Disclosure of Invention
The invention provides a motor energy-saving optimization and intelligent regulation platform, which mainly aims to solve the problem that the control accuracy is insufficient in motor energy-saving optimization of the existing motor control system.
In order to achieve the above purpose, the invention provides a motor energy-saving optimizing and intelligent regulating platform, comprising:
the system comprises a motor data analysis module, a motor system simulation module, a characteristic parameter extraction module and a control strategy generation module, and specifically comprises the following components:
The motor data analysis module is used for acquiring the operation data of the historical motor, carrying out pattern recognition on the operation data to obtain pattern probability, and calculating the performance data of the operation data according to the pattern probability, wherein the pattern recognition is carried out by utilizing the following formula:
;
wherein, theRepresenting operational dataThe probability of the corresponding pattern is determined,Is the first in the preset weight matrixThe weight of the individual pattern(s),As the total number of weights in the weight matrix,Is the firstThe probability average value corresponding to the individual pattern,Is the firstA covariance matrix of probabilities corresponding to the individual patterns,For given dataBelonging to the firstProbability of individual patterns;
the motor system simulation module is used for constructing a dynamic simulation model by utilizing the collected motor system parameters;
The characteristic parameter extraction module is used for calculating dynamic characteristics according to the performance data and the dynamic simulation model and extracting mode characteristic parameters of the operation data based on the dynamic characteristics;
and the control strategy generation module is used for optimizing the control parameters of the simulation model based on the mode characteristic parameters, generating a candidate parameter set, and carrying out parameter screening on the candidate parameter set to obtain the optimal control parameters.
Optionally, the motor system simulation module is specifically configured to, when executing the function of constructing the dynamic simulation model by using the collected motor system parameters:
fitting a preset electromagnetic model according to the parameters of the motor system to obtain a motor electromagnetic model;
fitting a preset mechanical model according to the parameters of the motor system to obtain a motor mechanical model;
fitting a preset control model according to the parameters of the motor system to obtain a motor control model;
and constructing a simulation environment according to the motor control model, the motor mechanical model and the motor electromagnetic model to obtain a dynamic simulation model.
Optionally, the feature parameter extraction module is specifically configured to, when executing the function of calculating dynamic characteristics according to the performance data and the dynamic simulation model:
Constructing a state space equation according to the dynamic simulation model and the performance data;
Performing data discretization on the state space equation to obtain a discrete time matrix;
Performing matrix parameter estimation by using the performance data and the discrete time matrix to obtain a feature matrix;
and decomposing the characteristic value of the characteristic matrix to obtain dynamic characteristics.
Optionally, the feature parameter extraction module is specifically configured to, when executing the function of constructing a state space equation according to the dynamic simulation model and the performance data:
extracting an input variable and an output variable of a motor system from the dynamic simulation model;
acquiring the relation between an input variable and an output variable of the motor system based on the performance data;
Defining a state variable of the motor system by using the dynamic simulation model and the performance data;
and constructing a state space equation according to the relation between the state variable and the input variable and the output variable of the motor system.
Optionally, when executing the function of discretizing the state space equation to obtain a discrete time matrix, the feature parameter extraction module is specifically configured to:
Determining a time step of a state space equation according to the motor system parameters;
Discretizing the state space equation by using a time step length to generate a discretized continuous time equation;
and calculating a discrete time matrix according to the discretized continuous time equation.
Optionally, the feature parameter extraction module is specifically configured to, when executing the function of extracting the mode feature parameter of the operation data based on the dynamic characteristic:
Carrying out data segmentation on the operation data by utilizing the dynamic characteristics to obtain time sequence data of different stages;
Calculating a stability index of the motor system by using the time sequence data and the dynamic characteristics;
calculating an efficiency interval critical point of the time sequence data;
calculating the dynamic response time of the time series data;
And carrying out data combination on the dynamic response time, the efficiency interval critical point, the stability index and the performance data corresponding to the operation data to obtain a mode characteristic parameter.
