CN116128467A - Power utilization system maintenance method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a user system maintenance method, a user system maintenance device, computer equipment and a storage medium. The method comprises the following steps: collecting information data of each station area; constructing a high-dimensional random matrix according to the information data; judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix; when the power utilization system operates abnormally, analyzing abnormal conditions and acquiring a coping scheme according to analysis results. By adopting the method, the abnormal position of the power utilization system can be efficiently detected, and a scheme for coping with the abnormality can be timely given, so that the power utilization system can maintain normal operation.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method and apparatus for maintaining an electrical system, a computer device, and a storage medium.
Background
The low-voltage distribution network is used as a ring for directly supplying power to users, and the normal operation of the low-voltage distribution network influences the power consumption behavior and the power consumption quality of the users. At present, along with popularization of intelligent electric meters and perfection of an electric power consumer electricity consumption information acquisition system, electric power data resources are rapidly increased. At present, abnormal electricity utilization behaviors at a user side can be identified to a certain extent by utilizing line loss management functions and the like of an electricity utilization information acquisition system, but the method cannot be positioned to specific users and specific moments of abnormal electricity utilization of the users; therefore, how to utilize the widely distributed data information of the intelligent electric meter and the electricity consumption information of the electricity consumption information acquisition system to carry out data mining, thereby realizing comprehensive, intelligent and accurate analysis of the abnormal electricity consumption behaviors of the user, and being a research subject with very practical significance for management of the abnormal electricity consumption behaviors of the user side in the big data age.
In the maintenance process of the power utilization system, the problem that the release of fault information is slow and the call repair is not finished is frequently caused in the power supply process, the problems promote us to start using the artificial intelligence technology, improve the efficiency in work and avoid human errors to a certain extent, and are the application benefits of the artificial intelligence technology. Along with the rapid development of the science and technology in China, the artificial intelligence technology is well developed, and many enterprises are currently applying the artificial intelligence technology, and the maintenance process of the power utilization system in the power enterprises cannot be delayed.
The adoption of artificial intelligence technology is a primary task at present, and the artificial intelligence technology can perform language identification, image identification, natural language processing, and conversation with a user, etc., and the technology can provide effective help for the maintenance process of an electric system. The problem of user electricity utilization potential safety hazard is solved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a power system maintenance method, apparatus, computer device, and computer-readable storage medium that can efficiently maintain a power system.
In a first aspect, the present application provides a method of maintaining an electrical system. The method comprises the following steps:
collecting information data of each station area;
constructing a high-dimensional random matrix according to the information data;
judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix;
when the power utilization system operates abnormally, analyzing abnormal conditions and acquiring a coping scheme according to analysis results.
In one embodiment, the information data includes active power; the step of collecting information data of each area comprises the following steps:
and collecting the active power recorded in the intelligent ammeter of each area according to the time sequence.
In one embodiment, constructing a high-dimensional random matrix from the information data includes:
preprocessing each collected active power to obtain a preprocessing result; the pretreatment result is that no abnormal active power data exists;
and constructing a high-dimensional random matrix according to the preprocessing result.
In one embodiment, the preprocessing each collected active power to obtain a preprocessing result includes:
constructing an electricity consumption abnormality analysis model according to each active power;
determining abnormal active power data in a plurality of active powers based on the electricity utilization abnormality analysis model;
and detecting and repairing the abnormal data to obtain a preprocessing result.
In one embodiment, the information data further includes at least one of a voltage value, a current value, reactive power, three-phase voltage data, and a communication line type; constructing a high-dimensional random matrix from the information data further includes:
and establishing a corresponding high-dimensional random matrix according to each information data.
In one embodiment, the determining whether the power utilization system of each area operates normally according to the high-dimensional random matrix includes:
carrying out normalized representation on the high-dimensional random matrix to obtain state data of a corresponding station area of the high-dimensional random matrix;
and judging whether the power utilization system of the platform area normally operates according to the state data.
In one embodiment, the status data includes abnormal and normal; the normalizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix comprises the following steps:
obtaining a covariance matrix of a high-dimensional random matrix;
acquiring a characteristic value of the covariance matrix;
judging whether the characteristic values are intensively distributed in a circular ring obtained according to the single-ring theorem or not according to the single-ring theorem;
if the characteristic values are not distributed in the circular ring in a concentrated mode, judging that the state data are abnormal, otherwise, judging that the state data are normal.
