SVSEmbedded will do new innovative thoughts. Any latest idea will comes we will take that idea & implement that idea in a few days. We always encourage the students to take good ideas/projects.
SVSEmbedded providing latest innovative electronics projects to B.E/B.Tech/M.E/M.Tech students. We developed thousands of projects for engineering student to develop their skills in electrical and electronics
SmartGuard-3P: IoT-Based Real-Time Health Monitoring and Protection of Three-Phase Transformers Using ESP32 and ThingSpeak. ************************************************ 🛠️ Do You Want to Purchase the Full Working Project KIT? 🛠️ Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link 🔌 CODE & CIRCUIT DIAGRAMS FOR SALE 🔧 💡 Reliable – Affordable – Ready to Use http://svsembedded.com/
M1: +91 9491535690 M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / India Post / Blue Dart We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos & Working Video links Latest Projects with Year Wise YouTube video Links 148 Projects https://svsembedded.com/ieee_2026.php
1.IoT-Based Three-Phase Transformer Health Monitoring and Protection Using ESP32 and ThingSpeak. 2.SmartGuard-3P: IoT-Based Real-Time Health Monitoring and Protection of Three-Phase Transformers Using ESP32. 3.Three-Phase Transformer Health Monitoring Using ESP32 and ThingSpeak | IoT-Based Condition Monitoring System. 4.IoT-Based Real-Time Monitoring and Fault Detection of Three-Phase Distribution Transformers Using ESP32. 5.Design and Implementation of an IoT-Based Three-Phase Transformer Health Monitoring and Protection System. 6.Smart Three-Phase Transformer Monitoring and Protection System Using ESP32 and ThingSpeak. 7.IoT-Enabled Real-Time Condition Monitoring of Three-Phase Distribution Transformers Using ESP32. 8.ESP32-Based IoT System for Real-Time Three-Phase Transformer Health Monitoring and Fault Detection. 9.IoT-Based Three-Phase Transformer Condition Monitoring and Early Fault Detection Using ESP32. 10.Real-Time Three-Phase Transformer Health Monitoring and Protection Using ESP32 and IoT. 11.Design and Implementation of an IoT-Enabled Real-Time Condition Monitoring and Protection System for Three-Phase Distribution Transformers. 12.An ESP32-Based IoT Framework for Real-Time Condition Monitoring and Fault Detection in Three-Phase Distribution Transformers. 13.IoT-Enabled Real-Time Condition Monitoring and Protection of Three-Phase Transformers Using ESP32 and ThingSpeak. 14.Design and Development of an ESP32-Based IoT System for Three-Phase Transformer Health Assessment and Fault Detection. 15.Real-Time IoT-Based Monitoring of Electrical and Thermal Parameters for Three-Phase Transformer Condition Assessment. 16.An IoT-Based Framework for Real-Time Voltage, Current, Temperature and Oil-Level Monitoring of Three-Phase Transformers. 17.Development of an ESP32-Enabled Smart Monitoring System for Real-Time Three-Phase Transformer Health Assessment. 18.IoT-Based Condition Monitoring and Early Fault Detection of Three-Phase Distribution Transformers Using ESP32. 19.A Smart IoT Architecture for Real-Time Monitoring and Protection of Three-Phase Distribution Transformers. 20.Real-Time Transformer Health Monitoring and Fault Protection Using ESP32-Based IoT Technology. 21.TransfoSense: An IoT-Enabled Intelligent Three-Phase Transformer Health Monitoring and Fault Detection System. 22.Transformer360: Smart IoT-Based Real-Time Monitoring and Protection of Three-Phase Distribution Transformers. 23.GridGuard-3P: An IoT-Enabled Smart Transformer Monitoring, Fault Detection and Protection System Using ESP32. 24.TransfoShield: Intelligent IoT-Based Condition Monitoring and Protection of Three-Phase Distribution Transformers. 25.SmartTrans-3P: An ESP32 and IoT-Based Intelligent Three-Phase Transformer Health Monitoring System. 