CN117951167A - Modeling system and method for dynamic digital model of power system equipment - Google Patents
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Abstract
The application relates to a modeling system and a modeling method of a dynamic digital model of power system equipment, which relate to the field of modeling of dynamic digital models and comprise an acquisition module, a data processing module and an analysis and calculation module; the acquisition module acquires information data; the data processing module analyzes, processes and stores the information data; the analysis and calculation module divides a group of data into a plurality of loop entities, and divides the data in the loop entities into a plurality of data tables; setting independent data identification for each data in a data table of each loop entity; programming is carried out according to the data identification in the data table in the loop entity, the corresponding input value is queried according to the data identification in the data table, the calculated result is output to the data of the corresponding data identification, and the data analysis unit outputs the data with the received calculated result to the upper communication equipment. The application has the effect of improving the operation efficiency of the embedded edge computing device on dynamic data.
Description
Technical Field
The application relates to the field of modeling of dynamic digital models, in particular to a modeling system and method of a dynamic digital model of power system equipment.
Background
Along with the proposal of the construction strategy of the ubiquitous electric power Internet of things of the national electric network company, sensing layer Internet of things equipment in a transformer substation is gradually increased, the data volume provided by various Internet of things sensors is also gradually increased, the newly increased Internet of things data is difficult to incorporate into an original electric power data system, and high requirements are put forward on the digital modeling design of a station-end edge computing device by how to utilize the Internet of things data and timely make effective feedback in the station in combination with the traditional electric power data.
The software digital model of a conventional station-side edge computing device is typically built with reference to an "electrical loop". The basic electrical data such as voltage, current, switch position and the like in one electrical closed loop are collected and stored into a real-time database through analog signals or digital signals, and edge calculation is carried out. In practice, since the working logic of the sensors of the internet of things is different from that of the conventional power device, most of the sensors of the internet of things do not serve a certain "electric loop", and most of the data of the sensors of the internet of things cannot be stored in a real-time database according to the digital model induction classification of the "electric loop" and are subjected to edge calculation, even if the data can be established with the edge calculation device of the station end through a communication protocol, the edge calculation device of the station end simply forwards the original data to the upper communication equipment.
The partial station side edge computing device opens script application for processing the numerical variables collected by communication for processing the data of the Internet of things, but the script program is slower in operation efficiency, so that the embedded edge computing device loses the performance advantage of high operation efficiency.
Disclosure of Invention
In order to improve the operation efficiency of an embedded edge computing device on dynamic data, the application provides a modeling system and a modeling method of a dynamic digital model of power system equipment.
The modeling system and method for the dynamic digital model of the power system equipment provided by the application adopt the following technical scheme:
in a first aspect, a modeling system of a dynamic digital model of an electric power system device includes an acquisition module, a data processing module, and an analysis calculation module;
The acquisition module acquires information data and transmits the information data to the data processing module;
The data processing module receives and stores the information data, analyzes and processes the information data and outputs the information data;
the analysis and calculation module comprises a data analysis unit, a first calculation unit and a second calculation unit;
The data analysis unit receives the data output by the data processing module, divides one or more groups of data into a plurality of entities, constructs a plurality of loop entities, and sets a unique loop identifier for each loop entity; dividing data in a loop entity into a plurality of data tables, wherein the data tables are compatible with various data types corresponding to five tele-control in a power system; constructing an index table according to the independent loop identifications of the loop entities, wherein the index table points to the data table; setting independent data identification for each data in a data table of each loop entity; the data analysis unit outputs the data with the received calculation result to the upper communication equipment;
The first calculation unit is programmed according to the data identification in the data table in the loop entity, inquires corresponding input values according to the data identification in the data table, and outputs the result of program calculation to the data of the corresponding data identification;
the second calculation unit calculates a data value corresponding to the data identifier in the data table in the loop entity, and outputs a calculated result to the data of the corresponding data identifier.
By adopting the technical scheme, the modeling system collects information data, wherein the information data can be Internet of things data or electric data, the modeling system analyzes the collected data and then calculates the data through the mounted program, and outputs a calculation result to the data analysis unit.
