CN206114822U - Many information detection means of power transformer winding deformation state - Google Patents
Many information detection means of power transformer winding deformation state Download PDFInfo
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- CN206114822U CN206114822U CN201621118098.1U CN201621118098U CN206114822U CN 206114822 U CN206114822 U CN 206114822U CN 201621118098 U CN201621118098 U CN 201621118098U CN 206114822 U CN206114822 U CN 206114822U
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Abstract
The utility model provides a many information detection means of power transformer winding deformation state, belongs to electrical equipment failure diagnosis technical field. The utility model discloses a detect power transformer frequency characteristic, detect high -low pressure side electric current, voltage and phase angle, detect the supersound signal, detect the vibration signal, realize that winding deformation state's on -line measuring, trouble do not detects mutually, fault location detects and fault type detects, the detected signal simplification has effectively been solved, the problem of power transformer winding operation conditions, fault location and type can't be effectively detected. The utility model discloses a voltage transformer, current transformer, ultrasonic transducer, vibration sensor, signal conditioning circuit, AD converting circuit and central processing unit, voltage transformer, current transformer, ultrasonic transducer and vibration sensor's output links to each other with signal conditioning circuit's input, and signal conditioning circuit's output links to each other with central processing unit's input through AD converting circuit, and central processing unit's output links to each other with communication bus.
Description
Technical field
The utility model belongs to Fault Diagnosis for Electrical Equipment technical field, more particularly to a kind of Winding in Power Transformer shape
Change state multi information detection means.
Background technology
With reform and opening-up and China's economy rapid growth, demand sharp increase of the user to electric power, an urgent demand I
State's electrical network provides the user safe and reliable electric power.Power transformer is one of power transmission and transforming equipment important in power system, even
Multiple electric pressures are connect, hinge status is in electrical network.The security reliability of its operation directly affects the operation safety of electrical network,
The operational reliability of power transformer is improved, the safe and reliable operation tool of whole electrical network is of great significance.At present, I
The existing more transformer station high-voltage side bus time limit of state more than 20 years, these operating transformers be faced with increasingly serious equipment fault and
Problem of aging, the probability that accident occurs is continuously increased.Asset of equipments and power failure etc. be likely to result in and once accident in transformer there is
Massive losses, or even serious social influence can be produced, therefore it is that current national grid is badly in need of that detection is carried out to transformer fault
The key issue of solution.
The internal fault of transformer is divided from transformer device structure, including winding, iron core (i.e. magnetic circuit) and annex failure,
Divide from fault type, including mechanical breakdown, insulation fault, overheating fault etc., come from the conventional fault diagnosis case of transformer
See, the failure majority of transformer is not Insulation Problems, but mechanical performance problems cause electric fault, with winding in mechanical breakdown
Occupy more with iron core, maximum is affected on transformer stable operation.Accident analysis about transformer shows：Winding is that event occurs
One of more part of barrier, according to incompletely statistics during 1999-2003, the transformer of China's 110kVA aboves,
The damage accident directly resulted in because being subjected to short-circuit current impact is about 72, accounts for the 27.5% of total accident number of units.China
220kV in 2005 and above transformer unplanned outage are pressed the classification situation of trouble location and are shown：In 220kV grade transformers by
The unplanned outage time caused in winding accounts for 79.49%, the 330kV grades of total unplanned outage time and accounts for 72.31%,
500kV grades account for 98.92%.Therefore in order to can guarantee that the security reliability of transformer station high-voltage side bus, carry out deformation of transformer winding shape
The research tool of state detection is of great significance.
At present, mainly there are frequency response method, Low Voltage Impulse Method and short-circuit reactance to the detection method of deformation of transformer winding
Method.Above-mentioned three kinds of methods are respectively provided with respective limitation：Project And Exploring through years of researches and repeatedly, usage frequency response
Method test winding deformation of power transformer is accepted extensively by power industry department.But due to unit impact response function and biography
Mapping relations between delivery function and the deformation extent of Transformer Winding lack theories integration, using FRA methods to Transformer Winding
Deformation extent be analyzed, there is no unified quantitative analysis standard.Additionally, frequency response method must be by shutdown, furred ceiling etc.
