Data Analysis for Solar System Performance

Understanding Pyranometers, GHI, GTI, and Performance Benchmarking Across Solar Plant Blocks to study plant performance effectively. In utility-scale solar plants, accurate irradiance measurement is the foundation of performance analysis. Here's a simplified yet technically strong breakdown for those managing multi- sites of solar assets or looking to enhance plant monitoring systems. 1. What is a Pyranometer? A pyranometer is a precision sensor that measures solar radiation on a surface (W/m²). It’s essential for: GHI (Global Horizontal Irradiance) GTI (Global Tilted Irradiance) Key for PR calculation, fault diagnostics, real data validation, and prediction on expected energy output and plant pros and cons study 2. GHI vs GTI – What's the Difference? GHI: Solar radiation on a flat surface. Direct sunlight Diffused radiation Ground-reflected radiation GTI: Radiation on the module’s tilt. Better represents energy received by your panels. Use GTI for real performance correlation across inverters. 3. Irradiance vs Insolation Irradiance: Instant solar power (W/m²). Example: 1000 W/m² at noon or real time . Insolation: Total daily energy (kWh/m²/day) – used in Helioscope, PVsyst, etc. to analysis Use both to understand short-term vs. daily trends. 4. Managing Multiple GTIs Across 3–5 km When managing large solar sites with multiple blocks: Installation Tips: Match module tilt & azimuth. Avoid shadow zones Clean glass regularly Calibrate every 2 years Performance Check: Compare GTIs via SCADA or datalogger Acceptable variation: 3–5% Investigate if >5% consistently: Sensor drift Dirt or droppings Loose cables Local cloud pattern 5. Advanced Considerations Spectral mismatch: Pyranometers and PV cells behave differently under cloudy/filtered light. Temperature effect: Ensure ISO Class A-grade sensors for stability. Ventilation units: Prevent fog/dust on high-end sensors (e.g., SMP22, SR30). Shadow rings/albedometers: For diffuse/reflected radiation data. GTI-inverter drop alerts: Use GTI drops + relay trips to predict snow/dust events or plant anomalies. Conclusion Pyranometer data = Solar plant intelligence. Consistent GTI data block-wise = Accurate inverter benchmarking. Better visibility = Better decisions!

Solar Performance Monitoring: Practical Examples with Fault Analysis To understand how data analysis helps in fault detection and performance optimization, let’s look at real-world scenarios with sample values. Example 1: Underperformance Due to Soiling Losses 🔹 Expected Power Output: 500 kW 🔹 Actual Power Output: 450 kW 🔹 Performance Ratio (PR) = (450 / 500) × 100 = 90% ✅ (Good) After a week: 🔹 Expected Power Output: 500 kW 🔹 Actual Power Output: 400 kW 🔹 PR = (400 / 500) × 100 = 80% ⚠ (Declining) 🔹 Soiling Loss Estimate: 10-12% 📌 Diagnosis: Increased dust accumulation on panels is reducing efficiency. 📌 Action: Schedule panel cleaning and monitor PR improvement. Example 2: Inverter Failure Leading to Downtime 🔹 Total Plant Capacity: 1 MW 🔹 Number of Inverters: 10 (Each handling 100 kW) 🔹 Before Issue: • Expected Output: 950 kW (considering minor losses) • Actual Output: 940 kW ✅ (Good Performance) 🔹 After Issue: • Expected Output: 950 kW • Actual Output: 840 kW ⚠ (Significant Drop) • Inverter Logs: • Inverter 6: No output • Fault Code: Overvoltage error 📌 Diagnosis: One inverter failure resulted in a 100 kW generation loss. 📌 Action: Restart the inverter remotely via SCADA, if unsuccessful, perform on-site inspection for hardware issues. Example 3: Faulty Solar Panel String Detection 🔹 Total Plant Capacity: 500 kW 🔹 Number of Strings: 50 (Each handling 10 kW) 🔹 Normal Operation: • Each string generating 9.5 - 10 kW 🔹 Current Readings: • 49 Strings: 9.8 kW ✅ (Normal) • 1 String: 6.5 kW ⚠ (Underperforming) 📌 Diagnosis: Possible issues include: ✅ Loose connection in the junction box. ✅ Module degradation in one or more panels. ✅ Partial shading from nearby object. 📌 Action: Perform IR thermographic scanning to check for hotspots and replace faulty panels if needed. Example 4: Impact of High Temperature on Efficiency 🔹 Ambient Temperature: 45°C 🔹 Panel Temperature: 70°C 🔹 Power Output Drop: 5-6% compared to normal conditions 📌 Diagnosis: High temperatures reduce panel efficiency due to the negative temperature coefficient (-0.5% per °C above 25°C). 📌 Action: ✅ Install cooling solutions (e.g., water mist or ventilation). ✅ Use bifacial or high-temperature-resistant panels for future installations. Example 5: Grid Instability Causing Shutdown 🔹 Normal Grid Voltage: 415V 🔹 Recorded Grid Voltage: 470V ⚠ (Overvoltage) 🔹 Inverter Logs: “Grid Overvoltage Protection Activated – Shutdown Initiated” 📌 Diagnosis: ✅ Overvoltage from the grid triggered the inverter’s protective shutdown. ✅ Possible transformer tap setting issue or reactive power injection problem. 📌 Action: ✅ Coordinate with the grid operator to stabilize voltage fluctuations. ✅ Enable reactive power control in the inverter to manage voltage spikes. #SolarMonitoring #DataAnalytics #IoT #SCADA #PredictiveMaintenance #RenewableEnergy #IliosPower