Optionally, when executing the function of performing data segmentation on the operation data by using the dynamic characteristic to obtain time series data in different stages, the feature parameter extraction module is specifically configured to:
Constructing a state evaluation function according to the dynamic characteristics;
And detecting the change point of the operation data by using the state evaluation function to obtain a state change probability value, wherein the change point detection can be performed by using the following formula:
;
wherein, theFor the point in timeThe corresponding state change probability value is used to determine,Is the base of the natural logarithm,In order to set the constant value of the preset value,For the point in timeThe amount of change in the operational data at the time,Operating data for a preset significance threshold;
Constructing a dynamic window adjustment function based on the state change probability value;
and carrying out data segmentation on the operation data by utilizing the dynamic window adjusting function and a preset initialization window to obtain time sequence data.
Optionally, the control policy generation module is specifically configured to, when executing the function of optimizing the simulation model control parameters based on the mode feature parameters to generate the candidate parameter set:
screening out control key parameters from the operation data by utilizing the mode characteristic parameters;
generating a simulation result by using the control key parameters and the dynamic simulation model;
calculating the performance index of the simulation result;
performing parameter adjustment on the control key parameters by using the performance indexes to obtain candidate control parameters;
And returning to the step of generating a simulation result by using the control key parameters and the dynamic simulation model until the number of parameter adjustment reaches a preset threshold value, and obtaining a candidate parameter set.
Optionally, the control policy generation module is specifically configured to, when executing the function of screening the control key parameter from the operation data by using the mode feature parameter:
calculating a correlation score between the mode feature parameter and the operational data;
Screening preliminary related data from the operation data by utilizing the correlation score and a preset threshold value;
performing multiple correlation verification on the preliminary correlation data to obtain a verification result;
and screening out control key parameters from the preliminary related data based on the verification result.
Optionally, when executing the function of performing parameter screening on the candidate parameter set to obtain the optimal control parameter, the control policy generating module is specifically configured to:
performing motor simulation operation based on the candidate parameter set, and calculating a corresponding energy-saving effect;
Evaluating performance indexes of each group of candidate control parameters on motor system stability, response speed and energy consumption based on the energy-saving effect;
scoring each performance index according to a preset weight formula to obtain a scoring result;
And screening the candidate control parameter with the highest comprehensive score according to the scoring result to serve as the optimal control parameter.
The invention constructs a highly realistic motor system model through detailed motor system parameters and advanced simulation tools, ensures the accuracy and reliability of the model, calculates dynamic characteristics by using a matrix expression method through using input performance data and a dynamic simulation model, extracts key characteristic parameters which have obvious influence on motor performance, and provides accurate data support for optimizing a control strategy. Therefore, the motor energy-saving optimization and intelligent regulation platform provided by the invention can solve the problem that the control accuracy is insufficient in motor energy-saving optimization of the current motor control system.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a functional block diagram of a motor energy-saving optimization and intelligent regulation platform according to an embodiment of the present invention is shown. In this embodiment, the motor energy-saving optimization and intelligent regulation platform 100 may be installed in an electronic device. According to the implemented functions, the motor energy-saving optimization and intelligent regulation platform 100 may include a motor data analysis module 101, a state calculation module 102, a fault detection module 103, and an automatic adjustment module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the embodiment of the invention, the motor data analysis module 101 comprises the steps of acquiring the operation data of a historical motor, carrying out mode identification on the operation data to obtain the mode probability, and calculating the performance data of the operation data according to the mode probability;
in the embodiment of the present invention, the motor system simulation module 102 includes a dynamic simulation model constructed by using collected motor system parameters;
in the embodiment of the present invention, the feature parameter extraction module 103 calculates dynamic characteristics according to the performance data and the dynamic simulation model, and extracts mode feature parameters of the operation data based on the dynamic characteristics;
in the embodiment of the present invention, the control policy generation module 104 includes optimizing the control parameters of the simulation model based on the mode feature parameters, generating a candidate parameter set, and performing parameter screening on the candidate parameter set to obtain the optimal control parameters.