In one embodiment, the status data includes normal and abnormal; the normalizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix further comprises:
acquiring a characteristic value spectral density function of the high-dimensional random matrix;
judging whether the characteristic value spectral density function complies with the Marchenko-Pasteur theorem;
if the characteristic value spectral density function complies with the Marchenko-Pasteur theorem, the state data is normal, otherwise, the state data is abnormal.
In one embodiment, analyzing the abnormal situation and obtaining the countermeasure solution according to the analysis result includes:
determining an anomaly type according to the state data; the abnormal type comprises at least one of electricity stealing by a user and damage of the intelligent electric meter;
and sending the abnormal data type to a service platform to instruct the service platform to formulate an operation and maintenance scheme according to the abnormal type and send abnormal early warning information to a user.
In a second aspect, the present application also provides an electrical system maintenance device. The device comprises:
the data acquisition module is used for acquiring multi-source data of each area according to the time sequence;
the data processing module is used for processing the multi-source data to construct a high-dimensional random matrix;
the judging module is used for judging whether the power utilization system of each area normally operates according to the high-dimensional random matrix;
and the abnormal maintenance module is used for analyzing abnormal conditions and acquiring a coping scheme according to an analysis result when the power utilization system operates abnormally.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the method steps of any of the embodiments described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method steps of any of the embodiments described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the method steps of any of the embodiments described above.
The maintenance method, the device, the computer equipment and the storage medium of the power utilization system collect the information data of each area; constructing a high-dimensional random matrix according to the information data; judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix; when the power utilization system runs abnormally, the abnormal condition is analyzed, a coping scheme is obtained according to the analysis result, the abnormal position of the power utilization system can be detected efficiently, and the coping scheme is given to the abnormality in time, so that the power utilization system can maintain normal running.
Drawings
FIG. 1 is a flow chart of a method of maintaining an electrical system in one embodiment;
FIG. 2 is a flow chart of a method for obtaining a preprocessing result in one embodiment;
FIG. 3 is a schematic diagram of a status data flow for obtaining a high-dimensional random matrix corresponding to a region in one embodiment;
FIG. 4 is a flow chart of another method for obtaining status data of a corresponding area of a high-dimensional random array according to one embodiment;
FIG. 5 is a block diagram of a service platform architecture in one embodiment;
FIG. 6 is a block diagram of an electrical maintenance system in one embodiment;
FIG. 7 is a block diagram of an electrical system maintenance device in one embodiment;
fig. 8 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in the power system maintenance method flowchart of FIG. 1, the present application provides a power system maintenance method comprising the following steps 102-108.
The transformer area refers to a power supply range or area of a transformer, which is a noun of power economy operation management and is also an area place name in a specific statement.
And 104, constructing a high-dimensional random matrix according to the information data.
And step 106, judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix.
And step 108, when the power utilization system operates abnormally, analyzing abnormal conditions and acquiring a coping scheme according to an analysis result.
In the maintenance method of the power utilization system, information data of each area are collected; constructing a high-dimensional random matrix according to the information data; judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix; when the power utilization system runs abnormally, the abnormal condition is analyzed, a coping scheme is obtained according to the analysis result, the abnormal position of the power utilization system can be detected efficiently, and the coping scheme is given to the abnormality in time, so that the power utilization system can maintain normal running.
In one embodiment, the information data includes active power; the step of collecting information data of each area comprises the following steps: and collecting the active power recorded in the intelligent ammeter of each area according to the time sequence.
The step of collecting the active power of each station area according to the time sequence refers to storing the active power in the station areas according to the time sequence logic.
In this embodiment, the active power may also be obtained from an electricity consumption information collection system and an online detection system. Active power data of each station area is collected according to the time sequence, so that the electricity utilization condition of each station area can be analyzed more accurately, and the maintenance efficiency of the electricity utilization system of each station area is improved.
In one embodiment, constructing a high-dimensional random matrix from the information data comprises: preprocessing each collected active power to obtain a preprocessing result; the pretreatment result is that no abnormal active power data exists; and constructing a high-dimensional random matrix according to the preprocessing result.
