CN116915122A - Self-adaptive control method and system for coal mine frequency conversion equipment - Google Patents
Self-adaptive control method and system for coal mine frequency conversion equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
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- H—ELECTRICITY
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- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
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Abstract
The invention provides a self-adaptive control method and a self-adaptive control system for coal mine variable frequency equipment, which relate to the technical field of equipment control and comprise the following steps: the method comprises the steps of carrying out data acquisition aiming at equipment specifications and reference braking parameters, acquiring real-time braking scenes by an interactive control target, acquiring and determining braking monitoring data, carrying out real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, outputting a plurality of groups of variable frequency control parameters, optimizing the plurality of groups of variable frequency control parameters, determining a target variable frequency control parameter set, constructing a real braking model, carrying out trial control analysis and tuning to determine a pre-variable frequency control parameter set, matching and transmitting the pre-variable frequency control parameter set to corresponding coal mine variable frequency equipment, and carrying out motor soft braking control of equipment groups. The invention solves the technical problems that the operation working conditions of the electric control system of the coal mine equipment are changeable, and the defects of unstable action, unstable action delay, serious signal feedback lag, dependence on object model precision and the like of a mechanical executing mechanism are caused, and the performance and implementation effect of the control system are directly influenced.
Description
Technical Field
The invention relates to the technical field of equipment control, in particular to a self-adaptive control method and system for coal mine variable frequency equipment.
Background
Modern coal mine equipment has the characteristics of high efficiency, energy conservation and safety based on an automatic and intelligent principle, promotes the development of the coal mine industry, and along with the increase of the resource scarcity and the environmental pressure, the coal mine industry faces the difficult problems of improving the energy utilization efficiency and reducing the carbon emission, so that the control of the coal mine equipment also brings new challenges.
The existing control method of the coal mine equipment has the defects that the operation working condition of an electric control system of the coal mine equipment is variable, the control mechanism is complex, the action of a mechanical executing mechanism is unstable, the action delay is not fixed, the signal feedback lag is serious, the accuracy of an object model is depended, and the like, so that the randomness and the time variability of the system are caused, and the performance and the implementation effect of the control system are directly influenced. Therefore, a certain lifting space exists for the control of coal mine equipment.
Disclosure of Invention
The application provides a self-adaptive control method and a self-adaptive control system for coal mine variable frequency equipment, and aims to solve the technical problems that the operation condition of an electric control system of the coal mine equipment is changeable, the control mechanism is complex, the defects of unstable action of a mechanical executing mechanism, unstable action delay, serious signal feedback lag, dependence on object model precision and the like exist, the randomness and the time variability of the system are caused, and the performance and the implementation effect of the control system are directly influenced.
In view of the problems, the application provides a self-adaptive control method and a self-adaptive control system for coal mine frequency conversion equipment.
The application discloses a first aspect, which provides a self-adaptive control method of coal mine frequency conversion equipment, comprising the following steps: determining a device group to be subjected to variable frequency control, traversing the device group, and carrying out data acquisition aiming at device specifications and reference braking parameters to generate a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value; the method comprises the steps of interactively controlling a target, acquiring a real-time braking scene, and acquiring and determining braking monitoring data based on a sensing monitoring device; performing the real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups; taking the control reference value as constraint, taking the control target as response, optimizing the multiple groups of variable frequency control parameters, and determining a target variable frequency control parameter set; connecting a visual simulation platform, and constructing an actual braking model based on the real-time braking scene, the coal mine variable frequency equipment and the equipment group; based on the simulated braking model, performing trial control analysis and tuning on the target variable frequency control parameter set, and determining a pre-variable frequency control parameter set; and matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment to perform motor soft braking control of the equipment group.
In another aspect of the disclosure, an adaptive control system for a coal mine variable frequency device is provided, where the system is used in the method, and the system includes: the basic data acquisition module is used for determining a device group to be subjected to variable frequency control, traversing the device group to acquire data aiming at device specifications and reference braking parameters, and generating a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value; the brake data acquisition module is used for interactively controlling the target, acquiring a real-time brake scene, and acquiring and determining brake monitoring data based on the sensing monitoring device; the braking scene analysis module is used for carrying out real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups; the control parameter optimizing module is used for optimizing the plurality of groups of variable frequency control parameters by taking the control reference value as a constraint and the control target as a response, and determining a target variable frequency control parameter set; the brake model building module is used for connecting a visual simulation platform, and building a simulated brake model based on the real-time brake scene, the coal mine variable frequency equipment and the equipment group; the pilot control analysis tuning module is used for carrying out pilot control analysis and tuning on the target variable frequency control parameter set based on the simulated braking model, and determining a pre-variable frequency control parameter set; and the soft braking control module is used for matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment to perform motor soft braking control of the equipment group. One or more technical schemes provided by the application have at least the following technical effects or advantages:
Determining a device group to be subjected to variable frequency control, traversing the device group, carrying out data acquisition aiming at device specifications and reference braking parameters to generate a device database, acquiring real-time braking scenes by an interaction control target, acquiring and determining braking monitoring data, analyzing the real-time braking scenes, inputting the braking monitoring data into a frequency modulation analysis model, outputting a plurality of groups of variable frequency control parameters, optimizing the plurality of groups of variable frequency control parameters, determining a target variable frequency control parameter set, constructing a real braking model, carrying out trial control analysis and optimization to determine a pre-variable frequency control parameter set, matching and transmitting the pre-variable frequency control parameter set to corresponding coal mine variable frequency devices, and carrying out motor soft braking control of the device group. The method solves the technical problems that the operation working conditions of an electric control system of coal mine equipment are changeable, the control mechanism is complex, the defects of unstable action, unstable action delay, serious signal feedback lag, dependence on object model precision and the like of a mechanical executing mechanism are caused, the randomness and time variability of the system are caused, the performance and implementation effect of the control system are directly influenced, the self-adaptive closed-loop control is realized, the control performance requirements of different equipment under various working conditions are met, the precision and stability requirements required by automatic control are met, and further the technical effects of powerful support are provided for the realization of comprehensive automation and intelligent coal mine.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of a self-adaptive control method of a coal mine frequency conversion device according to an embodiment of the application;
fig. 2 is a schematic flow chart of a possible real-time braking scene analysis in the adaptive control method of the coal mine variable frequency device according to the embodiment of the present application;
fig. 3 is a schematic diagram of a possible flow for building a frequency modulation analysis model in a self-adaptive control method of a coal mine frequency conversion device according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an adaptive control system of a coal mine variable frequency device according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a basic data acquisition module 10, a brake data acquisition module 20, a brake scene analysis module 30, a control parameter optimizing module 40, a brake model building module 50, a test control analysis optimizing module 60 and a soft brake control module 70.
