CN115833142A - Power grid automatic voltage regulation and control method and system based on multi-source data load analysis - Google Patents
Power grid automatic voltage regulation and control method and system based on multi-source data load analysis Download PDFInfo
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Abstract
The invention discloses a method and a system for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis, wherein the method comprises the steps of predicting load data corresponding to each load analysis sub-element through a power grid load change trend prediction model, and generating a load change trend prediction result; dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods; and generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction. The technical problem that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time and influences equipment and even power grid safety is solved. According to the method, the strategy of automatic voltage regulation and control of the power grid is optimized by realizing the fusion of the accurate load change trend and the automatic voltage control strategy, and unnecessary operation of equipment is avoided, so that the service life of the equipment is prolonged, and the stability and the economy of automatic voltage control of the power system are improved.
Description
Technical Field
The invention relates to the technical field of voltage regulation, in particular to a power grid automatic voltage regulation method and system based on multi-source data load analysis.
Background
Along with the development of social economy, people put forward higher requirements on electric energy, meet the requirements of people on the electric energy, and are the responsibility and the charge of power grid enterprises. The voltage is used as an important index of the quality of the electric energy, and the reasonable voltage reactive power control can not only improve the qualified level of the voltage of the power grid, but also obviously reduce the loss of the power network, thereby promoting the safe, economic and high-quality operation of the power grid.
The automatic voltage control technology of regional power grids is widely applied in most regions, the control mode of the existing Automatic Voltage Control (AVC) system is that after the reactive power and voltage out-of-limit are detected, a strategy algorithm is started to control, then the out-of-limit quantity is pulled back to a reasonable operation range through equipment regulation, the execution of the control strategy lags behind the voltage and reactive power out-of-limit, the voltage and power factor indexes cannot be timely improved, and even frequent regulation of reactive equipment caused by normal fluctuation of load can occur, so that the safety of the equipment and even the power grid is influenced.
Disclosure of Invention
The invention provides a power grid automatic voltage regulation and control method and system based on multi-source data load analysis, and solves the technical problems that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time, and even frequent regulation of reactive equipment caused by normal fluctuation of load possibly occurs, so that the equipment and even the power grid safety are influenced.
The invention provides a power grid automatic voltage regulation and control method based on multi-source data load analysis, which comprises the following steps:
responding to a received voltage regulation request, and acquiring multi-source data corresponding to the voltage regulation request;
establishing a power grid load change trend prediction model according to the multi-source data;
load data corresponding to each load analysis sub-element is predicted through the power grid load change trend prediction model, and a load change trend prediction result is generated;
dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods;
and generating a voltage regulation and control instruction according to the voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction.
Optionally, the multi-source data comprises historical load data, historical meteorological data, planned overhaul data, and social characteristic data; the step of responding to the received voltage regulation and control request and acquiring multi-source data corresponding to the voltage regulation and control request comprises the following steps:
responding to the received voltage regulation and control request, and acquiring the historical load data, the historical meteorological data, the planned maintenance data and the social characteristic data corresponding to the voltage regulation and control request.
Optionally, the step of establishing a power grid load change trend prediction model according to the multi-source data includes:
converting data formats corresponding to the historical load data, the historical meteorological data, the planned overhaul data and the social characteristic data into load prediction data formats respectively to generate updated historical load data, updated historical meteorological data, updated planned overhaul data and updated social characteristic data;
establishing a plurality of load analysis sub-elements based on a plurality of time nodes and space characteristics corresponding to the time characteristics;
associating the updated historical load data, the updated historical meteorological data, the updated planned maintenance data, the updated social characteristic data and the corresponding load analysis sub-elements, and respectively inputting the data into a data memory corresponding to each load analysis sub-element;
and establishing a power grid load change trend prediction model by adopting all the load analysis sub-elements.
Optionally, the step of predicting load data corresponding to each load analysis subelement by using the power grid load change trend prediction model to generate a load change trend prediction result includes:
determining reference predicted load data corresponding to each load analysis subelement according to updated historical load data, updated social characteristic data and a preset load increase coefficient corresponding to each load analysis subelement corresponding to the power grid load change trend prediction model;
determining a meteorological factor correction coefficient, a planned maintenance factor correction coefficient and a social characteristic factor correction coefficient corresponding to each load analysis sub-element according to a preset coefficient value taking table and updated historical meteorological data, updated planned maintenance data and updated social characteristic data corresponding to the load analysis sub-elements;
generating target predicted load data corresponding to each load analysis subelement by using the reference predicted load data, the meteorological factor correction coefficient, the planned maintenance factor correction coefficient and the social characteristic factor correction coefficient;
and generating a load change trend prediction result by combining the target prediction load data corresponding to each load analysis sub-element.
