Non-intrusive Real-time Solar PV Monitoring System
How we revolutionized solar energy monitoring for leading research institutions using IoT and AI automation
Trusted by Leading Research Institutions
SGS Weather
National Institute of Solar Energy
National Institute of Wind Energy
The Challenge
Leading renewable energy research institutions faced critical challenges in monitoring solar PV generation across vast installations. Traditional monitoring approaches required expensive technical consulting and complex infrastructure modifications:
- • Intrusive monitoring systems requiring expensive sensors and circuit modifications
- • Fragmented data collection from 15 different inverter types across 200 acres
- • Manual performance analysis leading to delayed issue detection
- • High operational costs due to sophisticated monitoring equipment
- • Lack of real-time insights for optimizing energy generation
Our AI-Powered IoT Solution
Internet of Photovoltaics (IoP) - A revolutionary non-intrusive monitoring system powered by our AI automation expertise
IoT Integration
Raspberry Pi 3B+ devices connected to each inverter using existing communication protocols. Learn more about our IoT consulting services.
AI Analytics
Intelligent data processing system for real-time performance analysis and anomaly detection. Discover our full range of AI solutions.
Centralized Network
Long-range antenna network connecting all devices to a centralized command center
Technical Architecture
Hardware Components
- • Raspberry Pi 3B+ (1.2GHz quad-core, 1GB RAM)
- • Long-range WiFi antennas for network coverage
- • RS485 to Ethernet converters
- • Bluetooth 4.1 connectivity modules
- • Centralized server infrastructure
Software Features
- • Real-time data acquisition (1-minute intervals)
- • Multi-protocol communication support
- • AI-powered performance analytics
- • Web-based monitoring dashboard
- • Automated alert system
Implementation Process
Phase 1: Infrastructure Assessment
Comprehensive analysis of existing inverter infrastructure across NISE's 200-acre campus
- • Cataloged 15 different inverter types and communication protocols
- • Mapped network topology requirements
- • Identified optimal antenna placement locations
Phase 2: IoT Network Deployment
Strategic deployment of Raspberry Pi devices and network infrastructure
- • Installed 15 Raspberry Pi units with custom configurations
- • Established long-range antenna network
- • Configured multi-protocol communication adapters
Phase 3: AI System Development
Custom AI analytics engine for intelligent data processing and insights
- • Developed machine learning algorithms for performance analysis
- • Created real-time dashboard with predictive analytics
- • Implemented automated alert and reporting system
Phase 4: Testing & Optimization
Comprehensive testing and system optimization for maximum efficiency
- • Conducted 30-day performance validation
- • Optimized data collection intervals and network performance
- • Trained client teams on system operation
Measurable Results
Transforming solar energy monitoring with quantifiable improvements through our AI automation services
Technical Specifications
System Capabilities
Monitored Parameters
AC Parameters
- • AC Power Output (kW)
- • AC Voltage
- • AC Current
- • Frequency
- • Power Factor
DC Parameters
- • DC Power Input
- • DC Voltage
- • DC Current
- • MPPT Efficiency
- • String Performance
Environmental Data
- • Solar Irradiance • Ambient Temperature • Module Temperature
Future Enhancements
Continuous innovation for next-generation solar monitoring
Predictive Maintenance
AI-powered predictive analytics to forecast equipment failures and optimize maintenance schedules.
Mobile App Integration
Native mobile applications for real-time monitoring and remote control capabilities on-the-go. Explore our mobile app development services.
Cloud Analytics
Advanced cloud-based analytics platform for multi-site management and comparative analysis.
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Join leading research institutions in revolutionizing renewable energy monitoring with our AI-powered IoT solutions.
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