In the embodiment, the collected active power is preprocessed, so that the high-dimensional random matrix formed by the active power can be normalized and characterized in real time, and a data base is provided for the user side running state and abnormal positioning analysis of the low-voltage power distribution network. Further, the pretreatment result after pretreatment is limited in a certain range, so that adverse effects caused by singular sample data are reduced, and the accuracy of analyzing whether the power consumption system is abnormal is improved.
In one embodiment, a flowchart of the preprocessing result is shown in fig. 2; the preprocessing the collected active powers to obtain a preprocessing result includes the following steps 202 to 206;
step 202: and constructing an electricity consumption abnormality analysis model according to each active power.
Wherein the electrical anomaly analysis model is also called the cluster analysis of K-means.
Step 204: abnormal active power data in a plurality of the active powers is determined based on the electrical anomaly analysis model.
Step 206: and detecting and repairing the abnormal data to obtain a preprocessing result.
In the embodiment, abnormal data is obtained according to the electricity consumption abnormality analysis model, and detection, repair and cleaning of the abnormal data are performed on the basis of the cluster analysis of K-means. Through the process, the conditions of data loss or abnormal occurrence caused by factors such as equipment, environment and running state can be reduced, data analysis errors are reduced, data accuracy is improved, and a foundation is laid for further obtaining prepared analysis results.
In one embodiment, the clustering of K-means from information data includes the following: establishing a data set d= { D 1 ，D 2 ，…D j ，…，D F }；D J ＝{x j1 ，x j2 ，…，x jn -a }; f is the information data type number in the data set; n is the number of sampling points of a certain information data type; x is x ji (j=1, 2, …, F; i=1, 2, …, n) is the value of the i-th sampling point of the j-th type information data. Obtaining a relevant sample and Euclidean distance according to K-means data clustering analysis:
wherein D (D) s ，D j ) Is the Euclidean distance; obtaining a minimum value of an average error criterion function of the data set: />
Wherein k is a cluster group number, n i For the mean value of the data in the i group, q i The number of the data in the i group; x is x ij Is the value of the j data of the i group. The detection, repair and cleaning of abnormal data based on the cluster analysis of K-means comprises the steps of paradox according to the Pearson-Stephens methodDetecting and repairing the normal data; the step of detecting the abnormal data includes: arranging a group of data in the cluster group in order of from the top to the bottom (y) 1 ，y 2 ，…，y n ) The method comprises the steps of carrying out a first treatment on the surface of the According to the formula->
Calculating the ratio of the polar difference to the standard deviation, and comparing the value with a certain significant level alpha so as to judge whether the value is an abnormal value, wherein R is the polar difference of a data set; s is the standard deviation of the data set; />
A mean of the data set. The repairing of the abnormal data specifically refers to repairing the abnormal data of the same group, different data sets, the same position and the same field data respectively to obtain a repairing value. The formula for obtaining the repair value is:
wherein y is i Representing a repair value; n is n num The number of data groups included in the cluster group is represented.
In this embodiment, specifically, in the practical engineering application process, a matrix with a number of rows and columns exceeding 100 is generally regarded as a high-dimensional matrix. The low-voltage power distribution network is assumed to contain n observation points (n intelligent electric meters), and each observation point can obtain actual electricity utilization data information of a user at the observation point and can store historical electricity utilization data information of the user in a period of time. At the observation time t there is a time series vector z; (i=1, 2, …, n), then during the observation period [0, t]The data information of the user-side intelligent ammeter acquired in the method can form an n multiplied by t time sequence matrix Z n×t ，
When the number of rows n is smaller than the number of columns t, Z tn Splitting into m segments in sequence, i.e. Z tn ＝(Z 1n ，Z 2n ，…，Z mn ) And fold the split m segments line by lineTo generate a high-dimensional random matrix Z M×N Converting the n×t-dimensional matrix of the original measurement data set into a (nm) x (t/M) -dimensional random matrix, ensuring that the number of rows (m=nm) and the number of columns (n=t/M) of the random matrix are of the same order of magnitude, wherein Z' M×N ＝(Z 11 ，Z 21 ，…，Z m1 ，…，Z 1n ，Z 2n ，…，Z nm ) T . Normalizing the high-dimensional matrix comprises normalizing the data of each dimension, i.e. normalizing the data of each dimension, i.e +.>
Wherein Z is ij * The data value of the j state quantity in the ith dimension after normalization; z is Z ij The data is the ith dimension and the jth state quantity data; mu (mu) i ， normal The average value of the i-th dimension state quantity history normal operation data is obtained; sigma (sigma) i ， normal And (5) the standard deviation of the normal operation data of the i-th dimension state quantity history.
