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Benchmarking RF, KNN, MLP, and CNN for FFT-Based PV Arc Fault Detection: Scaling Choice, Temporal Cross-Validation, and Latency Trade-Offs Toward Edge Deployment
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DOI：10.3390/en19163787.png)
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Abstract
En 中文
Ensuring the safety and reliability of photovoltaic (PV) installations requires accurate electrical arc fault detection. This work presents a computational arc fault detection framework that combines fixed-length windowing, Fast Fourier Transform (FFT)-based features, and supervised machine learning classifiers. Data were acquired using an Arc Fault Circuit Interrupter (AFCI) test bench developed based on IEC 63027. Current and voltage signals were partitioned into 200-sample windows, DC-offset corrected, and Hann-windowed signals. Each window generated 204 statistical and spectral attributes used to train and evaluate Random Forest (RF), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) models. Hyperparameters were tuned by grid search with TimeSeriesSplit cross-validation, comparing min–max normalization and Z–Score standardization. On a 15% hold-out test set, CNN with Z–Score achieved F1 = 0.9982 and recall = 0.9975, followed by MLP (F1 = 0.9957) and RF (F1 = 0.9821). Amortized per-window inference latencies were ≈ 0.0035 ms for RF, ≈ 0.0016 ms for MLP with Z–Score, and ≈ 0.032 ms for CNN. These classifier-stage timings indicate computational compatibility with edge-oriented implementation but do not constitute an end-to-end IEC 63027 AFCI compliance assessment. The framework targets integration into PV inverters at Mackenzie Presbyterian University’s solar plant.
Keywords:
arc fault detection
photovoltaic systems
machine learning
convolutional neural networks
fast fourier transform
edge computing
real-time classification
time series cross-validation
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3.2
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1.4W
Citations:
14.2W
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