Industrial Load Harmonic Control Using Probabilistic Impact Modeling
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Solution Overview
Problem
Current methods for monitoring and adjusting harmonic emission levels in industrial loads lack accuracy and adaptability, impacting the overall safety and efficiency of power grid operations.
Innovation Solution
A system and method that utilize a processor to obtain grid load data, construct a generalized probabilistic model for industrial loads based on harmonic monitoring data, and adjust industrial loads by optimizing parameters to determine harmonic impact factors, generating instructions to reduce electricity consumption or cut off power supplies when preset conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional harmonic monitoring methods are used, then the monitoring system is simple to implement, but the modeling accuracy and adaptability of harmonic emission levels are insufficient
Solution Approach 1:
The patent introduces a generalized probabilistic model as an intermediary between raw harmonic monitoring data and harmonic emission level assessment. This model acts as a mediator that transforms complex, variable harmonic data into standardized emission level classifications, thereby improving measurement precision without requiring direct complex measurement systems.
Solution Approach 2:
The patent changes the parameter representation from direct harmonic data to probabilistic model parameters. By transforming harmonic monitoring data into probabilistic distributions and using parameter optimization, the system achieves higher modeling accuracy while managing complexity through mathematical transformation rather than physical complexity.
2Adaptability or versatility
If fixed threshold methods are used for harmonic assessment, then the control logic is simple, but the adaptability to different industrial loads and grid conditions is poor
Solution Approach 1:
The patent implements dynamic adaptability through the generalized probabilistic model that can adjust to different industrial load characteristics. The model parameters are optimized based on specific load types and grid conditions, allowing the system to adapt dynamically rather than using fixed thresholds, thereby improving versatility while maintaining manageable control complexity.
Solution Approach 2:
The generalized probabilistic model serves as a universal framework that can handle multiple types of industrial loads and various grid conditions through a single unified approach. This multi-functional model structure provides adaptability across different scenarios without requiring separate control systems for each case.
3Measurement precision
If detailed harmonic analysis is performed for each industrial load, then the harmonic impact assessment is accurate, but the monitoring time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing harmonic analysis on the most significant parameters and using probabilistic modeling to capture essential characteristics without performing exhaustive detailed analysis on all harmonic components. This approach maintains assessment accuracy while reducing the time and computational resources required.
Solution Approach 2:
The patent uses probabilistic model copies that represent industrial load characteristics without requiring direct detailed analysis of each actual load. By creating and optimizing probabilistic representations, the system achieves accurate harmonic impact assessment while significantly reducing monitoring time compared to direct detailed measurement of each load.
Data Source
AI summary
Disclosed is a system and a method for monitoring and adjusting a harmonic emission level of an industrial load. The method may include obtaining a grid load for a current time period, and determining, for each of at least one type of industrial load, a generalized probabilistic model for the industrial load by obtaining harmonic monitoring data of the industrial load at a preset frequency, determining a harmonic characteristic dataset for the industrial load, constructing an initial generalized probabilistic model for target harmonic data, and obtaining the generalized probabilistic model for the target harmonic data. The method may further include determining a harmonic impact factor of the industrial load, and in response to determining that the harmonic impact factor of the industrial load satisfies a first preset condition, generating and sending an adjustment instruction to a control device to adjust the at least one type of industrial load.