26.TransfoWatch: IoT-Based Real-Time Monitoring of Voltage, Current, Temperature and Oil Level in Three-Phase Transformers. 27.IntelliTrafo: An ESP32-Based IoT System for Intelligent Three-Phase Transformer Health Monitoring and Protection. 28.SmartTrafo: IoT-Enabled Real-Time Three-Phase Transformer Monitoring and Early Fault Detection Using ESP32. 29.TrafoGuard 360: An Intelligent IoT Architecture for Three-Phase Transformer Health Monitoring and Protection. 30.TrafoSentinel: Continuous IoT-Based Health Monitoring and Early Fault Detection for Three-Phase Transformers. 31.IoT-Based Three-Phase Transformer Monitoring of Voltage, Current, Temperature and Oil Level Using ESP32. 32.Real-Time Transformer Health Assessment Using Multi-Parameter IoT Sensing and ESP32. 33.ESP32-Based Transformer Condition Monitoring System for Voltage, Current, Temperature and Oil-Level Detection. 34.IoT-Based Multi-Parameter Health Monitoring and Fault Detection of Three-Phase Transformers. 35.Real-Time Monitoring of Electrical and Thermal Parameters in Three-Phase Transformers Using ESP32 and ThingSpeak. 36.Smart IoT-Based Transformer Condition Monitoring Using Voltage, Current, Temperature and Oil-Level Sensors. 37.Three-Phase Distribution Transformer Health Assessment Through Real-Time IoT-Based Multi-Parameter Monitoring. 38.IoT-Enabled Multi-Parameter Transformer Monitoring and Early Warning System Using ESP32. 39.ESP32-Based Real-Time Three-Phase Transformer Health and Safety Monitoring System. 40.Real-Time IoT-Based Three-Phase Transformer Condition Assessment and Fault Warning System. 41.IoT-Driven Transformer Health Intelligence: Real-Time Monitoring and Fault Detection Using ESP32. 42.Edge-IoT-Based Intelligent Condition Assessment and Health Monitoring of Three-Phase Distribution Transformers. 43.An Intelligent Edge-IoT Framework for Real-Time Anomaly Detection and Health Assessment of Three-Phase Transformers. 44.Multi-Parameter IoT-Based Condition Assessment and Predictive Fault Detection of Three-Phase Distribution Transformers. 45.Edge-Enabled Transformer Health Intelligence Using Multi-Parameter Sensing and Real-Time IoT Analytics. 46.Intelligent IoT Architecture for Continuous Condition Monitoring, Anomaly Detection and Predictive Protection of Three-Phase Transformers. 47.IoT-Based Predictive Health Assessment and Early Fault Detection of Three-Phase Distribution Transformers Using ESP32. 48.Smart Transformer Health Intelligence Using ESP32-Based Edge Monitoring and Cloud Analytics. 49.IoT-Enabled Intelligent Transformer Condition Assessment and Real-Time Anomaly Detection Using ESP32. 50.Advanced IoT-Based Three-Phase Transformer Health Monitoring, Fault Detection and Predictive Condition Assessment. 51.IoT-Based Three-Phase Transformer Health Monitoring and Protection Using ESP32 and ThingSpeak. 52.SmartGuard-3P: IoT-Based Real-Time Health Monitoring and Protection of Three-Phase Transformers Using ESP32. 53.IoT-Based Real-Time Monitoring and Fault Detection of Three-Phase Distribution Transformers Using ESP32. 54.Design and Implementation of an IoT-Based Three-Phase Transformer Health Monitoring and Protection System. 55.TransfoSense: An IoT-Enabled Intelligent Three-Phase Transformer Health Monitoring and Fault Detection System. 56.Three-Phase Transformer Health Monitoring Using ESP32 and ThingSpeak \| IoT-Based Condition Monitoring System. 57.Transformer360: Smart IoT-Based Real-Time Monitoring and Protection of Three-Phase Distribution Transformers. 58.An ESP32-Based IoT Framework for Real-Time Condition Monitoring and Fault Detection in Three-Phase Distribution Transformers. 59.IoT-Based Three-Phase Transformer Condition Monitoring and Early Fault Detection Using ESP32. 60.Real-Time Monitoring of Electrical and Thermal Parameters in Three-Phase Transformers Using ESP32 and ThingSpeak.
