CN116345447B - Power generation electric energy transmission loss evaluation system - Google Patents
Power generation electric energy transmission loss evaluation system Download PDFInfo
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- CN116345447B CN116345447B CN202310389250.8A CN202310389250A CN116345447B CN 116345447 B CN116345447 B CN 116345447B CN 202310389250 A CN202310389250 A CN 202310389250A CN 116345447 B CN116345447 B CN 116345447B
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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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
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention relates to a power generation electric energy transmission loss evaluation system, comprising: the content mapping mechanism is used for intelligently analyzing the single loss percentage data of the target electric device at the set time based on the transmission distance of each level of transmission links, the loss percentage data of each part of each day at the set time and the single loss percentage data of the previous time at the set time; and the electric quantity configuration mechanism is used for determining the power value of the power plant oversubscribed to the target electric appliance at the set moment compared with the rated transmission power based on the single loss percentage data at the set moment. The power generation electric energy transmission loss evaluation system is simple and convenient to operate and stable in operation. The method can intelligently analyze the single loss percentage data of the power supply system at the set moment, and realize the on-site distribution of the excess electric quantity value which is required to be supplemented by the loss of each level and is transmitted to each target electric appliance by the power plant based on the intelligent analysis result, so that the intelligent level of the management of the power supply system is improved.
Description
Technical Field
The invention relates to the field of power supply system management, in particular to a power generation electric energy transmission loss evaluation system.
Background
A power plant (power plant) refers to a power generation site that incorporates power grid operation such as fire power (coal, fuel oil, gas, and biomass), water power, nuclear, wind power, solar energy, geothermal energy, ocean energy, and the like.
A power plant is also called a power plant, and is a plant that converts various primary energy sources stored in nature into electric energy (secondary energy sources). As the demand for electricity increases, the assumption of establishing an electricity production center has been put forward. The development of motor manufacturing technology expands the application range of electric energy, the need for electricity for production increases rapidly, and power plants are accompanied. There are a variety of power generation pathways in power plants: thermal power plants, hydroelectric power plants, solar (photovoltaic) power plants, wind power plants, tidal power plants and the like. While nuclear power plants, which use nuclear fuel as an energy source, have played an increasing role in many countries around the world.
Currently, in a power supply system, electric energy is sent to a power transmission line from a power plant to a power distribution substation, then to a power supply station and finally to target electric devices, so that configuration of a transmission line from the power plant to the target electric devices is completed, in actual use, the power plant respectively provides fixed electric power consumption for different target electric devices, and for each target electric device, the fixed electric power consumption exceeds or is equal to the maximum electric power consumption of each target electric device, so that charging electric energy supply of each target electric device is ensured. Obviously, this power supply configuration mode easily causes consumption and loss of a large amount of electric power.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides a power generation electric energy transmission loss evaluation system which can analyze each part of loss percentage data of electric energy existing in each historical moment in a power supply system from a power plant to a power transmission line, to a power supply and distribution station and finally to each power transmission line branch of a target electric appliance, and intelligently analyze the single part of loss percentage data at the set moment by adopting a BP neural network model based on each part of loss percentage data at the set moment and the single part of loss percentage data at the moment before the set moment in each past day, so that the on-site distribution of excess electric quantity values which are required to be supplemented because of each stage of loss is realized for the power plant to be transmitted to each target electric appliance.
According to an aspect of the present invention, there is provided a generated electric power transmission loss evaluation system including:
a first detecting device provided between the power plant and the power transmission and transformation line for detecting a loss percentage of the unit transmission power transmitted from the power plant to the power transmission and transformation line to output as a first loss percentage;
a second detecting device provided between the power transmission and transformation line and the power supply and distribution substation, for detecting a loss percentage of unit transmission power transmitted from the power transmission and transformation line to the power supply and distribution substation as a second loss percentage output;
a third detecting device provided between the power supply and distribution station and the target electric device for detecting a loss percentage of the unit transmission electric energy transferred from the power supply and distribution station to the target electric device to output as a third loss percentage;
the information extraction mechanism is respectively connected with the first detection device, the second detection device and the third detection device and is used for taking the first loss percentage, the second loss percentage and the third loss percentage at each moment as corresponding single loss percentage data at each moment, acquiring loss percentage data of each part at the set moment in each past day and acquiring single loss percentage data of the moment before the set moment;
the content mapping mechanism is connected with the information extraction mechanism and is used for analyzing the single loss percentage data at the set time by adopting a BP neural network model based on the distance from a power plant to a power transmission and transformation circuit, the distance from the power transmission and transformation circuit to a power supply and distribution station, the distance from the power supply and distribution station to a target electric device, the loss percentage data at the set time in each past day and the single loss percentage data at the time before the set time;
the electric quantity configuration mechanism is connected with the content mapping mechanism and is used for determining the power value of the power plant, which is overstocked to the target electric appliance at the set moment compared with the rated transmission power, based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment;
wherein determining the power value of the power plant over-distributed to the target electric device at the set time compared with the rated transmission power based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time comprises: the power value that is oversupplied to the target electric device at the set time in comparison with the rated transmission power is monotonically and positively correlated with any one of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time.
