CN116846074A - Intelligent electric energy supervision method and system based on big data - Google Patents
Intelligent electric energy supervision method and system based on big data Download PDFInfo
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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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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- H—ELECTRICITY
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- 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
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses a smart electric energy supervision method and system based on big data, and belongs to the technical field of smart electric energy supervision. The invention comprises the following steps: the intelligent power supply and distribution system comprises a power supply and distribution data storage module, a start-stop plan management module, an electric quantity data loss analysis module, an early warning module and an intelligent electric energy supervision module; the output end of the power supply and distribution data storage module is connected with the input end of the start-stop plan management module; the output end of the start-stop plan management module is connected with the input end of the electric quantity data loss analysis module; the output end of the electric quantity data loss analysis module is connected with the input end of the early warning module and the input end of the intelligent electric energy supervision module; the invention gives early warning of data based on the coming of the fault of the boarding bridge; the method can analyze and process the electric energy according to the airport start-stop plan, find out the optimal electric energy loss plan and help the airport to realize electric energy supervision.
Description
Technical Field
The invention relates to the technical field of intelligent electric energy supervision, in particular to an intelligent electric energy supervision method and system based on big data.
Background
With the application of technologies such as cloud computing, big data, internet of things and the like in airports, the deployment mode of airport infrastructure is changed, and new changes are also needed for airport construction. In smart airports, the distribution network is a vital link.
The power is used as a basis for all facilities, and the operation of the distribution network, like other production flows within the facilities, needs to be monitored and managed. The power distribution management is to connect intelligent power and energy equipment with a communication function to a software system for data collection, state visualization and analysis and report function, so as to realize continuous monitoring and comprehensive management of the power distribution system.
In the current intelligent airport, the boarding bridges in the airplane starting and stopping process are controlled by adopting an electric power system, and the use frequency and time of each boarding bridge are different due to different airplane starting and stopping plans of each airport, so that when the intelligent airport is not used, the electric power system of each boarding bridge is not generally started for saving electric energy, and the loss of internal circuit equipment of each boarding bridge is also different due to the use frequency and time difference, and the early warning of the missing data of the coming fault is performed; meanwhile, the aging of the internal circuits and the devices also aggravates the electric energy loss, and how to seek the optimal electric energy loss is not mentioned in the current technical means.
Disclosure of Invention
The invention aims to provide an intelligent electric energy supervision method and system based on big data, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent electric energy supervision method based on big data comprises the following steps:
s1, acquiring power supply and distribution historical data of airport boarding bridge equipment, and marking distribution loss data of each power distribution before faults occur;
s2, any group of airport start-stop plans is obtained, any airport boarding bridge in the selected start-stop plans is output in a future time period T 0 The number of times of use K, T 0 Taking 24 hours, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data, and counting the K groups of data into a training set, wherein the K groups of data are selected forward by taking the last group of data before the occurrence of a fault as a first group until the K groups are fully selected;
s3, calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time, constructing an electric quantity data loss analysis model, outputting a predicted data growth rate, outputting fault early warning information to an administrator port if the output result exceeds the data growth rate of a first group of data of any group of distribution loss data in the training set, and marking a currently selected start-stop plan;
and S4, if the data growth rate of the first group of data with the output result exceeding any group of power distribution loss data in the training set does not exist, calculating the power distribution loss value in the whole start-stop plan according to the obtained data growth rate, marking the power distribution loss value in the start-stop plan, sending the power distribution loss value into a screening library, calculating the power distribution loss value of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to output to an administrator port as an airport start-stop plan implemented on the same day.
According to the technical scheme, the distribution loss data comprise the sum of line loss and equipment loss in the distribution process, and the distribution loss data are equal to the total output electric energy of the distribution end minus the total use electric energy of the use end.