1.AttendX: An Intelligent Edge-to-Cloud RFID–IoT Platform for Automated Student Attendance, Real-Time Parent Alerts, and Cloud Analytics. 2.Smart RFID Attendance System Using ESP32 and IoT with Real-Time Parent Notifications and Cloud Analytics. 3.Design and Development of an ESP32-Based RFID Student Attendance System with Cloud Integration and Real-Time Parent Notifications. 4.AttendSphere: A Secure Edge-to-Cloud Framework for Intelligent Student Attendance and Presence Management. 5.An IoT-Enabled RFID Framework for Automated Student Attendance Monitoring and Real-Time Parent Alert Generation. 6.ESP32-Based Smart RFID Attendance System with IoT Connectivity, Cloud Monitoring, and Real-Time Alerts. 7.AttendSync: A Smart RFID–IoT Framework for Real-Time Student Attendance and Parent Engagement. 8.EduPulse: An Intelligent IoT Platform for Real-Time Student Attendance, Monitoring, and Notifications. 9.Design and Implementation of a Smart RFID–IoT Architecture for Real-Time Student Attendance and Cloud-Based Monitoring. 10.PresenceGuard: An Intelligent RFID and Edge-IoT Framework for Secure Student Attendance and Real-Time Notifications. 11.Smart Student Attendance System Using ESP32, RFID, IoT, Cloud Computing, and Real-Time Alerts. 12.AttendIQ: Intelligent RFID-Based Attendance Automation with Cloud Integration and Attendance Analytics. 13.EduTrack: An Intelligent RFID and IoT-Based Student Attendance Monitoring and Alert System. 14.Real-Time Student Attendance Monitoring Using ESP32, RFID, IoT Connectivity, and Cloud-Based Alert Mechanisms. 15.ClassGuard: An IoT-Enabled RFID System for Automated Attendance and Real-Time Parent Alerts. 16.A Cloud-Integrated IoT Framework for Automated RFID-Based Student Attendance and Real-Time Stakeholder Notifications. 17.AttendLink: A Cloud-Integrated RFID–IoT Ecosystem for Student Attendance and Parent Communication. 18.ESP32 RFID Attendance System with Cloud Database, Real-Time Notifications, and Student Attendance Analytics. 19.EduSentinel: A Smart RFID–IoT Architecture for Continuous Student Presence Monitoring and Automated Alerts. 20.SmartPresence: A Real-Time IoT Framework for Automated Student Attendance and Parent Notification. 21.AttendGuard: A Secure Smart Attendance Ecosystem with RFID Authentication, Edge Processing, and Real-Time Alerts. 22.IoT-Based RFID Student Attendance System Using ESP32 with Real-Time Parent Alerts and Cloud Dashboard. 23.EduWatch: An Intelligent IoT-Based Student Presence, Attendance, and Alert Management System. 24.CampusSense: An ESP32–RFID Smart Attendance and Student Presence Monitoring Platform. 25.AttendCloud: A Smart RFID–ESP32 Framework for Automated Attendance and Real-Time Stakeholder Notifications. 26.Intelligent Student Attendance Monitoring Using RFID and ESP32 with IoT-Based Cloud Integration. 27.Automated Student Attendance System Using RFID and ESP32 with Real-Time IoT Notifications. 28.ClassPulse: Real-Time RFID Attendance Monitoring with ESP32, Cloud Connectivity, and Parent Alerts. 29.EduTrace: A Cloud-Connected Student Presence Intelligence Platform Using RFID and Edge IoT. 30.SmartCampus360: An IoT-Based Student Attendance and Real-Time Monitoring Platform Using RFID and ESP32. 31.An ESP32-Enabled Intelligent Attendance Monitoring System Using RFID, Cloud Computing, and Real-Time Notification Services. 32.IoT-Enabled Smart Attendance Management Using RFID and ESP32 with Cloud-Based Analytics and Alerts. 33.PresenceNet: An Intelligent RFID–IoT Network for Automated Attendance and Stakeholder Communication. 34.Intelligent RFID-Based Student Attendance and Presence Monitoring System with ESP32 and Cloud Services. 35.AttendAIoT: An Intelligent Edge-to-Cloud Architecture for Automated Student Attendance and Behavioral Insights. 36.SchoolSync: An Intelligent RFID Attendance System with ESP32, Cloud Monitoring, and Parent Alerts. 37.SmartClass360: A Connected RFID–IoT Platform for Student Attendance and Presence Management. 