TRANSFORMER MONITORING

![[Pasted image 20250625134249.png]]

Project Title: Real-Time Transformer Monitoring System

  • Name: Brian Kamau Muthoni

  • Reg No: TLE/4906/21

  • Date: 24/06/2025

  • Superviser: Dr Serem

I. INTRODUCTION

The modern power grid is the backbone of global infrastructure, enabling the delivery of electricity to homes, industries, and essential services. At the heart of this network are power transformers, responsible for stepping voltage levels up or down for efficient long-distance transmission and safe distribution. Despite their importance, transformer failures continue to pose significant risks to grid stability, public safety, and economic productivity. In light of recent grid disruptions, there is a growing call to modernize transformer monitoring and integrate smart grid technologies.

With advancements in microcontrollers and sensor networks, it is now feasible to deploy low-cost, real-time transformer monitoring systems that collect and analyze key performance indicators such as current, voltage, gas emission levels, and temperature. When integrated with cloud platforms like Firebase, these systems can alert utilities before failures occur—ushering in a new era of smart maintenance and grid resilience.

II. PROBLEM STATEMENT

When a power transformer fails, the resulting consequences can be catastrophic. Utilities may face months of downtime, millions in repair and replacement costs, and service disruption to thousands or millions of customers. Traditionally, transformer condition assessment has been manual and infrequent, leaving utilities reactive rather than proactive.

Most transformers in existing electrical infrastructure operate without real-time monitoring. Utilities often depend on manual inspections or periodic sampling, which may fail to detect critical fault conditions early enough. This reactive approach can lead to:

  • Unexpected failures and prolonged outages

  • Manual dispatches to remote areas for testing

  • Cascading failures due to overloading of adjacent transformers

  • Increased operational costs and customer dissatisfaction

The objective of this project is to design and implement a low-cost, real-time transformer monitoring system using the ESP32 microcontroller. The system will collect data from sensors (voltage, current, gas), display it locally, and upload it to Firebase for remote monitoring and fault prediction.

CHALLENGE

Conventional vs. Smart Transformer Monitoring System

Lets look at a utility that has not deployed an advanced transformer monitoring system and technicians need to manually inspect the transformer for potential faults.

  • Samples of gases taken show no indication of potential failure , but 2 weeks later the transformer has a catastrophic failure

  • The inspection of the transformer is not scheduled for another 4 weeks

  • In this scenario the utility is unable to prevent a transformer failure ![[Pasted image 20250624124057.png]]

CONSEQUENCES

The immediate result is :

  • A large Blackout that affects many customers.

  • Customers are moved to another circuit on the distribution network while they replace the failed transformer

  • This in turn places undue strain on the second circuit and accelerates asset failures.

  • Months and Millions of Kenya Shillings are spent to bring a new transformer online.

![[Pasted image 20250624124214.png]]

III. OBJECTIVES

With Real Time Monitoring:

  • Enable Anomaly detection

  • Predict failure in advance

  • Schedule maintenance and minimize operational costs

  • Increased life span of transformers

Core Design Objectives:

  • Real-time monitoring of AC voltage, current, and gas leaks

  • Alert generation on overload, undervoltage, or gas threshold exceedance

  • Cloud database for remote monitoring and historical logging

  • A low-cost, scalable, microcontroller-based prototype

Motivation and Relevance

This project aims to emulate the core monitoring functions of industrial systems using affordable, easily sourced components:

Industry Function
Our Prototype Equivalent

Gas Analysis (Photo-acoustic)

MQ2 Gas Sensor

Partial Discharge / Bushing

Not implemented (future upgrade)

Load Current Monitoring

ACS712 Current Sensor

Voltage Monitoring

ZMPT101B Voltage Sensor

Cloud Dashboard & Alerting

Firebase Integration via Wi-Fi

Local Display

LED 16x2 I2C

Literature Review and Industry Background

A fragmented energy sector

Recent years have witnessed catastrophic grid failures due to transformer breakdowns. For instance:

Kenya's energy sector relies heavily on renewables such as geothermal and wind energy. But experts say there are potential weaknesses in the system.

According to Victor Kenga, a renewable energy expert, the fragmented nature of the energy sector presents challenges that may not exist in a more centralized setting.

"It should be looked at that we don't have a lot of monopolization in Kenya power and KenGen," Kenga said.

Experts say a lack of clear leadership on energy further muddles the picture. Kenya also has an energy minister and a principal secretary in charge of energy and various departments.

Cabinet Secretary for Energy and Petroleum David Chirchir has blamed the nationwide power outages on overloaded transmission lines.

