CN111612129A - Method and device for predicting state of isolating switch and storage medium - Google Patents
Method and device for predicting state of isolating switch and storage medium Download PDFInfo
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- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
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Abstract
The invention discloses a method for predicting the state of an isolating switch, which comprises the following steps: (1) establishing a neural network classifier, taking the monitoring state quantity of the isolating switch as an input state quantity, taking the alarm level of the isolating switch as an output quantity, and carrying out learning training on the neural network by using the state data of the historical record; (2) predicting the predicted state of each recorded input state quantity at a certain future time point by adopting a binary linear regression analysis method; (3) the predicted state of each state quantity is used as the input of a trained neural network classifier, and the warning level output by the neural network is the warning level of the isolating switch at a certain time point; (4) and according to the trend of the early warning level change in the future time period, corresponding measures are taken according to the operation and maintenance regulations of the power system, and targeted and preventable maintenance of the isolating switch is realized. The invention also provides a device and a storage medium based on the method, which can improve the operation safety and save the operation and maintenance cost.
Description
Technical Field
The invention relates to the field of monitoring and maintaining of equipment states of a power system, in particular to a method and a device for predicting states of an isolating switch and a storage medium.
Background
The isolating switch is a core device in power transmission and transformation of a power grid, and the quantity of the isolating switch rapidly rises along with the high-speed development of the power grid in China. Once the isolating switch breaks down, the isolating switch not only influences the power supply of the city, but also is more likely to cause accidents of casualties such as explosion and the like. Among them, the overheating defect is the most dominant fault of the disconnection switch.
In recent years, the on-line monitoring technology of the power equipment has developed to a mature degree, and various parameters of the isolating switch can be accurately and comprehensively monitored in real time by installing various sensors in the isolating switch. At present, the common alarm method for determining the fault of the isolating switch in the power industry judges whether to need to alarm or not by comparing the historical monitoring data of single or few state quantities with the alarm threshold value of the equipment operation and maintenance standard. The alarm method only judges whether the current state of the equipment has problems according to historical monitoring data, and does not predict the state trend of the isolating switch in a combined mode.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for predicting the state of an isolating switch. The invention also aims to provide a disconnecting switch state prediction device and a computer storage medium based on the method.
The technical scheme is as follows: the invention discloses a method for predicting the state of an isolating switch, which comprises the following steps:
(1) acquiring historical data of monitoring state quantity of the isolating switch, and normalizing the historical data;
(2) taking the historical data of the monitoring state quantities of multiple dimensions of the normalized isolating switch as an input sample, taking the alarm level of the isolating switch as output, and constructing and utilizing a classifier to perform machine learning;
(3) predicting the normalized value of the monitoring state quantity of the isolating switch by adopting a binary linear regression analysis method, and predicting a corresponding predicted value at a certain future moment;
(4) the predicted value corresponding to a certain predicted future time is used as an input value of a trained classifier, and the alarm level output by the classifier is used as the early warning level corresponding to the isolating switch equipment at a certain future time;
(5) repeating the step (4) to obtain the trend of the early warning level change of the isolation switch equipment in a certain period of time in the future;
(6) and (5) early warning the state of the isolating switch equipment according to the trend obtained in the step (5).
Further, in step (1), the monitoring state quantity of the disconnecting switch includes: load current value, hot point temperature value, environment temperature value, normal heating temperature value, phase-to-phase temperature difference value and relative temperature difference value.
Further, the step (1) comprises: normalizing the historical data of the monitoring state quantity of each dimension to a value between 0 and 1 according to the normalization model to obtain a data sample set, and taking the data sample set as a learning sample library of the classifier; the normalization model is as follows:
x＝(xd-xmin)/(xmax-xmin)
wherein x represents a normalized value, xdHistorical data, x, representing monitored state quantities for each dimensionminMinimum value, x, of history data representing monitored state quantities of each dimensionmaxAnd a maximum value of the historical data representing the monitored state quantity of each dimension.
Further, in the step (2), the classifier is a BP network classifier, and a gradient descent method is adopted as a machine learning method.