Optionally, the step of dividing the load peak-valley period according to the load variation trend prediction result, and determining the voltage control optimization strategy according to the load variation trend corresponding to the load peak-valley period includes:
dividing the load change trend prediction result according to the peak-valley characteristics of the load to generate a plurality of load peak-valley time periods;
and optimizing a preset automatic voltage control strategy according to the load change trend corresponding to the load peak-valley period to generate a voltage control optimization strategy.
Optionally, the step of generating a voltage regulation and control instruction according to the voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction includes:
generating a corresponding remote control instruction file according to the voltage control optimization strategy;
generating a voltage regulation and control instruction according to the remote control instruction file;
and implementing voltage regulation according to the voltage regulation instruction.
Optionally, the method further comprises:
calculating a load difference between the target predicted load data and the actual load data;
and adjusting the coefficient value taking table according to the load difference.
The invention provides a power grid automatic voltage regulation and control system based on multi-source data load analysis in a second aspect, which comprises:
the multi-source data module is used for responding to the received voltage regulation and control request and acquiring multi-source data corresponding to the voltage regulation and control request;
the power grid load change trend prediction model module is used for establishing a power grid load change trend prediction model according to the multi-source data;
the load change trend prediction result module is used for predicting load data corresponding to each load analysis sub-element through the power grid load change trend prediction model to generate a load change trend prediction result;
the voltage control optimization strategy module is used for dividing load peak-valley time periods according to the load change trend prediction result and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods;
and the voltage regulation and control module is used for generating a voltage regulation and control instruction according to the voltage control optimization strategy and implementing voltage regulation and control according to the voltage regulation and control instruction.
The electronic device provided by the third aspect of the present invention includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for regulating and controlling automatic voltage of a power grid based on multi-source data load analysis according to any one of the above-mentioned items.
A fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed, implements the method for automatic voltage regulation and control of a power grid based on multi-source data load analysis according to any one of the above.
According to the technical scheme, the invention has the following advantages:
the method comprises the steps of responding to a received voltage regulation request, and obtaining multi-source data corresponding to the voltage regulation request; establishing a power grid load change trend prediction model according to the multi-source data; load data corresponding to each load analysis subelement is predicted through a power grid load change trend prediction model, and a load change trend prediction result is generated; dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods; and generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction. The technical problems that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time, and even the frequent regulation of reactive equipment possibly occurs due to the normal fluctuation of load, so that the equipment and even the power grid safety are influenced are solved. According to the voltage regulation and control method and system based on the multisource data load analysis decision, fusion of an accurate load change trend and an automatic voltage control strategy is achieved, the automatic voltage regulation and control strategy of a power grid is optimized, unnecessary operation of equipment is avoided, the service life of the equipment is prolonged, and the stability and the economical efficiency of automatic voltage control of a power system are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a method for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a method for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis according to a second embodiment of the present invention;
fig. 3 is a block diagram of a structure of an automatic power grid voltage regulation and control system based on multi-source data load analysis according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a power grid automatic voltage regulation and control method and system based on multi-source data load analysis, which are used for solving the technical problems that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time, and even frequent regulation of reactive equipment possibly occurs due to normal fluctuation of load, so that the safety of the equipment and even the power grid is influenced.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below 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.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a method for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis according to an embodiment of the present invention.
The invention provides a power grid automatic voltage regulation and control method based on multi-source data load analysis, which comprises the following steps of:
101. and responding to the received voltage regulation and control request, and acquiring multi-source data corresponding to the voltage regulation and control request.
It should be noted that the multi-source data includes historical load data, historical meteorological data, planned maintenance data, and social characteristic data.
And acquiring corresponding historical load data and scheduled maintenance data through the power grid operation monitoring system and the power grid operation management system.
And acquiring historical meteorological data by connecting with a meteorological monitoring and predicting system.