Detailed Description
The embodiment of the application solves the technical problems that the randomness and the time variability of a system are caused by the defects of unstable action, unstable action delay, serious signal feedback lag, dependence on object model precision and the like of a mechanical executing mechanism, directly influence the performance and implementation effect of a control system, realize self-adaptive closed-loop control, meet the control performance requirements of different equipment under various working conditions, meet the precision and stability requirements required by automatic control, and further provide powerful support for the comprehensive automation and the intelligent realization of the coal mine by providing the self-adaptive control method of the coal mine variable-frequency equipment.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Embodiment one:
as shown in fig. 1, an embodiment of the present application provides an adaptive control method for a coal mine frequency conversion device, where the method includes:
step S100: determining a device group to be subjected to variable frequency control, traversing the device group, and carrying out data acquisition aiming at device specifications and reference braking parameters to generate a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value;
Specifically, according to the type and operation requirement of equipment in a coal mine, equipment groups needing to be subjected to variable frequency control, such as an emulsion pump, an air compressor, a belt conveyor, a winch and the like, are determined, the selected equipment groups are traversed, data acquisition is carried out aiming at equipment specifications and reference braking parameters, the equipment specifications and the reference braking parameters comprise information of equipment types, rated power, working modes, operation parameters and the like, the acquired equipment basic information is arranged and stored to form an equipment database, the equipment database comprises an equipment basic database and a motor basic database, wherein the equipment basic database comprises basic information of equipment, such as the types, the rated power, manufacturers and the like, the motor basic database records key information, such as the motor types, the rated voltage, the rated frequency and the like, of each equipment, and the control reference values of each equipment are marked, wherein the control reference values are preset parameters or reasonable ranges summarized according to the characteristics and requirements of the coal mine variable frequency equipment under the premise of considering factors such as safety, efficiency and the like.
Step S200: the method comprises the steps of interactively controlling a target, acquiring a real-time braking scene, and acquiring and determining braking monitoring data based on a sensing monitoring device;
Specifically, different running states of the equipment group and control targets in a specific scene are set according to requirements, for example, power-on buffering is needed for start-stop control, invalid work compensation is needed for the running states, emergency control is needed for the different states, and the control targets can be determined according to running requirements and safety requirements of equipment in a coal mine. The method comprises the steps of obtaining current real-time braking scenes by interaction with equipment groups, wherein the current real-time braking scenes comprise information such as the running state and the current working mode of each equipment in the equipment groups, and acquiring corresponding braking monitoring data which relate to parameters such as current, voltage, frequency and temperature of the equipment and other key indexes by utilizing a sensing monitoring device of each equipment in the equipment groups.
Step S300: performing the real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups;
further, as shown in fig. 2, step S300 of the present application includes:
step S310: based on the real-time braking scene, identifying target equipment to determine a frequency modulation mode, wherein the frequency modulation mode comprises an independent frequency modulation mode and a cooperative frequency modulation mode;
Step S320: based on the real-time braking scene, identifying the running state of the equipment, and determining a target state, wherein the target state comprises start-stop braking, running frequency, running time zone and fluctuation trend;
step S330: and determining a scene analysis result based on the frequency modulation mode and the target state, wherein the scene analysis result is at least one item.
Specifically, according to the characteristics of a real-time braking scene and the working states of all the devices, identifying target devices by analyzing and judging the data of the real-time braking scene, and determining a frequency modulation mode according to the identification result of the target devices, wherein when one device is determined to be the target device, the independent frequency modulation mode is adopted, namely the frequency conversion control of the device is not affected with the frequency conversion control of other devices, and each device independently operates and adjusts the frequency to meet the independent control requirement; when a plurality of devices are identified as target devices and certain association or cooperative operation requirements exist between the devices, a cooperative frequency modulation mode is adopted, namely the devices are linked, and the frequencies are regulated together according to the relation and constraint conditions of the devices, so that a better cooperative work effect is achieved.
The running states of the equipment are identified and classified by analyzing the real-time braking scene data, and whether the equipment is in a starting state, a stopping state or a braking state is judged according to the working characteristics of different equipment and the monitoring data of the sensors. After the operational status of the device is identified, a target status is further determined, including start-stop braking, operating frequency, operating time zone, and trend of fluctuation.