In one embodiment, the information data further includes at least one of a voltage value, a current value, reactive power, three-phase voltage data, and a communication line type; constructing a high-dimensional random matrix from the information data further includes: and establishing a corresponding high-dimensional random matrix according to each information data.
In this embodiment, the power consumption system may also be subjected to anomaly analysis from other information data angles through other data in the information data, such as a voltage value, a current value, reactive power, three-phase voltage data, a communication line type, and the like, so as to obtain multidimensional data, and more accurately analyze the anomaly condition of the power consumption system to make a more accurate response scheme.
In one embodiment, the determining whether the power utilization system of each area operates normally according to the high-dimensional random matrix includes: carrying out normalized representation on the high-dimensional random matrix to obtain state data of a corresponding station area of the high-dimensional random matrix; and judging whether the power utilization system of the platform area normally operates according to the state data.
In this embodiment, the state data of the area corresponding to the high-dimensional random matrix is obtained by performing normalized characterization on the dimensional random matrix, so that accurate state data can be obtained to determine the use state of the power utilization system.
In one embodiment, a state data flow diagram of a corresponding area of the high-dimensional random matrix is obtained as shown in fig. 3; the status data includes abnormal and normal; the normalizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix includes the following steps 302 to 308;
step 302: and obtaining a covariance matrix of the high-dimensional random matrix.
Step 304: and obtaining the eigenvalue of the covariance matrix.
Wherein the eigenvalues of the covariance matrix are also referred to as the average spectral radius of the covariance matrix.
Step 306: judging whether the characteristic values are intensively distributed in a circular ring obtained according to the single-ring theorem or not according to the single-ring theorem.
Step 308: if the characteristic values are not distributed in the circular ring in a concentrated mode, judging that the state data are abnormal, otherwise, judging that the state data are normal.
In this embodiment, specifically, a high-dimensional random matrix including a time sequence is formed according to the thought of 'a station area- & gt a power supply station is changed- & gt a user side node set' from a large area to a small area, and the time sequence and the acquisition time step are combined to analyze and evaluate the user side power consumption condition in the acquisition time window through the MSR change trend condition. In this embodiment, the change of the eigenvalue of the covariance matrix may directly reflect the change of the information data, where the information data may also be referred to as user-side electricity data. Obviously, when the conditions such as load mutation and the like occur in the electricity consumption data of the user side, the information data obtained by the intelligent electric meter also becomes mutated, and at the moment, a high-dimensional random matrix formed by data cleaning, high-dimensional characterization and normalization processing of the history and the real-time information data is no longer in accordance with the random matrix principle, and the occurrence of abnormal electricity consumption behavior of the user side can be judged through the verification of the single-ring theorem and the M-P theorem. Further, the ratio of the active power of the selected nodes for observing the electricity larceny point is formedThe covariance matrix eigenvalue average spectrum radius (i.e. eigenvalue) change rule along with time, and can judge the electricity stealing points and the electricity stealing types. Specifically, it is known from the single-loop theorem that for the NxT-th order non-Hermitian matrix X N×T When the elements in the matrix are independently and uniformly distributed, the expected value is 0, and the variance is 1, the matrix U can be used for matching X N×T Performing odd-differentiation on the sample covariance matrix of the number to obtain an equivalent matrix
Let->
Where L is the number of equivalent matrices, when N, T approaches infinity and N/t=c e (0, 1]When (I)>
Eigenvalue lambda of t Is at radius +.>
Within the defined circle. Wherein the characteristic value lambda t The probability density function of (2) can be expressed as: />
I.e. high-dimensional matrix->
Is distributed at a radius +.>
And 1.
In one embodiment, as shown in fig. 4, another flow chart for acquiring the state data of the high-dimensional random array corresponding to the area is shown; the status data includes normal and abnormal; the normalizing and characterizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix further includes the following steps 402 to 406;
step 402: and obtaining the characteristic value spectral density function of the high-dimensional random matrix.
