CN109034400B - Transformer substation abnormal measurement data prediction platform system - Google Patents
Transformer substation abnormal measurement data prediction platform system Download PDFInfo
- Publication number
- CN109034400B CN109034400B CN201810526732.2A CN201810526732A CN109034400B CN 109034400 B CN109034400 B CN 109034400B CN 201810526732 A CN201810526732 A CN 201810526732A CN 109034400 B CN109034400 B CN 109034400B
- Authority
- CN
- China
- Prior art keywords
- data
- model
- server
- value
- platform system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 52
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 28
- 238000002955 isolation Methods 0.000 claims abstract description 20
- 238000005065 mining Methods 0.000 claims abstract description 20
- 238000004891 communication Methods 0.000 claims abstract description 11
- 238000007418 data mining Methods 0.000 claims abstract description 9
- 230000010354 integration Effects 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 22
- 230000005540 biological transmission Effects 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000007476 Maximum Likelihood Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 4
- 241001123248 Arma Species 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000013459 approach Methods 0.000 claims description 2
- 238000013075 data extraction Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims description 2
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 238000013112 stability test Methods 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
- H04L63/0442—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply asymmetric encryption, i.e. different keys for encryption and decryption
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a transformer substation anomaly measurement data prediction platform system which is characterized by at least comprising a data acquisition layer, a data integration layer, a data mining layer and a display layer; the data acquisition layer comprises a plurality of communication managers, routers, switches and longitudinal encryption authentication devices; the data integration layer comprises a front-end server, an SCADA server, an HIS disk array and a first forward isolation device; the data mining layer comprises a data acquisition server and a data and relation mining server; the display layer comprises a second forward isolation device, a firewall and a display workstation, the firewall is used for preventing illegal access, and the display workstation visualizes related time sequence data, trend data and related environment information. By adopting the technical scheme: the invention can analyze and mine the measurement data uploaded by the transformer substation, early warn abnormal data in real time, and take measures to prevent the abnormal data in advance before the transformer substation measurement device is abnormal.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a substation anomaly measurement data prediction platform system based on big data.
Background
With the increasing automation degree of the power grid, particularly the operation of a new generation of intelligent station, a great deal of services in the station generate huge data including real-time measurement values, remote control values, remote signaling values, SOE signals and the like, and massive historical data is generated, and meanwhile, the intelligent equipment generates relevant information of the equipment. The scale of the power grid is getting larger and the data of the transformer substation is getting more and more, the most critical measurement data of the power grid is analyzed, a big data prediction platform is established, and monitoring and displaying of abnormal measurement information are very important.
At present, measured data of a transformer substation are mainly collected through a communication management machine and are remotely transmitted to a power grid automatic SCADA system through network equipment. When the automated master station finds that the measurement information is abnormal, the general measurement device is already abnormal. After the measuring device is abnormal, the maintenance period is long and the influence range is large. How to utilize the measurement data to predict the abnormal measurement in advance and achieve the maintenance in advance, and the working timeliness of the automatic operation and maintenance of the power grid is greatly improved.
Disclosure of Invention
Aiming at the defects in the collection and storage of the existing transformer substation measurement data, the invention aims to provide an abnormal measurement monitoring platform based on a big data algorithm, which can be used for carrying out real-time monitoring, display and early warning on the transformer substation measurement data. And generating a relevant rule according to deep mining of the historical data of the transformer substation, and obtaining abnormal measurement when the measured data is not consistent with the rule. When the abnormal measurement of a certain measuring device is predicted, the display workstation can generate related alarm information, and an automatic operation and maintenance worker can maintain the measuring device in advance at the first time according to the alarm information to ensure the completeness and correctness of the measured data.