38.Real-Time RFID-Based Student Attendance and Parent Notification System Using ESP32 and IoT. 39.Secure Cloud-Connected RFID Attendance Architecture for Automated Student Identification and Attendance Management. 40.IoT-Based Smart School Attendance System Using ESP32 and RFID with Automated Parent Notifications. 41.Automated RFID Student Attendance Monitoring with ESP32, Cloud Synchronization, and Real-Time Alerts. 42.Cloud-Integrated Smart Attendance System Using ESP32 and RFID for Real-Time Student Monitoring. 43.EduConnect: Smart RFID Attendance with ESP32, IoT Cloud Integration, and Real-Time Parent Communication. 44.ClassSentinel: An IoT-Driven Student Attendance, Presence Verification, and Parent Alert Ecosystem. 45.MyAttend: A Smart RFID and ESP32-Based Student Attendance and Real-Time Notification System. 46.Next-Generation Student Attendance Monitoring Using RFID, ESP32, IoT, and Cloud Analytics. 47.Smart Attendance and Student Presence Intelligence Using ESP32, RFID, IoT, and Real-Time Notifications. 48.An Intelligent Cloud-Based RFID Attendance Management System with ESP32 and Automated Stakeholder Alerts. 49.Connected Student Attendance Management Using RFID and ESP32 with IoT-Based Monitoring and Analytics. 50.Automated Student Attendance and Presence Monitoring Using ESP32, RFID, Cloud Computing, and Real-Time Alerts. 51.AttendX: An Intelligent Edge-to-Cloud RFID–IoT Platform for Automated Student Attendance, Real-Time Parent Alerts, and Cloud Analytics. 52.Design and Development of an ESP32-Based RFID Student Attendance System with Cloud Integration and Real-Time Parent Notifications. 53.Smart RFID Attendance System Using ESP32 and IoT with Real-Time Parent Notifications and Cloud Analytics. 54.An IoT-Enabled RFID Framework for Automated Student Attendance Monitoring and Real-Time Parent Alert Generation. 55.AttendSphere: A Secure Edge-to-Cloud Framework for Intelligent Student Attendance and Presence Management. 56.AttendIQ: Intelligent RFID-Based Attendance Automation with Cloud Integration and Attendance Analytics. 57.EduPulse: An Intelligent IoT Platform for Real-Time Student Attendance, Monitoring, and Notifications. 58.AttendSync: A Smart RFID–IoT Framework for Real-Time Student Attendance and Parent Engagement. 59.AttendGuard: A Secure Smart Attendance Ecosystem with RFID Authentication, Edge Processing, and Real-Time Alerts. 60.ESP32-Based Smart RFID Attendance System with IoT Connectivity, Cloud Monitoring, and Real-Time Alerts.
Yes. This can be developed as a complete academic/industrial-style IoT project combining:
ESP32 as the edge controller
Three-phase transformer sensing
Local protection/interlocking
ThingSpeak cloud dashboard
n8n automation
AI-based fault interpretation
Telegram text + voice alerts
Google Sheets event/history logging
A custom IoT web dashboard
Optional AI-agent commands and remote control
The most important design principle is: the ESP32 must perform the immediate protection locally; cloud/AI/n8n must never be the only protection layer. Internet or AI failure must not prevent the transformer from being protected.
The ESP32 Arduino platform officially supports Wi-Fi and ADC/peripheral functionality suitable for this type of edge-monitoring application. ThingSpeak provides REST APIs for writing and reading channel data. n8n provides Webhook and Telegram nodes for event-driven automation.
1. Proposed Project Title
IoT-Based Three-Phase Transformer Monitoring, Protection and AI-Agentic Alert System Using ESP32, ThingSpeak and n8n Automation
Alternative title
AI-Powered Agentic IoT Three-Phase Transformer Monitoring and Protection System Using ESP32, n8n, ThingSpeak, Telegram and Google Sheets
2. Complete Project Concept
The proposed system continuously monitors a three-phase transformer and measures parameters such as:
Phase-R voltage
Phase-Y voltage
Phase-B voltage
Phase-R current
Phase-Y current
Phase-B current
Transformer/load temperature
Frequency
Phase imbalance
Overvoltage
Undervoltage
Overcurrent
Overtemperature
Power/load condition
Protection status
Internet/device status
The ESP32 collects the sensor information and performs local fault detection.