In detail, each module in the motor energy-saving optimization and intelligent regulation platform 100 in the embodiment of the present invention adopts the same technical means as the motor energy-saving optimization and intelligent regulation platform in the drawings when in use, and can produce the same technical effects, and the details are not repeated here.
The following description is made with reference to the specific embodiments, and the specific workflow and the respective components of the motor energy-saving optimization and intelligent regulation platform are respectively:
The motor data analysis module 101 is configured to obtain operation data of a historical motor, perform pattern recognition on the operation data to obtain a pattern probability, and calculate performance data of the operation data according to the pattern probability.
In the embodiment of the invention, the long-term performance trend and the state change of the motor are analyzed by acquiring the operation data, the fluctuation and the potential problem of the motor performance are identified, and the support is provided for the follow-up optimization.
In the embodiment of the invention, the operation data refers to data for recording the actual operation condition of the motor in the past period of time, and comprises operation time, starting times, motor load data, motor temperature data, voltage and current data, fault and alarm data and the like.
Referring to fig. 2, in the embodiment of the present invention, when the motor data analysis module performs the performance analysis on the operation data according to the mode probability to obtain the performance data, the motor data analysis module is specifically configured to:
S21, carrying out mode division on the operation data according to the mode probability to obtain a data mode;
s22, carrying out regression analysis on the data pattern to obtain a performance change trend;
And S23, carrying out structural processing on the operation data based on the performance change trend to obtain performance data.
In the embodiment of the invention, the following formula is utilized for pattern recognition:
;
wherein, theRepresenting operational dataThe probability of the corresponding pattern is determined,Is the first in the preset weight matrixThe weight of the individual pattern(s),As the total number of weights in the weight matrix,Is the firstThe probability average value corresponding to the individual pattern,Is the firstA covariance matrix of probabilities corresponding to the individual patterns,For given dataBelonging to the firstProbability of individual patterns.
In detail, the data mode and the operation data are fitted by using a multi-variable linear regression equation, regression coefficient analysis is carried out on the fitted multi-variable linear regression equation to obtain a performance change trend, and the regression coefficient is analyzed to obtain the performance change trend because positive and negative values of the regression coefficient indicate the influence direction of independent variables on dependent variables and the absolute value of the coefficient indicates the influence intensity.
In the embodiment of the invention, the data are reclassified based on the performance change trend. Specifically, originally scattered and disordered motor operation data are grouped according to different performance trends (such as normal operation, overload operation, abnormal state and the like), each classified data is organized according to a certain rule (such as time, load, rotating speed and the like) to form a structured table or database, the motor operation data in different time periods are summarized to form records similar to fields such as date, load, temperature, rotating speed, power and the like, and a unified and representative performance data set, namely performance data, is formed through reclassifying and structuring.
In the embodiment of the invention, the mode identification can calculate the probability that each operation data point belongs to a less mode, identify complex distribution and multiple modes in the motor operation data, provide probability distribution information of the data points and provide basis for the subsequent mode division.
In the embodiment of the invention, based on the mode probability, the cleaned data are divided into different modes, each data point is allocated to the mode with the highest probability, so that each mode corresponds to one running state (such as normal, overload, low efficiency and the like) of the motor, the data are divided into meaningful running states, the subsequent analysis is convenient, the classification result of the running state of the motor is provided, and the state monitoring and fault diagnosis are supported.
In the embodiment of the invention, the risk of fault occurrence can be reduced and the stability and reliability of a motor system can be improved by finding out the performance decline or potential fault of the motor in advance.
The motor system simulation module 102 is configured to construct a dynamic simulation model using the collected motor system parameters.
