CN117154945A - Intelligent monitoring method for transformer and distribution station based on cloud computing technology - Google Patents
Intelligent monitoring method for transformer and distribution station based on cloud computing technology Download PDFInfo
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Classifications
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
- H02B1/00—Frameworks, boards, panels, desks, casings; Details of substations or switching arrangements
- H02B1/24—Circuit arrangements for boards or switchyards
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
Abstract
The invention relates to the technical field of intelligent monitoring. The invention relates to an intelligent monitoring method for a transformer and distribution substation based on a cloud computing technology. Which comprises the following steps: a cloud computing platform is built, and operation data of the transformer and distribution stations are collected; the cloud computing platform extracts historical operation data from the operation data, and predicts future operation data of the transformer and distribution station according to the development trend of the historical operation data; performing intelligent fault detection according to the predicted data and the historical operation data; carrying out load fluctuation analysis on the operation of the substation according to the predicted data; according to the invention, the future operation data of the power transformation and distribution station is predicted, the corresponding operation mode is regulated according to different load fluctuation control power transformation and distribution station, the situation that the power supply is excessive or insufficient in the state that the same operation mode of the power transformation and distribution station faces different power loads is avoided, the operation of an enterprise is unstable, and the guarantee of the operation of the enterprise is improved.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring method for a transformer and distribution substation based on a cloud computing technology.
Background
The power transformation and distribution station is a hub of an enterprise transmission and distribution network, the safety state of the power transformation and distribution station is the basis of the safe operation of the whole power grid system, but in the operation process, if a problem occurs and fault maintenance is carried out, the power utilization of the enterprise is required to be suspended, the power utilization of the enterprise is suspended, the data transmission is interrupted, the enterprise operation is lost, meanwhile, the power required by the operation of the enterprise in different periods is different, the loaded state of the power transformation and distribution station is different, the power supply is excessive or insufficient in the state that the same operation mode of the power transformation and distribution station faces different power loads, and the operation of the enterprise is unstable, so that the intelligent monitoring method of the power transformation and distribution station based on the cloud computing technology is provided.
Disclosure of Invention
The invention aims to provide an intelligent substation monitoring method based on a cloud computing technology, so as to solve the problems in the background technology.
In order to achieve the above purpose, the intelligent monitoring method of the transformer and distribution station based on the cloud computing technology comprises the following steps:
s1, a cloud computing platform is built, and operation data of a power transformation and distribution station are collected;
s2, the cloud computing platform extracts historical operation data from the operation data, and predicts future operation data of the transformer and distribution substation according to the development trend of the historical operation data;
s3, performing intelligent fault detection according to the data predicted by the S2 and the historical operation data;
s4, carrying out load fluctuation analysis on the operation of the power transformation and distribution station according to the data predicted in the S2, and obtaining an intelligent control scheme for different load fluctuation of the power transformation and distribution station;
and S5, monitoring operation data by the cloud computing platform, and intelligently controlling the power transformation and distribution station according to a monitoring result and an intelligent control scheme.
As a further improvement of the technical scheme, for the time series operation data, S1 selects OpenStack cloud platform software to build a cloud computing platform.
As a further improvement of the technical scheme, the S1 is used for acquiring the current and the voltage of the power equipment of the substation in real time by deploying a data collection sensor comprising a current sensor and a voltage sensor at the substation.
As a further improvement of the technical scheme, the step of predicting the future operation state of the substation by S2 is as follows:
s2.1, extracting historical current and voltage data of the transformer and distribution substation from operation data;
s2.2, acquiring a future operation decision of the power transformation and distribution station;
s2.3, an intelligent prediction model is built, and the historical current and voltage data extracted in the step S2.1 are input into the intelligent prediction model by combining with future operation decisions of the step S2.2;
s2.4, the intelligent prediction model outputs future operation prediction data of the transformer and distribution substation.
As a further improvement of the technical scheme, the step S3 is as follows according to the steps of performing intelligent fault detection:
s3.1, extracting a load state of current and voltage in normal operation of the transformer and distribution substation in historical operation data;
and S3.2, extracting the load state of the time-varying power distribution station operated by the same current and voltage as the current and voltage in the step S3.1 from the future operation prediction data, carrying out intelligent fault detection by combining the load state extracted in the step S3.1, and sending a fault overhaul notification to the time-varying power distribution station through a cloud computing platform according to the detection result.
As a further improvement of the technical scheme, the step S3 further comprises the steps of detecting and comparing the operation data with the future operation prediction data, sending a fault maintenance notification to the power transformation and distribution station through the cloud computing platform when the data difference is more than five percent, collecting feedback data after the fault maintenance of workers, and correcting the future operation data.
