CN112348339A - Power distribution network planning method based on big data analysis - Google Patents
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Abstract
A power distribution network planning method based on big data analysis firstly establishes an intelligent power distribution network standardized application process to form a corresponding management standard, a working standard and a technical standard, links from data acquisition to auxiliary planning are connected in series, a hardware and software system is deployed in the second step, and in the aspect of hardware convenience, a low-voltage high-speed carrier chip is installed in a field acquisition device, an acquisition channel and a power distribution terminal for upgrading, so that the typical power distribution network is upgraded, the real-time acquisition of power distribution network operation information and topology information is realized, and then data value mining is completed by using a big data technology; and finally, after the data analysis of the power distribution network is completed, data such as equipment faults, data abnormity, load adjustment suggestions and the like are obtained, the state of the power distribution network is visually displayed through an automatic drawing technology, demonstration grid planning is carried out on the power distribution network, and intelligent management and control are carried out on the grid planning of the power distribution network according to an application flow.
Description
Technical Field
The invention relates to the technical field of planning and transformation of power distribution networks, in particular to a power distribution network planning method based on big data analysis.
Background
With the continuous improvement of social science and technology, the development of big data technology is mature day by day and is widely applied to various industry fields, and on the other hand, with the massive popularization and use of new energy, the number of power grid users is increased day by day, and the power grid structure is more complicated. In order to scientifically plan the power grid layout, improve the reliability, economy and foresight of a power distribution network and enhance the planning and management level of the power distribution network, more and more power enterprises introduce big data to analyze the load characteristics of the power grid, accurately calculate various data of power grid operation management by constructing a load model and reasonably and scientifically arrange the operation mode of the power distribution network, so that business application scenes of user power utilization behavior analysis, power saving, power utilization prediction, grid optimization, peak-load shifting and the like are realized, the technical expansion of big data technology in the field of smart power grids is realized, the intellectualization level and the comprehensive benefit of the power grid are improved, and the requirements of deep construction of the smart power grids on fusion and mining of multi-source data are met.
The construction of the power distribution network is an important content for promoting the intellectualization of the power grid and is an important stage for applying big data in a power grid system. At present, because the development starting time of the power distribution network in China is relatively late, the phenomena of heavy construction and light planning are relatively common, and the planning design of the power distribution network is obviously lagged behind the development of urban economy and is not suitable for the requirement of the whole urban economy construction. The problems that the power distribution network is unreasonable in planning, insufficient in professional overall planning, heavy overload of equipment, light idle load, low system fault diagnosis efficiency and the like are increasingly prominent. Therefore, under the big data era, the potential efficiency increase is performed based on the mass data of the power distribution network, and the method has important significance for improving the management efficiency and the development quality of the power distribution network.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a power distribution network planning method based on big data analysis.
The invention adopts the technical scheme for solving the technical problems that: a power distribution network planning method based on big data analysis comprises the following steps:
step one, a standardized application flow is formulated based on a service mode of fault elimination, auxiliary planning, project storage and tracking of real-time acquisition of power distribution network data, and a power distribution network data, service, management and personnel integration operation flow is formed;
step two, deploying a hardware and software environment system according to a standardized application process, and carrying out standardized application on the power failure event of the power distribution network and curve information of equipment power, voltage, current and phase; the hardware and software environment system comprises a data acquisition module, an Hbase non-relational database and an Oracle relational database, wherein the data acquisition module consists of a field acquisition device provided with a low-voltage high-speed carrier chip, an acquisition channel and a power distribution terminal;
step three, acquiring running data and topological data of the power distribution network in real time through a data acquisition module, storing the running data of the power distribution network in an Hbase non-relational database, and storing the topological data in an Oracle relational database;
fourthly, performing big data analysis on the operation data and the topology data of the power distribution network in the database, wherein the big data analysis comprises fault diagnosis analysis and fault prediction analysis, and the fault diagnosis analysis analyzes historical operation data of the equipment and excavates a normal fluctuation range of the operation of the equipment to form an abnormal data judgment result of the operation parameters of the distribution network equipment; a fault diagnosis model is established by using a machine learning technology, and abnormal data judgment results are input to automatically acquire weak links and fault types of equipment, so that the position of the fault equipment is quickly positioned, and the first-aid repair speed is increased; the failure prediction analysis establishes an equipment failure prediction model through equipment historical operation data and a distribution network intelligent auxiliary optimization algorithm, and predicts a future failure point for active first-aid repair; outputting data analysis and diagnosis results according to the fault type, the position and the future fault point; the data analysis and diagnosis result also comprises data abnormity and load adjustment suggestion data.