Operation is tested again to winding, therefore the method is difficult to avoid that there is efficiency aspect defect；Although own Jing's Low Voltage Impulse Method exists
Power industry department, especially winding deformation of power transformer and motion monitoring field are widely used, but its defect
Be also fairly obvious, such as relatively low signal to noise ratio, poor repeatability, in test process the impact of various electromagnetic interferences compared with
Greatly, it is and insensitive to the failure response of winding head end position, it is more difficult to judge that winding deformation position all greatly limit action of low-voltage pulse
Method reliability in use；The essence of short-circuit reactance method is to judge that winding is by the short-circuit impedance of measuring transformer winding
It is no to there is the defects such as deformation, displacement and turn-to-turn open circuit or short circuit.The method needs offline inspection after shutdown, while sensitivity is not
It is high, it is difficult to ensure certainty of measurement, therefore at the scene using there is very big difficulty.
Comprehensive existing several detection methods, it can be seen that：Current power deformation of transformer winding state-detection is profit
Testing result is drawn with single test parameters, there is detection signal unification, it is impossible to effective detection Winding in Power Transformer failure
Position and the problem of type.
Utility model content
For the problem that prior art is present, the utility model provides a kind of Winding in Power Transformer deformed state multi information
Detection means.The utility model is by detecting power transformer frequency characteristic, detection high and low pressure side electric current, voltage and phase angle, inspection
Ultrasonic signal is surveyed, vibration signal is detected, on-line checking, the separate detection of failure, the abort situation inspection of winding deformed state is realized
Survey and fault type detection；Efficiently solve detection signal unification, it is impossible to effective detection Winding in Power Transformer operation conditions,
The problem of abort situation and type.
To achieve these goals, the utility model is adopted the following technical scheme that：A kind of Winding in Power Transformer deformation shape
State multi information detection means, including voltage transformer, current transformer, ultrasonic probe, vibrating sensor, signal condition electricity
Road, A/D converter circuit and central processing unit；The voltage transformer, current transformer, ultrasonic probe and vibrating sensor
Output end is connected respectively with the input of signal conditioning circuit, output end Jing A/D converter circuit and the central authorities of signal conditioning circuit
The input of processor is connected, and the output end of central processing unit is connected with communication bus.
The beneficial effects of the utility model：
The utility model is diagnosed based on multi signal, overcomes the limitation of single signal detection, improves detection knot
The accuracy of fruit.The utility model can be examined to frequency characteristic, current and voltage signals, ultrasonic signal and vibration signal simultaneously
Survey, enrich detection signal database, supplement the fault type of winding；Both can be with on-line checking winding state, while can be with
Accurate tuning on-line diagnosis is carried out to winding abort situation, the on-line checking of various winding deformation fault types is also achieved.
Description of the drawings
Fig. 1 is the flow chart of the detection method using detection means of the present utility model；
Fig. 2 is structure of the detecting device of the present utility model and its functional schematic；
Fig. 3 is the hardware system structure figure of one embodiment of detection means of the present utility model；
Fig. 4 is the Winding in Power Transformer shape of one embodiment of the detection method using detection means of the present utility model
The fault type diagnostic flow chart of change；
Fig. 5 is the circuit theory diagrams of the double limiting circuit of one embodiment of detection means of the present utility model；
Fig. 6 is the circuit theory diagrams of the signal conditioning circuit of one embodiment of detection means of the present utility model.
Specific embodiment
Below in conjunction with the accompanying drawings the utility model is described in further detail with specific embodiment.
By taking S11-M-500/35 type power transformers as an example, multi information inspection is carried out to Winding in Power Transformer deformed state
Survey.A kind of Winding in Power Transformer deformed state multi information detection means, including voltage transformer, current transformer, ultrasonic wave
Probe, vibrating sensor, signal conditioning circuit, A/D converter circuit and central processing unit；The voltage transformer, Current Mutual Inductance
The output end of device, ultrasonic probe and vibrating sensor is connected respectively with the input of signal conditioning circuit, signal condition electricity
The output end Jing A/D converter circuit on road is connected with the input of central processing unit, the output end and communication bus of central processing unit
It is connected.
The voltage transformer adopt TVS1908 type voltage transformers, or adopt specified primary side voltage for Specified secondary side voltage isAccuracy class is
0.05 HZ12-35R type voltage transformers；Current transformer adopts specified primary side current for 5～1000A, specified secondary side
Electric current is 5A, and accuracy class is 0.05 HL28-5 type current transformers；The vibrating sensor adopts model BK-4507B
Vibrating sensor；The utility model is used to send the swept signal generator of frequency response signal to be tested using HY3310 winding deformations
Instrument, model THD-M1 that ultrasonic generator is adopted, receiving transducer adopts the ultrasonic probe of 2P10N models.A/D converter circuit
Using 2 12 AD conversion chips AD1672AP；Central processing unit adopts dual-cpu structure, by dsp processor and arm processor
Composition, data exchange is carried out between dsp processor and arm processor using dual port RAM.Dsp processor be responsible for data processing and
Communication connection, arm processor is responsible for data real-time storage.DSP core of the dsp processor from TMS320LF2401A models
Core, it has the advantages that height system is integrated and smaller size smaller；Arm processor is declined using the embedded of EP7309-IBZ models
Controller；Dual port RAM adopts the high speed twoport static state RAM of model CY7C026, and two ports have independent control signal
Line, address wire and data wire, can simultaneously carry out data access, realize the resource-sharing of chip.Upper computer software system is adopted
MATLAB programmings software and SQL Server databases.It is that protection AD conversion chip is devised in data acquisition
Double limiting circuit, as shown in Figure 5；In order to improve the precision of AD samplings, signal conditioning circuit is devised, as shown in fig. 6, can be with
The frequency glitches signal in sampling process is filtered, the function of polynary amplifier and current potential translation is realized.
Using the detection method of described Winding in Power Transformer deformed state multi information detection means, as shown in figure 1, bag
Include following steps：
Step 1：The amplitude-frequency response characteristic of Transformer Winding is obtained using frequency sweep detection mode,
Apply sine-wave excitation source in Transformer Winding one end, the continuous frequency for changing sine-wave excitation source is measured not
The ratio of the signal amplitude of response terminal voltage and excitation terminal voltage under same frequency, obtains and specifies in the case of excitation end and responder
Winding amplitude-frequency response characteristic；By the way that the amplitude-frequency response characteristic of each winding of the transformer for obtaining and historical data are carried out into longitudinal ratio
Compared with, waveform maximum variation coefficient is calculated, waveform maximum variation coefficient is winding Deformation Anomalies more than 5%；
The waveform maximum variation coefficient is A%=(A1- A)/A, wherein, A1For the amplitude that test signal changes maximum point
Than A is the historical data of corresponding points.
Step 2：Online acquisition is carried out to transformer two ends electric current, voltage and phase angle,
According to the electric current under different load, voltage and phase angle signal, the short circuit electricity of Three-Phase Transformer winding is online calculated
Anti-, the short-circuit reactance for calculating obtains short-circuit reactance rate of change divided by short-circuit reactance factory-said value；When the short circuit of similar Transformer Winding
When reactance change rate is more than standard setting, then judge that Transformer Winding is deformed upon；Specify in DL/T1093-2008：Impedance
Voltage UKThe Winding in Power Transformer short circuit of the concentric circles winding pair of ＞ 4%, capacity 100MVA and following and below voltage 220kV
The relative change of reactance not should be greater than ± 2%, therefore, described standard setting is ± 2%, i.e.,：When similar Transformer Winding
When short-circuit reactance rate of change is more than ± 2%, then judge that Transformer Winding is deformed upon.
Step 3：The ultrasonic signal of online acquisition Transformer Winding correspondence position,
Several ultrasonic probes are moved along the winding correspondence position on oil tank of transformer surface, is simultaneously emitted by and is received
Ultrasonic signal, by ultrasonic probe winding all surfaces, and the corresponding ultrasonic signal of test record winding various point locations are scanned,
Obtain data of the winding surface each point with respect to tank body of oil tank surface distance；By the relative tank body of oil tank table of the winding surface each point for obtaining
Identity distance from data be compared with transformer factory data, if range data occur change, may determine that it is corresponding therefore
Barrier position；
Winding surface each point is with respect to the computing formula of tank body of oil tank surface distance：
H=vt
In formula, H is winding surface each point with respect to tank body of oil tank surface distance, and v is sound propagation velocity, and t is for reception and sends out
Go out the time difference of signal.
Step 4：Collection transformer winding vibration signal,
Five vibrating sensors are arranged in the fuel tank sidewall of Transformer Winding correspondence position, transformer is separately fixed at
At ABC three-phase windings correspondence tank surface 5/6,2/3,1/2,1/3,1/6 position, while gathering five, surface of oil tank of transformer position
The vibration signal put.
Step 5：Transformer winding vibration signal characteristic is extracted,
Characteristics extraction is carried out to transformer winding vibration signal, the characteristic value of vibration signal is using wavelet package transforms and energy
Amount entropy method is extracted.
4 layers of wavelet package transforms are carried out to the sampled data of vibration signal,Respectively the 4th layer vibration letter
The vibration signal of number frequency contents all from low to high.
Single reconstruct is carried out to WAVELET PACKET DECOMPOSITION coefficient：To the sequence in 16 frequency bands obtaining through 4 layers of WAVELET PACKET DECOMPOSITION
Row are reconstructed, and obtain 16 wavelet package reconstruction signals, and each reconstruction signal contains respectively mechanical oscillation signal from low frequency to height
The information of frequency.Ask for the characteristic vector of wavelet-packet energy entropy composition：When Transformer Winding deforms, transformer machine is shown as
The energy of each frequency content of tool vibration signal there occurs respective change.Therefore, can be with a certain or several frequency content energy values
Change characterizing the fault mode corresponding to Transformer Winding；If Transformer Winding mechanical oscillation signal length is N, to the letter
4 floor WAVELET PACKET DECOMPOSITION number are carried out, the Decomposition Sequence for obtaining is X4k, (k=1～16)；Decomposition coefficient is carried out to obtain after single reconstruct
It is S to reconstruction signal component4kIf, E4kFor power of the reconstruction signal on the 4th layer of k-th node, then E4k=| S4k(i)|2, formula
In, i is i-th signal segment for decomposing.Make ε4k=E4k/ E, in formula, E is the general power of the 3rd layer of all nodes, then
Wavelet-packet energy entropy HkComputing formula be：
16 wavelet-packet energy entropys are obtained respectively by above formula, 1 is may be constructed as element with this 16 wavelet-packet energy entropys
Characteristic vector Q, then：
Q=[H1, H2, H3..., H16] (2)
By characteristic vector Q normalized, orderThe mechanical oscillation signal characteristic value that (j=1～16) obtain
Q ' is：
Step 6：It is respectively directed to different conditions type and sets up Method Using Relevance Vector Machine sorter model, by the change of known state type
The vibration signal characteristics value of depressor winding is instructed as the input of the model in corresponding Method Using Relevance Vector Machine sorter model
Practice study, calculate the hyper parameter and associated weight vector of each Method Using Relevance Vector Machine sorter model；Each Status Type is corresponding each
Signal characteristic value calculated posterior probability in its Method Using Relevance Vector Machine being input into sorter model is the output of the model, if
Determine threshold value, record posterior probability and the magnitude relationship of threshold value and corresponding Status Type, and then determine each Method Using Relevance Vector Machine
Sorter model；The Status Type includes the radial compression of Transformer Winding, radial stretching, axial dielectric comes off and end is folded
Set；
Step 6.1：It is respectively directed to different conditions type and sets up Method Using Relevance Vector Machine sorter model RVM1 to RVM4, RVM1 is used
In radial compression is diagnosed to be, RVM2 is used to be diagnosed to be radial stretching, and RVM3 comes off for being diagnosed to be axial dielectric, and RVM4 is used to examine
