Energy-saving optimization method and system for high-voltage frequency converter

Abstract

The invention discloses an energy-saving optimization method and system for a high-voltage frequency converter. The method comprises the steps of determining historical feature operation data and historical feature control data required by different motor working conditions according to historical operation logs; analyzing the historical feature operation data required by the same motor working condition, determining optimal feature operation data according to an analysis result, and generating an application evaluation value; according to the application evaluation value and an energy-saving principle, judging whether the historical feature control data needs to be optimized, if so, determining the historical feature control data needing to be optimized and an optimization strategy to obtain energy-saving optimization data, and constructing an energy-saving optimization control model according to the motor working condition requirement and the energy-saving optimization data; based on the energy-saving optimization control model, optimal control data of actual motor working condition requirements are obtained, the high-voltage frequency converter is accurately and efficiently controlled according to a requirement instruction, the equipment operation efficiency and the energy-saving efficiency are improved, and safe and efficient operation of the system is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of energy-saving of high-voltage frequency converters, and in particular to an energy-saving optimization method and system for high-voltage frequency converters. Background Art

[0002] A high-voltage inverter is a power regulation device used to control and adjust the operation of high-voltage motors. It adjusts the speed and torque of the motor by changing the power supply frequency and voltage, thereby achieving speed control of the motor. However, the existing control method of high-voltage inverters adopts a fixed method and cannot achieve optimal and precise control of motor requirements, which not only reduces the operating efficiency of the equipment, but also affects the stability and reliability of the system. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides an energy-saving optimization method and system for a high-voltage inverter, which determines the optimized characteristic operation data of different motor working conditions, calculates the application evaluation value of the optimized characteristic operation data to generate the corresponding historical characteristic control data, determines the energy-saving optimization data according to the application evaluation value and the energy-saving principle, constructs an energy-saving optimization control model according to the different motor working conditions and the corresponding energy-saving optimization data, determines the optimal control data of the actual motor working conditions based on the energy-saving optimization control model, and accurately and efficiently controls the high-voltage inverter according to the demand instructions, thereby improving the equipment operation efficiency and energy-saving efficiency, and realizing safe and efficient operation of the system.

[0004] In some embodiments of the present application, a method for optimizing energy saving of a high-voltage inverter is provided, comprising:

[0005] Obtain the historical operation log of the high-voltage inverter, and determine the historical characteristic operation data and historical characteristic control data of different motor working conditions based on the historical operation log;

[0006] Analyze the historical characteristic operation data of the same motor working condition demand, determine the preferred characteristic operation data of each motor working condition demand according to the analysis results, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data;

[0007] According to the application evaluation value and energy-saving principle, determine whether the historical characteristic control data needs to be optimized. If necessary, determine the historical characteristic control data to be optimized and the optimization strategy;

[0008] Optimize the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and build an energy-saving optimization control model based on different motor working conditions and energy-saving optimization data;

[0009] The actual motor working condition requirements are obtained, and based on the energy-saving optimization control model, the optimal control data of the actual motor working condition requirements is obtained, and control instructions are generated for the high-voltage inverter according to the optimal control data.

[0010] In some embodiments of the present application, determining historical characteristic operation data and historical characteristic control data for different motor operating conditions according to historical operation logs includes:

[0011] Obtain the historical operation log of the high-voltage inverter, extract the historical demand instructions corresponding to each historical operation log, analyze and classify the historical demand instructions, and obtain multiple different motor working condition requirements;

[0012] Using the historical running time in each historical running log as the time reference line, set the collection node based on the preset time interval;

[0013] According to the collection nodes in each time reference line, the completion degree of the corresponding motor working condition requirements, the historical operation data of the motor and the historical control data of the high-voltage inverter are collected and mapped to the corresponding time reference line to generate a change relationship curve;

[0014] Determine a linear correlation according to the change relationship curve, and determine the degree of correlation between the historical operation data of the motor and the historical control data of the high-voltage inverter and the degree of completion of the corresponding motor working condition requirements according to the linear correlation;

[0015] The historical operation data and the historical control data with a correlation degree greater than a preset correlation degree threshold are respectively set as the historical characteristic operation data and the historical characteristic control data of the motor working condition demand corresponding to the time reference line;

[0016] Compare the historical characteristic operation data and the historical characteristic control data according to the same motor working condition requirements, and determine the credibility of the historical characteristic operation data and the historical characteristic control data according to the comparison results;

[0017] The historical feature operation data and historical feature control data whose credibility is less than the preset credibility threshold are eliminated.

