Electrical automation control system and method
By predicting load demand curves and optimizing models, combined with a master-slave tracking strategy, the problems of response lag and low energy efficiency in motor group control were solved, achieving stable coordinated operation of the motor group and maximizing energy efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-19
- Publication Date
- 2026-04-07
AI Technical Summary
The existing motor group control has not established a collaborative scheduling system with the overall system energy efficiency as the core, and has not made predictions based on historical operating patterns, environmental parameters and production plans. As a result, the motor group has a lagging response and unstable operating conditions when the load fluctuates, making it difficult to achieve the optimal collaborative operation of each motor and maximize the overall system energy efficiency.
By collecting historical data and real-time information on the operation of motor groups, using time series algorithms to predict future load demand curves, and combining dynamic efficiency model libraries and particle swarm optimization models, the system determines the number of motors, load allocation, and speed setpoints. It also employs a master-slave tracking strategy to execute load transfer operations, thereby achieving smooth switching of the motor group.
It enables timely response and improved operating stability of the motor group, achieving optimal coordinated operation of each motor and maximizing the overall energy efficiency of the system.
Smart Images
Figure CN121813918A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electrical automation technology, and in particular to a control system and method for electrical automation. Background Technology
[0002] In industrial and civil applications such as industrial production lines, central air conditioning systems, and water supply pumping stations, multi-motor coordinated drive is the mainstream operating mode. By having multiple motors work together, the continuous operation and load output requirements of the system can be met. Currently, most existing motor group control adopts a single-motor independent control strategy. That is, each motor independently adjusts its operating parameters such as speed and load distribution based on its corresponding local load feedback signal. The control logic is mainly based on real-time collected load data for passive response. Some solutions combine simple fixed-cycle optimization algorithms to adjust the motor's operating status to ensure the stable operation of individual devices.
[0003] In the aforementioned existing technologies, the existing motor group control has not established a collaborative scheduling system with the overall system energy efficiency as the core. It still follows the inertia of single-machine control and does not combine historical operating patterns, environmental parameters and production plans to predict future load demand. As a result, the motor group responds slowly and operates in an unstable manner when the load fluctuates. It is impossible to achieve the optimal collaborative operation of each motor and it is difficult to achieve the core requirement of maximizing the overall system energy efficiency. Summary of the Invention
[0004] The purpose of this invention is to provide a control system and method for electrical automation, aiming to solve the technical problem that existing motor group control technologies do not establish a collaborative scheduling system with the overall system energy efficiency as the core, still follow the inertia of single-machine control, and fail to combine historical operating patterns, environmental parameters, production plans and other factors to predict future load demand. As a result, the motor group responds slowly and operates in an unstable manner when the load fluctuates, which makes it impossible to achieve the optimal collaborative operation of each motor and the core requirement of maximizing the overall system energy efficiency.
[0005] To achieve the above objectives, the present invention employs an electrical automation control method, comprising the following steps: Collect historical data and real-time information related to the operation of the motor group, predict the total load demand curve for a future preset period using a time series algorithm, and output the total load demand curve. Based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, the number of motors to be operated, the target load allocation of each motor, and the speed setpoint are determined by constructing and solving the optimization model, and the decision result is output. Based on the decision results, a master-slave tracking strategy is adopted to perform motor load transfer operations, thereby achieving a smooth switch of the operating status of the motor group.
[0006] Among them, in the steps of collecting historical data and real-time information related to the operation of the motor group, predicting the total load demand curve for a future preset period through time series algorithms, and outputting the total load demand curve: Collect historical data and real-time information. The historical data includes the historical operating load of the motor group, energy consumption data, and production task records under the corresponding operating conditions. The real-time information includes the current production plan, ambient temperature, and equipment operating status parameters. Perform standardized preprocessing on historical data and real-time information, remove outlier data, and output a preprocessed dataset; A load forecasting model is constructed using a long short-term memory time series algorithm. The preprocessed dataset is input into the load forecasting model, and the total load demand curve for the next working cycle in the future preset period is output.