Further, the BP network classifier comprises an input layer, a hidden layer and an output layer, wherein an input sample of the input layer corresponds to a normalized value of the monitoring state quantity, and the number of neurons of the input layer is the dimension n of the monitoring state quantity; the output of the output layer corresponds to the digital quantization result of the alarm level of the isolating switch, and the neuron number of the output layer is the alarm level number m; the number k of nodes of the hidden layer is
Rounding off to obtain integer, a being empirical coefficient, a ∈ [1,10 ]]。
Further, the alarm levels of the isolating switch comprise no alarm, blue alarm, yellow alarm and red alarm: the numeric quantization result corresponding to the no-alarm has a value range of [0, 0.25], the numeric quantization result corresponding to the blue alarm has a value range of (0.25,0.5], the numeric quantization result corresponding to the yellow alarm has a value range of (0.5, 0.75), and the numeric quantization result corresponding to the red alarm has a value range of (0.75, 1).
The invention adopts a binary linear regression analysis method to predict various monitoring indexes (including load current, hot spot temperature, environment temperature, normal heating temperature, interphase temperature difference and relative temperature difference) of the isolating switch respectively, and then uses the predicted parameters as the input of a BP neural network classifier, wherein the output of the BP neural network classifier is the alarm state of the isolating switch in a period of time in the future. The invention discloses a state prediction device of an isolating switch, which comprises: the device comprises a memory, a processor and a program for predicting the state of the isolating switch, wherein the program for predicting the state of the isolating switch is stored in the memory and can be operated, and the steps of the method for predicting the state of the isolating switch are realized when the program for predicting the state of the isolating switch is executed by the processor.
The computer readable storage medium of the present invention stores thereon a program for predicting a state of an isolator switch, which when executed by a processor implements the steps of the method for predicting a state of an isolator switch as described above.
Has the advantages that: the invention monitors various indexes of the isolating switch (including load current, hot spot temperature, environment temperature, normal heating temperature, interphase temperature difference and relative temperature difference) to obtain the alarm state of the isolating switch in a period of time in the future, can realize targeted and preventive maintenance on the equipment before equipment failure occurs, improves the operation safety of the equipment, reduces the operation risk of the equipment and saves the operation and maintenance cost.
Drawings
FIG. 1 is a block diagram of the steps of the method of the present invention;
FIG. 2 is a block diagram of a BP neural network classifier.
Detailed Description
The technical scheme of the invention is further described in the following by combining the attached drawings and the detailed description.
Fig. 2 is a diagram of a BP neural network classifier. The classifier adopts a three-layer neural network structure, wherein an input layer comprises 6 input nodes X1-X6Respectively corresponding to normalized load current, hot spot temperature, environment temperature, normal heating temperature, interphase temperature difference and relative temperature difference; the output layer comprises 4 output nodes Y1-Y4Respectively corresponding to a red alarm, a yellow alarm, a blue alarm and no alarm. Hidden layer node Z1-ZlThe number of hidden layer nodes is according to formula
After calculation, rounding off to get an integer. Wherein n is the number of neurons in the input layer, m is the number of neurons in the output layer, a is an empirical coefficient, and the value range is [1,10 ]]To (c) to (d); preferably, the number l of hidden layer nodes is 8 according to actual experience; the weight between the input layer and the hidden layer is vijThe weight between the hidden layer and the output layer is Wjk. i is the input layer node number, j is the hidden layer node number, and k is the output layer node number.
As shown in fig. 1, the method for predicting the state of the disconnecting switch according to the present invention includes the following steps:
(1) historical monitoring data of monitoring state quantities of a plurality of dimensionalities of the isolating switch are acquired, and the dimensionalities comprise: the load current dimension, the hot spot temperature dimension, the environment temperature dimension, the normal heating temperature dimension, the interphase temperature dimension and the relative temperature dimension; and carrying out normalization processing, normalizing the data samples of each dimension to a value between 0 and 1, wherein the normalization model is represented as:
x＝(yd-xmin)/(xmax-xmin)
wherein x isdRepresenting the original value, x representing the normalized value, xmaxDenotes the maximum value, xminMeans of maximumA small value. The normalized values of the dimensions serve as a learning sample library of the neural network classifier.
(2) Constructing a neural network classifier, and selecting a neural network with a 3-layer structure; the input layer is 6 input elements which respectively correspond to a load current value, a hot point temperature value, an environment temperature value, a normal heating temperature value, an interphase temperature difference value and a normalization value of a relative temperature difference value in historical monitoring data; the output layer is 4 output elements corresponding to the digital quantized values of 4 alarm levels of the isolating switch. The appointed alarm is no alarm when the alarm is greater than or equal to 0 and less than or equal to 0.25, blue alarm when the alarm is greater than 0.25 and less than or equal to 0.5, yellow alarm when the alarm is greater than 0.5 and less than or equal to 0.75, and red alarm when the alarm is greater than 0.75 and less than or equal to 1.
(3) Setting weight v between input layer and hidden layerijWeight w between hidden layer and output layerjkAssigned as random numbers between (-1, 1), respectively. Wherein i is the number of the input layer node, j is the number of the hidden layer node, and k is the number of the output layer node. The target error is set to be 0.001, the neural network learning rate is 0.05, and the maximum learning frequency is 10000.
(4) Randomly choosing the r-th input sample (x)1(r)……x6(r)) and its desired output (y)1(r)……y4(r))；
(5) Calculating input zin of each neuron of hidden layerj(r) and output zoutj(r)；
Wherein f1() is the hidden layer transfer function, tansig function is selected, f2() is the output layer excitation function, logsig function is selected, bjBias of hidden layer neuron j, bkIs the deviation of output layer neuron k.
(6) Calculating the derivation number of each neuron of the output layer by using the network expected output and actual output and adopting mse mean square error functiono(r)；
(7) Using neurons of the output layero(r) and output zout of neurons of hidden layerj(r) correcting the weight w by adopting an adaptive learning algorithm thingdx function with momentum termsij，
(8) Using neurons of the hidden layeri(r) and the input x (r) of each neuron of the input layer, and correcting the weight v by adopting a training dx function of an adaptive learning algorithm with momentum termsij，
(9) A global error is calculated and the global error is calculated,
wherein d isk(i) To corresponding expectation, yk(i) Is the actual output.
If the global error E (r) is less than the target error 0.001 or the learning frequency is more than the maximum learning frequency and 10000 times, ending the learning, otherwise, selecting the next learning sample and the corresponding expected output, and returning to the step 5 to enter the next round of learning.
(10) And 6 state quantities of the disconnecting switches, such as load current, hot spot temperature, environment temperature, normal heating temperature, interphase temperature difference, relative temperature difference and the like in the last 30 days are selected as a prediction sample library. The calculation is slow since too high a sampling frequency would result in too large a data volume. Therefore, the sampling frequency should be as low as possible without affecting the detection accuracy. As can be seen from empirical data, the sampling frequency was set to 4 hours per time.
(11) Predicting each isolation switch state quantity sample y according to the date x1And time of day x2Two variables construct a binary linear regression equation:
y＝β0+β1x1+β2x2
wherein, β0，β1，β2Is a partial regression coefficient;
(12) calculating the predicted data within 15 days in the future by using a binary linear regression analysis method;
(13) inputting the predicted data serving as test data into the trained BP neural network model to obtain the predicted change trend of the early warning level;
(14) and carrying out active early warning according to the change trend of early warning levels (red early warning, yellow early warning and blue early warning in sequence according to the severity).
(15) And updating the training sample base by using the latest 30-day measured state quantity and the corresponding early warning level data every 30 days, and retraining the BP network model according to the steps 3-9. When the latest data sample is selected to replace the older data in the training sample library, the latest data sample is updated according to different early warning levels. For example, the data corresponding to the early warning with the early warning level of red can only be updated and replaced with the data with the early warning level of red in the historical sample data.
The embodiment of the invention also provides a device for predicting the state of the isolating switch, which comprises: the device comprises a memory, a processor and a program for predicting the state of the isolating switch, wherein the program for predicting the state of the isolating switch is stored in the memory and can be operated, and when the program for predicting the state of the isolating switch is executed by the processor, part or all of the steps of the method for predicting the state of the isolating switch are realized.
The embodiment of the invention also provides a computer readable storage medium, on which the program for predicting the state of the isolating switch is stored, and when the program for predicting the state of the isolating switch is executed by a processor, the program for predicting the state of the isolating switch realizes part or all of the steps of the method for predicting the state of the isolating switch.
Claims (8)
1. A method for predicting the state of an isolating switch is characterized by comprising the following steps:
(1) acquiring historical data of monitoring state quantity of the isolating switch, and normalizing the historical data;
(2) taking the historical data of the monitoring state quantities of multiple dimensions of the normalized isolating switch as an input sample, taking the alarm level of the isolating switch as output, and constructing and utilizing a classifier to perform machine learning;
(3) predicting the normalized value of the monitoring state quantity of the isolating switch by adopting a binary linear regression analysis method, and predicting a corresponding predicted value at a certain future moment;
(4) the predicted value corresponding to a certain predicted future time is used as an input value of a trained classifier, and the alarm level output by the classifier is used as the early warning level corresponding to the isolating switch equipment at a certain future time;
(5) repeating the step (4) to obtain the trend of the early warning level change of the isolation switch equipment in a certain period of time in the future;
(6) and (5) early warning the state of the isolating switch equipment according to the trend obtained in the step (5).
2. The method for predicting the state of the disconnector according to claim 1, wherein in the step (1), the monitoring state quantity of the disconnector comprises: load current value, hot point temperature value, environment temperature value, normal heating temperature value, phase-to-phase temperature difference value and relative temperature difference value.
3. The disconnector state prediction method of claim 1, wherein step (1) comprises: normalizing the historical data of the monitoring state quantity of each dimension to a value between 0 and 1 according to the normalization model to obtain a data sample set, and taking the data sample set as a learning sample library of the classifier; the normalization model is as follows:
x＝(xd-xmin)/(xmax-xmin)
wherein x represents a normalized value, xdHistorical data, x, representing monitored state quantities for each dimensionminMinimum value, x, of history data representing monitored state quantities of each dimensionmaxAnd a maximum value of the historical data representing the monitored state quantity of each dimension.
4. The disconnector state prediction method according to claim 1, characterized in that: in the step (2), the classifier is a BP network classifier, and a gradient descent method is adopted as a machine learning method.
5. The disconnector state prediction method of claim 4, characterized in that: the BP network classifier comprises a data input unitThe input layer comprises an input layer, a hidden layer and an output layer, wherein an input sample of the input layer corresponds to a normalized value of the monitoring state quantity, and the number of neurons of the input layer is the dimension n of the monitoring state quantity; the output of the output layer corresponds to the digital quantization result of the alarm level of the isolating switch, and the neuron number of the output layer is the alarm level number m; the number k of nodes of the hidden layer is
Rounding off to obtain integer, a being empirical coefficient, a ∈ [1,10 ]]。
6. The disconnector state prediction method according to claim 1 or 5, characterized in that: the alarm levels of the isolating switch comprise no alarm, blue alarm, yellow alarm and red alarm: the numeric quantization result corresponding to the no-alarm has a value range of [10, 0.25], the numeric quantization result corresponding to the blue alarm has a value range of (0.25, 0.5), the numeric quantization result corresponding to the yellow alarm has a value range of (0.5, 0.75), and the numeric quantization result corresponding to the red alarm has a value range of (0.75, 1).
7. A disconnector state prediction device, characterized in that it comprises: memory, processor and a program for predicting the state of an isolator switch stored and executable on the memory, which program, when executed by the processor, carries out the steps of the method for predicting the state of an isolator switch according to any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for disconnector state prediction, which program, when being executed by a processor, carries out the steps of the disconnector state prediction method as claimed in any one of claims 1 to 6.
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| CN116203298A (en) * | 2023-01-31 | 2023-06-02 | 赛福凯尔(绍兴)医疗科技有限公司 | Power protection method and system based on magnetic coupling digital isolator |
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| CN110472772A (en) * | 2019-07-09 | 2019-11-19 | 长沙能川信息科技有限公司 | A kind of disconnecting switch overheat method for early warning and a kind of disconnecting switch overheat early warning system |
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| CN110472772A (en) * | 2019-07-09 | 2019-11-19 | 长沙能川信息科技有限公司 | A kind of disconnecting switch overheat method for early warning and a kind of disconnecting switch overheat early warning system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116203298A (en) * | 2023-01-31 | 2023-06-02 | 赛福凯尔(绍兴)医疗科技有限公司 | Power protection method and system based on magnetic coupling digital isolator |
| CN116203298B (en) * | 2023-01-31 | 2024-04-02 | 赛福凯尔(绍兴)医疗科技有限公司 | Power protection method and system based on magnetic coupling digital isolator |
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| RJ01 | Rejection of invention patent application after publication |