Step five, carrying out grid planning on the power distribution network by using an automatic drawing technology according to the data analysis and diagnosis result, and generating a planning power grid diagram; tracking and comparing the difference between the current power grid diagram and the planned power grid diagram, and providing a difference analysis report; and predicting the influence of the unplanned project on the planned grid according to the difference analysis report, so that managers, scheduling operation and maintenance personnel and planning personnel can intuitively and clearly acquire the operation situation of the power distribution network, and the power distribution network grid planning is intelligently controlled according to the standardized application flow.
For further improvement, the hardware and software environment system in step two further includes:
the data acquisition interface program module is used for acquiring data acquired by the data acquisition module;
the topology analysis program module is used for processing and converting the data acquired by the topology analysis data acquisition interface program module into a format which needs to be finally stored and storing the format into an Oracle relational database;
and the big data analysis module obtains data analysis and diagnosis results through data screening, data summarization and technical analysis of the data acquisition interface program module.
Further perfecting, the big data analysis module comprises a Hadoop distributed computing platform for screening by using a Map/Reduce algorithm and a Spark component for carrying out technical analysis on the screened data by loading a fault diagnosis algorithm.
Further perfecting, the data analysis and diagnosis result also comprises data abnormity and load adjustment suggestion data.
And further perfecting, wherein the fault diagnosis algorithm comprises abnormal data judgment, fault diagnosis model construction, equipment fault prediction model construction and correlation analysis.
Further perfecting, the abnormal data judgment comprises the following steps: 1) the method comprises the steps of data preprocessing, filtering and denoising acquired data, filtering redundant information, adjusting wavelet coefficients according to set threshold thresholds at various scales by utilizing a wavelet threshold denoising method according to the characteristics of different rules of the signal under different resolutions, and completing the filtering and denoising of the data to obtain available equipment state monitoring information; 2) judging the data type, and dividing the difference of the direct availability of the collected data after filtering and denoising the power grid into current state data and future state data; 3) respectively judging abnormal data, wherein the current state data directly judges the state information of the current power grid operation equipment through a direct analysis method according to the current real-time measurement information and by combining the equipment and the rated parameters of the power grid state quantity, and judges whether the current power grid operation equipment is in an abnormal state or not; the future state data is subjected to big data mining through a big data analysis module, an autocorrelation function is calculated by using a time sequence method, curve fitting is performed by using an autoregressive moving average model, the autoregressive model and the moving average model, the running state data in the future time period is quantitatively predicted, deviation check is performed on the running state data and power grid and equipment running parameters collected in real time, and an abnormal data judgment result is given.
Further perfecting, the fault diagnosis model adopts a cluster analysis method and a deep belief network machine learning method to classify faults and mine fault characteristics.
And further perfecting, screening out the correlation between the line loss and the load by utilizing the characteristics and the change rule of various line losses of the distribution line according to the correlation between the load and the line loss and the topological structure and the operation parameter data of a typical distribution network through the correlation analysis, and obtaining the correlation between the load and the line loss in the operation of the distribution network through a big data correlation analysis model based on an Apriori algorithm.
