CN117458722B - Data monitoring method and system based on electric power energy management system - Google Patents
Data monitoring method and system based on electric power energy management system Download PDFInfo
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Abstract
The present disclosure relates to the field of data monitoring technologies, and in particular, to a data monitoring method and system based on an electric power energy management system. According to the method, the device and the system, the identification is carried out on the digital map, the running state of the power station is displayed, so that the power state can be monitored in real time, then the power consumption data of the power equipment are counted and analyzed, whether the power equipment has power consumption abnormality or not is judged according to the power consumption data, when the power consumption abnormality exists, the real-time state data are subjected to abnormality analysis to obtain an abnormality analysis result, then the power equipment is managed according to the abnormality analysis result, the abnormality of the power equipment can be timely identified, abnormality early warning management is carried out when the abnormality data occur, and the abnormal state is responded timely.
Description
Technical Field
The application relates to the technical field of data monitoring, in particular to a data monitoring method and system based on an electric power energy management system.
Background
Most of current power management systems adopt simple statistical tables to display data of site equipment, and the display mode is static and abstract, so that real-time monitoring of sites cannot be realized. The power manager cannot quickly and intuitively grasp the real-time operation state of a site or an area. When abnormal data occur, an effective real-time monitoring and management means is lacked, so that response is not timely, reliability of power supply and stable operation of a power grid are reduced, and the situation needs to be further improved.
Disclosure of Invention
In order to solve the problem that the existing power monitoring means is not timely in response, the application provides a data monitoring method and system based on a power energy management system, and the following technical scheme is adopted:
in a first aspect, the present application provides a data monitoring method based on an electric power energy management system, including the steps of:
collecting real-time state data of power equipment corresponding to a power station;
marking the position of the power station on the constructed digital map, and displaying the corresponding station running state at the corresponding position of the power station according to the real-time state data;
counting and analyzing the power consumption data of the power equipment according to the real-time state data, judging whether the power equipment has power consumption abnormality according to the power consumption data, and performing abnormality analysis on the real-time state data when the power equipment has power consumption abnormality to obtain an abnormality analysis result;
and carrying out abnormality early warning management on the power equipment according to the abnormality analysis result.
Through adopting above-mentioned technical scheme, this application is through gathering the real-time status data of the power equipment that the electric website corresponds, then sign on the digital map, and demonstrate the running state of electric website, thereby can carry out real-time supervision to the electric power state, then judge whether there is the power consumption unusual according to the power consumption data of power equipment through statistics and analysis power consumption data, when there is the power consumption unusual, carry out the anomaly analysis to real-time status data, obtain the anomaly analysis result, then manage power equipment according to the anomaly analysis result, thereby can in time discern the power equipment abnormal condition, and carry out the unusual early warning management when the unusual data takes place, in time respond to the unusual state.
Optionally, the real-time state data includes a voltage parameter, a current parameter, a temperature parameter and a power factor, and the calculating and analyzing the electricity consumption data of the electric power equipment according to the real-time state data, and judging whether the electric power equipment has an electricity consumption abnormality according to the electricity consumption data, and when the electric power equipment has the electricity consumption abnormality, performing abnormality analysis on the real-time state data to obtain an abnormality analysis result specifically includes:
calculating the active power of each sampling time point in the preset time period according to the real-time state data, and counting the total power consumption in the preset time period;
comparing the total electricity consumption with the historical contemporaneous electricity consumption of the power equipment, and judging whether the power equipment has abnormal electricity consumption according to a comparison result;
when the power equipment has abnormal power consumption, extracting time domain sample data corresponding to voltage parameters and current parameters of the power equipment;
performing wavelet transformation on the time domain sample data to obtain an energy feature vector corresponding to the time domain sample data;
and inputting the energy characteristic vector into a preset abnormal type identification model, determining the abnormal type of electricity consumption of the power equipment, and obtaining an abnormal analysis result.
By adopting the technical scheme, the application specifically discloses calculating the total power consumption in the preset time according to the active power of each sampling time point in the preset time, comparing the total power consumption with the historical synchronous total power consumption, judging whether the power station has abnormal power consumption according to the comparison result, extracting time domain sample data corresponding to the voltage parameter and the current parameter when the power consumption is abnormal, performing wavelet transformation to obtain energy feature vectors corresponding to the time domain sample data, inputting a preset abnormal type identification model, determining the abnormal power consumption type, and obtaining an abnormal analysis result.
