CN114065667A - SF6 equipment gas pressure prediction method based on Prophet-LSTM model - Google Patents

SF6 equipment gas pressure prediction method based on Prophet-LSTM model Download PDF

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CN114065667A
CN114065667A CN202111391722.0A CN202111391722A CN114065667A CN 114065667 A CN114065667 A CN 114065667A CN 202111391722 A CN202111391722 A CN 202111391722A CN 114065667 A CN114065667 A CN 114065667A
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陆敏安
郑真
陈敬德
顾华
樊汝森
黄强
陈亚杰
徐友刚
张红燕
李建宁
黄一楠
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Abstract

The invention relates to a gas pressure prediction method of SF6 equipment based on a Prophet-LSTM model, which comprises the following steps: collecting data of SF6 electrical equipment during operation, preprocessing the data, and dividing the data into a training set and a test set after normalization; training the data of the training set respectively through a Prophet model and an LSTM (long short term memory network) model optimized by Bayes; inputting the prediction results of the training set of the Prophet model and the LSTM model and the real data of the training set into an optimal weight coefficient acquisition module to obtain the combined weight of the two models; inputting test set data to the combined model, and checking the prediction performance of the combined model according to the test set prediction result and the test set real data; finally, the perfect combined prediction model is used for SF6 pressure prediction. The Prophet-LSTM model is applied to prediction of the pressure value in SF6 electrical equipment, so that the method is beneficial to judging possible overheating faults and leakage faults in the equipment through the gas pressure value, and plays a role in early warning.