[0018] In some embodiments of the present application, determining the preferred characteristic operation data required for each motor operating condition according to the analysis results includes:

[0019] Analyze the historical characteristic operation data of different historical operation durations of the same motor working condition requirements and the completion degree of the corresponding motor working condition requirements;

[0020] Generate the change efficiency according to the change value of the completion degree within the corresponding historical operation time, and the comparison result between the final completion degree and the preset completion degree threshold, and remove the historical operation time and historical characteristic operation data whose change efficiency is less than the preset change efficiency threshold and the final completion degree is less than the preset completion degree threshold;

[0021] Calculate the fluctuation degree of the remaining multiple historical characteristic operation data within the same historical operation time, and generate the fluctuation evaluation value of the historical characteristic operation data within the corresponding historical operation time according to the fluctuation degree of the multiple historical characteristic operation data and the corresponding weight coefficient;

[0022] A completion evaluation value is generated for the final completion degree and change efficiency of the motor operating condition requirements corresponding to the historical characteristic operating data of the remaining historical operating time, the completion evaluation values ​​are sorted, and the fluctuation evaluation values ​​are judged in turn according to the sorting results, and the historical characteristic operating data with a fluctuation evaluation value less than a preset fluctuation evaluation value threshold is set as the preferred characteristic operating data corresponding to the motor operating condition requirements.

[0023] In some embodiments of the present application, generating an application evaluation value corresponding to historical feature control data according to the preferred feature operation data includes:

[0024] Compare the preferred characteristic operation data with the preset standard characteristic operation data interval corresponding to the motor working condition requirement, and obtain the number of preferred characteristic operation data that are not in the preset standard characteristic operation data interval and the characteristic operation data deviation amount that the preferred characteristic operation data are not in the preset standard characteristic operation data interval;

[0025] Generate an operation evaluation coefficient based on the number and deviation of characteristic operation data;

[0026] Generate a fluctuation evaluation coefficient according to the fluctuation degree of the historical characteristic control data corresponding to the preferred characteristic operation data within the historical operation time;

[0027] The preferred characteristic operation data and the corresponding historical characteristic control data are converted into energy consumption to obtain the energy consumption array in the corresponding historical operation time, the energy consumption array at the same acquisition node is time-aligned and the energy consumption value of the aligned energy consumption array is calculated, and the energy consumption evaluation coefficient is generated according to the energy consumption values ​​at multiple acquisition nodes in the historical operation time;

[0028] Calculate the comprehensive use cost of the historical feature control data corresponding to the preferred feature operation data within the historical operation time, and subtract the comprehensive use cost from the preset use cost threshold to obtain the use cost difference, and set the compensation coefficient according to the use cost difference;

[0029] An application evaluation value corresponding to the historical characteristic control data is generated according to the operation evaluation coefficient, the fluctuation evaluation coefficient, the energy consumption evaluation coefficient and the compensation coefficient.

[0030] In some embodiments of the present application, the calculation formula of the application evaluation value is:

[0031]

[0032] Among them, Y is the application evaluation value, a1 is the operation evaluation conversion coefficient, y1 is the weight coefficient of the operation evaluation coefficient, m is the number of preferred feature operation data that are not in the preset standard feature operation data interval, n is the total number of preferred feature operation data, △Ps is the deviation of the sth feature operation data, B0 is the fluctuation degree threshold, B is the fluctuation degree of the historical feature control data within the historical operation time, y2 is the weight coefficient of the fluctuation evaluation coefficient, a2 is the fluctuation evaluation conversion coefficient, w is the number of collection nodes within the corresponding historical application time, Ni is the energy consumption value of the energy consumption array at the i-th collection node, y3 is the weight coefficient of the energy consumption evaluation coefficient, a3 is the energy consumption evaluation conversion coefficient, and c is the compensation coefficient.

[0033] In some embodiments of the present application, judging whether the historical control data needs to be optimized according to the application evaluation value and the energy-saving principle includes:

[0034] Presetting a preset application evaluation value threshold;

[0035] When the application evaluation value is less than the preset application evaluation value threshold, the optimized feature operation data to be optimized is selected according to the feature operation data deviation amount and the optimization coefficient is calculated;

[0036] When the application evaluation value is greater than the preset application evaluation value threshold, the standard data interval of each energy consumption indicator is determined based on the energy-saving principle. According to the degree of correlation between the preferred characteristic operation data and the energy consumption indicator, the optimized characteristic operation data to be optimized for each energy consumption indicator is determined and the optimization coefficient is calculated.

[0037] In some embodiments of the present application, determining the historical characteristic control data to be optimized and the optimization strategy includes:

[0038] Determine the degree of association between the optimization feature operation data to be optimized and the historical feature control data according to the synchronous change time nodes and synchronous change degrees of the optimization feature operation data to be optimized and the historical feature control data within the historical operation time;

[0039] The historical feature control data with a correlation degree greater than a preset correlation degree is set as the historical feature control data to be optimized, and an optimization strategy for the corresponding historical feature control data is generated according to the optimization coefficient. The corresponding historical feature control data is optimized according to the optimization strategy to obtain energy-saving optimization data.