[0007] In the steps of standardizing and preprocessing historical data and real-time information, removing outlier data, and outputting the preprocessed dataset: The three-standard-deviation criterion is used to identify outliers in historical data and real-time information, and outliers are replaced by interpolation. The outliers after interpolation are processed by normalization, and the data is mapped to the 0,1 interval to obtain the standardized dataset and output it.
[0008] Before the step of constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library for motor groups, to determine the number of motors to be operated, the target load allocation for each motor, and the speed setpoint, and outputting the decision result: The input and output power data of each motor in the motor group under different load rates were collected through bench tests, and the load rate power dataset of each motor was output. Based on the load rate power dataset, calculate the efficiency value of each motor at the corresponding load rate, and output the load rate efficiency dataset of each motor. The load rate efficiency datasets of each motor are fitted to obtain the efficiency curve of each motor. The efficiency curves of all motors are then integrated into a dynamic efficiency model library and updated in real time.
[0009] In the step of calculating the efficiency value of each motor at the corresponding load rate based on the load rate power dataset and outputting the load rate efficiency dataset of each motor: The load rate adjustment range of the bench test is 10%-100%. A set of input power and output power data is collected every 5% load rate. The output load rate power dataset covers the main operating conditions of the motor.
[0010] Among the steps, the following steps are involved: fitting the load rate efficiency datasets of each motor to obtain the efficiency curve of each motor; integrating the efficiency curves of all motors into a dynamic efficiency model library and updating it in real time: A polynomial fitting method is used to fit the load rate efficiency dataset to obtain the efficiency curve equation for each motor. The dynamic efficiency model library stores the efficiency curve equation and the corresponding load rate range for each motor.
[0011] In the step of constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library for motor groups, to determine the number of motors to be operated, the target load allocation for each motor, and the speed setpoint, and then outputting the decision result: To determine whether to trigger an optimization decision, the triggering condition is based on a preset triggering event rule. The absolute value of the difference between the load value corresponding to the total load demand curve and the current actual total load exceeds a preset threshold, which ranges from 5% to 15%. If this condition is met, an optimization decision is triggered. If the decision is triggered, the pre-built dynamic efficiency model library of the motor group is invoked, and the total load demand curve is used as the constraint benchmark. The particle swarm optimization algorithm is then used to build the optimization model. Based on the dynamic efficiency model library, the efficiency values of each motor under different load rates are extracted, and an objective function is constructed by combining the motor output power. The objective function takes the highest overall system efficiency as its core. The overall system efficiency is calculated by integrating the output power and corresponding efficiency of each running motor and the input power. Set the particle swarm optimization algorithm parameters, specifically: population size 50-100, maximum number of iterations 30-50, inertia weight 0.5-0.9, and learning factors c1 and c2 both 1.5-2.0, to complete the construction of the optimization model; The optimization model is solved using the particle swarm optimization algorithm. During the solution process, the maximum load rate, minimum load rate, and speed adjustment range of the motor are used as constraints. The final output is the optimal solution, which is the number of motors to be operated, the target load distribution ratio of each operating motor, and the corresponding speed setting value.
[0012] In the step of performing motor load transfer operations based on the decision results using a master-slave tracking strategy to achieve a smooth switching of the motor group's operating state: Analyze the decision results to clarify the motor load transfer commands to be executed, the target load values and speed setpoints for each motor; A master-slave tracking strategy is adopted to perform load transfer. One motor is set as the master motor and the rest are slave motors. The slave motors track the load change rate of the master motor in real time. Motor load transfer operations are performed based on a master-slave tracking strategy to achieve smooth switching of the operating status of the motor group.
