Overload monitoring equipment and monitoring method for electric dust removal equipment
By predicting dust emissions using a neural network model and combining it with automated electric field control and dust removal devices, the problem of complex changes in dust volume in electrostatic precipitators is solved, achieving automated adjustment and equipment protection, and reducing power consumption and equipment wear.
CN121869591APending Publication Date: 2026-04-17HUANENG POWER INT INC
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Patent Information
- Application Number
- CN202410206153.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-02-26
- Publication Date
- 2026-04-17
AI Technical Summary
Technical Problem
In existing electrostatic precipitators, the amount of dust flue gas varies in a complex manner, requiring manual adjustment of the electric field load. This operation is complicated and detrimental to health, and also results in unnecessary energy consumption and equipment wear.
Method used
A neural network model is used to predict dust emissions and adjust the electric field current in real time. Combined with the impact unit and the collection unit, automated control is achieved, reducing manual intervention.
Benefits of technology
It achieves automated adjustment of electric field load, reduces power consumption, extends equipment life, reduces spark discharge, protects equipment, and improves ash removal efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of electric precipitation, in particular to overload monitoring equipment and a monitoring method for electric precipitation equipment, and the method comprises the following steps: acquiring target production data; determining a target dust discharge amount according to the target production data; and determining the target current magnitude according to the target dust discharge amount. The method has the advantages that the neural network is used for conducting deep learning on online collected production data, a corresponding relation model between the dust amount and the power plant production data is established, the smoke dust emission amount is predicted based on the model, the average secondary current amount of the electric field is adjusted in real time on the basis, and the purposes of reducing consumption and reducing power consumption are achieved. According to the invention, the control aims of improving the reliability of the IGBT and prolonging the service life of the IGBT are fulfilled, the spark discharge phenomenon can be reduced or avoided as much as possible, equipment such as the IGBT and a trigger circuit board can be better protected, and unnecessary electric energy consumption can be effectively reduced at the same time.
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