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MACHINE LEARNING APPLICATIONS IN�POWER SYSTEM FAULT LOCATION WITH ADMS AND AMI

GROUP 11

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THE TEAM

Michael Mederos

CpE

Yuejun (Pearl) Guan

EE

Julio Barreto

EE

David Silvieus

EE

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MOTIVATIONS

Increasing stability of the distribution system
Better communication between grid equipment and operators
Reduce the duration of outages

David Silvieus, EE

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GOALS AND OBJECTIVES

Create a fault locator that is applicable to various feeder systems

Use ADMS and AMI data to assist in determining fault status & location

Measure and compare impedance values along the feeder to determine location of the fault

Verify fault status and location with machine learning algorithms

David Silvieus, EE

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TABLE OF ENGINEERING SPECIFICATIONS

Pearl Guan

EE

For the hardware comminction portion of this project, The PCB must be able to commicate with the computer at a reasonable speed. For our Uart com. We decied on a baud rate of 9600 or 115200 baud because its is tylpcial serial com. Speed

This project also requires us to collect current and voltage data to calulate the impedances, these data serve as a important input to the fault detection and location alrgoithum. Per the scope of this project, we will be building and testing a small one line grid with 4 loads and one laterals disbirtion line. having the ability to to compute at least 4 signals and the power supply is necessary

After the system is calibrated, ground fault and high impendace fault should be determined within 15 seconds.

The scalbility of the machine learning is also critical because the Models’ detection algorithm must be able scale depending on the size of grid or number of nodes involved for this to be appliable in the real world setting

Another critical specification involves identifying the type and phases of the fault. In a 3-phase power system, there are eight types of faults, ranging from phase-to-ground faults, phase-to-phase faults, to phase-to-phase ground faults. Determining the phase of a fault is important for precisely isolating and addressing the faulted part of the power system, based on the type of fault, different fault location algthirum will be used. To determine which phase the fault occurred on, we compare the absolute values of the per-phase current with the respective 0-sequence component of the line current.

The calculated location should be accurate within a 10% margin of error relative to the actual fault location along the section.

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GENERAL PCB BLOCK DIAGRAM

Julio Barreto - EE

Now we are going to go over our hardware and significant PCB design.

The main objective of our PCB is to collect data from our microgrid representation. In this case, we will be measuring for current and voltage in different nodes. Then we will send this data to the microprocessor so it can transform it into impedance.

In this simplified block diagram we can see the main function of each of the systems of our PCB.

In red there is the Power supply system which it’ll be responsible for powering the processor and it peripherals.

Then in yellow we have the current sensors which will be clamped around the wires of our model to measure for current

Finally, in blue we see our voltage sensor which will have probes that will be touching the nodes we want to get information from.

As we can see all of these systems will end up communicating with the STM32, who's going to get this data and use it to find impedance

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POWER SUPPLY SYSTEM

Julio Barreto - EE

Now we are gonna go more in depth with each of the systems in our pcb.

First we will start with the power supply, which its main function is to deliver enough power to the op amps, multiplexers and the mcu.

Our power supply is shown in screen, this will deliver 15 V and -15 V DC.

From this power supply we’ll use a voltage regulator to get 3.3 V to supply the MCU. As said before in the part selection, since we are going from 15 to 3.3 V(almost 12), we’ll need a power sink to dissipate heat in the voltage regulator

A 12 V and a -12 Vregulator we’ll be used to power the op amps and the multiplexers. It is necessary to power the op amps and multiplexers with + /- 12 V since our microgrid model will have a maximum value of 12 V AC, if we were to use our PCB to measure higher voltages, all the peripherals related to the voltage meter will get permanently damaged.