According to the above technical solution, the constructing the electric quantity data loss analysis model includes:
acquiring any group of airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
calculating adjacent difference values for each group of distribution loss data in the training set to form a data growth rate in unit time:
wherein P is t The maximum value of t+1 is taken to be K, which represents the data growth rate of the t group in unit time; e (E) t+1 、E t Distribution loss data respectively representing the t-th group; m is M t+1 、M t Time-consuming data representing group t, respectively;
forming a set of data growth rates per unit time for each set of distribution loss data, any one of which is denoted as { P ] 1 、P 2 、…、P K-1 }；
Randomly numbering all sets of data growth rate sets, wherein any two sets of numbers are different, and selecting any one set to construct a prediction model:
wherein P is K Representing the predicted output result of the K group, wherein sigma and mu represent the gray action quantity and the development coefficient respectively;
the ash action amount and the development coefficient satisfy the relation:
wherein P is 1 (i) Representing data generated after gray accumulation generation processing is carried out on any one group of selected data growth rate set, wherein i represents a serial number;
selecting other groups of data different from the current number, calculating a prediction output result according to the current prediction model, acquiring actual results of the other groups of data different from the current number, calculating the absolute value of difference data between the prediction value and the actual value, and taking the average value of the sum of the absolute values of the difference data as a calibration value of the current prediction model;
calculating a calibration value of a prediction model of each group of numbers based on all the numbered group data, and selecting the smallest value as an electric quantity data loss analysis model;
if the minimum data are simultaneously provided with a plurality of groups, evaluating the maximum value of the absolute value of the difference data of each group of data, and selecting the maximum value according to the sequence from small to large; (if the same is present, the system sets a random selection process)
Based on the constructed electric quantity data loss analysis model, substituting the initial data of any airport boarding bridge at present, and outputting the predicted data growth rate.
In the above technical solution, mainly for a certain start-stop plan, the start-stop plan includes the stop and take-off time of the aircraft on the same day, and in the stop and take-off process, the system needs to perform power distribution processing for the boarding bridges, and through inquiring the start-stop plan, the number of times of use of each boarding bridge on the same day can be obtained, after marking the number of times of use, the historical data set is used to form a training set, that is, the fault leading data with the same number of times is used as the training set, because in the whole electric energy monitoring process, the fault is the worst case, the same number of times of data closest to the fault is used as the training set, the growth rate is formed, and the current data is combined with the worst growth rate, so as to generate a prediction, and can ensure that the result is as close to a safe value as possible, if the result exceeds the data growth rate under a certain set of historical data, the use plan is indicated that the fault may occur, and the alarm processing should be performed.
According to the above technical solution, in step S4, further includes:
acquiring the output predicted data growth rate;
if the output result exceeds the data growth rate of the first group of data of any group of distribution loss data in the training set, outputting fault early warning information to an administrator port, and marking a currently selected start-stop plan;
if the data growth rate of the first group of data of which the output result exceeds any group of distribution loss data in the training set does not exist, calculating the distribution loss value in the whole start-stop plan according to the obtained data growth rate each time:
wherein E is n+1 Distribution loss data representing the n+1th group; m is M n 、M n+1 Respectively representing the duration of the n+1th group; e (E) n The power distribution loss data representing the nth group, wherein the initial value of n is 1, and the termination value is K-1; p (P) n Representing the data growth rate of the nth group through the n+1th group;
calculating the sum of the calculated distribution loss data of each group, marking the distribution loss data in a start-stop plan, sending the distribution loss data into a screening library, calculating to obtain distribution loss values of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day.
An intelligent power monitoring system based on big data, the system comprising: the intelligent power supply and distribution system comprises a power supply and distribution data storage module, a start-stop plan management module, an electric quantity data loss analysis module, an early warning module and an intelligent electric energy supervision module;
the power supply and distribution data storage module is used for acquiring power supply and distribution historical data of airport boarding bridge equipment and marking distribution loss data of each power distribution before faults occur; the start-stop plan management module is used for acquiring any group of start-stop plans in the airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The number of times of use K, T 0 Taking 24 hours, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data, and counting the K groups of data into a training set, wherein the K groups of data are selected forward by taking the last group of data before the occurrence of a fault as a first group until the K groups are fully selected; the electric quantity data loss analysis module is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate in unit time, constructing an electric quantity data loss analysis model and outputting a predicted data growth rate; the early warning module is used for outputting fault early warning information to an administrator port if the data growth rate of the first group of data of which the output result exceeds any group of power distribution loss data in the training set exists, and marking a currently selected start-stop plan; the intelligent electric energy supervision module is used for outputting a first group of data exceeding any group of distribution loss data in the training set if any group of output results do not existCalculating the power distribution loss value in the whole start-stop plan according to the obtained data growth rate of each time, marking the power distribution loss value in the start-stop plan, sending the power distribution loss value into a screening library, calculating the power distribution loss value of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to output to an administrator port as an airport start-stop plan implemented on the same day;
the output end of the power supply and distribution data storage module is connected with the input end of the start-stop plan management module; the output end of the start-stop plan management module is connected with the input end of the electric quantity data loss analysis module; and the output end of the electric quantity data loss analysis module is connected with the input end of the early warning module and the input end of the intelligent electric energy supervision module.