[0013] In the step of performing motor load transfer operations based on a master-slave tracking strategy to achieve a smooth switching of the operating state of the motor group: Real-time acquisition of motor current, voltage and speed data; monitoring of equipment operating status during switching process; output of real-time monitoring data. The real-time monitoring data is compared with the preset threshold. If the current overshoot exceeds 10%, the master-slave tracking rate is adjusted immediately.
[0014] This invention also provides a control system for electrical automation, including a load demand prediction module, an optimization decision module, and a smooth switching module; wherein: The load demand prediction module is used to collect historical data and real-time information related to the operation of the motor group, predict the total load demand curve for a future preset period through a time series algorithm, and output the total load demand curve. The optimization decision module is used to determine the number of motors to be operated, the target load allocation for each motor, and the speed setpoint by constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, and output the decision result. The smooth switching module is used to perform motor load transfer operations based on the decision results and using a master-slave tracking strategy to achieve smooth switching of the operating status of the motor group.
[0015] This invention discloses an electrical automation control system and method that collects historical and real-time data related to the operation of a motor group. It predicts the total load demand curve for a future preset period using a time series algorithm and outputs this curve. Based on the total load demand curve, and combining preset trigger event rules with a pre-built dynamic efficiency model library for the motor group, it constructs and solves an optimization model to determine the number of motors to operate, the target load allocation for each motor, and the speed setpoint, outputting the decision result. Based on the decision result, it employs a master-slave tracking strategy to execute motor load transfer operations, achieving a smooth switching of the motor group's operating state. Thus, by combining historical and real-time data to predict load, constructing a system-level collaborative scheduling system and optimization model, it improves the timeliness of motor group response and operational stability, achieving optimal collaborative operation of each motor and maximizing the overall energy efficiency of the system. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart of the steps of the electrical automation control method of the present invention.
[0018] Figure 2 This is a flowchart of steps S100 of the present invention.
[0019] Figure 3 This is a flowchart of steps S102 of the present invention.
[0020] Figure 4 This is a flowchart of steps S200 of the present invention.
[0021] Figure 5 This is a flowchart of steps S300 of the present invention.
[0022] Figure 6 This is a flowchart of steps S303 of the present invention.
[0023] Figure 7 This is a schematic diagram of the electrical automation control system of the present invention.
[0024] Figure 8 This is a schematic diagram of the electronic device of the present invention.
[0025] 401 - Load demand prediction module, 402 - Optimization decision module, 403 - Smooth switching module. Detailed Implementation
[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.
[0027] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.
[0028] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."
[0029] Please see Figures 1-6 The present invention provides a control method for electrical automation, comprising the following steps: S100: Collects historical data and real-time information related to the operation of the motor group, predicts the total load demand curve for a future preset period through time series algorithm, and outputs the total load demand curve. In this embodiment, historical data and real-time information related to the operation of the motor group are collected, and the total load demand curve for a future preset period is predicted using a time series algorithm, and the total load demand curve is output. The specific process is as follows: S101: Collect historical data and real-time information. The historical data includes the historical operating load of the motor group, energy consumption data, and production task records under the corresponding operating conditions. The real-time information includes the current production plan, ambient temperature, and equipment operating status parameters. S102: Perform standardized preprocessing on historical data and real-time information, remove outlier data, and output the preprocessed dataset; S1021: Use the three-standard-deviation criterion to identify outliers in historical data and real-time information, and perform interpolation replacement on outliers; S1022: The outliers after interpolation replacement are processed by normalization, and the data is mapped to the 0,1 interval to obtain the standardized dataset and output it.
[0030] S103: A load forecasting model is constructed using a long short-term memory time series algorithm. The preprocessed dataset is input into the load forecasting model, and the total load demand curve for the next working cycle in the future preset period is output.
