CN111460727A - Method for predicting service life of transformer by using multiple parameters - Google Patents
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Abstract
The invention discloses a method for predicting the service life of a transformer by utilizing multiple parameters, which comprises the following steps: s1, performing state evaluation on the transformer by using the transformer state data; s2, constructing an assessment database by using the historical data of the transformers and the retired data of the transformers of the same type; and S3, predicting the transformer evaluation parameters by using an Elman network prediction method to obtain the predicted residual life. The transformer state data comprises transformer electrical test project data, analysis data of dissolved gas in oil, transformer oil characteristic data and working condition data; the analysis data of the dissolved gas in the oil comprises hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide and carbon dioxide; the transformer oil characteristic data comprises micro-water content, acid value, breakdown voltage and oil dielectric loss. The method can be used for predicting the service life of the transformer, accurately replacing the transformer and providing a technical method for guaranteeing the healthy operation of the transformer.
Description
Technical Field
The invention relates to the field of life prediction of power transmission and transformation equipment, in particular to a life prediction method of a transformer by utilizing multiple parameters.
Background
In order to meet the needs of economic development in China, power systems are continuously developed, and the technical level is continuously improved. The power transformer is an important electrical device in a power system and plays a key role in power generation, power transmission and power distribution links.
It is essential to ensure healthy operation of the transformer, which is associated with a number of factors, the internal insulation being one of the most important. The evaluation of the service life of the transformer is a complex technical problem, the aging mechanism of the insulation inside the transformer must be deeply researched, the field nondestructive detection and judgment are carried out on the aging degree of the transformer, and a correct method is discussed to evaluate the aging condition of the insulation of the transformer so as to predict the operation risk and reliability of the insulation of the transformer, so that an effective maintenance and replacement strategy is made by combining economic management, and the accident rate is reduced.
The large-capacity transformer is required to be configured with an online monitoring system, and a complete transformer file including basic information of the transformer, running state, overhaul and maintenance, test, defect and defect elimination record and the like must be established in the power production management system. The information is comprehensively and effectively utilized, the operation level of the power transformer is correctly evaluated, and a method for accurately evaluating the operation life of the transformer is established, so that the method has important significance for guaranteeing the efficient and stable operation of the transformer.
Disclosure of Invention
The invention aims to solve the problem that the operation life of a transformer is difficult to evaluate by using comprehensive information in the prior art, and provides a multi-parameter transformer life prediction method.
In order to achieve the technical purpose, the invention provides a technical scheme that a method for predicting the service life of a transformer by using multiple parameters comprises the following steps:
s1, performing state evaluation on the transformer by using the transformer state data;
s2, constructing an assessment database by using the historical data of the transformers and the retired data of the transformers of the same type;
and S3, predicting the transformer evaluation parameters by using an Elman network prediction method to obtain the predicted residual life.
In the step S1, in the step S,
and S11, recording the transformer state data as a state vector:
Y＝(y1,y2,...,yi,...,yn)(n＝17)
s12, determining each state quantity yiInitial value of (2)
And attention value
Calculating the score condition of each state data by using a formula (1);
wherein x isiIs the score value of the status quantity of the ith item, y'iIs the deterioration value of the i-th state, if the state quantity belongs to a positive deterioration condition, i.e., the deterioration is about severe as the value of the state quantity is larger,
if the state quantity is negative deterioration, take
S13, determining the constant weight value of each index by adopting an analytic hierarchy process: w ═ W1,w2,...,wn]；
S14, because the real state of the transformer can not be reflected when a certain performance is seriously reduced under a constant weight system, the state variable weight vector in the variable weight theory is used for the construction of the weight:
wherein the state-variable weight vector S (X) ═ S1(X),...,Sn(X)) is:
the variable weight coefficient can be obtained according to equation (2) as:
s15, evaluating the transformer state by using a formula (5) according to the scores of the state quantities and the variable weight coefficients corresponding to the state quantities;
the transformer state data comprises transformer electrical test project data, analysis data of dissolved gas in oil, transformer oil characteristic data and working condition data;
the electrical test project data comprise insulation resistance, absorption ratio, leakage current and direct current resistance unbalance coefficient;
the analysis data of the dissolved gas in the oil comprises hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide and carbon dioxide;
the transformer oil characteristic data comprises micro-water content, acid value, breakdown voltage and oil dielectric loss;
the working condition data comprises the load rate of the transformer and the operating environment grade of the transformer.
When the state evaluation is carried out, a state variable weight vector is introduced to carry out weight processing on the state variable weight vector, and the specific steps are as follows:
a1, calculating the single-phase score of the item by using the actual measured value, the attention value and the initial value of the state data;
a2, determining the initial weight of each test item by using an analytic hierarchy process;
a3, calculating a variable weight coefficient by using the state variable weight vector and the initial weight;
and A4, calculating the transformer state evaluation result by using the variable weight coefficient and the item score.