Optionally, the performing wavelet transformation on the time domain sample data to obtain an energy feature vector corresponding to the time domain sample data specifically includes:
performing L-layer wavelet decomposition on the time domain sample data to obtain 1 low-frequency approximate signal A and L high-frequency detail signals D1 to DL;
calculating energy EA of the low-frequency approximation signal A as a first energy characteristic;
calculating energy ED1 to EDL of the L high-frequency detail signals D1 to DL respectively as second to L+1 energy characteristics;
the first energy feature and the second to l+1 energy feature constitute an energy feature vector [ EA, ED1, ED2, ].
Through adopting above-mentioned technical scheme, this application is through carrying out L layer wavelet decomposition to time domain sample data, obtains 1 low frequency approximate signal A and L high frequency detail signal D1 to DL, then calculates corresponding energy characteristic, constitutes energy characteristic vector to carry out the unusual discernment according to energy characteristic vector.
Optionally, the process for establishing the anomaly type recognition model includes:
acquiring historical electricity utilization data of the power equipment with different anomaly types;
performing wavelet transformation on the historical electricity utilization data, extracting energy characteristics, and constructing a sample characteristic vector;
labeling the sample feature vectors according to a preset labeling rule, and determining different types of abnormal electricity consumption samples;
and training the neural network model to be trained through a support vector machine algorithm according to the marked power consumption abnormal samples to obtain an abnormal type identification model based on wavelet transformation energy characteristics.
Through adopting above-mentioned technical scheme, this application is through carrying out wavelet transformation to historical electricity consumption data in advance, draws energy characteristic, builds sample feature vector, then marks, confirms the unusual sample of different grade type electricity consumption, then treats training neural network model through support vector machine algorithm, obtains the unusual type recognition model based on wavelet transformation energy characteristic to follow-up according to energy feature vector carries out unusual recognition.
Optionally, the real-time status data includes a voltage parameter, a current parameter, a temperature parameter and a power factor, and the displaying, according to the real-time status data, a site running status corresponding to the power site position specifically includes:
comparing the deviation of the real-time measured values of the voltage parameter, the current parameter, the temperature parameter and the power factor with the normal threshold;
carrying out state hierarchy classification on the real-time state data according to the deviation of the voltage parameter, the current parameter, the temperature parameter and the power factor, wherein different colors and areas are adopted for displaying different state hierarchies;
displaying the visualized areas of different state levels at the corresponding power station positions;
and when a preset instruction is received, displaying corresponding real-time state information in a superposition manner on the visual area, and popping up a trend chart to display the running state of the station.
Through adopting above-mentioned technical scheme, this application specifically discloses carrying out the state level classification with real-time status data according to the deviation size of voltage parameter, current parameter and temperature parameter's real-time measured value and normal threshold value, then the visual region of different state levels is shown at the electric website position of digital map, when receiving the instruction of predetermineeing, overlaps the real-time parameter numerical value of display correspondence on visual region to pop up the trend chart, thereby demonstrate the website running state directly perceivedly, be convenient for monitor the website running state.
Optionally, performing abnormality early warning management on the electrical equipment according to the abnormality analysis result specifically includes:
performing power consumption abnormality early warning monitoring on the power equipment according to the abnormality analysis result;
when the electricity utilization abnormality early warning information is triggered, generating an operation and maintenance task of the power equipment;
and issuing the operation and maintenance task to a corresponding operation and maintenance person so that the operation and maintenance person can carry out operation and maintenance management on the power equipment.
Through adopting above-mentioned technical scheme, this application carries out the unusual early warning control of power consumption to power equipment according to unusual analysis result, when triggering the unusual early warning information of power consumption, generates power equipment's fortune dimension task, issues fortune dimension task to corresponding fortune dimension personnel to make fortune dimension personnel carry out fortune dimension management to power equipment, thereby in time respond to the unusual power consumption.
Optionally, the method further comprises:
recording all event information generated in the operation process of the power equipment, wherein the event information comprises power utilization abnormality, abnormality early warning, operation and maintenance tasks and operation and maintenance operation information;
and generating a life cycle operation and maintenance file of the power equipment according to the all event information.