This is particularly important because transformer monitoring involves potentially lethal voltages.
Do not connect transformer primary or secondary mains directly to ESP32 GPIO/ADC pins.
Use properly rated:
Isolation transformers / voltage transformers
Current transformers
Hall-effect current sensors
Opto-isolation where appropriate
Fuses
MCB
Surge protection
Proper earthing
Isolation barriers
Rated contactors
Proper enclosure
The ESP32 side should operate at its low-voltage logic level, while measurement/protection interfaces provide the necessary electrical isolation.
For an academic prototype, it is much safer to demonstrate using a low-voltage isolated three-phase source or laboratory transformer model.
5. Hardware Components
Main controller
ESP32 Development Board
Recommended:
ESP32 DevKit
ESP32-WROOM-based board
USB programming interface
Wi-Fi connectivity
Espressif's Arduino documentation currently documents the ESP32 Arduino core and supported ESP32 families.
Sensors
A practical prototype can use:
Parameter
Sensor/interface
Voltage R
Isolated voltage sensor
Voltage Y
Isolated voltage sensor
Voltage B
Isolated voltage sensor
Current R
CT/Hall current sensor
Current Y
CT/Hall current sensor
Current B
CT/Hall current sensor
Temperature
DS18B20 / PT100 interface
Frequency
Zero-crossing isolated circuit
Trip feedback
Digital input
Contactor status
Digital input
6. Recommended Pin Allocation
One possible ESP32 mapping:
ESP32
────────────────────────────
GPIO 34 ← Voltage R
GPIO 35 ← Voltage Y
GPIO 32 ← Voltage B
GPIO 33 ← Current R
GPIO 36 ← Current Y
GPIO 39 ← Current B
GPIO 4 ← Temperature sensor
GPIO 25 ← Trip relay
GPIO 26 ← Alarm relay
GPIO 27 ← Reset input
GPIO 14 ← Contactor feedback
GPIO 13 ← Emergency-stop feedback
GPIO 2 → Status LED
Important: exact ADC suitability and pin availability depend on the specific ESP32 board. Verify the board's pinout before building.
These values can be used for warning and protection decisions.
9. Protection Logic
Example engineering thresholds:
Condition
Example threshold
Undervoltage
< 90% nominal
Overvoltage
> 110% nominal
Overcurrent warning
> 90% rated
Overcurrent trip
> 110% rated
Temperature warning
70°C
Temperature trip
85°C
Voltage imbalance warning
> 3%
Voltage imbalance trip
> 5%
These are example values only. Actual thresholds must come from the transformer rating, protection study, applicable standards, sensor characteristics and engineering requirements.
10. Two-Level Protection
This project should deliberately use two separate layers.
Layer 1 — Local protection
ESP32 immediately evaluates:
Voltage
Current
Temperature
Phase imbalance
↓
Protection algorithm
↓
Fault?
↓
Relay/Trip
ThingSpeak can store the measurements and display them as charts. Its REST API supports channel writes using HTTP GET or POST.
A suggested channel structure:
Field
Parameter
Field 1
Voltage R
Field 2
Voltage Y
Field 3
Voltage B
Field 4
Current R
Field 5
Current Y
Field 6
Current B
Field 7
Temperature
Field 8
Fault code
Additional calculated values can be sent through another channel if necessary.
For example:
ThingSpeak Channel
│
├── Field 1 = V_R
├── Field 2 = V_Y
├── Field 3 = V_B
├── Field 4 = I_R
├── Field 5 = I_Y
├── Field 6 = I_B
├── Field 7 = Temperature
└── Field 8 = Fault Code
ThingSpeak uses channel Write API Keys for writing data.
Voltage R = 231 V
Voltage Y = 230 V
Voltage B = 229 V
Current R = 13.2 A
Current Y = 13.0 A
Current B = 13.5 A
Temperature = 91°C
Fault = Overtemperature
and generates:
TRANSFORMER FAULT ANALYSIS
Device: TX-001
Severity: HIGH
Primary condition:
Transformer temperature exceeded the configured
trip threshold.