"Sometimes the network trips when it is overloaded, there was sudden demand in the line between Kisumu and Muhoroni and cascaded down to the rest of the country," Chirchir said.

  • January 3, 2024 - A woman pictured in the glow of an emergency light during a power outage in Kenya in August 2023

  • Global infrastructure aging—70% of transformers and transmission lines are beyond their designed lifespan.

V. SMART GRIDS: THE FUTURE OF RESILIENT ENERGY DELIVERY

The traditional electrical grid was designed for one-way power flow: from central generation stations to end users. It lacks the intelligence to handle distributed renewable sources, variable demand, or autonomous control. This leads to inefficiencies, vulnerabilities, and increased blackout risk.

What is a Smart Grid?

A Smart Grid is a next-generation power system that integrates:

  • Sensors for real-time voltage, current, and frequency monitoring

  • Communications between substations, transformers, and utilities

  • Automation to trigger load balancing, isolation, and fault response

  • Computational intelligence for predictive maintenance and demand forecasting

Smart grids are capable of:

  • Detecting small faults before they become major failures

  • Isolating problems to local microgrids

  • Automatically shedding load during peak times or faults

  • Integrating renewable energy (solar, wind, hydro)

A smart grid integrates digital sensors, IoT devices, and automated controls to monitor, analyze, and optimize the flow of electricity in real-time. Smart grids can detect early signs of failure, reroute power dynamically, and isolate faults before they escalate.

Key smart grid technologies include:

  • Multi-Gas Monitoring: Identifies fault-related gases in transformer oil.

  • Partial Discharge Detection: Monitors bushing capacitance and dielectric breakdown.

  • Microgrids: Decentralized clusters that can operate independently during grid failures.

  • Advanced Communications: Use of IEC 61850 protocol for secure, real-time data exchange.

Industry Practice:

Modern solutions, like GE’s Transfix multi-gas monitor and BMT 300, use:

  • Photoacoustic Spectroscopy to detect trace gases

  • Capacitance Monitoring for bushings

  • Partial Discharge Analysis for early failure symptoms

These parameters are continuously monitored and sent to control centers over secure networks (e.g., IEC 61850 protocol), allowing operators to receive predictive alerts up to six months before potential failure.

“GE’s Transfix and BMT-300 series continuously monitor dissolved gases, bushing capacitance, and partial discharge in transformers using optical sensors and advanced analytics platforms integrated via IEC 61850 protocols.”

These systems, while effective, are prohibitively expensive for developing regions and local utilities. There’s a need for affordable, scalable, and wireless systems that mimic industrial capabilities on a smaller scale.

Project Justification and Design Goals

This project aims to emulate the core monitoring functions of industrial systems using affordable, easily sourced components:

Industry Function
Prototype Equivalent

Gas Analysis (Photo-acoustic)

MQ2 Gas Sensor

Partial Discharge / Bushing

Not implemented (future upgrade)

Load Current Monitoring

ACS712 Current Sensor

Voltage Monitoring

ZMPT101B Voltage Sensor

Cloud Dashboard & Alerting

Firebase Integration via Wi-Fi

VII. PROTOTYPE DESIGN: LOW-COST TRANSFORMER MONITORING SYSTEM

To replicate similar functionalities in a low-cost educational setup, components such as the ESP32 microcontroller, ACS712 current sensor, ZMPT101B voltage sensor, MQ2 gas sensor, relay module, OLED display, and Firebase IoT backend are used.

Features:

  • AC Voltage and Current Monitoring

  • Gas Leak Detection (Dissolved Gases Proxy)

  • Fault Shutdown via Relay

  • Real-Time Display and Cloud Logging

Components Used

ZMPT101B (AC Voltage Sensor Module)

Data Collection: - The ZMPT101B is a voltage sensor that uses a precision voltage transformer and an operational amplifier. - It scales down high AC voltages to a safe, readable level for the microcontroller.

Data Interpretation: - The module outputs a proportional AC signal that can be read by an ADC (Analog-to-Digital Converter) pin on a microcontroller. - The ADC reads the signal as a varying voltage, and a calibration algorithm calculates the actual AC voltage.

  • ![[Pasted image 20250624125010.png]] ## ✅ ZMPT101B AC Voltage Measurement Script (0–250V) Here’s a MicroPython script that:

  • Reads the ADC value rapidly.

  • Converts it to voltage.

  • Calculates the RMS AC voltage.