The start-stop braking state is to judge whether the equipment needs to be started, stopped or subjected to braking operation currently, and can be determined according to the control requirement of the equipment and related data in a real-time braking scene; the operating frequency is a variable frequency for determining that the equipment should operate in a target state, and proper operating frequency can be selected according to the equipment type, the operating requirement and the data of a real-time braking scene; the running time zone is used for determining which running time zone the equipment should be in within a specific time period, and the difference of energy consumption and output requirements of different time periods can be considered, and the equipment is classified or distinguished according to scenes and requirements; the fluctuation trend is the fluctuation trend of the frequency or other key parameters when the equipment is analyzed according to the data of the real-time braking scene, and a proper control strategy can be determined to stabilize the working state of the equipment.
Matching and associating the frequency modulation mode with the target state according to the actual operation requirement of the equipment group and the specific scene requirement, determining a scene analysis result according to the matching result, for example, analyzing the equipment operation mode, and judging the current operation mode of the equipment, such as a starting mode, a stopping mode or a braking mode, according to the target state and the frequency modulation mode; frequency adjustment analysis, namely determining a frequency adjustment mode of equipment according to a target state and a frequency modulation mode, such as determining a variable frequency control parameter according to a target frequency and a fluctuation trend so as to control frequency output of the equipment; and the running time zone analysis is carried out, and the running time zone of the equipment is determined to be a specific time period according to the target state and the frequency modulation mode so as to meet the requirements of energy consumption management and production scheduling.
Further, as shown in fig. 3, step S300 of the present application includes:
step S340: the equipment group is used as an index to search and call historical variable frequency control data;
step S350: based on the historical variable frequency control data, historical variable frequency control data of a first device in the device group is extracted, and a sample scene analysis result, sample brake monitoring data and sample variable frequency control parameters are determined through data regularity;
Step S360: mapping and connecting the sample scene analysis result, the sample brake monitoring data and the sample variable frequency control parameters, determining construction data and training to generate a first equipment frequency modulation unit;
step S370: screening the historical variable frequency control data and training samples to sequentially complete construction of an N-th equipment frequency modulation unit;
step S380: and integrating the first equipment frequency modulation unit to the Nth equipment frequency modulation unit to generate the frequency modulation analysis model.
Specifically, according to the equipment group as an index, corresponding records are retrieved from the historical variable frequency control data storage, and the historical variable frequency control data set related to the equipment group is screened out by utilizing the unique identifier or other associated information of the equipment group.
The data records associated with the first device are screened from the historical variable frequency control data set and subjected to a normalization process including time alignment, data sampling interval adjustment, outlier processing, etc., for further analysis and application. And deducing a sample scene analysis result according to the running state, running model, frequency adjustment condition and the like of the equipment by analyzing the regular historical variable frequency control data. And extracting and sorting brake monitoring data and variable frequency control parameters of the first equipment. Through the steps, the data of the first equipment can be extracted from the historical variable frequency control data, the data are regulated and analyzed, a sample scene analysis result, sample brake monitoring data and sample variable frequency control parameters are obtained, and the sample data can provide basis and reference for subsequent modeling and algorithm optimization.
The sample scene analysis result, the sample brake monitoring data and the sample variable frequency control parameters are mapped and connected to form a complete data set, the corresponding relation and consistency between the data are ensured, the constructed data set is utilized to train the frequency modulation unit, the frequency modulation unit is generated for practical application after training and verification according to the input sample data and target output, namely the expected frequency adjustment effect, and the frequency modulation unit can automatically adjust the variable frequency control parameters according to the real-time brake scene and the brake monitoring data, so that accurate frequency adjustment is realized, and the operation effect of the first equipment is optimized.
And sequentially selecting data related to the N-th device from the historical variable frequency control data set, screening the historical variable frequency control data of the corresponding device by using the unique identifier of the device, and sequentially completing the construction of the frequency modulation unit of the N-th device by adopting the completely same method.
The method comprises the steps of collecting first equipment frequency modulation units to N equipment frequency modulation units, ensuring that interfaces and parameters of the first equipment frequency modulation units to the N equipment frequency modulation units can be mutually connected and cooperatively work, integrating the collected frequency modulation units of all the equipment to form an integral frequency modulation analysis model, ensuring cooperation and unified operation among a plurality of frequency modulation units, comprehensively considering running states and control requirements of a plurality of equipment, having the capacity of carrying out coordinated frequency conversion control on equipment groups, and providing a basis decision and guidance for accurate frequency modulation of the coal mine frequency conversion equipment groups.
Further, step S360 of the present application includes:
step S361: performing time sequence segmentation on the construction data to determine a frequency modulation analysis sample and a frequency modulation prediction sample;
step S362: generating a first frequency modulation analysis branch by performing neural network training based on the frequency modulation analysis sample;
step S363: based on the frequency modulation prediction sample, carrying out anomaly identification and incentive tracing positioning identification, and training to construct a first frequency modulation prediction branch;
step S364: constructing a parameter screening layer by taking identification as a screening mechanism;
step S365: and sequentially connecting and integrating the first frequency modulation analysis branch, the first frequency modulation prediction branch and the parameter screening layer to generate the first equipment frequency modulation unit.
Specifically, the constructed data is divided according to a certain time window or time period, for example, a sliding window, a fixed time interval or the like is used for dividing, and continuity and relative independence of sample data are ensured. And selecting a part of the segmented samples as frequency modulation analysis samples, wherein the samples comprise real-time braking scene analysis results, braking monitoring data and variable frequency control parameters and are used for analyzing, optimizing and adjusting the current state of equipment, and in addition, selecting a part of the segmented samples as frequency modulation prediction samples, wherein the samples mainly comprise historical braking monitoring data and variable frequency control parameters and are used for training a model to predict future frequency modulation requirements and running states.