Step 404: and judging whether the characteristic value spectral density function complies with the Marchenko-Pasteur theorem.
Step 406: if the characteristic value spectral density function complies with the Marchenko-Pasteur theorem, the state data is normal, otherwise, the state data is abnormal.
In the present embodiment, it is known from Marchenko-Pasteur theorem that for an NxT order non-Hermitian matrix X N×T The covariance matrix is S N×T Assume an NxT order non-Hermitian matrix X N×T The elements in the matrix are independently distributed at the same time, and the average value mu=0, the variance sigma < ≡infinity, when the number N is N, T tends to infinity and N/T=c ε (0, 1]When X covariance matrix S N×T Is non-random convergence of the empirical spectral distribution of (a) to a density function
Wherein (1)>
As covariance matrix S N×N Characteristic value of>
/>
In one embodiment, analyzing and evaluating the user side electricity consumption condition in the collection time window through the MSR change trend condition includes: the linear eigenvalue statistic reflects the high-dimensional random matrix eigenvalue distribution. For a random matrix, the characteristic value statistics are as follows
Wherein lambda is i (i=1, 2, …, N) is the feature root of X; />
Is a test function. By law of large numbersKnow->
Where ρ (λ) is the probability density function of the feature root λ.
In one embodiment, the change rule of the average spectrum radius of covariance matrix eigenvalue over time, which is respectively formed by 4 parameters of node voltage, node current, total active power of a branch and the ratio of active power to reactive power of the node, of the observed electricity stealing point is taken, and the electricity stealing type of the electricity stealing point is judged. After transformation, the time series data of each parameter of the electricity stealing points can obtain a matrix with 86000 multiplied by 1 dimension, and a high-dimension random matrix with 100 multiplied by 860 dimension can be formed. Table 1 shows the covariance eigenvalue average spectrum radius of each electricity theft type in the observation time, and the situation that the mutation point exists in the observation time can be concluded as follows:
TABLE 1 covariance eigenvalue average spectral radius for each power theft type parameter with or without abrupt change
| Parameters (parameters) | Under-current method for stealing electricity | Under-voltage method for stealing electricity | No-meter method for stealing electricity |
| Node voltage | Without any means for | Has the following components | Without any means for |
| Node current | Has the following components | Without any means for | Without any means for |
| Branch total active power | Has the following components | Has the following components | Has the following components |
| Node active-reactive ratio | Without any means for | Without any means for | Without any means for |
As can be seen from Table 1, when the average spectrum radius of the node voltage of the electricity stealing point and the ratio of the node active power to the node reactive power does not have obvious mutation points, the average spectrum radius of the node current of the electricity stealing point and the total active power of the branch circuit have mutation points at the starting time and the ending time of electricity stealing, and at the moment, the occurrence of under-current electricity stealing at the electricity stealing point can be judged. When the average spectrum radius of the node current of the electricity stealing point and the ratio of the node active power to the node reactive power does not appear obvious mutation points; the average spectrum radius of the node voltage and the total active power of the branch circuit has abrupt change points at the starting time and the ending time of electricity stealing, and at the moment, the occurrence of undervoltage electricity stealing at the electricity stealing point can be judged. When the node current of the electricity stealing point is changed, abnormal fluctuation points do not appear in the average spectrum radius change rate of the node voltage and the ratio of the node active power to the node reactive power; only the total active power of the branch circuit to which the node belongs has abrupt change points at the starting time and the ending time of electricity stealing, and at the moment, the phenomenon that the electricity stealing point cannot steal electricity in a meter can be judged. The conventional meter-free electricity larceny judging method is to compare the total electricity consumption measured by the line total electricity meter with the sum of the electricity consumption measured by the electricity meters on the connected branches so as to judge whether the electricity larceny of the type occurs.
In one embodiment, analyzing the abnormal situation and obtaining the countermeasure solution according to the analysis result includes: determining an anomaly type according to the state data; the abnormal type comprises at least one of electricity stealing by a user and damage of the intelligent electric meter; and sending the abnormal data type to a service platform to instruct the service platform to formulate an operation and maintenance scheme according to the abnormal type and send abnormal early warning information to a user.