In the embodiment of the invention, the accurate dynamic simulation model is constructed through the parameters of the motor system, the performance of the motor under different running conditions is simulated, the high-cost and high-risk actual test is avoided, and multiple simulation verification is carried out.
In the embodiment of the invention, the motor system parameters refer to various key parameters and structural information for describing the motor and related systems thereof, the information is the basis for constructing a dynamic simulation model, and the accuracy and the functional performance of the model are determined, wherein the motor system parameters comprise motor body information, control system motor system parameters, control parameters, motor system environment, load information and the like.
In the embodiment of the invention, when executing the function of constructing the dynamic simulation model by using the collected motor system parameters, the motor system simulation module is specifically used for:
fitting a preset electromagnetic model according to the parameters of the motor system to obtain a motor electromagnetic model;
fitting a preset mechanical model according to the parameters of the motor system to obtain a motor mechanical model;
fitting a preset control model according to the parameters of the motor system to obtain a motor control model;
and constructing a simulation environment according to the motor control model, the motor mechanical model and the motor electromagnetic model to obtain a dynamic simulation model.
In the embodiment of the invention, the mathematical expression of the electromagnetic model is as follows:
;
wherein, theIs the stator voltage in the parameters of the motor system,Is the stator resistance in the parameters of the motor system,For the stator current in the parameters of the motor system,Is the stator inductance in the parameters of the motor system,For a preset back-emf,The rate of change of the stator current over time,Is a differential sign.
In the embodiment of the invention, the mathematical expression of the mechanical model is as follows:
;
wherein, theIs the moment of inertia among the parameters of the motor system,In order to fit the resulting angular velocity,For angular acceleration, the rate of change of angular velocity over time is described,Indicating a small change in the angular velocity of the wheel,For electromagnetic torque in the parameters of the motor system,For the load torque in the parameters of the motor system,Is a predetermined coefficient of friction.
In the embodiment of the present invention, the mathematical expression of the mathematical model is:
;
wherein, theAs a result of the predetermined error,Is the first of the motor system parametersThe control signal of the respective moment in time,For proportional gain in the motor system parameters,For the integral gain in the motor system parameters,Is a differential gain in the motor system parameters.
In the embodiment of the invention, a physical model and a mathematical model are integrated, the real-time behavior of a motor system is simulated, and the change (such as current, torque and speed) of key variables is output.
In the embodiment of the invention, by fitting the models, a more accurate dynamic simulation model can be established based on actual motor system parameters (such as motor parameters, electrical characteristics, mechanical loads and the like). For example, the electromagnetic model of the motor can accurately describe the relationship between current and voltage, the mechanical model can describe the influence of factors such as moment of inertia and friction on the motor motion, and the control model can predict the response of the control system, so that the motor can still keep running stably when the load changes.
And the feature parameter extraction module 103 is configured to calculate dynamic characteristics according to the performance data and the dynamic simulation model, and extract mode feature parameters of the operation data based on the dynamic characteristics.
In the embodiment of the invention, the key characteristic parameters of the motor are extracted by calculation based on the performance data and the dynamic simulation model, so that the dynamic characteristics of the motor are accurately reflected.
Referring to fig. 3, the feature parameter extraction module is specifically configured to, when executing the function of calculating dynamic characteristics according to the performance data and the dynamic simulation model:
s31, constructing a state space equation according to the dynamic simulation model and the performance data;
S32, performing data discretization on the state space equation to obtain a discrete time matrix;
S33, performing matrix parameter estimation by using the performance data and the discrete time matrix to obtain a feature matrix;
s34, decomposing the characteristic value of the characteristic matrix to obtain dynamic characteristics.
In the embodiment of the present invention, when executing the function of constructing a state space equation according to the dynamic simulation model and the performance data, the feature parameter extraction module is specifically configured to:
extracting an input variable and an output variable of a motor system from the dynamic simulation model;
acquiring the relation between an input variable and an output variable of the motor system based on the performance data;
Defining a state variable of the motor system by using the dynamic simulation model and the performance data;
and constructing a state space equation according to the relation between the state variable and the input variable and the output variable of the motor system.