By adopting the technical scheme, all event information generated in the operation process of the power equipment is recorded, including power utilization abnormality, abnormality early warning, operation and maintenance tasks and operation and maintenance operation information, and then life cycle operation and maintenance files of the power equipment are generated so as to monitor the equipment state.
In a second aspect, the present application provides an electrical energy management system comprising:
the real-time state data acquisition module is used for acquiring real-time state data of the power stations and the power equipment corresponding to the stations;
the site running state display module is used for marking the position of the power site on the digital map and displaying the site running state corresponding to the position of the power site according to the real-time state data;
the power consumption analysis module is used for counting and analyzing the power consumption data of the power equipment according to the real-time state data, judging whether the power equipment has power consumption abnormality according to the power consumption data, and carrying out abnormality analysis on the real-time state data when the power equipment has power consumption abnormality to obtain an abnormality analysis result;
and the management module is used for managing the power equipment according to the abnormality analysis result.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the above-mentioned data monitoring method based on the power energy management system when the computer program is executed.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described data monitoring method based on an electrical power energy management system.
In summary, the present application includes at least one of the following beneficial technical effects:
the method comprises the steps of collecting real-time state data of power equipment corresponding to a power station, identifying on a digital map, displaying the running state of the power station, monitoring the power state in real time, counting and analyzing the power data of the power equipment, judging whether the power equipment has power utilization abnormality according to the power utilization data, carrying out abnormality analysis on the real-time state data when the power utilization abnormality exists, obtaining an abnormality analysis result, managing the power equipment according to the abnormality analysis result, and accordingly timely identifying the abnormal condition of the power equipment, carrying out abnormality early warning management when the abnormality data occurs and timely responding to the abnormality state;
the method comprises the steps of calculating the total power consumption in a preset time period according to the active power of each sampling time point in the preset time period, comparing the total power consumption with the historical contemporaneous total power consumption, judging whether power stations have power consumption abnormality according to comparison results, extracting time domain sample data corresponding to voltage parameters and current parameters when the power consumption abnormality exists, performing wavelet transformation to obtain energy feature vectors corresponding to the time domain sample data, inputting a preset abnormality type identification model, determining the power consumption abnormality type, and obtaining an abnormality analysis result, wherein the abnormality type can be automatically identified for the time period of the power consumption data abnormality, so that the power consumption abnormality can be responded in time, meanwhile, compared with the power consumption total power consumption comparison method which is directly judged according to parameter thresholds, the equipment condition can be reflected more reliably, transient alarm caused by transient parameter fluctuation and the like is reduced, wavelet analysis is not needed for each time period, and the calculation efficiency is higher;
the application specifically discloses a real-time state data is subjected to state hierarchy classification according to the deviation of real-time measured values of voltage parameters, current parameters and temperature parameters and normal thresholds, then visualized areas of different state hierarchies are displayed at the position of an electric power station of a digital map, when a preset instruction is received, corresponding real-time parameter values are displayed in a superimposed mode on the visualized areas, and a trend chart is popped up, so that the running state of the station is intuitively displayed, and the running state of the station is conveniently monitored.
Drawings
FIG. 1 is an exemplary flow chart of a method of data monitoring based on an electrical energy management system according to an embodiment of the present application;
FIG. 2 is an exemplary flow chart illustrating site operation status according to an embodiment of the present application;
FIG. 3 is an exemplary flow chart for anomaly analysis in accordance with an embodiment of the present application;
FIG. 4 is a block diagram of an electrical energy management system according to an embodiment of the present application;
fig. 5 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Description of the embodiments
The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application is intended to encompass any or all possible combinations of one or more of the listed items.
The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
Most of the existing power management systems adopt simple statistical tables to display data of site equipment, real-time monitoring of sites cannot be achieved, and when abnormal data occur, effective real-time monitoring and early warning management means are lacked, so that response is not timely.
The utility model provides a power energy data control and management method, through the real-time state data of the power equipment that gathers the electric website correspondence, then sign on the digital map, and demonstrate the running state of electric website, thereby can carry out real-time supervision to the electric power state, then judge whether there is the power consumption unusual according to the power consumption data through statistics and analysis power equipment's power consumption data, when there is the power consumption unusual, carry out the anomaly analysis to real-time state data, obtain the anomaly analysis result, then manage power equipment according to the anomaly analysis result, thereby can in time discern the power equipment abnormal condition, and carry out the unusual early warning management when the unusual data takes place, in time respond to the unusual state.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The method is executed by an electronic device, and the electronic device can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service. In this embodiment, the terminal device is an electronic device, but not limited to this, but may also be an intelligent tablet, a computer, or the like, where the terminal device and the server may be directly or indirectly connected through a wired or wireless communication manner, and the embodiment of the present application is not limited herein.
