免费看大片a-亚洲精品中文字幕乱码三区91-久久久在线视频-中文字幕免费高清在线观看-狼人狠狠干-www婷婷-欧美第一视频-国产中文字字幕乱码无限-色呦呦在线播放-男女羞羞无遮挡-成人男女视频-久久传媒-久久草精品-久久久精品综合-国产免费二区-四虎影院一区二区-国产操人-操操操爽爽爽-色就是色网站-久久77777-神马伦理影视-91手机在线看片-黄视频国产-中文字幕第100页-视频免费1区二区三区

    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