In detail, according to the definition in the dynamic simulation model, input variables (e.g., voltage, current), output variables (e.g., rotational speed, torque), and state variables (e.g., rotor position, speed) are automatically identified, and the identified variables are stored in a list or dictionary.
In detail, the performance data is matched with the input variables and the output variables, and statistical methods (such as correlation analysis, regression analysis and the like) are used to find out the relations between the input variables and the output variables and the changes of the relations with time.
In detail, according to the data expression of the dynamic simulation model, a state variable such as rotor position, speed and the like is preliminarily selected, and the validity of the state variable is verified by utilizing the performance data to obtain a final state variable.
In detail, the state space equation is:
;
;
wherein, theAs a derivative of the state variable,As a state variable, a state variable is used,In order to output the variable(s),In order to input the variable(s),The system matrix represents the relationship between states.
In detail, by extracting input variables and output variables of the motor system from the dynamic simulation model and determining a relationship between the two in combination with performance data, dynamic behavior of the motor system can be more precisely described. By constructing a state space equation, a more comprehensive state space equation can be constructed by utilizing the extracted state variables and the relationships between the extracted state variables and the input and output variables, and the regulation and control capability of the motor system in the aspect of energy efficiency optimization is enhanced.
In the embodiment of the present invention, when the feature parameter extraction module performs the function of discretizing the state space equation to obtain a discrete time matrix, the feature parameter extraction module is specifically configured to:
Determining a time step of a state space equation according to the motor system parameters;
Discretizing the state space equation by using a time step length to generate a discretized continuous time equation;
and calculating a discrete time matrix according to the discretized continuous time equation.
In detail, according to the specific motor system parameters of the motor system, taking the time of one tenth of the fastest dynamic change time constant in the motor system parameters as the time step.
In detail, the state space equation is discretized using a zero-order holder (ZOH) or other discretization method (e.g., bilinear transformation), and the discretized continuous-time equation is obtained by performing an equation transformation on a system matrix in the state space equation using a bilinear transformation formula.
In detail, according to the discretized continuous time equation and the state space equation, calculating a matrix index through a Taylor series expansion method, calculating a matrix integral through a numerical integration method such as a trapezoidal rule or a Simpson rule, and recombining the calculated matrix index and the matrix integral with a system matrix to obtain a discrete time matrix.
In detail, by discretizing the state space equation of continuous time, the dynamic behavior of the motor system can be more accurately represented in the digital control system, and the discretized equation is suitable for a computer control algorithm, which is helpful for improving the control precision of the system, ensuring the accurate matching between the control signal and the motor output under the discrete time step length, and avoiding the unstable system or inaccurate control caused by the discretization error.
In the embodiment of the invention, an error function is constructed according to the discrete time matrix, and the error function is optimized by using a least square method or a recursive least square method based on the configuration data and the performance data, so that the discrete time model can reflect the dynamic rule in the performance data, and finally the feature matrix is obtained.
In the embodiment of the invention, the following formula is utilized to decompose the characteristic values:
;
wherein, theAs a matrix of features,Is a corresponding diagonal matrix of eigenvalues,A feature vector matrix that is a feature vector matrix decomposition.
In the embodiment of the present invention, when executing the function of extracting the mode feature parameter of the operation data based on the dynamic characteristic, the feature parameter extracting module is specifically configured to:
Carrying out data segmentation on the operation data by utilizing the dynamic characteristics to obtain time sequence data of different stages;
Calculating a stability index of the motor system by using the time sequence data and the dynamic characteristics;
calculating an efficiency interval critical point of the time sequence data;
calculating the dynamic response time of the time series data;
And carrying out data combination on the dynamic response time, the efficiency interval critical point, the stability index and the performance data corresponding to the operation data to obtain a mode characteristic parameter.