[0031] In the above process, the historical data collection scope is as follows: 1) Select continuous operation data of the motor group within the past 6 months to ensure coverage of at least 100 complete working cycles to meet the training data requirements of the load prediction model; 2) Real-time information collection frequency: Ambient temperature is collected once every 5 minutes, equipment operating status parameters are collected once per second, and the current production plan is updated in real time with the production schedule; 3) Specific types of equipment operating status parameters: Including real-time current, voltage, speed, winding temperature and vibration frequency of each motor; 4) Definition of working conditions and production task records: Working conditions are divided into light load working conditions, heavy load working conditions and start-stop working conditions, and production task records include the production product model, planned output, single batch running time and load demand level; 5) Ambient temperature collection location: Ambient temperature is collected at a distance of 30cm around the motor body to ensure the correlation between the data and the motor operating status; 6) Data collection method: Equipment operating parameters are directly collected through the current, voltage and speed sensors built into the motor, ambient temperature is collected through temperature and humidity sensors deployed in the workshop, historical data and production task records are read and exported from the factory PLC control system, and all data are uniformly stored in the industrial control cloud platform; The min-max normalization method is adopted, and the specific calculation formula is: x'=(x - x_min) / (x_max - x_min), where x_min is the minimum value of the data, x_max is the maximum value of the data, and x' is the normalized output data. This process eliminates the interference of data of different magnitudes on the prediction model. When building the prediction model, the dataset is first divided into training, validation, and test sets in a 7:2:1 ratio. The training set is used for training model parameters, the validation set is used to adjust model hyperparameters, and the test set is used to verify the model's prediction accuracy. Then, the model is trained: the number of training iterations is set to 500, the initial learning rate is set to 0.001, and the mean squared error (MSE) is used as the loss function. When the loss function value of the validation set does not decrease or falls below 0.005 for 10 consecutive iterations, the model training is stopped. At the same time, dropout technology is used to prevent the model from overfitting, and the dropout rate is set to 0.2. 4) Model output format: the total load demand curve is output in the form of a two-dimensional array of "time-load value", the time interval is consistent with the real-time data acquisition frequency (i.e., 1 second / data point), the load value is in kW, and it is exported as an Excel file for subsequent steps. 5) Model validation standard: the prediction error of the test set must be controlled within 5% to ensure that the load prediction accuracy meets the requirements of subsequent scheduling decisions.
[0032] S200: Based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, the system constructs and solves optimization models to determine the number of motors that need to be operated, the target load allocation for each motor, and the speed setpoint, and outputs the decision result. In this embodiment, based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library for motor groups, an optimization model is constructed and solved to determine the number of motors that need to operate, the target load allocation for each motor, and the speed setpoint, and the decision result is output. The specific process is as follows: S201: Determine whether to trigger an optimization decision. The triggering condition is based on a preset triggering event rule. The absolute value of the difference between the load value corresponding to the total load demand curve and the current actual total load exceeds a preset threshold. The threshold range is 5%-15%. If this condition is met, the optimization decision is triggered. S202: If the decision is triggered, the pre-built dynamic efficiency model library of the motor group is called, and the total load demand curve is used as the constraint benchmark. The particle swarm algorithm is used to build the optimization model. S203: Based on the dynamic efficiency model library, extract the efficiency values of each motor under different load rates, and construct an objective function by combining the motor output power. The objective function takes the highest overall system efficiency as its core, and calculates the overall system efficiency by integrating the output power and corresponding efficiency of each running motor and the input power. S204: Set the particle swarm optimization algorithm parameters, specifically: population size 50-100, maximum number of iterations 30-50, inertia weight 0.5-0.9, and learning factors c1 and c2 both 1.5-2.0, to complete the construction of the optimization model; S205: The optimization model is solved using the particle swarm optimization algorithm. During the solution process, the maximum load rate, minimum load rate, and speed adjustment range of the motor are used as constraints. The final output is the optimal solution, which is the number of motors to be operated, the target load distribution ratio of each operating motor, and the corresponding speed setting value.
