Power Supply Line Scheduling for Accurate Load Switching
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Solution Overview
Problem
Existing power control methods lack evidence-based selection of dedicated power supply lines, leading to misjudgment and inefficiencies such as tripping out due to insufficient capacity or waste of electricity due to excessive capacity.
Innovation Solution
A power control method utilizing a first training model to rank power supply lines based on differential values and a second training model to adjust for status and operation data, ensuring optimal line selection through deep reinforcement learning and time series models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If experienced users select dedicated lines subjectively according to historical experience, then the operation can be carried out with simple procedures, but misjudgment occurs leading to tripping out when capacity is too small or waste of electricity when capacity is too large
Solution Approach 1:
The patent replaces the manual subjective selection process with an automated intelligent system that uses machine learning models and algorithms to objectively evaluate and select power supply lines based on real-time data, thereby eliminating human error while maintaining operational simplicity
Solution Approach 2:
The patent introduces an intelligent recommendation system as an intermediary between the operator and the power supply line selection, where the system processes historical data and real-time status to provide optimized recommendations, bridging the gap between simple operation and accurate decision-making
2Reliability
If the capacity of the dedicated line is increased to avoid tripping out, then the reliability of power supply is improved, but waste of electricity occurs when the capacity is too large
Solution Approach 1:
The patent implements dynamic capacity adjustment by continuously monitoring real-time power consumption data and automatically optimizing the dedicated line capacity selection, allowing the system to adapt to changing load requirements and avoid both tripping out and electricity waste
Solution Approach 2:
The patent establishes a feedback mechanism where the intelligent system continuously monitors power consumption patterns, evaluates the performance of selected power supply lines, and uses this information to refine future selections, thereby optimizing the balance between reliability and energy efficiency
3Device complexity
If historical experience is used for selecting power supply lines, then no complex data processing is needed, but evidence-based selection cannot be achieved leading to misjudgment
Solution Approach 1:
The patent performs preliminary data collection and processing by pre-processing historical power consumption data and building training datasets before actual operation, so that when power supply line selection is needed, the system can quickly provide accurate recommendations without complex real-time computation
Data Source
AI summary
A power control method optimized against both a shortfall in supply and an oversupply, comprising acquiring scheduling information of a plurality of power supply lines, the scheduling information including electric power data, status data, and operation data of the power supply lines; inputting the electric power data into a first training model to obtain a recommended schedule of turning on the plurality of power supply lines; modifying the recommended schedule according to the status data and the operation data of each of the plurality of power supply lines to obtain an optimal power supply line; turning on the loads connected to the optimal power supply line. The method recommends power supply lines according to the equipment being used in actuality and historically. A power control system and a power control device, are also provided.