According to the technical scheme, the power supply and distribution data storage module comprises a power supply and distribution data storage unit and a marking unit;
the power supply and distribution data storage unit is used for acquiring power supply and distribution historical data of airport boarding bridge equipment; the marking unit is used for marking the distribution loss data of each distribution before the occurrence of faults in the power supply and distribution data storage unit;
and the output end of the power supply and distribution data storage unit is connected with the input end of the marking unit.
According to the technical scheme, the start-stop plan management module comprises a start-stop plan generation unit and a selection unit;
the start-stop plan generation unit is used for acquiring any group of start-stop plans in the airport start-stop plans; the selection unit is used for outputting any airport boarding bridge in the selected start-stop plan in a future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
the output end of the start-stop plan generating unit is connected with the input end of the selecting unit.
According to the technical scheme, the electric quantity data loss analysis module comprises a data processing unit and an electric quantity data loss analysis unit;
the data processing unit is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time; the electric quantity data loss analysis unit is used for constructing an electric quantity data loss analysis model and outputting a predicted data growth rate;
the output end of the data processing unit is connected with the input end of the electric quantity data loss analysis unit.
According to the technical scheme, the early warning module comprises a data receiving unit and an early warning unit;
the data receiving unit is used for receiving data with the data growth rate of the first group of data with the output result exceeding any group of distribution loss data in the training set; the early warning unit is used for outputting fault early warning information to an administrator port and marking a currently selected start-stop plan;
the output end of the data receiving unit is connected with the input end of the early warning unit.
According to the technical scheme, the intelligent electric energy supervision module comprises a distribution loss summarizing unit and a screening unit;
the power distribution loss summarizing unit is used for acquiring the data growth rate of a first group of data without any group of output results exceeding any group of power distribution loss data in the training set, calculating the power distribution loss value in the whole start-stop plan according to each acquired data growth rate, marking the power distribution loss value in the start-stop plan, and sending the power distribution loss value into the screening library; the screening unit is used for calculating power distribution loss values of all start-stop plans in the screening library, and selecting a start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day;
and the output end of the distribution loss summarizing unit is connected with the input end of the screening unit.
Compared with the prior art, the invention has the following beneficial effects: based on analysis of an electric power system of an airport internal boarding bridge, the invention gives early warning of data about the coming of the boarding bridge fault by means of a data source of the loss and growth rate of internal line equipment of each boarding bridge; meanwhile, the electric energy analysis processing can be carried out according to the airport start-stop plan, the optimal electric energy loss plan is sought, the airport is helped to realize electric energy supervision, the data processing and intelligent support in the intelligent airport are finally realized, and the intelligent airport intelligent electric energy monitoring system is widely applied and deeply fused in the civil aviation field by new generation technologies such as the Internet of things, cloud computing and big data.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a smart power monitoring method and system based on big data according to the present invention.
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.