Optionally, the acquisition module comprises a first acquisition unit and a second acquisition unit, and the first acquisition unit acquires the electrical data in the electrical closed loop through an analog signal or a digital signal and outputs the electrical data; the second acquisition unit establishes data communication with the data source equipment to be acquired through a communication protocol to acquire the data of the Internet of things, and outputs the data of the Internet of things;
The data processing module comprises a first processing unit and a second processing unit, and the first processing unit receives and stores the electrical data; the first processing unit outputs the electrical data to the upper communication equipment and the data analysis unit; the second processing unit receives and stores the internet of things data, and outputs the internet of things data to the upper communication equipment and the data analysis unit.
By adopting the technical scheme, the modeling object of the modeling system is not limited to a fixed electric closed loop any more, and the station-side power edge computing gateway designed according to the modeling system and the method can compute power data and Internet of things data or compute the combination of the power data and the Internet of things data. Meanwhile, the electrical data can be subjected to edge calculation by using a traditional electric device, the electrical data can be directly communicated to the upper communication equipment, and the simple Internet of things data can also be directly communicated to the upper communication equipment.
Optionally, the data flow between the data analysis unit and the first processing unit, the second processing unit is unidirectional.
By adopting the technical scheme, edge calculation in the data analysis unit cannot influence the data of the first processing unit and the second processing unit. The unidirectional data flow design can ensure the data security and stability among various databases, and reduce the possibility of data collision and competition.
Alternatively, one loop application and one loop application script each serve a single loop entity, and the same loop application script may be multiplexed between different loop entities.
By adopting the technical scheme, compared with the traditional modeling structure, the freely-mountable application program and application script applied to the loop entity have very high reusability and flexibility. In practical application, the customization development period of different projects is reduced.
Optionally, the data processing module further includes a data cleaning unit, the first collecting unit and the second collecting unit are respectively connected with a data cleaning unit, the data cleaning unit connected with the first collecting unit receives the data output by the first collecting unit and outputs standard data after checking, processing and repairing, and the data cleaning unit connected with the second collecting unit receives the data output by the second collecting unit and outputs dynamic data after checking, processing and repairing.
By adopting the technical scheme, errors, missing values, repeated values and inconsistencies in the data can be identified and corrected by data cleaning, so that the accuracy and the integrity of the data are improved. The cleaned data is more reliable and accurate, is easier to process and analyze, can reduce the time of data processing and analysis, and improves the efficiency of data processing.
Optionally, the data processing module further includes a data analysis unit and a data classification unit, the data analysis unit is connected with the data cleaning unit connected with the second acquisition unit, the data analysis unit is connected with the data cleaning unit, the dynamic data output by the data cleaning unit is received, the data size and the data type of the dynamic data are analyzed and bound with the dynamic data to be output to the data classification unit, the data classification unit receives the data and classifies the data according to the data size and the data type to generate simple data and complex data, the data classification unit outputs the simple data and the complex data to the second processing unit, the second processing unit outputs the simple data to the upper communication equipment after receiving the data, and the complex data is output to the data analysis unit.
By adopting the technical scheme, the data processing module analyzes the size and the type of the data, divides the data into simple data and complex data, directly communicates the screened simple data to the upper communication equipment, outputs the complex data to the data analysis unit for calculation, classifies the data before the data enter the data analysis unit, can lighten the data quantity in the data analysis unit, reduces the storage cost, reduces the calculation cost of data operation, accelerates the data processing speed and improves the calculation efficiency.
Optionally, the data classification unit is preset with a threshold value and a type table, wherein the type table represents the difficulty level of different data types in data processing, and the difficulty level is represented by complexity or simplicity; the data classifying unit compares the data size of each dynamic data with a threshold value, and when the data size is larger than the threshold value, the dynamic data is directly output to the data analyzing unit as complex data; when the data size is smaller than the threshold value, matching the data type of the dynamic data with the data type in the type table, and when the difficulty corresponding to the matched data type is complex, outputting the dynamic data as complex data to a data analysis unit; and when the difficulty degree corresponding to the matched data type is simple, outputting the dynamic data to the upper communication equipment as simple data.