🔍 Performance Ratio (PR): One Metric, Many Truths 📊 Are you still using just the basic PR formula to assess your solar plant? You’re missing the full picture. Here’s the complete breakdown of all major PR types, formulas as per IEC 61724, and when to use what. Save this post 🔖 — It’s your go-to guide for solar asset benchmarking. ⚡ What is Performance Ratio (PR)? PR is a key metric used to evaluate how efficiently a solar PV system converts available solar radiation into usable AC electricity. It is dimensionless (%) and normalizes the output by irradiance and system size — making it ideal for cross-site or time-based comparisons. 🧮 IEC Standard Formula (PR as per IEC 61724-1:2021) ✅ Standard PR (Uncorrected) PR = (E_AC) / (G_POA × P_STC) × 100 • E_AC = Actual AC energy output (kWh) • G_POA = Plane-of-array irradiation (kWh/m²) • P_STC = Installed DC capacity at STC (kWp) Used for daily/monthly/yearly performance analysis. Assumes STC (25°C module temperature) and neglects real-time temperature variation. 🌡️ Temperature-Corrected PR (as per IEC) To account for the impact of temperature on module efficiency: PR_temp = (E_AC) / (G_POA × P_STC × (1 + γ × (T_mod - 25))) × 100 Where: • γ = Temperature coefficient (e.g., -0.0025 /°C) • T_mod = Avg Module Temperature (°C) • 25 = STC reference temperature (°C) Used for temperature-sensitive benchmarking across seasons or regions. 🧮 Alternative PR Formulas in Industry Practice 📘 1. Reference Yield-Based PR PR = Y_final / Y_ref × 100 Where: • Y_final = E_AC / P_STC (kWh/kWp) • Y_ref = G_POA (kWh/m²) Simple form, widely used in dashboards and monthly summaries. 📘 2. PR with Inverter Efficiency PR = (E_DC) / (G_POA × P_STC) × η_inv Where: • E_DC = DC energy from string monitoring (kWh) • η_inv = Inverter efficiency (decimal or %) Used when only DC-side energy is logged and inverter efficiency is separately known. 🧮 Let’s Crunch the Numbers ✅ Real site data: AC Energy Output : 139,930 kWh DC Capacity (STC) : 26,514 kWp Irradiation (POA) : 6.22 kWh/m² Module Temp : 41.93°C Temp Coefficient (γ) : -0.0025 /°C 📘 1. Standard PR (Uncorrected) Formula (IEC 61724-1 Basic) PR = (E_AC) / (G_POA × P_STC) × 100 = 139,930 / (6.22 × 26,514) × 100 = 84.9% 🌡️ 2. Temperature-Corrected PR Formula (IEC 61724-1:2021 – Class A) PR_temp = (E_AC) / (G_POA × P_STC × (1 + γ × (T_mod - 25))) × 100 = 139,930 / (6.22 × 26,514 × 0.9577) × 100 = 88.6% 🔚 Conclusion: Which PR Is Better? Standard PR 84.9% Temp-Corrected PR 88.6% ✅ For everyday monitoring, Standard PR works fine. ✅ Use Temp-Corrected PR For audits, investor reviews, or comparing sites,benchmarking across seasons, locations, or technologies 🌞 PR is not just a number — it tells the story of your plant’s efficiency, losses, and behavior under real-world conditions.

🔍 Deep-Dive: Inverter-Level Breakdown Analysis in Solar Power Plants ☀️⚙️ Inverter performance is a critical factor in ensuring optimal generation in any solar power plant. Even minor issues—if undetected—can lead to significant energy losses over time. Recently, I conducted a comprehensive analysis of inverter-level breakdowns across multiple sites to assess their impact on overall plant performance. Here's what the investigation revealed: 📊 Key Findings: Several inverters consistently tripped during peak irradiance hours, indicating potential thermal derating or oversizing mismatches. Breakdown patterns aligned with high ambient temperature spikes, pointing to insufficient ventilation or stressed cooling systems. In some cases, the communication between SCADA and field devices failed to trigger alarms, leading to delayed detection of inverter outages. Sites with preventive maintenance strategies in place showed 30–40% less inverter downtime than those with purely reactive O&M models. ✅ Action Taken: Shared recommendations with the asset management and O&M teams to prioritize inverter cooling audits. Proposed string-level monitoring and tighter SCADA data validation to minimize response time. Developed inverter-wise performance dashboards in Power BI to enable real-time visibility and early anomaly detection. 📈 Outcome: Post-analysis, targeted actions were taken which led to a 2–3% improvement in plant performance across the affected sites—translating into better generation, reliability, and revenue protection.

Key Performance Indicators (KPIs) to Monitor in a Solar Power Plant SCADA System In modern solar power plants, SCADA is the heart of real-time monitoring, analytics, and performance optimization. To ensure reliable generation and maximum plant efficiency, these KPIs play a crucial role: 🌞 1. Generation KPIs • Instantaneous AC Power • Daily / Monthly / Annual Energy • Inverter-wise generation • DC power, voltage & current 📊 2. Performance KPIs • Performance Ratio (PR) • Specific Yield (kWh/kWp) • Capacity Utilization Factor (CUF) • Inverter & Transformer Efficiency 🏥 3. Equipment Health KPIs • Inverter Availability • String/Combiner Box current imbalance • DC insulation resistance • Inverter/Module temperature • Transformer alarms & trips 🌤️ 4. Weather & Environmental KPIs • Solar Irradiation (GHI/POA) • Ambient & Module Temperature • Wind speed • Soiling loss trend ⚡ 5. Grid KPIs • Grid Voltage & Frequency • Power Factor • Active / Reactive Power • Export vs Import energy 🟢 6. Availability KPIs • Plant Availability • Inverter Availability • Grid Availability • SCADA Data Availability 📉 7. Loss Analysis KPIs • Inverter clipping loss • Shading loss • Curtailment loss • DC/AC cable losses • Thermal and soiling losses ⸻ ✅ Why These KPIs Matter? Monitoring these indicators helps identify performance gaps early, reduce downtime, and increase revenue generation. A strong SCADA dashboard with these KPIs enables proactive O&M and long-term plant reliability.