Randomly dividing the frequency modulation analysis sample into a training data set and a verification data set, for example, dividing the frequency modulation analysis sample into the training data set and the verification data set in a ratio of 8:2, training the neural network by using the training data set and using optimization methods such as a back propagation algorithm, and the like, and enabling the neural network to realize an accurate frequency modulation analysis function by adjusting network parameters and weights; and verifying and evaluating the trained neural network by using the verification data set, and adjusting network parameters, a model structure and a training strategy according to a verification result so as to further optimize the performance and accuracy of the frequency modulation analysis.
After training and verification, a first frequency modulation analysis branch for practical application is obtained, and the branch can receive real-time braking scene analysis results, braking monitoring data and variable frequency control parameters as input and output accurate frequency modulation analysis results for optimizing the running state of equipment and a variable frequency control strategy.
Preprocessing and feature extraction are carried out on the frequency modulation prediction sample to obtain input data suitable for training, the frequency modulation prediction sample is trained by utilizing a training data set, a training model is used for identifying abnormal phenomena, and proper machine learning or deep learning algorithms such as abnormality detection, classifier and the like are used for analyzing and judging the input data. By analyzing the abnormal phenomenon and combining the related data in the frequency modulation prediction sample, the possible causes of the abnormality are traced and positioned, which can help identify specific causes of the abnormality, such as equipment faults, environmental changes and the like, and are used for subsequent problem solving and prevention control.
Based on the results of anomaly identification and incentive tracing, a first frequency modulation prediction branch is trained and constructed, the branch can identify anomaly and find incentive thereof according to real-time data, and meanwhile, future frequency modulation demands are predicted, so that a more accurate frequency modulation prediction and control strategy is provided for equipment groups.
And (3) using the trained model to identify the abnormal phenomenon of the frequency modulation prediction sample, determining whether the abnormal phenomenon exists or not by means of analysis data, pattern matching and the like, and identifying the corresponding abnormal event. And constructing a parameter screening layer based on the identification result of the abnormal phenomenon, inputting the parameters generated by the frequency modulation analysis branch and the frequency modulation prediction branch into the parameter screening layer for processing according to the setting of the parameter screening layer, and screening out parameters meeting the conditions or adjusting the values of the existing parameters according to the identification result of the abnormal phenomenon. And the optimized frequency modulation control parameters are obtained through the processing of the parameter screening layer, and the parameters can be better adapted to the influence of abnormal phenomena, so that the frequency modulation control effect is improved.
The first frequency modulation analysis branch is connected with the parameter screening layer, so that the output result of the analysis branch can be processed by the parameter screening layer, the first frequency modulation prediction branch is connected with the parameter screening layer, the output result of the prediction branch can be processed by the parameter screening layer, the frequency modulation analysis result and the frequency modulation prediction result processed by the parameter screening layer are integrated, and for example, weighing and comprehensive processing are carried out according to the characteristics and the requirements of the equipment group, so that an optimal frequency modulation control scheme is obtained. The first equipment frequency modulation unit which can be used for practical application is generated through connection and integration, the frequency modulation unit can comprehensively utilize the results of frequency modulation analysis and prediction models, intelligently conduct frequency modulation analysis and prediction, and provide a frequency conversion control strategy with high accuracy and adaptability through adjustment of a parameter screening layer.
Further, step S300 of the present application includes:
step S300-1: inputting the scene analysis result and the brake monitoring data into the frequency modulation analysis model;
step S300-2: performing unit matching to determine a target equipment frequency modulation unit based on the frequency modulation mode and the target state, and calling to construct a temporary processing module;
step S300-3: based on the temporary processing module, performing adaptive analysis on the scene analysis result and the brake monitoring data, and outputting the plurality of groups of variable frequency control parameters;
step S300-4: if the frequency modulation mode is an independent frequency modulation mode, independent analysis and output integration are carried out based on the temporary processing module; and if the frequency modulation mode is a cooperative frequency modulation mode, performing independent analysis and cooperative influence analysis on the temporary processing module.
Specifically, the obtained scene analysis result and brake monitoring data are input into a frequency modulation analysis model to be used as the input of the model, the frequency modulation analysis model is matched according to the determined frequency modulation mode and the target state, and a target equipment frequency modulation unit which needs to be called is determined according to the unit matching result, namely, the frequency modulation unit which accords with the target equipment is found and is called, so that the frequency conversion control of the target equipment is realized. And constructing a temporary processing module aiming at the temporary processing requirement of the equipment group, wherein the module comprises data processing, algorithm operation, decision logic and the like so as to meet the temporary processing requirement.
And inputting the obtained scene analysis result and the brake monitoring data into a temporary processing module, carrying out self-adaptive analysis and processing on the data, and generating a plurality of groups of variable frequency control parameters according to the self-adaptive analysis result, wherein the parameters can realize different frequency modulation schemes for different equipment according to the requirements of the equipment group and the scene change.
If the frequency modulation mode is an independent frequency modulation mode, which means that each device independently performs frequency modulation analysis and optimization and generates corresponding variable frequency control parameters, the temporary processing module analyzes the independent requirement of each device and outputs corresponding frequency modulation control parameters so as to realize independent control of each device; if the frequency modulation mode is a cooperative frequency modulation mode, in the mode, mutual influence among the devices needs to be considered, cooperative adjustment of the whole device group needs to be considered, the temporary processing module firstly carries out independent analysis on each device, and then generates variable frequency control parameters adapting to the whole device group by analyzing the cooperative influence and considering the mutual influence among the devices. In a word, according to different frequency modulation modes, the temporary processing module can perform independent frequency modulation analysis and output integration or independent analysis and synergistic effect analysis, so that the frequency modulation control strategy of the equipment group can be effectively optimized according to the requirements under different frequency modulation modes, and the overall performance and the synergistic effect of the equipment group are improved.