The power generation electric energy transmission loss evaluation system is simple and convenient to operate and stable in operation. The method can intelligently analyze the single loss percentage data of the power supply system at the set moment, and realize the on-site distribution of the excess electric quantity value which is required to be supplemented by the loss of each level and is transmitted to each target electric appliance by the power plant based on the intelligent analysis result, so that the intelligent level of the management of the power supply system is improved.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram showing an internal structure of a generated electric power transmission loss evaluation system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram showing an internal structure of the generated electric power transmission loss evaluation system according to the embodiment B of the present invention.
Fig. 3 is a schematic diagram showing an internal structure of the generated electric power transmission loss evaluation system according to the embodiment C of the present invention.
Description of the embodiments
Embodiments of the generated power transmission loss evaluation system of the present invention will be described in detail below with reference to the accompanying drawings.
Embodiment 1
Fig. 1 is a schematic diagram showing an internal structure of a generated electric power transmission loss evaluation system according to an embodiment of the present invention, the system including:
a first detecting device provided between the power plant and the power transmission and transformation line for detecting a loss percentage of the unit transmission power transmitted from the power plant to the power transmission and transformation line to output as a first loss percentage;
the first detection device comprises a dynamic storage unit for storing a percentage of loss of unit transmission power delivered from the power plant to the power transmission line;
a second detecting device provided between the power transmission and transformation line and the power supply and distribution substation, for detecting a loss percentage of unit transmission power transmitted from the power transmission and transformation line to the power supply and distribution substation as a second loss percentage output;
a third detecting device provided between the power supply and distribution station and the target electric device for detecting a loss percentage of the unit transmission electric energy transferred from the power supply and distribution station to the target electric device to output as a third loss percentage;
the information extraction mechanism is respectively connected with the first detection device, the second detection device and the third detection device and is used for taking the first loss percentage, the second loss percentage and the third loss percentage at each moment as corresponding single loss percentage data at each moment, acquiring loss percentage data of each part at the set moment in each past day and acquiring single loss percentage data of the moment before the set moment;
the content mapping mechanism is connected with the information extraction mechanism and is used for analyzing the single loss percentage data at the set time by adopting a BP neural network model based on the distance from a power plant to a power transmission and transformation circuit, the distance from the power transmission and transformation circuit to a power supply and distribution station, the distance from the power supply and distribution station to a target electric device, the loss percentage data at the set time in each past day and the single loss percentage data at the time before the set time;
the electric quantity configuration mechanism is connected with the content mapping mechanism and is used for determining the power value of the power plant, which is overstocked to the target electric appliance at the set moment compared with the rated transmission power, based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment;
wherein determining the power value of the power plant over-distributed to the target electric device at the set time compared with the rated transmission power based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time comprises: the power value which is compared with the rated transmission power and is oversupplied to the target electric appliance at the set moment is monotonically and positively correlated with any one of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment;
wherein analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time comprises: the BP neural network model is a BP neural network model after each learning of a set total number;
wherein analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time comprises: the value of the set total number is proportional to the distance from the power plant to the target electric device.
Therefore, the invention has the following outstanding technical effects:
the method comprises the steps that a first place intelligently analyzes single loss percentage data at a set moment by adopting a BP neural network model based on the distance from a power plant to a power transmission and transformation line, the distance from the power transmission and transformation line to a power supply and distribution substation, the distance from the power supply and distribution substation to a target electric device, the loss percentage data at the set moment in each day before and the single loss percentage data at the moment before the set moment, so that reliable reference information is provided for the subsequent power plant to deliver an excess electric quantity value compared with rated delivery power;
the second place, introduce the electric quantity and dispose the mechanism and is used for determining the power value that the power plant compares the rated power of delivery to the electric device of goal to send to the overrate at the moment of settlement on the basis of the first loss percentage, second loss percentage and third loss percentage in the single loss percentage data of the moment of settlement, wherein compare the rated power of delivery to send to the electric device of goal overrate at the moment of settlement to send to the power value of electric device of goal and any loss percentage in the single loss percentage data of moment of settlement to send to the overrate and send to the first loss percentage, second loss percentage and third loss percentage to be monotonically forward to be correlated;
and thirdly, performing intelligent analysis, wherein the BP neural network model is a BP neural network model subjected to various learning of a set total number, and the value of the set total number is in direct proportion to the distance from the power plant to the target electric device.