[0033] In the above process, input and output power data of each motor in the motor group under different load rates are collected through bench tests, and load rate power datasets of each motor are output. Based on the load rate power datasets, the efficiency value of each motor at the corresponding load rate is calculated, and load rate efficiency datasets of each motor are output. The load rate adjustment range of the bench test is 10%-100%, and a set of input and output power data is collected every 5% load rate. The output load rate power datasets cover the main operating conditions of the motors. The load rate efficiency datasets of each motor are fitted to obtain the efficiency curve of each motor. The efficiency curves of all motors are integrated into a dynamic efficiency model library and updated in real time. The load rate efficiency datasets are fitted using a polynomial fitting method to obtain the efficiency curve equation of each motor. The dynamic efficiency model library stores the efficiency curve equations of each motor and the corresponding load rate range. When making judgments and decisions: The current actual total load is the sum of the current output loads of all running motors in the motor group. It is obtained by collecting the output power of each running motor in real time (the collection frequency is consistent with the time granularity of the total load demand curve, i.e., 1 second / time), and then summing them up; if there are motors that are not running, they are not included in the calculation. The default value of the preset threshold is 10%, which can be flexibly adjusted according to the application scenario. For light load conditions (total system load ≤ 30% of rated total load), the value is 5%-8%, and for heavy load conditions (total system load > 70% of rated total load), the value is 12%-15%. The adjustment is based on the load fluctuation range allowed by the production process and the motor start-stop loss threshold. The load value corresponding to the total load demand curve is the instantaneous load value (unit: kW) at each time point in the curve. The comparison logic is "the absolute value of the difference between the instantaneous value of the total load demand curve and the current actual total load exceeds the preset threshold for 3 consecutive seconds" to avoid false triggering caused by a single fluctuation. If the triggering condition is not met, the current motor running state is maintained, and the judgment process of S201 is re-executed after an interval of 10 seconds. When constructing the optimization model using the Particle Swarm Optimization (PSO) algorithm: a real-number encoding mechanism is employed to map the core decision variables of motor group scheduling (number of motors to be operated, target load rate of each motor, target speed of each motor) to particle dimension vectors of the PSO algorithm. Each particle uniquely corresponds to a complete scheduling scheme, and the particle dimension is determined by the total number of motors in the group, ensuring that all decision variables are effectively represented and achieving a one-to-one correspondence between scheduling schemes and particles. Maximizing the overall system efficiency is the core optimization objective, which is transformed into the fitness function of the PSO algorithm. By calling the motor group dynamic efficiency model library to obtain the load rate-efficiency characteristics of each motor, and combining this with the rated parameters of each motor, the overall system efficiency corresponding to different scheduling schemes is calculated. This efficiency is used as the particle fitness value, achieving a direct correlation between the optimization objective and the algorithm evaluation criteria. The total load demand matching constraint and motor operating parameter constraints (maximum / minimum load rate, speed adjustment range) are transformed into the search feasible region boundary of the PSO algorithm. A penalty function mechanism is employed to penalize particles that violate constraints, reducing the search priority of non-compliant solutions and ensuring that the algorithm searches for optimal solutions only within the physically feasible and technologically compliant range. Based on a preset parameter range, the core parameters of the particle swarm optimization algorithm are initialized, including population size, maximum number of iterations, inertia weight, and learning factor. By randomly generating an initial particle population, it is ensured that the initial search samples uniformly cover the feasible region, providing a sufficient search foundation for subsequent iterative optimization. Finally, the optimization model is constructed, possessing the ability to search for and solve optimal scheduling schemes.
[0034] S300: Based on the decision results, a master-slave tracking strategy is adopted to perform motor load transfer operations, thereby achieving a smooth switching of the operating status of the motor group.
[0035] In this embodiment, based on the decision results, a master-slave tracking strategy is adopted to perform motor load transfer operations, achieving a smooth switch in the operating state of the motor group. The specific process is as follows: S301: Analyze the decision results to determine the motor load transfer command to be executed, the target load value and speed set value of each motor; S302: The load transfer is performed using a master-slave tracking strategy. One motor is set as the master motor and the rest are slave motors. The slave motors track the load change rate of the master motor in real time. S303: Performs motor load transfer operations based on master-slave tracking strategy to achieve smooth switching of motor group operating status.
