CN113507120B - Method and device for calculating bearing capacity and electronic equipment - Google Patents
Method and device for calculating bearing capacity and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract
The invention provides a method and a device for calculating bearing capacity and electronic equipment, wherein the method comprises the following steps: acquiring a power grid data set in a target time period from a power grid dispatching automation system based on a data interface; converting the grid data set into target grid data of a plurality of target devices in a format supported by the distributed photovoltaic bearing capacity computing system, and inputting the target grid data into the distributed photovoltaic bearing capacity computing system; and determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system. According to the method, the device and the electronic equipment for calculating the bearing capacity, which are provided by the embodiment of the invention, manual data input is not needed, so that the working difficulty is greatly reduced, the time consumption of working is shortened, the calculation efficiency can be greatly improved, and the problem of calculation errors caused by manual errors can be avoided.
Description
Technical Field
The present invention relates to the technical field of bearing capacity calculation, and in particular, to a method, an apparatus, an electronic device, and a computer readable storage medium for calculating bearing capacity.
Background
The bearing capacity in the power grid is also called as bearing capacity, and refers to bearing capacity for power supply and load fluctuation, and generally refers to maximum capacity of the power grid, which can bear power supply and load under the conditions that equipment or nodes are not overloaded continuously and voltage, short-circuit current and harmonic waves are not out of limits.
For distributed power systems, the corresponding load capacity can currently be determined based on a distributed photovoltaic load capacity computing system (Distributed photovoltaic capacity calculation system, DPCCS). However, since there are many devices in the power grid and some time of data is needed to calculate the bearing capacity, the amount of data required when the DPCCS calculates the bearing capacity is large, and it is difficult to calculate the bearing capacity quickly.
Disclosure of Invention
In order to solve the existing technical problems, embodiments of the present invention provide a method, an apparatus, an electronic device, and a computer readable storage medium for calculating a bearing capacity.
In a first aspect, an embodiment of the present invention provides a method for calculating a bearing capacity, including:
acquiring a power grid data set in a target time period from a power grid dispatching automation system based on a data interface;
converting the power grid data set into target power grid data of a plurality of target devices in a format supported by a distributed photovoltaic bearing capacity computing system, and inputting the target power grid data into the distributed photovoltaic bearing capacity computing system;
And determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system.
In one possible implementation, the converting the grid dataset into target grid data for a plurality of target devices in a format supported by a distributed photovoltaic load capacity computing system includes:
determining the subordinate relation between devices in a power grid according to a preset power grid topological structure;
converting the grid data set into intermediate grid data of intermediate equipment in a format supported by a distributed photovoltaic bearing capacity computing system;
and determining intermediate equipment belonging to the target equipment according to the affiliation, and calculating to obtain target power grid data of the target equipment according to intermediate power grid data of all the intermediate equipment belonging to the target equipment.
In one possible implementation, the converting the grid dataset into the intermediate grid data of the intermediate device in a format supported by the distributed photovoltaic load capacity computing system includes:
extracting historical power grid data of the intermediate equipment from the power grid data set;
data element d of historical grid data of the intermediate device i,j Data element d 'as the intermediary device' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]；
All the data elements d 'of the intermediate device are processed' k Sequentially arranging and forming intermediate power grid data d 'of the intermediate equipment' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
In one possible implementation, the extracting historical grid data of an intermediate device from the grid dataset includes:
the method comprises the steps of firstly sorting the power grid data set according to a date field, secondly sorting the power grid data set according to a device name field, and adopting an bubbling sorting method for the secondary sorting;
and taking the n data corresponding to the equipment name field of the intermediate equipment in the power grid data set as historical power grid data of the intermediate equipment.
In one possible implementation manner, after the determining the load capacity of the target device according to the calculation result of the distributed photovoltaic load capacity calculation system, the method further includes:
determining the grade of the bearing capacity of the target equipment, and determining the color corresponding to the grade;
and setting the color update of the line corresponding to the target equipment in the power grid topological structure to be the color corresponding to the grade.
In a second aspect, an embodiment of the present invention further provides an apparatus for calculating a bearing capacity, including:
the data acquisition module is used for acquiring a power grid data set in a target time period from the power grid dispatching automation system based on the data interface;
the data conversion module is used for converting the power grid data set into target power grid data of a plurality of target devices in a format supported by the distributed photovoltaic bearing capacity computing system, and inputting the target power grid data into the distributed photovoltaic bearing capacity computing system;
and the data calculation module is used for determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system.
In one possible implementation, the data conversion module converting the grid dataset into target grid data for a plurality of target devices in a format supported by a distributed photovoltaic load capacity computing system includes:
determining the subordinate relation between devices in a power grid according to a preset power grid topological structure;
converting the grid data set into intermediate grid data of intermediate equipment in a format supported by a distributed photovoltaic bearing capacity computing system;
and determining intermediate equipment belonging to the target equipment according to the affiliation, and calculating to obtain target power grid data of the target equipment according to intermediate power grid data of all the intermediate equipment belonging to the target equipment.
In one possible implementation, the data conversion module converting the grid dataset into intermediate grid data for an intermediate device in a format supported by a distributed photovoltaic load capacity computing system includes:
extracting historical power grid data of the intermediate equipment from the power grid data set;
data element d of historical grid data of the intermediate device i,j Data element d 'as the intermediary device' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]；
All the data elements d 'of the intermediate device are processed' k Sequentially arranging and forming intermediate power grid data d 'of the intermediate equipment' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected by the bus, and where the computer program when executed by the processor implements the steps in the method for calculating a bearing capacity described in any one of the above.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of calculating a load bearing capacity according to any one of the above.
According to the method, the device, the electronic equipment and the computer readable storage medium for calculating the bearing capacity, the needed power grid data set is obtained from the power grid dispatching automation system, and the power grid data set is converted into the target power grid data of the target equipment in the format supported by the distributed photovoltaic bearing capacity calculation system, so that the bearing capacity of the target equipment can be calculated conveniently based on the distributed photovoltaic bearing capacity calculation system. According to the method, manual data input is not needed, so that the working difficulty is greatly reduced, the working time consumption is shortened, the calculation efficiency can be greatly improved, and the problem of calculation errors caused by manual errors can be avoided.
Drawings
In order to more clearly describe the embodiments of the present invention or the technical solutions in the background art, the following description will describe the drawings that are required to be used in the embodiments of the present invention or the background art.
FIG. 1 is a flow chart of a method for calculating bearing capacity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a power grid topology in a method for calculating a load capacity according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for calculating bearing capacity according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of an electronic device for performing a method for calculating a bearing capacity according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.
Fig. 1 is a flowchart of a method for calculating a bearing capacity according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step 101: a grid dataset within a target time period is obtained from a grid dispatching automation system based on the data interface.
The power grid dispatching automation system, such as IES600, is an open power control center application integrated platform adopting domestic and foreign electronic information technology and various system technologies, and supports a modular system structure based on middleware technology, and comprises an information organization mode based on a Common Information Model (CIM), data access based on a Component Interface Specification (CIS) and the like. Because the power grid dispatching automation system can collect power grid data such as current, voltage and the like generated in the power grid operation process, the embodiment of the invention obtains the original data for calculating the bearing capacity, namely a power grid data set, from the power grid dispatching automation system.
In the embodiment of the invention, a data interface for acquiring data is developed in advance, and the data interface can receive the data in a specified date from a power grid dispatching automation system; furthermore, optionally, since the data formed by the grid dispatching automation system are numerous and complex, and only part of the data is needed in calculating the bearing capacity, the data interface can also set the required data type, namely the grid dispatching automation system only returns the grid data of the data type designated by the data interface. In the embodiment of the invention, the data stored by the power grid dispatching automation system is the generated power grid data, so the embodiment refers to the generated power grid data as historical power grid data, and each device corresponds to the historical power grid data. When the bearing capacity needs to be calculated, the required time, namely the target time period, is designated, and the historical grid data of the plurality of devices in the target time period are formed into a grid data set.
Step 102: and converting the grid data set into target grid data of a plurality of target devices in a format supported by the distributed photovoltaic bearing capacity computing system, and inputting the target grid data into the distributed photovoltaic bearing capacity computing system.
Step 103: and determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system.
In the embodiment of the invention, a large amount of historical power grid data can be conveniently and rapidly acquired from the power grid dispatching automation system through the data interface, and the efficiency of acquiring the power grid data can be improved; however, the DPCCS of the distributed photovoltaic load-bearing capability calculation system requires data in a specific format when calculating the load-bearing capability, which is different from the format output by the grid dispatching automation system, so the embodiment of the present invention requires converting the grid dataset into data in a specific format.
Specifically, the grid data set includes historical grid data of a plurality of devices, and part or all of the devices can be used as target devices so as to calculate the bearing capacity of the target devices; in general, a transformer, a circuit breaker, a transformer, a bus bar, or the like may be selected as the target device. Accordingly, the corresponding historical grid data in the grid data set can be converted into the grid data of the target device, namely the target grid data, and the target grid data accords with the format supported by the distributed photovoltaic bearing capacity computing system. And then the target power grid data of the target equipment can be input into the distributed photovoltaic bearing capacity computing system, and the bearing capacity of the target equipment can be computed by utilizing the bearing capacity computing function of the distributed photovoltaic bearing capacity computing system.
According to the method for calculating the bearing capacity, the needed power grid data set is obtained from the power grid dispatching automation system, and the power grid data set is converted into the target power grid data of the plurality of target devices in the format supported by the distributed photovoltaic bearing capacity calculation system, so that the bearing capacity of the target devices can be calculated conveniently based on the distributed photovoltaic bearing capacity calculation system. According to the method, manual data input is not needed, so that the working difficulty is greatly reduced, the working time consumption is shortened, the calculation efficiency can be greatly improved, and the problem of calculation errors caused by manual errors can be avoided.
Based on the above embodiment, the step 102 "converting the grid data set into the target grid data of the plurality of target devices in the format supported by the distributed photovoltaic load capacity computing system" may specifically include:
step A1: and determining the subordinate relations among the devices in the power grid according to a preset power grid topological structure.
In the embodiment of the invention, the power grid is preset with a corresponding topological structure, namely a power grid topological structure, so as to represent the connection relation among all the devices in the power grid. Based on the grid topology, it is possible to determine the dependencies between the devices in the grid, i.e. which device a certain device belongs to, or which device is the superior device to, etc. A schematic diagram of the power grid topology structure can be seen in fig. 2, taking the bus 110kV hong gang xi station 110kVI bus and the transformer 110kV hong gang xi station #1 main transformer and the transformer 2 main transformer in fig. 2 as examples, based on the power grid topology structure, two transformers 110kV hong gang xi station 110kVI bus, namely the subordinate device of the bus 110kV hong gang xi station 110kVI bus are known; or, the bus bar 110kV Longgang western station 110kVI bus bar is the superior equipment of the transformer Longgang western station #1 main transformer and Longgang western station #2 main transformer.
Step A2: and converting the grid data set into intermediate grid data of intermediate equipment in a format supported by the distributed photovoltaic bearing capacity computing system.
Step A3: and determining intermediate devices belonging to the target device according to the affiliation, and calculating to obtain target power grid data of the target device according to the intermediate power grid data of all the intermediate devices belonging to the target device.
In the embodiment of the invention, the intermediate device is a device contained in the power grid data set, that is, the power grid data set contains historical power grid data of the intermediate device. If the grid data set does not contain the target equipment, the embodiment firstly determines the intermediate grid data of the intermediate equipment, and then determines the target grid data of the corresponding target equipment according to the subordinate relation. Still referring to fig. 2, if the target device is the bus "110 kV" of the "kVI" station, the intermediate devices belonging to the target device include "the" main transformer of the "1 st" station and "the" main transformer of the "2 nd" station, so the target grid data of the target device may be calculated based on the intermediate grid data of the "main transformer of the" 1 st "station and the" main transformer of the "2 nd station" at this time. For example, the sum of the active powers of the two transformers is the active power of the target device, etc.
It should be noted that, the above-mentioned historical grid data, intermediate grid data and target grid data are all grid-related data, but a distinction may also be made between them. For example, the historical grid data is the raw data collected by the grid dispatching automation system, and mainly includes voltage, current, etc., while the intermediate grid data and the target grid data are processed data, which may include voltage, current, or active power, reactive power, etc. that need to be calculated, and specifically, the data included in the intermediate grid data and the target grid data may be determined based on which data the DPCCS needs to calculate the bearing capacity.
Optionally, the step A2 "converting the grid data set into the intermediate grid data of the intermediate device in the format supported by the distributed photovoltaic load capacity computing system" specifically includes:
step A21: historical grid data for the intermediate device is extracted from the grid dataset.
In the embodiment of the invention, the power grid data set contains the data of a plurality of intermediate devices, namely, the historical power grid data, and the historical power grid data of the intermediate devices can be directly extracted from the power grid data set.
Optionally, the grid data set generated by the grid dispatching automation system may have a problem of data dislocation, and in this embodiment, the grid data sets are first sorted, and then the historical grid data of each intermediate device is uniformly extracted. Specifically, the step a21 "extracting the historical grid data of the intermediate device from the grid data set" may specifically include: the method comprises the steps of firstly sorting a power grid data set according to a date field, secondly sorting the power grid data set according to a device name field, and adopting an bubbling sorting method for secondary sorting; and taking the n data corresponding to the equipment name field of the intermediate equipment in the power grid data set as the historical power grid data of the intermediate equipment.
In the embodiment of the invention, the power grid dispatching automation system sequentially generates data according to the time sequence, and sets the numerical values in the date fields from small to large according to the time sequence. And then, carrying out second sorting according to the equipment name field, so as to ensure that the grid data with the same equipment name (namely, the values of the equipment name fields are the same) are arranged together to form grid data in a matrix form, namely, historical grid data. And in the second sorting, an bubbling sorting method is adopted, and for the power grid data with the same equipment name, the second sorting does not interfere with the time sequence, so that the plurality of lines of power grid data determined after the second sorting are also arranged according to the time sequence, and the historical power grid data arranged according to the time sequence can be directly extracted in blocks based on the equipment name.
Step A22: data element d of historical grid data of the intermediate device i,j Data element d 'as an intermediary' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment ]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]。
Step A23: all data elements d 'of the intermediate device' k Intermediate network data d 'which are arranged in sequence to form an intermediate device' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
Since the grid data set collected from the grid dispatching automation system is in a specific format, i.e. the historical grid data of the intermediate device is in accordance with the format supported by the grid dispatching automation system, it is also required to convert the historical grid data into the data in the DPCCS supported format, i.e. the intermediate electricityNetwork data. In the embodiment of the present invention, the historical grid data of the intermediate device is essentially matrix data, for example, m rows and n columns of data, and the embodiment converts the data into data in an array form supported by DPCCS. In particular, data element d of historical grid data i,j Data element d 'as an intermediary' k I.e. d' k ＝d i,j Thereby based on a plurality of data elements d' k Combining to form intermediate grid data d' 1 ,d' 2 ,…,d' k ,…,d' m×n . Where k=i×n+j-n to ensure that the data elements in the intermediate grid data are in the same timing as the data elements in the historical grid data.
In the embodiment of the invention, the target power grid data of more complete target equipment can be determined through the power grid topological structure; data element d of historical grid data i,j Data element d 'as an intermediary' k Format conversion can be realized quickly; and k=i×n+j-n, so that the time sequence of the data elements in the intermediate power grid data is identical to the time sequence of the data elements in the historical power grid data, and the target power grid data obtained through conversion can be conveniently and directly input into the DPCS to calculate the bearing capacity.
Optionally, after the step 103 of determining the load capacity of the target device according to the calculation result of the distributed photovoltaic load capacity calculation system, the method further includes:
step B1: and determining the grade of the bearing capacity of the target equipment, and determining the color corresponding to the grade.
Step B2: and setting the color update of the line corresponding to the target equipment in the power grid topological structure to be the color corresponding to the grade.
In the embodiment of the invention, a plurality of bearing capacity grades are preset, and corresponding colors are set for each grade. For example, four levels are set, and colors are set for the levels in order from high to low in bearing capacity: green, orange, yellow, red. After the grade corresponding to the target equipment is determined, the color of the target equipment can be determined, and the circuit corresponding to the target equipment in the power grid topological structure is displayed according to the color, so that a user can quickly position the circuit possibly having a problem based on the power grid topological structure. As shown in fig. 2, if the target device is "110kV hong gang xi station 110kVI master", and the bearing capacity of the target device is the highest level, the corresponding line color of the target device is green, and at this time, the line between "110kV hong gang xi station 110kVI master" to "hong gang xi station #1 master" and "hong gang xi station #2 master" may be set to green.
The method for calculating the bearing capacity provided by the embodiment of the invention is described in detail above, and the method can also be realized by a corresponding device, and the device for calculating the bearing capacity provided by the embodiment of the invention is described in detail below.
Fig. 3 is a schematic structural diagram of an apparatus for calculating a bearing capacity according to an embodiment of the present invention. As shown in fig. 3, the apparatus for calculating the bearing capacity includes:
a data acquisition module 31, configured to acquire a grid data set within a target period from a grid dispatching automation system based on a data interface;
a data conversion module 32, configured to convert the grid dataset into target grid data of a plurality of target devices in a format supported by a distributed photovoltaic load capacity computing system, and input the target grid data to the distributed photovoltaic load capacity computing system;
and the data calculation module 33 is used for determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system.
On the basis of the above embodiment, the data conversion module 32 converts the grid data set into target grid data of a plurality of target devices in a format supported by the distributed photovoltaic load capacity computing system includes:
Determining the subordinate relation between devices in a power grid according to a preset power grid topological structure;
converting the grid data set into intermediate grid data of intermediate equipment in a format supported by a distributed photovoltaic bearing capacity computing system;
and determining intermediate equipment belonging to the target equipment according to the affiliation, and calculating to obtain target power grid data of the target equipment according to intermediate power grid data of all the intermediate equipment belonging to the target equipment.
Based on the above embodiments, the data conversion module 32 converts the grid dataset into intermediate grid data of an intermediate device in a format supported by the distributed photovoltaic load capacity computing system includes:
extracting historical power grid data of the intermediate equipment from the power grid data set;
data element d of historical grid data of the intermediate device i,j Data element d 'as the intermediary device' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]；
All the data elements d 'of the intermediate device are processed' k Sequentially arranging and forming intermediate power grid data d 'of the intermediate equipment' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
On the basis of the above embodiment, the data conversion module 32 extracts the historical grid data of the intermediate device from the grid dataset, including:
The method comprises the steps of firstly sorting the power grid data set according to a date field, secondly sorting the power grid data set according to a device name field, and adopting an bubbling sorting method for the secondary sorting;
and taking the n data corresponding to the equipment name field of the intermediate equipment in the power grid data set as historical power grid data of the intermediate equipment.
On the basis of the embodiment, the device further comprises a display module;
after the load capacity of the target device is determined according to the calculation result of the distributed photovoltaic load capacity calculation system, the display module is configured to:
determining the grade of the bearing capacity of the target equipment, and determining the color corresponding to the grade;
and setting the color update of the line corresponding to the target equipment in the power grid topological structure to be the color corresponding to the grade.
In addition, the embodiment of the invention also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for calculating the bearing capacity can be realized, and the same technical effect can be achieved, so that repetition is avoided and redundant description is omitted.
In particular, referring to FIG. 4, an embodiment of the invention also provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, perform the processes of the method embodiments described above for calculating load bearing capacity.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In an embodiment of the invention, represented by bus 1110, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits, including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and a memory controller, a peripheral bus, an accelerated graphics port (Accelerate Graphical Port, AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA (EISA) bus, video electronics standards association (Video Electronics Standards Association, VESA) bus, peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units (Central Processing Unit, CPU), network processors (Network Processor, NP), digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA), complex programmable logic devices (Complex Programmable Logic Device, CPLD), programmable logic arrays (Programmable Logic Array, PLA), micro control units (Microcontroller Unit, MCU) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The steps of the method disclosed in connection with the embodiments of the present invention may be performed directly by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules may be located in a random access Memory (Random Access Memory, RAM), flash Memory (Flash Memory), read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), registers, and so forth, as are known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Accordingly, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in embodiments of the present invention, the memory 1150 may further comprise memory located remotely from the processor 1120, such remotely located memory being connectable to a server through a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet, an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and a combination of two or more of the above-described networks. For example, the cellular telephone network and wireless network may be a global system for mobile communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced mobile broadband (Enhance Mobile Broadband, embbb) system, a mass machine type communication (massive Machine Type of Communication, mctc) system, an ultra reliable low latency communication (Ultra Reliable Low Latency Communications, uirllc) system, and the like.
It should be appreciated that the memory 1150 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-Only Memory (ROM), programmable ROM (PROM), erasable Programmable EPROM (EPROM), electrically Erasable EPROM (EEPROM), or Flash Memory (Flash Memory).
The volatile memory includes: random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRAM). The memory 1150 of the electronic device described in embodiments of the present invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various system programs, such as: a framework layer, a core library layer, a driving layer and the like, which are used for realizing various basic services and processing tasks based on hardware. The applications 1152 include various applications such as: a Media Player (Media Player), a Browser (Browser) for implementing various application services. A program for implementing the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above embodiment of the method for calculating a bearing capacity, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
The computer-readable storage medium includes: persistent and non-persistent, removable and non-removable media are tangible devices that may retain and store instructions for use by an instruction execution device. The computer-readable storage medium includes: electronic storage, magnetic storage, optical storage, electromagnetic storage, semiconductor storage, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), non-volatile random access memory (NVRAM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical coding (e.g., punch cards or bump structures in grooves with instructions recorded thereon), or any other non-transmission medium that may be used to store information that may be accessed by a computing device. In accordance with the definition in the present embodiments, the computer-readable storage medium does not include a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a pulse of light passing through a fiber optic cable), or an electrical signal transmitted through a wire.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus, electronic device, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one position, or may be distributed over a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the scheme of the embodiment of the application.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present invention is essentially or partly contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the storage medium includes various media as exemplified above that can store program codes.
In the description of the embodiments of the present invention, those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Thus, embodiments of the present invention may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be implemented in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer diskette, hard disk, random Access Memory (RAM), read-only Memory (ROM), erasable programmable read-only Memory (EPROM), flash Memory (Flash Memory), optical fiber, compact disc read-only Memory (CD-ROM), optical storage device, magnetic storage device, or any combination thereof. In embodiments of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The computer program code embodied in the computer readable storage medium may be transmitted using any appropriate medium, including: wireless, wire, fiber optic cable, radio Frequency (RF), or any suitable combination thereof.
Computer program code for carrying out operations of embodiments of the present invention may be written in assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages, including an object oriented programming language such as: java, smalltalk, C ++, also include conventional procedural programming languages, such as: c language or similar programming language. The computer program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computers may be connected via any sort of network, including: a Local Area Network (LAN) or a Wide Area Network (WAN), which may be connected to the user's computer or to an external computer.