The invention adopts the following specific technical scheme:
a transformer substation anomaly measurement data prediction platform system at least comprises:
the system comprises a data acquisition layer, a data integration layer, a data mining layer and a display layer; wherein:
the data acquisition layer comprises a plurality of communication managers, routers, switches and longitudinal encryption authentication devices, the data acquisition layer acquires and remotely transmits measurement data from the transformer substation to the front-end server, the communication managers are used for acquiring measurement values of secondary circuits in the transformer substation, the routers and the switches are used for data transmission, and the longitudinal encryption authentication devices are used for encrypting the data to ensure the safety of the data;
the data integration layer comprises a front-end server, an SCADA server, an HIS disk array and a first forward isolation device; the front-end server carries out curing treatment on the data sent by the communication management machine, so that an SCADA system can identify, the SCADA server runs real-time services of a power grid, the HIS disk array stores historical data, the first forward isolation device ensures single-phase transmission of the data, and the safety of SCADA service is ensured;
the data mining layer comprises a data acquisition server and a data and relation mining server, the data acquisition server acquires data required by the prediction platform, and the data and relation mining server performs data mining on the data transmitted by the data acquisition server;
the display layer comprises a second forward isolation device, a firewall and a display workstation, the second forward isolation device ensures single-phase transmission of data, the abnormal measurement prediction platform system is ensured to be isolated from an external network except the display workstation, the firewall is used for preventing illegal access, and the display workstation visualizes related time sequence data, trend data and related environment information.
Further: the transformer substation anomaly measurement data prediction platform system is a distributed cluster architecture consisting of hardware equipment; the HIS disk array is a local storage and is in a RIAD-5 disk array mode.
Further: the monitored data includes voltage, current, power factor and switching signals within the substation.
Further: and the data and relation mining server is processed by a correlation algorithm and then is compared with a judgment rule, if the data and relation mining server accords with the specified rule, the judgment is normal, and if the data and relation mining server does not accord with the specified rule, the judgment is abnormal.
Further: the longitudinal encryption device encrypts related data by using a hash algorithm and/or an SM2 encryption algorithm.
Further: the first forward isolation device and the second forward isolation device are physical isolation devices, and ensure that data can only be transmitted in a forward single phase, namely the data are transmitted in a single phase from the HIS disk array, the SCADA server to the data acquisition server and from the data and relationship mining server to the display workstation.
Further: the display workstation is a JavaScript-based display interface and dynamically displays relevant time sequence data, trend data and relevant environment information.
Further: the data transmission process is based on TCP/IP communication protocol.
The invention has the advantages and positive effects that:
by adopting the technical scheme, the abnormal measurement monitoring platform based on the big data algorithm can be used for monitoring, displaying and early warning the measured data of the transformer substation in real time. And generating a relevant rule according to deep mining of the historical data of the transformer substation, and obtaining abnormal measurement when the measured data is not consistent with the rule. When the abnormal measurement of a certain measuring device is predicted, the display workstation can generate related alarm information, and the automatic operation and maintenance personnel can maintain the measuring device in advance at the first time according to the alarm information to ensure the completeness and the correctness of the measured data; the invention can analyze and mine the measurement data uploaded by the transformer substation, early warn abnormal data in real time, and take measures to prevent the abnormal data in advance before the transformer substation measurement device is abnormal.
Drawings
Fig. 1 is a block diagram of the structure of an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
Please refer to fig. 1: a transformer substation anomaly measurement data prediction platform system comprises a data acquisition layer, a data integration layer, a data mining layer, a display layer and corresponding modules;
the transformer substation anomaly measurement prediction platform is a distributed cluster architecture consisting of hardware equipment; the HIS disk array is a local storage and is in a RIAD-5 disk array mode. The HIS disk array is mainly used for storing various historical data of the transformer substation.
The monitored data includes voltage, current, power factor, switching signals, etc. within the substation.
And the data and relation mining server is processed by a correlation algorithm and then compared with a judgment rule, if the data and relation mining server accords with the specified rule, the judgment is normal, and if the data and relation mining server does not accord with the specified rule, the judgment is abnormal.
The longitudinal encryption device encrypts related data by using a hash algorithm, namely an SM2 encryption algorithm.
The forward isolation device is a physical isolation device and ensures that data can be transmitted only in a forward single phase, namely the data are transmitted from the HIS disk array, the SCADA server to the data acquisition server and from the data and relationship mining server to the display workstation in a single phase.
The firewall is mainly a network security device, and can specify a specified IP, MAC, port and user access abnormity measurement data prediction platform by setting an access control list, so as to protect the platform.
The display workstation is mainly based on a JavaScript display interface, and dynamically displays relevant time sequence data, trend data, relevant environment information and the like for visualization. When abnormal measurement data is found or a device which is about to generate abnormal measurement is found, an alarm message is generated, and the related operation and maintenance personnel of the transformer substation can maintain the related device according to the information.
