CN102607643B - Overheat fault diagnosis and early warning method for electrical equipment of traction substation of electrified railway - Google Patents
Overheat fault diagnosis and early warning method for electrical equipment of traction substation of electrified railway Download PDFInfo
- Publication number
- CN102607643B CN102607643B CN201210016061.8A CN201210016061A CN102607643B CN 102607643 B CN102607643 B CN 102607643B CN 201210016061 A CN201210016061 A CN 201210016061A CN 102607643 B CN102607643 B CN 102607643B
- Authority
- CN
- China
- Prior art keywords
- data
- temperature
- early warning
- environment
- fault
- 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.)
- Expired - Fee Related
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 35
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 5
- 230000001939 inductive effect Effects 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 5
- 230000000875 corresponding Effects 0.000 claims description 2
- 238000005259 measurement Methods 0.000 claims 3
- 244000171263 Ribes grossularia Species 0.000 description 27
- 238000006243 chemical reactions Methods 0.000 description 3
- 238000004458 analytical methods Methods 0.000 description 2
- 238000010586 diagrams Methods 0.000 description 2
- 230000005059 dormancy Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 241001081830 Degeneriaceae Species 0.000 description 1
- 206010069870 Device damage Diseases 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000023298 conjugation with cellular fusion Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000001419 dependent Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000000835 fibers Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000003365 glass fibers Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006011 modification reactions Methods 0.000 description 1
- 238000010301 surface-oxidation reactions Methods 0.000 description 1
- 238000004861 thermometry Methods 0.000 description 1
- 230000021037 unidirectional conjugation Effects 0.000 description 1
Abstract
The invention discloses an overheat fault diagnosis and early warning system and an overheat fault diagnosis and early warning method for electrical equipment of a traction substation of an electrified railway. The fault diagnosis and early warning system comprises wireless sensors, concentrators, a computer and the like, wherein the wireless sensors comprise wireless temperature sensors, wireless temperature and humidity sensors and wireless current sensors, and measure the temperature of the electrical equipment, the temperature and humidity of an environment and the current of a transmission line respectively; the concentrators receive monitoring point data from each wireless sensor in the whole traction substation, and upload the received data to the computer; and the computer finishes the acquisition and storage of the monitoring data, and finishes intelligent diagnosis and early warning for an overheat fault of the electrical equipment according to the acquired data. By the system, a scientific basis can be provided for the running and overhauling of the traction substation of the electrified railway to ensure the power supply security of the railway.
Description
Technical field
The present invention relates to a kind of fault diagnosis and early warning system and method, particularly a kind of electric railway traction Substation Electric Equipment overheat fault diagnostic and method for early warning.
Background technology
A large amount of high voltage isolators is there is in traction substation, mutual inductor, isolating switch, the electrical equipment such as transformer and high-tension switch cabinet, its joint One's name is legion, these electrical equipments are responsible for the vital task of power converter and conveying, but usually can because of joint loose contact when long-time continuous is run, the reasons such as aging or surface oxidation cause contact point resistance to increase, and then occur that temperature rise causes the situations such as heating too much burns out, if the temperature of these heating positions is not monitored timely and effectively, finally device damage may be caused, thus cause the generation of the even fire failure of having a power failure, to the great potential safety hazard that the operation of electric railway brings.Therefore necessary detection method should be taked to understand each electrical equipment current operating conditions, according to equipment real-time running state data and in conjunction with its variation tendency of historical data analysis, thus reach minimizing electric power system accident potential, reduce accident rate, shorten troubleshooting, repair time, guarantee the object of electric power system safe and reliable operation.
The main electrical equipment temperature monitoring technology adopted has two large classes both at home and abroad at present: one is adopt infrared technique (infrared temperature instrument or infrared thermoviewer) in manual inspection mode to carry out temperature detection; Two is by being furnished with line sensor network, carries out temperature detection as adopted optical fiber.But these methods all cannot meet the temperature monitoring demand of traction substation.Mainly because: traction substation is different from general transformer station, when its supply lines section has train to pass through, related electric equipment just has electric current to pass through, apparatus overheat potential faults just can display, and when not having train to pass through, because electrical equipment load current is very little, even if now monitor relevant device temperature also cannot find potential faults.Infrared technique thermometric is owing to being manual detection, real-time online cannot be realized detect, also cannot measure the temperature of device interior and there is comparatively big error, therefore cannot realize overheating fault timely, accurately find, Fiber Optic Pyrometer is unfavorable for large-scale application due to the complexity that connects up, and is easy to be polluted thus causes thermometric accuracy significantly to decline.