In this embodiment, when the abnormal type is determined, or the fault type of the ammeter is determined through the state data, the abnormal condition is sent to the service platform to instruct the service platform to make an operation and maintenance scheme according to the abnormal type, repair the fault ground, and generate fault information according to the abnormal type and send the fault information to the user to early warn the user.
In one embodiment, as shown in the service platform structure block diagram of fig. 5, the service platform 500 includes a user service command module 510, a coordination command module 520, a distribution network management control module 530, and an electrical system quality supervision module 540. The user service module 510 can provide internet and propaganda service, command non-rush repair work orders, command user emergency, monitor the condition of each service node and maintain the normal knowledge base; the coordination command module 520 is used for managing power outage information, commanding emergency repair work, managing emergency management, monitoring power-saving tasks and uniformly releasing service information; the distribution network management control module 530 is configured to manage and control a shutdown state of the distribution network, analyze a distribution network defect, perform statistical analysis on the distribution network defect, perform early warning on a distribution network operation risk, generate comprehensive evaluation according to a distribution network operation condition, and automatically process a work order; the power consumption system quality monitoring module 540 is used for monitoring and analyzing a business development report, managing and controlling power consumption users of an electronic channel, executing and analyzing a power outage plan, monitoring user service events, managing and controlling and analyzing the operation state of a distribution network, displaying service panorama of the distribution network, and checking the quality of marketing and distribution data.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiments of the present application provide an electricity maintenance system for implementing the above-mentioned related electricity system maintenance method. The power consumption maintenance system specifically shown in fig. 6 comprises a terminal power grid intelligent sensing module, a user power consumption information acquisition module, a service platform and a terminal power grid, wherein the terminal power grid is in communication connection with the terminal power grid intelligent sensing module, the terminal power grid intelligent sensing module is in communication connection with the user power consumption information acquisition module, the user power consumption information acquisition module is connected with the service platform, the terminal power grid comprises a distribution transformer, a branch box and an electric energy meter, and the distribution transformer, the branch box and the electric energy meter are connected with the terminal power grid intelligent sensing module through detection equipment; the monitoring equipment is used for monitoring real-time operation data and operation states of the distribution transformer, the branch box and the electric energy meter, the terminal power grid intelligent sensing module is used for dynamically sharing link information of distribution, electricity utilization and rush repair, the user electricity utilization information acquisition module is used for acquiring and analyzing the electricity utilization data of the distribution transformer, the branch box and the electric energy meter, and the service platform is used for overall dispatching and commanding and can directly negotiate with a user side; the intelligent sensing module of the terminal power grid monitors the power data of the terminal power grid through monitoring equipment, the monitoring equipment can integrate the power data into the intelligent sensing module of the terminal power grid, dynamically share information of all links such as power distribution, power consumption, rush repair and the like, and then sends the power data to the power consumption information acquisition module of a user, so that accurate fault positioning and active operation and maintenance service are realized; the user electricity information acquisition module analyzes the electric power data to obtain fault judging and impedance data, then the fault judging and impedance data are pushed to the power supply service platform, the power supply service platform and the PMS system distribute shipping and rush repair work orders according to the information, the user electricity information acquisition module, the service platform and the PMS system are completely penetrated, island operation of the information system is effectively eliminated, propaganda and production can be effectively fused, and cross-professional efficient operation and closed-loop management of distribution network active operation and fault rush repair flow are realized; the monitoring equipment monitors the switch states of the distribution transformer, the branch box and the electric energy meter in real time, acquires the power failure range and the fault point in advance, judges the power failure type and the area in advance, combines the work orders, and reasonably configures the rush-repair resources, so that the rush-repair cost of unnecessary rush-repair vehicles, invalid dispatchers and the like is reduced; the intelligent sensing module of the terminal power grid utilizes analysis and calculation of power data to realize the functions of phase separation, segmentation and real-time line loss, analyzes the proportion of the line loss of the phase separation stage by stage in real time, and timely finds out the electric quantity loss caused by line impedance, ground leakage and metering faults; the service platform and the PMS system can dispatch a shipping and rush repair work order to the power supply company, so that the power supply company changes from passively coping with user report to actively grasping power failure information in real time and actively and efficiently distributing rush repair resources, and the power failure duration of a user is reduced; the intelligent sensing module of the terminal power grid can monitor the running state of the power grid, improve the power supply emergency maintenance level, and improve the metering monitoring and line loss analysis level through monitoring and analyzing the distribution transformer, the branch box and the electric energy meter, so