Embodiment II
Fig. 2 is a schematic diagram showing an internal structure of the generated electric power transmission loss evaluation system according to the embodiment B of the present invention.
Unlike the embodiment of the present invention a, the generated electric energy transmission loss evaluation system shown in the embodiment of the present invention B may further include the following components:
the numerical value temporary storage mechanism is connected with the electric quantity configuration mechanism and is used for temporarily storing the power numerical value which is compared with the rated transmission power and is excessively distributed to the target electric appliance at the set moment by the power plant;
the numerical scratch mechanism may be implemented using a static memory device, an MMC memory device, or a TF memory device, for example.
Embodiment III
Fig. 3 is a schematic diagram showing an internal structure of the generated electric power transmission loss evaluation system according to the embodiment C of the present invention.
Unlike the embodiment of the present invention a, the generated electric energy transmission loss evaluation system shown in the embodiment of the present invention C may further include the following components:
and the quartz oscillation mechanism is connected with the electric quantity configuration mechanism and is used for providing a reference clock signal required by the electric quantity configuration mechanism.
Next, a further explanation of the specific configuration of the generated power transmission loss evaluation system of the present invention will be continued.
In the generated electric power transmission loss evaluation system according to the various embodiments of the present invention:
the monotonic positive correlation of any one of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data of the power value oversupplied to the target electric device at the set time compared with the rated transmission power, comprises: the numerical mapping formula of three-input and one-output is adopted to represent the numerical mapping relation between the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment and the power value which is compared with the rated transmission power and is overstocked to the target electric appliance at the set moment;
the numerical mapping relation between the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time and the power value which is compared with the rated transmission power and is oversupplied to the target electric device at the set time is expressed by adopting a numerical mapping formula with three inputs and one output, and the numerical mapping relation comprises the following steps: and adopting a simulation mode to realize the test and training of the three-input and one-output numerical mapping formula.
In the generated electric power transmission loss evaluation system according to the various embodiments of the present invention:
analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power supply and distribution substation, the distance from the power supply and distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time, comprising: the method comprises the steps of taking the distance from a power plant to a power transmission and transformation line, the distance from the power transmission and transformation line to a power supply and distribution station, the distance from the power supply and distribution station to a target electric device, loss percentage data of each part at a set time in each past day and single loss percentage data at the time before the set time as multiple input contents of a BP neural network model;
the method for inputting the BP neural network model comprises the following steps of taking the distance from a power plant to a power transmission and transformation line, the distance from the power transmission and transformation line to a power supply and distribution substation, the distance from the power supply and distribution substation to a target electric device, loss percentage data of each part at a set time in each past day and single loss percentage data at the time before the set time as a plurality of input contents of the BP neural network model: the normalization processing is performed on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data of each part of each day at the set time and the loss percentage data of each part of each day before the set time, before the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data of each part of each day before the set time and the loss percentage data of each part of each day before the set time are input to the BP neural network model.
In the generated electric power transmission loss evaluation system according to the various embodiments of the present invention:
analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power supply and distribution substation, the distance from the power supply and distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time, comprising: and taking the single loss percentage data at the set moment as single output content of the BP neural network model.
And in the generated electric power transmission loss evaluation system according to the various embodiments of the present invention:
taking the first loss percentage, the second loss percentage and the third loss percentage at each moment as corresponding single loss percentage data at each moment, acquiring each loss percentage data of each day at the set moment, and acquiring the single loss percentage data at the moment before the set moment comprises the following steps: the time axes are uniformly arranged at intervals at all times;
the step of obtaining each loss percentage data of each day at the set time and obtaining the single loss percentage data of the previous time at the set time by taking the first loss percentage, the second loss percentage and the third loss percentage of each time as the single loss percentage data corresponding to each time comprises the following steps: the previous time to the set time is the half-pel time.
In the generated power transmission loss evaluation system, analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time includes: and the single loss percentage data at the set moment output by the BP neural network model is normalized data.
