Affiliations: [a] National Key Laboratory of Power Grid Safety, China Electric Power Research Institute, Beijing, China | [b] Northeast Electric Power University, Jilin, China
Correspondence:
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Corresponding author: Han Gao, Northeast Electric Power University, Jilin, China. E-mail: 13844609588@163.com.
Abstract: In the past few decades, China’s power demand has been increasing, and the power fiber plays a key role in ensuring the orderly dispatching of all links of the power system. The study used a wavelet decomposition and reconstruction method, which is a signal processing technique used to decompose complex optical power data into low-frequency and high-frequency signals with different frequency components. Through this decomposition, we can more clearly observe periodic fluctuations, trend changes, and noise components in optical power data. The study also examined different prediction models, including GRU, LSTM, ARMA), etc. The performance of these models in predicting optical power trends is then analyzed, taking into account their accuracy, stability, and computational efficiency. Finally, we carefully evaluated the GRU-ARMA combined prediction model and determined its superior performance in predicting optical power trends. The outcomes show that after adjusting the input data length of the gating cycle cell model and the relevant parameters of the autoregressive sliding mean model, the residual mean value was 0.0141. At the same time, the root mean square error calculated by the combined prediction model of the gating cycle unit-autoregressive moving mean model was 0.000618, which successfully improved the accuracy of predicting the optical power trend of power fiber. This research result provides an important reference for the aging state assessment of power fiber lines, and has an important practical application value for the maintenance of power fiber lines.
Keywords: Feature extraction, network situation, fault diagnosis, security early warning, power system