Thermometry in recent years based on wireless senser starts to be widely used.But it exists following limitation in Substation Electric Equipment monitoring: detected object is only the temperature of point being monitored, by simply setting a temperature threshold, determine whether to have occurred device of overheating of electrical fault.According to general knowledge, we know under different environment temperatures and humidity, same electrical equipment temperature often represents different overheating fault results, different line currents also can cause different running temperature results, therefore whether there is overheating fault, not only relevant with the device temperature of measured point, also relevant with environment temperature, ambient humidity and line current, be the result of a comprehensive descision.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of electric railway traction Substation Electric Equipment overheat fault diagnostic and method for early warning, realized electrical equipment temperature by wireless senser, environment temperature, ambient humidity and line current carry out on-line checkingi, set up the fault diagnosis based on least square method supporting vector machine (least squaressupport vector machines is called for short LSSVM) and Early-warning Model, thus the Accurate Diagnosis realized device of overheating of electrical fault and early warning, for the reliability service of electrical equipment provides foundation, significantly reduce rate of breakdown.
For achieving the above object, the present invention adopts following technological means:
Electric railway traction Substation Electric Equipment overheat fault diagnostic and early warning system, it is characterized in that: primarily of wireless senser, concentrator and computing machine composition, wherein wireless senser comprises radio temperature sensor, the wireless environment Temperature Humidity Sensor for measurement environment humiture, the radio flow sensor for measuring circuit electric current for measuring electrical equipment temperature; Concentrator is responsible for collecting wireless sensor data and being uploaded to computing machine, and computing machine realizes intelligent diagnostics and the on-line early warning of device of overheating of electrical fault according to the data separate least square method supporting vector machine overheating fault Early-warning Model uploaded.
Described radio temperature sensor installs one on each electrical equipment needing to carry out overheating fault monitoring; The region that described wireless environment Temperature Humidity Sensor has common environmental feature respectively installs one; Respectively on described radio flow sensor has common current feature circuit at every bar install one;
Described various wireless senser all with the CC2430 chip based on ZIGBEE technology for core; Described radio temperature sensor adopts DS18B20 induction electric device temperature; Described wireless humiture sensor adopts DHT21 induced environment humiture; Described radio flow sensor adopts inductive circular loop to obtain current signal;
A kind of electric railway traction Substation Electric Equipment overheat fault diagnostic and method for early warning, comprise the following steps:
(1) wireless senser measures electrical equipment monitoring point temperature T
equipment, environment temperature T
environment, ambient humidity H
environmentwith line current I
circuit, then these data are transferred to concentrator;
(2) concentrator receive wireless senser transmission data after, these data are sent to computing machine with Ethernet, then by the report cycle of above-mentioned data responsively packet be back to corresponding sensor;
(3), after computing machine receives the reported data of concentrator transmission, first store, then process according to following rule according to data type: if data are environment temperature, ambient humidity or line current, then do not process; If data are electrical equipment temperature, then according to the temperature data determination point position of electrical equipment, from computing machine, then recall ambient temperature data, ambient humidity data, and line current data, composition measurement data { T
equipment, T
environment, H
environment, I
circuit;
(4) by measurement data { T that step (3) obtains
equipment, T
environment, H
environment, I
circuitinput the least square method supporting vector machine overheating fault Early-warning Model set up, obtain fault diagnosis and early warning result.