[0040] In some embodiments of the present application, an energy-saving optimization control model is constructed according to different motor operating conditions and energy-saving optimization data, including:

[0041] Acquire characteristic operating condition data of different motor operating condition requirements, and construct training data sets and test data sets with the characteristic operating condition data and energy-saving optimization data corresponding to the motor operating condition requirements;

[0042] The characteristic operating condition data in the training data set is set as input data, and the energy-saving optimization data corresponding to the characteristic operating condition data in the training data set is set as output data, and a neural network training is performed to obtain an initial energy-saving optimization control model;

[0043] The accuracy of the initial energy-saving optimization control model generated based on the test data set;

[0044] If the accuracy is less than the preset accuracy threshold, the training data set is iteratively trained to rebuild the initial energy-saving optimization control model until the accuracy is greater than the preset accuracy threshold, and the energy-saving optimization control model is generated;

[0045] If the accuracy is greater than a preset accuracy threshold, the initial energy-saving optimization control model is set as the energy-saving optimization control model.

[0046] In some embodiments of the present application, generating a control instruction for a high-voltage inverter according to optimal control data includes:

[0047] Obtaining actual motor working condition requirements and obtaining actual characteristic working condition data of the actual motor working condition requirements;

[0048] The actual characteristic operating condition data is input into the energy-saving optimization control model to obtain the optimal control data required by the actual motor operating condition, and control instructions are generated for the high-voltage inverter according to the optimal control data.

[0049] In some embodiments of the present application, an energy-saving optimization system for a high-voltage frequency converter is also included:

[0050] An acquisition module is used to acquire the historical operation log of the high-voltage inverter, and determine the historical characteristic operation data and historical characteristic control data of different motor working conditions according to the historical operation log;

[0051] A generation module is used to analyze the historical characteristic operation data of the same motor working condition demand, determine the preferred characteristic operation data of each motor working condition demand according to the analysis result, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data;

[0052] A judgment module is used to judge whether the historical characteristic control data needs to be optimized according to the application evaluation value and the energy-saving principle, and if necessary, determine the historical characteristic control data to be optimized and the optimization strategy;

[0053] A construction module is used to optimize the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and to construct an energy-saving optimization control model according to different motor working conditions and energy-saving optimization data;

[0054] The control module obtains the actual motor working condition requirements, obtains the optimal control data of the actual motor working condition requirements based on the energy-saving optimization control model, and generates control instructions for the high-voltage inverter according to the optimal control data.

[0055] Compared with the prior art, the energy-saving optimization method and system of a high-voltage inverter in the embodiment of the present application have the following beneficial effects:

[0056] By determining the optimized characteristic operation data of different motor working conditions, calculating the optimized characteristic operation data to generate the application evaluation value of the corresponding historical characteristic control data, determining the energy-saving optimization data according to the application evaluation value and the energy-saving principle, building an energy-saving optimization control model according to the different motor working conditions and the corresponding energy-saving optimization data, determining the optimal control data for the actual motor working conditions based on the energy-saving optimization control model, and accurately and efficiently controlling the high-voltage inverter according to the demand instructions, the equipment operation efficiency and energy-saving efficiency are improved, and the safe and efficient operation of the system is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of an energy-saving optimization method for a high-voltage frequency converter in a preferred embodiment of the present application;

[0058] Figure 2 It is a schematic diagram of an energy-saving optimization system for a high-voltage inverter in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0059] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0060] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0061] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0062] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0063] like Figure 1 As shown, a method for energy saving optimization of a high-voltage inverter according to a preferred embodiment of the present application includes:

[0064] Step S101: obtaining a historical operation log of a high-voltage inverter, and determining historical characteristic operation data and historical characteristic control data of different motor working condition requirements according to the historical operation log;

[0065] Step S102: Analyze the historical characteristic operation data of the same motor working condition requirement, determine the preferred characteristic operation data of each motor working condition requirement according to the analysis result, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data;

[0066] Step S103: judging whether the historical characteristic control data needs to be optimized according to the application evaluation value and the energy-saving principle, and if so, determining the historical characteristic control data to be optimized and the optimization strategy;

[0067] Step S104: Optimizing the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and constructing an energy-saving optimization control model according to different motor working condition requirements and the energy-saving optimization data;

[0068] Step S105: Acquire the actual motor working condition requirements, obtain optimal control data of the actual motor working condition requirements based on the energy-saving optimization control model, and generate control instructions for the high-voltage inverter according to the optimal control data.

[0069] In this embodiment, the historical demand instructions include adjustment ranges of the motor's operating parameters such as speed and torque. The historical demand instructions are classified according to different operating conditions and the sizes of the adjustment ranges to obtain multiple different motor operating conditions. The historical characteristic operating data refers to the historical operating data of the motor that changes according to different motor operating conditions. The historical characteristic control data refers to the historical control data of the high-voltage inverter that changes according to different motor operating conditions.