Step S400: taking the control reference value as constraint, taking the control target as response, optimizing the multiple groups of variable frequency control parameters, and determining a target variable frequency control parameter set;
specifically, by using an optimizing algorithm, such as a particle swarm optimization algorithm, a control reference value and a control target are used as constraint conditions, a plurality of groups of variable frequency control parameters are searched and optimized, an optimal variable frequency control parameter combination is selected as a target variable frequency control parameter set, and the optimal control effect and performance of the equipment can be realized under the condition that the control reference value is met after the parameter set is optimized.
Specifically, in the optimization process, a certain number of particles are randomly generated, and initial positions and speeds are randomly allocated to each particle, wherein each particle represents one possible solution, namely a set of variable frequency control parameters, and for each particle, a fitness value is calculated according to a corresponding variable frequency control parameter set, wherein a fitness function can be defined according to a control target and constraint conditions, and represents the fitting degree or the quality degree between the particle solution and the target. Recording the current position and fitness of each particle, updating the optimal position of each particle, namely an individual optimal solution, finding the particle with the best fitness from the group, recording a variable frequency control parameter set and the corresponding fitness, updating the global optimal position, namely a group optimal solution, and updating the speed and position of the particle according to the current position of the particle, the individual optimal solution and the group optimal solution by using a particle swarm optimization algorithm. And if the set stopping condition is met, for example, the maximum iteration number is met or the target fitness is met, ending the optimization algorithm, and outputting the variable frequency control parameter set corresponding to the global optimal position as a target variable frequency control parameter set.
Through the steps, the particle swarm optimization algorithm can search and find the optimal target variable frequency control parameter set to obtain the optimal frequency modulation control effect and performance, and can search among a plurality of solutions by introducing concepts of an individual optimal solution and a group optimal solution and take a global optimal solution as a final result.
Step S500: connecting a visual simulation platform, and constructing an actual braking model based on the real-time braking scene, the coal mine variable frequency equipment and the equipment group;
specifically, according to actual demands and system requirements, a visual simulation platform, such as a general simulation platform or a platform special for electric power/energy system simulation, is selected, a simulation braking model is constructed based on data of a real-time braking scene, coal mine variable frequency equipment and equipment groups by using modeling tools and functions of the selected platform, the model reflects the running condition, control strategy, interaction relation and the like of equipment in a coal mine, and dynamic simulation and visualization can be performed through the simulation platform. On the visual simulation platform, simulation analysis can be carried out on the built simulated braking model, dynamic operation of the equipment set is simulated, a foundation is provided for follow-up trial control analysis and tuning, and meanwhile, the visual interface can display variable frequency control and corresponding braking effect of equipment in real time, so that engineers can observe and analyze conveniently.
Step S600: based on the simulated braking model, performing trial control analysis and tuning on the target variable frequency control parameter set, and determining a pre-variable frequency control parameter set;
further, step S600 of the present application includes:
step S610: based on the simulated braking model, matching and regulating the target variable frequency control parameters to obtain simulated control information;
step S620: the simulation control information is interacted, and control offset analysis is carried out to determine a variable frequency offset coefficient;
step S630: and judging whether the variable frequency offset coefficient meets an offset threshold value, if so, calculating the difference value between the variable frequency offset coefficient and the offset threshold value, and performing feedback compensation on the target variable frequency control parameter set to generate the pre-variable frequency control parameter set.
Specifically, according to the target variable frequency control parameters, the target variable frequency control parameters are matched with an actual brake model, namely, the target parameters are converted into input formats and units required by the actual brake model, so that the consistency of the parameters is ensured, and according to the matched control parameters, the actual brake model simulates brake response, parameter adjustment and effect evaluation of equipment to generate actual control information which is consistent with the target variable frequency control parameters and comprises actual frequency adjustment values, brake time, energy consumption and other brake effect indexes and other control information.
According to the obtained simulated control information, comparing and analyzing the simulated control information with the expected control effect, including comparing the difference between the frequency adjustment difference, the braking time, the energy consumption and the like, so as to determine the deviation condition between the actual control and the expected control, and deriving a variable frequency deviation coefficient based on the result of the control deviation analysis, wherein the coefficient represents the deviation degree between the simulated control and the expected control and can be used for quantifying the difference between the actual control and the expected control.
And determining an offset threshold according to actual requirements, determining whether compensation operation is required, comparing the calculated variable frequency offset coefficient with a preset offset threshold, judging whether the variable frequency offset coefficient meets the preset offset threshold according to a comparison result, if not, no further processing is required, and if yes, the compensation operation is required. When the threshold value is exceeded, calculating a difference value between the variable frequency offset coefficient and the offset threshold value, wherein the difference value is used for representing the actual offset degree, and performing feedback compensation operation on the target variable frequency control parameter set according to the calculated difference value, wherein the feedback compensation operation comprises the steps of adjusting related parameters such as a frequency adjustment value, braking time, energy consumption and the like, so that the target variable frequency control parameter set is closer to an expected effect. And by performing feedback compensation operation, an adjusted target variable frequency control parameter set, namely a pre-variable frequency control parameter set, is obtained, and the parameters can more accurately reflect the actual application requirements and can better meet the expected variable frequency control effect.