Breaking, it is nestable end；
Step 6.2：Choose the radial compression of known Transformer Winding, radial stretching, axial dielectric come off, end nestable four
Plant the Transformer Winding mechanical oscillation signal characteristic value under malfunction；
Step 6.3：Using the vibration signal characteristics value of the Transformer Winding of known state type as the model input,
Study is trained in corresponding Method Using Relevance Vector Machine sorter model, the hyper parameter of each Method Using Relevance Vector Machine sorter model is calculated
With associated weight vector, the corresponding each signal characteristic value of each Status Type falls into a trap in its Method Using Relevance Vector Machine sorter model being input into
The posterior probability for obtaining is the output of the model, sets threshold value, record posterior probability and the magnitude relationship of threshold value and right
The Status Type answered, and then determine each Method Using Relevance Vector Machine sorter model RVM1 to RVM4；
Step 6.3.1：Parameter initialization, arranges hyper parameter α initial values, and end condition is α convergences, maximum iteration time；
Step 6.3.2：The vibration signal characteristics value of the Transformer Winding of the known state type chosen by step 6.2And kernel function K (xi, xj), (j=1～N) calculates design matrix Φ；
Φ=[φ (x1), φ (x2) ..., φ (xN)]T (4)
Wherein：
φ(xi)=[1, K (xi, x1), K (xi, x2) ..., K (xi, xN)]T (5)
In formula, N is signal characteristic value dimension；
Step 6.3.3：The current hyper parameter α of fixation, using second order Newton iterative method associated weight vector w is solved, and is calculated
Gradient vector g and hessian matrix H；Because H is symmetrical matrix, therefore H is carried out into Cholesky decomposition, w is updated, so as to reduce meter
Calculation amount；
H=- ΦTB Φ-A=UTU (6)
G=ΦT(t-Y)-Aw (7)
In formula, U is upper triangular matrix；Δ w is the Power Interpolation of iteration；A=diag (α1, α2..., αN), αi(i=1～
N) be iterative calculation hyper parameter；B=diag (β1, β2..., βN), βi=[Yi(1-Yi)], i=1～N；T is object vector, t
=[t1, t2..., tN]T；W is that associated weight is vectorial, w=[w1, w2..., wN]；Y=[Y1, Y2..., YN]T, Yi=σ [y (xi,
W)], i=1～N, σ () be sigmoid functions, y (xi, w) it is i-th Method Using Relevance Vector Machine sorter model；
Step 6.3.4：The w that step 6.3.3 iteration is obtained as weight Posterior Mean, i.e., associated weight vector, by
In carrying out Cholesky decomposition to H, therefore the quick calculation method that can be given by formula (9) calculates ∑I, i,
In formula, | | | |2Represent 2 norms, MiI-th row of representing matrix M, ∑I, iFor i-th diagonal entry in ∑；
Step 6.3.5：Method using marginal likelihood function is maximized, according to formula (10) and (11) hyper parameter α is updated,
γi=1- αi∑I, i (11)
In formula,And αiI-th yuan in i-th element and initial hyper parameter in hyper parameter after respectively updating
Element, ∑I, iFor i-th diagonal entry, w in ∑iFor i-th element of associated weight vector w；
Step 6.3.6：Repeat step 6.3.3～step 6.3.5 is until α restrains；
Step 6.3.7：Calculate the hyper parameter α after RVM1 to RVM4 updatesnewWith associated weight vector w；
Step 6.3.8：According to the hyper parameter and associated weight that obtain after known radial compression, signal characteristic value and training
Vector, obtains corresponding posterior probability of the signal characteristic value of radial compressive state in RVM1；
IfFor input signal characteristic value, t=[t1, t2..., tN]TFor object vector, Method Using Relevance Vector Machine grader mould
Type is：
In formula, w0For initial weight, w is that associated weight is vectorial, w=[w1, w2..., wN]；K (x, xi) be kernel function, wiFor
I-th element of associated weight vector w；
For binary classification problems, desired value0 or 1 is only, whole data set likelihood function is：
In formula, σ () is sigmoid functions, to avoid producing over-fitting, it is ensured that openness, the sparse Bayesian of model
Method gives zero-mean gaussian prior distribution to associated weight vector w：
In formula, αnewFor the N+1 dimension hyper parameters after renewal, αnew=(α1, α2..., αN)T,I-th N after for renewal
Dimension hyper parameter, N () is normal distyribution function；
For signal characteristic value x known to Transformer Winding*, desired value t*Posterior probability be：
P(t*| t)=∫ p (t*| w, αnew) p (w | t, αnew)p(αnew|w)dw dαnew (15)
Corresponding posterior probability of the signal characteristic value of radial compressive state in RVM1 is calculated according to formula (15)；
Step 6.3.9：Threshold value is set as 70%, posterior probability and the magnitude relationship of threshold value and corresponding state is recorded
Type, the posterior probability of the normal condition for such as calculating is more than 70%, then when the output posterior probability for recording RVM1 is more than 70%
It is otherwise remaining state for radial compression.
Carry out the operation of step 6.3.1 to step 6.3.8 to RVM2 to RVM4 successively, and then determine Method Using Relevance Vector Machine classification
Device model RVM1 to RVM4.
Step 7：Using the Transformer Winding of the Method Using Relevance Vector Machine sorter model and unknown state type determined in step 6
Each signal characteristic value fault diagnosis is carried out to Transformer Winding,
The signal characteristic value of the Transformer Winding of the unknown state type extracted is input in RVM1, according to step 6.3.1
To step 6.3.8, the corresponding posterior probability of the signal characteristic value in RVM1 of the Transformer Winding of unknown state type is calculated,
And judge the magnitude relationship of the posterior probability and threshold value, it is right to carry out with the magnitude relationship of threshold value with the posterior probability of record
According to；If both equivalents, it is determined that the corresponding Status Type of current signal characteristics value, and then determine the transformer of unknown state type
The corresponding whole Status Types of each signal characteristic value of winding, that is, obtain transformer winding fault type detection result.
If the posterior probability calculated in the signal characteristic value input RVM1 of unknown state type is more than threshold value 70%, and
Posterior probability is radial compression more than 70% in the RVM1 of record, then the signal characteristic value for diagnosing the unknown state type is corresponding
Status Type is radial compression, exports radial compression；Otherwise, the corresponding Status Type of signal characteristic value of the unknown state type
For other states, the signal characteristic value of unknown state type is input in RVM2.
To be diagnosed in the signal characteristic value input RVM2 of unknown state type, RVM2 is used to be diagnosed to be radial stretching,
Corresponding posterior probability of the signal characteristic value of unknown state type in RVM2 is calculated, and judges the posterior probability and threshold value
Magnitude relationship, with RVM2 record posterior probability compareed with the magnitude relationship of threshold value, if corresponding Status Type
For radial stretching, it is determined that Transformer Winding has radial stretching；Otherwise, the signal characteristic value of the unknown state type is corresponding
Status Type is other states, by the signal characteristic value input RVM3 of unknown state type.
To be diagnosed in the signal characteristic value input RVM3 of unknown state type, RVM3 takes off for being diagnosed to be axial dielectric
Fall, calculate corresponding posterior probability of the signal characteristic value of unknown state type in RVM3, and judge the posterior probability and door
The magnitude relationship of limit value, is compareed, if corresponding state with the posterior probability recorded in RVM3 with the magnitude relationship of threshold value
Type comes off for axial dielectric, it is determined that there is Transformer Winding axial dielectric to come off；Otherwise, the signal of the unknown state type
The corresponding status of characteristic value are other states, by the signal characteristic value input RVM4 of unknown state type.
To be diagnosed in the signal characteristic value input RVM4 of unknown state type, RVM4 is nested for being diagnosed to be end,
Corresponding posterior probability of the signal characteristic value of unknown state type in RVM4 is calculated, and judges the posterior probability and threshold value
Magnitude relationship, with RVM4 record posterior probability compareed with the magnitude relationship of threshold value, if corresponding Status Type
It is nestable for end, it is determined that there is Transformer Winding end to be nested；Otherwise, other failures are exported.
Although the foregoing describing specific embodiment of the present utility model, those skilled in the art in the art should
Work as understanding, these are merely illustrative of, various changes or modifications can be made to these embodiments, it is new without departing from this practicality
The principle and essence of type.Scope of the present utility model is only limited by the claims that follow.
Claims (1)
1. a kind of Winding in Power Transformer deformed state multi information detection means, it is characterised in that including voltage transformer, electric current
Transformer, ultrasonic probe, vibrating sensor, signal conditioning circuit, A/D converter circuit and central processing unit；The mutual induction of voltage
The output end of device, current transformer, ultrasonic probe and vibrating sensor is connected respectively with the input of signal conditioning circuit,
The output end Jing A/D converter circuit of signal conditioning circuit is connected with the input of central processing unit, the output end of central processing unit
It is connected with communication bus.
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| CN107748314A (en) * | 2017-10-18 | 2018-03-02 | 国网重庆市电力公司北碚供电分公司 | Transformer Faults Analysis system based on sound wave shock detection |
| CN108267100A (en) * | 2018-01-11 | 2018-07-10 | 国家电网公司 | A kind of deformation of transformer winding monitors system |
| CN109443190A (en) * | 2018-11-20 | 2019-03-08 | 武汉拓清科技有限公司 | Transient traveling wave-based transformer winding deformation online monitoring method and device |
| CN109444656A (en) * | 2018-12-21 | 2019-03-08 | 浙江大学 | A kind of inline diagnosis method of deformation of transformer winding position |
| CN109612564A (en) * | 2018-12-29 | 2019-04-12 | 国网上海市电力公司 | Transformer winding state vibration monitoring device in a kind of short-circuit test |
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| CN107748314A (en) * | 2017-10-18 | 2018-03-02 | 国网重庆市电力公司北碚供电分公司 | Transformer Faults Analysis system based on sound wave shock detection |
| CN108267100A (en) * | 2018-01-11 | 2018-07-10 | 国家电网公司 | A kind of deformation of transformer winding monitors system |
| CN108267100B (en) * | 2018-01-11 | 2020-09-18 | 国家电网公司 | Transformer winding deformation monitoring system |
| CN109443190A (en) * | 2018-11-20 | 2019-03-08 | 武汉拓清科技有限公司 | Transient traveling wave-based transformer winding deformation online monitoring method and device |
| CN109444656B (en) * | 2018-12-21 | 2020-06-09 | 浙江大学 | Online diagnosis method for deformation position of transformer winding |
| CN109444656A (en) * | 2018-12-21 | 2019-03-08 | 浙江大学 | A kind of inline diagnosis method of deformation of transformer winding position |
| CN109612564A (en) * | 2018-12-29 | 2019-04-12 | 国网上海市电力公司 | Transformer winding state vibration monitoring device in a kind of short-circuit test |
| CN109669101A (en) * | 2019-02-13 | 2019-04-23 | 云南电网有限责任公司电力科学研究院 | A kind of method and device that transformer winding self-oscillation wave characteristic is extracted |
| CN109669100A (en) * | 2019-02-13 | 2019-04-23 | 云南电网有限责任公司电力科学研究院 | A kind of transformer self-oscillation wave extracting method and system |
| CN110763964A (en) * | 2019-11-12 | 2020-02-07 | 保定天威新域科技发展有限公司 | Operating voltage phase triggering wireless device based on transformer vibration |
| CN110763964B (en) * | 2019-11-12 | 2022-04-12 | 国网新疆电力有限公司乌鲁木齐供电公司 | Operating voltage phase triggering wireless device based on transformer vibration |
| CN111273100A (en) * | 2020-02-20 | 2020-06-12 | 浙江大学 | Power transformer winding state evaluation method based on vibration phase |
| CN111273100B (en) * | 2020-02-20 | 2021-04-27 | 浙江大学 | Power transformer winding state evaluation method based on vibration phase |
| CN115825794A (en) * | 2022-01-07 | 2023-03-21 | 宁德时代新能源科技股份有限公司 | Battery core sampling circuit, circuit fault early warning method and battery management system |
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