[0070] In some embodiments of the present application, determining historical characteristic operation data and historical characteristic control data for different motor operating conditions according to historical operation logs includes:

[0071] Obtain the historical operation log of the high-voltage inverter, extract the historical demand instructions corresponding to each historical operation log, analyze and classify the historical demand instructions, and obtain multiple different motor working condition requirements;

[0072] Using the historical running time in each historical running log as the time reference line, set the collection node based on the preset time interval;

[0073] According to the collection nodes in each time reference line, the completion degree of the corresponding motor working condition requirements, the historical operation data of the motor and the historical control data of the high-voltage inverter are collected and mapped to the corresponding time reference line to generate a change relationship curve;

[0074] Determine a linear correlation according to the change relationship curve, and determine the degree of correlation between the historical operation data of the motor and the historical control data of the high-voltage inverter and the degree of completion of the corresponding motor working condition requirements according to the linear correlation;

[0075] The historical operation data and the historical control data with a correlation degree greater than a preset correlation degree threshold are respectively set as the historical characteristic operation data and the historical characteristic control data of the motor working condition demand corresponding to the time reference line;

[0076] Compare the historical characteristic operation data and the historical characteristic control data according to the same motor working condition requirements, and determine the credibility of the historical characteristic operation data and the historical characteristic control data according to the comparison results;

[0077] The historical feature operation data and historical feature control data whose credibility is less than the preset credibility threshold are eliminated.

[0078] In this embodiment, the completion degree of the motor operating condition requirement refers to the evaluation result of the completion of the corresponding demand instruction, the linear correlation means that the completion degree of the motor operating condition requirement changes with the historical operation data and historical control data, and the credibility refers to the number of times the historical characteristic operation data (or historical characteristic control data) in the same motor operating condition requirement appears in the corresponding time reference line and the total number of corresponding time references of the same motor operating condition requirement. The accuracy of the historical characteristic operation data and the historical characteristic control data is judged according to the credibility, which lays a data foundation for the subsequent determination of the preferred characteristic operation data, ensures the accuracy of the subsequent energy-saving optimization data, and accurately and efficiently controls the high-voltage inverter according to the demand instruction, improves the equipment operation efficiency and energy-saving efficiency, and realizes safe and efficient operation of the system.

[0079] In some embodiments of the present application, determining the preferred characteristic operation data required for each motor operating condition according to the analysis results includes:

[0080] Analyze the historical characteristic operation data of different historical operation durations of the same motor working condition requirements and the completion degree of the corresponding motor working condition requirements;

[0081] Generate the change efficiency according to the change value of the completion degree within the corresponding historical operation time, and the comparison result between the final completion degree and the preset completion degree threshold, and remove the historical operation time and historical characteristic operation data whose change efficiency is less than the preset change efficiency threshold and the final completion degree is less than the preset completion degree threshold;

[0082] Calculate the fluctuation degree of the remaining multiple historical characteristic operation data within the same historical operation time, and generate the fluctuation evaluation value of the historical characteristic operation data within the corresponding historical operation time according to the fluctuation degree of the multiple historical characteristic operation data and the corresponding weight coefficient;

[0083] A completion evaluation value is generated for the final completion degree and change efficiency of the motor operating condition requirements corresponding to the historical characteristic operating data of the remaining historical operating time, the completion evaluation values ​​are sorted, and the fluctuation evaluation values ​​are judged in turn according to the sorting results, and the historical characteristic operating data with a fluctuation evaluation value less than a preset fluctuation evaluation value threshold is set as the preferred characteristic operating data corresponding to the motor operating condition requirements.

[0084] In this embodiment, the change value = change value / corresponding historical operating time, the change value refers to the final completion degree within the historical operating time - the initial completion degree, the preset completion degree threshold is set according to the minimum completion status of the demand instruction, and the historical characteristic operating data within the historical operating time of the same motor working condition requirement is screened according to the change efficiency and the final completion degree, so as to eliminate the historical characteristic operating data that is less than the preset change efficiency threshold and the preset completion threshold.

[0085] In this embodiment, the fluctuation evaluation value of the historical characteristic operation data corresponding to the historical operation time is calculated according to the fluctuation degree of multiple historical characteristic operation data within the remaining historical operation time. The smaller the fluctuation evaluation value, the higher the stability of the historical characteristic operation data, that is, the higher the safety stability of the system. The corresponding fluctuation evaluation value is judged according to the completion evaluation value sorting result, and the first historical characteristic operation data that is less than the preset fluctuation evaluation value threshold is set as the preferred characteristic operation data for the corresponding motor operating condition requirement.

[0086] In this embodiment, the preferred characteristic operation data of the motor operating condition requirement refers to the historical characteristic operation data in the historical operation log that can meet the maximum completion degree of the demand instruction and maintain stable operation of the system. The application evaluation value of the historical characteristic control data within the same historical operation time is obtained according to the preferred characteristic operation data of each motor operating condition requirement, which lays the foundation for the subsequent construction of the energy-saving optimization control model and improves the accuracy of the energy-saving optimization control model, so as to accurately and efficiently control the high-voltage inverter according to the demand instruction, improve the equipment operation efficiency and energy-saving efficiency, and realize safe and efficient operation of the system.