[0036] S3031: Real-time acquisition of motor current, voltage and speed data, monitoring of equipment operating status during switching process, and output of real-time monitoring data; S3032: Compare real-time monitoring data with preset thresholds. If the current overshoot exceeds 10%, immediately adjust the master-slave tracking rate.
[0037] In the above process, when parsing the decision results: based on the output two-dimensional table of "motor number - operating status - target load rate - target speed", the parsing is performed according to the principle of "unique matching of motor number", distinguishing three core commands: "starting a new motor", "stopping the running motor", and "adjusting the load / speed of the existing motor". Target parameter conversion: the "target load rate" in the decision result is combined with the rated output power of the motor to convert it into a specific target load value (unit: kW), that is, target load value = target load rate × rated output power of the motor, ensuring that the command parameters are intuitive and executable. Parsing result output format: output in the form of a structured instruction set, each instruction includes "execution object (motor number) - operation type (start / stop / adjust) - target parameter (load value kW / speed rpm) - execution sequence (priority)", where the start / stop instruction has higher priority than the load / speed adjustment instruction. Parsing anomaly handling: if anomalies such as "motor number matching failure" or "target parameter exceeding the rated range of the equipment" occur during the parsing process, the parsing is immediately paused and an alarm is triggered. At the same time, the parsing instruction of the last valid decision result is called to temporarily maintain operation, and parsing is restarted after the maintenance personnel handle it. During load transfer: First, the main motor selection criteria are established: the running motor with the current load rate closest to the target load rate and an efficiency ≥ 85% is selected as the main motor; if none of the running motors meet this criterion, the motor with the highest rated power matching degree with the target total load among the motors to be started is selected as the main motor; once the main motor is selected, it remains unchanged during this load transfer process; then, the slave motor matching rules are implemented: according to the parsed load allocation instructions, the remaining running motors are divided into slave motors, and they are grouped and tracked according to the principle of "similar rated power" (slave motors in the same group synchronously track the changes of the main motor), avoiding response imbalance caused by small-power motors tracking large-power motors; at this time, the initial tracking rate is set: the master-slave tracking rate is set to 0.02kW / ms by default (i.e., the load change per second does not exceed 20kW), which can be adjusted according to the motor type, with asynchronous motors set to 0.01~0.02kW / ms and synchronous motors set to 0.02~0.03kW / ms.
[0038] Corresponding to the aforementioned embodiments of the control method for electrical automation, this application also provides embodiments of a control system for electrical automation.
[0039] Figure 7 This is a block diagram of an electrical automation control system according to an exemplary embodiment. (Refer to...) Figure 7 The system may include: a load demand prediction module 401, an optimization decision module 402, and a smooth switching module 403, wherein: The load demand prediction module 401 is used to collect historical data and real-time information related to the operation of the motor group, predict the total load demand curve for a future preset period through a time series algorithm, and output the total load demand curve. The optimization decision module 402 is used to, based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, construct and solve an optimization model to determine the number of motors that need to be operated, the target load allocation of each motor, and the speed setpoint, and output the decision result. The smooth switching module 403 is used to perform motor load transfer operations based on the decision results and adopt a master-slave tracking strategy to achieve smooth switching of the operating state of the motor group.
