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Convolutional Neural Networks (CNNs) have been explored to detect forced oscillations in windfarm systems in the past. However, these CNNs require a significant amount of data samples between inference queries and a significant amount of computational power and time. This leads to systems that have a large delay between a forced oscillation occurring and detecting the forced oscillation. This paper presents a novel approach applying Hyperdimensional Computing (HDC) as an effective solution for the first time in forced oscillation detection to overcome the problems of CNNs. HDC is able to reduce the time to detect forced oscillations in two ways: First, by reducing the time needed to collect data to create a new inference sample by reducing the number of data points required. Second, by providing a significantly smaller, more energy efficient, and faster model for detection than current state-of-the-art. Our results show that HDC, with an FPGA implementation, is able to achieve 55× faster detection of forced oscillations in windfarms while achieving the same accuracy as the best current CNN models using software solutions.