And obtaining social characteristic data by connecting with a big data center or a mobile operator.
In the embodiment of the invention, corresponding historical load data, historical meteorological data, planned maintenance data and social characteristic data are obtained by responding to the received voltage regulation and control request.
102. And establishing a power grid load change trend prediction model according to the multi-source data.
The power grid load change trend prediction model refers to a prediction model established by collecting all historical load data, historical meteorological data, planned maintenance data and social characteristic data.
In the embodiment of the invention, all historical load data, historical meteorological data, scheduled maintenance data and social characteristic data are divided according to a certain time node and spatial characteristics, the divided historical load data, historical meteorological data, scheduled maintenance data, social characteristic data and a plurality of load analysis sub-elements are correlated, and all the load analysis sub-elements are aggregated to establish a power grid load change trend prediction model.
103. And predicting load data corresponding to each load analysis sub-element through the power grid load change trend prediction model to generate a load change trend prediction result.
It should be noted that the load analysis sub-element has a data storage, and can store the related data of the load analysis sub-element.
The load change trend prediction result refers to the predicted load change trend.
In a specific embodiment, load data corresponding to each load analysis subelement is predicted through a power grid load change trend prediction model, and a prediction result of load trend increase and load trend decrease is obtained by forming a curve by all load data points.
104. And dividing the load peak-valley time period according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time period.
It should be noted that the load peak-valley period refers to a peak period and a valley period.
The load variation tendency refers to an increase in load tendency or a decrease in load tendency.
The voltage control optimization strategy refers to a voltage control optimization strategy obtained by optimizing on the basis of an automatic voltage control strategy based on voltage change.
In a specific embodiment, a load peak time period or a load valley time period is divided by a prediction curve generated by a load change trend prediction result, and an optimal adjustment direction of reactive equipment is determined according to a load change trend of a corresponding time period so as to achieve optimization of automatic voltage regulation and control, thereby generating a voltage control optimization strategy.
105. And generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction.
The voltage regulation command refers to a control command for regulating the voltage.
In a specific embodiment, a corresponding voltage regulation and control instruction is generated according to a voltage control optimization strategy, and voltage regulation and control operation is implemented according to the voltage regulation and control instruction.
The method comprises the steps of responding to a received voltage regulation request, and obtaining multi-source data corresponding to the voltage regulation request; establishing a power grid load change trend prediction model according to multi-source data; load data corresponding to each load analysis sub-element is predicted through a power grid load change trend prediction model, and a load change trend prediction result is generated; dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods; and generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction. The technical problems that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time, and even the frequent regulation of reactive equipment possibly occurs due to the normal fluctuation of load, so that the equipment and even the power grid safety are influenced are solved. According to the voltage regulation and control method and system based on the multisource data load analysis decision, fusion of an accurate load change trend and an automatic voltage control strategy is achieved, the automatic voltage regulation and control strategy of a power grid is optimized, unnecessary operation of equipment is avoided, the service life of the equipment is prolonged, and the stability and the economical efficiency of automatic voltage control of a power system are improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a method for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis according to a second embodiment of the present invention.
The invention provides a power grid automatic voltage regulation and control method based on multi-source data load analysis, which comprises the following steps of:
201. and responding to the received voltage regulation and control request, and acquiring historical load data, historical meteorological data, planned maintenance data and social characteristic data corresponding to the voltage regulation and control request.
In a specific embodiment, historical load data such as historical current and power of a power grid are acquired by butting a storage database of a power grid dispatching automation system.
By connecting with a power grid meteorological system, the forecasting conditions of factors influencing load change, such as temperature, rainfall and the like, are acquired. Specifically, the load prediction system and the meteorological system develop a data interface, a meteorological data file sent to the load prediction system by the meteorological system is received regularly through the data interface, the system analyzes the meteorological data file, and the meteorological data corresponding to geographic coordinates and the coordinates of the power grid equipment are obtained to be associated, so that the meteorological data influencing the load change factors are obtained.
And acquiring the power grid maintenance and outage equipment and the maintenance time interval corresponding to the equipment object by butting a planned maintenance management module of the power grid operation management system.