By adopting the technical scheme, the data are roughly classified according to the preset threshold value through the data size, and then the data type of the screened data with smaller data size is analyzed, so that the classification of simple data and complex data is realized. Because the process of comparing the data size with the threshold value is simpler, according to the principle that the data size of most of complex data is larger, small data can be rapidly and effectively screened out, and then the small data is subdivided, so that the workload in the subdivision is reduced, and the data classification efficiency is improved.
Optionally, the data processing module further includes a counting unit connected to the operation unit, the counting unit measures the number of dynamic data with the data size smaller than the threshold value, the number value is used as a screening number to be output to the operation unit, the measured data type is the number of complex dynamic data, and the number value is used as a return number to be output to the operation unit; the operation unit is preset with a threshold proportion range and a single adjustment value, receives the screening quantity and the return quantity, divides the screening quantity by the return quantity to obtain a return proportion, compares the return proportion with the threshold proportion range, calls a threshold value when the return proportion is larger than the upper limit of the threshold proportion range, subtracts the single adjustment value from the threshold value, adds the single adjustment value to the threshold value when the return proportion is larger than the lower limit of the threshold proportion range, and covers the calculated result with the threshold value.
By adopting the technical scheme, the screened data with smaller data size and the complex data in the smaller data are counted, the duty ratio of the complex data relative to the screened small data is calculated, and the preset threshold value is reversely adjusted, so that more effective and reliable screening is realized.
In a second aspect, a modeling method for a dynamic digital model of an electric power system device includes the steps of: collecting information data;
Analyzing, processing and storing the acquired information data;
Constructing a plurality of independent loops by using the information data;
Setting a unique loop identifier for each loop;
dividing the data in each loop into a plurality of data tables;
constructing an index table according to the independent loop identification of the loop, wherein the index table points to the data table;
setting independent data identification for each data in each data table;
programming according to the data identification, and inquiring corresponding input values;
calculating a result according to the input value and outputting the result to the data of the corresponding data identifier;
And outputting the data with the received calculation result.
By adopting the technical scheme, the modeling system collects information data, analyzes the collected data, calculates the data through the mounted program, and outputs a calculation result, compared with a script calculation mode, the hardware resources are saved by using the program calculation which is completed by development and compiling, and the calculation speed is faster.
Optionally, analyzing and storing the collected information data includes the following steps:
presetting a threshold value and a type table, and presetting a threshold proportion range and a single adjustment value;
The collected data is checked, processed and repaired to output dynamic data;
Analyzing the data size and the data type of the dynamic data and binding the dynamic data;
Comparing the data size of each dynamic data with a threshold value;
when the data size is larger than the threshold value, the dynamic data is directly used as complex data;
When the data size is smaller than the threshold value, counting the dynamic data with the data size smaller than the threshold value to form screening quantity, and matching the data type of the dynamic data with the data type in the type table;
when the difficulty degree corresponding to the matched data type is complex, counting the dynamic data with the complex data type to form a return number; meanwhile, the dynamic data is used as complex data;
When the difficulty degree corresponding to the matched data type is simple, the dynamic data is used as simple data;
Dividing the return number by the screening number to obtain a return proportion;
Comparing the return ratio to a threshold ratio range;
When the return proportion is larger than the upper limit of the threshold proportion range, subtracting the single adjustment value from the threshold value and covering the threshold value;
And when the return proportion is larger than the lower limit of the threshold proportion range, adding the threshold value with a single adjustment value, and covering the threshold value.
Through adopting above-mentioned technical scheme, carry out the coarse screening to data according to data size before internet of things data gets into data analysis unit, carry out the fine screening to the data of screening according to the data type again, directly communicate the simple data of screening to upper communication equipment, output the complex data for data analysis unit and calculate, can alleviate the data volume in the data analysis unit, reduce storage cost, reduce the computational cost of data operation unit, accelerate data processing speed, improve computational efficiency. Meanwhile, the preset threshold value is reversely adjusted through the duty ratio of complex data in the screened data, so that more reasonable screening is realized.