[0087] In some embodiments of the present application, generating an application evaluation value corresponding to historical feature control data according to the preferred feature operation data includes:

[0088] Compare the preferred characteristic operation data with the preset standard characteristic operation data interval corresponding to the motor working condition requirement, and obtain the number of preferred characteristic operation data that are not in the preset standard characteristic operation data interval and the characteristic operation data deviation amount that the preferred characteristic operation data are not in the preset standard characteristic operation data interval;

[0089] Generate an operation evaluation coefficient based on the number and deviation of characteristic operation data;

[0090] Generate a fluctuation evaluation coefficient according to the fluctuation degree of the historical characteristic control data corresponding to the preferred characteristic operation data within the historical operation time;

[0091] The preferred characteristic operation data and the corresponding historical characteristic control data are converted into energy consumption to obtain the energy consumption array in the corresponding historical operation time, the energy consumption array at the same acquisition node is time-aligned and the energy consumption value of the aligned energy consumption array is calculated, and the energy consumption evaluation coefficient is generated according to the energy consumption values ​​at multiple acquisition nodes in the historical operation time;

[0092] Calculate the comprehensive use cost of the historical feature control data corresponding to the preferred feature operation data within the historical operation time, and subtract the comprehensive use cost from the preset use cost threshold to obtain the use cost difference, and set the compensation coefficient according to the use cost difference;

[0093] An application evaluation value corresponding to the historical characteristic control data is generated according to the operation evaluation coefficient, the fluctuation evaluation coefficient, the energy consumption evaluation coefficient and the compensation coefficient.

[0094] In this embodiment, the degree of fluctuation refers to the average value of the fluctuation change values ​​of adjacent acquisition nodes of multiple historical feature control data during the historical operation time. The energy consumption conversion is to match and convert the preferred feature operation data and historical feature control data associated with energy consumption, and is obtained according to the data-energy consumption mapping table. The data-energy consumption mapping table is the energy consumption value matched according to the feature operation data and the feature control data.

[0095] In this embodiment, the operation evaluation coefficient is used to evaluate the operating status of the preferred characteristic operation data for the motor operating condition requirements. The smaller the number and the deviation of the characteristic operation data, the larger the operation evaluation coefficient, that is, the better the operating status. The fluctuation evaluation coefficient is used to evaluate the stability of the historical characteristic control data. When the fluctuation degree is smaller, the fluctuation evaluation coefficient is smaller, which means that the historical characteristic control data is more stable, that is, the system operates stably. The energy consumption evaluation coefficient is used to evaluate the energy consumption status of the preferred characteristic operation data and the historical characteristic control data. When the energy consumption value is smaller, the energy consumption evaluation coefficient is smaller, that is, the comprehensive energy consumption of the high-voltage inverter and the motor is less. The compensation coefficient is used to be set according to the difference between the maximum usage cost and the comprehensive usage cost. When the difference is larger, the compensation coefficient is larger, and when the difference is smaller, the compensation coefficient is smaller.

[0096] In this embodiment, a comprehensive analysis is performed based on the operating status of the optimized characteristic operating data under each motor operating condition requirement, the stability status of the historical characteristic control data, the energy consumption status of the high-voltage inverter and the motor, and the usage cost status, to obtain an application evaluation value of the corresponding historical characteristic control data, and determine whether to optimize the corresponding historical characteristic control data based on the application evaluation value, so as to accurately and efficiently control the high-voltage inverter according to the demand instructions, improve the equipment operating efficiency and energy-saving efficiency, and achieve safe and efficient operation of the system.

[0097] In some embodiments of the present application, the calculation formula of the application evaluation value is:

[0098]

[0099] Among them, Y is the application evaluation value, a1 is the operation evaluation conversion coefficient, y1 is the weight coefficient of the operation evaluation coefficient, m is the number of preferred feature operation data that are not in the preset standard feature operation data interval, n is the total number of preferred feature operation data, △Ps is the deviation of the sth feature operation data, B0 is the fluctuation degree threshold, B is the fluctuation degree of the historical feature control data within the historical operation time, y2 is the weight coefficient of the fluctuation evaluation coefficient, a2 is the fluctuation evaluation conversion coefficient, w is the number of collection nodes within the corresponding historical application time, Ni is the energy consumption value of the energy consumption array at the i-th collection node, y3 is the weight coefficient of the energy consumption evaluation coefficient, a3 is the energy consumption evaluation conversion coefficient, and c is the compensation coefficient.

[0100] In this embodiment, the value range of c is (0.75, 1.25), y1 is 0.6, y2 is 0.1, and y3 is 0.3. The smaller the preset usage cost difference interval in which the usage cost difference is located, the larger the compensation coefficient.

[0101] In some embodiments of the present application, judging whether the historical control data needs to be optimized according to the application evaluation value and the energy-saving principle includes:

[0102] Presetting a preset application evaluation value threshold;

[0103] When the application evaluation value is less than the preset application evaluation value threshold, the optimized feature operation data to be optimized is selected according to the feature operation data deviation amount and the optimization coefficient is calculated;

[0104] When the application evaluation value is greater than the preset application evaluation value threshold, the standard data interval of each energy consumption indicator is determined based on the energy-saving principle. According to the degree of correlation between the preferred characteristic operation data and the energy consumption indicator, the optimized characteristic operation data to be optimized for each energy consumption indicator is determined and the optimization coefficient is calculated.