By connecting a large data center, people stream data is acquired according to regions and time periods, and regional life coefficients are acquired according to regional development levels. Specifically, an area is defined according to the load forecasting objects, historical loads, people flow data and load characteristics of corresponding time periods are obtained, and the corresponding area life coefficients are obtained by integrating the condition inversion of the multiple load forecasting objects. The historical load mainly refers to current and power at corresponding moment and comes from stored data of a dispatching automation system. The people flow data mainly refers to the number of people flows at the corresponding moment and comes from a large data center of an operator. The load characteristics are determined according to the main electricity utilization properties of the region, such as rural areas, towns, industrial parks and the like. The mobile operator is required to provide the data for importing the streaming data without a direct data interface.
202. And establishing a power grid load change trend prediction model according to the multi-source data.
Optionally, step 202 comprises the following steps S11-S14:
s11, converting data formats respectively corresponding to the historical load data, the historical meteorological data, the planned overhaul data and the social characteristic data into load prediction data formats, and generating updated historical load data, updated historical meteorological data, updated planned overhaul data and updated social characteristic data;
s12, establishing a plurality of load analysis sub-elements based on a plurality of time nodes and space characteristics corresponding to the time characteristics;
s13, correlating and updating historical load data, historical meteorological data, planned maintenance data, social characteristic data and corresponding load analysis sub-elements, and respectively inputting the data into a data memory corresponding to each load analysis sub-element;
and S14, establishing a power grid load change trend prediction model by adopting all load analysis sub-elements.
It should be noted that each system data file has its own format, and data association is first implemented for comprehensive analysis and collected into a unified model. Therefore, the data formats corresponding to the historical load data, the historical meteorological data, the scheduled maintenance data and the social characteristic data are required to be converted into a unified load prediction data format, so as to generate updated historical load data, updated historical meteorological data, updated scheduled maintenance data and updated social characteristic data.
Specifically, a certain power grid 10kV line power supply area is used as a load prediction object and is used as a model base, and the model can directly obtain historical loads of the equipment-associated power grid; the meteorological data file has longitude and latitude coordinates of monitoring points, and related meteorological data can be associated to the load prediction object model by associating the coordinates of the monitoring points with the coordinates of equipment on a 10kV power grid line; the method comprises the steps that people flow data are divided mainly according to administrative regions, and the people flow data are related to corresponding load prediction object models by detecting that the administrative regions to which the people flow data belong are related to power supply administrative regions of a power grid 10kV line; and through data association and conversion, a data format of the power grid load change trend prediction modeling corresponding to the prediction object is formed.
The time characteristics refer to time nodes corresponding to the load analysis sub-elements; the spatial characteristics refer to spatial positions such as position coordinates or area coordinates or device coordinates corresponding to the load analysis sub-elements.
In a specific embodiment, historical load data, historical meteorological data, planned overhaul data and social characteristic data are collected, effective data are screened out, data formats corresponding to the screened historical load data, historical meteorological data, planned overhaul data and social characteristic data are converted into a unified load prediction data format, and the data are conveniently correlated.
And establishing a plurality of load analysis sub-elements according to the time nodes corresponding to the time characteristics and the coordinates corresponding to the space characteristics, associating the load analysis sub-elements according to each time node and each updated historical load data, updated historical meteorological data, updated scheduled maintenance data and updated social characteristic data in the coordinate range corresponding to the time node, and respectively inputting the load analysis sub-elements into the data memory of each load analysis sub-element.
On the basis of the time characteristics and the space characteristics, all load analysis sub-elements are aggregated, and a power grid load change trend prediction model is established.
203. And predicting load data corresponding to each load analysis subelement through a power grid load change trend prediction model to generate a load change trend prediction result.
Optionally, step 203 comprises the following steps S21-S24:
determining reference predicted load data corresponding to each load analysis subelement according to updated historical load data, updated social characteristic data and a preset load increase coefficient corresponding to each load analysis subelement corresponding to the power grid load change trend prediction model;
determining a meteorological factor correction coefficient, a planned maintenance factor correction coefficient and a social characteristic factor correction coefficient corresponding to each load analysis subelement according to the updated historical meteorological data, the updated planned maintenance data and the updated social characteristic data corresponding to the preset coefficient value taking table and the load analysis subelement;
generating target predicted load data corresponding to each load analysis sub-element by adopting the reference predicted load data, the meteorological factor correction coefficient, the planned maintenance factor correction coefficient and the social characteristic factor correction coefficient;
and generating a load change trend prediction result by combining the target prediction load data corresponding to each load analysis sub-element.
