EPIC 2018 Graduate Student Symposium

Graduate Student Symposium Moderated by: Dr. Brandon Grainger 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Graduations Since EPIC 2017 (Full Time)

Samantha Morello Mitsubishi Electric

Dr. Hashim Al Hassan Switched Source LLC

JJ Petti Dominion Energy

Jacob Friedrich Aptiv

Mathieu Bertin Elsys Design (France)

Powering Resilient Cities Alekhya Velagapudi Erick Bittenbender Jenna DeLozier John Kieffer 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Connecting smart cities to sustainability: Embedding sustainability at the local level Prepared by: Alekhya Velagapudi Ph.D. Student (School of Computing and Information) 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Powering Resilient Cities Sustainability in smart cities at local level

Motivation and Problem • Cities a central focus for sustainable development, no such development observed • To connect “smart” and "sustainable” using Information and Communication Technologies (ICT).

• Understand real-world problems associated with smart and sustainable cities • Understand the informational vacuum.

“The gaps in US smart city initiatives are leading to a lack of sustainable development in terms of achieving our long-term social, environmental, and economic goals”

5

Powering Resilient Cities Sustainability in smart cities at local level

Methodology

6

Powering Resilient Cities Sustainability in smart cities at local level

Preliminary Analysis • No interconnections between different aspects of a smart city • No connections between sustainability goals • Interconnections between innovation and economy has not been made which is crucial to meet the sustainability goals

Pyramid of Sustainability for Cities

7

Powering Resilient Cities Sustainability in smart cities at local level

Cities performance by United Nations • All the cities except Portland are in a bad situation in terms of the sustainability development index • Portland: overall 10th rank in Sustainable Development Index (2017) • All the cities have no climate action initiatives • All the cities are bad in terms of Goal 11: sustainable cities and communities • 2018 Sustainable Development Goals report states US has lack of sustainable implementations when compared with other G20 countries

8

Powering Resilient Cities Sustainability in smart cities at local level

What is missing? Cities performance based on Interviewing city officials • • • • • • • •

Policy recommendations Balanced focus on different aspects of smart city Use of innovative ideas Use of data for decision making throughout the city Performance Analysis of the cities Inter-city information exchange about these performance indicators Innovative partnerships for sustainable ideas Governance structure and model that every city can follow

9

Powering Resilient Cities Sustainability in smart cities at local level

Governance as the key to sustainable cities?

Academic Industry City Agencies State and Federal Agencies Citizens and Communities ICT Decision-making

Septa-Helix Model

10

Powering Resilient Cities Sustainability in smart cities at local level

Recommendations to US cities for sustainable development

11

Thermal Baseline Study of Uptown and the Role of Grid Resiliency in Reducing Carbon Emissions Prepared by: Erick Bittenbender M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Powering Resilient Cities Thermal Baseline Study of Uptown

Pittsburgh Climate Action Plan (CAP) 3.0 • CAP 3.0 goals relating to energy include: • 50% reduction in energy and water usage • 100% renewable energy use (City Operations) • Emissions reductions • Completion target of 2030 • City-wide effort, but different neighborhoods at different stages Target Year

% Target GHG Emissions Reductions

2023

20%

2030

50%

2050

80% CAP 3.0 Greenhouse Gas Emissions Goals

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Powering Resilient Cities Thermal Baseline Study of Uptown

Current Emissions Profile • Changing how energy is generated is important, but it’s not the entire picture • Emissions reduction targets not attainable with renewables alone • Need to address consumption behaviors Carbon Emissions Reductions Year

2025

2050

Goal

27%

80%

Current Gen. Profile

13.54%

26.40%

All Natural Gas

13.85%

26.50%

All Renewables

32.68%

32.68%

Projections for Reductions Due to Generation

14

Powering Resilient Cities Thermal Baseline Study of Uptown

Thermal Baseline • Preliminary Baseline Approaches Assume uniform NG consumption throughout Uptown

Calculate total energy use with Total NG Consumption data from NETL

Compare with CHP Viability Figure to assess CHP potential

NG Consumption (Normalized by Area): 716.9 TJ/mi2

Approach 1: Uniform Distribution of NG Consumption Categorize properties by zoning designation (e.g. residential)