In summary, the present application includes at least one of the following beneficial technical effects:
for the embedded device, compared with a script computing mode, the first computing unit which is developed and compiled is used for computing, so that hardware resources are saved, and the computing speed is faster. In actual products, the method has more performance advantages and price advantages.
The freely mountable first and second computing units applied to the loop entity have a very high reusability and flexibility compared to the previous modeling structure. In practical application, the customization development period of different projects is reduced.
The modeling object of the modeling system is not limited to a fixed electric closed loop any more, and the station-side power edge computing gateway designed according to the modeling system and the method can compute power data, internet of things data or combination of the power data and the Internet of things data.
Drawings
FIG. 1 is a system block diagram of an embodiment of the present application.
Fig. 2 is a block diagram of a first embodiment of the application.
Fig. 3 is a block diagram of a second embodiment of the present application.
Reference numerals illustrate: 1. an acquisition module; 11. a first acquisition unit; 12. a second acquisition unit; 2. a data processing module; 21. a first processing unit; 22. a second processing unit; 23. a data cleaning unit; 24. a data analysis unit; 25. a data classification unit; 26. a counting unit; 27. an arithmetic unit; 3. an analysis and calculation module; 31. a data analysis unit; 32. a first calculation unit; 33. a second calculation unit; 4. and the upper communication equipment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings 1-3 and examples. 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 application.
The embodiment of the application discloses a modeling system and a modeling method for a dynamic digital model of power system equipment.
Referring to fig. 1, a modeling system of a dynamic digital model of an electric power system device includes an acquisition module 1, a data processing module 2 and an analysis calculation module 3; the acquisition module 1 acquires information data and transmits the information data to the data processing module 2; the data processing module 2 receives data, analyzes, processes and stores information data, and outputs the information data to the analysis and calculation module 3; the analysis and calculation module 3 receives the data, performs calculation processing on the data, and outputs the data with the calculation result to the upper communication device 4.
Referring to fig. 2, the acquisition module 1 includes a first acquisition unit 11 and a second acquisition unit 12, and the first acquisition unit 11 acquires and outputs electrical data in an electrically closed loop through an analog signal or a digital signal. The second acquisition unit 12 establishes data communication with the data source equipment to be acquired through the communication protocol to acquire the internet of things data, and outputs the internet of things data. The second acquisition unit 12 and the data source device to be acquired can be in communication connection through USB, HDMI, thunderbolt, TCP/IP, PS/2, EPCALE, netBIOS, GSM multiple protocol interfaces, and the adopted communication protocols include, but are not limited to, TCP/IP, netBEUI, IPX/SPX.
Referring to fig. 2, the data processing module 2 includes a first processing unit 21 and a second processing unit 22.
The first processing unit 21 is connected with the first acquisition unit 11, receives and stores the electrical data, the first processing unit 21 is connected with the upper communication equipment 4 and the analysis and calculation module 3, and the first processing unit 21 can utilize a traditional electric device to perform edge calculation on the electrical data and can also perform calculation on the electrical data through the analysis and calculation module 3.
The second processing unit 22 is connected with the second acquisition unit 12, receives and stores the data of the internet of things, and the second processing unit 22 outputs complex data in the data of the internet of things to the analysis and calculation module 3 and outputs the simple data to the upper communication equipment 4.
Referring to fig. 2, the analysis calculation module 3 includes a data analysis unit 31 and first and second calculation units 32 and 33.
The data of the data analysis unit 31 may be specification data linked from the first processing unit 21 through a unidirectional pipe, or may be dynamic data mapped from the second processing unit 22 through unidirectional mapping. The data flow between the data analysis unit 31 and the first processing unit 21, the second processing unit 22 is unidirectional, ensuring that the edge calculation of the analysis calculation module 3 does not affect the data of the first processing unit 21 and the second processing unit 22.