The invention has the beneficial effects that: the method comprises the steps of establishing an intelligent power distribution network standardized application process, forming a corresponding management standard, a working standard and a technical standard, serially connecting links from data acquisition to auxiliary planning, deploying a hardware and a software system in the second step, installing a low-voltage high-speed carrier chip for upgrading in a field acquisition device, an acquisition channel and a power distribution terminal, realizing the upgrading of a typical power distribution network, realizing the real-time acquisition of the operation information and the topology information of the power distribution network, deploying a corresponding software environment and a server in the software aspect, acquiring data acquired on the hardware layer through a data receiving interface and a data processing program, respectively storing topology and operation data of the power distribution network by using a relational database and a non-relational database, and then mining the data value by using a big data technology; finally, after the data analysis of the power distribution network is completed, data such as equipment faults, data abnormity, load adjustment suggestions and the like are obtained, the state of the power distribution network is visually displayed through an automatic drawing technology, demonstration grid planning is carried out on the power distribution network, the current situation power grid is tracked and compared, the transverse and longitudinal differences among the power grids are planned, a difference analysis report is provided, the influence of a non-planning project on the planned grid is predicted, intelligent management and control are carried out on the grid planning of the power distribution network according to an application flow, operation and maintenance resources are reasonably distributed, the equipment detection time and the power failure maintenance time are reduced, and the power supply reliability of the system is improved. According to the invention, through big data analysis of mass data accessed to the operation management of the power distribution network, the accurate processing of the power distribution data is realized, the state of the power distribution network is accurately evaluated, and meanwhile, technical support is provided for the structure optimization of the power distribution network.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic diagram of a hardware and software environment system architecture of the present invention;
FIG. 3 is a flow chart of the anomalous data determination of the present invention;
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
with reference to the accompanying drawings: in this embodiment, a power distribution network planning method based on big data analysis includes the following steps:
step one, a standardized application flow is formulated based on a service mode of fault elimination, auxiliary planning, project storage and tracking of real-time acquisition of power distribution network data, and a power distribution network data, service, management and personnel integration operation flow is formed; the standardized application process comprises the steps of serially connecting management standards, working standards and technical standards of all links from data acquisition to auxiliary planning;
step two, deploying a hardware and software environment system according to a standardized application process, and carrying out standardized application on the power failure event of the power distribution network and curve information of equipment power, voltage, current and phase; the hardware and software environment system comprises a data acquisition module, an Hbase non-relational database and an Oracle relational database, wherein the data acquisition module consists of a field acquisition device provided with a low-voltage high-speed carrier chip, an acquisition channel and a power distribution terminal;
step three, acquiring running data and topological data of the power distribution network in real time through a data acquisition module, storing the running data of the power distribution network in an Hbase non-relational database, and storing the topological data in an Oracle relational database;
fourthly, performing big data analysis on the operation data and the topology data of the power distribution network in the database, wherein the big data analysis comprises fault diagnosis analysis and fault prediction analysis, and the fault diagnosis analysis analyzes historical operation data of the equipment and excavates a normal fluctuation range of the operation of the equipment to form an abnormal data judgment result of the operation parameters of the distribution network equipment; a fault diagnosis model is established by using a machine learning technology, and abnormal data judgment results are input to automatically acquire weak links and fault types of equipment, so that the position of the fault equipment is quickly positioned, and the first-aid repair speed is increased; the failure prediction analysis establishes an equipment failure prediction model through equipment historical operation data and a distribution network intelligent auxiliary optimization algorithm, and predicts a future failure point for active first-aid repair; outputting data analysis and diagnosis results according to the fault type, the position and the future fault point;
step five, carrying out grid planning on the power distribution network by using an automatic drawing technology according to the data analysis and diagnosis result, and generating a planning power grid diagram; tracking and comparing the difference between the current power grid diagram and the planned power grid diagram, and providing a difference analysis report; and predicting the influence of the unplanned project on the planned grid according to the difference analysis report, so that managers, scheduling operation and maintenance personnel and planning personnel can intuitively and clearly acquire the operation situation of the power distribution network, and the power distribution network grid planning is intelligently controlled according to the standardized application flow. When a fault occurs, different comprehensive emergency repair schemes are formulated according to different fault severity degrees, and a proposed route and emergency repair resource supply size are given. By analyzing the running state of the distribution network equipment, running and maintenance resources are reasonably distributed, the equipment detection time and the power failure maintenance time are reduced, and the power supply reliability of the system is improved.