Measured temperature: 91°C
Recommended checks:
1. Verify cooling system.
2. Check transformer loading.
3. Inspect ventilation.
4. Check recent current trend.
5. Do not re-energize until temperature and cause
are verified safe.
Operator:
"What's the transformer status?"
AI Agent:
"TX-001 is operating normally.
R/Y/B voltages are within limits.
Temperature is 53°C.
Load current is approximately 4.2 A."
Operator:
"Why did it trip yesterday?"
AI Agent:
"At 14:32 the temperature reached 87°C.
The trip was preceded by increasing phase current.
The likely cause is excessive loading or inadequate cooling."
Operator:
"Reset the transformer."
AI Agent:
"Reset command is not permitted until the local
interlock confirms the transformer is safe."
That final safety behavior is important.
19. Telegram Alert Workflow
Telegram's Bot API provides a sendVoice method for playable voice messages.
The workflow can be:
Fault
↓
n8n
↓
AI Agent
↓
Generate alert text
↓
Text-to-Speech
↓
Audio file
↓
Telegram
↓
Operator phone
Example alert:
🚨 TRANSFORMER ALERT
Device: TX-001
Fault: Overtemperature
Temperature: 88.4 °C
Status: TRIPPED
Phase currents:
R = 12.7 A
Y = 12.5 A
B = 12.9 A
Immediate inspection required.
Then a voice notification:
"Attention. Transformer TX-001 has tripped due to high temperature. The measured temperature is 88.4 degrees Celsius. Please inspect the transformer before re-energizing."
Webhook
↓
Code/Set
↓
Google Sheets
↓
Switch
├── NORMAL → END
│
└── FAULT
↓
AI Agent
↓
Alert Text
↓
Telegram
↓
Text-to-Speech
↓
Telegram Voice
27. AI Prompt for n8n
A useful system prompt for the AI Agent is:
You are an industrial transformer monitoring assistant.
Your task is to analyze telemetry from a three-phase
transformer.
Never claim that the transformer is safe unless the
measured values support that conclusion.
Never override or bypass a local protection trip.
Local ESP32 protection has priority over all AI decisions.
Analyze:
- Three-phase voltage
- Three-phase current
- Temperature
- Voltage imbalance
- Current imbalance
- Fault code
- Trip state
Classify the condition as:
NORMAL
WARNING
CRITICAL
TRIPPED
For a fault:
1. Identify the most significant abnormal parameter.
2. Compare it with the configured threshold.
3. Explain the likely condition.
4. Recommend inspection steps.
5. Clearly state that physical inspection is required
before re-energization after a trip.
Return concise output suitable for Telegram.
A more advanced version could query ThingSpeak's REST API for historical data. ThingSpeak supports reading channel and field data through its REST API.
29. Example AI Conversation
Operator
/status
AI Agent
Transformer TX-001
Status: NORMAL 🟢
R voltage: 230.4 V
Y voltage: 229.8 V
B voltage: 231.1 V
R current: 4.2 A
Y current: 4.1 A
B current: 4.3 A
Temperature: 52.6 °C
Voltage imbalance: 0.57%
Current imbalance: 4.76%
No active protection fault.
Operator
Why did TX-001 trip?
AI
TX-001 tripped at 14:32.
The primary trip condition was overtemperature.
Temperature:
87.4 °C
The temperature increased together with phase
current, suggesting that transformer loading may
have contributed.
Recommended checks:
1. Check transformer loading.
2. Check cooling/ventilation.
3. Inspect connections.
4. Review current trend.
5. Do not re-energize until the cause is verified.
30. Voice Alert
The n8n flow can convert the AI response to audio:
AI response
↓
Text-to-Speech
↓
MP3/voice-compatible audio
↓
Telegram
Telegram's Bot API specifically distinguishes ordinary audio from the sendVoice method intended for voice messages.
For an actual electrical installation, the contactor/trip circuit should be engineered independently and appropriately rated.
The ESP32 should not directly drive a large contactor coil.
