and breaking waves. The advancement to metamodels overcomes two fundamental limitations: the sparsity of long-term jointly distributed offshore data for extreme conditions and the complexity of state-of-the-art models of the offshore environment and associated aero/hydrodynamic loads, negating their ability to predict innovative system-level performance metrics.

Learning Objectives: Students will learn about 1) validation methods using time series modeling in the program FAST; 2) how to plan numerical and physical experiments to characterize breaking wave loads on offshore wind turbines; 3) wind energy fundamentals and its impact on the global energy delivery.

Role of REU Student: The student will assist in 1) the development, validation, execution, and uncertainty quantification of a model of the U.S. Atlantic coast which estimates seastate conditions during hurricanes, 2) the development of a metamodel designed to emulate the results of the coastal model, 3) the development of a frequency-domain model of an offshore wind turbine.

A 500mV-5V USB charging circuit using energy harvesting from single cell solar cell with less than 100nW quiescent power consumption (Aatmesh Shrivastava)
Abstract: Internet-of-things (IoT) promises an exponential growth in the number of connected devices and sensors in our environment. It is possible to provide energy autonomy to IoT devices by incorporating energy harvesting from ambient energy sources, such as by using solar cells to power from indoor/outdoor lighting. However, ambient energy can range anywhere from 1uW-100uW. Energy harvesting at this power level requires ultra-low power and high efficiency circuits. In this project, we propose a research to develop a 0.5V to 5V energy harvesting boost converter which can be used as a USB charger for various IoT devices. The proposed boost converter will have ultra-low quiescent power consumption of less than 100nW while achieving an efficiency greater than 90%. The higher efficiency and ultra-low power consumption will be achieved using a new analog sub-threshold control scheme, an efficient boost conversion technique, and a single cell solar cell. The efficient energy harvesting from single cell will also reduce the device size.

Learning Objectives: Students will learn the basic principles of power management integrated circuit (IC) design. They will learn standard IC design in Cadence simulation environment. They will also learn PCB layout, soldering, and how to test and characterize integrated circuits.

Role of REU Student: The REU student will characterize the available power in a single cell in different ambient conditions. Further, the student will engage in design and simulation of different circuit components of the solar energy USB charger. The design work will include developing power switches, designing small comparator, layout effort, and running experimental validations.

Tunable Inductors for Adaptive Power Electronics (Nian Sun)
Abstract: Because portable devices are on-the-move and in unknown environmental conditions, it is necessary to operate the power management devices in wide ranges of operating conditions. This research proposes to investigate novel compact and power efficient adaptive power electronics with voltage tunable magnetoelectric inductors. This is possible by building magnetoelectric magnetic/ferroelectric heterostructures, which allows for voltage control of magnetic permeability and therefore, voltage tunable inductors which are solenoid inductors with a magnetic/ferroelectric heterostructure core. Research will develop these devices and then utilize them in adaptive power supplies to improve the low efficiency of power electronics at low load, such as in the idle states of smartphones, laptops, etc.

Learning Objectives: Students will learn the designing and building of power inductors, magnetic theory, and energy management strategies. They will also learn power and magnetic software tools.

Role of REU Student: The REU student will test different magnetic materials and then build power converters (soldering, board layout, experimental testing). Simulations will also be run.

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