12: LM7812

-12: LM7912

3.3: LM317

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CURRENT SENSOR

Julio Barreto - EE

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VOLTAGE SENSOR

Julio Barreto - EE

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COMPARISON AND SELECTION OF HARDWARE (POWER SUPPLY)

Application	Characteristic	Advantages	Disadvantages
chroma programmable ac source 61704	0-300V 3 phase programmable AC sources	Synchronized 3-phase low voltage output 2) Uses SCPI, which is commonly use most research and industry grade signal generator 3) Allow current cap relay	More Complicate than the regular signal generator
Tektronix AFG3022C Arbitrary/Function Generator	a single-phase 12V sine wave input couple with a 0.125A fuse	Easy access in the lab	No three-phase option
California Instruments MX22.5 amplifier	120V three phase sine wave signals	A good three-phase power supply with built-in protection and a user-friendly interface	1) Too many moving parts 2) Extremely expensive

12V three phase AC delta configuration

Pearl Guan

EE

For our grid design, we went thru Multiple change for power supply trying to decied the amount of phase and power input level. We chose 3 phase delta configueration to avoid the returning neatual path, and 12v input level allow us to prefrom necessary testing safely.
Chorma: Can provide sync 3 phase power at our desire voltage level and has a programmable interface that allows us to gaage he ststauate of the system. It progaamming lang. SCPI is commonly use amount proesional level power equipments. The RS-232 com. Protocol allows us to transfer the Current and impendace value to the computer.
Tektronix: can provide single phase power and couple with the 0.125A fuse we can ensure a safe testing enmvirment.
Amplifier: can provide sync 3 phase power at our desire voltage level but it does not have a easy to read interface and it is very expenasive to replace

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MICROCONTROLLER SELECTION

Microcontroller	Reason	Cost
STM32F103C8T6	(+) 2x 8-channel 12-bit ADC (+) Powerful IDE (+) Availability of reference material	$6.42
ATMEGA328P	(-) 1x 8-channel 10-bit ADC (+) Simple to use IDE (-) Expensive for performance (-) IDE can be limiting	$2.89
MSP430FR6989IPZ	(+) 1x 16-channel 12-bit ADC (+) Powerful IDE (-) Not as much reference material (-) Expensive for similar performance to STM32	$10.77

Michael Mederos

CpE

Michael

�For our microcontroller, we originally were going to use the ATMega328p as our MCU of choice. However, as we were advancing along our project we realized that the available flash and RAM on the ATMega328p, 32kb of flash and 2kb of ram, was simply not enough for our project as we are planning on implementing a real-time operating system and require multiple tasks as well as a machine learning model. Another reason was that the ATMega328p only had one ADC, and even then it was a 10-bit ADC compared to our other candidates.

This led us to choosing the STM32F103C8, we believe this one is the best choice due to it having two dedicated 12-bit ADCs, which means a considerable increase in sampling resolution (4x). The STM32CubeIDE is also much more powerful than the Arduino IDE, allowing for the implementation of a FreeRTOS Kernel as well as more granular control of the MCU’s operations. Documentation and reference material was also more plentiful, which proved to be useful as we were learning and developing our project. It’s price increase is also well worth it considering it has double the flash at 64kb and 20kb of RAM. This will prove plenty for both our FreeRTOS implementation as well as our machine learning model.

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POWER RESISTOR SELECTION

Power resistor	Application	Cost
HS100 4R7 J and HS 5R7 J	Load with high wattage	$10.57 (free for us)
AVT10006E1R000KE	Potential meter with high wattage for fault	$17.17 (free for us)
HS100 1K J and HSC1001R5J	Line resistance to simulate distance on the mini grid	$24.4

Pearl Guan

EE

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3.3 POWER REGULATOR SELECTION

Microcontroller	Reason	Size	Delivery time	Cost
TPSM82902SISR	(+) Small footprint (+) Efficient switching regulator (-) Complex soldering (-) Expensive	3.0 mm × 2.8 mm	1-week shipping	$6.42
LM317	(+) Simple (+) Fast delivery (-) Less efficient linear regulator (-) Junction-to-ambient coefficient 37.9*C/W, requires heatsink	14.48mm x 10.16mm	2-day shipping	$2.89
MIC29300	(+) Simple (+) Fixed 3.3V out (-) Heatsink required	15.11mm x 10.66 mm	1-week shipping	$4.65

Julio Barreto - EE

Julio

An important part of the pcb is to power the STM32, for that, we are using a 3.3 voltage regulator.