In the embodiment of the invention, the historical data is divided into different time periods according to the dynamic characteristics of the motor system, and the dynamic characteristics can help to identify key events or state change points in the data, so that the running data is segmented through a time window with variable length (the window length is adaptively adjusted according to the dynamic characteristics) to obtain time sequence data.
In the embodiment of the invention, the performance of the motor system in different running states can be accurately identified by segmenting the running data and combining the dynamic characteristics to calculate the stability index, the efficiency interval critical point and the dynamic response time. By calculating the stability index, the stability of the motor system in each operation stage can be analyzed, the problems of vibration, overheat, stall and the like of the motor are avoided, the working state of the motor in each stage is comprehensively known, and therefore key data support is provided for system optimization, fault diagnosis, performance prediction and the like.
In the embodiment of the present invention, when the feature parameter extraction module performs the function of segmenting the running data by using the dynamic characteristic to obtain time series data in different stages, the feature parameter extraction module is specifically configured to:
Constructing a state evaluation function according to the dynamic characteristics;
Detecting the change point of the operation data by using the state evaluation function to obtain a state change probability value;
Constructing a dynamic window adjustment function based on the state change probability value;
and carrying out data segmentation on the operation data by utilizing the dynamic window adjusting function and a preset initialization window to obtain time sequence data.
In detail, the state evaluation function is used to describe the operating state of the motor system at different points in time. These states are typically evaluated based on some characteristic values (such as rotational speed, load, current, etc.), and the state evaluation function is finally obtained by weighting the data of the state characteristics. In detail, the change point detection refers to identifying a significant change (such as a sudden load change, a speed regulation process, etc.) occurring during the operation of the motor, and calculating a "state change probability value" of each time point through a state evaluation function, that is, reflecting the degree of state change between the current time point and the previous time point.
In detail, by constructing a state evaluation function in combination with the change point detection, the state change (such as load change, temperature fluctuation, etc.) of the motor system during operation can be accurately identified. Such dynamic segmentation processes can segment the operational data of the system into a plurality of meaningful time windows, thereby providing careful data support for further analysis (e.g., performance optimization, fault prediction, etc.).
In detail, the change point detection can be performed using the following formula:
;
wherein, theFor the point in timeThe corresponding state change probability value is used to determine,Is the base of the natural logarithm,In order to set the constant value of the preset value,For the point in timeThe amount of change in the operational data at the time,And running data for a preset significance threshold.
In detail, a piecewise function is constructed based on the calculated state change probability value and a preset threshold value, and a dynamic window adjustment function is obtained.
In detail, the data segmentation of the running data by using the dynamic window adjustment function and a preset initialization window is performed by calculating an S (t) and state change probability value and current dynamic characteristics at each moment through the initialization window, performing data accumulation on the initialization window by using the dynamic window adjustment function, generating a time sequence segment when the accumulated data reaches the last data, performing overlapping control, that is, if adjacent segments overlap by more than 15%, merging into a single segment, and finally obtaining the time sequence data.
In the embodiment of the invention, time series analysis is carried out on the data of each time period, and the autocorrelation coefficient is calculated.
In the embodiment of the invention, the system efficiency in each time period is calculated, which is generally defined as the ratio of output power to input power, and then the average efficiency in each time period is calculated by using a sliding window, so as to detect the mutation point of the efficiency change.
In the embodiment of the invention, step input (such as suddenly increasing load) is applied to a motor system, the time required by the system from an initial state to a stable state is recorded, the rising time, the peak time and the adjusting time are calculated, and the calculated time is stored in a vector form to obtain the dynamic response time.
In the embodiment of the invention, the dynamic response time, the efficiency interval critical point, the stability index and the performance data (such as energy consumption, temperature and the like) corresponding to the operation data are integrated together, and each characteristic parameter is obtained as a dimension by combining all the characteristic parameters into one characteristic vector.