The data analysis unit 31 receives the data output by the data processing module 2, divides one or more groups of data into a plurality of entities, constructs a plurality of loop entities, and sets each loop entity with a unique loop identifier; dividing the data in the loop entity into 6 data tables of 'event', 'measurement', 'control', 'parameter', 'constant value' and 'accumulated' according to the data characteristics, wherein the data tables are compatible with various data types corresponding to 'five remote' in the electric power system; constructing an index table according to the independent loop identifications of the loop entities, wherein the index table points to the data table; setting independent data identification for each data in a data table of each loop entity; the data analysis unit 31 outputs the data received with the calculation result to the host computer.
The first calculation unit 32 includes a plurality of loop application programs, and the loop application programs query corresponding input values according to the data identifiers in the data table in the loop entity, and output the results of program calculation to the data of the corresponding data identifiers. To ensure the versatility of the first computing unit 32, its developer may design variables in the loop application. The variable value may be determined as loop application variable parameters when the loop application is installed; the specific data may also be used to identify the "parameter" and "constant value" data tables stored in the data analysis unit 31, and these two data tables may be modified by the user in real time during the running process of the program through a fixed "constant value issuing" interface.
The second calculation unit 33 includes a plurality of loop application scripts, and the loop application scripts calculate data values corresponding to the data identifiers in the data table in the loop entity, and output the calculated results to the data of the corresponding data identifiers. The loop application script can be selected in which loop entities are mounted through the interface, and the action range of the loop application script is consistent with that of the loop application program and is limited in the loop entities.
Both a loop application and a loop application script serve a single loop entity, and the same loop application script can be multiplexed between different loop entities.
The embodiment of the application discloses a modeling system of a dynamic digital model of power system equipment, which comprises the following implementation principles: the modeling system collects electrical data (namely board acquisition data) in the electrical closed loop through analog signals or digital signals, establishes data communication with data source equipment to be collected through a communication protocol to collect Internet of things data, constructs a plurality of independent loops from the collected data, and sets a unique loop identifier for each loop. The data in each loop is divided into a plurality of data tables, and each data in each data table is provided with an independent data identifier. Programming according to the data identification, and inquiring corresponding input values; and calculating a result according to the input value, outputting the result to the data of the corresponding data identifier, and outputting the data with the calculation result. The modeling object of the modeling system is not limited to a fixed electric closed loop, and the station-side power edge computing gateway designed according to the modeling system can compute power data, internet of things data or a combination of the power data and the Internet of things data. Meanwhile, compared with a script computing mode, the embedded device uses a developed and compiled program to perform computation, so that hardware resources are saved, and the embedded device has a faster computing speed.
The second embodiment is different from the first embodiment in that:
Referring to fig. 3, the data processing module 2 further includes a data cleansing unit 23, a data analyzing unit 24, a data classifying unit 25, a counting unit 26, and an arithmetic unit 27.
The first acquisition unit 11 and the second acquisition unit 12 are respectively connected with a data cleaning unit 23, and the data cleaning unit 23 connected with the first acquisition unit 11 receives the data output by the first acquisition unit 11 and outputs standard data after checking, processing and repairing; the data cleaning unit 23 connected to the second acquisition unit 12 receives the data output from the second acquisition unit 12 and outputs dynamic data after inspection, processing and repair. The data cleaning types include: null and non-null checks, prefix and suffix checks, data length checks, numerical range checks, enumerated value checks, regular checks, etc.
The data analysis unit 24 is connected with the data cleaning unit 23 connected with the second acquisition unit 12, receives the dynamic data output by the data cleaning unit 23, analyzes the data size and data type of each dynamic data, binds the dynamic data and outputs the dynamic data to the data classification unit 25.
The data classifying unit 25 is preset with threshold values and a type table, the type table represents the difficulty level of different data types in data processing, and the difficulty level is represented by complexity or simplicity; the data classifying unit 25 receives the dynamic data bound with the data size and the data type, compares the data size of each dynamic data with a threshold value, and directly outputs the dynamic data as complex data to the analysis calculating module 3 when the data size is greater than the threshold value. When the data size is smaller than the threshold value, the data type of the dynamic data is matched with the data type in the type table, and when the difficulty corresponding to the matched data type is complex, the dynamic data is used as complex data to be output to the analysis and calculation module 3; when the difficulty level corresponding to the matched data type is simple, the dynamic data is output to the upper communication device 4 as simple data.
