Intelligent Substation Equipment Status Early Warning Method Based on ICA Algorithm

Abstract

The present invention provides an intelligent substation equipment state early warning method based on ICA algorithm, which includes the following steps: (1) information data collection; (2) information data transmission; (3) information data processing: the control center in the control room reads in real time Collect the data set sent by the collector, and use the ICA algorithm to clean up the collected data set, data integration and conversion, data dimensionality reduction, data discretization, etc.; (4) Early warning diagnosis: the control center of the station control room uses Bayeux The Sri Lankan algorithm analyzes the data processed in step (3), and through the methods of data classification, association and prediction, trains and models historical data and integrates the existing experience of staff to obtain strategies, and outputs corresponding early warning signals. The invention can simply and quickly extract effective data information, and provide more accurate early warning for the operation status of the primary / secondary equipment of the substation, so that the monitoring and early warning of the substation is more timely and accurate. The invention is suitable for risk assessment and early warning of all communication equipment in the power system.

Description

technical field [0001] The invention belongs to the communication field of electric power systems, and in particular relates to an ICA algorithm-based intelligent substation equipment state early warning method. Background technique [0002] With the continuous development of substation intelligence, unattended substations are gradually increasing, and the tasks of online real-time monitoring of important equipment such as high-voltage switchgear, bus connectors, cable connectors, power transmission and transformation line connectors, and outdoor knife switches in substations are also increasing. These are easy to generate heat due to insulation aging or excessive contact resistance during long-term operation, resulting in fires and large-scale power outages, resulting in major economic losses and serious social impacts. Therefore, real-time temperature monitoring of smart substation equipment is a must very necessary. [0003] Due to the poor contact, oxidation, aging and ...

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Problems solved by technology

[0002] With the continuous development of substation intelligence, unattended substations are gradually increasing, and the tasks of online real-time monitoring of important equipment such as high-voltage switchgear, bus connectors, cable connectors, power transmission and transformation line connectors, and outdoor knife switches in substations are also increasing. These are easy to generate heat due to insulation aging or excessive contact resistance during long-term operation, resulting in fires and large-scale power outages, resulting in major economic losses and serious social impacts. Therefore, real-time temperature monitoring of smart substation equipment is a must very necessary

[0003] Due to the poor contact, oxidation, aging and other reasons of the contacts of the substation equipment, the resistance will increase and the sudden high temperature will appear. The rise of the contact temperature is the most intuitive manifestation of the equipment failure. Before the failure occurs, the general contact temperature is consistent. When the load increases, it will cause the temperature of the equipment to change, causing it to rise suddenly. Therefore, the substation equipment early warning system in the prior art is mainly to warn the temperature of each equipment in the substation, and the existing substation equipment early warning system is important to the substation. When monitoring the temperature of the equipment, the temperature information of each equipment is directly collected, and then the temperature information is directly analyzed and processed. The disadvantage of this processing method is that the temperature data of the same type of contacts collected are actually one A collection of data, including original temperature data, other equipment electromagnetic noise interference data, bypass contact temperature interference data, etc. If these invalid data are not eliminated during data analysis in the early warning system, it is easy to cause wrong analysis As a result, the early warning system makes wrong predictions, which will greatly affect the normal operation of substation equipment

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