Although embodiments of the present invention have been described to some extent with particular examples, it is to be understood that the invention is not so limited. It will be appreciated by those skilled in the art that various changes and modifications can be made to the invention without departing from the spirit and scope of the invention.
Claims (9)
1. A generated power delivery loss evaluation system, the system comprising:
a first detecting device provided between the power plant and the power transmission and transformation line for detecting a loss percentage of the unit transmission power transmitted from the power plant to the power transmission and transformation line to output as a first loss percentage;
a second detecting device provided between the power transmission and transformation line and the power supply and distribution substation, for detecting a loss percentage of unit transmission power transmitted from the power transmission and transformation line to the power supply and distribution substation as a second loss percentage output;
a third detecting device provided between the power supply and distribution station and the target electric device for detecting a loss percentage of the unit transmission electric energy transferred from the power supply and distribution station to the target electric device to output as a third loss percentage;
the information extraction mechanism is respectively connected with the first detection device, the second detection device and the third detection device and is used for taking the first loss percentage, the second loss percentage and the third loss percentage at each moment as corresponding single loss percentage data at each moment, acquiring loss percentage data of each part at the set moment in each past day and acquiring single loss percentage data of the moment before the set moment;
the content mapping mechanism is connected with the information extraction mechanism and is used for analyzing the single loss percentage data at the set time by adopting a BP neural network model based on the distance from a power plant to a power transmission and transformation circuit, the distance from the power transmission and transformation circuit to a power supply and distribution station, the distance from the power supply and distribution station to a target electric device, the loss percentage data at the set time in each past day and the single loss percentage data at the time before the set time;
the electric quantity configuration mechanism is connected with the content mapping mechanism and is used for determining the power value of the power plant, which is overstocked to the target electric appliance at the set moment compared with the rated transmission power, based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment;
wherein determining the power value of the power plant over-distributed to the target electric device at the set time compared with the rated transmission power based on the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time comprises: the power value which is compared with the rated transmission power and is oversupplied to the target electric appliance at the set moment is monotonically and positively correlated with any one of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment;
analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power supply and distribution substation, the distance from the power supply and distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time, comprising: the BP neural network model is a BP neural network model after each learning of a set total number;
wherein analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time comprises: the value of the set total number is proportional to the distance from the power plant to the target electric device.
2. The generated power delivery loss evaluation system of claim 1, further comprising:
the numerical value temporary storage mechanism is connected with the electric quantity configuration mechanism and is used for temporarily storing the power numerical value which is compared with the rated transmission power and is overstocked to the target electric appliance at the set moment.
3. The generated power delivery loss evaluation system of claim 1, further comprising:
and the quartz oscillation mechanism is connected with the electric quantity configuration mechanism and is used for providing a reference clock signal required by the electric quantity configuration mechanism.
4. A generated power delivery loss evaluation system as set forth in any one of claims 1-3 wherein:
the monotonic positive correlation of any one of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data of the power value oversupplied to the target electric device at the set time compared with the rated transmission power, comprises: and a three-input and one-output numerical mapping formula is adopted to express the numerical mapping relation between the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set moment and the power numerical value which is compared with the rated transmission power and is overstocked to the target electric appliance at the set moment.
5. The generated power delivery loss evaluation system of claim 4, wherein:
the numerical mapping relation of the first loss percentage, the second loss percentage and the third loss percentage in the single loss percentage data at the set time and the power value which is compared with the rated transmission power and is overstocked to the target electric device at the set time is expressed by adopting a three-input and one-output numerical mapping formula, and the numerical mapping relation comprises the following steps: and adopting a simulation mode to realize the test and training of the three-input and one-output numerical mapping formula.
6. A generated power delivery loss evaluation system as set forth in any one of claims 1-3 wherein:
analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power supply and distribution substation, the distance from the power supply and distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time, comprising: the distance from the power plant to the power transmission and transformation line, the distance from the power transmission and transformation line to the power supply and distribution station, the distance from the power supply and distribution station to the target electric device, the loss percentage data of each part at the set time in each past day and the loss percentage data of a single part at the time before the set time are used as a plurality of input contents of the BP neural network model.
7. The generated power delivery loss evaluation system of claim 6, wherein:
the method comprises the steps of taking the distance from a power plant to a power transmission and transformation line, the distance from the power transmission and transformation line to a power supply and distribution substation, the distance from the power supply and distribution substation to a target electric device, loss percentage data of each part at a set time in each past day and single loss percentage data at the time before the set time as a plurality of input contents of a BP neural network model, wherein the input contents comprise: the normalization processing is performed on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data of each part of each day at the set time and the loss percentage data of each part of each day before the set time, before the distance from the power plant to the power transmission line, the distance from the power transmission line to the power distribution substation, the distance from the power distribution substation to the target electric device, the loss percentage data of each part of each day before the set time and the loss percentage data of each part of each day before the set time are input to the BP neural network model.
8. A generated power delivery loss evaluation system as set forth in any one of claims 1-3 wherein:
analyzing the single loss percentage data at the set time using the BP neural network model based on the distance from the power plant to the power transmission line, the distance from the power transmission line to the power supply and distribution substation, the distance from the power supply and distribution substation to the target electric device, the loss percentage data at the set time for each day, and the single loss percentage data at the time immediately before the set time, comprising: and taking the single loss percentage data at the set moment as single output content of the BP neural network model.
9. A generated power delivery loss evaluation system as set forth in any one of claims 1-3 wherein:
taking the first loss percentage, the second loss percentage and the third loss percentage at each moment as corresponding single loss percentage data at each moment, acquiring each loss percentage data of each day at the set moment, and acquiring the single loss percentage data at the moment before the set moment comprises the following steps: the time axes are uniformly arranged at intervals at all times;
the step of obtaining each loss percentage data of each day at the set time and obtaining the single loss percentage data of the previous time at the set time by taking the first loss percentage, the second loss percentage and the third loss percentage of each time as the single loss percentage data corresponding to each time comprises the following steps: the previous time to the set time is the half-pel time.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202856381U (en) * | 2012-10-31 | 2013-04-03 | 厦门天圣能源科技有限公司 | System capable of compensating power grid transmission loss through wind power generation |
| CN108694467A (en) * | 2018-05-22 | 2018-10-23 | 中国电力科学研究院有限公司 | A kind of method and system that Line Loss of Distribution Network System rate is predicted |
| CN110363334A (en) * | 2019-06-21 | 2019-10-22 | 西安工程大学 | Grid-connected grid line loss prediction technique based on Grey Neural Network Model |
| CN111398885A (en) * | 2020-03-27 | 2020-07-10 | 天津大学 | Intelligent electric meter operation error monitoring method combining line loss analysis |
| CN112365024A (en) * | 2020-10-10 | 2021-02-12 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | High-voltage direct-current converter station energy efficiency prediction method and system based on deep learning |
| CN115081743A (en) * | 2022-07-20 | 2022-09-20 | 阳光电源(南京)有限公司 | Charging station system configuration method, device, equipment and computer readable storage medium |
| CN115619594A (en) * | 2022-12-21 | 2023-01-17 | 南京六的平方信息技术有限公司 | Wisdom management system based on engineering construction equipment power consumption data |
Family Cites Families (1)
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-
2023
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202856381U (en) * | 2012-10-31 | 2013-04-03 | 厦门天圣能源科技有限公司 | System capable of compensating power grid transmission loss through wind power generation |
| CN108694467A (en) * | 2018-05-22 | 2018-10-23 | 中国电力科学研究院有限公司 | A kind of method and system that Line Loss of Distribution Network System rate is predicted |
| CN110363334A (en) * | 2019-06-21 | 2019-10-22 | 西安工程大学 | Grid-connected grid line loss prediction technique based on Grey Neural Network Model |
| CN111398885A (en) * | 2020-03-27 | 2020-07-10 | 天津大学 | Intelligent electric meter operation error monitoring method combining line loss analysis |
| CN112365024A (en) * | 2020-10-10 | 2021-02-12 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | High-voltage direct-current converter station energy efficiency prediction method and system based on deep learning |
| CN115081743A (en) * | 2022-07-20 | 2022-09-20 | 阳光电源(南京)有限公司 | Charging station system configuration method, device, equipment and computer readable storage medium |
| CN115619594A (en) * | 2022-12-21 | 2023-01-17 | 南京六的平方信息技术有限公司 | Wisdom management system based on engineering construction equipment power consumption data |
Non-Patent Citations (3)
| Title |
|---|
| 基于BP神经网络的线损不良数据辨识及其预测;周福举 等;节能;第39卷(第1期);第27-31页 * |
| 神经网络在配网电能损耗预测中的应用研究;王昕 等;软件;第38卷(第8期);全文 * |
| 论发电厂高压输电线路线损分析及降损措施;朱晓伟;;科技创新与应用(第14期);全文 * |
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