It should be noted that the initial value of the increase coefficient is obtained mainly by analyzing the load increase condition through manual experience. For example, in the residential electricity utilization area, the residential electricity load is mainly used, the number of the permanent population is increased, the electricity load is increased in proportion, and the load increase coefficient is determined according to the increase proportion. For example, the industrial power utilization area corresponds to the increase of the area factory building and the scale, the power utilization load is increased according to the proportion, and the load increase coefficient is determined according to the increase proportion.
Specifically, the preset load increase coefficient may be understood as a load prediction coefficient that is preliminarily set to be increased according to the electricity consumption object.
In a specific embodiment, the load change trend analysis is performed in units of each load sub-element. Load trend analysis to historical load data x o Taking the increase coefficient y into consideration according to the local economic level corresponding to the updated social characteristic data as a reference 0 To obtain the child elementReference predicted load (x) of element o *y 0 )。
Considering the factors influencing the load change, according to the updated historical meteorological data, the updated scheduled maintenance data and the updated social characteristic data associated with each load sub-element corresponding to the power grid load change trend prediction model, and the coefficient value-taking algorithm corresponding to the change factors obtains the meteorological factor correction coefficient y 1 Correcting coefficient y of planned maintenance factor 2 And a social characteristic factor correction coefficient y 3 And calculating to obtain target predicted load data through the reference predicted load data, the meteorological factor correction coefficient, the planned maintenance factor correction coefficient and the social characteristic factor correction coefficient.
Specifically, a power supply area of a power grid 10kV line is used as a load prediction object, a data model corresponding to the prediction object is established, model-associated historical load, meteorological information, overhaul information and people flow data are obtained, and a meteorological factor correction coefficient y is obtained by looking up a coefficient value taking table obtained by continuously inverting a reference model by a system 1 Correcting coefficient y of planned maintenance factor 2 Social characteristic factor correction coefficient y 3 Finally according to the calculation formula x 1 ＝(x o *y 0 )*y 1 *y 2 *y 3 And calculating to obtain a final result.
Further, a set of target predicted load data of the sub-elements is analyzed according to the loads of a plurality of continuous time nodes of the load node of the specific power grid equipment, and a load change trend prediction result of the power grid equipment can be formed.
204. And dividing the load peak-valley time period according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time period.
Optionally, step 205 comprises the following steps S31-S32:
s31, dividing the load change trend prediction result according to the peak-valley characteristics of the load to generate a plurality of load peak-valley time periods;
and S32, optimizing a preset automatic voltage control strategy according to the load change trend corresponding to the load peak-valley period to generate a voltage control optimization strategy.
It should be noted that the load peak-valley period refers to a period of 24 hours per day divided into a peak period, a normal period and a valley period according to the load change condition of the power grid. And obtaining target predicted load data corresponding to each time node according to the power grid load change trend prediction model, and connecting the target predicted load data to generate a corresponding load change trend curve.
In the embodiment of the invention, on the basis of an Automatic Voltage Control (AVC) control strategy based on voltage change, a plurality of load peak-valley periods are divided by utilizing the peak-valley characteristics of the load and combining with a load change trend curve, and the optimal regulation direction of reactive equipment is determined according to the load change trend of the corresponding period so as to achieve the optimization of automatic voltage regulation and control. The specific adjustment means are shown in the following table (table 1):
| tendency of load | Variation of voltage | Means of adjustment |
| Rise up | Descend | Throw electric capacity, cut reactance, up-regulation tap |
| Descend | Rise up | Cut electric capacity, throw reactance, down regulation tap |
TABLE 1
Specifically, as shown in table 1, when the voltage is in a decreasing state and the load analysis shows an increasing trend, adjustment means such as a capacitor, a cut-off reactor, and an upper-regulation transformer tap are used.
And for the condition that the voltage is in a descending change and the load analysis shows an ascending change trend, adjusting means such as cutting off a capacitor, putting in a reactor and lowering down a transformer tap are adopted.
205. And generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction.
Optionally, step 205 comprises the following steps S41-S43:
s41, generating a corresponding remote control instruction file according to a voltage control optimization strategy;
s42, generating a voltage regulation and control instruction according to the remote control instruction file;
and S43, voltage regulation is implemented according to the voltage regulation instruction.