Calculate energy use for end-use sectors and sum all for total energy demand

Compare sum with CHP Viability Figure to assess CHP potential

Approach 2: End-Use Sector Specific

NG Consumption (Normalized by Area): 73.3 TJ/mi2

15

Powering Resilient Cities Thermal Baseline Study of Uptown

Grid Resiliency • Vulnerability Indexing by Neighborhood

Vulnerability Index Using DHS Data and Metrics

16

Using Public Spaces for Urban Electrification: A Case Study on EV Integration Prepared by: Jenna DeLozier M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Powering Resilient Cities Case Study in EV Integration

Current Electric Vehicle Status • 1,000 EVs in a medium-sized city (population of 0.5 million) • 20-year projection to be over 100,000 EVs • Most common charger is Level 2 • 200 deployed in city • 220 VAC connection

Level 2 charging port

EV projected demand curve

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Powering Resilient Cities Case Study in EV Integration

Electric Vehicle Charging • • • •

Case Study uses an EV with a 200 mile range/battery capacity Level 2 charge 417 minutes is just under 7 hours for full-charge Average commute is 16 miles one way Level 2 charger would charge in less than 10 minutes

Charging rates by connection type

19

Powering Resilient Cities Case Study in EV Integration

Parking Garage Case Study • • • •

Garage pictured has multiple levels and 450 spots Solar canopy has 115 kW of potential Each charger needs a supply of 7.2 kW for Level 2 Could lead to almost 16 simultaneous charging cars

Parking garage solar example

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Creating an Efficient, Resilient, and Affordable Neighborhood Microgrid Prepared by: John Kieffer M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Powering Resilient Cities Neighborhood Microgrids

Current Electric Sector Status • We must address the electrical needs of the neighborhood, while helping the city achieve its emissions reductions goals, and reducing neighborhood energy costs.

22

Powering Resilient Cities Neighborhood Microgrids

Solar Energy Potential • Approximately 2 MW of rooftop solar could be installed in Uptown.

23

Powering Resilient Cities Neighborhood Microgrids

Microgrid Efficiency Methods • Peak-shifting or a peak-reduction can even out the power consumption of the microgrid, while base load reductions can decrease total energy use. • Residential electric bills are weighted toward total energy consumption, while commercial electric bills are weighted towards peak demand.

Peak-shifting

Peak-reduction with batteries

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Control and Design of Power Electronics Zachary Smith Mohammed Hatatah Santino Fiorello Graziani Patrick Lewis 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Multi-Port Current-Fed Dual Active Bridge Converter Prepared by: Zachary T. Smith M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Control and Design of Power Electronics Multi-Port Current-Fed Dual Active Bridge Converter

Multi-Port Converter Features • • • •

Initial research: Multi-input/output configurations Power flow control along multiple voltage levels Redundant paths for isolation or reliability Tie in battery storage to a DC system

Multi-Port Converter Benefits: • Centralized Control • Simplified Power Flow Control • Bidirectional Operation • Galvanic Isolation Multi-Port DAB

27

Control and Design of Power Electronics Multi-Port Current-Fed Dual Active Bridge Converter

Current-Fed Dual Active Bridge Features • Dual Active Bridge (DAB) converter is suitable for: – MVDC applications – Bidirectional power flow control – Soft-switching operation • Current-Fed DAB (b) offers some benefits vs the traditional VoltageFed DAB (a): – Wide input voltage range – Fault tolerance ABB Florida State CAPS – DC circuit opening

(a)

(b)

DAB (a) and Current-Fed DAB (b)

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Control and Design of Power Electronics Multi-Port Current-Fed Dual Active Bridge Converter

Multi-Port Current-Fed DAB Features • New topology has benefits of both current-fed DAB and multiport designs • Conclusions from initial analysis and simulations demonstrate that the converter should be able to provide similar benefits to the current-fed DAB at MVDC levels

Multi-Port Current-Fed DAB

29

Control and Design of Power Electronics Multi-Port Current-Fed Dual Active Bridge Converter

Ongoing Analysis • Multi-Port DAB configurations operate under soft switching conditions for wide voltage ranges and duty cycles[1] • Current-Fed DABs provide passive component size reduction and DC circuit opening[2] • The ongoing analysis aims to establish characteristic equations for soft switching with respect to duty cycle, phase shift, and component values

Soft switching operation of DAB [1]

[1] H. Tao, A. Kotsopoulos, J. L. Duarte, and M. A. M. Hendrix, “Transformer-coupled multiport ZVS bidirectional DC-DC converter with wide input range,” IEEE Trans. Power Electron., vol. 23, no. 2, pp. 771–781, 2008.

[2] Y. Shi and H. Li, “Isolated Modular Multilevel DC-DC Converter With DC Fault Current Control Capability Based on Current-Fed Dual Active Bridge for MVDC Application,” IEEE Trans. Power Electron., vol. 33, no. 3, pp. 2145–2161, 2018.

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Fault-Tolerant Capability of a QAB DC-DC Converter for MV Modular Inverters Prepared by: Mohammed Hatatah Ph.D. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Introduction • Solid-state transformer SST is one of the smart grid technologies that has been receiving more attention for several reasons. • This work will present the operation of the Quad Active Bridge QAB as a module in the SST as shown in Figure 1. Then, a fault-tolerant operation method in case of device failure is proposed Figure 1. SST architecture using the QAB module for the dc-dc.

32

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Operation Principle • The triangular current modulation TCM was used for the QAB converter. • Voltages Va and Vb must have the waveforms presented in Figure 2.

Figure 2. Waveforms of the QAB. Power flow from MV to LV side.

33

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Design Procedure • The design procedure is made considering the specifications of the modular SST which is shown in Figure 1 and illustrated in Table 1.

• To control the QAB converter, three control circuits are used: - output voltage control - duty cycle control - power balance control Table 1. The SST design’s parameters

34

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Fault-Tolerant Operation â&#x20AC;˘ To detect the fault, a control circuit used to monitor switch/transformer currents continuously. â&#x20AC;˘ The duty cycle of MV-side đ??ˇ2 is updated by adjusting the duty cycle of LV-side đ??ˇ1 to control the power transferred. â&#x20AC;˘ â&#x2C6;&#x2020;đ?&#x2018;&#x2013;đ??żđ?&#x2018;? (0<đ?&#x2018;Ą<đ??ˇ2 đ?&#x2018;&#x2021;đ?&#x2018; ) = â&#x2C6;&#x2020;đ?&#x2018;&#x2013;đ??żđ?&#x2018;? (đ??ˇ2đ?&#x2018;&#x2021;đ?&#x2018; <đ?&#x2018;Ą<đ?&#x2018;&#x2021;đ?&#x2018; /2) .

â&#x20AC;˘ The proposed control topology maintains the output voltage and achieves ZCS operation in less than 50 đ?&#x153;&#x2021;đ?&#x2018; and therefore minimizes the switching losses.

35

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Results

Figure 3. Output voltage.

Figure 4. Power of the MV cells.

Figure 5. Duty cycle at the LV-side.

36

Control and Design of Power Electronics Fault-Tolerant Capability of a QAB DC-DC Converter for MV

Results

Figure 6. Simulation results for the QAB: normal and fault-tolerant operation. (a) main waveforms for voltages and currents, (b) voltage and current on the semiconductors at the LV side and (c) voltage and current on the semiconductors at the MV side.

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A Flying Capacitor Multilevel Flyback Converter (FCMFC) for DC-DC and Pulsed Power Prepared by: Santino Fiorello Graziani Ph.D. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

Research and Design Motivation • DC-DC Applications  High step-up conversion with high efficiency  Constant demand for higher power dense topologies  Lowering device stresses allows for use of smaller and more efficient components

 Electrical isolation is often required for protection purposes • Pulsed Power Applications  Require fast rise times to provide quick bursts of power to loads  Lowering device stress to allow for high repetition rates  Applications emerging in the handheld area require high power density  Flyback transformer good for high gain

39

Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

Description of Topology

N Voltage Levels

Ll

â&#x20AC;&#x153;MxNâ&#x20AC;? used to describe number of flybacks and also the number of voltage levels (N-1) = # of capacitors

D11

1

Lm1

M Modified Flyback Converters

Ll

2

C11 S11

S1

Lm2

D12

D21

C1o

S12

L O A D

+ V -

D22 C21

C2o

Vin Lowest order FCMFC 3x2 single SDC stage outlined in red

S2

S21

S22

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Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

Flying Capacitor Energy Benefit • • • •

Output circuit for a flyback converter with flying capacitor Inductor energy is complemented by the energy from VC1 Higher energy transfer per switching period Increase in gain (N-1) and decrease in stress on secondary side switches

1:1

FCMFC Output Circuit

Equivalent Output Circuit

41

Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

Gain for Varying N Levels 10x more gain with 10 capacitors

Flyback and Boost Converters

42

Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

DC-DC Boosting Results 100V input 72kHz Switching Freq.

7-Level FCML Boost Converter [1]

1:n 1x4 FCMFC (n=1) Converter FCML Boost [1] FCMFC

Vavg 994.5 V 994.5 V

Vripple 12.4 V 12.8 V

Operational Comparison

Switches 6 4

Diodes 6 3

Capacitors 6 3

Component Comparison

[1] Z. Liao, Y. Lei, R. C. N. Pilawa-podgurski, and N. W. Street, “A GaN-based Flying-Capacitor Multilevel Boost Converter for High Step-up Conversion,” 2016.

43

Control and Design of Power Electronics FCMFC for DC-DC and Pulsed Power

Pulsed Power Results

Ll1 Co1

Lm1 S1

S

S2

S3

S4

Ll2 Lm2

Co2 S1

S

S2

S3

S4

Ll3 Lm3

Co3 S1

S

S2

S3

L O A D

S4

+ V -

Ll4 Co4

Lm4 S1

S

S2

S3

S4

Ll5 Lm5

Co5

Vin S

Converter Flyback [1] FCMFC

Voltage Peak 35kV 36kV

Slew Rate (dv/dt) 8.7kV/μs 8.0kV/μs

Operational Comparison

S1

S2

S3

S4

5x5 FCMFC FCMFC achieves the same power pulse while drawing half the source current and utilizing half as many transformer components.

[1] P. Davari et al., “Parallel and series configurations of flyback converter for pulsed power applications,” Proc. 2012 7th IEEE Conf. Ind. Electron. Appl. ICIEA 2012, vol. 40, no. 10, pp. 1517–1522, 2012.

44

Preserving the Life of Power Electronics through Active Thermal Boundary Control Prepared by: Patrick Lewis Ph.D. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Control and Design of Power Electronics Active Thermal Boundary Control

Research Motivation â&#x20AC;˘ Device failure caused by thermal stress â&#x20AC;˘ Thermal cycling leads to device failure Structure of an IGBT module [12]

IGBT bond wire damage: (a) cracking and lift-off (b) bond wire lift-off

46

Control and Design of Power Electronics Active Thermal Boundary Control

Active Thermal Control â&#x20AC;˘ Active Thermal Control (ATC) is an increasingly popular method of reducing thermal cycling (Î&#x201D;đ?&#x2018;&#x2021;đ?&#x2018;&#x2014; ) by means of discontinuous pulse-width modulation or variable switching frequency, etc.

Thermal cycling during LVRT event without ATC

Thermal cycling with optimized modulation technique for ATC [18]

47

Control and Design of Power Electronics Active Thermal Boundary Control

Natural Switching Surface Control â&#x20AC;˘ Switching structures of the dual active bridge for each natural operation

Switching Structures : (a) đ?&#x2018;Łđ??ż = đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;? + đ?&#x2018;Ł0 (b) đ?&#x2018;Łđ??ż = đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;Ł0 (c) đ?&#x2018;Łđ??ż = â&#x2C6;&#x2019; đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;Ł0 (d) đ?&#x2018;Łđ??ż = â&#x2C6;&#x2019;đ?&#x2018;&#x2030;đ?&#x2018;?đ?&#x2018;? + đ?&#x2018;Ł0 (a) I, (b) II, (c) III, (d) IV

48

Control and Design of Power Electronics Active Thermal Boundary Control

Natural Switching Surface Control â&#x20AC;˘ The dual active bridge performs along these natural switching surface trajectories in both the buck and boost modes of operation

Buck mode

Boost mode

49

Control and Design of Power Electronics Active Thermal Boundary Control

Natural Switching Surface Control â&#x20AC;˘ Steady state operation of DAB in boost mode showing switching trajectories on the state plane with corresponding signal waveforms

Steady State Operation on the State Plane Showing Switching Trajectories

System Signal Waveforms Corresponding to State Plane

50

Control and Design of Power Electronics Active Thermal Boundary Control

Manipulation of NSS for Active Thermal Boundary Control â&#x20AC;˘ NSS control enables manipulation of pseudo switching frequency and power losses for the benefit of life preservation for interval based loads

Steady State Operation on the State Plane Showing Switching Trajectories

System Signal Waveforms Corresponding to State Plane

51

Extreme Events and Environments Corey Weimann Thibaut Harzig Thomas Cook 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

DC Arc Flash Incident Energy Calculation Summary and its Need in Workplace Safety Prepared by: Corey Weimann M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Extreme Events and Environments DC Arc Flash Incident Energy Calculation Summary

Motivation • • • •

Increase in PV and Plug-In Electrical Vehicles Safety Arc Flash Incident Energy is Established for AC NFPA 70E

54

Extreme Events and Environments DC Arc Flash Incident Energy Calculation Summary

Arc Resistance Method Ammerman Model • Based on Stokes and Oppenlander • Arcing Current and Resistance Equations • Open-Air Arc Energy Equations

Minimum Arc Voltage at Different Gap Widths

Simplified DC Equivalent-Circuit

55

Extreme Events and Environments DC Arc Flash Incident Energy Calculation Summary

Max Power Transfer Method Doan’s Method •

Based on max power transfer

•

Developed Conservative Incident Energy Equation

NFPA 70E 2015 Method

•

Electrical Safety in the Workplace

•

Provides Arcing Current and Incident Energy Equations

56

Extreme Events and Environments DC Arc Flash Incident Energy Calculation Summary

Photovoltaic System Max Power Method â&#x20AC;˘ Standard 70E Method Insufficient â&#x20AC;˘ Incident Energy Calculation for PV Systems

I-V Curve of 300W PV Module

57

Extreme Events and Environments DC Arc Flash Incident Energy Calculation Summary

Conclusion • Differences / Drawbacks • Assumes total conversion of electrical arc energy into heat • Accuracy • Plans Grid Tie

PV Plant Microgrid Disconnect

~ Commercial Load and PV Generation =

~

Community Energy Storage

=

Residential Load and PV Generation ~

=

~

~

=

=

Example Time-Current Curve

58

Improved Sequence Network of a Current Controlled Inverter Prepared by: Thibaut HARZIG M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Extreme Events and Environments Improved Sequence Network of a Current Controlled Inverter

Goal • Develop an improved sequence network of a grid-tied current controlled inverter not interfaced with a Wye-Delta transformer and experiencing a single line-to-ground fault • The model predicts the fault current and the asymmetry of the inverter’s output current due to control limitations

Grid-Tied Current Controlled Inverter experiencing a Single Line-to-Ground Fault

60

Extreme Events and Environments Improved Sequence Network of a Current Controlled Inverter

Classical Model of Sequence Network â&#x20AC;˘ The model assumes a symmetric output current from the inverter to compute the fault current magnitude If

Sequence network assuming the inverter provides a symmetric output current

61

Extreme Events and Environments Improved Sequence Network of a Current Controlled Inverter

Improved Sequence Network â&#x20AC;˘ The inverter is not able to produce a zero sequence voltage to regulate the zero component of its output current

Improved Sequence Network taking into account Control Limitations

62

Extreme Events and Environments Improved Sequence Network of a Current Controlled Inverter

Validation of the Improved Sequence Network • A sequence analysis of the output current of the inverter under unbalanced conditions reveals that the output current of the inverter is not symmetric • The improved sequence network showed better accuracy in its estimation of the fault current magnitude and the asymmetry of the inverter’s output current

Sequence analysis of the inverter’s output current

Comparison of PLECS simulation results with the results from the models

63

Extreme Events and Environments Improved Sequence Network of a Current Controlled Inverter

Conclusion and Future work • A single line-to-ground sequence network of a grid-tied current controlled inverter without any interfacing transformer has shown to predict the fault current magnitude accurately • The model is also able to predict the asymmetry of the inverter’s output current during the fault • The model is likely to be used for other applications such as “ Transient Detection” application.

64

Radiation-Tolerant, GaN-based Point of Load Converters for Small Spacecraft Missions Prepared by: Thomas Cook M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Extreme Events and Environments Radiation-Tolerant, GaN-based Point of Load Converters

Why GaN? • Power Electronic Benefits • Fast turn on speed and reduced switching losses • Low ON resistance • ¼ size of Silicon Transistors • Radiation Performance Benefits • Schottky metal gate reduces chances of Total Ionizing Dose (TID) • Small depletion region reduces damage caused by heavy ions • Wide band-gap requires high energy particles to achieve ionization Collaborative Project between Electric Power Systems Lab, NSF Center for Space, High - Performance, & Resilient Computing and DoD Space Test Program (STP) Houston

66

Extreme Events and Environments Radiation-Tolerant, GaN-based Point of Load Converters

Synchronous Buck Converter Design Synchronous Buck Controllers

• 12V input to 5V or 3.3V output • LTC3833 and LM25141-Q1 controllers • Simple output voltage level adjustment • Direct, protected gate drive for GaN HEMTs achieved through clamped bootstrap circuit • Teledyne TDG100E15B and EPC 2014C GaN HEMTs

Parameter

LTC3833

LM25141-Q1

Input Voltage Range

4.5 – 38 V

3.8 – 42 V

Output Voltage Range

0.6 – 5.5 V

1.5 – 15 V

Switching Frequency

1.9 MHz

2.2 MHz

Gate Drive Voltage

5.3 V

5V

Parameter

TDG100E15B

EPC2014C

Drain-Source Voltage

3.8 - 42V

4.5 - 38 V

1.3 V

1.4 V

21 mΩ

16 mΩ

6.2 nC

2.5 nC

GaN HEMTs

Threshold Gate-Source Voltage Maximum On Resistance Gate Charge

67

Extreme Events and Environments Radiation-Tolerant, GaN-based Point of Load Converters

Assembled Converters

EPC Test Converter Area: 0.62in2 Weight: 2.32g

E2V Test Converter Area: 0.85in2 Weight: 4.74g

Representative Rad-hard PoL Converter Area: 1.16in2 Weight: 16g

68

Extreme Events and Environments Radiation-Tolerant, GaN-based Point of Load Converters

Switching Waveforms Average inductor current ripple of 31.2% measured differential over sense resistor Gate drive voltage signals for top gate (blue) and bottom gate (green)

Average voltage of 5V with ripple value of 8.2mV or 0.1%

69

Extreme Events and Environments Radiation-Tolerant, GaN-based Point of Load Converters

Efficiency

5V Output Efficiency

• Efficiencies of 94% to 96% for 5V output • Efficiencies of 70% to 85% for 3.3V output • EPC HEMTs see higher efficiencies

Future Work • Radiation and efficiency testing on ISS when launched in Spring 2019 • LANSCE radiation testing on controllers and Smart Module • Integration into STP-H7 with CASPR Experiment

3.3V Output Efficiency

70

Energy Management and Monitoring Rui Hu Ryan Brody Adam Emes Christian Perenyi 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Self-learning Load Management Strategy for a Communication Microgrid Prepared by: Rui Hu Ph.D. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Energy Management and Monitoring Load management for a communication microgrid

Communication microgrid in natural disasters

Bus

Main grid

Scheme of a cellar network

73

Energy Management and Monitoring Load management for a communication microgrid

Plan base station load using game theory

Load and solar power curves

Battery SoC distribution

đ?&#x2018;&#x201A;đ?&#x2018;?đ?&#x2018;&#x2014; = đ?&#x2018;¤đ?&#x2018;&#x17E; đ?&#x153;&#x17D; + đ?&#x2018;¤đ?&#x2018;&#x17D; đ?&#x2018;&#x201C;(đ?&#x2018;&#x2020;đ?&#x2018;&#x153;đ??ś) Player II Player I đ?&#x153;&#x17D;11 đ?&#x153;&#x17D;12

Ď&#x192;11 Ď&#x192;12

Ď&#x192;21 C1 C3

Strategic form game

Ď&#x192;22 C2 C4

74

Energy Management and Monitoring Load management for a communication microgrid

Learn from the environment

Transmit more data

Base station

Whatâ&#x20AC;&#x2122;s left?