The hardware and software environment system in step two further comprises:
the data acquisition interface program module is used for acquiring data acquired by the data acquisition module;
the topology analysis program module is used for processing and converting the data acquired by the topology analysis data acquisition interface program module into a format which needs to be finally stored and storing the format into an Oracle relational database;
and the big data analysis module obtains data analysis and diagnosis results through data screening, data summarization and technical analysis of the data acquisition interface program module. The big data analysis module comprises a Hadoop distributed computing platform for screening by using a Map/Reduce algorithm and a Spark component for carrying out technical analysis on the screened data by loading a fault diagnosis algorithm.
The abnormal data determination includes the steps of: 1) the method comprises the steps of data preprocessing, filtering and denoising acquired data, filtering redundant information, adjusting wavelet coefficients according to set threshold thresholds at various scales by utilizing a wavelet threshold denoising method according to the characteristics of different rules of the signal under different resolutions, and completing the filtering and denoising of the data to obtain available equipment state monitoring information; 2) judging the data type, and dividing the difference of the direct availability of the collected data after filtering and denoising the power grid into current state data and future state data; 3) respectively judging abnormal data, wherein the current state data directly judges the state information of the current power grid operation equipment through a direct analysis method according to the current real-time measurement information and by combining the equipment and the rated parameters of the power grid state quantity, and judges whether the current power grid operation equipment is in an abnormal state or not; the future state data is subjected to big data mining through a big data analysis module, an autocorrelation function is calculated by using a time sequence method, curve fitting is performed by using an autoregressive moving average model, the autoregressive model and the moving average model, the running state data in the future time period is quantitatively predicted, deviation check is performed on the running state data and power grid and equipment running parameters collected in real time, and an abnormal data judgment result is given. The fault diagnosis model adopts a cluster analysis method and a deep belief network machine learning method to realize fault classification and excavation of fault characteristics. The correlation analysis screens out the correlation between the line loss and the line loss according to the correlation between the load and the line loss and the topological structure and the operation parameter data of a typical distribution network by utilizing the characteristics and the change rule of various line losses of the distribution line, and obtains the correlation between the load and the line loss in the operation of the distribution network through a big data correlation analysis model based on an Apriori algorithm.
While the invention has been shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the appended claims.
Claims (8)
1. A power distribution network planning method based on big data analysis is characterized by comprising the following steps:
step one, a standardized application flow is formulated based on a service mode of fault elimination, auxiliary planning, project storage and tracking of real-time acquisition of power distribution network data, and a power distribution network data, service, management and personnel integration operation flow is formed; the standardized application process comprises the steps of serially connecting management standards, working standards and technical standards of all links from data acquisition to auxiliary planning;
step two, deploying a hardware and software environment system according to a standardized application process, and carrying out standardized application on the power failure event of the power distribution network and curve information of equipment power, voltage, current and phase; the hardware and software environment system comprises a data acquisition module, an Hbase non-relational database and an Oracle relational database, wherein the data acquisition module consists of a field acquisition device provided with a low-voltage high-speed carrier chip, an acquisition channel and a power distribution terminal;
step three, acquiring running data and topological data of the power distribution network in real time through a data acquisition module, storing the running data of the power distribution network in an Hbase non-relational database, and storing the topological data in an Oracle relational database;
fourthly, performing big data analysis on the operation data and the topology data of the power distribution network in the database, wherein the big data analysis comprises fault diagnosis analysis and fault prediction analysis, and the fault diagnosis analysis analyzes historical operation data of the equipment and excavates a normal fluctuation range of the operation of the equipment to form an abnormal data judgment result of the operation parameters of the distribution network equipment; a fault diagnosis model is established by using a machine learning technology, and abnormal data judgment results are input to automatically acquire weak links and fault types of equipment, so that the position of the fault equipment is quickly positioned, and the first-aid repair speed is increased; the failure prediction analysis establishes an equipment failure prediction model through equipment historical operation data and a distribution network intelligent auxiliary optimization algorithm, and predicts a future failure point for active first-aid repair; outputting data analysis and diagnosis results according to the fault type, the position and the future fault point;
step five, carrying out grid planning on the power distribution network by using an automatic drawing technology according to the data analysis and diagnosis result, and generating a planning power grid diagram; tracking and comparing the difference between the current power grid diagram and the planned power grid diagram, and providing a difference analysis report; and predicting the influence of the unplanned project on the planned grid according to the difference analysis report, so that managers, scheduling operation and maintenance personnel and planning personnel can intuitively and clearly acquire the operation situation of the power distribution network, and the power distribution network grid planning is intelligently controlled according to the standardized application flow.