The data transmission process is based on a TCP/IP communication protocol.
On the basis of the scheme, all the hierarchical servers, workstations and related network equipment are connected through RJ45 ports.
On the basis of the scheme, the data and association relation mining server has a watchdog program, and when the system has faults such as crash and the like, the program can be automatically started.
The working process of the invention comprises the following steps:
s1: data extraction; the method specifically comprises the following steps: measuring historical data in the HIS disk array, and inquiring the YCdata table through an SQL database so as to obtain the measured historical data of each transformer substation; the real-time data is acquired by the data acquisition server, and the acquired data is transmitted by the front-end server through the isolation device;
s2: analyzing data;
s3: data cleaning; the method specifically comprises the following steps: finding typical values of the same measuring point by a K-means cluster analysis method, and removing extreme values and repeated values;
assuming that a total of N data points need to be divided into K clusters, what the K-means needs to do is to minimize:
in the above function, J is the sum of the distances of the points of the dataset to the center point, μkThe value of (d) should be the average of the data points in all cluster K; 1 when data point n is classified as cluster K, and 0 otherwise; xn represents the nth data; r isnkA data filter;
finding rnkAnd mukTo minimize J, an iterative approach is taken here: firstly fix mukSelecting the optimum rnkClassifying a data point to the center closest to him ensures that J is minimal; then r is fixednkThen, find the optimum muk(ii) a J to mukDerivative and make the derivative equal to zero to obtain the time mu when J is minimumkIt should satisfy:
since the minimum value of J is taken in each iteration, J is only continuously reduced or unchanged but not increased, and the K-means is ensured to finally reach a minimum value; analyzing the central point by a K-mean cluster analysis method, finding out a typical value of a measuring point, and removing an extreme value and a repeated value;
s4: constructing an attribute; each measured value comprises three information of a plant station name, interval information and acquisition time, the measured values after data cleaning are reconstructed, and all the information of the measured values are included in the names of the measured values;
s5: an anomaly measurement prediction model; the method specifically comprises the following steps: firstly, performing stability test on data, and if the data is not stable, performing stability processing; after the data are stable, carrying out white noise test on the data, identifying which model of AR, MA and ARMA the model belongs to, determining the order of the model, and determining the p and q parameters of the ARIMA parameters; after the model is identified, the model needs to be checked, whether the model residual sequence is white noise is detected, and if the model residual sequence does not pass the test, the model needs to be determined again; carrying out model parameter estimation on the passed model by using a maximum likelihood method; finally, testing the application model, and carrying out error analysis on the actual value and the predicted value; if the error value is within the service allowable range, the fitting is successful, the modeling of the model is finished, otherwise, the parameters are estimated again;
s6: checking stability; checking the stationarity by using a unit root inspection method or a time sequence chart for drawing a measurement value to prevent the occurrence of a pseudo regression phenomenon;
s7: white noise detection; the method specifically comprises the following steps: performing stationarity check on the differentiated data by using an LB statistic method;
selecting x of the measured data1、x2、x3、x4、x5First order difference smoothing, i.e. x2-x1Thus; performing white noise detection on the stabilized data; then, white noise detection is carried out by utilizing an lbqtest function in the MATLAB, and when the output h is 1, the white noise is detected, which indicates that the data is completely utilized; otherwise, the data comprises available data;
s8: identifying a model; estimating model parameters by adopting a maximum likelihood ratio method, and estimating the value of each parameter; then, aiming at each different model, adopting a BIC information criterion to rank the model, and determining p and q parameters so as to select an optimal model;
s9: inspecting the model; after the model is determined, checking whether a residual sequence is white noise, if not, indicating that the data is not used up, modifying model parameters or reusing the data;
s10: evaluating the model; comparing the measurement prediction result with a real-time value, and analyzing from three angles of an absolute value, a variance and an average error;
s11: and after the model evaluation reaches the standard, detecting the measured data by using the model, and giving an alarm when the predicted value jumps and/or the change rate is abnormal and/or the data exceeds the range.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (8)
1. A transformer substation anomaly measurement data prediction platform system is characterized by at least comprising: the system comprises a data acquisition layer, a data integration layer, a data mining layer and a display layer; wherein:
the data acquisition layer comprises a plurality of communication managers, routers, switches and longitudinal encryption authentication devices, the data acquisition layer acquires and remotely transmits measurement data from the transformer substation to the front-end server, the communication managers are used for acquiring measurement values of secondary circuits in the transformer substation, the routers and the switches are used for data transmission, and the longitudinal encryption authentication devices are used for encrypting the data to ensure the safety of the data;
