CN112115636A - Method and system for predicting insulation aging life of power cable in advance - Google Patents
Method and system for predicting insulation aging life of power cable in advance Download PDFInfo
- Publication number
- CN112115636A CN112115636A CN202010830377.5A CN202010830377A CN112115636A CN 112115636 A CN112115636 A CN 112115636A CN 202010830377 A CN202010830377 A CN 202010830377A CN 112115636 A CN112115636 A CN 112115636A
- Authority
- CN
- China
- Prior art keywords
- index
- cable
- insulation aging
- data
- life
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000032683 aging Effects 0.000 title claims abstract description 51
- 238000009413 insulation Methods 0.000 title claims abstract description 38
- 238000013528 artificial neural network Methods 0.000 claims abstract description 39
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 230000000306 recurrent effect Effects 0.000 claims abstract description 17
- 230000008569 process Effects 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 10
- 125000004122 cyclic group Chemical group 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 9
- 238000010586 diagram Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims description 2
- 230000006386 memory function Effects 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 229920003020 cross-linked polyethylene Polymers 0.000 description 1
- 239000004703 cross-linked polyethylene Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/16—Cables, cable trees or wire harnesses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a method and a system for predicting the insulation aging life of a power cable in advance, index historical data are selected, index prediction is carried out by adopting a recurrent neural network method, the recurrent neural network can have a memory function on the state of the last moment and is used for calculating the output vector of the current moment, the method and the system have unique superiority in predicting time sequence data, can accurately evaluate the operation reliability of the cable and calculate the residual insulation aging life of the cable, and have higher prediction precision compared with the traditional BP neural network method under the same data scale and calculation environment.
Description
Technical Field
The invention relates to the technical field of power system equipment management, in particular to a method and a system for predicting the insulation aging life of a power cable in advance.
Background
Compared with an overhead line, the cable has the advantages of small occupied area, high reliability, large distributed capacitance, small maintenance workload, small electric shock probability and the like. In addition, as the power system is developed, the cable has more favorable conditions for the development to extra-high voltage, large capacity and long distance, so that the proportion of the cable to the number of all transmission lines is gradually increased in the development process of the power system.
However, the above factors also aggravate the problems of serious repeated production, repeated construction, excess capacity and uneven process and quality existing in the cable production industry, and bring great challenges to the safe, efficient and economic operation of the power system. Therefore, the service life of the power cable is managed in a whole life cycle, various expenses can be comprehensively considered, the total cost is reduced, the reliability of equipment and a system is improved, the benefit is improved, the service life prediction and the state evaluation occupy the important position in the whole life cycle management of the power cable, and a power enterprise needs to monitor the state of the running power cable in real time, evaluate the reliability of the running state, predict the residual service life of the power cable, prevent possible faults or abnormal states of the power cable, and achieve the purposes of reducing the production cost and improving the benefit.
However, the existing work at present mainly develops research aiming at a certain specific index, and for a power cable in actual operation of a project, the service life and the state of the power cable cannot be predicted and comprehensively evaluated in real time, or the prediction and evaluation results are difficult to show the coupling with time. In addition, most of the existing methods for cable life prediction and state evaluation are to perform evaluation calculation through tests in a laboratory after a cable is returned, and the practical value is not high. With the continuous increase of the current online monitoring data volume and the development of the cloud computing technology, the advantages of the artificial intelligence method are gradually highlighted, the artificial intelligence method is applied to the whole life cycle management of the power cable, and the method has good development and application prospects and is a key research direction in the field.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the insulation aging life of a power cable in advance, which aim to solve the problem that the service life of the cable cannot be predicted in real time and comprehensively evaluated in the prior art, realize accurate evaluation of the operation reliability of the cable and improve the prediction precision.
In order to achieve the technical purpose, the invention provides a method for predicting the insulation aging life of a power cable in advance, which comprises the following steps:
s1, acquiring historical data of cable online state evaluation indexes, and converting the historical data of the indexes into time sequence data with the same time interval;
s2, training and learning index historical data by adopting a recurrent neural network method, and carrying out online advanced prediction on future index data;
and S3, constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the operation time and the reliability index.