As a further improvement of the technical scheme, the step of S4 obtaining the intelligent control scheme which is different from the transformer and distribution station and accords with fluctuation is as follows:
s4.1, extracting load fluctuation data and distribution station operation states in the operation of the transformer and the distribution station from future operation prediction data;
s4.2, screening the running state of the strain distribution station according to different fluctuation amplitude combinations of the load fluctuation data, and obtaining a fluctuation amplitude threshold value affecting the strain distribution station;
and S4.3, carrying out parameter analysis on load fluctuation data exceeding a fluctuation amplitude threshold by combining the power substation, and obtaining an intelligent control scheme for adjusting the operation parameters of the power substation when the fluctuation amplitude exceeds the threshold.
As a further improvement of the technical scheme, the step S4 further includes the following steps of adjusting the substation operation mode of the substation:
s4.4, collecting weather forecast data, extracting consumption states of different operation modes of the power transformation and distribution station under different weather by combining the operation data, screening the different operation modes of the power transformation and distribution station under different weather according to the consumption states, obtaining an operation mode with the lowest consumption state under the corresponding weather, and sending the operation mode to the cloud computing platform.
As a further improvement of the technical scheme, the step of intelligently controlling the transformer and distribution station in S5 is as follows:
and S5.1, the cloud computing platform monitors the operation data according to the fluctuation amplitude threshold value acquired in the step S4.2, when the fluctuation amplitude of the motion data is larger than the fluctuation amplitude threshold value, the substation is adjusted according to the intelligent control scheme acquired in the step S4.3, otherwise, if the fluctuation amplitude is smaller than the fluctuation amplitude threshold value, the monitoring is kept continuously.
Compared with the prior art, the invention has the beneficial effects that:
according to the cloud computing technology-based intelligent monitoring method for the transformer substation, future operation data of the transformer substation are predicted, corresponding operation modes are adjusted according to different load fluctuation control transformer substation, the situation that the transformer substation is in a state of facing different power loads in the same operation mode is avoided, energy supply is excessive or insufficient, enterprise operation is unstable, the operation assurance of the enterprise is improved, meanwhile, intelligent fault detection is conducted on the predicted data, fault maintenance is avoided when problems occur, power consumption of the enterprise is required to be suspended, data transmission is interrupted due to suspended power consumption of the enterprise, and enterprise operation loss is caused.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a block flow diagram of predicting future operational data of a substation in accordance with the present invention;
FIG. 3 is a flow chart diagram of the intelligent fault detection of the present invention;
fig. 4 is a flow chart of the intelligent control scheme for acquiring different load fluctuation of the transformer and distribution station.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1-4, a cloud computing technology-based intelligent substation monitoring method is provided, which comprises the following steps:
s1, a cloud computing platform is built, and operation data of a power transformation and distribution station are collected;
and S1, selecting OpenStack cloud platform software for building a cloud computing platform for the time sequence operation data.
The method comprises the following steps:
installation and configuration of OpenStack: according to the OpenStack official document, a proper version is selected, and the installation and configuration are performed according to the instruction. The process involves installation of an operating system, network configuration, database configuration, setting of computing nodes and storage nodes, etc.;
virtualized environment configuration: creating virtual machine instances for time-series data processing tasks using virtualization techniques (e.g., KVM) provided by OpenStack to utilize computing resources for data processing;
storing configuration: configuring a storage module of OpenStack, such as a Cinder (block storage), a Swift (object storage) and the like, so as to meet the storage requirement of time series data;
network configuration: using OpenStack to perform network configuration, and ensuring communication of time sequence data between virtual machines and connection with an external network;
deploying a time series data processing framework: corresponding software and tools are deployed on OpenStack according to the time-series data processing framework (e.g., spark, tensorFlow, etc.) you choose.
The S1 is used for acquiring the current and the voltage of the power equipment of the power substation in real time by deploying a data collection sensor comprising a current sensor and a voltage sensor at the power substation.
A current sensor: the current value of the power device is measured by a current sensor. The current sensor may be a non-contact current clamp sensor, a hall effect sensor, or the like.
A voltage sensor: the voltage value of the electrical device is measured by a voltage sensor. The voltage sensor may employ a voltage transformer or voltage divider or the like.
S2, the cloud computing platform extracts historical operation data from the operation data, and predicts future operation data of the transformer and distribution substation according to the development trend of the historical operation data;
the step of predicting the future operation state of the substation by the S2 is as follows:
s2.1, extracting historical current and voltage data of the transformer and distribution substation from operation data; and marking the transmitted voltage data and current data by combining the time nodes, and extracting the marked data.
S2.2, acquiring a future operation decision of the power transformation and distribution station; the method comprises the following steps:
data collection and analysis: historical operation data of the transformer and distribution substation is collected, wherein the historical operation data comprise key indexes such as current, voltage and load quantity. Carrying out statistics and trend analysis on historical data by utilizing a data analysis technology, finding rules and modes, and knowing the operation characteristics of the transformer and distribution station;
risk assessment and decision making: by performing risk assessment on future prediction results, possible risks and problems are assessed in consideration of the influence of various situations and factors. Based on the evaluation result, making corresponding operation decisions such as upgrading equipment, adjusting operation modes, optimizing network topology and the like;
s2.3, an intelligent prediction model is built, and the historical current and voltage data extracted in the step S2.1 are input into the intelligent prediction model by combining with future operation decisions of the step S2.2;
establishing a prediction model: based on historical data and load prediction, a prediction model is established to predict conditions such as load change, voltage and current fluctuation and the like of the transformer and distribution substation in the future. A commonly used prediction method is time series analysis;
s2.4, the intelligent prediction model outputs future operation prediction data of the transformer and distribution substation.
S3, performing intelligent fault detection according to the data predicted by the S2 and the historical operation data;
the step S3 is based on the steps of performing intelligent fault detection:
s3.1, extracting a load state of current and voltage in normal operation of the transformer and distribution substation in historical operation data; the method comprises the following steps:
and (3) data characteristic extraction: load status characteristics of current and voltage are extracted from the operational data. The average value, the maximum value, the minimum value or the peak value and the like of each time point can be calculated to represent the load states of the current and the voltage;
calibrating a load state: according to the operation rule and characteristics of the transformer substation, a certain threshold value or standard is set, and the extracted load state characteristics are classified, such as normal load, high load or low load and the like;
data analysis and visualization: and analyzing and visually displaying the extracted load state by using a data analysis method and a tool. Statistical analysis, data mining, or machine learning algorithms may be used to study the relationship of load status to other factors;
interpretation and application of results: and according to the analysis result, explaining the load states of the current and the voltage in the historical operation data of the transformer substation. These results can be applied to evaluate the stability of the power system, optimize the operation plan, and formulate maintenance strategies;
and S3.2, extracting the load state of the time-varying power distribution station operated by the same current and voltage as the current and voltage in the step S3.1 from the future operation prediction data, carrying out intelligent fault detection by combining the load state extracted in the step S3.1, and sending a fault overhaul notification to the time-varying power distribution station through a cloud computing platform according to the detection result. The method comprises the following steps:
data collection and pretreatment: predicted values of current and voltage are collected from future operational prediction data of the substation. Preprocessing the collected data, such as removing abnormal values or noise, filling missing values, and the like;
load state extraction: and extracting the load state of the substation based on the predicted values of the current and the voltage. The load state can be divided into normal load, high load or low load according to a preset threshold value or a set rule;
fault intelligent detection: and performing association analysis on the extracted load state and the historical fault data or establishing a fault detection model by utilizing technologies such as data analysis, machine learning and the like. Detecting possible fault conditions in the power transformation and distribution station through a model;
and (3) notification transmission: and according to the fault detection result, sending a fault maintenance notification to the power transformation and distribution station through the cloud computing platform. The timeliness and reliability of overhaul notification can be ensured by utilizing a message service or notification mechanism provided by the cloud computing platform;
fault handling and recording: and carrying out corresponding fault processing and maintenance work according to the detected fault condition. At the same time, fault information and processes are recorded for subsequent analysis and improvement.
And S3, detecting and comparing the operation data with future operation prediction data, and when the data difference is more than five percent, sending a fault overhaul notification to the transformer and distribution station through the cloud computing platform, collecting feedback data after the fault overhaul of workers, and correcting the future operation data. The method comprises the following steps:
calculating a data difference value: the difference between current and voltage is calculated by comparing the operational data with future predicted data. The formula may be used:
data difference = actual value-predicted value/actual value 100%;
abnormality detection: judging that when the data difference exceeds a preset threshold (such as five percent), an abnormal condition is indicated, namely a fault maintenance notification is required to be sent;
and (3) notification transmission: and sending the fault overhaul notification to the power transformation and distribution station through the cloud computing platform. Message service or notification mechanism provided by the cloud computing platform can be used to ensure timeliness and reliability of notification;
troubleshooting and recording: and after the power transformation and distribution station receives the notification, performing fault maintenance work. Meanwhile, recording the specific condition and the processing process when the fault occurs;
and (3) feedback data collection: collecting feedback data after the trouble shooting of workers, wherein the feedback data comprise repair time, repair method, repair result and the like;
data correction: and correcting the future operation prediction data according to the feedback data. The actual repair situation can be applied to future operation prediction data by using a correction factor or a correction model, so that the prediction accuracy is improved.