the data integration layer comprises a front-end server, an SCADA server, an HIS disk array and a first forward isolation device; the front-end server carries out curing treatment on the data sent by the communication management machine, so that an SCADA system can identify, the SCADA server runs real-time services of a power grid, the HIS disk array stores historical data, the first forward isolation device ensures single-phase transmission of the data, and the safety of SCADA service is ensured;
the data mining layer comprises a data acquisition server and a data and relation mining server, the data acquisition server acquires data required by the prediction platform, and the data and relation mining server performs data mining on the data transmitted by the data acquisition server;
the display layer comprises a second forward isolation device, a firewall and a display workstation, the second forward isolation device ensures single-phase transmission of data, ensures that the abnormal measurement prediction platform system is isolated from an external network except the display workstation, the firewall is used for preventing illegal access, and the display workstation visualizes related time sequence data, trend data and related environment information;
the working process of the transformer substation anomaly measurement data prediction platform system comprises the following steps:
s1: data extraction; the method specifically comprises the following steps: measuring historical data in the HIS disk array, and inquiring the YCdata table through an SQL database so as to obtain the measured historical data of each transformer substation; the real-time data is acquired by the data acquisition server, and the acquired data is transmitted by the front-end server through the isolation device;
s2: analyzing data;
s3: data cleaning; the method specifically comprises the following steps: finding typical values of the same measuring point by a K-means cluster analysis method, and removing extreme values and repeated values;
assuming that a total of N data points need to be divided into K clusters, what the K-means needs to do is to minimize
In the above function, J is the sum of the distances of the points of the dataset to the center point, μkThe value of (d) should be the average of the data points in all cluster K; 1 when data point n is classified as cluster K, and 0 otherwise; xn represents the nth data; r isnkA data filter;
finding rnkAnd mukTo minimize J, an iterative approach is taken here: firstly fix mukSelecting the optimum rnkClassifying a data point to be away from himThe nearest center can guarantee that J is minimal; then r is fixednkThen, find the optimum muk(ii) a J to mukDerivative and make the derivative equal to zero to obtain the time mu when J is minimumkIt should satisfy:
since the minimum value of J is taken in each iteration, J is only continuously reduced or unchanged but not increased, and the K-means is ensured to finally reach a minimum value; analyzing the central point by a K-mean cluster analysis method, finding out a typical value of a measuring point, and removing an extreme value and a repeated value;
s4: constructing an attribute; each measured value comprises three information of a plant station name, interval information and acquisition time, the measured values after data cleaning are reconstructed, and all the information of the measured values are included in the names of the measured values;
s5: an anomaly measurement prediction model; the method specifically comprises the following steps: firstly, performing stability test on data, and if the data is not stable, performing stability processing; after the data are stable, carrying out white noise test on the data, identifying which model of AR, MA and ARMA the model belongs to, determining the order of the model, and determining the p and q parameters of the ARIMA parameters; after the model is identified, the model needs to be checked, whether the model residual sequence is white noise is detected, and if the model residual sequence does not pass the test, the model needs to be determined again; carrying out model parameter estimation on the passed model by using a maximum likelihood method; finally, testing the application model, and carrying out error analysis on the actual value and the predicted value; if the error value is within the service allowable range, the fitting is successful, the modeling of the model is finished, otherwise, the parameters are estimated again;
s6: checking stability; checking the stationarity by using a unit root inspection method or a time sequence chart for drawing a measurement value to prevent the occurrence of a pseudo regression phenomenon;
s7: white noise detection; the method specifically comprises the following steps: performing stationarity check on the differentiated data by using an LB statistic method;
selecting x of the measured data1、x2、x3、x4、x5First order difference smoothing, i.e. x2-x1Thus; performing white noise detection on the stabilized data; then, white noise detection is carried out by utilizing an lbqtest function in the MATLAB, and when the output h is 1, the white noise is detected, which indicates that the data is completely utilized; otherwise, the data comprises available data;
s8: identifying a model; estimating model parameters by adopting a maximum likelihood ratio method, and estimating the value of each parameter; then, aiming at each different model, adopting a BIC information criterion to rank the model, and determining p and q parameters so as to select an optimal model;
s9: inspecting the model; after the model is determined, checking whether a residual sequence is white noise, if not, indicating that the data is not used up, modifying model parameters or reusing the data;
s10: evaluating the model; comparing the measurement prediction result with a real-time value, and analyzing from three angles of an absolute value, a variance and an average error;
s11: and after the model evaluation reaches the standard, detecting the measured data by using the model, and giving an alarm when the predicted value jumps and/or the change rate is abnormal and/or the data exceeds the range.