Described least square method supporting vector machine overheating fault Early-warning Model is set up according to following steps:
(1) definite kernel function kind: with radial basis function
as kernel function, wherein, x is present input data, x
nfor training sample set, δ is the width parameter of radial basis function;
(2) hyper parameter { δ of standard search algorithms Confirming model is adopted
2, γ }, wherein, γ is regularization parameter;
(3) using the sample in sample database as training dataset, to model training, model parameter { α is obtained
n, b}, wherein, α
nfor Lagrangian (also known as support factor), b is bias;
(4) fault diagnosis model is obtained:
Before carrying out fault diagnosis, first, the data obtained are normalized;
Computing machine adjusts according to the report cycle of the result of fault diagnosis to electrical equipment temperature data; Reporting of described line current adopts real-time calling mode, namely after computing machine receives the temperature data of cordless electrical appliance, and calling line current data;
Least square method supporting vector machine overheating fault Early-warning Model, collecting after new fault sample reaches some, carries out renewal training to model.
Compared with prior art, electric railway traction Substation Electric Equipment overheat fault diagnostic of the present invention and early warning system and method at least have the following advantages: present invention employs the measurement that wireless senser realizes electrical equipment temperature, efficiently solve the problem that high-tension apparatus is difficult to on-line real-time measuremen; Adopt much information comprehensive diagnos and early warning, effectively can eliminate the electrical equipment temperature change caused because of the change of environment temperature, humidity and line energizing flow amount, thus realize the Accurate Diagnosis of overheating fault, for power supply safety provides reliable guarantee.
Accompanying drawing explanation
Fig. 1 is traction substation device of overheating of electrical on-line fault diagnosis of the present invention and early warning system structural representation;
Fig. 2 is radio temperature sensor hardware structure diagram;
Fig. 3 is radio temperature sensor software workflow figure;
Fig. 4 is fault diagnosis and early warning process flow diagram.
Embodiment
Be described in detail below in conjunction with the embodiment of accompanying drawing to electric railway traction Substation Electric Equipment overheat fault diagnostic of the present invention and early warning system and method:
The present invention carries out inline diagnosis and early warning to electric railway traction Substation Electric Equipment overheating fault, its system global structure as shown in Figure 1, comprises the radio temperature sensor for measuring electrical equipment temperature, the wireless environment Temperature Humidity Sensor for measurement environment humiture, the radio flow sensor for measuring circuit electric current, the concentrator for collecting sensor data and the computing machine for fault diagnosis and unified monitoring.
In a traction substation, a radio temperature sensor installed by the electrical equipment that needs to carry out overheating fault monitoring for each (busbar etc. as in the device clamp at the places such as transformer, disconnector, isolating switch, current transformer, switch cubicle); A wireless environment Temperature Humidity Sensor is respectively installed for the region with common environmental feature, the environment temperature that measurement obtains and humidity data result share for all electrical equipment monitoring points fault diagnosis in this region, such as in traction substation outdoor mounted environment humidity sensor, its measurement result is for all outdoor electrical equipment overheat fault diagnostics, high pressure indoor location environment humidity sensor, its measurement result for all in hyperbaric chamber device of overheating of electrical fault diagnosis; Respectively on the circuit at every bar with common current feature install a radio flow sensor, its data result shares for all electrical equipment monitoring points fault diagnosis on this circuit.
Wireless senser completes electrical equipment monitoring point temperature T
equipment, environment temperature T
environment, ambient humidity H
environmentwith line current I
circuitmeasurement; And measurement result is wirelessly sent to concentrator; Concentrator can be installed multiple according to traction substation concrete condition, such as, outdoor mounted one, is responsible for the collection of outdoor sensor data; High pressure indoor location one, is responsible for the collection of hyperbaric chamber inner sensor data.
After concentrator receives wireless sensor data, data are sent to computing machine by Industrial Ethernet, and by the report cycle of the sensor responsively packet be back to respective sensor.
Computing machine, after receiving reported data, first carries out data storage, then processes according to following rule according to data type: if data are environment temperature, ambient humidity or line current, then do not process; If data are electrical equipment temperature, then perform overheat fault diagnostic and early warning program, be specially:
Computing machine receives electrical equipment temperature data, and after completing data storage, first data fit procedures is performed: according to the electrical equipment temperature data received, determine point position (i.e. electrical equipment temperature sensor ID), then environment humidity sensor ID, the line current sensor ID of mating with this point position is recalled, obtaining line current by calling mode, obtaining ambient temperature and humidity data by inquiring about latest data from historical data base, composition measurement data { T
equipment, T
environment, H
environment, I
circuit;
By the LSSVM overheating fault Early-warning Model that the input of the measurement data set of acquisition establishes, obtain fault diagnosis and early warning result, and result is shown on monitoring software interface.
Be illustrated below in conjunction with a concrete application example:
As shown in Figure 2, for the wireless senser that traction substation electrical equipment temperature is measured, the CC2430 chip based on ZIGBEE technology is adopted to realize, this chip internal is integrated with low-power scm, power management module, analog-to-digital conversion module, radio-frequency module and memory module etc., can realize collection, the work such as process and wireless transmission of data with a chip.Temperature acquisition is realized by DS18B20, and this chip temperature is digital temperature sensor, obtains temperature data by number bus mode, and its error is in ± 1.5 DEG C.500 meters can be reached by its communication distance of wireless senser of this technical design, meet the demand of electrical equipment temperature monitoring completely.
For the wireless senser that ambient temperature and humidity and line current are measured, difference unique on its hardware configuration is that selected front end sensors is different, wherein measurement environment humiture adopts DHT21, this sensor is the humiture compound sensor that digital signal exports, and measuring circuit electric current adopts homemade inductive circular loop to obtain current signal.
As shown in Figure 3, cordless electrical appliance temperature sensor, its workflow is: first judge whether dormancy period arrives, and dormancy period is to then entering into collecting temperature program, and complete the handling procedures such as data conversion, filtering, then collection result is sent to concentrator and wait-for-response packet; Determine whether change parameter packet (namely whether change reporting cycle) after receiving the response data packet of concentrator, if it is change relevant parameter as requested.Cordless electrical appliance temperature sensor, its report cycle can according to overheating fault degree self-adaptative adjustment, one group of representative value is: during non-fault, report cycle is 30 minutes, during minor failure, report cycle is 10 minutes, during moderate fault, report cycle is 5 minutes, and during catastrophic failure, report cycle is 1 minute.
For environment humidity sensor, its report cycle can manually be arranged, and representative value is report once in 30 minutes, for current sensor, adopts calling mode to obtain.
Computing machine obtains electrical equipment monitoring point temperature T
equipment, environment temperature T
environment, ambient humidity H
environmentwith line current I
circuitafter, adopt the fault diagnosis model based on LSSVM to carry out overheat fault diagnostic and early warning, its workflow is as shown in Figure 4.
First judge carry out fault model training or carry out fault diagnosis, model training carries out in both cases, and one is use when first Modling model; Two is elongated along with working time, collects (such as newly collecting 10 fault samples) when new fault sample reaches some and carries out to model renewals and train.
Fault diagnosis result is divided into four classes, is respectively non-fault, minor failure, moderate fault, moderate fault and catastrophic failure, and it corresponds to the output of LSSVM fault model and is respectively 0,1,2,3.Setting up in training sample set process, T
equipment, T
environment, H
environment, I
circuitby on-line monitoring system automatic acquisition, and the fault type of correspondence is rule of thumb specified with analysis result by operations staff.
After entering model training program, first definite kernel function kind, selects radial basis function
as the kernel function of LSSVM, wherein, x is present input data, x
nfor the sample set of training, δ is the width parameter of radial basis function; Then the Matlab mathematical tool case Plays searching algorithm of LSSVM is adopted to determine the hyper parameter { δ of LSSVM model
2, γ }, wherein, γ is regularization parameter; Then, using the sample in sample database as training dataset, to the training of LSSVM model, model parameter { α is obtained
n, b}, wherein, α
nfor Lagrangian (also known as support factor), b is bias, can obtain fault diagnosis model:
Normal monitor procedure, model enters fault diagnosis flow scheme, first the data of acquisition is normalized, and then adopts the model trained
obtain fault diagnosis result and be shown to monitoring interface, determine whether according to fault severity level the report cycle parameter changing cordless electrical appliance temperature sensor simultaneously, and using dependent failure data as sample stored in sample database, when newly-increased sample size reaches 10, then enter model modification flow process, existing fault diagnosis model is upgraded.
The traction substation device of overheating of electrical on-line fault diagnosis method that the present invention provides and method for early warning, by the convergence analysis to each side data such as monitoring point temperature, present position size of current, residing environment temperature and humidity, provide fault diagnosis result; Make diagnostic result various environment and transmission of electricity situation external condition under all can meet Accurate Diagnosis early warning, meet well traction substation monitoring intelligentized demand, ensure that railway power supply safety.
The foregoing is only one embodiment of the present invention, it not whole or unique embodiment, the conversion of those of ordinary skill in the art by reading instructions of the present invention to any equivalence that technical solution of the present invention is taked, is claim of the present invention and contains.
Claims (6)
1. the overheat fault diagnostic of an electric railway traction Substation Electric Equipment overheat fault diagnostic and early warning system and method for early warning, it is characterized in that: electric railway traction Substation Electric Equipment overheat fault diagnostic and early warning system are primarily of wireless senser, concentrator and computing machine composition, and wherein wireless senser comprises radio temperature sensor, the wireless environment Temperature Humidity Sensor for measurement environment humiture, the radio flow sensor for measuring circuit electric current for measuring electrical equipment temperature;
Described overheat fault diagnostic and method for early warning comprise the following steps:
(1) wireless senser measures electrical equipment monitoring point temperature T
equipment, environment temperature T
environment, ambient humidity H
environmentwith line current I
circuit, then these data are transferred to concentrator, described line current I
circuitreport and adopt real-time calling mode, namely after computing machine receives the temperature data of cordless electrical appliance, calling line current data;
(2) concentrator receive wireless senser transmission data after, these data are sent to computing machine with Ethernet, then by the report cycle of above-mentioned data responsively packet be back to corresponding sensor;
(3), after computing machine receives the reported data of concentrator transmission, first store, then process according to following rule according to data type: if data are environment temperature, ambient humidity or line current, then do not process; If data are electrical equipment temperature, then according to the temperature data determination point position of electrical equipment, then recall from computing machine and the ambient temperature data of this location matches, ambient humidity data, and line current data, composition measurement data { T
equipment, T
environment, H
environment, I
circuit;
(4) by measurement data { T that step (3) obtains
equipment, T
environment, H
environment, I
circuitinput the least square method supporting vector machine overheating fault Early-warning Model set up, obtain fault diagnosis and early warning result;
Described least square method supporting vector machine overheating fault Early-warning Model is set up according to following steps:
(1) definite kernel function kind: with radial basis function
as kernel function, wherein, x is present input data, x
nfor training sample set, δ is the width parameter of radial basis function;
(2) hyper parameter { δ of standard search algorithms Confirming model is adopted
2, γ }, wherein, γ is regularization parameter;
(3) using the sample in sample database as training dataset, to model training, model parameter { α is obtained
n, b}, wherein, α
nfor Lagrangian, b is bias;
(4) fault diagnosis model is obtained:
2. overheat fault diagnostic as claimed in claim 1 and method for early warning, is characterized in that: before carrying out fault diagnosis, first, is normalized the data obtained.
3. overheat fault diagnostic as claimed in claim 1 and method for early warning, is characterized in that: computing machine adjusts according to the report cycle of the result of fault diagnosis to electrical equipment temperature data.
4. overheat fault diagnostic as claimed in claim 1 and method for early warning, is characterized in that: least square method supporting vector machine overheating fault Early-warning Model, collecting after new fault sample reaches some, is carried out to model renewals and trained.
5. overheat fault diagnostic according to claim 1 and method for early warning, is characterized in that: described radio temperature sensor installs one on each electrical equipment needing to carry out overheating fault monitoring; The region that described wireless environment Temperature Humidity Sensor has common environmental feature respectively installs one; Respectively on described radio flow sensor has common current feature circuit at every bar install one.
6. overheat fault diagnostic according to claim 5 and method for early warning, is characterized in that: described various wireless senser all with the CC2430 chip based on ZIGBEE technology for core; Described radio temperature sensor adopts DS18B20 induction electric device temperature; Described wireless humiture sensor adopts DHT21 induced environment humiture; Described radio flow sensor adopts inductive circular loop to obtain current signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210016061.8A CN102607643B (en) | 2012-01-18 | 2012-01-18 | Overheat fault diagnosis and early warning method for electrical equipment of traction substation of electrified railway |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210016061.8A CN102607643B (en) | 2012-01-18 | 2012-01-18 | Overheat fault diagnosis and early warning method for electrical equipment of traction substation of electrified railway |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102607643A CN102607643A (en) | 2012-07-25 |
| CN102607643B true CN102607643B (en) | 2015-03-04 |
Family
ID=46525232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210016061.8A Expired - Fee Related CN102607643B (en) | 2012-01-18 | 2012-01-18 | Overheat fault diagnosis and early warning method for electrical equipment of traction substation of electrified railway |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102607643B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103220050B (en) * | 2013-03-21 | 2015-12-09 | 深圳市长龙铁路电子工程有限公司 | A kind of CIR comprehensive diagnos testing equipment and method |
| CN103346525A (en) * | 2013-07-16 | 2013-10-09 | 南京益瑞电气有限公司 | Arc light protecting device based on UVC sensors |
| CN103453998B (en) * | 2013-08-09 | 2016-08-10 | 国家电网公司 | A kind of self-energizing radio temperature sensor and its implementation |
| CN106200518A (en) * | 2015-04-29 | 2016-12-07 | 中国科学院电工研究所 | A kind of frequency self-adaption method of electric-vehicle remote monitoring system |
| CN107145951A (en) * | 2017-04-28 | 2017-09-08 | 河南润华通用装备有限公司 | Gong Wu mechanical cars cloud management system |
| CN108197396A (en) * | 2018-01-09 | 2018-06-22 | 国网福建省电力有限公司 | A kind of high voltage isolator superheat state Forecasting Methodology based on PSO-SVM |
| CN108663089A (en) * | 2018-06-21 | 2018-10-16 | 肇庆高新区徒瓦科技有限公司 | A kind of scenic spot wind tower system with online environment monitoring function |
| CN110379118A (en) * | 2019-07-26 | 2019-10-25 | 中车青岛四方车辆研究所有限公司 | Fire prevention intelligent monitor system and method under train vehicle |
| CN110426127B (en) * | 2019-08-20 | 2021-03-02 | 剑科云智(深圳)科技有限公司 | Power distribution cabinet, monitoring method and system of mother disc of power distribution cabinet and loss monitoring method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63289803A (en) * | 1987-05-22 | 1988-11-28 | Hitachi Ltd | Apparatus for diagnosing abnormal condition in oil-immersed electrical equipment |
| JPH09200918A (en) * | 1996-01-18 | 1997-07-31 | Nissin Electric Co Ltd | Diagnosis for internal overheating of electrical equipment |
| CN101158873A (en) * | 2007-09-26 | 2008-04-09 | 东北大学 | Non-linearity process failure diagnosis method |
| CN201364814Y (en) * | 2008-11-28 | 2009-12-16 | 北京新源绿网节能科技有限公司 | Passive high-voltage wireless current transformer |
| CN101800440A (en) * | 2010-03-26 | 2010-08-11 | 国网电力科学研究院武汉南瑞有限责任公司 | System for monitoring distribution network state and method thereof |
| CN101839773A (en) * | 2009-03-19 | 2010-09-22 | 胡宜敏 | Detecting system based on zigbee of temperature of pig body and temperature and humidity of piggery |
| CN101881968A (en) * | 2009-05-05 | 2010-11-10 | 同济大学 | Equipment fault diagnosis method based on model |
| CN101951633A (en) * | 2010-09-16 | 2011-01-19 | 江苏省电力公司无锡供电公司 | Wireless sensor network technology based computer-room temperature and humidity online monitoring system |
-
2012
- 2012-01-18 CN CN201210016061.8A patent/CN102607643B/en not_active Expired - Fee Related
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63289803A (en) * | 1987-05-22 | 1988-11-28 | Hitachi Ltd | Apparatus for diagnosing abnormal condition in oil-immersed electrical equipment |
| JPH09200918A (en) * | 1996-01-18 | 1997-07-31 | Nissin Electric Co Ltd | Diagnosis for internal overheating of electrical equipment |
