The solar energy sector is pivotal in driving the transition to sustainable energy sources. However, maintaining and optimizing solar plant performance involves numerous challenges, including monitoring energy production, predicting future output, and addressing equipment inefficiencies. NEXALYZE’s cutting-edge product, SolarFlux, revolutionizes solar energy monitoring and management by leveraging Inverter APIs, IoT devices, advanced analytics, and AI/ML-powered solutions. This case study explores how SolarFlux helped a leading “Solar Power Generating Company” enhance its energy production, identify faults promptly, and achieve optimal efficiency.

The company faced the following challenges:

1. Limited Real-Time Monitoring: The existing system provided basic production data but lacked insights into performance metrics like string-level inefficiencies and inverter issues.
2. Unanticipated Downtime: Faulty strings and inverter malfunctions often went unnoticed, leading to energy losses and increased downtime.
3. Inefficient Maintenance: Cleaning requirements were addressed reactively, resulting in reduced energy output due to soiling.
4. Lack of Predictive Capabilities: The system could not forecast energy production or detect anomalies, hindering proactive measures.
5. Data Overload: While data was collected from installed supplier platforms, it was not actionable due to the absence of advanced analytics.

The company required a comprehensive solution capable of:

1. Providing real-time and granular monitoring of energy production.
2. Predicting future energy output based on weather patterns and historical data.
3. Automatically identifying faulty strings and inverter issues.
4. Alerting operators for cleaning and maintenance requirements.
5. Offering actionable insights through advanced analytics to maximize energy output.

NEXALYZE collaborated with the company to deploy SolarFlux, a robust and intelligent energy monitoring system tailored to their needs. Our approach included:

1. Seamless API / IoT Integration
   • Connected SolarFlux with existing IoT-based devices, APIs and PLCs to gather real-time data on energy production, voltage, current, and temperature.
   • Designed a centralized data pipeline to consolidate inputs from all inverters and strings.
2. Advanced Monitoring and Dashboards
   • Developed intuitive dashboards displaying real-time solar energy metrics at both macro and granular levels.
   • Enabled live tracking of string performance, inverter health, and overall system efficiency.
3. Consolidated Platform
   • A singular, consolidated dashboard provides an overview of total numbers, offering a complete picture across the installation.
   • Whether the hardware is installed by a single vendor or multiple vendors, the dashboard provides consolidated values seamlessly.
4. Predictive Analytics and AI/ML Integration
   • Built machine learning models to forecast energy production based on historical data and weather forecasts.
   • Integrated anomaly detection algorithms to identify faulty strings, inverter issues, and soiling impacts before they escalated.
5. Proactive Alert System
   • Configured a robust alert system to notify operators of cleaning requirements, potential equipment failures, and energy production discrepancies.
   • Automated alert prioritization to address critical issues promptly.
6. Custom Reports and Insights
   • Automated the generation of customized energy production and consumption reports.
   • Provided actionable insights, including performance trends, loss analysis, and maintenance recommendations.

The implementation of SolarFlux transformed the company’s operations and energy management:

1. Enhanced Energy Production: Increased energy output by 15% within six months by addressing faulty strings and optimizing inverter performance.
2. Reduced Downtime: Minimized unplanned downtime by detecting and resolving issues proactively, improving operational reliability.
3. Streamlined Maintenance: Reduced soiling losses by 20% through timely cleaning alerts, ensuring optimal performance even during high-dust periods.
4. Improved Decision-Making: Empowered operators and management with detailed analytics, enabling data-driven decisions to enhance efficiency.
5. Cost Savings: Achieved significant cost savings by preventing equipment failures and optimizing maintenance schedules.