The counting unit 26 is connected to the data classifying unit 25, and the counting unit 26 counts the number of dynamic data whose data size is smaller than the threshold value, outputs the number value as a screening number to the operation unit 27, counts the number of dynamic data whose data type is complex, and outputs the number value as a return number to the operation unit 27.
The operation unit 27 presets a threshold proportion range and a single adjustment value, receives the screening quantity and the return quantity, divides the return quantity by the screening quantity to obtain a return proportion, compares the return proportion with the threshold proportion range, and when the return proportion is larger than the upper limit of the threshold proportion range, the operation unit 27 indicates that more complex data are in the data with the screened data size lower than the threshold value, the threshold value is too high, the workload of the data classification unit 25 is increased, and at the moment, the operation unit 27 calls the threshold value and subtracts the single adjustment value from the threshold value; when the return proportion is greater than the lower limit of the threshold proportion range, the data with the size lower than the threshold value is screened, the complex data is less, the threshold value is too low, the workload of the data operation unit 27 is increased, the operation unit 27 adds the threshold value with a single adjustment value, and the calculation result is covered with the threshold value.
The first processing unit 21 is connected with the data cleaning unit 23 connected with the first collecting unit 11, receives and stores the standard data, and the first processing unit 21 can perform edge calculation on the standard data by using a traditional electric device and can also perform calculation on the standard data by using the analysis and calculation module 3.
The second processing unit 22 is connected to the data classifying unit 25, receives and stores the complex data and the simple data output from the data classifying unit 25, and the second processing unit 22 outputs the complex data to the analysis and calculation module 3 and outputs the simple data to the upper communication device 4.
The implementation principle of the modeling system of the dynamic digital model of the power system equipment in the second embodiment of the application is as follows: the collected data of the internet of things is cleaned before the data of the internet of things enters the data analysis unit 31, and errors, missing values, repeated values and inconsistencies in the data are identified and corrected, so that the accuracy and the integrity of the data are improved. Then, the data are roughly classified according to the preset threshold value through the data size, the data with smaller data size are screened out and analyzed, the screened out simple data are directly communicated to the upper communication equipment 4, the complex data are output to the data analysis unit 31 for calculation, and meanwhile, the preset threshold value is reversely adjusted through the occupation ratio of the complex data in the screened out data, so that more reasonable screening is realized. Classifying the data before entering the data analysis unit 31 can reduce the data amount in the data analysis unit 31, reduce the storage cost, reduce the calculation cost of the data calculation unit 27, accelerate the data processing speed, and improve the calculation efficiency.
Examples
The modeling method of the dynamic digital model of the power system equipment comprises the modeling system of the dynamic digital model of the power system equipment, and specifically comprises the following steps:
S001, presetting a threshold value, a type table, a threshold proportion range and a single adjustment value;
S100, collecting information data;
s110, when the acquired information data are electrical data, cleaning the electrical data to generate standard data and storing the standard data;
S111, outputting the standard data and simultaneously constructing a plurality of independent loops
S120, when the acquired information data is the data of the Internet of things, cleaning the data of the Internet of things to generate dynamic data and storing the dynamic data;
S121, analyzing the data size and the data type of the dynamic data and binding the dynamic data;
S122, comparing the data size of each dynamic data with a threshold value;
S123, when the data size is larger than a threshold value, directly taking the dynamic data as complex data, and constructing a plurality of independent loops;
S124, when the data size is smaller than the threshold value, counting the dynamic data with the data size smaller than the threshold value to form screening quantity, and simultaneously matching the data type of the dynamic data with the data type in the type table;
S125, when the difficulty corresponding to the matched data type is complex, counting the dynamic data with the complex data type to form a return number; meanwhile, the dynamic data is used as complex data, and a plurality of independent loops are constructed;
s126, when the difficulty level corresponding to the matched data type is simple, the dynamic data is directly output as simple data;
s130, dividing the return quantity by the screening quantity to obtain a return proportion;
s131, comparing the return proportion with a threshold proportion range;
s132, subtracting the single adjustment value from the threshold value and covering the threshold value when the return proportion is larger than the upper limit of the threshold proportion range;
S133, adding a single adjustment value to the threshold value and covering the threshold value when the return proportion is larger than the lower limit of the threshold proportion range;
S200, setting a unique loop identifier for each loop;
s201, dividing the data in each loop into a plurality of data tables;
s202, constructing an index table according to the independent loop identification of the loop, wherein the index table points to the data table;
s203, setting independent data identification for each data in each data table;
s204, determining a design variable value or modifying a parameter and a constant value in a data table in real time by a user through a fixed constant value issuing interface;
S205, programming according to the data identification, and inquiring a corresponding input value; calculating a result according to the input value and outputting the result to the data of the corresponding data identifier;
s206, outputting the data with the received calculation result.