  • Maps that to the real AC voltage (0–250V) using a calibration factor.

from machine import ADC, Pin
from time import sleep
import math

# --- ZMPT101B Setup ---
VREF = 3.3
ADC_RESOLUTION = 4095
ADC_MIDPOINT = VREF / 2  # Midpoint around which waveform is biased
SAMPLES = 1000           # Number of samples for RMS

# Adjust this based on calibration (typical value: 250 for full scale)
CALIBRATION_FACTOR = 233  # volts per RMS output of sensor when mains is at 250V

# Setup ADC (GPIO 35)
zmpt_adc = ADC(Pin(35))
zmpt_adc.atten(ADC.ATTN_11DB)  # 0-3.3V input range

def read_voltage(adc):
    raw = adc.read()
    return (raw / ADC_RESOLUTION) * VREF

def read_ac_voltage(samples):
    sum_sq = 0
    for _ in range(samples):
        voltage = read_voltage(zmpt_adc)
        centered = voltage - ADC_MIDPOINT  # Remove DC offset
        sum_sq += centered ** 2
    mean_sq = sum_sq / samples
    sensor_rms = math.sqrt(mean_sq)
    
    # Convert sensor RMS voltage to actual AC voltage using calibration factor
    ac_voltage = sensor_rms * CALIBRATION_FACTOR
    return sensor_rms, ac_voltage

while True:
    sensor_rms, ac_voltage = read_ac_voltage(SAMPLES)
    adc_reading = zmpt_adc.read()
    print("ADC Value      : {}".format(adc_reading))
    print("Sensor RMS (V) : {:.3f} V".format(sensor_rms))
    print("AC Voltage     : {:.1f} V".format(ac_voltage))
    print("-" * 40)
    sleep(1)

Calibration Instructions

The CALIBRATION_FACTOR maps the sensor's RMS voltage to the actual AC voltage. To get an accurate value:

  1. Connect the ZMPT101B to AC mains (e.g. 233V measured in KENYA using a digital multimetee ).

  2. Use a multimeter to measure the real AC voltage.

  3. Run the script above and note the sensor RMS value.

  4. Use this formula to find the correct calibration factor: CALIBRATION_FACTOR = True_AC_Voltage / sensor_rms

  5. Replace the default value (250) with your calculated value.

# ACS712 (Current Sensor Module)

  • Data Collection: - The ACS712 measures current using the Hall effect. - It outputs a proportional analog voltage (e.g., 2.5V at 0A, 5V at max positive current, 0V at max negative current).

  • Data Interpretation: - The analog signal is fed to a microcontroller’s ADC pin. - The microcontroller reads the voltage and calculates the current based on the sensor's sensitivity (e.g., 185 mV per amp for 5A version).

![[Pasted image 20250624125044.png]]

How the ACS712 Works:

The ACS712 provides an analog voltage that varies linearly with current. For example:

Model
Sensitivity (mV/A)
Zero-current Voltage (Vout @ 0A)

ACS712-05B

185 mV/A

~2.5V

ACS712-20A

100 mV/A

~2.5V

ACS712-30A

66 mV/A

~2.5V

When no current is flowing, the sensor outputs approximately 2.5V (assuming 5V supply). A current in either direction shifts the output voltage linearly up or down.

Let’s assume you're using ACS712-20A for this example. Here's how to calculate the current:

current = (voltage - 2.5) / 0.100  # for 100 mV/A sensitivity

✅ Modify Your Script:

You can update your script to calculate and print the actual current like this:

from machine import ADC, Pin
from time import sleep
import math

# Constants
VREF = 3.3
ADC_RESOLUTION = 4095
ACS_OFFSET = 2.5
ACS_SENSITIVITY = 0.100  # V/A (adjust for your model)
SAMPLES = 1000           # Increase if needed for accuracy

# ADC setup
acs_adc = ADC(Pin(34))
acs_adc.atten(ADC.ATTN_11DB)

def read_voltage(adc):
    raw = adc.read()
    voltage = (raw / ADC_RESOLUTION) * VREF
    return voltage

def read_rms_current(samples):
    sum_sq = 0
    for _ in range(samples):
        voltage = read_voltage(acs_adc)
        current = (voltage - ACS_OFFSET) / ACS_SENSITIVITY
        sum_sq += current ** 2
    mean_sq = sum_sq / samples
    return math.sqrt(mean_sq)

while True:
    rms_current = read_rms_current(SAMPLES)
    print("AC Current (RMS): {:.3f} A".format(rms_current))
    print("-" * 40)
    sleep(1)

MQ Sensors (Gas Detection)

  • Output: Analog voltage

  • Application: Detect gas leaks, overheating

- MQ-2: Methane, Butane, LPG ![[Pasted image 20250624125141.png]]

What It Detects

  • Gases: LPG, propane, methane (CH4), hydrogen (H2), smoke, CO, alcohol

  • It's ideal for flame detection, gas leak detection, smoke alarms, and air quality monitoring

How It Works

  • MQ2 contains a SnO₂ (tin dioxide) semiconductor that changes resistance in the presence of target gases.