In the embodiment of the invention, accurate motor state monitoring can be realized through the characteristic parameters, and data support is provided for early fault diagnosis and maintenance.
The control policy generating module 104 is configured to optimize the control parameters of the simulation model based on the mode feature parameters, generate a candidate parameter set, and perform parameter screening on the candidate parameter set to obtain optimal control parameters.
In the embodiment of the invention, the dynamic simulation model is adjusted and the candidate parameter set is generated by optimizing the extracted characteristic parameters, so that the effect of the control strategy is improved.
In the embodiment of the present invention, when executing the function of optimizing the simulation model control parameters based on the mode feature parameters, the control policy generation module is specifically configured to:
screening out control key parameters from the operation data by utilizing the mode characteristic parameters;
generating a simulation result by using the control key parameters and the dynamic simulation model;
calculating the performance index of the simulation result;
performing parameter adjustment on the control key parameters by using the performance indexes to obtain candidate control parameters;
And returning to the step of generating a simulation result by using the control key parameters and the dynamic simulation model until the number of parameter adjustment reaches a preset threshold value, and obtaining a candidate parameter set.
In the embodiment of the invention, a characteristic selection method is used for selecting control parameters highly related to mode characteristic parameters from historical data, calculating and calculating the correlation coefficient between each control parameter and the mode characteristic parameters, and selecting parameters with the correlation coefficient larger than a preset correlation threshold value to obtain control key parameters.
In the embodiment of the present invention, when executing the function of screening the control key parameter from the operation data by using the mode feature parameter, the control policy generation module is specifically configured to:
calculating a correlation score between the mode feature parameter and the operational data;
Screening preliminary related data from the operation data by utilizing the correlation score and a preset threshold value;
performing multiple correlation verification on the preliminary correlation data to obtain a verification result;
and screening out control key parameters from the preliminary related data based on the verification result.
In detail, aligning the mode characteristic parameter with the operation data according to a time axis, respectively calculating covariance of the mode characteristic parameter and the operation data, standard deviation of the mode characteristic parameter and standard deviation of the operation data, calculating product of standard deviation of the mode characteristic parameter and standard deviation of the operation data, and dividing the covariance by the product of the standard deviation to obtain a correlation score.
In detail, calculating edge probability distribution and joint probability distribution of the mode characteristic parameters and the preliminary related data, respectively calculating univariate entropy and joint entropy of the mode characteristic parameters and the preliminary related data according to the edge probability distribution and the joint probability distribution, calculating mutual information entropy of the mode characteristic parameters and the preliminary related data according to the univariate entropy and the joint entropy, establishing trend mapping rules of the mode characteristic parameters and the preliminary related data according to the mutual information entropy, and matching the preliminary related data according to the trend mapping rules and the mutual information entropy to obtain verification results.
In the embodiment of the invention, the control key parameters are input into the dynamic simulation model to obtain the simulation result, for each simulated run result, a series of performance metrics are calculated to evaluate the system performance at that setting. Such performance metrics may include, but are not limited to, energy efficiency ratio, response time, stability, energy consumption, and the like.
In the embodiment of the invention, each control parameter is applied to a dynamic simulation model, the performance index corresponding to each control parameter is recorded in an operation mode, the fitness value of each control parameter is calculated according to the performance index, a part of control parameters are selected as parameters to be processed according to the fitness value, the parameters to be processed are randomly paired, a new control parameter combination is generated through the cross operation, and the control parameter combination is used as a candidate control parameter.
In the embodiment of the invention, the system can not only improve the energy efficiency, but also maintain the stability of the motor system in a changeable working environment through the optimization and iteration of the control parameters. The optimized control strategy can effectively influence different load fluctuation, temperature change and the like, and avoid faults of the motor caused by overload or imbalance, so that the overall reliability of the system is improved.
