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.