CN109034400A - A kind of substation's exception metric data predicting platform system - Google Patents
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Abstract
The invention discloses a kind of substation's exception metric data predicting platform systems, which is characterized in that includes at least data collection layer, Data Integration layer, data mining layer and presentation layer；Data collection layer includes more communication managers, router, interchanger and longitudinal encryption authentication device；Data Integration layer includes front server, SCADA server, HIS disk array and the first positive isolating device；Data mining layer includes data acquisition server, data and relation excavation server；Presentation layer includes the second positive isolating device, firewall and shows work station, and firewall is used to prevent unauthorized access, shows that work station visualizes correlation timing data, trend data, relevant context information.By using above-mentioned technical proposal: the metric data that the present invention can upload substation is analyzed and is excavated, and to abnormal data real-time early warning, is taken timely measure and is prevented trouble before it happens before exception occurs in substation's measuring equipment.
Description
Technical field
The invention belongs to technical field of power systems more particularly to a kind of substation's exception metric data based on big data
Predicting platform system.
Background technique
As grid automation degree is higher and higher, especially New Generation of Intelligent station is put into operation, stand in all multi-services, generate
Data of flood tide, including real-time measuring value, remote control, remote signalling value, SOE signal etc., also produce the historical data of magnanimity, simultaneously
Smart machine itself also generates the relevant information of this equipment.Power grid scale is increasing, substation data is also more and more, electricity
The metric data of net most critical is analyzed, and is established a big data predicting platform, is monitored, opens up to abnormal measurement information
Show, is just particularly important.
Present substation metric data is mainly acquired by communication manager, and network equipment teletransmission to grid automation is passed through
SCADA system.When automating main website discovery measurement information exception, general measuring equipment has occurred and that exception.Measuring equipment is different
Chang Hou, maintenance cycle is long, coverage is big.How metric data is being utilized, look-ahead measures extremely, accomplishes to safeguard in advance,
The work limitation of grid automation O&M will be greatly improved.
Summary of the invention
For the deficiency in the acquisition of present substation's metric data, storage, the purpose of the present invention is to provide a kind of bases
Abnormal in big data algorithm measures monitoring platform, can be monitored in real time, be shown and early warning to substation's metric data.Root
According to the deep excavation to substation's historical data, dependency rule is generated, when metric data and rule are not inconsistent, as abnormal amount
It surveys.When predicting that abnormal measure will occur in certain measuring equipment, shows that work station can generate related warning information, automate O&M
Personnel are according to warning information, so that it may be safeguarded to measuring equipment in advance at the first time, guarantee the complete, just of metric data
Really, the present invention provides a kind of substation's exception metric data predicting platform systems.
It is of the present invention the specific technical proposal is:
A kind of substation's exception metric data predicting platform system, includes at least:
Data collection layer, Data Integration layer, data mining layer and presentation layer；Wherein:
The data collection layer includes more communication managers, router, interchanger and longitudinal encryption authentication device, the number
According to acquisition layer, from the acquisition of substation's metric data, simultaneously teletransmission is used to acquire secondary in substation to front server, communication manager
The measuring value in circuit, router, interchanger are transmitted for data, and longitudinal authentication device that encrypts is used to encryption data, it is ensured that data
Safety；
The Data Integration layer includes front server, SCADA server, HIS disk array and the first positive isolation dress
It sets；Front server carries out maturation process to the data that communication manager is sent, and SCADA system is identified, SCADA
Server runs the real time business of power grid, HIS disk array store historical data, and the first positive isolating device ensures that data are single-phase
Transmission guarantees the safety of SCADA service；
The data mining layer includes data acquisition server, data and relation excavation server, acquisition server acquisition
Data required for predicting platform, data and relation excavation server carry out data mining to the data that acquisition server is passed to；
The presentation layer includes the second positive isolating device, firewall and shows work station, and the second positive isolating device is true
It protects data sheet mutually to transmit, guarantees that abnormal measurement predicting platform system is removed and show that work station is isolated with external network, firewall is used
It prevents unauthorized access, shows that work station visualizes correlation timing data, trend data, relevant context information.
Further, the distributed type assemblies that substation's exception metric data predicting platform system is made of hardware device
Framework；The HIS disk array is to be locally stored, and the HIS disk array is RIAD-5 disk array mode.
Further, the data of monitoring include voltage in substation, electric current, power, power factor and switching signal.
Further, being done after the data and relation excavation server are handled by related algorithm with judgment rule
Comparison, if meeting defined rule, judgement is normal, against regulation, judges exception.
Further, longitudinal encryption device adds related data using hash algorithm and/or SM2 Encryption Algorithm
It is close.
Further, the described first positive isolating device and the second positive isolating device are a kind of physical isolation apparatus, guarantee
Data may only positive single-phase transmission, i.e., dug from HIS disk battle array, SCADA server to data acquisition server, data and relationship
Pick server is mutually transmitted to the data sheet for showing work station.
Further, the displaying work station be the displaying interface based on JavaScript, Dynamic Display correlation timing data,
Trend data and relevant context information.
Further: data transmission procedure is based on TCP/IP communications protocol.
Advantages of the present invention and good effect are as follows:
By using above-mentioned technical proposal, the present invention provides a kind of abnormal measurement monitoring platform based on big data algorithm,
Substation's metric data can be monitored in real time, be shown and early warning.It is raw according to the deep excavation to substation's historical data
It is as abnormal to measure when metric data is not inconsistent with rule at dependency rule.When prediction, abnormal amount will occur in certain measuring equipment
It when survey, shows that work station can generate related warning information, automates operation maintenance personnel according to warning information, so that it may right at the first time
Measuring equipment is safeguarded in advance, guarantees the complete, correct of metric data；The metric data that the present invention can upload substation
It is analyzed and is excavated, to abnormal data real-time early warning, taken timely measure before exception occurs in substation's measuring equipment anti-
Suffer from possible trouble.
Detailed description of the invention
Fig. 1 is the structural block diagram of the embodiment of the present invention.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
Please refer to Fig. 1: a kind of substation's exception metric data predicting platform system, including data collection layer, Data Integration
Layer, data mining layer and presentation layer and corresponding module；
The substation measures the distributed type assemblies framework that predicting platform is made of hardware device extremely；HIS disk battle array
It is classified as and is locally stored, be RIAD-5 disk array mode.HIS disk array is mainly used for depositing for the various historical datas of substation
Storage.
The data of the monitoring include voltage in substation, electric current, power, power factor, switching signal etc..
The data and relation excavation server, after being handled by related algorithm, compare with judgment rule, if
It is to meet defined rule, judgement is normal, and it is against regulation, judge exception.
Longitudinal encryption device utilizes hash algorithm, and SM2 Encryption Algorithm encrypts related data.
The forward direction isolating device is a kind of physical isolation apparatus, guarantee data may only positive single-phase transmission, i.e., from HIS
Disk battle array, SCADA server to data acquisition server, data and relation excavation server are single-phase to the data for showing work station
Transmission.
The firewall, mainly a kind of network security device, by the way that accesses control list is arranged, it is possible to specify defined
IP, MAC, port and user's access exception metric data predicting platform, play the role of protection to platform.
The displaying interface for showing work station and being based primarily upon JavaScript, Dynamic Display correlation timing data, trend
Data, relevant context information etc. are visualized.When the metric data to note abnormalities or the device measured extremely will be generated,
Warning message will be generated, substation's correlation operation maintenance personnel can safeguard relevant apparatus according to information.
The data transmission procedure is based on TCP/IP communications protocol.
On the basis of above scheme, server, work station and the relevant network equipment of all levels pass through
The connection of the port RJ45.
On the basis of above scheme, there are house dog programs for data and incidence relation excavation server, occur in system
When the failures such as crash, self-starting can be carried out to program.
The course of work of the invention includes:
S1: data pick-up；Specifically: the historical data in HIS disk array is measured, SQL database, inquiry are passed through
YCdata table, and then obtain the historical data of the measurement of each substation；Real time data acquires number by data acquisition server
According to data collected are transmitted by front server by isolating device；
S2: data analysis；
S3: data cleansing；Specifically: by the method for K-means clustering, find the typical case of the same measuring point
Value, and reject extremum and repetition values；
If one, which shares N number of data point, needs to be divided into K cluster, what K-means to be done is exactly to minimize:
In above-mentioned function, J is distance and μ of the data set each point to central pointkValue should be all cluster K
The average value of middle data point；It is 1 when data point n is classified into cluster K, is otherwise 0；Xn indicates nth data；
rnkFor data filter；
Find rnkAnd μkJ is minimized, takes the method for iteration herein: first fixing μk, select optimal rnk, by data point
That closest to him center is referred to it is ensured that J is minimum；R is then fixed in next stepnk, then seek optimal μk；By J to μkDerivation
And derivative is enabled to be equal to zero, obtain μ when J minimumkIt should meet:
Since iteration is all to get the minimum value of J each time, J can be only steadily decreasing or constant, without increasing
Add, it ensure that K-means eventually reaches a minimum；By the method for K-mean clustering, analysis center's point is looked for
To the representative value of measuring point, and reject extremum and repetition values；
S4: attribute construction；Each measuring value includes three plant stand name, interval information and acquisition time information, by data
Measuring value after cleaning, is reconfigured, and all information of measuring value are included in the title of measuring value；
S5: abnormal to measure prediction model；Specifically: first by carrying out stationarity test to data, if unstable, into
The processing of row stationarity；After data are steady, white noise test is carried out to data, not over white noise test, identification model belongs to
Which kind of model in AR, MA and ARMA, and determine the order of model, determine p, the q parameter of ARIMA parameter；It is needed after model identification
It whether to be white noise to model checking, detection model residual sequence, if needed true again to model not over test
It is fixed；To the model passed through, model parameter estimation is carried out using maximum-likelihood method；Finally application model is tested, it will be practical
Value carries out error analysis with predicted value；If error amount in business allowed band, shows to be fitted successfully, model modeling terminates,
Otherwise parameter is reevaluated；
S6: stationarity inspection；Using the method for unit root test or the timing diagram of drafting measuring value, stationarity is checked,
The phenomenon that preventing shadowing property generation；
S7: white noise sound detection；Specifically: stationarity inspection is carried out to differentiated data using the method for LB statistic；
Choose the x of metric data1、x2、x3、x4、x5, progress first-order difference is steady, i.e. x2-x1In this way；To the number after steady
According to progress white noise sound detection；Then white noise sound detection is done using the lbqtest function in MATLAB, is white when the h of output is 1
Noise shows that data are utilized and is over；It otherwise include data available；
S8: model identification；Model parameter estimation is carried out using the method for Likelihood, estimates the value of parameters；So
It is directed to each different model afterwards, model is carried out using BIC information criterion to determine rank, determines p, q parameter, to select optimal mould
Type；
S9: model testing；After model determines, examine whether its residual sequence is white noise, if it is not, showing data also
It has not utilized, modify model parameter or has re-used data；
S10: model evaluation；It is compared by measuring prediction result with real value, from absolute value, variance, mean error
Three angles are analyzed；
S11: after model evaluation is up to standard, detecting metric data using model, when jump occurs for predicted value and/or becomes
When changing rate exception and/or data ultra-range, alarm is issued.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (8)
1. a kind of substation's exception metric data predicting platform system, which is characterized in that include at least: data collection layer, data
Conformable layer, data mining layer and presentation layer；Wherein:
The data collection layer includes that more communication managers, router, interchanger and longitudinal encryption authentication device, the data are adopted
Collect layer from the acquisition of substation's metric data and teletransmission is to front server, communication manager is for acquiring secondary circuit in substation
Measuring value, router, interchanger transmit for data, and longitudinal authentication device that encrypts is used to encryption data, it is ensured that the peace of data
Entirely；
The Data Integration layer includes front server, SCADA server, HIS disk array and the first positive isolating device；Before
It sets server and maturation process is carried out to the data that communication manager is sent, SCADA system is identified, SCADA service
Device runs the real time business of power grid, HIS disk array store historical data, and the first positive isolating device ensures that data sheet passes on from one to another
It is defeated, guarantee the safety of SCADA service；
The data mining layer includes data acquisition server, data and relation excavation server, acquisition server acquisition prediction
Data required for platform, data and relation excavation server carry out data mining to the data that acquisition server is passed to；
The presentation layer includes the second positive isolating device, firewall and shows work station, and the second positive isolating device ensures to count
According to single-phase transmission, guarantee the abnormal predicting platform system that measures except displaying work station is isolated with external network, firewall is used to prevent
Only unauthorized access shows that work station visualizes correlation timing data, trend data, relevant context information.
2. substation's exception metric data predicting platform system according to claim 1, which is characterized in that the substation
The distributed type assemblies framework that abnormal metric data predicting platform system is made of hardware device；The HIS disk array is this
Ground storage, the HIS disk array are RIAD-5 disk array mode.
3. substation's exception metric data predicting platform system according to claim 1, it is characterised in that: the data of monitoring
Including voltage, electric current, power, power factor and switching signal in substation.
4. substation's exception metric data predicting platform system according to claim 1, it is characterised in that: the data
It after being handled with relation excavation server by related algorithm, compares with judgment rule, if meeting defined rule, sentences
It is disconnected normal, it is against regulation, judge exception.
5. substation's exception metric data predicting platform system according to claim 1, it is characterised in that: the longitudinal direction
Encryption device encrypts related data using hash algorithm and/or SM2 Encryption Algorithm.
6. substation's exception metric data predicting platform system according to claim 1, it is characterised in that: described first just
A kind of physical isolation apparatus to isolating device and the second positive isolating device, guarantee data may only positive single-phase transmission, i.e.,
From HIS disk battle array, SCADA server to data acquisition server, data and relation excavation server to the number for showing work station
According to single-phase transmission.
7. substation's exception metric data predicting platform system according to claim 1, it is characterised in that: the displaying work
Making station is the displaying interface based on JavaScript, Dynamic Display correlation timing data, trend data and relevant context information.
8. substation's exception metric data predicting platform system according to claim 1-7, it is characterised in that: number
TCP/IP communications protocol is based on according to transmission process.
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| CN110492617A (en) * | 2019-09-05 | 2019-11-22 | 中电国科技术有限公司 | A kind of intelligent auxiliary monitoring management system, method and substation for substation |
| CN110648173A (en) * | 2019-09-19 | 2020-01-03 | 西安电子科技大学 | Unsupervised abnormal commodity data detection method based on good evaluation and poor evaluation rates of commodities |
| CN111190045A (en) * | 2019-12-27 | 2020-05-22 | 国网北京市电力公司 | Voltage abnormity prediction method and device and electronic equipment |
| CN112527470A (en) * | 2020-05-27 | 2021-03-19 | 上海有孚智数云创数字科技有限公司 | Model training method and device for predicting performance index and readable storage medium |
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