  • The analog output voltage drops or rises based on gas concentration.

  • It also has a digital output (threshold-based) that goes HIGH or LOW when concentration exceeds a set value (adjustable via onboard potentiometer).

Output

Use in Your Transformer Project

  • Detect flammable gases or transformer oil vapor

  • Trigger alerts when threshold exceeds safe levels

  • Can log data to Firebase and show on web dashboard

from machine import ADC, Pin
from time import sleep

# Setup ADC
mq2_adc = ADC(Pin(34))  # Use a valid ADC pin
mq2_adc.atten(ADC.ATTN_11DB)  # Full range 0-3.3V

VREF = 3.3
ADC_RES = 4095

def read_gas_voltage():
    raw = mq2_adc.read()
    voltage = (raw / ADC_RES) * VREF
    return voltage

while True:
    gas_voltage = read_gas_voltage()
    print("MQ2 Gas Sensor Voltage: {:.2f} V".format(gas_voltage))
    sleep(1)

ESP32 (Microcontroller)

  • Dual-core 32-bit MCU with built-in Wi-Fi and Bluetooth

  • 12-bit ADC for analog sensor interfacing

  • Acts as control, logic, and communication center

  • Sends data to Firebase and drives output devices

  • Data Collection and Processing: - The ESP32 Microcontroller can read analog and digital inputs and processes them

  • It interprets sensor data using libraries and code logic.

  • Data Interpretation: - It can directly interact with DHT22, ACS712, MQ sensors, etc. - It can also communicate with other modules ( e.g SIM800L) via UART, SPI, or I2C. Power Supply: 5V USB / Regulated 5V

![[Pasted image 20250624125208.png]]

Summary of Pin Usage

Sensor / Module
ESP32 Pin

ZMPT101B

GPIO35

ACS712

GPIO34

MQ2 (analog)

GPIO32

Ultrasonic TRIG

GPIO13

Ultrasonic ECHO

GPIO12

LED Alert

GPIO2

Ultrasonic Sensor (e.g., HC-SR04)

![[Pasted image 20250625141104.png]]

✅ What It Measures

  • Distance from the sensor to an object using sound waves

  • Used for level sensing (liquid, solid), obstacle detection, distance measurement

⚙️ How It Works

  • Sends a 40kHz ultrasonic pulse via the trigger pin

  • Measures the time it takes for the echo to bounce back to the echo pin

  • Calculates distance using the formula:

Distance=Time×Speed of Sound2​Distance=\frac{Time × Speed of Sound}2​

Accuracy & Range

  • Range: 2 cm to 400 cm

  • Accuracy: ±3 mm (under ideal conditions)

Power & Connections

  • Vcc: 5V

  • Trig: Digital Output (you pulse it HIGH for 10 µs)

  • Echo: Digital Input (read HIGH pulse duration)

  • GND: Ground

Use in My Transformer Project

  • Oil level detection: Measure oil height inside a tank if no mechanical float

  • Can be replaced by mechanical float or capacitive/oil-specific sensors in industrial setups

from machine import Pin, time_pulse_us
from time import sleep

TRIG = Pin(5, Pin.OUT)
ECHO = Pin(18, Pin.IN)

def get_distance():
    TRIG.off()
    sleep(0.002)
    TRIG.on()
    sleep(0.00001)
    TRIG.off()

    duration = time_pulse_us(ECHO, 1, 30000)  # Wait for ECHO HIGH
    if duration < 0:
        return -1  # Timeout
    distance_cm = (duration / 2) / 29.1  # Convert to cm
    return round(distance_cm, 2)

while True:
    dist = get_distance()
    if dist == -1:
        print("Out of range or timeout")
    else:
        print("Distance: {} cm".format(dist))
    sleep(1)

System Flowchart

![[Pasted image 20250624125240.png]]

Varying the load on a 230V AC supply line to test my voltage and current monitoring system was very doable and was done safely and deliberately.