Referring to fig. 1, fig. 1 is an exemplary flowchart of a data monitoring method based on an electric power energy management system according to an embodiment of the present application.
In a first aspect, the present application provides a data monitoring method based on an electric power energy management system, including the steps of:
s110, acquiring real-time state data of power equipment corresponding to the power station.
The power stations refer to power supply stations in regional power grids, are places for generating and providing power, such as power plants, substations and the like, and each power station can comprise a plurality of power generating sets, transformers and other power equipment, and real-time state data refer to data for monitoring key power equipment of the power stations in real time and collecting states.
It can be understood that the power stations include features such as location, responsible area, installed capacity, etc., the power stations have specific geographic locations, and require geographic coordinate positioning, each power station has a corresponding power supply coverage area, specific power capacity that can be generated and supplied, and each power device has specific model numbers and corresponds to different technical parameters.
And S120, marking the position of the power station on the constructed digital map, and displaying the corresponding station running state at the corresponding power station position according to the real-time state data.
The digital map is an electronic map which stores geographic information data such as topography, roads, place names and the like in a computer system in a digitalized mode, a map server in the geographic information system is responsible for generating and providing the digital map, and a user can call and display the map through a map application programming interface. Registering longitude and latitude coordinates of the power station on the digital map in advance, displaying the digital map in real time by the monitoring platform, marking the position of the power station on the digital map, and displaying the corresponding station running state according to the real-time state data.
Specifically, as shown in fig. 2, the real-time status data includes a voltage parameter, a current parameter, a temperature parameter, and a power factor, and step S120 includes:
s121, comparing deviation of real-time measured values of the voltage parameter, the current parameter, the temperature parameter and the power factor with a normal threshold.
The method comprises the steps of acquiring voltage parameters, current parameters, temperature parameters and power factors in real time by a sensor arranged on the power equipment, presetting a normal threshold in a monitoring system, searching a normal threshold range corresponding to each parameter in a parameter normal range database according to equipment identification of the power equipment, and obtaining parameter deviation by calculating deviation between a real-time measured value and the normal threshold range.
S122, carrying out state level grading on the real-time state data according to the deviation of the voltage parameter, the current parameter, the temperature parameter and the power factor, wherein different colors and areas are adopted for displaying different state levels.
Specifically, through state hierarchy classification, different state hierarchies display different colors and areas, so that the power parameter can be monitored conveniently. In one embodiment, the state hierarchy is divided into three stages of normal, early warning and abnormal, wherein the deviation value is 0 within the normal threshold range, green can be displayed, more than 5% of the states are abnormal, red can be displayed, early warning is displayed between the states, and yellow identification is adopted, so that monitoring staff can conveniently distinguish the running states of the power equipment according to colors.
It can be understood that when the parameters of any one of the power devices corresponding to the power station are abnormal, the abnormal state is classified as an abnormal state level.
S123, displaying the visualized areas of different state levels at the corresponding power station positions.
Specifically, longitude and latitude coordinates of the power station are obtained by calling a digital map, then a circular area is drawn at the coordinate position on the map, the size of the area represents the installed capacity, and the color represents the state level.
And S124, when a preset instruction is received, displaying corresponding real-time state information in a superposition manner on the visual area, and popping up a trend chart to show the running state of the station.
Specifically, when a monitoring person clicks a region corresponding to a site, a preset instruction is triggered, and corresponding real-time state information is displayed in a superimposed manner on the visual region. In some embodiments, recent fault history is loaded from a database, and a trend graph of the number of faults is plotted to display the fault status of the site equipment.
S130, counting and analyzing the power consumption data of the power equipment according to the real-time state data, judging whether the power consumption abnormality exists in the power equipment according to the power consumption data, and performing abnormality analysis on the real-time state data when the power consumption abnormality exists in the power equipment to obtain an abnormality analysis result.