It should be noted that the remote control instruction file refers to an instruction file for making a corresponding remote control according to a voltage control optimization strategy.
The voltage regulation instruction refers to an instruction for regulating and controlling the voltage of reactive power regulation equipment such as a capacitor, a reactor and a transformer.
In a specific embodiment, a corresponding remote control instruction file is generated according to the generated voltage regulation and control optimization strategy, the remote control instruction file is in butt joint with a power grid operation management system, voltage regulation and control instructions of reactive power regulation equipment such as a capacitor, an electric reactor and a transformer corresponding to a regional power grid are issued, and the voltage regulation and control optimization strategy is actually executed.
Optionally, the method further comprises the following steps S51-S52:
s51, calculating a load difference value between the target predicted load data and the actual load data;
and S52, adjusting the coefficient value taking table according to the load difference.
In a specific embodiment, the correction coefficient value-taking algorithm is mainly determined by inversion according to a plurality of prediction model data. For example, a power grid 10kV line power supply area is used as a load prediction reference object. The method comprises the steps of obtaining conditions of discontinuous reference objects in different time periods, weather, planned maintenance, people flow and the like in a historical state, inverting and resolving a coefficient value taking table corresponding to various correction conditions through a plurality of models by a computer program, comparing a continuous load prediction result with an actual result according to the coefficient value taking table, continuously correcting, and continuously improving load prediction accuracy.
The method comprises the steps of responding to a received voltage regulation request, and obtaining multi-source data corresponding to the voltage regulation request; establishing a power grid load change trend prediction model according to the multi-source data; load data corresponding to each load analysis sub-element is predicted through a power grid load change trend prediction model, and a load change trend prediction result is generated; dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods; and generating a voltage regulation and control instruction according to a voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction. The technical problems that the existing automatic voltage control technology cannot improve the voltage and power factor indexes in time, and even the frequent regulation of reactive equipment possibly occurs due to the normal fluctuation of load, so that the equipment and even the power grid safety are influenced are solved. According to the voltage regulation and control method and system based on the multisource data load analysis decision, fusion of an accurate load change trend and an automatic voltage control strategy is achieved, the automatic voltage regulation and control strategy of a power grid is optimized, unnecessary operation of equipment is avoided, the service life of the equipment is prolonged, and the stability and the economical efficiency of automatic voltage control of a power system are improved.
Referring to fig. 3, fig. 3 is a block diagram of a power grid automatic voltage regulation and control system based on multi-source data load analysis according to a third embodiment of the present invention.
The invention provides a power grid automatic voltage regulation and control system based on multi-source data load analysis, which comprises:
the multi-source data module 301 is configured to respond to the received voltage regulation request and obtain multi-source data corresponding to the voltage regulation request;
the power grid load change trend prediction model module 302 is used for establishing a power grid load change trend prediction model according to the multi-source data;
the load change trend prediction result module 303 is configured to predict load data corresponding to each load analysis sub-element through the power grid load change trend prediction model, and generate a load change trend prediction result;
the voltage control optimization strategy module 304 is configured to divide the load peak-valley period according to the load change trend prediction result, and determine a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley period;
and the voltage regulation and control module 305 is configured to generate a voltage regulation and control instruction according to the voltage control optimization strategy, and implement voltage regulation and control according to the voltage regulation and control instruction.
Optionally, the multi-source data module 301 includes:
and the multi-source data submodule is used for responding to the received voltage regulation and control request and acquiring historical load data, historical meteorological data, planned maintenance data and social characteristic data corresponding to the voltage regulation and control request.
Optionally, the power grid load trend prediction model module 302 includes:
the load analysis subelement submodule is used for establishing a plurality of load analysis subelements based on a plurality of time nodes and space characteristics corresponding to the time characteristics;
the data memory submodule is used for correlating and updating historical load data, historical meteorological data, planned maintenance data, social characteristic data and corresponding load analysis subelements and respectively inputting the data memory corresponding to each load analysis subelement;
and the power grid load change trend prediction model submodule is used for establishing a power grid load change trend prediction model by adopting all the load analysis subelements.