How to Vary the Load on a 230V Line

The goal is to change the current drawn by connecting or disconnecting resistive or inductive loads:

🔌 A. Use Multiple Household Appliances

Appliance
Typical Power
Notes

LED Bulb

7–15 W

Light load, quick baseline test

Incandescent Bulb

60–100 W

Easy to see changes

Fan/Heater

100–2000 W

For larger load variations

Kettle/Iron

1500–2500 W

Useful for near-overload testing

Refrigerator

200–600 W

Inductive load (good test case)

🔧 Use a power strip with switches:

  • Connect multiple devices.

  • Toggle one by one.

  • See voltage drop or current increase.

Testing and Experimental Results

⚙️ Setup

The system was tested in a simulated environment using:

  • A regulated 230V AC supply

  • Variable loads (bulbs, heaters, fans) to simulate current fluctuations

  • A gas lighter and ethanol drops for MQ2 testing

  • Firebase was monitored in real-time from a mobile dashboard

![[Pasted image 20250625130448.png]]

🔍 Key Testing Metrics

Parameter
Observed Range
Expected Behavior
Status

AC Voltage

215V – 240V

OLED showed accurate values

✅

AC Current

0.02A – 3.5A

ACS712 tracked correctly

✅

Gas Level

0.1V – 0.5V

MQ2 triggered alarm at >0.4V

✅

Firebase Logs

Every 5s or Fault

Synced with network connection

✅

BreadBoard Diagram

![[Pasted image 20250625161355.png]]

Schmatic Diagram

![[Pasted image 20250625161501.png]]

PCB Design

![[Pasted image 20250625162106.png]]

Firmware, Firebase Integration & Fault Handling

💻 MicroPython Firmware Logic Overview

The ESP32 firmware in this transformer monitoring project performs four key operations:

  1. Reads analog values from voltage (ZMPT101B), current (ACS712), and gas (MQ2) sensors

  2. Displays real-time readings on an LED screen

  3. Sends sensor data to Firebase for cloud-based visualization and logging

  4. Handles fault conditions, such as overcurrent, undervoltage, and gas leak detection, by activating a relay, buzzer, and LED alerts

⚙️ Main Functional Blocks in Code

🔹 1. ADC Reading & Conversion

def read_voltage(adc):     
	raw = adc.read()     
	return (raw / 4095.0) * 3.3

This is applied to each analog input (voltage, current, gas), scaled to reflect real-world measurements using calibrated constants.

🔹 2. RMS Current and AC Voltage Conversion

To get real values (RMS) from AC sensors:

def convert_acs712_to_current(vout):     
	sensitivity = 0.185  # For ACS712-5A version     
	v_zero = 2.5         # No-load output voltage     
	return (vout - v_zero) / sensitivity
def convert_zmpt_to_ac_voltage(vout, scale_factor):     
	return vout * scale_factor  # scale_factor = calibrated multiplier

🔹 3. OLED Display via I2C

oled.text("Voltage: {:.1f}V".format(voltage), 0, 0) 
oled.text("Current: {:.2f}A".format(current), 0, 10) 
oled.text("Gas: {:.2f}".format(gas_level), 0, 20) 
oled.show()

This gives local visibility even without internet connectivity.

🔹 4. Firebase Integration

Use urequests or a Firebase client to upload data via HTTPS POST:

import urequests  
firebase_url = "https://your-project.firebaseio.com/data.json" 
payload = {     
	"voltage": voltage,     
	"current": current,     
	"gas": gas_level,     
	"status": status 
}  
res = urequests.post(firebase_url, json=payload) 
res.close()

Firebase Realtime Dashboard

Data Structure in Firebase:

{
	"data":{   
		"timestamp": "2025-06-21 17:00",     
		"voltage": 229.1,     
		"current": 1.25,     
		"gas": 0.45,     
		"status": "OK"   
	} 
}

Optional Enhancements:

  • Add charts to display time-series data

  • Trigger email/SMS using Firebase Functions

  • Log historical data for predictive analysis

🔄 Data Refresh Rate

  • Sampling frequency: 1–2 Hz (every 0.5–1 sec)

  • Firebase upload rate: every 5 seconds or when fault is detected

  • LED update rate: every second

13. 🔔 Fault Handling Summary

Condition
Threshold
Action

Overcurrent

> 5 A (configurable)

Disconnect relay, activate buzzer

Overvoltage

> 250 V

Trigger visual and audible alarm

Undervoltage

< 180 V

Cut off relay

Gas Detected

> 0.4 V on MQ2

Alarm + Notification

Wi-Fi Lost

N/A

Store buffer data locally (optional)

