基于电力物联网的多模式大数据挖掘与分析

Gong Cui, Shuzhi Yi
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引用次数: 0

摘要

随着大数据挖掘和人工智能技术的发展,大数据分析在电力领域的应用越来越广泛。目前,由电力大数据组成的多模式数据主要包括电力设备管理的PMS系统、电力数据采集与监控的Scada系统、各种设备的在线监控系统、巡检系统、智能视频监控系统等。这些数据的规模和维度是巨大的。本文是基于物联网的逻辑算法和神经网络算法。通过实时监测数据对停电概率进行分析和预测,评估电力设备的状态,评估和预测电力运行中的潜在风险,主动预警,提高系统的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-mode Big Data Mining and Analysis Based on Internet of Things on Power
With the development of big data mining and artificial intelligence technology, the big data analysis in power is more and more used. At present, the big data is composed of power multi-mode data mainly includes PMS system of power equipment management, Scada system of power data acquisition and monitoring control, online monitoring system of various equipment, patrol system, intelligent video monitoring system and so on. The scale and dimension of these data are huge. This paper is based on Internet of Things with Logistic Algorithms and Neural Network Algorithms. By analyzing and predicting the probability of power failure through real-time monitoring data, the state of the power equipment can be evaluated, the potential risks in power operation can be evaluated and predicted, and the active warning can be given to improve the security of the system.
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