Optionally, the load trend prediction result module 303 includes:
the standard predicted load data submodule is used for determining standard predicted load data corresponding to each load analysis subelement according to updated historical load data, updated social characteristic data and a preset load increase coefficient corresponding to each load analysis subelement corresponding to the power grid load change trend prediction model;
the correction coefficient submodule is used for determining a meteorological factor correction coefficient, a planned maintenance factor correction coefficient and a social characteristic factor correction coefficient corresponding to each load analysis subelement according to the updated historical meteorological data, the updated planned maintenance data and the updated social characteristic data corresponding to the preset coefficient value taking table and the load analysis subelement;
the target predicted load data submodule is used for generating target predicted load data corresponding to each load analysis subelement by adopting the reference predicted load data, the meteorological factor correction coefficient, the planned maintenance factor correction coefficient and the social characteristic factor correction coefficient;
and the load change trend prediction result submodule is used for generating a load change trend prediction result by combining the target prediction load data corresponding to each load analysis subelement.
Optionally, the voltage control optimization strategy module 304 includes:
the load peak-valley time period submodule is used for dividing the load change trend prediction result according to the peak-valley characteristics of the load to generate a plurality of load peak-valley time periods;
and the voltage control optimization strategy submodule is used for optimizing a preset automatic voltage control strategy according to the load change trend corresponding to the load peak-valley period to generate a voltage control optimization strategy.
Optionally, the voltage regulation module 305 includes:
the remote control instruction file submodule is used for generating a corresponding remote control instruction file according to a voltage control optimization strategy;
the voltage regulation and control instruction submodule is used for generating a voltage regulation and control instruction according to the remote control instruction file;
and the voltage regulation and control submodule is used for implementing voltage regulation and control according to the voltage regulation and control instruction.
Optionally, the system further comprises:
the load difference submodule is used for calculating the load difference between the target predicted load data and the actual load data;
and the coefficient value-taking table submodule is used for adjusting the coefficient value-taking table according to the load difference value.
The fourth embodiment of the invention further provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program; when executed by the processor, the computer program causes the processor to execute the steps of the method for automatic voltage regulation and control of a power grid based on multi-source data load analysis according to any one of the embodiments.
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the method for automatically regulating and controlling voltage of a power grid based on multi-source data load analysis according to any one of the above embodiments is implemented.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A power grid automatic voltage regulation and control method based on multi-source data load analysis is characterized by comprising the following steps:
responding to a received voltage regulation and control request, and acquiring multi-source data corresponding to the voltage regulation and control request;
establishing a power grid load change trend prediction model according to the multi-source data;
load data corresponding to each load analysis sub-element is predicted through the power grid load change trend prediction model, and a load change trend prediction result is generated;
dividing load peak-valley time periods according to the load change trend prediction result, and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods;
and generating a voltage regulation and control instruction according to the voltage control optimization strategy, and implementing voltage regulation and control according to the voltage regulation and control instruction.
2. The automatic voltage regulation and control method of power grid based on multi-source data load analysis of claim 1, wherein the multi-source data comprises historical load data, historical meteorological data, planned maintenance data and social characteristic data; the step of responding to the received voltage regulation and control request and acquiring multi-source data corresponding to the voltage regulation and control request comprises the following steps:
responding to the received voltage regulation and control request, and acquiring the historical load data, the historical meteorological data, the planned maintenance data and the social characteristic data corresponding to the voltage regulation and control request.
3. The method for automatically regulating and controlling the voltage of the power grid based on the multi-source data load analysis according to claim 2, wherein the step of establishing a power grid load change trend prediction model according to the multi-source data comprises the following steps:
converting data formats corresponding to the historical load data, the historical meteorological data, the planned overhaul data and the social characteristic data into load prediction data formats respectively to generate updated historical load data, updated historical meteorological data, updated planned overhaul data and updated social characteristic data;
establishing a plurality of load analysis sub-elements based on a plurality of time nodes and space characteristics corresponding to the time characteristics;
associating the updated historical load data, the updated historical meteorological data, the updated planned maintenance data, the updated social characteristic data and the corresponding load analysis sub-elements, and respectively inputting the data into a data memory corresponding to each load analysis sub-element;
and establishing a power grid load change trend prediction model by adopting all the load analysis sub-elements.