The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Claims (10)
1. The modeling system of the dynamic digital model of the power system equipment is characterized in that: comprises an acquisition module (1), a data processing module (2) and an analysis and calculation module (3);
The acquisition module (1) acquires information data and transmits the information data to the data processing module (2);
The data processing module (2) receives and stores the information data, analyzes and processes the information data and outputs the information data;
the analysis and calculation module (3) comprises a data analysis unit (31), a first calculation unit (32) and a second calculation unit (33);
the data analysis unit (31) receives the data output by the data processing module (2), divides one or more groups of data into a plurality of entities, constructs a plurality of loop entities, and sets each loop entity with a unique loop identifier; dividing data in a loop entity into a plurality of data tables, wherein the data tables are compatible with various data types corresponding to five tele-control in a power system; constructing an index table according to the independent loop identifications of the loop entities, wherein the index table points to the data table; setting independent data identification for each data in a data table of each loop entity; the data analysis unit (31) outputs the data with the received calculation result to the upper communication equipment (4);
The first computing unit (32) comprises a plurality of loop application programs, the loop application programs inquire corresponding data values according to the data identifiers in the data tables in the loop entities, and the result of program computation is output to the data of the corresponding data identifiers;
The second calculation unit (33) comprises a plurality of loop application scripts, the loop application scripts calculate data values corresponding to the data identifications in the data table in the loop entity, and the calculated results are output to the data of the corresponding data identifications.
2. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 1, wherein: the acquisition module (1) comprises a first acquisition unit (11) and a second acquisition unit (12), wherein the first acquisition unit (11) acquires electrical data in the electrical closed loop through an analog signal or a digital signal and outputs the electrical data; the second acquisition unit (12) establishes data communication with the data source equipment to be acquired through a communication protocol to acquire the data of the Internet of things, and outputs the data of the Internet of things;
The data processing module (2) comprises a first processing unit (21) and a second processing unit (22), wherein the first processing unit (21) receives and stores electrical data; the first processing unit (21) outputs the electrical data to the upper communication device (4) and the data analysis unit (31); the second processing unit (22) receives and stores the Internet of things data, and the second processing unit (22) outputs the Internet of things data to the upper communication equipment (4) and the data analysis unit (31).
3. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 2, wherein: the data flow between the data analysis unit (31) and the first processing unit (21), the second processing unit (22) is unidirectional.
4. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 1, wherein: both a loop application and a loop application script serve a single loop entity, and the same loop application script can be multiplexed between different loop entities.
5. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 2, wherein: the data processing module (2) further comprises a data cleaning unit (23), the first collecting unit (11) and the second collecting unit (12) are respectively connected with the data cleaning unit (23), the data cleaning unit (23) connected with the first collecting unit (11) receives data output by the first collecting unit (11) and outputs standard data after checking, processing and repairing, and the data cleaning unit (23) connected with the second collecting unit (12) receives data output by the second collecting unit (12) and outputs dynamic data after checking, processing and repairing.
6. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 5 wherein: the data processing module (2) further comprises a data analysis unit (24) and a data classification unit (25), the data analysis unit (24) is connected with the data cleaning unit (23) connected with the second acquisition unit (12), dynamic data output by the data cleaning unit (23) are received, the data size and the data type of the dynamic data are analyzed and bound with the dynamic data to be output to the data classification unit (25), the data classification unit (25) receives the data and classifies the data according to the data size and the data type to generate simple data and complex data, the data classification unit (25) outputs the simple data and the complex data to the second processing unit (22), the second processing unit (22) outputs the simple data to the upper communication equipment (4) after receiving the data, and the complex data is output to the data analysis unit (31).
7. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 6 wherein: the data classifying unit (25) is preset with threshold values and a type table, the type table shows the difficulty level of different data types in data processing, and the difficulty level is expressed in a complex or simple way; the data classifying unit (25) compares the data size of each dynamic data with a threshold value, and when the data size is larger than the threshold value, the dynamic data is directly output to the data analyzing unit (31) as complex data; when the data size is smaller than the threshold value, the data type of the dynamic data is matched with the data type in the type table, and when the difficulty corresponding to the matched data type is complex, the dynamic data is used as complex data to be output to a data analysis unit (31); when the difficulty level corresponding to the matched data type is simple, the dynamic data is output to the upper communication equipment (4) as simple data.
8. A modeling system for a dynamic digital model of an electrical power system plant as claimed in claim 7 wherein: the data processing module (2) further comprises a counting unit (26) which is connected with the operation unit (27), the counting unit (26) is connected with the data classifying unit (25), the counting unit (26) measures the quantity of dynamic data with the data size smaller than a threshold value, the quantity value is used as screening quantity to be output to the operation unit (27), the measured data type is the quantity of complex dynamic data, and the quantity value is used as return quantity to be output to the operation unit (27); the operation unit (27) is preset with a threshold proportion range and a single adjustment value, receives the screening quantity and the return quantity, divides the screening quantity by the return quantity to obtain a return proportion, compares the return proportion with the threshold proportion range, calls a threshold value when the return proportion is larger than the upper limit of the threshold proportion range, subtracts the single adjustment value from the threshold value, adds the single adjustment value to the threshold value when the return proportion is larger than the lower limit of the threshold proportion range, and covers the calculated result with the threshold value.
9. A method of modeling a dynamic digital model of an electrical power system plant, comprising a system for modeling a dynamic digital model of an electrical power system plant as claimed in any one of claims 1-8, comprising the steps of:
Collecting information data;
Analyzing, processing and storing the acquired information data;
Constructing a plurality of independent loops by using the information data;
Setting a unique loop identifier for each loop;
dividing the data in each loop into a plurality of data tables;
constructing an index table according to the independent loop identification of the loop, wherein the index table points to the data table;
setting independent data identification for each data in each data table;
programming according to the data identification, and inquiring corresponding input values;
calculating a result according to the input value and outputting the result to the data of the corresponding data identifier;
And outputting the data with the received calculation result.
10. A method of modeling a dynamic digital model of electrical power system equipment as claimed in claim 9, wherein: analyzing and storing the collected information data comprises the following steps:
presetting a threshold value and a type table, and presetting a threshold proportion range and a single adjustment value;
The collected data is checked, processed and repaired to output dynamic data;
Analyzing the data size and the data type of the dynamic data and binding the dynamic data;
Comparing the data size of each dynamic data with a threshold value;
when the data size is larger than the threshold value, the dynamic data is directly used as complex data;
When the data size is smaller than the threshold value, counting the dynamic data with the data size smaller than the threshold value to form screening quantity, and matching the data type of the dynamic data with the data type in the type table;
when the difficulty degree corresponding to the matched data type is complex, counting the dynamic data with the complex data type to form a return number; meanwhile, the dynamic data is used as complex data;
When the difficulty degree corresponding to the matched data type is simple, the dynamic data is used as simple data;
Dividing the return number by the screening number to obtain a return proportion;
Comparing the return ratio to a threshold ratio range;
When the return proportion is larger than the upper limit of the threshold proportion range, subtracting the single adjustment value from the threshold value and covering the threshold value;
And when the return proportion is larger than the lower limit of the threshold proportion range, adding the threshold value with a single adjustment value, and covering the threshold value.
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