Further, step S620 of the present application includes:
step S621: determining expected control information based on the target variable frequency control parameter set;
step S622: mapping and checking the expected control information and the simulated control information to generate a plurality of groups of information sequences;
step S623: traversing the plurality of groups of information sequences to calculate based on a variable frequency offset coefficient expression, and obtaining the variable frequency offset coefficient;
step S624: the variable frequency offset coefficient expression is as follows:，
wherein, the liquid crystal display device comprises a liquid crystal display device,f is the variable frequency offset coefficient of any one device,configuring weights for the ith target variable frequency control parameters, +.>For the expected control information corresponding to the ith target variable frequency control parameter,/for the control information corresponding to the ith target variable frequency control parameter>And n is the parameter number of the target variable frequency control parameter set, wherein the n is the simulated control information corresponding to the ith target variable frequency control parameter.
Specifically, based on a set control target, a target variable frequency control parameter set is matched with an expected control requirement, so that each parameter in the target parameter set is ensured to be consistent with the requirement of expected control information, and the expected control information is determined according to a matched result, wherein the expected control information comprises relevant indexes such as a frequency adjustment value, braking time, energy consumption and the like expected in an actual application scene, and the limit and the requirement of compliance.
Relevant parameters such as a frequency adjustment value, braking time, energy consumption and the like are respectively extracted from the expected control information and the simulated control information, mapping and checking operations are carried out by comparing each corresponding parameter of the expected control information and the simulated control information, such as a lookup table, linear interpolation, curve fitting and the like, the corresponding relationship between the expected control information and the simulated control information is ensured to be accurate, a plurality of groups of information sequences are generated based on the mapping and checking result, the sequences comprise the values of each parameter of the expected control and the simulated control, and a complete control information sequence can be formed, so that the consistency between the actual control and the target control can be ensured.
And sequentially selecting the prepared multiple groups of information sequences, sequentially extracting parameters of each information sequence, performing traversal operation, substituting each group of parameters into the expression for calculation according to the information sequence obtained by traversal, and obtaining variable frequency offset coefficients corresponding to each group of information sequences after calculation is completed, wherein the coefficients are used for measuring the deviation degree between actual control and expected control and have guiding effect on subsequent adjustment and optimization.
Step S700: and matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment to perform motor soft braking control of the equipment group.
Specifically, according to the configuration and the topological structure of the equipment group, the pre-frequency conversion control parameter set is matched with each equipment, each equipment can receive corresponding control parameters, the matched pre-frequency conversion control parameters are transmitted to the corresponding coal mine frequency conversion equipment through methods of communication connection, network connection and the like, after the pre-frequency conversion control parameters are received, the coal mine frequency conversion equipment carries out motor soft braking control on the corresponding equipment according to parameter setting, namely, according to control requirements of frequency, voltage and the like specified by the parameters, the motion state and the performance of a motor of the equipment are adjusted.
Through the steps, the pre-frequency conversion control parameters are integrated with power transmission and applied to corresponding coal mine frequency conversion equipment, so that the motor soft braking control of the equipment group is realized, the running state and performance of the equipment can be effectively controlled on the premise of not damaging the equipment and improving the energy efficiency, and the safety and operation requirements of a coal mine are met.
In summary, the adaptive control method and system for the coal mine variable frequency equipment provided by the embodiment of the application have the following technical effects:
determining equipment groups to be subjected to variable frequency control, acquiring basic data to generate an equipment database, acquiring real-time braking scenes by an interactive control target, acquiring and determining braking monitoring data, analyzing the real-time braking scenes, inputting the braking monitoring data into a frequency modulation analysis model, outputting a plurality of groups of variable frequency control parameters, optimizing the plurality of groups of variable frequency control parameters, determining a target variable frequency control parameter set, constructing a simulated braking model, performing trial control analysis and tuning to determine a pre-variable frequency control parameter set, matching and transmitting the pre-variable frequency control parameter set to corresponding coal mine variable frequency equipment, and performing motor soft braking control of the equipment groups.
The method solves the technical problems that the operation working conditions of an electric control system of coal mine equipment are changeable, the control mechanism is complex, the defects of unstable action, unstable action delay, serious signal feedback lag, dependence on object model precision and the like of a mechanical executing mechanism are caused, the randomness and time variability of the system are caused, the performance and implementation effect of the control system are directly influenced, the self-adaptive closed-loop control is realized, the control performance requirements of different equipment under various working conditions are met, the precision and stability requirements required by automatic control are met, and further the technical effects of powerful support are provided for the realization of comprehensive automation and intelligent coal mine.
Embodiment two:
based on the same inventive concept as the adaptive control method of the coal mine frequency conversion equipment in the foregoing embodiment, as shown in fig. 4, the present application provides an adaptive control system of the coal mine frequency conversion equipment, which includes:
the basic data acquisition module 10 is used for determining a device group to be subjected to variable frequency control, traversing the device group to perform data acquisition aiming at device specifications and reference braking parameters, and generating a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value;
The brake data acquisition module 20 is used for interactively controlling the target, acquiring a real-time brake scene, and acquiring and determining brake monitoring data based on the sensing monitoring device;
the braking scene analysis module 30 is used for performing the real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups;
a control parameter optimizing module 40, where the control parameter optimizing module 40 is configured to optimize the multiple groups of variable frequency control parameters with the control reference value as a constraint and the control target as a response, and determine a target variable frequency control parameter set;
the brake model building module 50 is used for connecting a visual simulation platform, and building a simulated brake model based on the real-time brake scene, the coal mine variable frequency equipment and the equipment group;
the pilot control analysis tuning module 60, wherein the pilot control analysis tuning module 60 is used for performing pilot control analysis and tuning on the target variable frequency control parameter set based on the simulated braking model, and determining a pre-variable frequency control parameter set;
And the soft braking control module 70 is used for matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment based on the pre-variable frequency control parameter set, so as to perform motor soft braking control of the equipment group.