[0105] In this embodiment, the energy-saving principle refers to achieving minimum energy consumption under the premise of ensuring the normal operation of the motor and the high-voltage inverter. According to the category of the energy consumption array after the energy consumption conversion of the optimized characteristic operation data and the historical characteristic control data, multiple energy consumption indicators are determined, and the standard data interval corresponding to the lowest energy consumption of the energy consumption indicator is set in advance. According to the degree of correlation between the preferred characteristic operation data and the corresponding energy consumption indicator, if the degree of correlation is greater than 80%, it is set as the optimized characteristic operation data strongly correlated with the corresponding energy consumption indicator. If the strongly correlated optimized characteristic operation data of each energy consumption indicator is not in the corresponding standard data interval, it is the optimized characteristic operation data to be optimized, and the optimization coefficient is set according to the data difference amount that is not in the standard data interval.

[0106] In this embodiment, the corresponding optimization feature operation data to be optimized and the corresponding optimization coefficient are determined based on the comparison result between the application evaluation value and the preset application evaluation value threshold, laying the foundation for the subsequent determination of the historical feature control data to be optimized, thereby determining accurate energy-saving optimization data and improving the energy-saving optimization efficiency of the high-voltage inverter.

[0107] In some embodiments of the present application, determining the historical characteristic control data to be optimized and the optimization strategy includes:

[0108] Determine the degree of association between the optimization feature operation data to be optimized and the historical feature control data according to the synchronous change time nodes and synchronous change degrees of the optimization feature operation data to be optimized and the historical feature control data within the historical operation time;

[0109] The historical feature control data with a correlation degree greater than a preset correlation degree is set as the historical feature control data to be optimized, and an optimization strategy for the corresponding historical feature control data is generated according to the optimization coefficient. The corresponding historical feature control data is optimized according to the optimization strategy to obtain energy-saving optimization data.

[0110] In this embodiment, optimized optimization feature operation data is obtained according to the optimization coefficient, and corresponding prediction feature control data is obtained according to the optimized optimization feature operation data. The prediction feature control data is the historical control data corresponding to the optimized optimization feature operation data under the same motor working condition requirement. The optimization strategy with the shortest time and lowest cost is generated according to the degree of deviation between multiple prediction feature control data and the corresponding historical feature control data.

[0111] In this embodiment, according to the degree of correlation between the optimized characteristic operation data to be optimized and the historical characteristic control data, the historical characteristic control data to be optimized and the optimization strategy are determined, thereby improving the energy-saving optimization efficiency of the high-voltage inverter, reducing the data processing volume, and quickly determining the energy-saving optimization data required for each motor operating condition, so as to accurately and efficiently control the high-voltage inverter according to the demand instructions, improve the equipment operation efficiency and energy-saving efficiency, and realize safe and efficient operation of the system.

[0112] In some embodiments of the present application, an energy-saving optimization control model is constructed according to different motor operating conditions and energy-saving optimization data, including:

[0113] Acquire characteristic operating condition data of different motor operating condition requirements, and construct training data sets and test data sets with the characteristic operating condition data and energy-saving optimization data corresponding to the motor operating condition requirements;

[0114] The characteristic operating condition data in the training data set is set as input data, and the energy-saving optimization data corresponding to the characteristic operating condition data in the training data set is set as output data, and a neural network training is performed to obtain an initial energy-saving optimization control model;

[0115] The accuracy of the initial energy-saving optimization control model generated based on the test data set;

[0116] If the accuracy is less than the preset accuracy threshold, the training data set is iteratively trained to rebuild the initial energy-saving optimization control model until the accuracy is greater than the preset accuracy threshold, and the energy-saving optimization control model is generated;

[0117] If the accuracy is greater than a preset accuracy threshold, the initial energy-saving optimization control model is set as the energy-saving optimization control model.

[0118] In some embodiments of the present application, generating a control instruction for a high-voltage inverter according to optimal control data includes:

[0119] Obtaining actual motor working condition requirements and obtaining actual characteristic working condition data of the actual motor working condition requirements;

[0120] The actual characteristic operating condition data is input into the energy-saving optimization control model to obtain the optimal control data required by the actual motor operating condition, and control instructions are generated for the high-voltage inverter according to the optimal control data.

[0121] In some embodiments of the present application, Figure 2 As shown, it also includes an energy-saving optimization system for a high-voltage inverter:

[0122] An acquisition module is used to acquire the historical operation log of the high-voltage inverter, and determine the historical characteristic operation data and historical characteristic control data of different motor working conditions according to the historical operation log;

[0123] A generation module is used to analyze the historical characteristic operation data of the same motor working condition demand, determine the preferred characteristic operation data of each motor working condition demand according to the analysis result, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data;

[0124] A judgment module is used to judge whether the historical characteristic control data needs to be optimized according to the application evaluation value and the energy-saving principle, and if necessary, determine the historical characteristic control data to be optimized and the optimization strategy;

[0125] A construction module is used to optimize the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and to construct an energy-saving optimization control model according to different motor working conditions and energy-saving optimization data;

[0126] The control module obtains the actual motor working condition requirements, obtains the optimal control data of the actual motor working condition requirements based on the energy-saving optimization control model, and generates control instructions for the high-voltage inverter according to the optimal control data.

