★ 國網上海市電力公司信息通信公司 劉逸逸

摘要：為實現電力工程失穩狀態的遠程、精準校核，本文提出了一種基于改進卷積神經網絡的電力工程數字化校核技術。該技術架構分為校核層、站控層、間隔層：在間隔層，通過多組振動傳感器采集電力工程的電氣設備振動信號，并采用經驗模態分解算法對原始信號去噪；在站控層網關支持下，將去噪后的信號發送至校核層；校核層通過基于改進卷積神經網絡的電力工程電氣設備異常校核方法，完成去噪后電氣設備振動信號的特征提取與融合，并啟動Softmax分類器識別設備異常工況，以此判斷電力工程是否存在失穩情況，實現電力工程數字化智能校核。實驗結果表明，本文所提技術可遠程、精準完成電力工程失穩風險校核，校核性能可滿足實際應用要求。

關鍵詞：卷積神經網絡；電力工程；電氣設備；數字化；校核技術；特征提取

Abstract: To remotely and accurately assess instability in power engineering systems, this paper proposes a digital verification technology based on an improved convolutional neural network. The technical architecture is divided into a verification layer, a station control layer, and an bay layer. The bay layer collects vibration signals of electrical equipment in power engineering using multiple sets of vibration sensors, and then employs a signal denoising method based on empirical mode decomposition to filter out noise information mixed into the signals. With the support of the gateway in the station control layer, the denoised signals are transmitted to the verification layer. At the verification layer, an electrical-equipment anomaly verification method based on the improved convolutional neural network is used to extract and fuse features from the denoised vibration signals. A Softmax classifier is then used to identify abnormal operating conditions and determine whether the power engineering system is unstable, thereby completing digital intelligent verification. Experimental results show that the proposed technology can remotely and accurately verify whether instability exists in power engineering, and its digital verification capability meets application requirements.

Key words: Convolutional neural network; Power engineering; Electrical equipment; Digital verification; Verification technology; Feature extraction

在線預覽：基于改進卷積神經網絡的電力工程數字化校核技術研究.pdf

摘自《自動化博覽》2026年6月刊