35. Local Web Server
The ESP32 can also expose a local page:
http://ESP32-IP/
Example:
ESP32 LOCAL DASHBOARD
Transformer: TX-001
Status: NORMAL
Voltage:
R = 230 V
Y = 231 V
B = 229 V
Current:
R = 4.2 A
Y = 4.1 A
B = 4.3 A
Temperature = 52°C
Wi-Fi = Connected
ThingSpeak = OK
n8n = OK
ESP32 Wi-Fi station mode supports connecting to an access point for Internet-connected applications.
Chart 1:
Three-phase voltage
Chart 2:
Three-phase current
Chart 3:
Transformer temperature
Chart 4:
Voltage imbalance
Chart 5:
Current imbalance
Chart 6:
Fault code
Chart 7:
Load trend
ThingSpeak is specifically designed to aggregate, visualize and analyze live IoT data streams.
42. Project Operating Modes
Implement four modes:
NORMAL
WARNING
TRIPPED
MAINTENANCE
NORMAL
Everything within limits.
WARNING
Parameter approaching limit.
TRIPPED
Unsafe condition detected.
MAINTENANCE
Protection temporarily controlled under authorized maintenance procedures.
43. Example Warning
⚠️ TRANSFORMER WARNING
Device: TX-001
Temperature: 72.1°C
Warning limit: 70°C
Trip limit: 85°C
Current:
R = 8.8 A
Y = 8.6 A
B = 8.9 A
Recommendation:
Inspect loading and cooling conditions.
44. Example Critical Alert
🚨 CRITICAL TRANSFORMER FAULT
Device: TX-001
Fault: OVERTEMPERATURE
Temperature: 87.3°C
Trip status: ACTIVE
The local protection controller has isolated
the transformer.
Do not re-energize until the cause is inspected.
45. Google Sheets Database Design
Create columns:
Timestamp
Device_ID
Voltage_R
Voltage_Y
Voltage_B
Current_R
Current_Y
Current_B
Temperature
Frequency
Voltage_Imbalance
Current_Imbalance
Fault_Code
Status
Trip_State
AI_Severity
AI_Diagnosis
Action
Operator
This becomes your maintenance database.
46. AI Maintenance Prediction
Once sufficient historical data exists, the AI can identify patterns.
Example:
Week 1:
Temperature = 48°C
Week 2:
Temperature = 54°C
Week 3:
Temperature = 61°C
Week 4:
Temperature = 69°C
AI:
Temperature trend is increasing.
The transformer has shown a gradual increase
in operating temperature over the last four weeks.
Suggested maintenance:
Inspect cooling system and loading conditions.
This is more useful than simple threshold alarms.
47. Agentic Predictive Maintenance
Eventually:
Historical Data
↓
ThingSpeak
↓
n8n
↓
AI Agent
↓
Trend Analysis
↓
Risk Score
↓
Maintenance Recommendation
↓
Telegram
Example:
TX-001 Maintenance Risk
Temperature trend: HIGH
Current trend: MEDIUM
Voltage imbalance: LOW
Overall risk: MEDIUM
Recommended:
Cooling-system inspection within maintenance window.
48. Suggested Project Modules
For a final-year engineering project, divide the project into:
This architecture is excellent for a prototype, academic project, laboratory demonstrator, or non-safety-critical monitoring system.
For an actual utility/industrial transformer protection installation, an ESP32 + cloud/AI system should not replace certified transformer protection equipment. Protection such as overcurrent, differential, earth fault, temperature, breaker trip circuits, interlocks and emergency systems should be engineered using appropriate industrial protection hardware and standards.
The AI component should be treated as decision support and maintenance intelligence, not as the primary protection relay.
58. Recommended Final Project Name
For a university report, I would use:
“Design and Implementation of an IoT-Based Three-Phase Transformer Monitoring and Protection System Using ESP32 with ThingSpeak, n8n Automation, AI-Agentic Fault Analysis, Telegram Voice Alerts and Cloud Dashboard”
That title captures essentially the whole architecture without making the project sound like only a simple ESP32 sensor project.
Useful official documentation
If you are turning this into a final-year project report, the next logical step is to convert this architecture into a formal Chapter 1–10 project document with abstract, literature survey, block diagram, circuit/schematic, component specifications, mathematical calculations, ESP32 source code, n8n workflow design, AI-agent prompt, Telegram setup, Google Sheets setup, ThingSpeak setup, testing tables, results, conclusion, future scope, references, and viva questions/answers.