In step S2, the method includes the steps of:
s21, obtaining the factory score g of the same type of transformer0Score g of frequent and near-retirement faultsendRecording the data in the transformer file;
and S22, periodically measuring the transformer state data, calculating the state evaluation score of the transformer at the moment by using the step S1, recording the state evaluation score in a transformer file, and constructing a test transformer state evaluation database.
Step S3 includes the following steps:
s31, constructing an Elman' network with 3 inputs and 1 output;
s32, using the state score of the transformer in the previous 3 years as input, and using the state score in the next 1 year as output;
s33, dividing historical data of the transformer in N years to obtain N-3 groups of training sets, and training a network;
s34, predicting the state of the transformer by using the trained Elman network;
and S35, determining a threshold value by using the state of the retired transformer, and analyzing the residual life of the transformer by using the predicted state.
The invention has the beneficial effects that: the method for predicting the service life of the transformer by using the multiple parameters has the advantages that the multiple characteristic quantity data of the transformer are comprehensively utilized to evaluate the state of the transformer, the real level of the aging of each part of the transformer is reflected, the state variable weight vector is integrated, the degradation degree of a single state quantity is changed along with an index value, and the wooden barrel effect of an index in the aging is better reflected. The local feedback characteristic and the dynamic memory function of the Elman network are utilized for predicting the state quantity time sequence of the transformer, the high prediction precision is shown, the residual life of the transformer can be predicted, and the significance of guiding operation and maintenance staff to replace the transformer is provided.
Drawings
FIG. 1 is a flow chart of a method for predicting the life of a transformer using multiple parameters according to the present invention.
FIG. 2 is a state quantity index system for a method for predicting transformer life using multiple parameters according to the present invention.
FIG. 3 is a prediction network Elman network topology diagram of the multi-parameter transformer life prediction method of the present invention.
Detailed Description
For the purpose of better understanding the objects, technical solutions and advantages of the present invention, the following detailed description of the present invention with reference to the accompanying drawings and examples should be understood that the specific embodiment described herein is only a preferred embodiment of the present invention, and is only used for explaining the present invention, and not for limiting the scope of the present invention, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the scope of the present invention.
Example (b): as shown in fig. 1, a flowchart of a method for predicting the life of a transformer using multiple parameters includes the following steps:
and S1, performing state evaluation on the transformer by using the transformer state data.
S11, as shown in fig. 2, the transformer state data is composed of transformer electrical test project data, analysis data of dissolved gas in oil, transformer oil characteristic data, and working condition data, and specifically includes: the data of the electrical test items are insulation resistance, absorption ratio, leakage current and unbalance coefficient of direct current resistance; the analysis data of the dissolved gas in the oil are hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide and carbon dioxide; the transformer oil characteristic data comprise micro-water content, acid value, breakdown voltage and oil medium loss; the working condition data are the load rate of the transformer and the running environment grade of the transformer; record as a state vector:
Y＝(y1,y2,...,yi,...,yn)(n＝17)。
s12, determining each state quantity yiInitial value of (2)
And attention value
And calculating the score condition of each state data by using the formula (1):
in the formula (1), xiIs the score value of the status quantity of the ith item, y'iIs the deterioration value of the i-th state, if the state quantity belongs to a positive deterioration condition, i.e., the deterioration is about severe as the value of the state quantity is larger,
if the state quantity is negative deterioration, take
S13, determining the constant weight value of each index according to an analytic hierarchy process:
W＝[w1,w2,…,wn]。
s14, the real state of the transformer when a certain performance is seriously reduced can not be reflected under a constant weight system, and the state variable weight vector in the variable weight theory is used for the construction of the weight:
wherein the state-variable weight vector S (X) ═ S1(X),…,Sn(X)) is:
the variable weight coefficient can be obtained according to equation (2) as:
and S15, evaluating the transformer state by using an equation (5) according to the variable weight coefficient corresponding to the score of each state quantity and the state quantity, and recording the evaluation result into a transformer file.
And S2, constructing an assessment database by using the historical transformer data and the retired transformer data of the same type.
S21, obtaining the factory score g of the same type of transformer0Score g of frequent and near-retirement faultsendRecorded in such transformer files.
And S22, periodically measuring the state data of the transformer, calculating in the step 1 to obtain the state score of the transformer at the moment, recording the state score in a transformer file, and constructing a test transformer state evaluation database.
And S3, as shown in FIG. 3, predicting the transformer evaluation parameters by using a prediction method to obtain the predicted residual life.