4. The method for automatically regulating and controlling the voltage of the power grid based on the multi-source data load analysis according to claim 1, wherein the step of predicting the load data corresponding to each load analysis sub-element through the power grid load change trend prediction model to generate a load change trend prediction result comprises the following steps:
determining reference predicted load data corresponding to each load analysis subelement according to updated historical load data, updated social characteristic data and a preset load increase coefficient corresponding to each load analysis subelement corresponding to the power grid load change trend prediction model;
determining a meteorological factor correction coefficient, a planned maintenance factor correction coefficient and a social characteristic factor correction coefficient corresponding to each load analysis sub-element according to a preset coefficient value taking table and updated historical meteorological data, updated planned maintenance data and updated social characteristic data corresponding to the load analysis sub-elements;
generating target predicted load data corresponding to each load analysis sub-element by adopting the reference predicted load data, the meteorological factor correction coefficient, the planned maintenance factor correction coefficient and the social characteristic factor correction coefficient;
and generating a load change trend prediction result by combining the target prediction load data corresponding to each load analysis subelement.
5. The method for automatically regulating and controlling the voltage of the power grid based on the multi-source data load analysis according to claim 1, wherein the step of dividing the load peak-valley period according to the load change trend prediction result and determining the voltage control optimization strategy according to the load change trend corresponding to the load peak-valley period comprises the following steps:
dividing the load change trend prediction result according to the peak-valley characteristics of the load to generate a plurality of load peak-valley time periods;
and optimizing a preset automatic voltage control strategy according to the load change trend corresponding to the load peak-valley period to generate a voltage control optimization strategy.
6. The method for automatically regulating and controlling the voltage of the power grid based on the multi-source data load analysis according to claim 1, wherein the step of generating a voltage regulation and control instruction according to the voltage control optimization strategy and implementing the voltage regulation and control according to the voltage regulation and control instruction comprises the following steps:
generating a corresponding remote control instruction file according to the voltage control optimization strategy;
generating a voltage regulation and control instruction according to the remote control instruction file;
and implementing voltage regulation according to the voltage regulation instruction.
7. The automatic voltage regulation and control method of power grid based on multi-source data load analysis according to claim 4, further comprising:
calculating a load difference between the target predicted load data and the actual load data;
and adjusting the coefficient value taking table according to the load difference.
8. The utility model provides an automatic voltage regulation and control system of electric wire netting based on multisource data load analysis which characterized in that includes:
the multi-source data module is used for responding to the received voltage regulation and control request and acquiring multi-source data corresponding to the voltage regulation and control request;
the power grid load change trend prediction model module is used for establishing a power grid load change trend prediction model according to the multi-source data;
the load change trend prediction result module is used for predicting load data corresponding to each load analysis sub-element through the power grid load change trend prediction model to generate a load change trend prediction result;
the voltage control optimization strategy module is used for dividing load peak-valley time periods according to the load change trend prediction result and determining a voltage control optimization strategy according to the load change trend corresponding to the load peak-valley time periods;
and the voltage regulation and control module is used for generating a voltage regulation and control instruction according to the voltage control optimization strategy and implementing voltage regulation and control according to the voltage regulation and control instruction.
9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the method for automatic voltage regulation of a power grid based on multi-source data load analysis according to any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed, implements the method for automatic voltage regulation of a power grid based on multi-source data load analysis according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117134366A (en) * | 2023-10-27 | 2023-11-28 | 南方电网数字电网研究院有限公司 | Load control method, device, equipment and storage medium |
| CN117955110A (en) * | 2024-03-26 | 2024-04-30 | 国网甘肃省电力公司武威供电公司 | Auxiliary optimization method for innovative power system load regulation and control |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117134366A (en) * | 2023-10-27 | 2023-11-28 | 南方电网数字电网研究院有限公司 | Load control method, device, equipment and storage medium |
| CN117134366B (en) * | 2023-10-27 | 2024-02-23 | 南方电网数字电网研究院有限公司 | Load control method, device, equipment and storage medium |
| CN117955110A (en) * | 2024-03-26 | 2024-04-30 | 国网甘肃省电力公司武威供电公司 | Auxiliary optimization method for innovative power system load regulation and control |
| CN117955110B (en) * | 2024-03-26 | 2024-05-31 | 国网甘肃省电力公司武威供电公司 | Auxiliary optimization method for innovative power system load regulation and control |
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