that the power supply service capacity of a power supply company is improved; after each node such as a distribution transformer, a branch box and an electric energy meter of the terminal power grid is powered off, the monitoring equipment can report power failure information to a service platform within 30 seconds, and report active operation and maintenance of a power supply company, meanwhile, the service platform actively pushes real-time information to a mobile phone of a user, and the user can timely acquire power failure and repair information through a short message function of the service platform, so that active power supply service is obtained. When a fault occurs, a short message prompt can be sent to a user in time, the device is different from a conventional manual short message, a power failure area can be automatically identified, the process of manually searching information is reduced, the user is informed of a man-machine conversation function of an artificial intelligent system at the highest speed, the user can directly conduct conversation with an electricity user, the user is helped to solve the problem, a powerful multithread processing function is achieved, the problem of a plurality of merchants is solved at the same time, and the high-efficiency working mode is very suitable for being applied to a power supply service command center. The method is applied to power consumption peaks in maintenance of the power consumption system. Aiming at the phenomenon of power consumption peak outage, an artificial intelligence technology can be applied, and the method can diagnose the place where the power grid fails, effectively forecast circuit load and control part of the field of the power grid. In the power generation link, the working condition of the power generation equipment can be detected through an artificial intelligence technology, meanwhile, the fault position can be expected to be detected, the fault rate of the equipment is reduced, and the production efficiency of the power generation equipment is integrally improved. In the power transmission link, the power transmission network is effectively monitored, faults are diagnosed and the power transmission stability is improved through an artificial intelligence technology. If faults occur in the power utilization process, the artificial intelligence technology can also solve the problems, so that the power utilization requirement of a user is met, and the satisfaction degree of the user is improved; artificial intelligence technology is applied to power dispatching in the maintenance process of the power utilization system. For the problem of power scheduling, there are many benefits to applying artificial intelligence techniques in daily management. The dispatcher does not carry out troublesome work any more, the shift-over records can be completed through an artificial intelligence technology for daily work records, the manpower is greatly reduced to a certain extent, and particularly, the harm caused by frequent errors of staff in the line maintenance records is avoided, the damage is successfully avoided by applying the artificial intelligence technology, the data such as line maintenance, communication defect record and the like can be processed through the artificial intelligence technology, the running condition of a power grid is mastered at any time, and high-efficiency work is generated.
Based on the same inventive concept, the embodiment of the application also provides an electricity system maintenance device for realizing the above related electricity system maintenance method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitations in one or more embodiments of the maintenance device for an electrical system provided below may be referred to above as limitations on the maintenance method for an electrical system, which are not repeated here.
In one embodiment, such as the power system maintenance device 700 shown in fig. 7, the present application provides a power system maintenance device 700, the power system maintenance device 700 comprising: a data acquisition module 710, a data processing module 720, a judgment module 730, and an exception maintenance module 740, wherein: the data acquisition module 710 is configured to acquire multi-source data of each zone according to a time sequence. A data processing module 720, configured to process the multi-source data to construct a high-dimensional random matrix. And the judging module 730 is configured to judge whether the power utilization system of each area operates normally according to the high-dimensional random matrix. And the abnormal maintenance module 740 is used for analyzing abnormal conditions and acquiring a coping scheme according to an analysis result when the power utilization system operates abnormally.
The various modules in the power system maintenance device 700 described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile 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 computer device is used for storing various data involved in the maintenance method of the power utilization system according to the above embodiments. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement a power system maintenance method.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of a portion of the structure associated with the present application and is not intended to limit the computer device to which the present application is applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the method steps according to any of the embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
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, database, or other medium used in the various 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, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. 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 databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (13)
1. An electrical system maintenance method, comprising:
collecting information data of each station area;
constructing a high-dimensional random matrix according to the information data;
judging whether the power utilization system of each area operates normally or not according to the high-dimensional random matrix;
when the power utilization system operates abnormally, analyzing abnormal conditions and acquiring a coping scheme according to analysis results.
2. The power system maintenance method of claim 1, wherein the information data includes active power; the step of collecting information data of each area comprises the following steps:
and collecting the active power recorded in the intelligent ammeter of each area according to the time sequence.