S31, selecting historical state scoring data G of the transformer from the state database
G＝(g1,g2,...,gn)。
And S32, dividing the training samples and the test samples. Wherein the division rule of the training samples is that g is extracted1～gNIs the first sample, and (g)1,g2,…,gN-1) Is an independent variable, gNIs an objective function; drawing out g2～gN+1Is the second sample, and (g)2,g3,…,gN) Is an independent variable, gN+1Is an objective function; sampling in turn, the following training set can be obtained:
and S33, constructing an Elman neural network with N-1 input and 1 output to obtain a predicted network topological structure.
And S34, training the network, namely training the data of the first N-1 rows in the first column in the training set as network input, and the Nth row as output to obtain the trained network parameters and the trained network model after training the data of the i groups. Before training, input and output are normalized so that the network can obtain better performance and stability:
in the formula (6), gminPresentation score giThe minimum value in the data; gmaxPresentation score giThe maximum value in the data.
And S35, testing the network. And performing network test by using the normalized data, and normalizing the actual output result into a score value to obtain the prediction precision of the network.
S36, transforming the transformerHistorical state score gnAnd outputting the state score as input to obtain the next state score, and comparing the state score with the retired transformer threshold value to obtain the predicted service life of the transformer.
The above-mentioned embodiments are preferred embodiments of the method for predicting the lifetime of a transformer using multiple parameters, and the scope of the invention is not limited thereto, and all equivalent changes in shape and structure according to the invention are within the scope of the invention.
Claims (6)
1. A method for predicting the service life of a transformer by using multiple parameters is characterized by comprising the following steps:
s1, performing state evaluation on the transformer by using the transformer state data;
s2, constructing an assessment database by using the historical data of the transformers and the retired data of the transformers of the same type;
and S3, predicting the transformer evaluation parameters by using an Elman network prediction method to obtain the predicted residual life.
2. The method of claim 1, wherein the method comprises the steps of: in the step S1, in the step S,
and S11, recording the transformer state data as a state vector:
Y＝(y1,y2,...,yi,...,yn)(n＝17)；
s12, determining each state quantity yiInitial value of (2)
And attention value
Calculating the score condition of each state data by using a formula (1);
wherein x isiIs the score value of the status quantity of the ith item, y'iIs the deterioration value of the i-th state, if the state quantity belongs to a positive deterioration condition, i.e., the deterioration is about severe as the value of the state quantity is larger,
if the state quantity is negative deterioration, take
S13, determining the constant weight value of each index by adopting an analytic hierarchy process: w ═ W1,w2,...,wn]；
S14, because the real state of the transformer can not be reflected when a certain performance is seriously reduced under a constant weight system, the state variable weight vector in the variable weight theory is used for the construction of the weight:
wherein the state-variable weight vector S (X) ═ S1(X),...,Sn(X)) is:
the variable weight coefficient can be obtained according to equation (2) as:
s15, evaluating the transformer state by using a formula (5) according to the scores of the state quantities and the variable weight coefficients corresponding to the state quantities;
3. the method of claim 2, wherein the method comprises predicting the life of the transformer using multiple parameters
The transformer state data comprises transformer electrical test project data, analysis data of dissolved gas in oil, transformer oil characteristic data and working condition data;
the electrical test project data comprise insulation resistance, absorption ratio, leakage current and direct current resistance unbalance coefficient;
the analysis data of the dissolved gas in the oil comprises hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide and carbon dioxide;
the transformer oil characteristic data comprises micro-water content, acid value, breakdown voltage and oil dielectric loss;
the working condition data comprises the load rate of the transformer and the operating environment grade of the transformer.
4. The method of claim 2, wherein the method comprises the steps of: when the state evaluation is carried out, a state variable weight vector is introduced to carry out weight processing on the state variable weight vector, and the specific steps are as follows:
a1, calculating the single-phase score of the item by using the actual measured value, the attention value and the initial value of the state data;
a2, determining the initial weight of each test item by using an analytic hierarchy process;
a3, calculating a variable weight coefficient by using the state variable weight vector and the initial weight;
and A4, calculating the transformer state evaluation result by using the variable weight coefficient and the item score.
5. The method of claim 1, wherein the method comprises the steps of: in step S2, the method includes the steps of:
s21, obtaining the factory score g of the same type of transformer0Score g of frequent and near-retirement faultsendRecording the data in the transformer file;
and S22, periodically measuring the transformer state data, calculating the state evaluation score of the transformer at the moment by using the step S1, recording the state evaluation score in a transformer file, and constructing a test transformer state evaluation database.
6. The method of claim 1, wherein the method comprises the steps of: step S3 includes the following steps:
s31, constructing an Elman network with 3 inputs and 1 output;
s32, using the state score of the transformer in the previous 3 years as input, and using the state score in the next 1 year as output;
s33, dividing historical data of the transformer in N years to obtain N-3 groups of training sets, and training a network;
s34, predicting the state of the transformer by using the trained Elman network;
and S35, determining a threshold value by using the state of the retired transformer, and analyzing the residual life of the transformer by using the predicted state.
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Application publication date: 20200728 |