VIII. ADVANTAGES OF SMART TRANSFORMER MONITORING

  • Prevents catastrophic failures

  • Enables predictive maintenance

  • Reduces manual inspection frequency

  • Supports grid stability and efficiency

  • Reduces operating costs and outage penalties

Industrial Case Study: GE Advanced Monitoring Solutions

In an actual utility scenario, transformer failure occurred two weeks after gas samples were collected, long before the next test was scheduled. The utility failed to detect a growing fault, resulting in:

  • Catastrophic failure

  • Blackout affecting thousands

  • Strain on alternate circuits, accelerating other failures

  • High replacement costs, both financial and time-wise

In Contrast: GE’s Smart Monitoring

GE’s Transfix multi-gas monitor and BMT 300 detect:

  • Minute changes in gas concentrations

  • Partial discharges and bushing capacitance

  • All results are reported in real-time via IEC 61850 over wireless links

  • Anomaly detection triggered alerts months in advance

  • Utility pre-emptively de-energized the transformer, avoiding failure

This industrial-grade system demonstrates how proactive monitoring changes the game.

Transition to Industrial-Grade System

For the Industrial Transition Considerations we will include EMI-Resistant Components such as the GE Intellix BMT 300 and GE Transfix for more robust readings and integrating LTE/4G modules for reliable remote monitoring Why DGA?

  • Traditional current, voltage, and temperature sensors provide surface-level data but do not give insights into internal transformer conditions.

  • Dissolved Gas Analysis (DGA) detects key fault gases (e.g., hydrogen, methane, ethane) dissolved in transformer oil, providing early warning of issues like overheating, arcing, or insulation degradation.

How DGA Works: Transformer oil samples are analyzed for gases like hydrogen (H₂), methane (CH₄), ethylene (C₂H₄), ethane (C₂H₆), and carbon monoxide (CO).

  • Each gas is associated with specific fault conditions:

  • Hydrogen (H₂): Partial discharge

  • Methane (CH₄): Thermal faults

  • Ethylene (C₂H₄): High-temperature arcing

  • Carbon Monoxide (CO): Insulation degradation

The GE Intellix BMT 300 The Intellix BMT 300 is a multi-parameter transformer condition monitoring system that combines:

  • Online DGA (gas-in-oil monitoring)

  • Bushing monitoring

  • Oil temperature and moisture monitoring

  • Load current and voltage monitoring

It’s installed permanently on power transformers (mostly high-voltage ones) and continuously collects and transmits data to SCADA or asset management systems.

Bushing Risk Alerts Detects changes in:

  • Capacitance (increasing = insulation breakdown)

  • Power factor (increasing = aging or moisture ingress)

  • Leakage current (increasing = partial discharge or tracking)

  • Triggers alerts before catastrophic bushing failure (a major cause of transformer explosions)

How DGA Provides Deeper Insights into Transformer Health

Early Fault Detection: - DGA can detect thermal faults, arcing, and partial discharge long before they manifest as external temperature rises or current anomalies. Fault Classification: - By analyzing specific gas ratios (e.g., Duval Triangle method), DGA can pinpoint the type and severity of internal faults, allowing for targeted maintenance. Bushing Condition Monitoring: - The BMT 300 monitors bushing capacitance and power factor. Changes in these parameters can indicate moisture ingress, insulation breakdown, or mechanical displacement.

Parameters Monitored

Category

Parameter

Description

Dissolved Gases

H₂, CO, CO₂, CH₄, C₂H₂, C₂H₄, C₂H₆, O₂, N₂

Used for fault detection (thermal, electrical, insulation degradation)

Temperature

Top-oil temperature, ambient temp

Indicates thermal stress and helps detect overload or cooling failure

Moisture in Oil

ppm

High moisture can reduce insulation life and lead to arcing/failure

Load Current

Real-time current through transformer

Tracks load conditions and load-induced heating

Bushing Monitoring

Capacitance, power factor (tan δ), leakage current

Detects bushing aging, insulation failure, partial discharge risk

Pressure/Vacuum

Oil tank pressure (optional)

Detects leaks or vacuum loss (if sensors added)

GE Transfix and GE Intellix BMT 300: Why Consider Them?

  • These devices are designed specifically for DGA and can operate reliably in high-EMI environments due to their industrial-grade construction and advanced signal processing.

  • GE Transfix: Continuous multi-gas monitoring with built-in data analysis for real-time fault detection.

  • °GE Intellix BMT 300: Focuses on bushing condition monitoring, integrating both DGA and bushing capacitance analysis for comprehensive health assessment.

  • Both devices can transmit data via industrial protocols like Modbus, DNP3, or Ethernet, reducing the reliance on GSM modules

GE Transfix and GE Intellix BMT 300

Feature

GE Intellix BMT 300

GE Transfix

Type

Multi-parameter monitor

Dedicated DGA monitor

Dissolved Gas Monitoring

✅ 9 gases (full DGA)

✅ 9 gases (full DGA)

Bushing Monitoring

✅

❌

Moisture & Oil Temp

✅

✅ (limited)

Load/Current Monitoring

✅

❌

Communication Protocols

Modbus, DNP3, IEC 61850

Modbus, DNP3

Transformer Health Index

✅ Combines all diagnostics

❌ (gas-only health index)

Ideal Use

High-voltage, critical assets

Medium-voltage, gas-only needs

Cost

💲💲💲 (more expensive)

💲💲 (less expensive)

Summary of What the GE System Does

The GE advanced transformer monitoring solution includes:

  1. Gas monitoring inside transformer oil (dissolved gases)

  2. Bushing diagnostics (capacitance change, partial discharge)

  3. Temperature monitoring

  4. Electrical fault detection (voltage/current anomalies)

  5. Data logging and trend analysis

  6. Wireless transmission (via IEC 61850 or private LTE)

  7. Cloud/dashboard analysis (Perception Commander software)

🔧 What I Can Mimic in My DIY Setup

Industrial Feature
What You Can Use in Your Prototype

Dissolved gas detection

MQ2 gas sensor (general leak/gas detection, not dissolved gases)

Bushing diagnostics

⚠️ Very advanced — hard to mimic cheaply

Current monitoring

✅ ACS712 (already in your setup)

Voltage monitoring

✅ ZMPT101B (already in your setup)

Partial discharge sensing

⚠️ Not feasible at low cost; needs high-frequency sensors

Oil level monitoring

JSN-SR04T ultrasonic sensor + sealed probe

Local alerts (overload/fault)

Relay module, buzzer, OLED display

Data logging

Firebase Realtime DB (replaces SD card and trend logs)

Web dashboard/alerts

Firebase + Freeboard or custom HTML/Chart.js

Wireless transmission

ESP32 Wi-Fi (replaces GE’s wireless LTE or IEC 61850)

ESP32 MicroPython Code (Post Data to Firebase)

📦 Required library: urequests.py (install via Thonny or WebREPL)

import urequests
from machine import ADC, Pin
from time import sleep
import network
import math

# Wi-Fi config
SSID = 'YOUR_WIFI_SSID'
PASSWORD = 'YOUR_WIFI_PASSWORD'

# Firebase config
FIREBASE_URL = 'https://my-project-id.firebaseio.com'
FIREBASE_SECRET = 'YOUR_FIREBASE_SECRET'

# Setup ADCs
acs = ADC(Pin(34))
zmpt = ADC(Pin(35))
mq2 = ADC(Pin(32))

for adc in (acs, zmpt, mq2):
    adc.atten(ADC.ATTN_11DB)

# Constants
VREF = 3.3
ADC_RES = 4095
ACS_OFFSET = 2.5
ACS_SENS = 0.185
ZMPT_SCALE = 230.0

# Connect Wi-Fi
def connect_wifi():
    wlan = network.WLAN(network.STA_IF)
    wlan.active(True)
    wlan.connect(SSID, PASSWORD)
    while not wlan.isconnected():
        sleep(1)
    print("Connected:", wlan.ifconfig())

# Compute RMS voltage from sensor
def get_rms(adc, samples=100):
    values = []
    for _ in range(samples):
        raw = adc.read()
        voltage = (raw / ADC_RES) * VREF
        values.append(voltage)
        sleep(0.005)
    mean_square = sum([v**2 for v in values]) / len(values)
    return round(math.sqrt(mean_square), 2)

# Push data to Firebase
def push_to_firebase(vrms, irms, gasv):
    url = f"{FIREBASE_URL}/transformer_data.json?auth={FIREBASE_SECRET}"
    payload = {
        "voltage": vrms,
        "current": irms,
        "gas": gasv
    }
    try:
        r = urequests.post(url, json=payload)
        print("Data pushed:", r.text)
        r.close()
    except:
        print("Failed to send data.")