3. The method of claim 2, wherein constructing a high-dimensional random matrix from the information data comprises:
preprocessing each collected active power to obtain a preprocessing result; the pretreatment result is that no abnormal active power data exists;
and constructing a high-dimensional random matrix according to the preprocessing result.
4. The method of claim 3, wherein preprocessing each collected active power to obtain a preprocessing result comprises:
constructing an electricity consumption abnormality analysis model according to each active power;
determining abnormal active power data in a plurality of active powers based on the electricity utilization abnormality analysis model;
and detecting and repairing the abnormal data to obtain a preprocessing result.
5. The method of claim 1, wherein the information data further comprises at least one of a voltage value, a current value, reactive power, three-phase voltage data, and a communication line type; constructing a high-dimensional random matrix from the information data further includes:
and establishing a corresponding high-dimensional random matrix according to each information data.
6. The method of claim 1, wherein determining whether each of the power systems operates properly based on the high-dimensional random matrix comprises:
carrying out normalized representation on the high-dimensional random matrix to obtain state data of a corresponding station area of the high-dimensional random matrix;
and judging whether the power utilization system of the platform area normally operates according to the state data.
7. The method of claim 6, wherein the status data includes abnormal and normal; the normalizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix comprises the following steps:
obtaining a covariance matrix of a high-dimensional random matrix;
acquiring a characteristic value of the covariance matrix;
judging whether the characteristic values are intensively distributed in a circular ring obtained according to the single-ring theorem or not according to the single-ring theorem;
if the characteristic values are not distributed in the circular ring in a concentrated mode, judging that the state data are abnormal, otherwise, judging that the state data are normal.
8. The method of claim 6, wherein the status data includes normal and abnormal; the normalizing the high-dimensional random matrix to obtain the state data of the station area corresponding to the high-dimensional random matrix further comprises:
acquiring a characteristic value spectral density function of the high-dimensional random matrix;
judging whether the characteristic value spectral density function complies with the Marchenko-Pasteur theorem;
if the characteristic value spectral density function complies with the Marchenko-Pasteur theorem, the state data is normal, otherwise, the state data is abnormal.
9. The method according to claim 7 or 8, wherein analyzing the abnormal situation and acquiring the countermeasure scheme based on the analysis result includes:
determining an anomaly type according to the state data; the abnormal type comprises at least one of electricity stealing by a user and damage of the intelligent electric meter;
and sending the abnormal data type to a service platform to instruct the service platform to formulate an operation and maintenance scheme according to the abnormal type and send abnormal early warning information to a user.
10. An electrical system maintenance device, the device comprising:
the data acquisition module is used for acquiring multi-source data of each area according to the time sequence;
the data processing module is used for processing the multi-source data to construct a high-dimensional random matrix;
the judging module is used for judging whether the power utilization system of each area normally operates according to the high-dimensional random matrix;
and the abnormal maintenance module is used for analyzing abnormal conditions and acquiring a coping scheme according to an analysis result when the power utilization system operates abnormally.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 9 when the computer program is executed.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 9.
13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 9.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116881617A (en) * | 2023-07-12 | 2023-10-13 | 武汉万数科技有限公司 | Equipment abnormality detection method and device |
| CN117201187A (en) * | 2023-11-01 | 2023-12-08 | 国网湖北省电力有限公司武汉供电公司 | Power data secure sharing method, system and storage medium |
| CN117559407A (en) * | 2023-11-15 | 2024-02-13 | 国网四川省电力公司营销服务中心 | Abnormal electricity utilization behavior positioning method suitable for new energy distribution network |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116881617A (en) * | 2023-07-12 | 2023-10-13 | 武汉万数科技有限公司 | Equipment abnormality detection method and device |
| CN117201187A (en) * | 2023-11-01 | 2023-12-08 | 国网湖北省电力有限公司武汉供电公司 | Power data secure sharing method, system and storage medium |
| CN117201187B (en) * | 2023-11-01 | 2024-01-05 | 国网湖北省电力有限公司武汉供电公司 | Power data secure sharing method, system and storage medium |
| CN117559407A (en) * | 2023-11-15 | 2024-02-13 | 国网四川省电力公司营销服务中心 | Abnormal electricity utilization behavior positioning method suitable for new energy distribution network |
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