2. The substation anomaly measurement data prediction platform system according to claim 1, wherein the substation anomaly measurement data prediction platform system is a distributed cluster architecture composed of hardware devices; the HIS disk array is a local storage and is in a RIAD-5 disk array mode.
3. The substation anomaly measurement data prediction platform system according to claim 1, wherein: the monitored data includes voltage, current, power factor and switching signals within the substation.
4. The substation anomaly measurement data prediction platform system according to claim 1, wherein: and the data and relation mining server is processed by a correlation algorithm and then is compared with a judgment rule, if the data and relation mining server accords with the specified rule, the judgment is normal, and if the data and relation mining server does not accord with the specified rule, the judgment is abnormal.
5. The substation anomaly measurement data prediction platform system according to claim 1, wherein: the longitudinal encryption authentication device encrypts related data by using a hash algorithm and/or an SM2 encryption algorithm.
6. The substation anomaly measurement data prediction platform system according to claim 1, wherein: the first forward isolation device and the second forward isolation device are physical isolation devices, and ensure that data can only be transmitted in a forward single phase, namely the data are transmitted in a single phase from the HIS disk array, the SCADA server to the data acquisition server and from the data and relationship mining server to the display workstation.
7. The substation anomaly measurement data prediction platform system according to claim 1, wherein: the display workstation is a JavaScript-based display interface and dynamically displays relevant time sequence data, trend data and relevant environment information.
8. The substation anomaly measurement data prediction platform system according to any one of claims 1-7, wherein: the data transmission process is based on TCP/IP communication protocol.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810526732.2A CN109034400B (en) | 2018-05-29 | 2018-05-29 | Transformer substation abnormal measurement data prediction platform system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810526732.2A CN109034400B (en) | 2018-05-29 | 2018-05-29 | Transformer substation abnormal measurement data prediction platform system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109034400A CN109034400A (en) | 2018-12-18 |
| CN109034400B true CN109034400B (en) | 2021-10-15 |
Family
ID=64611395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810526732.2A Active CN109034400B (en) | 2018-05-29 | 2018-05-29 | Transformer substation abnormal measurement data prediction platform system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109034400B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110474426A (en) * | 2019-08-17 | 2019-11-19 | 国网湖北省电力有限公司荆门供电公司 | Telecontrol channel monitoring and fault diagnosis system and method |
| 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 |
| CN110648173B (en) * | 2019-09-19 | 2023-04-07 | 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 |
| CN111611055B (en) * | 2020-05-27 | 2020-12-18 | 上海有孚智数云创数字科技有限公司 | Virtual equipment optimal idle time migration method and device and readable storage medium |
| CN112578222B (en) * | 2020-11-27 | 2022-09-13 | 国网山东省电力公司济宁供电公司 | Power distribution terminal off-line detection method, system and platform |
| CN113552860A (en) * | 2021-07-28 | 2021-10-26 | 南京新和普软件技术有限公司 | Intelligent verification system based on intelligent substation telecontrol configuration |
| CN114745170B (en) * | 2022-04-07 | 2023-08-18 | 鹏城实验室 | Internet of things abnormality real-time detection method, device, terminal and readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663537A (en) * | 2012-03-13 | 2012-09-12 | 凯里供电局 | Maintenance system of power equipment based on risk assessment |
| CN104201775A (en) * | 2014-08-19 | 2014-12-10 | 江苏省电力设计院 | Distribution automation system configuration method based on city and county level integration |
| CN104320478A (en) * | 2014-11-03 | 2015-01-28 | 国家电网公司 | Transformer substation real-time information browsing system based on digital remote centralized management scheme |
| KR101732060B1 (en) * | 2017-01-23 | 2017-05-04 | (주)국제전기 | Control system of the substation device |