At first we thought about using the TPSM82902, mostly because of its compact design, its efficiency, and it could hold 15 V DC supplied

But During the implementation of our design, we notice that it was too small to be soldered.

We looked up several regulators like the MIC29300 that could do the work, but Since we wanted to have it done as quickly as possible, we ended up going with the lm317 because it satisfies all of our needs. We can easily solder it to our pcb, it can be supplied with 15 V Dc, and also it is cheaper and has a faster delivery time than other regulators.

So we ended up going with the LM317. Although since we are converting 15 V into 3.3, a lot of heat has to be dissipated. By looking at the datasheet we noticed that we needed to use a heat sink since if the regulator gets too hot, it could start drifting from our desired output voltage. And that’s what we ended up implementing in our final design.

�

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COMPARISON AND SELECTION OF SOFTWARE (SYSTEM MODELING)

Application	Advantages	Disadvantages
MATLAB and Simulink	Professional level software that’s very user-friendly. Combining graphical power system case editor and power system analysis	It's different from traditional one line diagram simulation
PowerWorld	Block based diagram works best for dynamic systems. It can be used for system simulation as well as coding, especially for machine learning.	Unable to preform unbalanced load study
OpenDSS	Recommended for 3-phase unbalanced load modeling. Would be great for scalability down the road	Steeper learning curve

Pearl Guan

EE

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COMPARISON AND SELECTION OF SOFTWARE (SYSTEM MODELING)

Pearl Guan

EE

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MACHINE LEARNING SOFTWARE COMPARISON

Tensor Flow	Multinomial Logistic Regression
Scalable Uses static values as inputs Gives a binary result Much more complex	Scalable number of output classifications Uses logarithmic expressions to determine classification Static values as inputs Easier to use

David Silvieus, EE

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CHOICE OF CAD: EAGLE VS KICAD

Original design called for an ATMega328p-based PCB.

Reference Arduino material available already in EAGLE, as well as Junior Design material.

For the updated design using the STM32F103C8T6, KiCad was chosen due to it’s user-friendliness.

KiCad also has a stronger userbase, which can be helpful when asking for guidance or referencing material on the internet.

Michael Mederos

CpE

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Michael Mederos

CpE

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PCB DESIGN

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PCB - PERIPHERALS

VOLTMETER

POWER

CURRENT METER

MCU

Julio Barreto - EE

Components:

(11) OPA277
(1) LM7812 (12V Reg)
(1) LM7912 (-12V Reg)
(1) LM317 (3.3V Reg)
(2) CD4051BM
(3) 2n2222

This is the final product of our pcb. We highlighted the areas with different colors to have a better understanding of each of the zones of our pcb.

We ended up using 11 OPA277 which functions vary between isolating, amplifying and rectifying the inputs from the microgrid. Either current or voltage signals.

We are also using three 2n2222 transistors which are used to change channels in our mux. Since we are powering our mux with +/-12 v, we need to send around 8 volt signals to change channels. The problem is that th stm32 can only send 3.3 signals. What we did to overcome this is that we connected the base of the transistor to the mcu, the emitter to ground, and the collector to both multiplexers. What this does is that if we don’t apply any voltages to the transistors, the multiplexer will see it as a 1, and when we apply a voltage, a 0. Meaning that if we send a 111, the multiplexer will read it as a 000, or, read ch. 0.

In yellow we have the current meter area, clamps are connected through the pcb through a screw connector. current meter passes through a buffer to isolate the signal from the rest of the system, and afterwards they all connect to a multiplexer. Depending on the channel selected, the chosen signal wil pass through another buffer, and then will be amplified by 157. Finally we rectified the ac signal into dc and we stepped it down so it has a maximum of 3.3v.