Specifically, as shown in fig. 3, step S130 includes:
s131, calculating the active power of each sampling time point in the preset time period according to the real-time state data, and counting the total power consumption in the preset time period.
Wherein, the active power p=uicos phi, U is a voltage parameter, I is a current parameter, cos phi is a power factor. The voltage parameter, current parameter and power factor are derived from the collected real-time status data. Specifically, in one embodiment, the sampling is performed according to a set sampling frequency, for example, every 1 minute is used as a sampling point, the instantaneous power of the preset duration is integrated, and the total power consumption of one hour, one day and seven days is obtained through accumulation, that is, the preset duration can be one hour, one day or one week, and the power consumption statistics analysis is performed through time windows of multiple dimensions of each hour, each day and each week, so that the situation that a single window causes erroneous judgment is avoided.
And S132, comparing the total power consumption with the historical contemporaneous total power consumption of the power equipment, and judging whether the power equipment has abnormal power consumption according to a comparison result.
The historical synchronous electricity consumption total is stored in the equipment electricity consumption database, the electricity consumption abnormality is judged when the percentage exceeds the threshold value by comparing the percentages of the difference between the historical electricity consumption total and the historical electricity consumption total, compared with the electricity consumption total which is judged directly according to the parameter threshold value, the electricity consumption total can reflect the equipment condition more reliably, the instantaneous alarm caused by instantaneous parameter fluctuation and the like is reduced, meanwhile, wavelet analysis is not needed to be carried out on each time period, and the calculation efficiency is higher.
S133, when the power equipment has abnormal power consumption, extracting time domain sample characteristics corresponding to voltage parameters and current parameters of the power equipment.
And extracting a voltage waveform segment and a current waveform segment from waveform data of the real-time state data in a preset duration as time domain samples.
S134, performing wavelet transformation on the time domain sample characteristics to obtain energy characteristic vectors corresponding to the time domain sample data.
Specifically, step S134 includes:
s1341, performing L-layer wavelet decomposition on the time domain sample data to obtain 1 low-frequency approximate signal A and L high-frequency detail signals D1 to DL.
Specifically, in one embodiment, 5 layers of db wavelet decomposition are performed on the voltage waveform and the current waveform, each layer of decomposition resulting in a low frequency portion and a high frequency portion, together obtaining 1 low frequency signal a and 5 high frequency signals D1-D5.
S1342, calculating the energy EA of the low-frequency approximation signal a as the first energy characteristic.
The energy EA of the signal is obtained by analyzing the square sum of waveform amplitude of the low-frequency signal a.
Specifically, for a low frequency approximation signal A (t) consisting of N sample points, the sequence of sample points is A= { a (1), a (2),. The energy EA of signal A may be calculated according to the following formula EA= Σ_ { n=1 } N [ a (N) ]2, where a (N) represents the amplitude of the nth sample point. And accumulating and summing the squares of each sample point to obtain the low-frequency signal energy EA.
S1343, respectively calculating the energies ED1 to EDL of the L high frequency detail signals D1 to DL as second to l+1 energy features.
Wherein the energies ED1-ED5 of the 5 high frequency detail signals D1-D5 are calculated in the same way.
S1344, the first energy feature and the second to l+1 energy feature constitute an energy feature vector [ EA, ED1, ED2, ] EDL.
S135, inputting the energy feature vector into a preset abnormal type identification model, determining the abnormal type of electricity consumption of the power equipment, and obtaining an abnormal analysis result.
And inputting the energy feature vector into a preset abnormality type identification model, and finally determining the power consumption abnormality type of the power equipment to obtain an abnormality analysis result.
The process for establishing the abnormal type identification model comprises the following steps:
s1351, acquiring historical power consumption data of the power equipment with different anomaly types.
The historical power consumption data selects waveform data of typical power equipment such as a generator, a transformer and the like in different fault modes, wherein the fault modes include insulation faults, acidification faults, overload and the like.
S1352, performing wavelet transformation on the historical electricity utilization data, extracting energy characteristics, and constructing a sample characteristic vector.
The db wavelet is used for 5-layer decomposition, and the energy feature vector is extracted according to the method of step S134.
S1353, labeling the sample feature vectors according to a preset labeling rule, and determining different types of abnormal electricity consumption samples.