Further, the system further comprises:
the frequency modulation mode determining module is used for identifying and determining a frequency modulation mode of target equipment based on the real-time braking scene, wherein the frequency modulation mode comprises an independent frequency modulation mode and a cooperative frequency modulation mode;
the state identification module is used for identifying the running state of the equipment based on the real-time braking scene and determining a target state, wherein the target state comprises start-stop braking, running frequency, running time zone and fluctuation trend;
and the analysis result determining module is used for determining a scene analysis result based on the frequency modulation mode and the target state, wherein the scene analysis result is at least one item.
Further, the system further comprises:
the historical control data acquisition module is used for searching and calling historical variable frequency control data by taking the equipment group as an index;
the sample information determining module is used for extracting the historical variable frequency control data of the first equipment in the equipment group based on the historical variable frequency control data, and carrying out data normalization to determine sample scene analysis results, sample brake monitoring data and sample variable frequency control parameters;
The first unit generation module is used for mapping and connecting the sample scene analysis result, the sample brake monitoring data and the sample variable frequency control parameters, determining construction data and training to generate a first equipment frequency modulation unit;
the N unit construction module is used for screening the historical variable frequency control data and training samples in order to sequentially finish the construction of the N equipment frequency modulation unit;
and the unit integration module is used for integrating the first equipment frequency modulation unit to the Nth equipment frequency modulation unit to generate the frequency modulation analysis model.
Further, the system further comprises:
the time sequence segmentation module is used for performing time sequence segmentation on the construction data to determine a frequency modulation analysis sample and a frequency modulation prediction sample;
the first branch training module is used for generating a first frequency modulation analysis branch by training a neural network based on the frequency modulation analysis sample;
the positioning identification module is used for carrying out abnormal phenomenon identification and incentive tracing positioning identification based on the frequency modulation prediction sample, and training and constructing a first frequency modulation prediction branch;
the parameter screening layer construction module is used for constructing a parameter screening layer by taking identification as a screening mechanism;
And the branch integration module is used for sequentially connecting and integrating the first frequency modulation analysis branch, the first frequency modulation prediction branch and the parameter screening layer to generate the first equipment frequency modulation unit.
Further, the system further comprises:
the analysis result input module is used for inputting the scene analysis result and the brake monitoring data into the frequency modulation analysis model;
the calling module is used for determining a target equipment frequency modulation unit based on unit matching between the frequency modulation mode and the target state and calling the target equipment frequency modulation unit to construct a temporary processing module;
the self-adaptive analysis module is used for carrying out self-adaptive analysis on the scene analysis result and the brake monitoring data based on the temporary processing module and outputting the multiple groups of variable frequency control parameters;
the independent analysis module is used for carrying out independent analysis and output integration based on the temporary processing module if the frequency modulation mode is an independent frequency modulation mode; and if the frequency modulation mode is a cooperative frequency modulation mode, performing independent analysis and cooperative influence analysis on the temporary processing module.
Further, the system further comprises:
the matching regulation and control module is used for carrying out matching regulation and control on the target variable frequency control parameters based on the simulated braking model to obtain simulated control information;
The control offset analysis module is used for interacting the simulation control information, and performing control offset analysis to determine a variable frequency offset coefficient;
and the offset coefficient judging module is used for judging whether the variable frequency offset coefficient meets an offset threshold value, if so, calculating the difference value between the variable frequency offset coefficient and the offset threshold value, and carrying out feedback compensation on the target variable frequency control parameter set to generate the pre-variable frequency control parameter set.
Further, the system further comprises:
the expected control information determining module is used for determining expected control information based on the target variable frequency control parameter set;
the information sequence generation module is used for mapping and checking the expected control information and the simulated control information to generate a plurality of groups of information sequences;
the traversing module is used for traversing the plurality of groups of information sequences to calculate based on the variable frequency offset coefficient expression, and acquiring the variable frequency offset coefficient;
the expression description module is used for the variable frequency offset coefficient expression as follows:，
wherein f is the variable frequency offset coefficient of any equipment,configuring weights for the ith target variable frequency control parameters, +.>For the expected control information corresponding to the ith target variable frequency control parameter,/for the control information corresponding to the ith target variable frequency control parameter >Variable frequency control for the ith targetAnd (3) generating simulation control information corresponding to the parameters, wherein n is the parameter number of the target variable frequency control parameter set.
The foregoing detailed description of the adaptive control method of the coal mine frequency conversion device in this embodiment may be clearly known to those skilled in the art, and the device disclosed in this embodiment is relatively simple to describe, and the relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An adaptive control method for coal mine frequency conversion equipment is characterized by comprising the following steps:
Determining a device group to be subjected to variable frequency control, traversing the device group, and carrying out data acquisition aiming at device specifications and reference braking parameters to generate a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value;
the method comprises the steps of interactively controlling a target, acquiring a real-time braking scene, and acquiring and determining braking monitoring data based on a sensing monitoring device;
performing the real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups;
taking the control reference value as constraint, taking the control target as response, optimizing the multiple groups of variable frequency control parameters, and determining a target variable frequency control parameter set;
connecting a visual simulation platform, and constructing an actual braking model based on the real-time braking scene, the coal mine variable frequency equipment and the equipment group;
based on the simulated braking model, performing trial control analysis and tuning on the target variable frequency control parameter set, and determining a pre-variable frequency control parameter set;
and matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment to perform motor soft braking control of the equipment group.
