Servo Motor Base Speed Control for Peak Cut Energy Optimization
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Solution Overview
Problem
The existing methods for setting the base speed of motors for peak cut in industrial machines are often too high, leading to increased power losses and reduced bearing life due to uniform settings that do not account for varying operating states of the machines.
Innovation Solution
A control device that learns and adjusts the base speed of motors for peak cut using data from industrial machines through a machine learning model, allowing for dynamic setting based on the current operating state to optimize regenerative energy generation and reduce losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the base speed of the motor for peak cut is uniformly set high to ensure sufficient regenerative energy generation capability, then the motor can generate regenerative energy at high power consumption timing, but power losses in the motor increase and bearing life is shortened due to increased iron losses
Solution Approach 1:
The base speed of the motor for peak cut is changed from a fixed uniform value to a dynamically adjustable value that varies according to the operating state of industrial machines. The control device learns appropriate base speed values for different operating states through machine learning and adjusts the base speed accordingly, allowing the motor to operate at optimal speeds rather than constantly high speeds, thereby reducing iron losses and power consumption while maintaining regenerative energy generation capability when needed.
Solution Approach 2:
The base speed parameter of the motor is changed from a fixed high value to a variable parameter that adapts to different operating conditions. By using machine learning to determine appropriate base speed values for different operating states of industrial machines, the system optimizes the base speed parameter to balance regenerative energy generation capability with reduced power losses and extended bearing life.
2Reliability
If the base speed is set high to handle peak power consumption, then regenerative energy generation is sufficient during high power demand, but the motor operates at unnecessarily high speeds during low power demand periods
Solution Approach 1:
The base speed is made dynamic and adaptive to the actual operating conditions. The control device learns the relationship between industrial machine operating states and appropriate base speed values, allowing the motor to operate at lower base speeds during low power demand periods while maintaining sufficient regenerative energy generation capability during high power demand periods through optimized speed control.
Solution Approach 2:
The system uses machine learning to automatically determine appropriate base speed settings based on observed operating patterns of industrial machines. The control device learns from historical data and autonomously adjusts base speed settings without requiring manual intervention, optimizing the balance between reliability of regenerative energy generation and motor power consumption.
3Ease of operation
If a fixed base speed is used for simplicity of control, then the control system is easy to operate, but it cannot adapt to varying operating states of industrial machines
Solution Approach 1:
The manual experience-based speed setting method is replaced with an automated machine learning-based control system. The control device learns appropriate base speed settings for different operating states through analyzing operational data, automatically adapting to varying conditions without requiring manual adjustment. This substitution of manual decision-making with automated learning maintains ease of operation while dramatically improving adaptability.
Solution Approach 2:
The control device implements a learning feedback mechanism where it continuously observes the operating states of industrial machines and the effectiveness of base speed settings, then uses this information to improve future base speed decisions. This feedback loop enables the system to adapt to varying operating states automatically while maintaining simple operation for the user.
Data Source
AI summary
A control device according to the present invention is provided with a data acquisition unit configured to acquire data on at least an operating state of an industrial machine, a learning model storage unit configured to store a learning model in which the value of a setting action for a base speed of a servomotor for peak cut is associated with the operating state of the industrial machine, and a decision making unit configured to settle the setting action for the base speed of the servomotor for peak cut based on the data on the operating state of the industrial machine acquired by the data acquisition unit, by using the learning model stored in the learning model storage unit.