2. The power distribution network planning method based on big data analysis according to claim 1, wherein: the hardware and software environment system in step two further comprises:
the data acquisition interface program module is used for acquiring data acquired by the data acquisition module;
the topology analysis program module is used for processing and converting the data acquired by the topology analysis data acquisition interface program module into a format which needs to be finally stored and storing the format into an Oracle relational database;
and the big data analysis module obtains data analysis and diagnosis results through data screening, data summarization and technical analysis of the data acquisition interface program module.
3. The power distribution network planning method based on big data analysis according to claim 2, wherein: the big data analysis module comprises a Hadoop distributed computing platform for screening by using a Map/Reduce algorithm and a Spark component for carrying out technical analysis on the screened data by loading a fault diagnosis algorithm.
4. The power distribution network planning method based on big data analysis according to claim 3, wherein: the data analysis and diagnosis result also comprises data abnormity and load adjustment suggestion data.
5. The power distribution network planning method based on big data analysis according to claim 4, wherein: the fault diagnosis algorithm comprises abnormal data judgment, fault diagnosis model construction, equipment fault prediction model construction and correlation analysis.
6. The power distribution network planning method based on big data analysis according to claim 5, wherein: the abnormal data determination includes the steps of: 1) the method comprises the steps of data preprocessing, filtering and denoising acquired data, filtering redundant information, adjusting wavelet coefficients according to set threshold thresholds at various scales by utilizing a wavelet threshold denoising method according to the characteristics of different rules of the signal under different resolutions, and completing the filtering and denoising of the data to obtain available equipment state monitoring information; 2) judging the data type, and dividing the difference of the direct availability of the collected data after filtering and denoising the power grid into current state data and future state data; 3) respectively judging abnormal data, wherein the current state data directly judges the state information of the current power grid operation equipment through a direct analysis method according to the current real-time measurement information and by combining the equipment and the rated parameters of the power grid state quantity, and judges whether the current power grid operation equipment is in an abnormal state or not; the future state data is subjected to big data mining through a big data analysis module, an autocorrelation function is calculated by using a time sequence method, curve fitting is performed by using an autoregressive moving average model, the autoregressive model and the moving average model, the running state data in the future time period is quantitatively predicted, deviation check is performed on the running state data and power grid and equipment running parameters collected in real time, and an abnormal data judgment result is given.
7. The power distribution network planning method based on big data analysis according to claim 6, wherein: the fault diagnosis model adopts a cluster analysis method and a deep belief network machine learning method to realize fault classification and excavation of fault characteristics.
8. The power distribution network planning method based on big data analysis according to claim 7, wherein: the correlation analysis screens out the correlation between the line loss and the line loss according to the correlation between the load and the line loss and the topological structure and the operation parameter data of a typical distribution network by utilizing the characteristics and the change rule of various line losses of the distribution line, and obtains the correlation between the load and the line loss in the operation of the distribution network through a big data correlation analysis model based on an Apriori algorithm.
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