Preferably, the index history data includes a dielectric loss tangent value tan, a direct current leakage current I, and a ground capacitance current Ic。
Preferably, the specific process of online advanced prediction of the future index data is as follows:
s201, determining the number of neuron nodes of each layer of the recurrent neural network;
s202, setting the input vector truncation length of the neural network;
s203, training and learning index historical data;
and S204, carrying out advanced prediction on the future index data.
Preferably, the specific process for constructing and calculating the cable reliability index by using the fuzzy analytic hierarchy process is as follows:
s301, layering the final decision target and each influence factor according to the mutual relation to obtain a hierarchical structure diagram;
s302, constructing a fuzzy judgment matrix;
s303, transforming and adjusting the fuzzy judgment matrix to obtain a consistency matrix;
s304, calculating the weight of the evaluation index;
s305, normalizing the predicted index data to obtain the relative aging degree of the cable represented by various indexes, and calculating the final reliability index of the cable according to the relative aging degree and the weight of the cable.
Preferably, the calculation formula of the relative aging degree of the cable is as follows:
in the formula uiNormalized index value, U, representing the relative degree of ageing of the cableifDenotes a pass threshold, U, of the index iioIndicates the initial value, U, of the index iiRepresenting the measured value of the index i.
Preferably, the reliability index is calculated as follows:
preferably, the calculation formula of the residual insulation aging life of the cable is as follows:
in the formula, TSIndicating the remaining life of the cable, TRIndicating the commissioned time of the cable.
The invention also provides a power cable insulation aging life advance prediction system, which comprises:
the cable historical data processing module is used for acquiring cable online state evaluation index historical data and converting the index historical data into time sequence data with the same time interval;
the future data prediction module is used for training and learning the index historical data by adopting a cyclic neural network method and carrying out online advanced prediction on the future index data;
and the cable life calculation module is used for constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the commissioning time and the reliability index.
Preferably, the index history data includes a dielectric loss tangent value tan, a direct current leakage current I, and a ground capacitance current Ic。
Preferably, the calculation formula of the residual insulation aging life of the cable is as follows:
in the formula, TSIndicating the remaining life of the cable, TRRepresents the commissioning time of the cable, y is the reliability index, uiNormalized index value, k, representing the relative degree of ageing of the cableiIs the weight of the index i, UifDenotes a pass threshold, U, of the index iioIndicates the initial value, U, of the index iiRepresenting the measured value of the index i.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
compared with the prior art, the method selects index historical data, adopts a recurrent neural network method to predict the indexes, the recurrent neural network can generate a memory function on the state of the last moment and is used for calculating the output vector of the current moment, the method has unique superiority in predicting time sequence data, can accurately evaluate the operation reliability of the cable and calculate the residual insulation aging life of the cable, and has higher prediction precision compared with the traditional BP neural network method under the same data scale and calculation environment.
Drawings
Fig. 1 is a flowchart of a method for predicting insulation aging life of a power cable according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a general framework of a method for predicting insulation aging life advance of a power cable according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a dielectric loss tangent lead prediction curve provided in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a DC leakage current lead prediction curve according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a ground capacitance current lead prediction curve provided in the embodiment of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
The following describes a method and a system for predicting the insulation aging life of a power cable in advance according to embodiments of the present invention in detail with reference to the accompanying drawings.
As shown in fig. 1 and 2, an embodiment of the present invention discloses a method for predicting the insulation aging life of a power cable, where the method includes the following operations:
s1, acquiring historical data of cable online state evaluation indexes, and converting the historical data of the indexes into time sequence data with the same time interval;
wherein, the related index historical data comprises a dielectric loss tangent value tan, a direct current leakage current I and a grounding capacitance current Ic. To ensure the effectiveness of training, learning, and prediction, the data must be of a certain scale. For these numbers varying continuously with timeAccording to the method, the situation that the data change continuously in one day cannot be completely predicted, and the historical data of the three indexes needs to be processed into a discrete time sequence, wherein a certain time interval delta t is taken between two adjacent moments in the time sequence.
S2, training and learning index historical data by adopting a recurrent neural network method, and carrying out online advanced prediction on future index data;
in a more traditional neural network structure, neurons between layers are generally in full connection, and neurons in each layer are not connected pairwise, namely an output vector at the current moment is only related to an input vector at the current moment.
For the cyclic neural network structure, connection exists among neurons between hidden layers of the cyclic neural network, namely, the output at the current moment is not only dependent on the input at the current moment, but also dependent on the system state at the previous moment, namely, the cyclic neural network can memorize the past system state and use the past system state to calculate the output vector at the current moment, and the time sequence problem can be better processed.
The calculation process is as follows:
st＝f(Ugit+Wgst-1+b)
ot＝softmax(Vst+c)
in the formula, stAnd f is the state of the hidden layer at the moment t, f is the activation function of the neural network, b is the offset vector, W is the weight of the input, and U is the weight of the sample input at the moment. When t is 0, s is defaulted-1＝0。otV represents the sample weight of the output, and c is the offset vector.
From the above equation, the output of the recurrent neural network is related to the input at all previous times, but in the actual calculation, considering that the connection between two times with too long interval tends to decrease, the neural network is truncated to have a maximum length, that is, the output at a certain time is related to all the input at a certain time.
The specific process of adopting the recurrent neural network to carry out the advanced prediction is as follows:
s201, determining the number of neuron nodes of each layer of the recurrent neural network; the cyclic neural network is composed of a plurality of neuron nodes and is divided into an input layer, a hidden layer and an output layer, the neuron nodes between the layers are generally in full connection, in addition, the nodes between the hidden layers in the cyclic neural network are also connected, and before the neural network is used for training, learning and predicting, the scale of the cyclic neural network, namely the number of the neurons of each layer and the connection relation, must be determined;
s202, when a recurrent neural network is trained, the input vector truncation length of the neural network is set to be l, namely the index prediction value at a certain moment is related to the index data and the change trend in the previous time of l multiplied by delta t;
s203, training and learning the index historical data, and reserving a verification data set in a certain proportion to prevent overfitting;
s204, carrying out advanced prediction on future index data;
s205, calculating the root mean square error between the prediction result and the true value, wherein the calculation formula is as follows:
wherein X is a set of predicted values at each time of a certain index, N is the number of predicted times, X (t) is the predicted value of the index X at the t-th time,
the true value of the index X at the t-th moment.
And S3, constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the operation time and the reliability index.
The specific process for constructing and calculating the reliability index of the cable by using the fuzzy analytic hierarchy process is as follows:
s301, layering the final decision target and each influence factor according to the mutual relation to obtain a hierarchical structure diagram;
s302, construct the blur determination matrix a ═ (a)ij)n×nElement a in the matrixijThe index j represents the importance of the index i relative to the index j, can be obtained by evaluating the importance of the index according to a 0.1-0.9 scaling method, and satisfies aij+aji1(i, j ═ 1,2, …, n), the scale rule is shown in table 1:
TABLE 1
S303, carrying out conversion adjustment on the fuzzy judgment matrix to obtain a consistency matrix B, wherein the conversion formula is as follows:
bij＝(bi-bj)/[2(n-1)]+0.5
s304, calculating the weight k of the evaluation index iiThe calculation formula is as follows:
s305, normalizing the predicted index data to obtain the relative aging degree of the cable represented by the indexes, wherein the formula is as follows:
in the formula uiNormalized index value, U, representing the relative degree of ageing of the cableifDenotes a pass threshold, U, of the index iioIndicates the initial value, U, of the index iiRepresenting the measured value of the index i. u. ofiThe larger the value of (A)The less the cable ages.
And calculating the final reliability index y of the cable according to the relative aging degree and the weight of the cable, wherein the calculation formula is as follows:
in the formula, the larger the value of y, the longer the remaining life of the cable.
Calculating the residual insulation aging life of the cable according to the operation time and the reliability index, wherein the calculation formula is as follows:
in the formula, TSIndicating the remaining life of the cable, TRIndicating the commissioned time of the cable.
In the embodiment of the invention, a certain 110kV voltage-class crosslinked polyethylene cable which is put into operation at 9, 16 and 2007 is taken as an example, and the specific implementation process of the invention is further explained.
Selecting index real-time monitoring data between 1 month and 1 day of 2010 and 12 months and 31 days of 2017 of the cable to train and learn the neural network, wherein the sampling time interval is 1 hour, and 70128 groups of data are counted. The truncation length of the recurrent neural network is set to be 48, namely the index value at a certain moment is related to the index value in the previous 48 hours, so that the online prediction of the index data between 1 and 1 in 2018 and 12 and 31 in 2019 is carried out. And comparing the prediction result with a traditional BP neural network (BPNN) prediction result. The online prediction results of various indexes are shown in fig. 3-5, and the root mean square error between the predicted value and the true value of each index is shown in table 2:
TABLE 2
Therefore, the prediction based on the recurrent neural network provided by the embodiment of the invention can accurately predict the insulation aging indexes of various power cables on line, and the accuracy is higher than that of the traditional BP neural network, so that the effectiveness and superiority of the power cables are proved.
And (4) combining the prediction results of all indexes to obtain the cable with the residual insulation aging life of about 11 years and 4 months by 2019 years.
The embodiment of the invention adopts the recurrent neural network method to predict the indexes, the recurrent neural network can generate a memory function on the state of the last moment and is used for calculating the output vector of the current moment, the method has unique superiority in predicting time sequence data, can accurately evaluate the operation reliability of the cable and calculate the residual insulation aging life of the cable, and has higher prediction precision compared with the traditional BP neural network method under the same data scale and calculation environment.
The embodiment of the invention also discloses a power cable insulation aging life advanced prediction system, which comprises:
the cable historical data processing module is used for acquiring cable online state evaluation index historical data and converting the index historical data into time sequence data with the same time interval;
the future data prediction module is used for training and learning the index historical data by adopting a cyclic neural network method and carrying out online advanced prediction on the future index data;
and the cable life calculation module is used for constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the commissioning time and the reliability index.
The method for predicting the insulation aging life of the power cable is realized by the system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A power cable insulation aging life advance prediction method is characterized by comprising the following steps:
s1, acquiring historical data of cable online state evaluation indexes, and converting the historical data of the indexes into time sequence data with the same time interval;
s2, training and learning index historical data by adopting a recurrent neural network method, and carrying out online advanced prediction on future index data;
and S3, constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the operation time and the reliability index.
2. The method for predicting the insulation aging life of the power cable in advance as claimed in claim 1, wherein the index historical data comprises a dielectric loss tangent value tan, a direct current leakage current I and a grounding capacitance current Ic。
3. The method for predicting the insulation aging life of the power cable in advance as claimed in claim 1, wherein the specific process of predicting the future index data in advance on line is as follows:
s201, determining the number of neuron nodes of each layer of the recurrent neural network;
s202, setting the input vector truncation length of the neural network;
s203, training and learning index historical data;
and S204, carrying out advanced prediction on the future index data.
4. The method for predicting the insulation aging life of the power cable in advance as claimed in claim 1, wherein the specific process of constructing and calculating the reliability index of the power cable by using the fuzzy analytic hierarchy process is as follows:
s301, layering the final decision target and each influence factor according to the mutual relation to obtain a hierarchical structure diagram;
s302, constructing a fuzzy judgment matrix;
s303, transforming and adjusting the fuzzy judgment matrix to obtain a consistency matrix;
s304, calculating the weight of the evaluation index;
s305, normalizing the predicted index data to obtain the relative aging degree of the cable represented by various indexes, and calculating the final reliability index of the cable according to the relative aging degree and the weight of the cable.
5. The method for predicting the insulation aging life of the power cable in advance as claimed in claim 4, wherein the calculation formula of the relative aging degree of the cable is as follows:
in the formula uiNormalized index value, U, representing the relative degree of ageing of the cableifDenotes a pass threshold, U, of the index iioIndicates the initial value, U, of the index iiRepresenting the measured value of the index i.
6. The method for predicting the insulation aging life advance of the power cable as claimed in claim 5, wherein the reliability index is calculated by the following formula:
7. the method for predicting the insulation aging life of the power cable in advance as claimed in claim 6, wherein the calculation formula of the residual insulation aging life of the cable is as follows:
in the formula, TSIndicating the remaining life of the cable, TRIndicating the commissioned time of the cable.
8. A power cable insulation aging life advance prediction system, characterized in that the system comprises:
the cable historical data processing module is used for acquiring cable online state evaluation index historical data and converting the index historical data into time sequence data with the same time interval;
the future data prediction module is used for training and learning the index historical data by adopting a cyclic neural network method and carrying out online advanced prediction on the future index data;
and the cable life calculation module is used for constructing and calculating a cable reliability index by using a fuzzy analytic hierarchy process according to the index prediction result, and calculating the residual insulation aging life of the cable according to the commissioning time and the reliability index.
9. The system of claim 8, wherein the index history data comprises a dielectric loss tangent tan, a DC leakage current I and a ground capacitance current Ic。
10. The system for predicting the insulation aging life of the power cable as claimed in claim 8, wherein the residual insulation aging life of the cable is calculated by the following formula:
in the formula, TSIndicating the remaining life of the cable, TRRepresents the commissioning time of the cable, y is the reliability index, uiNormalized index value, k, representing the relative degree of ageing of the cableiIs the weight of the index i, UifDenotes a pass threshold, U, of the index iioIndicates the initial value, U, of the index iiRepresenting the measured value of the index i.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010830377.5A CN112115636A (en) | 2020-08-18 | 2020-08-18 | Method and system for predicting insulation aging life of power cable in advance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010830377.5A CN112115636A (en) | 2020-08-18 | 2020-08-18 | Method and system for predicting insulation aging life of power cable in advance |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112115636A true CN112115636A (en) | 2020-12-22 |
Family
ID=73803768
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010830377.5A Pending CN112115636A (en) | 2020-08-18 | 2020-08-18 | Method and system for predicting insulation aging life of power cable in advance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112115636A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128111A (en) * | 2021-04-12 | 2021-07-16 | 中国南方电网有限责任公司超高压输电公司昆明局 | Diagnostic method and diagnostic device for operating parameters in ultra-high voltage circuit |
| CN113204857A (en) * | 2021-03-15 | 2021-08-03 | 北京锐达芯集成电路设计有限责任公司 | Method for predicting residual life of electronic device based on extreme gradient lifting tree algorithm |
| CN113763205A (en) * | 2021-09-07 | 2021-12-07 | 南方电网电力科技股份有限公司 | Cable insulation state detection method and related device |
| CN115600478A (en) * | 2021-06-28 | 2023-01-13 | 中企网络通信技术有限公司（Cn） | Software-defined wide area network analysis system and method of operation thereof |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735970A (en) * | 2012-06-15 | 2012-10-17 | 安徽中兴继远信息技术股份有限公司 | Insulation monitoring and life prediction method of cross linked polyethylene cable |
| CN104166788A (en) * | 2014-07-22 | 2014-11-26 | 国家电网公司 | Overhead transmission line optimal economic life range assessment method |
| CN107516015A (en) * | 2017-08-29 | 2017-12-26 | 武汉大学 | Composite insulator ageing state comprehensive estimation method based on multi-characteristicquantity quantity |
| CN109031014A (en) * | 2017-12-28 | 2018-12-18 | 国网湖北省电力公司宜昌供电公司 | A kind of transformer synthesis reliability assessment and prediction technique based on operation data |
| CN109902336A (en) * | 2019-01-15 | 2019-06-18 | 国网浙江省电力有限公司 | Cable insulation lifetime estimation method based on Fuzzy AHP |
| CN110321601A (en) * | 2019-06-14 | 2019-10-11 | 山东大学 | A kind of overhead transmission line dynamic current-carrying capability advanced prediction method and system |
| CN110378052A (en) * | 2019-07-25 | 2019-10-25 | 北京航空航天大学 | It is looked to the future the equipment method for predicting residual useful life of operating condition based on Recognition with Recurrent Neural Network |
-
2020
- 2020-08-18 CN CN202010830377.5A patent/CN112115636A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735970A (en) * | 2012-06-15 | 2012-10-17 | 安徽中兴继远信息技术股份有限公司 | Insulation monitoring and life prediction method of cross linked polyethylene cable |
| CN104166788A (en) * | 2014-07-22 | 2014-11-26 | 国家电网公司 | Overhead transmission line optimal economic life range assessment method |
| CN107516015A (en) * | 2017-08-29 | 2017-12-26 | 武汉大学 | Composite insulator ageing state comprehensive estimation method based on multi-characteristicquantity quantity |
| CN109031014A (en) * | 2017-12-28 | 2018-12-18 | 国网湖北省电力公司宜昌供电公司 | A kind of transformer synthesis reliability assessment and prediction technique based on operation data |
| CN109902336A (en) * | 2019-01-15 | 2019-06-18 | 国网浙江省电力有限公司 | Cable insulation lifetime estimation method based on Fuzzy AHP |
| CN110321601A (en) * | 2019-06-14 | 2019-10-11 | 山东大学 | A kind of overhead transmission line dynamic current-carrying capability advanced prediction method and system |
| CN110378052A (en) * | 2019-07-25 | 2019-10-25 | 北京航空航天大学 | It is looked to the future the equipment method for predicting residual useful life of operating condition based on Recognition with Recurrent Neural Network |
Non-Patent Citations (2)
| Title |
|---|
| 廖瑞金 等: "基于模糊综合评判的电力变压器运行状态评估模型", 电力系统自动化, vol. 32, no. 3, 10 February 2008 (2008-02-10), pages 70 - 75 * |
| 黄肖为 等: "基于改进模糊层次分析法的电缆绝缘寿命评估模型", 电气自动化, vol. 41, no. 4, 30 July 2019 (2019-07-30), pages 107 - 110 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113204857A (en) * | 2021-03-15 | 2021-08-03 | 北京锐达芯集成电路设计有限责任公司 | Method for predicting residual life of electronic device based on extreme gradient lifting tree algorithm |
| CN113128111A (en) * | 2021-04-12 | 2021-07-16 | 中国南方电网有限责任公司超高压输电公司昆明局 | Diagnostic method and diagnostic device for operating parameters in ultra-high voltage circuit |
| CN115600478A (en) * | 2021-06-28 | 2023-01-13 | 中企网络通信技术有限公司（Cn） | Software-defined wide area network analysis system and method of operation thereof |
| CN115600478B (en) * | 2021-06-28 | 2023-08-15 | 中企网络通信技术有限公司 | Software defined wide area network analysis system and method of operation thereof |
| CN113763205A (en) * | 2021-09-07 | 2021-12-07 | 南方电网电力科技股份有限公司 | Cable insulation state detection method and related device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112115636A (en) | Method and system for predicting insulation aging life of power cable in advance | |
| CN113156917B (en) | Power grid equipment fault diagnosis method and system based on artificial intelligence | |
| CN111736084B (en) | Valve-regulated lead-acid storage battery health state prediction method based on improved LSTM neural network | |
| CN111539515A (en) | Complex equipment maintenance decision method based on fault prediction | |
| CN110059891B (en) | Photovoltaic power station output power prediction method based on VMD-SVM-WSA-GM combined model | |
| CN111784061B (en) | Training method, device and equipment for power grid engineering cost prediction model | |
| CN116167527B (en) | Pure data-driven power system static safety operation risk online assessment method | |
| CN113987294A (en) | CVT (continuously variable transmission) online fault diagnosis method based on genetic optimization GRU (generalized regression Unit) neural network | |
| CN113065715A (en) | Multi-load ultra-short-term prediction method for comprehensive energy system | |
| CN113516271A (en) | Wind power cluster power day-ahead prediction method based on space-time neural network | |
| CN115689114A (en) | Submarine cable running state prediction method based on combined neural network | |
| CN116186633A (en) | Power consumption abnormality diagnosis method and system based on small sample learning | |
| CN113758652B (en) | Oil leakage detection method and device for converter transformer, computer equipment and storage medium | |
| CN111680712A (en) | Transformer oil temperature prediction method, device and system based on similar moments in the day | |
| CN114595883A (en) | Oil-immersed transformer residual life personalized dynamic prediction method based on meta-learning | |
| CN114970665A (en) | Model training method, electrolytic capacitor residual life prediction method and system | |
| CN112734201B (en) | Multi-equipment overall quality evaluation method based on expected failure probability | |
| Lei et al. | Photovoltaic power missing data filling based on multiple matching and long‐and short‐term memory network | |
| CN104732107A (en) | Transformer bushing remaining life prediction method taking medium parameters as assessment parameters | |
| CN116401610A (en) | Inverter parameter fault diagnosis method based on depth residual error network and joint distribution | |
| CN115221731A (en) | Transformer life evaluation method based on data fusion and Wiener model | |
| Yang et al. | Prediction of top oil Temperature for oil-immersed transformers Based on PSO-LSTM | |
| CN111008779A (en) | Transformer health state assessment method based on multi-factor assessment model | |
| CN111612227A (en) | Load prediction method based on K-means clustering and bat optimization neural network | |
| CN112685933B (en) | Method for predicting residual service life of roller screw pair |
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 |