S4, carrying out load fluctuation analysis on the operation of the power transformation and distribution station according to the data predicted in the S2, and obtaining an intelligent control scheme for different load fluctuation of the power transformation and distribution station;
the step of S4 obtaining the intelligent control scheme which accords with fluctuation to the power transformation and distribution station is as follows:
s4.1, extracting load fluctuation data and distribution station operation states in the operation of the transformer and the distribution station from future operation prediction data; the method comprises the following steps:
load fluctuation extraction: and calculating the fluctuation degree of the load of the power substation by analyzing the prediction data. Statistical indicators such as standard deviation or variance can be used to measure the variation amplitude of the load and extract fluctuation data;
and (3) extracting an operation state: based on the predicted data and the load fluctuation data, an operating state of the substation is extracted. The running state can be judged according to the degree and trend of load change, such as high load, low load, stability, change trend and the like;
data analysis and visualization: and analyzing and visually displaying the extracted load fluctuation data and the running state by using a data analysis method and a tool. Statistical analysis, time series analysis, data mining, etc. may be used to study the relationship between load fluctuations and operating conditions.
S4.2, screening the running state of the strain distribution station according to different fluctuation amplitude combinations of the load fluctuation data, and obtaining a fluctuation amplitude threshold value affecting the strain distribution station; the method comprises the following steps:
and (3) data collection: acquiring fluctuation amplitude data and corresponding running state information of the power transformation and distribution station from the load fluctuation data;
wave amplitude screening: and screening the fluctuation amplitude data according to the definition of the running state of the transformer and distribution substation. A determination is made as to which of the amplitudes of the fluctuations are considered to be thresholds affecting the operation of the substation.
And S4.3, carrying out parameter analysis on load fluctuation data exceeding a fluctuation amplitude threshold by combining the power substation, and obtaining an intelligent control scheme for adjusting the operation parameters of the power substation when the fluctuation amplitude exceeds the threshold. The method comprises the following steps:
parameter analysis: and analyzing the data exceeding the fluctuation amplitude threshold value and the operation parameters of the power transformation and distribution station. Determining which operating parameters are associated when the fluctuation amplitude exceeds a threshold;
the intelligent control scheme is designed: based on the result of the parameter analysis, an intelligent control scheme aiming at the operation parameters of the time-varying power distribution station with the fluctuation amplitude exceeding the threshold value is provided. According to different fluctuation conditions, the operation mode of the equipment can be adjusted, the load can be increased or reduced, the power supply configuration can be changed, and the like;
implementation and monitoring: the intelligent control scheme is applied to the power substation, and is implemented and monitored. And adjusting and optimizing according to actual conditions so as to improve the operation efficiency and stability of the power transformation and distribution station.
The step S4 further comprises the following steps of adjusting the substation operation mode of the substation:
s4.4, collecting weather forecast data, extracting consumption states of different operation modes of the power transformation and distribution station under different weather by combining the operation data, screening the different operation modes of the power transformation and distribution station under different weather according to the consumption states, obtaining an operation mode with the lowest consumption state under the corresponding weather, and sending the operation mode to the cloud computing platform. The method comprises the following steps:
weather forecast data acquisition: collecting weather forecast data from reliable weather forecast channels or services, wherein the weather forecast data comprise weather parameters such as temperature, humidity, wind speed and the like related to the operation of the substation;
and (3) collecting operation data: collecting actual operation data of a transformer substation, wherein the actual operation data comprise key indexes such as load, current, voltage and the like;
data preprocessing: preprocessing the collected weather forecast data and operation data, including data quality inspection, missing value processing and the like;
consumption state extraction: and extracting the consumption states of the transformer and distribution station under different days by combining weather forecast data and operation data. The consumption state may be determined based on load, energy consumption, or other relevant indicators;
operation mode screening: screening the operation modes of the power transformation and distribution station under different weather according to the consumption state, and searching the operation mode with the lowest consumption state under specific weather;
the result is sent to the cloud computing platform: and sending the operation mode result of the lowest consumption obtained by screening to the cloud computing platform, and realizing the operation mode result by using a message service or notification mechanism provided by the cloud computing platform.
And S5, monitoring operation data by the cloud computing platform, and intelligently controlling the power transformation and distribution station according to a monitoring result and an intelligent control scheme.
The step of intelligently controlling the power transformation and distribution station in S5 is as follows:
and S5.1, the cloud computing platform monitors the operation data according to the fluctuation amplitude threshold value acquired in the step S4.2, when the fluctuation amplitude of the motion data is larger than the fluctuation amplitude threshold value, the substation is adjusted according to the intelligent control scheme acquired in the step S4.3, otherwise, if the fluctuation amplitude is smaller than the fluctuation amplitude threshold value, the monitoring is kept continuously. The formula is as follows:
if the fluctuation amplitude > the fluctuation amplitude threshold:
adjustment status = intelligent control scheme (run data);
otherwise: adjustment status = continue monitoring.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. The intelligent monitoring method for the transformer and distribution station based on the cloud computing technology is characterized by comprising the following steps of: the method comprises the following steps:
s1, a cloud computing platform is built, and operation data of a power transformation and distribution station are collected;
s2, the cloud computing platform extracts historical operation data from the operation data, and predicts future operation data of the transformer and distribution substation according to the development trend of the historical operation data;
s3, performing intelligent fault detection according to the data predicted by the S2 and the historical operation data;
s4, carrying out load fluctuation analysis on the operation of the power transformation and distribution station according to the data predicted in the S2, and obtaining an intelligent control scheme for different load fluctuation of the power transformation and distribution station;
and S5, monitoring operation data by the cloud computing platform, and intelligently controlling the power transformation and distribution station according to a monitoring result and an intelligent control scheme.
2. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: and S1, selecting OpenStack cloud platform software for building a cloud computing platform for the time sequence operation data.
3. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the S1 is used for acquiring the current and the voltage of the power equipment of the power substation in real time by deploying a data collection sensor comprising a current sensor and a voltage sensor at the power substation.
4. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the step of predicting the future operation state of the substation by the S2 is as follows:
s2.1, extracting historical current and voltage data of the transformer and distribution substation from operation data;
s2.2, acquiring a future operation decision of the power transformation and distribution station;
s2.3, an intelligent prediction model is built, and the historical current and voltage data extracted in the step S2.1 are input into the intelligent prediction model by combining with future operation decisions of the step S2.2;
s2.4, the intelligent prediction model outputs future operation prediction data of the transformer and distribution substation.
5. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the step S3 is based on the steps of performing intelligent fault detection:
s3.1, extracting a load state of current and voltage in normal operation of the transformer and distribution substation in historical operation data;
and S3.2, extracting the load state of the time-varying power distribution station operated by the same current and voltage as the current and voltage in the step S3.1 from the future operation prediction data, carrying out intelligent fault detection by combining the load state extracted in the step S3.1, and sending a fault overhaul notification to the time-varying power distribution station through a cloud computing platform according to the detection result.
6. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: and S3, detecting and comparing the operation data with future operation prediction data, and when the data difference is more than five percent, sending a fault overhaul notification to the transformer and distribution station through the cloud computing platform, collecting feedback data after the fault overhaul of workers, and correcting the future operation data.
7. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the step of S4 obtaining the intelligent control scheme which accords with fluctuation to the power transformation and distribution station is as follows:
s4.1, extracting load fluctuation data and distribution station operation states in the operation of the transformer and the distribution station from future operation prediction data;
s4.2, screening the running state of the strain distribution station according to different fluctuation amplitude combinations of the load fluctuation data, and obtaining a fluctuation amplitude threshold value affecting the strain distribution station;
and S4.3, carrying out parameter analysis on load fluctuation data exceeding a fluctuation amplitude threshold by combining the power substation, and obtaining an intelligent control scheme for adjusting the operation parameters of the power substation when the fluctuation amplitude exceeds the threshold.
8. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the step S4 further comprises the following steps of adjusting the substation operation mode of the substation:
s4.4, collecting weather forecast data, extracting consumption states of different operation modes of the power transformation and distribution station under different weather by combining the operation data, screening the different operation modes of the power transformation and distribution station under different weather according to the consumption states, obtaining an operation mode with the lowest consumption state under the corresponding weather, and sending the operation mode to the cloud computing platform.
9. The intelligent substation monitoring method based on the cloud computing technology according to claim 1, wherein the intelligent substation monitoring method is characterized by comprising the following steps of: the step of intelligently controlling the power transformation and distribution station in S5 is as follows:
and S5.1, the cloud computing platform monitors the operation data according to the fluctuation amplitude threshold value acquired in the step S4.2, when the fluctuation amplitude of the motion data is larger than the fluctuation amplitude threshold value, the substation is adjusted according to the intelligent control scheme acquired in the step S4.3, otherwise, if the fluctuation amplitude is smaller than the fluctuation amplitude threshold value, the monitoring is kept continuously.
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