Towards practical oscillation detection in wind farms: Comparative study of AI models, novel metrics, and edge implementations

Abstract

Fast oscillations observed in wind farms and other renewable energy systems interfaced with power grids must be addressed promptly to prevent devastating shutdown and damage. Existing approaches mainly target high‐magnitude oscillations using data‐intensive convolutional neural networks (CNNs), leading to delayed detection and overlooking mild oscillations that can still cause long‐term degradation. This paper proposes a dual detection framework that rapidly identifies high‐magnitude oscillations using time‐domain analysis while detecting mild oscillations through FFT‐based frequency‐domain feature extraction. The framework leverages hyperdimensional computing (HDC) and machine learning models, including one‐class support vector machine (SVM), 1D‐CNN, 2D‐CNN, and a one‐class autoencoder. To enhance performance evaluation, we introduce true positive confidence (TPC) and false positive confidence (FPC) metrics. Unlike prior work limited to server‐based evaluation, we deploy HDC on edge platforms, including a Jetson Nano graphics processing unit (GPU), tensor processing unit (TPU), and field programmable gate array (FPGA). Experimental results on real‐world wind farm data and Simulink‐generated datasets show that HDC outperforms all baseline models, achieving at least 99% confidence in both TPC and FPC for high‐magnitude and mild oscillations. Notably, HDC operates up to 2100 times faster than 1D‐CNN on GPU and 6800 times faster on FPGA, while being four times smaller in model size, demonstrating that FPGA‐based HDC is the minimum suitable hardware for reliable real‐time deployment in wind farms.