In blue we see the voltmeter section. The probes connected to the screw connector are also connected to the microgrid nodes that we want to measure. The voltage reading will be selected by the multiplexer and afterwards we will isolate it from the rest of the circuit with a buffer. Finally as with the current meter, we rectified the signal and stpped it down.

As said before in the parts selections. The 3.3 voltage regulator that we implemented in our design was too small to be soldered. So we have to create an extension of the pcb in the bottom left part

As you can see.

Michael

12: LM7812

-12: LM7912

3.3: LM317

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MINI GRID FOR DEMO

Pearl Guan

EE

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SOFTWARE DESIGN OF MCU

MCU software will be an interrupt-based approach

Every 5 seconds, interrupt is triggered
Both ADC are polled to grab voltage sensor and current sensor values
Impedance is calculated and stored as an array of n size, where n is number of nodes (6 for our project)
Impedance array sent to ML model through serial communication

Michael Mederos

CpE

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SOFTWARE DESIGN (FAULT DETECTION AND LOCATION)

Data acquisition
Estimating the faulted section
Modified radical system
Modeling of loads
Estimating sequence voltages and currents at the fault and at the remote node

Pearl Guan

EE

The following fault detection classivcfictaion and location method was not used in the final delibale due to the time constraints

Data: pre fault and at fault value of per phase voltage, current, impendace, and their respective positive, negative, and zero sequence components

The type of fault is determine based on the phase to phase or phase to ground current

After the type of fault is determined, the algrithum will Estimate faluty section by look for high reactance in the system from the power source side in a what if loop, pls keep in mind the calculation will look slightly different based on different type of fault

Then the not at fault laterals and load would be group togther by their equielant impendace, then esitimate the sequence voltages and currents at the fault and at the remote node, all the not at fault sections will be condensed at this point

Finally based on the at fault impedance, we should be able to locate the fault with less than 10% error within the section

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IMPLEMENTATION OF MULTINOMIAL LOGISTIC REGRESSION

Training the Machine Learning Model using data acquired from grid simulation
Import the model into our active program and use it to make real-time predictions
Ability to differentiate between ground and high-impedance faults
One model for high-impedance faults and one model for ground faults

David Silvieus, EE

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BUDGET

BOM shows all purchases made to assemble grid
Purchased 5 PCBs
Total cost came out to ~$350 after tax.

David Silvieus, EE

Item	Quantity Purchased	Total Price
8:1 MUX	10	$4.30
IC Regulators	4	$3.44
Fixed IND 12UH	4	$3.44
Zener Diodes 5.1V	10	$2.21
Current Sense XFMR	3	$26.50
Adjustable Inductors	10	$5.17
IC MCU	2	$5.78
Crystal 16MHZ Clock	2	$1.00
Capacitors	10	$1.54
STD Diode 1000V 1A	10	$.86
ADJ PWR RES 5OHM	1	$9.36
IC REG Linear -12V	2	$6.26
Zener Diodes 3.3V	10	$1.35
PCB Parts & Assembly	5	$217.43
Current Sensor	2	$15.99
Current Sensor	1	$12.99
Screws	106pc	$17.72

Grand Total	$335.34

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WORK DISTRIBUTION

Michael	Julio	Pearl	David
Primary Role: Designer of MCU and software associated with MCU. Secondary Role: PCB Design, voltmeter and current meter design	Primary Role: PCB Design and building in KICAD. Voltmeter and current meter design Secondary Role: Designer of MCU and software associated with MCU.	Primary Role: Designing and building the feeder and lateral grid simulation and physical representation	Primary Role: Implementation of Machine Learning into the fault detection system

David Silvieus, EE

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ACKNOWLEDGEMENT

Thank you,

Dr. Deepal Rodrigo
Dr. Qu Zhihua
Dr. Dimitrovski
Kwasi Opoku
Dr. Weeks
Kenneth McDonald

David Silvieus, EE