Specifically, each dimension characteristic value of the wavelet energy characteristic vector is drawn into a line graph so as to reflect energy distribution of different frequency bands, and then different fault mode samples are marked by judging the view form of the line graph of the characteristic vector, so that different types of abnormal electricity consumption samples are obtained.
S1354, training the neural network model to be trained through a support vector machine algorithm according to the marked power consumption abnormal samples to obtain an abnormal type identification model based on wavelet transformation energy characteristics.
And S140, carrying out abnormality early warning management on the power equipment according to an abnormality analysis result.
In one embodiment, a method of managing electrical devices includes:
s141, carrying out electricity consumption abnormality early warning monitoring on the power equipment according to an abnormality analysis result.
After the abnormality of the power equipment is determined according to the abnormality analysis result, an early warning generation module is called through an internal interface, early warning content is generated according to the abnormality analysis result, then early warning is popped up on a monitoring interface, and meanwhile, the subsequent operation and maintenance flow is triggered.
And S142, when the electricity utilization abnormality early warning information is triggered, generating an operation and maintenance task of the power equipment.
After the early warning content is received, a corresponding operation and maintenance work order is automatically generated according to a predefined abnormal processing flow, and the work order is issued to operation and maintenance personnel corresponding to the responsible area through short messages, mails and the like.
And S143, issuing the operation and maintenance task to a corresponding operation and maintenance person so that the operation and maintenance person can carry out operation and maintenance management on the power equipment.
S144, recording all event information generated in the operation process of the power equipment.
The event information comprises product information, installation information, operation data, alarms, operation and maintenance records and the like.
S145, generating a life cycle operation file of the power equipment according to all the event information.
All event information is input into the equipment operation and maintenance database, and when equipment is checked in the database, the complete life cycle file from installation and debugging to operation and maintenance of the equipment can be checked, so that long-term management is facilitated.
In one embodiment, a periodic service plan for the electrical equipment is generated from the lifecycle operation and maintenance profile, and equipment service reminders and overdue service alarms are performed.
In one embodiment, each power device generates a corresponding two-dimensional code, so that operation and maintenance personnel report inspection and repair records and operation and maintenance operation records, when the power device is started for a period of time plus an inspection period, an inspection work order is automatically pushed, and the records are submitted to form a closed loop after inspection.
The implementation principle of the data monitoring method based on the electric power energy management system in the embodiment of the application is as follows: according to the method, the device and the system, the identification is carried out on the digital map, the running state of the power station is displayed, so that the power state can be monitored in real time, then the power consumption data of the power equipment are counted and analyzed, whether the power equipment has power consumption abnormality or not is judged according to the power consumption data, when the power consumption abnormality exists, the real-time state data are subjected to abnormality analysis to obtain an abnormality analysis result, then the power equipment is managed according to the abnormality analysis result, the abnormality of the power equipment can be timely identified, abnormality early warning management is carried out when the abnormality data occur, and the abnormal state is responded timely.
In a second aspect, the present application provides an electric power energy management system, and the electric power energy management system of the present application is described below in conjunction with the above data monitoring method based on the electric power energy management system. Referring to fig. 4, fig. 4 is a schematic block diagram of an electric power energy management system according to an embodiment of the present application.
An electrical power energy management system, comprising:
the real-time status data acquisition module 410 is configured to acquire real-time status data of power equipment corresponding to a power station;
the site running state display module 420 is configured to identify a power site location on the constructed digital map, and display a corresponding site running state at a corresponding power site location according to the real-time state data;
the electricity analysis module 430 is configured to count and analyze electricity consumption data of the electrical device according to the real-time status data, determine whether the electrical device has electricity consumption abnormality according to the electricity consumption data, and perform abnormality analysis on the real-time status data when the electrical device has electricity consumption abnormality, so as to obtain an abnormality analysis result;
the early warning management module 440 is configured to perform an abnormal early warning management on the electrical device according to the abnormal analysis result.
