PV Santrallerinde DC Dizi Elektriksel Ölçümlerinden AC Gücün Yüksek Doğrulukla Yeniden Yapılandırılması için Fizik Tabanlı Derin Artık Öğrenme

Öz

DC tarafındaki elektriksel ölçümlerden fotovoltaik santralin AC aktif gücünü doğru bir şekilde yeniden oluşturmak, dijital ikiz kalibrasyonu, sensör arıza toleransı ve SCADA doğrulaması açısından faydalıdır. Bu çalışma, toplu dizi DC gücüne dayalı fiziksel temelli bir DC-AC tahminini, invertör kusurları, çalışma rejimleri ve çevresel koşullara atfedilebilen sistematik sapmaları öğrenen hafif bir derin kalıntı modeli ile birleştirerek, invertörün AC gücünü yeniden oluşturan fizik odaklı bir derin kalıntı öğrenme çerçevesi önermektedir. Model, 6778 örnek içeren ve 01.05.2024 ile 31.07.2025 tarihlerini kapsayan saatlik veri setinden DC dizi ölçümlerini, iç sıcaklığı ve meteorolojik değişkenleri kullanır. Gündüz performansı sMAPE'nin %8,10'u kadar çok daha iyi olan önerilen yeniden yapılandırma, sırasıyla 0,1907 kW, 0,2704 kW, %34,73 ve 0,9999 değerlerinde MAE, RMSE, sMAPE ve R2 değerlerine ulaşmaktadır. İnvertör sıcaklığına karşı kalıntı tanıları, çalışma aralıkları genelinde düşük sapma gösterir ve invertörlerdeki bilinen termal güç düşürme etkileriyle tutarlı olarak yüksek sıcaklıklarda öngörülemeyen hata artışını ortaya çıkarır. Sonuçlar, fizik odaklı kalıntı öğrenmenin, operasyonel izleme ve dijital ikiz uygulamaları için uygun olan DC dizisi ölçümlerinden yüksek doğrulukta AC güç rekonstrüksiyonu sağlayabileceğini göstermektedir.

Anahtar Kelimeler

• PV invertör
• AC güç tahmini
• Artık öğrenme
• Dijital ikiz
• SCADA

Physics-Guided Deep Residual Learning for High-Accuracy Reconstruction of PV Plant AC Power from DC String Electrical Measurements

Öz

Reconstructing photovoltaic plant AC active power accurately from DC-side electrical measurements is useful for digital twin calibration, sensor failure tolerance, and SCADA validation. By combining a physically motivated DC to AC estimate based on aggregated array DC power with a lightweight deep residual model that learns systematic deviations attributable to inverter non-idealities, operating regimes, and environmental conditions, this work proposes a physics-guided deep residual learning framework that reconstructs inverter AC power. The model makes use of DC string measurements, internal temperature, and meteorological variables from an hourly dataset covering 2024/05/01 to 2025/07/31 with 6778 samples. With much better daytime performance as 8.10% of sMAPE, the suggested reconstruction achieves MAE, RMSE, sMAPE, and R2 as 0.1907 kW, 0.2704 kW, 34.73%, and 0.9999, respectively. Residual diagnostics versus inverter temperature demonstrate low bias across operating ranges and reveal unpredictable error growth at high temperatures, consistent with known thermal derating effects in inverters. The results show that physics-guided residual learning can provide high accuracy AC power reconstruction from DC string measurements suitable for operational monitoring and digital twin applications.

Anahtar Kelimeler

• PV inverter
• AC power estimation
• Physics-guided ML
• Residual learning
• Digital twin
• SCADA

Kaynakça

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