| CN107634988A (en) * | 2017-08-25 | 2018-01-26 | 深圳市盛路物联通讯技术有限公司 | A kind of method and server of the Fault Diagnosis for Substation based on Internet of Things |
| CN207398929U (en) * | 2017-08-08 | 2018-05-22 | 广西电网有限责任公司电力科学研究院 | Power transmission and transforming equipment safe early warning decision-making platform based on electric power big data analysis |
| CN107948008B (en) * | 2017-10-23 | 2021-01-05 | 珠海许继芝电网自动化有限公司 | Modeling method for low-voltage distribution network monitoring system |
-
2018
- 2018-05-29 CN CN201810526732.2A patent/CN109034400B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663537A (en) * | 2012-03-13 | 2012-09-12 | 凯里供电局 | Maintenance system of power equipment based on risk assessment |
| CN104201775A (en) * | 2014-08-19 | 2014-12-10 | 江苏省电力设计院 | Distribution automation system configuration method based on city and county level integration |
| CN104320478A (en) * | 2014-11-03 | 2015-01-28 | 国家电网公司 | Transformer substation real-time information browsing system based on digital remote centralized management scheme |
| KR101732060B1 (en) * | 2017-01-23 | 2017-05-04 | (주)국제전기 | Control system of the substation device |
| CN207398929U (en) * | 2017-08-08 | 2018-05-22 | 广西电网有限责任公司电力科学研究院 | Power transmission and transforming equipment safe early warning decision-making platform based on electric power big data analysis |
| CN107634988A (en) * | 2017-08-25 | 2018-01-26 | 深圳市盛路物联通讯技术有限公司 | A kind of method and server of the Fault Diagnosis for Substation based on Internet of Things |
| CN107948008B (en) * | 2017-10-23 | 2021-01-05 | 珠海许继芝电网自动化有限公司 | Modeling method for low-voltage distribution network monitoring system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109034400A (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109034400B (en) | Transformer substation abnormal measurement data prediction platform system | |
| CN111881452B (en) | Safety test system for industrial control equipment and working method thereof | |
| CN106787169B (en) | Method for diagnosing telemetering fault of transformer substation by using multiple data source comparison technology | |
| CN113239132B (en) | Online out-of-tolerance identification method for voltage transformer | |
| CN109063885A (en) | A kind of substation's exception metric data prediction technique | |
| CN114978568A (en) | Data center management using machine learning | |
| CN108199891B (en) | Cps network attack identification method based on artificial neural network multi-angle comprehensive decision | |
| CN112734977B (en) | Equipment risk early warning system and algorithm based on Internet of things | |
| CN106571689A (en) | Online monitoring system for diagnosing substation telemetering fault based on multi-data source comparison technology | |
| CN113542017A (en) | Network fault positioning method based on network topology and multiple indexes | |
| CN116719664B (en) | Application and cloud platform cross-layer fault analysis method and system based on micro-service deployment | |
| CN117041029A (en) | Network equipment fault processing method and device, electronic equipment and storage medium | |
| US11657148B2 (en) | Event analysis in an electric power system | |
| Kummerow et al. | Cyber-physical data stream assessment incorporating Digital Twins in future power systems | |
| CN111371570B (en) | Fault detection method and device for NFV network | |
| CN104579784A (en) | Electric power industry control system network management method based on multi-dimensional virtual link | |
| US9581995B2 (en) | Systems and methods for monitoring of equipment using a configurable rule base | |
| CN116108376A (en) | Monitoring system and method for preventing electricity stealing, electronic equipment and medium | |
| CN115640698A (en) | Fault early warning system for hydropower station operation equipment | |
| CN110455370B (en) | Flood-prevention drought-resisting remote measuring display system | |
| CN106125643A (en) | A kind of industry control safety protection method based on machine learning techniques | |
| CN114997804A (en) | Method and system for cooperatively sensing running state of complex power distribution network information physical system | |
| CN109558258B (en) | Method and device for positioning root fault of distributed system | |
| CN112486096A (en) | Machine tool operation state monitoring method | |
| CN117493498B (en) | Electric power data mining and analysis system based on industrial Internet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |