Intelligent mutual inductor

文档序号：613559 发布日期：2021-05-07 浏览：12次中文

阅读说明：本技术 一种智能互感器 (Intelligent mutual inductor ) 是由丁飞石颉杜国庆苏新雅胡倩黄佳悦朱家坤申海锋于 2020-10-23 设计创作，主要内容包括：本发明公开了一种智能互感器,其设计要点在于,包括：电流检测模块、温度检测模块、数据存储模块、远程通信模块、智能诊断模块、智能终端；所述电流检测模块用于对导线电流的检测；所述温度检测模块用于测量智能互感器多个部位的温度；所述数据存储模块用于对电流检测模块、温度检测模块采集到的信息进行存储,用于与智能诊断模块得到的信息交换信息,用于与智能终端交换信息。采用本申请的智能互感器,能够大幅的提高诊断效率与诊断精度,可实现对互感器电流及温度进行实时检测,并可将检测数据进行实时传输及存储,同时互感器自带智能诊断模块,采用ART神经网络融合BP算法,可根据实时数据进行互感器状态诊断。(The invention discloses an intelligent mutual inductor, which has the design points that: the intelligent diagnosis system comprises a current detection module, a temperature detection module, a data storage module, a remote communication module, an intelligent diagnosis module and an intelligent terminal; the current detection module is used for detecting the current of the lead; the temperature detection module is used for measuring the temperatures of a plurality of parts of the intelligent mutual inductor; the data storage module is used for storing the information collected by the current detection module and the temperature detection module, exchanging information with the information obtained by the intelligent diagnosis module and exchanging information with the intelligent terminal. By adopting the intelligent mutual inductor, the diagnosis efficiency and the diagnosis precision can be greatly improved, the current and the temperature of the mutual inductor can be detected in real time, the detected data can be transmitted and stored in real time, meanwhile, the mutual inductor is provided with an intelligent diagnosis module, an ART neural network is adopted to fuse a BP algorithm, and the state diagnosis of the mutual inductor can be carried out according to the real-time data.)

1. An intelligent transformer, comprising: the intelligent diagnosis system comprises a current detection module, a temperature detection module, a data storage module, a remote communication module, an intelligent diagnosis module and an intelligent terminal;

the current detection module is used for detecting the current of the lead;

the temperature detection module is used for measuring the temperatures of a plurality of parts of the intelligent mutual inductor;

the data storage module is used for storing the information acquired by the current detection module and the temperature detection module, exchanging information with the information acquired by the intelligent diagnosis module and exchanging information with the intelligent terminal;

the intelligent diagnosis module reads the data of the data storage module, is used for diagnosing the state of the mutual inductor and can write the diagnosis result into the data storage module;

the intelligent terminal is used for inquiring and displaying the information stored in the data storage module;

the output end of the current detection module is connected with the input end of the data storage module;

the output end of the temperature detection module is connected with the input end of the data storage module;

wherein the data storage module is connected with the intelligent diagnosis module;

the data storage module is connected with the intelligent terminal, and the data storage module is in communication connection with the intelligent terminal through the remote communication module.

2. The intelligent transformer according to claim 1, wherein the current detection module employs patch thermistor sensors, and 8 patch thermistor sensors are averagely attached to the inner ring and the outer ring of the transformer; and a high-resolution analog-to-digital conversion chip is adopted to convert the current signal into a digital signal and then transmit the digital signal to the data storage module.

3. The intelligent mutual inductor according to claim 1, wherein the temperature detection module adopts thermistor sensors, and the thermistor sensors are mounted at a plurality of positions of the mutual inductor to realize the detection of the temperature of the mutual inductor; and 8 patch type thermistor sensors are averagely attached to the inner ring and the outer ring of the mutual inductor to form a Wheatstone bridge for detecting the temperature of each point and simultaneously detecting the difference value between the cable and the ambient temperature, and a high-resolution analog-to-digital conversion chip is adopted for converting a current signal into a digital signal and then transmitting the digital signal to a data storage module.

4. The intelligent transformer of claim 1, wherein the data storage module is a mobile data storage.

5. The intelligent transformer according to claim 1, wherein the intelligent diagnosis module employs a digital processing chip, which can acquire, store, remotely communicate and intelligently diagnose the collected current and temperature information, and employs a metal mesh enclosure to form a shielding enclosure to prevent electromagnetic interference of the digital chip under strong magnetic field.

6. The intelligent mutual inductor according to claim 1, wherein the intelligent diagnosis module stores a mutual inductor calculation model, and the mutual inductor calculation model adopts an ART calculation model;

the ART computational model consists of two layers of neurons including two subsystems: a comparison layer C and a recognition layer R; further comprising: three control signal RESET signals, logic control signals G1 and G2;

wherein, the comparison layer C has n nodes, each node receives signals from 3 aspects: one is an input signal x from the outside world_iThe other is an outward vector T from the R-layer winning neuron_jIs returned to_ijAnd a control signal from G1; the outputs of the nodes in layer C are generated according to the "majority vote" principle of 2/3, i.e. the output value C_iAnd x_i、t_ijG1, most of the 3 signals have the same value; when the network starts to operate, G1 is 1, the identification layer does not generate competition winning neurons, so that the feedback return signal is 0, the rule 2/3 shows that the output of the layer C is determined by the input signal, and C is X; when the comparison signal, x, of the feedback loop-back signal and the feedback signal occurs in the identification layer_i＝t_ijThen c is_i＝x_iOtherwise c_i0; that is, the control signal G1 is used to distinguish different stages of network operation by the comparison layer, the network start operation stage G1 is used to make the C layer directly output the input signal, and then G1 is used to make the C layer perform the comparison function, at this moment C_iIs to x_iAnd t_ijWhen both are 1, c_iIs 1, otherwise is 0, i.e. signal t returned from the R layer_ijThe output of the C layer is regulated;

the identification layer R is composed of a multi-layer feedforward neural network and is provided with m nodes for representing m input mode classes, and m can be dynamically increased to establish a new mode class; the internal weight vector connected from the C layer to the jth node of R is represented by R_j＝(k_1f，k_2j，......k_nf) Represents; the output vector C of the C layer is along m inner weight vectors R_j(j is 1, 2, …, m), forwards, generates a winning node j after competition after reaching each neuron node of the R layer, and indicates the category of the input mode; winning node output r_jThe output of the other nodes is 0;

each neuron of the R layer corresponds to two weight vectors: one is an internal weight vector R for converging C-layer feedforward signals to R layer_j(ii) a The other is an outer weight vector T for distributing R-layer feedback signals to C-layer_jThe vector is a typical vector corresponding to each mode class node of the R layer;

the control signals G1, G2, Reset respectively have the following functions: g1 indicates that the logical or of the X elements in the input pattern is X0, and the logical or of the R elements is R0, then G1 equals X0R0, that is, G1 equals 1 only when the R-layer output vectors R are all 0 and the input X are not all 0, otherwise G1 equals 0; the signal G2 detects whether the input pattern X is 0, which is equal to the logical OR of the X components, if X is_i(i-1, 2, …, n) is all 0, then G2 is 0, otherwise G2 is 1; the response signal is effective when the R layer competition winning neuron is ineffective, if according to a certain preset measurement standard, T_jIf the similarity between the X and the X is not equal to the preset similarity rho, the two are not sufficiently close to each other, and then the system sends out a Reset signal to invalidate the winning neuron;

the input layer is responsible for receiving external information and transmitting an input sample to the competition layer to play an observation role, the competition layer is responsible for analyzing and comparing, analyzing according to a known training model and correctly classifying, and if the result obtained by analysis does not exist in the known model, a new category is automatically created; the control signal is responsible for controlling the similarity rho of the analysis result of each layer, and if the result does not reach the preset similarity rho, the analysis is carried out again.

7. The intelligent transformer according to claim 6, wherein the ART calculation model operates by the following process:

when the network runs, receiving an input sample from an environment, checking the matching degree between the input sample and all categories of the R layer, and for the sample with the highest matching degree, continuously examining the similarity degree between a typical vector of the sample and the current input mode by the network; the similarity is examined according to a pre-designed reference threshold, and the occurrence conditions are not two:

firstly, if the similarity exceeds a reference threshold, selecting the mode class as a representative class of the current input mode; the weight adjustment rule is that the mode class with the similarity exceeding the reference threshold adjusts the corresponding internal and external weight vectors so as to obtain larger similarity when meeting a sample close to the current input mode in the future and make no change to other weight vectors;

if the similarity does not exceed the threshold value, the similarity of the pattern spills with the next highest matching degree of the R layer is inspected, if the similarity exceeds the reference threshold, the operation of the network returns to the condition 1, otherwise, the network still returns to the condition 2; the operation returns to the case 2 repeatedly, which means that the similarity between all the mode classes and the current input mode does not exceed the reference threshold, and at the moment, a node representing the new mode class needs to be established at the network output end to represent and store the mode so as to participate in the subsequent matching process;

the network follows the above operation process for each new input sample received; for each input, the mode network operation process can be generalized into three stages, namely an identification stage, a comparison stage and a search stage:

(1) identification phase

Before the network has no input mode, the network is in a waiting state; at this time, the input X is equal to 0, and the concatenation control signal G2 is equal to 0; therefore, the output of the R layer units is all 0, and the same winning chance exists in competition; when the network input is not all 0, setting G2 to be 1; information flows from bottom to top, G1 ═ G2R0 ═ 1, as can be seen by the 2/3 rule, when the C layer output C ═ X, and C feeds upward, acting with an upward weight vector B spills, producing a vector T, which feeds upward into the R layer, causing the R layer to start competing inside; assuming that the winning node is j, the R-level output Rj is 1, and the other node outputs are 0;

(2) comparison phase

The output information of the R layer returns to the C layer from top to bottom, and Rj is 1, so that Tj from top to bottom connected with the j node of the R layer is activated and returns to the C layer downwards;

at this time, the R-layer outputs are not all 0, and G1 is 0, so the next output C' of the C layer depends on the weight vector Tj from top to bottom of the R layer and the input pattern X of the network;

testing similarity spills by using a threshold specified in advance, if C' gives enough similar information, indicating that competition is correct, otherwise, indicating that the competition result does not meet the requirement, sending a Reset signal to invalidate the last winning node, and enabling the node not to win any more in the matching process of the mode; then entering a searching stage;

(3) search phase

Starting invalid setting of a winning stage by a Reset signal, and entering a searching stage by a network spills, wherein R is all 0, G1 is 1, and the current input mode X is obtained at the output end of the C layer; therefore, the network spills into the identification and comparison stage to obtain a new winning node; repeating the steps until a certain winning node K is searched and is fully matched with the input vector X to meet the requirement, compiling the mode X into the mode category connected with the R-layer K nodes, namely modifying the weight vectors of the points from bottom to top and from top to bottom according to a certain method, and adding an R-layer node to represent the mode of the X or the mode close to the X if the network meets the X or does not find the mode close to the X if all R-layer output nodes are searched;

if the reference threshold is greater than rho, accepting j as a winning node, modifying weight vectors of the R layer nodes from bottom to top and from top to bottom to make the X similar inputs more easily obtained later, recovering the R layer nodes restrained by Reset signals with higher similarity, and turning to a comparison stage to meet the next input; otherwise, a Reset signal is sent, j is set to be 0, and the search phase is started.

8. The intelligent mutual inductor according to claim 6, wherein the recognition layer R is a feedforward neural network model, that is, a multilayer feedforward neural network composed of two layers of neurons by using a BP neural network algorithm;

the feedforward neural network comprises an input layer formed by 10 neurons, a hidden layer formed by 10 neurons, an output layer formed by 2 outputs, and the input layer corresponds to the hidden layer: temperature (T), voltage (V), load (VA), load ratio (%), current phase difference, composite error, deviation; the output layer corresponds to: operating state, remaining life.

9. The intelligent mutual inductor according to claim 8, wherein the training method of the feedforward neural network model comprises the following steps:

firstly, data acquisition: the method comprises the following steps of carrying out operation experiments through a common sensor to obtain m groups of parameters under different operation states, recording the parameters as a data set D, wherein each group of parameters comprises X1-Xi and is recorded as a vector X, the operation state comprises Y1-yj (the operation state can be manually divided into j states, and j classification learning can also be carried out through unsupervised learning), recording the parameters as a vector Y, and judging the operation state of the mutual inductor to convert the operation state into a j classification task with i characteristic parameters:

secondly, sampling data obtained by an experiment by using a self-service sampling method, and dividing a training set and a testing set: in particular, given a data set D containing m samples, sampling it produces a data set D': randomly picking a sample from D each time, copying the sample into D', and then putting the sample back into the initial data set D, so that the sample can still be picked when sampling next time; after the process is repeatedly executed m times, a data set D' containing m samples is obtained, and the result is the self-help sampling result;

obviously, some samples in D will appear in D' multiple times, while another part of samples will not appear; a simple estimate can be made that the probability that a sample will never be taken in m samples is (1-1/m)^mWhen m is infinite, (1-1/m)^m1/e, D' is a training set of the machine learning model,to test the set

Thirdly, training is carried out: for each training sample, the BP algorithm executes the following operations of providing the input sample to an input layer neuron, and then forwarding signals layer by layer until a result of an output layer is generated; then calculating the error of an output layer, reversely transmitting the error to a hidden layer nerve, and finally adjusting the connection weight and the threshold value according to the error of the hidden layer nerve cell; the iterative process loops until a stop condition is reached.

Technical Field

The invention belongs to the field of intelligent diagnosis of power equipment, and relates to an intelligent diagnosis system of a power transformer and a diagnosis method based on decision tree classification.

Background

The mutual inductor is widely applied to a modern power grid, the operation reliability and the performance stability of the mutual inductor have great influence on the stable and reliable operation of the power grid, so that a plurality of solutions are provided for health management problems of fault diagnosis, aging, service life and the like of the power mutual inductor in the academic world, but a large number of problems are still unsolved at present, and most of the existing solutions do not produce commercial technical products.

For example, in patent CN103531340A, the temperature detection of the transformer only collects one point (in this solution 1, only one temperature detection point is set at 5), but in actual operation, because the measured wire cannot make the transformer uniformly receive the magnetic field (as shown in fig. 1, the magnetic field distribution of the wire in different operation states), this causes different temperatures generated by the current effect at each point of the transformer.

In addition, due to the influence of the production process, the specific heat capacity and the heat conduction coefficient of materials of all layers of the mutual inductor are different, so that the single-point temperature of the mutual inductor cannot reflect the whole condition.

At present, a mathematical model is obtained by analyzing the existing data in the fault diagnosis of the mutual inductor, the accuracy of the method in the initial operation stage of the mutual inductor is high, but as the operation time of equipment increases, the mathematical model is misaligned due to material aging, mechanical vibration, electromagnetic interference and the like, so that misjudgment or missed judgment is caused, and the problem needs to be continuously researched.

Disclosure of Invention

The invention aims to provide an intelligent transformer aiming at the defects of the prior art.

An intelligent transformer, comprising: the intelligent diagnosis system comprises a current detection module, a temperature detection module, a data storage module, a remote communication module, an intelligent diagnosis module and an intelligent terminal;