[0127] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A method for energy saving optimization of a high voltage inverter, characterized in that: include: Obtain the historical operation log of the high-voltage inverter, and determine the historical characteristic operation data and historical characteristic control data of different motor working conditions based on the historical operation log; Analyze the historical characteristic operation data of the same motor working condition demand, determine the preferred characteristic operation data of each motor working condition demand according to the analysis results, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data; According to the application evaluation value and energy-saving principle, determine whether the historical characteristic control data needs to be optimized. If necessary, determine the historical characteristic control data to be optimized and the optimization strategy; Optimize the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and build an energy-saving optimization control model based on different motor working conditions and energy-saving optimization data; The actual motor working condition requirements are obtained, and based on the energy-saving optimization control model, the optimal control data of the actual motor working condition requirements is obtained, and control instructions are generated for the high-voltage inverter according to the optimal control data.

2. The energy-saving optimization method for a high-voltage inverter according to claim 1, characterized in that: Determine the historical characteristic operation data and historical characteristic control data for different motor working conditions based on the historical operation log, including: Obtain the historical operation log of the high-voltage inverter, extract the historical demand instructions corresponding to each historical operation log, analyze and classify the historical demand instructions, and obtain multiple different motor working condition requirements; Using the historical running time in each historical running log as the time reference line, set the collection node based on the preset time interval; According to the collection nodes in each time reference line, the completion degree of the corresponding motor working condition requirements, the historical operation data of the motor and the historical control data of the high-voltage inverter are collected and mapped to the corresponding time reference line to generate a change relationship curve; Determine a linear correlation according to the change relationship curve, and determine the degree of correlation between the historical operation data of the motor and the historical control data of the high-voltage inverter and the degree of completion of the corresponding motor working condition requirements according to the linear correlation; The historical operation data and the historical control data with a correlation degree greater than a preset correlation degree threshold are respectively set as the historical characteristic operation data and the historical characteristic control data of the motor working condition demand corresponding to the time reference line; Compare the historical characteristic operation data and the historical characteristic control data according to the same motor working condition requirements, and determine the credibility of the historical characteristic operation data and the historical characteristic control data according to the comparison results; The historical feature operation data and historical feature control data whose credibility is less than the preset credibility threshold are eliminated.

3. The energy-saving optimization method for a high-voltage inverter according to claim 2, characterized in that: Based on the analysis results, the optimal characteristic operating data for each motor operating condition is determined, including: Analyze the historical characteristic operation data of different historical operation durations of the same motor working condition requirements and the completion degree of the corresponding motor working condition requirements; Generate the change efficiency according to the change value of the completion degree within the corresponding historical operation time, and the comparison result between the final completion degree and the preset completion degree threshold, and remove the historical operation time and historical characteristic operation data whose change efficiency is less than the preset change efficiency threshold and the final completion degree is less than the preset completion degree threshold; Calculate the fluctuation degree of the remaining multiple historical characteristic operation data within the same historical operation time, and generate the fluctuation evaluation value of the historical characteristic operation data within the corresponding historical operation time according to the fluctuation degree of the multiple historical characteristic operation data and the corresponding weight coefficient; A completion evaluation value is generated for the final completion degree and change efficiency of the motor operating condition requirements corresponding to the historical characteristic operating data of the remaining historical operating time, the completion evaluation values ​​are sorted, and the fluctuation evaluation values ​​are judged in turn according to the sorting results, and the historical characteristic operating data with a fluctuation evaluation value less than a preset fluctuation evaluation value threshold is set as the preferred characteristic operating data corresponding to the motor operating condition requirements.

4. The energy-saving optimization method for a high-voltage frequency converter according to claim 3, characterized in that: Generate application evaluation values ​​of corresponding historical feature control data according to the preferred feature operation data, including: Compare the preferred characteristic operation data with the preset standard characteristic operation data interval corresponding to the motor working condition requirement, and obtain the number of preferred characteristic operation data that are not in the preset standard characteristic operation data interval and the characteristic operation data deviation amount that the preferred characteristic operation data are not in the preset standard characteristic operation data interval; Generate an operation evaluation coefficient based on the number and deviation of characteristic operation data; Generate a fluctuation evaluation coefficient according to the fluctuation degree of the historical characteristic control data corresponding to the preferred characteristic operation data within the historical operation time; The preferred characteristic operation data and the corresponding historical characteristic control data are converted into energy consumption to obtain the energy consumption array in the corresponding historical operation time, the energy consumption array at the same acquisition node is time-aligned and the energy consumption value of the aligned energy consumption array is calculated, and the energy consumption evaluation coefficient is generated according to the energy consumption values ​​at multiple acquisition nodes in the historical operation time; Calculate the comprehensive use cost of the historical feature control data corresponding to the preferred feature operation data within the historical operation time, and subtract the comprehensive use cost from the preset use cost threshold to obtain the use cost difference, and set the compensation coefficient according to the use cost difference; An application evaluation value corresponding to the historical characteristic control data is generated according to the operation evaluation coefficient, the fluctuation evaluation coefficient, the energy consumption evaluation coefficient and the compensation coefficient.