For example, in the historical operation data of the motor system, the mode characteristic parameters can help to screen out the most critical control parameters, such as the rotating speed, the torque and the current of the motor, the parameters directly influence the power consumption and the efficiency of the motor, the simulation system can predict the response, the energy efficiency and the like of the motor under the load change by adjusting the rotating speed and the torque, the simulation result is evaluated through a series of performance indexes (such as efficiency, power factor, energy consumption and the like), namely the motor performance under each group of candidate control parameters is calculated, the optimal operation range is found, the current and the rotating speed control strategy in the control system are adjusted according to the energy efficiency calculation result of the motor, energy saving optimization is carried out, then a candidate parameter set is generated through continuous iteration and adjustment, and a group of candidate parameter sets after adjustment and verification are finally obtained through a plurality of optimization iterations, and the candidate parameter set can maximize the energy efficiency of the motor, and the stability and the high efficiency of the motor system are ensured in actual operation.
In the embodiment of the present invention, when the control policy generation module performs the function of performing parameter screening on the candidate parameter set to obtain the optimal control parameter, the control policy generation module is specifically configured to:
performing motor simulation operation based on the candidate parameter set, and calculating a corresponding energy-saving effect;
Evaluating performance indexes of each group of candidate control parameters on motor system stability, response speed and energy consumption based on the energy-saving effect;
scoring each performance index according to a preset weight formula to obtain a scoring result;
And screening the candidate control parameter with the highest comprehensive score according to the scoring result to serve as the optimal control parameter.
In the embodiment of the invention, a group of candidate control parameters are selected firstly, then, the performance of the parameters in actual operation is simulated by using a motor simulation model, operation data are obtained, the energy saving ratio corresponding to each operation data is calculated, and the energy saving ratio is used as an energy saving effect.
In the embodiment of the invention, the influence of each group of candidate control parameters on the stability, the response speed and the energy consumption of the motor system is evaluated, and the calculation of the response speed refers to the reaction time of the motor simulation model simulation system after receiving an input signal, and the evaluation is performed on the consumed electric energy and the results of whether different loads can work normally or not when executing tasks, so that the comprehensive performance is obtained.
In the embodiment of the invention, the comprehensive performance is weighted and calculated to obtain a scoring result.
In the embodiment of the invention, after the comprehensive scores of each group of candidate control parameters are obtained, the ranking can be performed according to the scores. The control parameter set with the highest overall score will be selected as the optimal control parameter, meaning that it achieves the best balance in multiple dimensions, providing the best performance (e.g., energy savings, response speed, stability, etc.).
In the embodiment of the invention, the motor system is ensured to efficiently and stably operate under different working conditions by screening the optimal control parameters, the service life of the motor is prolonged, and the maintenance cost is reduced.
Fig. 4 is a schematic flow chart of a motor energy-saving optimization and intelligent regulation method according to an embodiment of the invention. In the embodiment of the invention, the motor energy-saving optimization and intelligent regulation method comprises the following steps:
S401, acquiring operation data of a historical motor, performing pattern recognition on the operation data to obtain pattern probability, and calculating performance data of the operation data according to the pattern probability, wherein the pattern recognition is performed by using the following formula:
;
wherein, theRepresenting operational dataThe probability of the corresponding pattern is determined,Is the first in the preset weight matrixThe weight of the individual pattern(s),As the total number of weights in the weight matrix,Is the firstThe probability average value corresponding to the individual pattern,Is the firstA covariance matrix of probabilities corresponding to the individual patterns,For given dataBelonging to the firstProbability of individual patterns;
S402, constructing a dynamic simulation model by using the collected motor system parameters;
s403, calculating dynamic characteristics according to the performance data and the dynamic simulation model, and extracting mode characteristic parameters of the operation data based on the dynamic characteristics;
S404, optimizing simulation model control parameters based on mode characteristic parameters, generating candidate parameter sets, and carrying out parameter screening on the candidate parameter sets to obtain optimal control parameters.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology.
Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems stated in the system may also be implemented by one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.