In one embodiment, the present application provides an electronic device, which may be a server, whose internal structure may be as shown in fig. 5. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the electronic device is for storing data. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a data monitoring method based on an electrical energy management system.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the electronic device to which the present application is applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided an electronic device including a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method embodiments described above when executing the computer program.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (5)
1. The data monitoring method based on the electric power energy management system is characterized by comprising the following steps of:
collecting real-time state data of power equipment corresponding to a power station;
marking the position of the power station on the constructed digital map, and displaying the corresponding station running state at the corresponding position of the power station according to the real-time state data;
counting and analyzing the power consumption data of the power equipment according to the real-time state data, judging whether the power equipment has power consumption abnormality according to the power consumption data, and performing abnormality analysis on the real-time state data when the power equipment has power consumption abnormality to obtain an abnormality analysis result;
performing abnormality early warning management on the power equipment according to the abnormality analysis result;
the real-time state data comprises a voltage parameter, a current parameter, a temperature parameter and a power factor, the power consumption data of the power equipment are counted and analyzed according to the real-time state data, whether the power equipment has abnormal power consumption is judged according to the power consumption data, when the power equipment has abnormal power consumption, the real-time state data is subjected to abnormal analysis to obtain an abnormal analysis result, and the method specifically comprises the following steps:
calculating the active power of each sampling time point in the preset time period according to the real-time state data, and counting the total power consumption in the preset time period;
comparing the total electricity consumption with the historical contemporaneous electricity consumption of the power equipment, and judging whether the power equipment has abnormal electricity consumption according to a comparison result;
when the power equipment has abnormal power consumption, extracting time domain sample data corresponding to voltage parameters and current parameters of the power equipment;
performing wavelet transformation on the time domain sample data to obtain an energy feature vector corresponding to the time domain sample data;
inputting the energy feature vector into a preset abnormal type identification model, determining the power consumption abnormal type of the power equipment, and obtaining an abnormal analysis result;
the step of performing wavelet transformation on the time domain sample data to obtain an energy feature vector corresponding to the time domain sample data specifically comprises the following steps:
performing L-layer wavelet decomposition on the time domain sample data to obtain 1 low-frequency approximate signal A and L high-frequency detail signals D1 to DL;
calculating energy EA of the low-frequency approximation signal A as a first energy characteristic;
calculating energy ED1 to EDL of the L high-frequency detail signals D1 to DL respectively as second to L+1 energy characteristics;
the first energy feature and the second to l+1 energy feature constitute an energy feature vector [ EA, ED1, ED2, ].
2. The method for monitoring data based on an electric power energy management system according to claim 1, wherein the process for establishing the abnormality type identification model includes:
acquiring historical electricity utilization data of the power equipment with different anomaly types;
performing wavelet transformation on the historical electricity utilization data, extracting energy characteristics, and constructing a sample characteristic vector;
labeling the sample feature vectors according to a preset labeling rule, and determining different types of abnormal electricity consumption samples;
and training the neural network model to be trained through a support vector machine algorithm according to the marked power consumption abnormal samples to obtain an abnormal type identification model based on wavelet transformation energy characteristics.
3. The method for monitoring data based on an electric power energy management system according to claim 1, wherein the real-time status data includes a voltage parameter, a current parameter, a temperature parameter and a power factor, and the displaying the corresponding site operation status at the corresponding electric power site location according to the real-time status data specifically includes:
comparing the deviation of the real-time measured values of the voltage parameter, the current parameter, the temperature parameter and the power factor with the normal threshold;
carrying out state hierarchy classification on the real-time state data according to the deviation of the voltage parameter, the current parameter, the temperature parameter and the power factor, wherein different colors and areas are adopted for displaying different state hierarchies;
displaying the visualized areas of different state levels at the corresponding power station positions;
and when a preset instruction is received, displaying corresponding real-time state information in a superposition manner on the visual area, and popping up a trend chart to display the running state of the station.
4. The data monitoring method based on the electric power energy management system according to claim 1, wherein the performing the abnormality early warning management on the electric power equipment according to the abnormality analysis result specifically includes:
performing power consumption abnormality early warning monitoring on the power equipment according to the abnormality analysis result;
when the electricity utilization abnormality early warning information is triggered, generating an operation and maintenance task of the power equipment;
and issuing the operation and maintenance task to a corresponding operation and maintenance person so that the operation and maintenance person can carry out operation and maintenance management on the power equipment.
5. The method of data monitoring based on an electrical energy management system of claim 4, further comprising:
recording all event information generated in the operation process of the power equipment, wherein the event information comprises product information, installation information, operation data, alarm and operation and maintenance record information;
and generating a life cycle operation and maintenance file of the power equipment according to the all event information.
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