[0040] In this embodiment, the load demand prediction module 401 collects historical data and real-time information related to the operation of the motor group, predicts the total load demand curve for a future preset period using a time series algorithm, and outputs the total load demand curve. The optimization decision module 402, based on the total load demand curve, combines preset trigger event rules and a pre-built dynamic efficiency model library for the motor group, constructs and solves an optimization model, determines the number of motors that need to operate, the target load allocation for each motor, and the speed setpoint, and outputs the decision result. The smooth switching module 403, based on the decision result, uses a master-slave tracking strategy to perform motor load transfer operations, achieving a smooth switching of the motor group's operating state. By combining historical and real-time data to predict the load, constructing a system-level collaborative scheduling system and optimization model, the timely response and operational stability of the motor group are improved, achieving optimal collaborative operation of each motor and maximizing the overall energy efficiency of the system. For the dynamic efficiency model library, the model library has a real-time update mechanism. The update trigger conditions are divided into two categories: timed updates and event-triggered updates. The timed update frequency is once a month. Based on the actual monitoring data of the operating load rate and efficiency of the motor group in the past month, the efficiency curve equation of each motor is updated through a polynomial fitting method. The event-triggered update conditions include the completion of motor maintenance, the addition of a new motor to the group control system, the expansion of the existing motor load rate operating range to the range not covered by bench testing, and the absolute value of the deviation between the actual efficiency of the motor and the predicted efficiency of the model library exceeds 3% during operation. Once any condition is met, the update is triggered immediately. The efficiency curve is refitted by supplementing bench testing or online data collection, and the efficiency curve equation and load rate range of the corresponding motor in the model library are updated.
[0041] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.
[0042] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0043] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the electrical automation control method described above. Figure 8 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which is part of an electrical automation control system provided by an embodiment of the present invention. Except for... Figure 8 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.
[0044] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the electrical automation control method described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.
[0045] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.
[0046] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.
Claims
1. A control method for electrical automation, characterized in that, Includes the following steps: Collect historical data and real-time information related to the operation of the motor group, predict the total load demand curve for a future preset period using a time series algorithm, and output the total load demand curve. Based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, the number of motors to be operated, the target load allocation of each motor, and the speed setpoint are determined by constructing and solving the optimization model, and the decision result is output. Based on the decision results, a master-slave tracking strategy is adopted to perform motor load transfer operations, thereby achieving a smooth switch of the operating status of the motor group.
2. The electrical automation control method as described in claim 1, characterized in that, In the steps of collecting historical and real-time data related to the operation of the motor group, predicting the total load demand curve for a future preset period using time series algorithms, and outputting the total load demand curve: Collect historical data and real-time information. The historical data includes the historical operating load of the motor group, energy consumption data, and production task records under the corresponding operating conditions. The real-time information includes the current production plan, ambient temperature, and equipment operating status parameters. Perform standardized preprocessing on historical data and real-time information, remove outlier data, and output a preprocessed dataset; A load forecasting model is constructed using a long short-term memory time series algorithm. The preprocessed dataset is input into the load forecasting model, and the total load demand curve for the next working cycle in the future preset period is output.
3. The electrical automation control method as described in claim 2, characterized in that, In the steps of standardizing and preprocessing historical data and real-time information, removing outlier data, and outputting the preprocessed dataset: The three-standard-deviation criterion is used to identify outliers in historical data and real-time information, and outliers are replaced by interpolation. The outliers after interpolation are processed by normalization, and the data is mapped to the 0,1 interval to obtain the standardized dataset and output it.
4. The electrical automation control method as described in claim 1, characterized in that, Before the steps of constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library for motor groups, to determine the number of motors to operate, the target load allocation for each motor, and the speed setpoint, and output the decision result: The input and output power data of each motor in the motor group under different load rates were collected through bench tests, and the load rate power dataset of each motor was output. Based on the load rate power dataset, calculate the efficiency value of each motor at the corresponding load rate, and output the load rate efficiency dataset of each motor. The load rate efficiency datasets of each motor are fitted to obtain the efficiency curve of each motor. The efficiency curves of all motors are then integrated into a dynamic efficiency model library and updated in real time.