The embodiment of the invention describes a method, a device and electronic equipment through flowcharts and/or block diagrams.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The foregoing is merely a specific implementation of the embodiment of the present invention, but the protection scope of the embodiment of the present invention is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the embodiment of the present invention, and the changes or substitutions are covered by the protection scope of the embodiment of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A method of calculating load bearing capacity, comprising:
acquiring a power grid data set in a target time period from a power grid dispatching automation system based on a data interface;
converting the power grid data set into target power grid data of a plurality of target devices in a format supported by a distributed photovoltaic bearing capacity computing system, and inputting the target power grid data into the distributed photovoltaic bearing capacity computing system;
determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system;
wherein converting the grid dataset into target grid data for a plurality of target devices in a format supported by a distributed photovoltaic load capacity computing system comprises:
determining the subordinate relation between devices in a power grid according to a preset power grid topological structure;
Converting the grid data set into intermediate grid data of intermediate equipment in a format supported by a distributed photovoltaic bearing capacity computing system; and
determining intermediate devices belonging to the target device according to the affiliation, and calculating to obtain target power grid data of the target device according to intermediate power grid data of all the intermediate devices belonging to the target device;
the converting the grid dataset into intermediate grid data for an intermediate device in a format supported by a distributed photovoltaic load capacity computing system comprises:
extracting historical power grid data of the intermediate equipment from the power grid data set;
data element d of historical grid data of the intermediate device i,j Data element d 'as the intermediary device' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]；
All the data of the intermediate deviceElement d' k Sequentially arranging and forming intermediate power grid data d 'of the intermediate equipment' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
2. The method of claim 1, wherein the extracting historical grid data for an intermediate device from the grid dataset comprises:
The method comprises the steps of firstly sorting the power grid data set according to a date field, secondly sorting the power grid data set according to a device name field, and adopting an bubbling sorting method for the secondary sorting;
and taking the n data corresponding to the equipment name field of the intermediate equipment in the power grid data set as historical power grid data of the intermediate equipment.
3. The method according to claim 1 or 2, further comprising, after said determining the load bearing capacity of the target device from the calculation result of the distributed photovoltaic load bearing capacity calculation system:
determining the grade of the bearing capacity of the target equipment, and determining the color corresponding to the grade;
and setting the color update of the line corresponding to the target equipment in the power grid topological structure to be the color corresponding to the grade.
4. An apparatus for calculating load bearing capacity, comprising:
the data acquisition module is used for acquiring a power grid data set in a target time period from the power grid dispatching automation system based on the data interface;
the data conversion module is used for converting the power grid data set into target power grid data of a plurality of target devices in a format supported by the distributed photovoltaic bearing capacity computing system, and inputting the target power grid data into the distributed photovoltaic bearing capacity computing system;
The data calculation module is used for determining the bearing capacity of the target equipment according to the calculation result of the distributed photovoltaic bearing capacity calculation system;
the data conversion module converts the grid data set into target grid data of a plurality of target devices in a format supported by a distributed photovoltaic bearing capacity computing system, including:
determining the subordinate relation between devices in a power grid according to a preset power grid topological structure;
converting the grid data set into intermediate grid data of intermediate equipment in a format supported by a distributed photovoltaic bearing capacity computing system; and
determining intermediate devices belonging to the target device according to the affiliation, and calculating to obtain target power grid data of the target device according to intermediate power grid data of all the intermediate devices belonging to the target device;
the data conversion module converting the grid dataset into intermediate grid data for an intermediate device in a format supported by a distributed photovoltaic load capacity computing system includes:
extracting historical power grid data of the intermediate equipment from the power grid data set;
data element d of historical grid data of the intermediate device i,j Data element d 'as the intermediary device' k The method comprises the steps of carrying out a first treatment on the surface of the Wherein data element d i,j I E [1, m for data elements of ith row and jth column in historical grid data of intermediate equipment]，j∈[1,n]And k=i×n+j-n, k ε [1, m×n ]]；
All the data elements d 'of the intermediate device are processed' k Sequentially arranging and forming intermediate power grid data d 'of the intermediate equipment' 1 ,d' 2 ,…,d' k ,…,d' m×n 。
5. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor realizes the steps in the method of calculating a bearing capacity according to any one of claims 1 to 3.
6. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, realizes the steps in the method of calculating a bearing capacity according to any one of claims 1 to 3.
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