CN110991934A - Research method for application of Internet of things big data to AI table entries in power grid planning - Google Patents
Research method for application of Internet of things big data to AI table entries in power grid planning Download PDFInfo
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Abstract
The invention discloses a research method for application of Internet of things big data in AI table items in power grid planning, which comprises the following steps: the method comprises the following steps: the responsible person of the related technology refers to the related data, and can master the detailed rules of power grid planning implementation with each power department to ask a problem; step two: obtaining short-term and long-term power grid information big data planning values by utilizing load fluctuation and new energy output prediction, source grid load cooperative scheduling and grid frame development planning technologies; step three: the method adopts modern advanced sensing measurement technology, communication technology, information technology, computer technology and control technology to carry out actual measurement on the formed novel power grid to obtain short-term and long-term power grid information big data. The research method for the application of the Internet of things big data to the AI table entries in the power grid planning is stable and reliable, remarkable in effect, convenient to operate, strong in practicability and suitable for wide popularization and use.
Description
Technical Field
The invention belongs to the technical field of Internet of things big data production, and particularly relates to a research method for application of the Internet of things big data in AI table items in power grid planning.
Background
The smart grid is one of important technical application fields of big data. The smart grid big data platform is a basis for big data mining, and full data sharing of the smart grid can be realized through the smart grid big data platform, so that support is provided for business application development and operation. The existing research method for AI table item application of big data of the Internet of things in power grid planning cannot improve the reliability, self-healing property and stability of the system, cannot improve the prediction accuracy, cannot realize the smooth interaction of information among the three in a frame, and greatly improves the economical efficiency and reliability of power grid operation.
Disclosure of Invention
The invention aims to provide a research method for applying Internet of things big data to AI table entries in power grid planning, and aims to solve the problems that the reliability, self-healing performance and stability of a system cannot be improved, the prediction precision cannot be improved, information between the Internet of things big data and the system cannot be smoothly interacted in a frame, and the economy and reliability of power grid operation are greatly improved by the conventional research method for applying the Internet of things big data to the AI table entries in the power grid planning, which is proposed in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a research method for application of Internet of things big data in AI table items in power grid planning comprises the following steps:
the method comprises the following steps: the responsible person of the related technology refers to the related data, and can master the detailed rules of power grid planning implementation with each power department to ask a problem;
step two: obtaining short-term and long-term power grid information big data planning values by utilizing load fluctuation and new energy output prediction, source grid load cooperative scheduling and grid frame development planning technologies;
step three: the method comprises the following steps of actually measuring a formed novel power grid by adopting a modern advanced sensing measurement technology, a communication technology, an information technology, a computer technology and a control technology to obtain short-term and long-term power grid information big data;
step four: comparing the planned value of the big data of the power grid information with the short-term and long-term big data of the power grid information obtained through actual measurement by adopting an internet intelligent information processing and comparing technology, finding out deviation and recording for record;
step five: planning each electric appliance according to the deviation data item, discovering the connection between the data by using a big data technology, actually measuring and debugging the data deviation by using a computer network technology, and recording;
step six: determining a debugging power supply scheme according to the minimum deviation data integrated in the step five, and making a new scheduling scheme to enable the power grid planning system to be in AI table item processing at all times;
step seven: and editing the big data in the step six, drawing a real-time power grid system data information curve graph by using a GPU image processor, and monitoring the trend.
Furthermore, the load prediction in the second step is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high, and the medium-term and long-term precision is poor.
Furthermore, in the second step, part of the users actively reduce or add part of the loads to balance the output force change of the power generation side, that is, the electric quantity balance of the system is realized through the management of the demand side.
Further, in the third step, the data set is sensed, acquired, managed, processed and analyzed by using the traditional IT technology, software and hardware tools and mathematical analysis method.
Further, the power grid information big data in the third step includes: the output fluctuation of new energy, the transmission capacity of a power grid line, the range of load reduction electric quantity and the real-time electricity price.
Furthermore, the GPU server in the seventh step is a fast, stable, and flexible computing service based on the GPU and applied to various scenes such as video encoding and decoding, deep learning, and scientific computing.
Compared with the prior art, the invention has the beneficial effects that:
(1) the smart grid is a novel grid formed by highly integrating modern advanced sensing measurement technology, communication technology, information technology, computer technology and control technology with a physical grid on the basis of the physical grid. The system covers all links such as power generation, power transmission, power transformation, power distribution, power utilization and scheduling, coordinates the requirements and functions of all interest parties in the power market, ensures efficient operation of all parts of the system, reduces operation cost and environmental influence, and improves the reliability, self-healing performance and stability of the system as far as possible.
(2) The load prediction at the present stage is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high, and the medium-term and long-term precision is poor. With the increase of the data collection range of the power grid, different types of data such as meteorological information, user work and rest rules, macroscopic economic indexes and the like can be perceived more accurately through the relationship between abstract quantitative index representation and the load by utilizing a big data technology, and the prediction precision is improved.
(3) The big data technology can be used for effectively reducing the prediction error of new energy, if a large amount of auxiliary information is needed for achieving network source load coordination optimization scheduling, such as the fluctuation of new energy output, the transmission capacity of a power network line, the range of load reduction electric quantity, real-time electricity price and the like, each factor is influenced by a plurality of conditions, so that the method is a very complicated electricity transaction process, and at the moment, the relation among data needs to be discovered by using the big data technology, so that an optimal scheduling scheme is made. The intelligent power grid and the traditional power grid are different from the bidirectional information flow among the source grid and the load, information among the source grid and the load can be interacted smoothly in a frame, and the economical efficiency and the reliability of the power grid operation are greatly improved.
(4) The research method for the application of the Internet of things big data to the AI table entries in the power grid planning is stable and reliable, remarkable in effect, convenient to operate, strong in practicability and suitable for wide popularization and use.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A research method for application of Internet of things big data in AI table items in power grid planning comprises the following steps:
the method comprises the following steps: the responsible person of the related technology refers to the related data, and can master the detailed rules of power grid planning implementation with each power department to ask a problem;
step two: obtaining short-term and long-term power grid information big data planning values by utilizing load fluctuation and new energy output prediction, source grid load cooperative scheduling and grid frame development planning technologies;
step three: the method comprises the following steps of actually measuring a formed novel power grid by adopting a modern advanced sensing measurement technology, a communication technology, an information technology, a computer technology and a control technology to obtain short-term and long-term power grid information big data;
step four: comparing the planned value of the big data of the power grid information with the short-term and long-term big data of the power grid information obtained through actual measurement by adopting an internet intelligent information processing and comparing technology, finding out deviation and recording for record;
step five: planning each electric appliance according to the deviation data item, discovering the connection between the data by using a big data technology, actually measuring and debugging the data deviation by using a computer network technology, and recording;
step six: determining a debugging power supply scheme according to the minimum deviation data integrated in the step five, and making a new scheduling scheme to enable the power grid planning system to be in AI table item processing at all times;
step seven: and editing the big data in the step six, drawing a real-time power grid system data information curve graph by using a GPU image processor, and monitoring the trend.
And in the second step, the load prediction is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high and the medium-term and long-term precision is poor.
In the second step, part of the users are used for actively reducing or adding part of the loads to balance the output force change of the power generation side, namely, the electric quantity balance of the system is realized through the management of the demand side.
And in the third step, the traditional IT technology, software and hardware tools and a mathematical analysis method are utilized to sense, obtain, manage, process and analyze the data set.
Wherein, the power grid information big data in the third step comprises: the output fluctuation of new energy, the transmission capacity of a power grid line, the range of load reduction electric quantity and the real-time electricity price.
And the GPU server in the seventh step is a fast, stable and elastic calculation service which is based on the GPU and is applied to various scenes such as video coding and decoding, deep learning, scientific calculation and the like.
Example 2
A research method for application of Internet of things big data in AI table items in power grid planning comprises the following steps:
the method comprises the following steps: the responsible person of the related technology refers to the related data, and can master the detailed rules of power grid planning implementation with each power department to ask a problem;
step two: obtaining short-term and long-term power grid information big data planning values by utilizing load fluctuation and new energy output prediction, source grid load cooperative scheduling and grid frame development planning technologies;
step three: the method comprises the following steps of actually measuring a formed novel power grid by adopting a modern advanced sensing measurement technology, a communication technology, an information technology, a computer technology and a control technology to obtain short-term and long-term power grid information big data;
step four: comparing the planned value of the big data of the power grid information with the short-term and long-term big data of the power grid information obtained through actual measurement by adopting an internet intelligent information processing and comparing technology, finding out deviation and recording for record;
step five: planning each electric appliance according to the deviation data item, discovering the connection between the data by using a big data technology, actually measuring and debugging the data deviation by using a computer network technology, and recording;
step six: determining a debugging power supply scheme according to the minimum deviation data integrated in the step five, and making a new scheduling scheme to enable the power grid planning system to be in AI table item processing at all times;
step seven: and editing the big data in the step six, drawing a real-time power grid system data information curve graph by using 3DMAX drawing software, and monitoring the trend.
And in the second step, the load prediction is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high and the medium-term and long-term precision is poor.
In the second step, part of the users are used for actively reducing or adding part of the loads to balance the output force change of the power generation side, namely, the electric quantity balance of the system is realized through the management of the demand side.
And in the third step, the traditional IT technology, software and hardware tools and a mathematical analysis method are utilized to sense, obtain, manage, process and analyze the data set.