# Run main loop
connect_wifi()
while True:
    vrms = get_rms(zmpt) * ZMPT_SCALE
    irms = abs((get_rms(acs) - ACS_OFFSET) / ACS_SENS)
    gasv = get_rms(mq2)
    push_to_firebase(vrms, irms, gasv)
    sleep(10)  # Push every 10s

Web Dashboard with Charts

Create index.html locally or host it on GitHub Pages / Firebase Hosting

<!DOCTYPE html>
<html>
<head>
  <title>Transformer Monitor Dashboard</title>
  <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
  <h2>Transformer Monitor</h2>
  <canvas id="voltageChart"></canvas>
  <canvas id="currentChart"></canvas>
  <canvas id="gasChart"></canvas>

  <script>
    const firebaseUrl = 'https://your-project-id.firebaseio.com/transformer_data.json';

    async function fetchData() {
      const res = await fetch(firebaseUrl);
      const data = await res.json();
      const labels = Object.keys(data).map((_, i) => i + 1);
      const voltage = Object.values(data).map(d => d.voltage);
      const current = Object.values(data).map(d => d.current);
      const gas = Object.values(data).map(d => d.gas);
      return { labels, voltage, current, gas };
    }

    async function drawCharts() {
      const { labels, voltage, current, gas } = await fetchData();

      const commonConfig = (label, data, color) => ({
        type: 'line',
        data: {
          labels: labels,
          datasets: [{
            label: label,
            data: data,
            borderColor: color,
            fill: false
          }]
        }
      });

      new Chart(document.getElementById('voltageChart'), commonConfig('Voltage (V)', voltage, 'blue'));
      new Chart(document.getElementById('currentChart'), commonConfig('Current (A)', current, 'green'));
      new Chart(document.getElementById('gasChart'), commonConfig('Gas (V)', gas, 'red'));
    }

    drawCharts();
  </script>
</body>
</html>

Summary

✅ ESP32 collects RMS sensor data ✅ Posts data to Firebase in JSON format ✅ Web app pulls data and renders it using Chart.js ✅ You can monitor real-time changes in voltage, current, and gas

Data Visualization

![[Pasted image 20250624125802.png]]

![[Pasted image 20250624125826.png]]

![[Pasted image 20250624125818.png]]

IX. LIMITATIONS AND FUTURE WORK

Overview of Challenges

1.EMI Interference High-voltage power lines and transformers, Switching transients and arcing, Relay operations and circuit breakers, Motor drives and large inductive loads can cause potential impact on sensor accuracy and data transmission

2.Sensor Limitations - ACS712 limited to specific current ranges (e.g., 5A, 20A, 30A) - MQ sensors prone to calibration drift over time - DHT22 sensitive to rapid temperature/humidity changes 3.Communication Limitations

  • Wi-Fi Connectivity Issues: - ESP32 may experience signal loss in enclosed transformer rooms - Potential use of external antennas or LTE modules for extended range - Data Latency:-Real-time data transmission may be delayed by network congestion

4.No bushing capacitance monitoring (as in GE BMT-300) 5.No partial discharge detection 6 Limited to single transformer phase

  • No internal battery backup if power fails

  • Low-cost sensors have limited precision compared to industrial-grade systems.

  • Oil level and bushing capacitance sensors are not implemented in basic prototypes.

  • Future work can integrate AI models for anomaly prediction and add cybersecurity layers.

Future Work

  • Advanced Data Analysis: - Predictive maintenance using AI/ML algorithms - Data pattern recognition for fault detection

  • System Expansion: - Integration with additional sensors (e.g., vibration sensors) - Cloud integration for long-term data storage and analytics

  • Real-Time Data Acquisition: - Data acquisition frequency and sampling intervals

  • Data Storage: - Local storage (SD Card, EEPROM) - Remote logging (Cloud, MQTT)

  • Visualization: - Real-time graphs using platforms like ThingSpeak, Firebase - Customized dashboards for specific data points

X. CONCLUSION

The prototype successfully demonstrates the feasibility of a low-cost, ESP32-based transformer monitoring system. It combines:

  • Real-time data acquisition

  • Local and remote data display

  • Automatic safety response

  • Scalable architecture

This system can empower local utilities or research institutions to monitor transformer health in real-time, detect faults early, and reduce response time dramatically — a step forward toward democratizing smart grid monitoring.

References

  1. G.E. Advanced Transformer Monitoring Solutions

  2. ACS712 Datasheet – Allegro Microsystems

  3. ZMPT101B Voltage Sensor Module – Open Source Hardware

  4. MQ2 Gas Sensor Specifications – SeeedStudio

  5. Firebase Realtime Database – Google Cloud Docs

  6. IEEE 61850 Communication Standard for Substations

  7. “Smart Grid Overview” – National Renewable Energy Laboratory (NREL)

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