An Application of Wavelet Transformation and Statistical Analysis for Frequency Event Detection

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2023 North American Power Symposium (NAPS)

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Power system disturbances, such as significant faults or major disruptions in generation or load, cause imbalance between supply and demand, which may result in severe frequency fluctuations. Following such disturbances, fast frequency response is needed to provide frequency support and avoid system collapse. As such, monitoring and detecting frequency events quickly and precisely is critical. This paper proposes an abnormal frequency deviation detection algorithm that uses two methods to process phasor measurement data and declare the frequency events. The first is a signal processing-based method that de-noises frequency measurements. This is followed by a second, statistics-based method that calculates rate of change of frequency, mean, variance, and standard deviation. The algorithm uses thresholds to declare frequency events. The proposed algorithm is an improvement over literature-relevant works as it has four tunable threshold parameters. Proper tuning of these threshold parameters enhances algorithm performance, thereby making the algorithm adaptable to different electric power Balancing Authority. The algorithm detection performance is evaluated using binary classification technique and evaluation metrics. The results show the effectiveness of the proposed algorithm for detecting various frequency events of different datasets.


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