Referring to fig. 1, in the first embodiment, taking a 4F-class hub international airport in a western region as an example, an integrated intelligent power distribution system is built, integrated protection MiCOM P143 and MiCOM P543 are connected to 1551, and meters PM5350P are connected to 5300, so as to integrally manage multiple sets of power quality management equipment such as active filtering equipment, capacitors and the like;
the utility model provides a based on EcoStruxureTM Power framework customization intelligent power distribution solution, preset electric power monitoring module and full life cycle fortune dimension service tool in the intelligent power distribution system, wherein preset electric power monitoring module is including organic field boarding bridge equipment power supply and distribution supervisory methods in, realizes the comprehensive management of electric energy quality through the combination of software and hardware, catches electric energy quality event, adjusts control electric energy loss, visual analysis, avoids large-scale equipment to shut down and extra cost spending, and the specific method includes:
a database is arranged to acquire power supply and distribution historical data of airport boarding bridge equipment, and the power distribution loss data of each power distribution before the occurrence of faults is marked;
the distribution loss data comprises the sum of line loss and equipment loss in the distribution process, and the distribution loss data is equal to the total output electric energy of a distribution end minus the total use electric energy of a use end.
The method comprises the steps of setting an airport start-stop plan access, starting an equipment electric energy system when the airport start-stop is started, closing when the airport start-stop is not used, obtaining any group of start-stop plans in the airport start-stop plans, outputting the use times K of any airport boarding bridge in the selected start-stop plans in a future time period T0, taking 24 hours by T0, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data to be counted into a training set, and selecting the K groups to take the last group of data before a fault occurs as a first group, and selecting the K groups forwards until the K groups are selected to be full; calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time, and constructing an electric quantity data loss analysis model;
acquiring any group of airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
calculating adjacent difference values for each group of distribution loss data in the training set to form a data growth rate in unit time:
wherein P is t The maximum value of t+1 is taken to be K, which represents the data growth rate of the t group in unit time; e (E) t+1 、E t Distribution loss data respectively representing the t-th group; m is M t+1 、M t Time-consuming data representing group t, respectively;
forming a set of data growth rates per unit time for each set of distribution loss data, any one of which is denoted as { P ] 1 、P 2 、…、P K-1 }；
Randomly numbering all sets of data growth rate sets, wherein any two sets of numbers are different, and selecting any one set to construct a prediction model:
wherein P is K Representing the predicted output result of the K group, wherein sigma and mu represent the gray action quantity and the development coefficient respectively;
the ash action amount and the development coefficient satisfy the relation:
wherein P is 1 (i) Representing data generated after gray accumulation generation processing is carried out on any one group of selected data growth rate set, wherein i represents a serial number;
selecting other groups of data different from the current number, calculating a prediction output result according to the current prediction model, acquiring actual results of the other groups of data different from the current number, calculating the absolute value of difference data between the prediction value and the actual value, and taking the average value of the sum of the absolute values of the difference data as a calibration value of the current prediction model;
calculating a calibration value of a prediction model of each group of numbers based on all the numbered group data, and selecting the smallest value as an electric quantity data loss analysis model;
if the minimum data are simultaneously provided with a plurality of groups, evaluating the maximum value of the absolute value of the difference data of each group of data, and selecting the maximum value according to the sequence from small to large;
based on the constructed electric quantity data loss analysis model, substituting the initial data of any airport boarding bridge at present, and outputting the predicted data growth rate.
Acquiring the output predicted data growth rate;
if the output result exceeds the data growth rate of the first group of data of any group of distribution loss data in the training set, outputting fault early warning information to an administrator port, and marking a currently selected start-stop plan;
if the data growth rate of the first group of data of which the output result exceeds any group of distribution loss data in the training set does not exist, calculating the distribution loss value in the whole start-stop plan according to the obtained data growth rate each time:
wherein E is n+1 Distribution loss data representing the n+1th group; m is M n 、M n+1 Respectively representing the duration of the n+1th group; e (E) n The power distribution loss data representing the nth group, wherein the initial value of n is 1, and the termination value is K-1; p (P) n Representing the data growth rate of the nth group through the n+1th group;
calculating the sum of the calculated distribution loss data of each group, marking the distribution loss data in a start-stop plan, sending the distribution loss data into a screening library, calculating to obtain distribution loss values of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day.