2. The method of claim 1, wherein the performing the real-time braking scenario parsing comprises:
based on the real-time braking scene, identifying target equipment to determine a frequency modulation mode, wherein the frequency modulation mode comprises an independent frequency modulation mode and a cooperative frequency modulation mode;
based on the real-time braking scene, identifying the running state of the equipment, and determining a target state, wherein the target state comprises start-stop braking, running frequency, running time zone and fluctuation trend;
and determining a scene analysis result based on the frequency modulation mode and the target state, wherein the scene analysis result is at least one item.
3. The method of claim 2, wherein the fm analysis model is built, the method comprising:
the equipment group is used as an index to search and call historical variable frequency control data;
based on the historical variable frequency control data, historical variable frequency control data of a first device in the device group is extracted, and a sample scene analysis result, sample brake monitoring data and sample variable frequency control parameters are determined through data regularity;
mapping and connecting the sample scene analysis result, the sample brake monitoring data and the sample variable frequency control parameters, determining construction data and training to generate a first equipment frequency modulation unit;
Screening the historical variable frequency control data and training samples to sequentially complete construction of an N-th equipment frequency modulation unit;
and integrating the first equipment frequency modulation unit to the Nth equipment frequency modulation unit to generate the frequency modulation analysis model.
4. The method of claim 3, wherein the determining builds data and trains generating a first device frequency modulation unit, the method comprising:
performing time sequence segmentation on the construction data to determine a frequency modulation analysis sample and a frequency modulation prediction sample;
generating a first frequency modulation analysis branch by performing neural network training based on the frequency modulation analysis sample;
based on the frequency modulation prediction sample, carrying out anomaly identification and incentive tracing positioning identification, and training to construct a first frequency modulation prediction branch;
constructing a parameter screening layer by taking identification as a screening mechanism;
and sequentially connecting and integrating the first frequency modulation analysis branch, the first frequency modulation prediction branch and the parameter screening layer to generate the first equipment frequency modulation unit.
5. The method of claim 4, wherein the outputting a plurality of sets of variable frequency control parameters, the method comprising:
inputting the scene analysis result and the brake monitoring data into the frequency modulation analysis model;
Performing unit matching to determine a target equipment frequency modulation unit based on the frequency modulation mode and the target state, and calling to construct a temporary processing module;
based on the temporary processing module, performing adaptive analysis on the scene analysis result and the brake monitoring data, and outputting the plurality of groups of variable frequency control parameters;
if the frequency modulation mode is an independent frequency modulation mode, independent analysis and output integration are carried out based on the temporary processing module; and if the frequency modulation mode is a cooperative frequency modulation mode, performing independent analysis and cooperative influence analysis on the temporary processing module.
6. The method of claim 1, wherein the target variable frequency control parameter set is subject to trial control analysis and tuning based on the simulated braking model, the method comprising:
based on the simulated braking model, matching and regulating the target variable frequency control parameters to obtain simulated control information;
the simulation control information is interacted, and control offset analysis is carried out to determine a variable frequency offset coefficient;
and judging whether the variable frequency offset coefficient meets an offset threshold value, if so, calculating the difference value between the variable frequency offset coefficient and the offset threshold value, and performing feedback compensation on the target variable frequency control parameter set to generate the pre-variable frequency control parameter set.
7. The method of claim 6, wherein the performing control offset analysis determines a variable frequency offset coefficient, the method comprising:
determining expected control information based on the target variable frequency control parameter set;
mapping and checking the expected control information and the simulated control information to generate a plurality of groups of information sequences;
traversing the plurality of groups of information sequences to calculate based on a variable frequency offset coefficient expression, and obtaining the variable frequency offset coefficient;
the variable frequency offset coefficient expression is as follows:，
wherein f is the variable frequency offset coefficient of any equipment,configuring weights for the ith target variable frequency control parameters, +.>For the expected control information corresponding to the ith target variable frequency control parameter,/for the control information corresponding to the ith target variable frequency control parameter>And n is the parameter number of the target variable frequency control parameter set, wherein the n is the simulated control information corresponding to the ith target variable frequency control parameter.
8. An adaptive control system for a coal mine variable frequency device, for implementing the adaptive control method for a coal mine variable frequency device of any one of claims 1-7, comprising:
the basic data acquisition module is used for determining a device group to be subjected to variable frequency control, traversing the device group to acquire data aiming at device specifications and reference braking parameters, and generating a device database, wherein the device database comprises a device basic database sub-database and a motor basic database sub-database, and the motor basic database sub-database is marked with a control reference value;
The brake data acquisition module is used for interactively controlling the target, acquiring a real-time brake scene, and acquiring and determining brake monitoring data based on the sensing monitoring device;
the braking scene analysis module is used for carrying out real-time braking scene analysis, inputting the braking monitoring data into a frequency modulation analysis model, and outputting a plurality of groups of variable frequency control parameters, wherein the plurality of groups of variable frequency control parameters are in one-to-one correspondence with the equipment groups;
the control parameter optimizing module is used for optimizing the plurality of groups of variable frequency control parameters by taking the control reference value as a constraint and the control target as a response, and determining a target variable frequency control parameter set;
the brake model building module is used for connecting a visual simulation platform, and building a simulated brake model based on the real-time brake scene, the coal mine variable frequency equipment and the equipment group;
the pilot control analysis tuning module is used for carrying out pilot control analysis and tuning on the target variable frequency control parameter set based on the simulated braking model, and determining a pre-variable frequency control parameter set;
And the soft braking control module is used for matching and transmitting the pre-variable frequency control parameter set to the corresponding coal mine variable frequency equipment to perform motor soft braking control of the equipment group.
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