5. The electrical automation control method as described in claim 4, characterized in that, In the step of calculating the efficiency value of each motor at the corresponding load rate based on the load rate power dataset and outputting the load rate efficiency dataset of each motor: The load rate adjustment range of the bench test is 10%-100%. A set of input power and output power data is collected every 5% load rate. The output load rate power dataset covers the main operating conditions of the motor.
6. The electrical automation control method as described in claim 4, characterized in that, In the steps of fitting the load rate efficiency datasets of each motor to obtain the efficiency curve of each motor, and integrating the efficiency curves of all motors into a dynamic efficiency model library and updating it in real time: A polynomial fitting method is used to fit the load rate efficiency dataset to obtain the efficiency curve equation for each motor. The dynamic efficiency model library stores the efficiency curve equation and the corresponding load rate range for each motor.
7. The electrical automation control method as described in claim 1, characterized in that, In the steps of constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library for motor groups, the number of motors to be operated, the target load allocation for each motor, and the speed setpoint are determined, and the decision result is output: To determine whether to trigger an optimization decision, the triggering condition is based on a preset triggering event rule. The absolute value of the difference between the load value corresponding to the total load demand curve and the current actual total load exceeds a preset threshold, which ranges from 5% to 15%. If this condition is met, an optimization decision is triggered. If the decision is triggered, the pre-built dynamic efficiency model library of the motor group is invoked, and the total load demand curve is used as the constraint benchmark. The particle swarm optimization algorithm is then used to build the optimization model. Based on the dynamic efficiency model library, the efficiency values of each motor under different load rates are extracted, and an objective function is constructed by combining the motor output power. The objective function takes the highest overall system efficiency as its core. The overall system efficiency is calculated by integrating the output power and corresponding efficiency of each running motor and the input power. Set the particle swarm optimization algorithm parameters, specifically: population size 50-100, maximum number of iterations 30-50, inertia weight 0.5-0.9, and learning factors c1 and c2 both 1.5-2.0, to complete the construction of the optimization model; The optimization model is solved using the particle swarm optimization algorithm. During the solution process, the maximum load rate, minimum load rate, and speed adjustment range of the motor are used as constraints. The final output is the optimal solution, which is the number of motors to be operated, the target load distribution ratio of each operating motor, and the corresponding speed setting value.
8. The electrical automation control method as described in claim 1, characterized in that, In the step of performing motor load transfer operations based on the decision results and adopting a master-slave tracking strategy to achieve a smooth switch of the operating state of the motor group: Analyze the decision results to clarify the motor load transfer commands to be executed, the target load values and speed setpoints for each motor; A master-slave tracking strategy is adopted to perform load transfer. One motor is set as the master motor and the rest are slave motors. The slave motors track the load change rate of the master motor in real time. Motor load transfer operations are performed based on a master-slave tracking strategy to achieve smooth switching of the operating status of the motor group.
9. The electrical automation control method as described in claim 8, characterized in that, In the steps of performing motor load transfer operations based on a master-slave tracking strategy to achieve a smooth switching of the operating state of the motor group: Real-time acquisition of motor current, voltage and speed data; monitoring of equipment operating status during switching process; output of real-time monitoring data. The real-time monitoring data is compared with the preset threshold. If the current overshoot exceeds 10%, the master-slave tracking rate is adjusted immediately.
10. A control system for electrical automation, employing the control method for electrical automation as described in claim 1, characterized in that, It includes a load demand forecasting module, an optimization decision-making module, and a smooth switching module; among which: The load demand prediction module is used to collect historical data and real-time information related to the operation of the motor group, predict the total load demand curve for a future preset period through a time series algorithm, and output the total load demand curve. The optimization decision module is used to determine the number of motors to be operated, the target load allocation for each motor, and the speed setpoint by constructing and solving an optimization model based on the total load demand curve, combined with preset trigger event rules and a pre-built dynamic efficiency model library of motor groups, and output the decision result. The smooth switching module is used to perform motor load transfer operations based on the decision results and using a master-slave tracking strategy to achieve smooth switching of the operating status of the motor group.