In a second embodiment, an intelligent power monitoring system based on big data is provided, the system includes: the intelligent power supply and distribution system comprises a power supply and distribution data storage module, a start-stop plan management module, an electric quantity data loss analysis module, an early warning module and an intelligent electric energy supervision module;
the power supply and distribution data storage module is used for acquiring power supply and distribution historical data of airport boarding bridge equipment and marking distribution loss data of each power distribution before faults occur; the start-stop plan management module is used for acquiring any group of start-stop plans in the airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The number of times of use K, T 0 Taking 24 hours, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data, and counting the K groups of data into a training set, wherein the K groups of data are selected forward by taking the last group of data before the occurrence of a fault as a first group until the K groups are fully selected; the electric quantity data loss analysisThe module is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time, constructing an electric quantity data loss analysis model and outputting a predicted data growth rate; the early warning module is used for outputting fault early warning information to an administrator port if the data growth rate of the first group of data of which the output result exceeds any group of power distribution loss data in the training set exists, and marking a currently selected start-stop plan; the intelligent electric energy supervision module is used for calculating the power distribution loss value in the whole start-stop plan according to the obtained data increment rate if any one group of output results exceeds the data increment rate of the first group of power distribution loss data in the training set, marking the power distribution loss value in the whole start-stop plan, sending the power distribution loss value into the screening library, calculating the power distribution loss value of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day;
the output end of the power supply and distribution data storage module is connected with the input end of the start-stop plan management module; the output end of the start-stop plan management module is connected with the input end of the electric quantity data loss analysis module; and the output end of the electric quantity data loss analysis module is connected with the input end of the early warning module and the input end of the intelligent electric energy supervision module.
The power supply and distribution data storage module comprises a power supply and distribution data storage unit and a marking unit;
the power supply and distribution data storage unit is used for acquiring power supply and distribution historical data of airport boarding bridge equipment; the marking unit is used for marking the distribution loss data of each distribution before the occurrence of faults in the power supply and distribution data storage unit;
and the output end of the power supply and distribution data storage unit is connected with the input end of the marking unit.
The start-stop plan management module comprises a start-stop plan generation unit and a selection unit;
the start-stop plan generation unit is used for acquiring any group of start-stop plans in the airport start-stop plans; the selection unit is used for outputting any airport boarding bridge in the selected start-stop planIn future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
the output end of the start-stop plan generating unit is connected with the input end of the selecting unit.
The electric quantity data loss analysis module comprises a data processing unit and an electric quantity data loss analysis unit;
the data processing unit is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time; the electric quantity data loss analysis unit is used for constructing an electric quantity data loss analysis model and outputting a predicted data growth rate;
the output end of the data processing unit is connected with the input end of the electric quantity data loss analysis unit.
The early warning module comprises a data receiving unit and an early warning unit;
the data receiving unit is used for receiving data with the data growth rate of the first group of data with the output result exceeding any group of distribution loss data in the training set; the early warning unit is used for outputting fault early warning information to an administrator port and marking a currently selected start-stop plan;
the output end of the data receiving unit is connected with the input end of the early warning unit.
The intelligent electric energy supervision module comprises a distribution loss summarizing unit and a screening unit;
the power distribution loss summarizing unit is used for acquiring the data growth rate of a first group of data without any group of output results exceeding any group of power distribution loss data in the training set, calculating the power distribution loss value in the whole start-stop plan according to each acquired data growth rate, marking the power distribution loss value in the start-stop plan, and sending the power distribution loss value into the screening library; the screening unit is used for calculating power distribution loss values of all start-stop plans in the screening library, and selecting a start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day;
and the output end of the distribution loss summarizing unit is connected with the input end of the screening unit.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent electric energy supervision method based on big data is characterized in that: the method comprises the following steps:
s1, acquiring power supply and distribution historical data of airport boarding bridge equipment, and marking distribution loss data of each power distribution before faults occur;
s2, any group of airport start-stop plans is obtained, any airport boarding bridge in the selected start-stop plans is output in a future time period T 0 The number of times of use K, T 0 Taking 24 hours, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data, and counting the K groups of data into a training set, wherein the K groups of data are selected forward by taking the last group of data before the occurrence of a fault as a first group until the K groups are fully selected;
s3, calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time, constructing an electric quantity data loss analysis model, outputting a predicted data growth rate, outputting fault early warning information to an administrator port if the output result exceeds the data growth rate of a first group of data of any group of distribution loss data in the training set, and marking a currently selected start-stop plan;
and S4, if the data growth rate of the first group of data with the output result exceeding any group of power distribution loss data in the training set does not exist, calculating the power distribution loss value in the whole start-stop plan according to the obtained data growth rate, marking the power distribution loss value in the start-stop plan, sending the power distribution loss value into a screening library, calculating the power distribution loss value of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to output to an administrator port as an airport start-stop plan implemented on the same day.
2. The intelligent power supervision method based on big data according to claim 1, wherein: the distribution loss data comprises the sum of line loss and equipment loss in the distribution process, and the distribution loss data is equal to the total output electric energy of a distribution end minus the total use electric energy of a use end.
3. The intelligent power supervision method based on big data according to claim 2, wherein: the construction of the electric quantity data loss analysis model comprises the following steps:
acquiring any group of airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
calculating adjacent difference values for each group of distribution loss data in the training set to form a data growth rate in unit time:
wherein P is t The maximum value of t+1 is taken to be K, which represents the data growth rate of the t group in unit time; e (E) t+1 、E t Distribution loss data respectively representing the t-th group; m is M t+1 、M t Time-consuming data representing group t, respectively;
forming a set of data growth rates per unit time for each set of distribution loss data, any one of which is denoted as { P ] 1 、P 2 、…、P K-1 }；
Randomly numbering all sets of data growth rate sets, wherein any two sets of numbers are different, and selecting any one set to construct a prediction model:
wherein P is K Representing the predicted output result of the K group, wherein sigma and mu represent the gray action quantity and the development coefficient respectively;
the ash action amount and the development coefficient satisfy the relation:
wherein P is 1 (i) Representing data generated after gray accumulation generation processing is carried out on any one group of selected data growth rate set, wherein i represents a serial number;
selecting other groups of data different from the current number, calculating a prediction output result according to the current prediction model, acquiring actual results of the other groups of data different from the current number, calculating the absolute value of difference data between the prediction value and the actual value, and taking the average value of the sum of the absolute values of the difference data as a calibration value of the current prediction model;
calculating a calibration value of a prediction model of each group of numbers based on all the numbered group data, and selecting the smallest value as an electric quantity data loss analysis model;
if the minimum data are simultaneously provided with a plurality of groups, evaluating the maximum value of the absolute value of the difference data of each group of data, and selecting the maximum value according to the sequence from small to large;
based on the constructed electric quantity data loss analysis model, substituting the initial data of any airport boarding bridge at present, and outputting the predicted data growth rate.
4. A smart power supervision method based on big data according to claim 3, wherein: in step S4, further comprising:
acquiring the output predicted data growth rate;
if the output result exceeds the data growth rate of the first group of data of any group of distribution loss data in the training set, outputting fault early warning information to an administrator port, and marking a currently selected start-stop plan;
if the data growth rate of the first group of data of which the output result exceeds any group of distribution loss data in the training set does not exist, calculating the distribution loss value in the whole start-stop plan according to the obtained data growth rate each time:
wherein E is n+1 Distribution loss data representing the n+1th group; m is M n 、M n+1 Respectively representing the duration of the n+1th group; e (E) n The power distribution loss data representing the nth group, wherein the initial value of n is 1, and the termination value is K-1; p (P) n Representing the data growth rate of the nth group through the n+1th group;
calculating the sum of the calculated distribution loss data of each group, marking the distribution loss data in a start-stop plan, sending the distribution loss data into a screening library, calculating to obtain distribution loss values of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day.
5. An wisdom electric energy supervisory systems based on big data, its characterized in that: the system comprises: the intelligent power supply and distribution system comprises a power supply and distribution data storage module, a start-stop plan management module, an electric quantity data loss analysis module, an early warning module and an intelligent electric energy supervision module;
the power supply and distribution data storage module is used for acquiring power supply and distribution historical data of airport boarding bridge equipment and marking distribution loss data of each power distribution before faults occur; the start-stop plan management module is used for acquiring any group of start-stop plans in the airport start-stop plans, outputting any airport boarding bridge in the selected start-stop plans in a future time period T 0 The number of times of use K, T 0 Taking 24 hours, calling distribution loss data in historical data, selecting K groups of data from each group of distribution loss data, and counting the K groups of data into a training set, wherein the K groups of data are selected forward by taking the last group of data before the occurrence of a fault as a first group until the K groups are fully selected; the electric quantity data loss analysis module is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate in unit time, constructing an electric quantity data loss analysis model and outputting a predicted data growth rate; the early warning module is used for outputting fault early warning information to an administrator port if the data growth rate of the first group of data of which the output result exceeds any group of power distribution loss data in the training set exists, and marking a currently selected start-stop plan; the intelligent electric energy supervision module is used for calculating the power distribution loss value in the whole start-stop plan according to the obtained data increment rate if any one group of output results exceeds the data increment rate of the first group of power distribution loss data in the training set, marking the power distribution loss value in the whole start-stop plan, sending the power distribution loss value into the screening library, calculating the power distribution loss value of all the start-stop plans in the screening library, and selecting the start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day;
the output end of the power supply and distribution data storage module is connected with the input end of the start-stop plan management module; the output end of the start-stop plan management module is connected with the input end of the electric quantity data loss analysis module; and the output end of the electric quantity data loss analysis module is connected with the input end of the early warning module and the input end of the intelligent electric energy supervision module.
6. The intelligent power monitoring system based on big data of claim 5, wherein: the power supply and distribution data storage module comprises a power supply and distribution data storage unit and a marking unit;
the power supply and distribution data storage unit is used for acquiring power supply and distribution historical data of airport boarding bridge equipment; the marking unit is used for marking the distribution loss data of each distribution before the occurrence of faults in the power supply and distribution data storage unit;
and the output end of the power supply and distribution data storage unit is connected with the input end of the marking unit.
7. The intelligent power monitoring system based on big data of claim 5, wherein: the start-stop plan management module comprises a start-stop plan generation unit and a selection unit;
the start-stop plan generation unit is used for acquiring any group of start-stop plans in the airport start-stop plans; the selection unit is used for outputting any airport boarding bridge in the selected start-stop plan in a future time period T 0 The using times K in the history data are called, the distribution loss data in the history data are called, and K groups of data are selected from each group of distribution loss data to be counted into a training set;
the output end of the start-stop plan generating unit is connected with the input end of the selecting unit.
8. The intelligent power monitoring system based on big data of claim 5, wherein: the electric quantity data loss analysis module comprises a data processing unit and an electric quantity data loss analysis unit;
the data processing unit is used for calculating adjacent difference values of each group of distribution loss data in the training set to form a data growth rate of unit time; the electric quantity data loss analysis unit is used for constructing an electric quantity data loss analysis model and outputting a predicted data growth rate;
the output end of the data processing unit is connected with the input end of the electric quantity data loss analysis unit.
9. The intelligent power monitoring system based on big data of claim 5, wherein: the early warning module comprises a data receiving unit and an early warning unit;
the data receiving unit is used for receiving data with the data growth rate of the first group of data with the output result exceeding any group of distribution loss data in the training set; the early warning unit is used for outputting fault early warning information to an administrator port and marking a currently selected start-stop plan;
the output end of the data receiving unit is connected with the input end of the early warning unit.
10. The intelligent power monitoring system based on big data of claim 5, wherein: the intelligent electric energy supervision module comprises a distribution loss summarizing unit and a screening unit;
the power distribution loss summarizing unit is used for acquiring the data growth rate of a first group of data without any group of output results exceeding any group of power distribution loss data in the training set, calculating the power distribution loss value in the whole start-stop plan according to each acquired data growth rate, marking the power distribution loss value in the start-stop plan, and sending the power distribution loss value into the screening library; the screening unit is used for calculating power distribution loss values of all start-stop plans in the screening library, and selecting a start-stop plan corresponding to the minimum value to be output to an administrator port as an airport start-stop plan implemented on the same day;
and the output end of the distribution loss summarizing unit is connected with the input end of the screening unit.
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