| CN101158873A (en) * | 2007-09-26 | 2008-04-09 | 东北大学 | Non-linearity process failure diagnosis method |
| CN201364814Y (en) * | 2008-11-28 | 2009-12-16 | 北京新源绿网节能科技有限公司 | Passive high-voltage wireless current transformer |
| CN101839773A (en) * | 2009-03-19 | 2010-09-22 | 胡宜敏 | Detecting system based on zigbee of temperature of pig body and temperature and humidity of piggery |
| CN101881968A (en) * | 2009-05-05 | 2010-11-10 | 同济大学 | Equipment fault diagnosis method based on model |
| CN101800440A (en) * | 2010-03-26 | 2010-08-11 | 国网电力科学研究院武汉南瑞有限责任公司 | System for monitoring distribution network state and method thereof |
| CN101951633A (en) * | 2010-09-16 | 2011-01-19 | 江苏省电力公司无锡供电公司 | Wireless sensor network technology based computer-room temperature and humidity online monitoring system |
Non-Patent Citations (3)
| Title |
|---|
| 基于最小二乘支持向量机的软测量建模;阎威武等;《系统仿真学报》;20031031;第15卷(第10期);第1494-1496页 * |
| 基于粗糙集与最小二乘支持向量机的密闭鼓风炉故障诊断方法研究;戴贤江;《中国优秀硕士学位论文全文数据库信息科技辑》;20090115(第1期);正文第47页第5行至第52页最后1行 * |
| 最小二乘支持向量机及其在故障诊断中的应用;崔世林等;《微计算机信息》;20060610;第22卷(第6-1期);第216页第1栏第12-34行 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102607643A (en) | 2012-07-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10222409B2 (en) | Method and system for detecting and locating single-phase ground fault on low current grounded power-distribution network | |
| CN201355384Y (en) | System for detecting and managing partial discharge of switch cabinet on-line | |
| CN103293443B (en) | A kind of distribution network overhead line Earth design method | |
| CN104267374B (en) | Metering device on-line checking and status assessing system | |
| CN103926510B (en) | A kind of cable sheath electric current and current-carrying capacity on-line monitoring and fault diagonosing localization method | |
| CN102426328B (en) | Capacitive equipment insulation state real-time on-line monitoring method | |
| CN102680798B (en) | Online measurement method and device for grounding resistor of rod tower | |
| CN105115626B (en) | Single-core high-voltage cable joint intelligent monitor system and intelligent monitoring method | |
| CN201707380U (en) | Portable MOA live detection device based on GPS synchronization | |
| CN102064537B (en) | Method for judging and processing single-phase earth faults by using electronic mutual inductor | |
| CN104796082A (en) | System and method for diagnosing faults of photovoltaic power generation systems in online manner | |
| CN102680799B (en) | Transformer substation capacitor on-line monitoring method and device based on wireless mode | |
| CN101894981A (en) | Intelligent monitoring, repair and control method of lead-acid battery pack and system thereof | |
| CN202041178U (en) | On-line monitoring system for power transmission line sag | |
| CN102411119B (en) | Intelligent monitoring device for temperature and insulation state of 330KV high-voltage cable in hydropower station | |
| CN102768321B (en) | A kind of power cable current-carrying capacity monitoring method | |
| CN201965191U (en) | Capacitor intelligentizing device | |
| CN204855690U (en) | Many parameter synthesis monitoring system of substation equipment | |
| CN103852744A (en) | Distributed online monitoring system and method for electric energy metering device | |
| CN102937675A (en) | Real-time data collecting and fault positioning system of power distribution network and working method thereof | |
| CN205665347U (en) | 10kV distribution lines broken string fault detection and positioning system based on three phase voltage electric current | |
| CN203502135U (en) | Temperature monitor apparatus for high-voltage electric power equipment | |
| CN102590670B (en) | High voltage cable smart grounding box | |
| CN102620769B (en) | On-line monitoring system and monitoring method for cold water in convertor station | |
| CN202230150U (en) | On-line monitoring intelligent terminal for electric power lightning arrester with fiber for power supply |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| C06 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| C10 | Entry into substantive examination | ||
| GR01 | Patent grant | ||
| C14 | Grant of patent or utility model | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150304 Termination date: 20180118 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |