Project

# Title Team Members TA Documents Sponsor
68 Power-Factor-Corrected Musical Tesla Coil
Ali Albaghdadi
Kartik Singh Maisnam
Shengyan Liu design_document1.pdf
final_paper1.pdf
proposal1.pdf
# Gentle Giant: A Power-Factor-Corrected Musical Tesla Coil

Team Members:
- Ali Albaghdadi (aalba9)
- Kartik Maisnam (maisnam2)

# Problem

Tesla coils are impressive visual and auditory devices; some can a surprising range of sounds using arc discharges, and thus have found uses as display pieces in entertainment and STEM education. A particularly large one is permanently mounted to a ceiling inside the Museum of Science and Industry in Chicago. However, for the majority of their existence, they have been crude instruments. The way they are built and operate typically results in a suboptimal use of AC power, also known as a poor power factor, and even with the advent of "solid-state" Tesla coils (SSTCs) that use power semiconductors, the problem has not improved. Areas with lower-voltage mains like the United States are often at a disadvantage due to details in many of these implementations. Further, when scaling up to large Tesla coils for use in performances, they can have a significant effect on the grid. Solving these problems can improve the efficiency and portability of these novelty constructions.

# Solution

We aim to build, for a comparatively low cost, a Dual-Resonance Solid State Tesla Coil (DRSSTC) with an active Power Factor Correction (PFC) front end. The combination of these two advancements puts our Tesla coil at the very forefront of Tesla coil hardware technology, and solves many of the technical issues with other modern designs.

Some background: Tesla coils are effectively giant transformers, with a secondary winding that has many times more turns than the primary. Conventional SSTCs operate by first rectifying mains AC to a high-voltage DC, then using a half-bridge or full-bridge of power semiconductors to switch the primary of the Tesla coil. This results in a very large voltage being generated in the secondary, which causes it to release arc discharges.

A major benefit that DRSSTCs like ours bring over SSTCs is that it operates more like a resonant converter. In the design phase of the transformer, the primary and secondary must be tuned to have close LC resonant frequencies. During operation, feedback from the primary is used to switch it at its resonant frequency, which results in energy being built up in the system more quickly and more impressive arc discharges. This energy buildup must be stopped intermittently by an external PWM signal called an interruptor (which can simultaneously be used to modulate music into the arc discharges). The primary feedback also enables zero-current switching (ZCS), reducing thermal losses in the power stage to near zero.

We choose to improve even further by designing a digitally controlled boost-type active PFC to create the high-voltage DC rail. This brings with it several benefits of its own, like improving system power factor, making the system agnostic to mains voltage and frequency, and allowing for smooth capacitor precharging without the use of a separate precharge circuit.

With a high power factor, both of the following are possible:
1. For the same apparent AC power, the generated arcs can be larger
2. Arcs of the same size can be generated for less apparent AC power

Thus the whole system consists of the PFC, the feedback controller, the power stage, and the transformer.

# Solution Components

## Boost-type PFC Stage

This subsystem draws power from the AC mains and creates a 400-volt DC rail. It is digitally controlled using an STM32F103 microcontroller, which allows it to ramp the voltage for precharging and compensate for different mains voltages and frequencies.

A boost-type PFC consists of a bridge rectifier, an input inductance, an output capacitance, a FET and an individual diode. We plan to use the Panjit KBJB bridge rectifier, Rohm SCT3120ALHR SiC FET and Wolfspeed C6D04065A SiC diode. Since we only need one of each in the product, their costs are negligible. A Texas Instruments UCC5710x gate driver can be used to allow the STM32F103 to drive the FET. The projected frequency of switching is 50kHz.

## Feedback controller

This subsystem implements a simple ZCS feedback controller using comparators and digital logic chips, and utilizes a long plastic optical cable to safely and remotely play simple musical notes via PWM (this is the interruptor signal). The optical receiver will be an Industrial Fiber Optics IF-D95T, which is an inexpensive device that has been highly proven in Tesla coil design history. Though in theory the microcontroller could also perform the logic task, we felt that it would not have low enough latency. The feedback itself is provided by a current transformer made of a Fair-Rite #77 ferrite core, which feeds into a burden resistor. Microchip MCP6561 comparators perform the zero crossing detection, and 74HCT logic chips manipulate the signal, combine it with the interruptor signal, and create gate drive waveforms for the power stage.

## Power stage

The power stage simply consists of a full bridge of four 60N65 IGBTs, and the primary LC is connected in the middle. The switches are driven by gate drive transformers (GDTs) to save cost and complexity versus developing a solution with isolated gate drive ICs. GDTs have been by far the leading solution to drive SSTC power semiconductors, and there is little incentive to do otherwise.

## Transformer

This is the Tesla coil itself. It will stand at around three feet tall once completed. It has no electronic components, but its physical design places some constraints on the electronic components. Preliminary calculations place the resonant frequency of the primary at around 200kHz.

# Criterion For Success

A PWM generator with an optical transmitter needs to be able to remotely start and operate the Tesla coil, causing it to release arc discharges. The arc discharges should be at least 1 foot in length, and the power factor of the whole system needs to be above 0.95 during normal operation.

Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

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