Dimmer System Classifies Lighting Loads Using Machine Learning
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Solution Overview
Problem
Configuring dimmer operating parameters to avoid flicker and compatibility issues with LED and other lighting loads is resource-intensive and time-consuming, and existing testing methods may not cover all lamp types, leading to potential flickering issues in end-user installations.
Innovation Solution
A dimmer system that includes a switching circuit, memory, and processing circuit, which uses machine learning to classify lighting loads based on electrical current data and configure operating parameters accordingly, enabling optimal dimming performance and minimizing flicker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used to configure dimmer operating parameters, then compatibility with lighting loads can be verified, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-configuring the dimmer with operating parameters before actual use. The system performs initial testing and configuration of multiple lighting load types, stores the results in a database, and uses this pre-established information to quickly determine compatibility without requiring extensive testing at the point of installation. This resolves the contradiction by preparing compatibility data in advance, reducing both time and resource consumption during actual deployment.
2Adaptability or versatility
If extensive testing is performed to cover all LED lamp types, then compatibility improves, but resources and time consumption increase significantly
Solution Approach 1:
The patent implements universality by creating a comprehensive database that stores operating parameters for multiple types of lighting loads (LED lamps, CFLs, incandescent bulbs) in a single system. The dimmer can universally handle different load types by querying this centralized database, eliminating the need for separate testing procedures for each lamp type. This approach maintains high adaptability across diverse lighting loads while significantly improving productivity by consolidating testing efforts into a reusable database.
3Reliability
If a fixed testing plan is used for dimmer configuration, then initial compatibility can be established, but new LED lamp types may not be covered leading to flickering issues
Solution Approach 1:
The patent applies dynamics by implementing an adaptive system that can update its operating parameters based on new information. When a new LED lamp type is encountered that isn't in the existing database, the system can dynamically add the new lamp type and its corresponding operating parameters to the database. This dynamic update capability ensures the system maintains reliability for known lamp types while simultaneously improving adaptability to cover new lamp types, preventing flickering issues that would occur with a static testing plan.
Data Source
AI summary
Approaches are provided for lighting load classification using machine learning models, and selection of parameters for dimmer operation based on the classification. Approaches provide requests and/or use of updated models, building datasets for model construction, model construction, evaluation, and selection, application of machine learning models and handling unsuccessful classification attempts or classification into a class corresponding to an unknown lighting load type, and selection of operating parameters based on the foregoing.