Wherein, the power grid information big data in the third step comprises: the output fluctuation of new energy, the transmission capacity of a power grid line, the range of load reduction electric quantity and the real-time electricity price.
When the invention works: the smart grid is a novel grid formed by highly integrating modern advanced sensing measurement technology, communication technology, information technology, computer technology and control technology with a physical grid on the basis of the physical grid. The system covers all links such as power generation, power transmission, power transformation, power distribution, power utilization and scheduling, the requirements and functions of all interest parties in the power market are coordinated, efficient operation of all parts of the system is guaranteed, operation cost and environmental influence are reduced, and meanwhile, the reliability, self-healing performance and stability of the system are improved as much as possible; the load prediction at the present stage is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high, and the medium-term and long-term precision is poor. With the increase of the data collection range of the power grid, different types of data such as meteorological information, user work and rest rules, macroscopic economic indexes and the like can be more accurately perceived through the relationship between abstract quantitative index representation and the load by utilizing a big data technology, so that the prediction precision is improved; the big data technology can be used for effectively reducing the prediction error of new energy, if a large amount of auxiliary information is needed for achieving network source load coordination optimization scheduling, such as the fluctuation of new energy output, the transmission capacity of a power network line, the range of load reduction electric quantity, real-time electricity price and the like, each factor is influenced by a plurality of conditions, so that the method is a very complicated electricity transaction process, and at the moment, the relation among data needs to be discovered by using the big data technology, so that an optimal scheduling scheme is made. The intelligent power grid and the traditional power grid are different from the bidirectional information flow among the source power grid, the source power grid and the load power grid, and information among the three can be smoothly interacted in a frame, so that the economical efficiency and the reliability of the power grid operation are greatly improved; the research method for the application of the Internet of things big data to the AI table entries in the power grid planning is stable and reliable, remarkable in effect, convenient to operate, strong in practicability and suitable for wide popularization and use.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A research method for application of Internet of things big data in AI table items in power grid planning is characterized by comprising the following steps:
the method comprises the following steps: the responsible person of the related technology refers to the related data, and can master the detailed rules of power grid planning implementation with each power department to ask a problem;
step two: obtaining short-term and long-term power grid information big data planning values by utilizing load fluctuation and new energy output prediction, source grid load cooperative scheduling and grid frame development planning technologies;
step three: the method comprises the following steps of actually measuring a formed novel power grid by adopting a modern advanced sensing measurement technology, a communication technology, an information technology, a computer technology and a control technology to obtain short-term and long-term power grid information big data;
step four: comparing the planned value of the big data of the power grid information with the short-term and long-term big data of the power grid information obtained through actual measurement by adopting an internet intelligent information processing and comparing technology, finding out deviation and recording for record;
step five: planning each electric appliance according to the deviation data item, discovering the connection between the data by using a big data technology, actually measuring and debugging the data deviation by using a computer network technology, and recording;
step six: determining a debugging power supply scheme according to the minimum deviation data integrated in the step five, and making a new scheduling scheme to enable the power grid planning system to be in AI table item processing at all times;
step seven: and editing the big data in the step six, drawing a real-time power grid system data information curve graph by using a GPU image processor, and monitoring the trend.
2. The method for researching the application of the Internet of things big data to the AI table items in the power grid planning as claimed in claim 1, wherein: and in the second step, the load prediction is mainly to predict the load size by using similar days through load historical data, and the short-term prediction precision is high and the medium-term and long-term precision is poor.
3. The method for researching the application of the Internet of things big data to the AI table items in the power grid planning as claimed in claim 1, wherein: in the second step, part of the users actively reduce or add part of the load to balance the output force change of the power generation side, namely, the electric quantity balance of the system is realized through the management of the demand side.
4. The method for researching the application of the Internet of things big data to the AI table items in the power grid planning as claimed in claim 1, wherein: and in the third step, the traditional IT technology, software and hardware tools and a mathematical analysis method are utilized to sense, acquire, manage, process and analyze the data set.
5. The method for researching the application of the Internet of things big data to the AI table items in the power grid planning as claimed in claim 1, wherein: the power grid information big data in the third step comprises the following steps: the output fluctuation of new energy, the transmission capacity of a power grid line, the range of load reduction electric quantity and the real-time electricity price.
6. The method for researching the application of the Internet of things big data to the AI table items in the power grid planning as claimed in claim 1, wherein: and the GPU server in the seventh step is a fast, stable and elastic calculation service which is based on the GPU and is applied to various scenes such as video coding and decoding, deep learning, scientific calculation and the like.
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| CN102184475A (en) * | 2011-05-11 | 2011-09-14 | 浙江大学 | Optimizing and dispatching method for microgrid economical operation on basis of multiple time scale coordination |
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Application publication date: 20200410 |