5. The energy-saving optimization method for a high-voltage inverter according to claim 4, characterized in that: The calculation formula of the application evaluation value is: Among them, Y is the application evaluation value, a1 is the operation evaluation conversion coefficient, y1 is the weight coefficient of the operation evaluation coefficient, m is the number of preferred feature operation data that are not in the preset standard feature operation data interval, n is the total number of preferred feature operation data, △Ps is the deviation of the sth feature operation data, B0 is the fluctuation degree threshold, B is the fluctuation degree of the historical feature control data within the historical operation time, y2 is the weight coefficient of the fluctuation evaluation coefficient, a2 is the fluctuation evaluation conversion coefficient, w is the number of collection nodes within the corresponding historical application time, Ni is the energy consumption value of the energy consumption array at the i-th collection node, y3 is the weight coefficient of the energy consumption evaluation coefficient, a3 is the energy consumption evaluation conversion coefficient, and c is the compensation coefficient.

6. The energy-saving optimization method for a high-voltage inverter according to claim 5, characterized in that: Based on the application evaluation value and energy-saving principles, determine whether the historical control data needs to be optimized, including: Presetting a preset application evaluation value threshold; When the application evaluation value is less than the preset application evaluation value threshold, the optimized feature operation data to be optimized is selected according to the feature operation data deviation amount and the optimization coefficient is calculated; When the application evaluation value is greater than the preset application evaluation value threshold, the standard data interval of each energy consumption indicator is determined based on the energy-saving principle. According to the degree of correlation between the preferred characteristic operation data and the energy consumption indicator, the optimized characteristic operation data to be optimized for each energy consumption indicator is determined and the optimization coefficient is calculated.

7. The energy-saving optimization method for a high-voltage inverter according to claim 6, characterized in that: Determine the historical characteristic control data and optimization strategies that need to be optimized, including: Determine the correlation degree between the optimization feature operation data to be optimized and the historical feature control data according to the synchronization change time nodes and synchronization change degrees of the optimization feature operation data to be optimized and the historical feature control data within the historical operation time; The historical feature control data with a correlation degree greater than a preset correlation degree is set as the historical feature control data to be optimized, and an optimization strategy for the corresponding historical feature control data is generated according to the optimization coefficient. The corresponding historical feature control data is optimized according to the optimization strategy to obtain energy-saving optimization data.

8. The energy-saving optimization method for a high-voltage inverter according to claim 7, characterized in that: Construct an energy-saving optimization control model based on different motor operating conditions and energy-saving optimization data, including: Acquire characteristic operating condition data of different motor operating condition requirements, and construct training data sets and test data sets with the characteristic operating condition data and energy-saving optimization data corresponding to the motor operating condition requirements; The characteristic operating condition data in the training data set is set as input data, and the energy-saving optimization data corresponding to the characteristic operating condition data in the training data set is set as output data, and a neural network training is performed to obtain an initial energy-saving optimization control model; The accuracy of the initial energy-saving optimization control model generated based on the test data set; If the accuracy is less than the preset accuracy threshold, the training data set is iteratively trained to rebuild the initial energy-saving optimization control model until the accuracy is greater than the preset accuracy threshold, and the energy-saving optimization control model is generated; If the accuracy is greater than a preset accuracy threshold, the initial energy-saving optimization control model is set as the energy-saving optimization control model.

9. The energy-saving optimization method for a high-voltage inverter according to claim 8, characterized in that: Generate control instructions for the high-voltage inverter according to the optimal control data, including: Obtaining actual motor working condition requirements and obtaining actual characteristic working condition data of the actual motor working condition requirements; The actual characteristic operating condition data is input into the energy-saving optimization control model to obtain the optimal control data required by the actual motor operating condition, and control instructions are generated for the high-voltage inverter according to the optimal control data.

10. An energy-saving optimization system for a high-voltage inverter, characterized in that: include: An acquisition module is used to acquire the historical operation log of the high-voltage inverter, and determine the historical characteristic operation data and historical characteristic control data of different motor working conditions according to the historical operation log; A generation module is used to analyze the historical characteristic operation data of the same motor working condition demand, determine the preferred characteristic operation data of each motor working condition demand according to the analysis result, and generate the application evaluation value of the corresponding historical characteristic control data according to the preferred characteristic operation data; A judgment module is used to judge whether the historical characteristic control data needs to be optimized according to the application evaluation value and the energy-saving principle, and if necessary, determine the historical characteristic control data to be optimized and the optimization strategy; A construction module is used to optimize the historical characteristic control data according to the optimization strategy to obtain energy-saving optimization data, and to construct an energy-saving optimization control model according to different motor working conditions and energy-saving optimization data; The control module obtains the actual motor working condition requirements, obtains the optimal control data of the actual motor working condition requirements based on the energy-saving optimization control model, and generates control instructions for the high-voltage inverter according to the optimal control data.

Images