Store more energy Learning mechanism

Original grid 75

Energy Management and Monitoring Load management for a communication microgrid

Simulation results

Simulated microgrid scheme

System average PSNR

Learning curve of an agent

System SoC during training

Trained TSF and SoC curves

Obtained TSF strategy

76

Energy Management and Monitoring Load management for a communication microgrid

Simulation results cont.

Unexpected structure change

System SoC after BS loss

Trained TSF and SoC curves

System SINR after BS loss

77

Energy Management and Monitoring Load management for a communication microgrid

Conclusion and future work • The learning mechanism has a reasonable performance.

• No prior environment or load data is needed.

• Adjusting capability of the learning method could be further invested.

• Combination of game & learning?

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Nonintrusive Load Monitoring (NILM) - Undergraduate Research Prepared by: Ryan Brody M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Motivation • NILM – determining what household loads are on based on voltage and current measured at fuse box • Increasing popularity of smart home loads

Commercial NILM - Sense Home Energy Monitor [1]

Sense User Interface [1]

80

[1] https://theblueadapter.com/sense-home-energy-monitor-review/

Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Goal â&#x20AC;˘ Investigate fundamental principals of NILM using equipment in the Electric Power Systems Lab (EPSL) at Pitt

EPSL Bench One-Line Diagram

Resistive, Capacitive, Inductive and CFL Loads

81

Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Event Detection â&#x20AC;˘ Implemented in experiment using Dranetz Power Visa â&#x20AC;˘ Replicated using the approach proposed in [1] [1] M. Nait Meziane, P. Ravier, G. Lamarque, J. Le Bunetel and Y. Raingeaud, "High accuracy event detection for Non-Intrusive Load Monitoring," 2017 IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), New Orleans, LA, 2017, pp. 2452-2456. doi: 10.1109/ICASSP.2017.7952597

Dranetz Power Visa Used for Measurements

Example Event Waveform

82

Nonintrusive Load Monitoring (NILM) â&#x20AC;&#x201C; Undergraduate Research Prepared by: Adam Emes M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Waveform Pre-Processing for Feature Extraction

Sampled Current Waveform

Time (s)

Current (A)

Post-Transient Current

Current (A)

Current (A)

Raw Current Waveform

Voltage (V)

• Steady state current waveforms from before and after the transient were used for classification Post-Transient Voltage

Time (s)

Time (s)

Waveform Sampling Procedure and Representation of the Training Array used for “Training” the Algorithm

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Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Event Type and Power Consumption Results • The classify function was used to determine the load type for each sampled waveform - combined with event detection algorithm

Event Type Results – “turn on/off” detection combined with load-type classification

Power Consumption Results – Percent Difference Between the Dranetz Readings and the Calculated Power Levels

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Energy Management and Monitoring Nonintrusive Load Monitoring (NILM)

Further Testing through Simulation â&#x20AC;˘ A Simulink model of the lab bench was developed to test the algorithm under different impedance levels

Parts of Lab Bench Model Created in Simulink

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Technical Support of a Thermal Plant - BESS & Virtual Sensors Prepared by: Christian Perenyi M.S. Student 13th Annual Electric Power Industry Conference Swanson School of Engineering Graduate Student Symposium October 15th, 2018

Energy Management and Monitoring Technical Support of a Thermal Plant

Background

Pittsburgh USA

Lyon France

Bogota Colombia

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Energy Management and Monitoring Technical Support of a Thermal Plant

BESS project â&#x20AC;˘ Objective: Integrate a power bank battery to the power plant to regulate the primary frequency.

â&#x20AC;˘

Added value: Accelerate primary frequency response.

Declare a higher energy production.

Charge and discharge the battery according to the price of energy.

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Energy Management and Monitoring Technical Support of a Thermal Plant

Virtual sensor project • Objective:

• Added value:

Implement virtual sensors thanks to the diffuse logic firstly and secondly using machine learning.

Identify plant’s needs and opportunities.

Facilitate the work of the operators by accelerating the fault causes analyze.

Predict the behavior of the machine to anticipate the faults.

Flux

Good

Alarm

Fault

Level

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Energy Management and Monitoring Technical Support of a Thermal Plant

Other responsibilities • Perform everyday TS tasks as oil quality analysis, fault analyses, local technician support, project development.

• Insure being conform with the resolution CREG 038 – Measure Code.

• Communicate with local engineering school (UN). Proposal from both parties for very concrete projects.

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Thank You!