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基于多模型仿真的變電站數據監控與性能評估研究

來源：電工電氣發布時間：2025-04-27 12:27瀏覽次數：167

基于多模型仿真的變電站數據監控與性能評估研究

蔣亞坤，林旭，黃博

(云南電網有限責任公司，云南昆明 650011)

摘要：各模型組合運用為變電站數據監控提供了技術支持，在數據分析和故障預警方面具有重要應用價值。采用卡爾曼濾波、自回歸積分滑動平均(ARIMA)模型、高斯混合模型(GMM)、移動平均模型和系統性能評估方法對變電站數據監控的多種場景進行了仿真測試與分析。研究結果表明：卡爾曼濾波在噪聲較大的觀測數據中具備良好的平滑效果和狀態估計能力；ARIMA 模型能夠準確捕捉時間序列的長期趨勢和短期波動，適用于負荷預測；GMM 模型通過概率密度分析成功識別低概率的異常點，實現異常檢測；移動平均模型在不同窗口大小下能夠平滑數據并分析短期趨勢。通過系統性能評估實驗，驗證了系統在實時監控中的處理能力，發現高吞吐量和低延遲是系統高效運行的關鍵指標。

關鍵詞： 變電站；數據監控；卡爾曼濾波；自回歸積分滑動平均(ARIMA) 模型；高斯混合模型；移動平均模型；異常檢測；系統性能評估

中圖分類號：TM63 ；TM743 文獻標識碼：A 文章編號：1007-3175(2025)04-0053-06

Research on Substation Data Monitoring and Performance

Evaluation Based on Multi-Model Simulation

JIANG Ya-kun, LIN Xu, HUANG Bo

（Yunnan Power Grid Co., Ltd, Kunming 650011, China）

Abstract: The integrated utilization of multiple models offers technical support for substation data monitoring, demonstrating significant applied value in data analysis and fault warning systems. In this study, simulation tests and comprehensive analysis were conducted on multiple scenarios of substation data monitoring by employing Kalman filter, auto-regressive integrated moving average(ARIMA)model, Gaussian mixture model (GMM), moving average (MA) model, and systematic performance evaluation methodologies. The results show that Kalman filtering has good smoothing effect and state estimation ability in noisy observation data; ARIMA model can accurately capture long-term trend and short-term fluctuation of time series, which is suitable for load forecasting; the GMM successfully identifies low-probability anomalies through probability density analysis and achieves anomaly detection; the moving average model is capable of smoothing the data under different window sizes and analyzing short-term trends. Ultimately, system performance evaluation experiments were conducted to verify the processing capabilities in real-time monitoring scenarios, with experimental results demonstrating that high throughput and low latency are critical indicators for efficient system operation.

Key words: substation; data monitoring; Kalman filter; auto-regressive integrated moving average model; Gaussian mixture model; moving average model; anomaly detection; system performance evaluation

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基于多模型仿真的變電站數據監控與性能評估研究