Description

SF6 equipment gas pressure prediction method based on Prophet-LSTM model
Technical Field
The invention relates to a gas pressure prediction method of SF6 equipment based on a Prophet-LSTM model, which is used in the field of power equipment detection.
Background
SF6 (sulfur hexafluoride) gas has good insulation and arc extinguishing characteristics, and is widely applied to power transformation equipment at present, however, SF6 has some hidden troubles as an insulation medium of the power transformation equipment. For example, in the gas leakage problem generally existing in the closed GIS (gas insulated substation) equipment, once leakage occurs, due to the effect of a high-voltage arc, SF6 is decomposed to generate some toxic substances, so that the air in a GIS room is anoxic and toxic, and the safety of personnel is threatened, and on the other hand, SF6 gas leakage also reduces the insulation and arc extinguishing performance of the equipment, so that measures are necessary to be taken to realize the rapid and effective monitoring of SF6 gas pressure, and thus the leakage fault of the equipment is judged. Besides, most faults of the primary equipment of the power transformation are expressed in the form of heat, the heat is released through SF6 gas, the SF6 gas pressure is inevitably changed in the heat exchange process, and therefore real-time monitoring of the SF6 gas pressure is also of great significance for analyzing overheating faults of the equipment.
At present, for the prediction of time series data such as SF6 gas pressure, a single time series model or a prediction method based on a neural network is mostly adopted, however, time series generally comprises linear components and nonlinear components, the single prediction method can only guarantee the prediction accuracy of one of the components, and in addition, the single prediction model has a defect in effectively capturing the composite characteristics of the time series.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a gas pressure prediction method of SF6 equipment based on a Prophet-LSTM model, and by applying a combined prediction model, the advantages of a time series model and a prediction method based on a neural network are fully exerted, and compared with a single model, the prediction method has a better prediction effect. The time sequence models of Prophet and LSTM optimized by Bayes are used for predicting SF6 air pressure time sequence data respectively, and the prediction results of the two models are subjected to linear weighting to serve as the final SF6 air pressure prediction result, so that SF6 air pressure change can be predicted more accurately and effectively.
One technical scheme for achieving the above purpose is as follows: a gas pressure prediction method of SF6 equipment based on a Prophet-LSTM model comprises the following steps:
s1, acquiring SF6 air pressure time sequence data in the past year through an air pressure sensor of SF6 equipment;
s2, preprocessing the acquired SF6 historical air pressure data: the method comprises the steps of processing missing data, repeated and redundant data, abnormal or wrong data, further normalizing the data in consideration of the problem that the value range difference of the data in a training model is overlarge and possibly causes the wrong weight distribution of the model, and then dividing the data into a training set and a test set;
s3, training: respectively inputting the training set data into a Prophet model and an LSTM model optimized by Bayes for training, and simultaneously obtaining the optimal weight coefficient of the combined model;
s4, testing: after the training of the Prophet-LSTM combined model is finished, inputting the test set data into the Prophet-LSTM combined model for prediction, and taking the linear weighted value of the output results of the Prophet-LSTM combined model and the Prophet-LSTM combined model as the final test set prediction result;
and S5, testing the prediction effect of the combined model, if the error between the prediction result of the test set and the actual test set meets the requirement, keeping the structure and parameters of the current combined prediction model, and if not, returning to the training stage.
Further, the step S2 specifically includes:
s21, processing missing data by adopting a proximity value filling method, and replacing the missing value by the average value of the front and back adjacent position data;
s22, deleting repeated or redundant data;
s23, recognizing abnormal and error data by using a manually set range, and processing the abnormal and error data as a missing value;
s24, in order to accelerate the convergence rate and the prediction precision of the prediction model, the data is further processed by adopting a minimum-maximum (min-max) normalization method, the data is compressed to an interval [0,1], and meanwhile, the influence of different dimensions is eliminated, and the conversion formula is as follows:
Figure BDA0003364548840000031
wherein x is the actual value of the historical pressure data of SF6, max and min are the maximum value and the minimum value respectively, and x*The normalized data is represented.
Further, the Prophet model in step S3 can effectively analyze the time series characteristics of the data, such as periodicity, holiday effect, and future trend, and the steps of training with the Prophet model are as follows:
the Prophet model decomposes the equipment data into three parts, g (t) represents an aperiodic term in the data, h (t) represents a holiday term in the data, and s (t) represents a periodic term in the data, and the formula is as follows:
P＝g(t)+s(t)+h(t)+εt
for data of non-periodic terms, modeling is carried out by using a logic growth model, and the formula is as follows:
Figure BDA0003364548840000032
wherein C represents the capacity of the prediction model, k is the trend growth rate, and n represents the offset parameter;
the periodic effect is simulated using a fourier series for the data of the holiday and periodic terms, the formula being:
Figure BDA0003364548840000033
and determining parameters in the final model through testing to obtain a Prophet model predicted value P.
Further, the LSTM model in step S3 alleviates the problems of gradient extinction and gradient explosion when the recurrent neural network processes long-sequence data by adding forgetting gates to the neuron part, and is suitable for training long-distance time-series data, and the LSTM model is implemented as follows:
s41, initializing the weight and the offset parameter of the neural network, and then searching the optimal hyper-parameter combination of the LSTM model through a Bayesian optimization method, wherein the Bayesian optimization does not need derivation, thereby being suitable for adjusting the hyper-parameter of the neural network and finding a better hyper-parameter combination with fewer steps;
and S42, inputting training set data to train the LSTM neural network.
Further, in step S3, the training set data are respectively input to the Prophet model and the LSTM model optimized by bayes, the obtained training set prediction results are respectively represented as p (t) and l (t), the two are linearly weighted and then input to the optimal weight coefficient obtaining module together with the training set real data, and the training set prediction results obtained by combining the models are as follows:
R(t)＝ω1 *P(t)+ω2 *L(t)
in the formula, ω1 *+ω2 *When the training set prediction result r (t) is closest to the actual training set, the weight coefficient ω is 11 *And ω2 *The value of (a) is determined by the least squares method.
Further, in step S4, the test set data is input into the combined model, and a final test set prediction result is obtained according to the prediction results of the two models and the optimal combined weight.
Further, the step S5 is implemented as follows:
the two indexes of average absolute percentage and root mean square error are adopted to test the prediction effect of the combined model, and the formulas are respectively as follows:
Figure BDA0003364548840000041
Figure BDA0003364548840000042
wherein p isiAnd aiAnd respectively representing the prediction result of the combined model at the moment i on the test data and the true value of the test data, if the MAPE and RMSE indexes meet the requirements, storing the structure and parameters of the current combined model, and otherwise, continuing to train the model.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, the Prophet-LSTM combined model is adopted to predict SF6 air pressure time sequence data, compared with a single prediction model, the characteristics of time sequence data can be better analyzed, and the accuracy is higher when complex nonlinear time sequences are predicted;
2. the LSTM model adopted by the invention can effectively improve the problems of gradient explosion and disappearance of the simple circulating neural network, and meanwhile, the LSTM model is optimized by using a Bayesian method, so that the optimal hyper-parameter combination of the neural network is searched in a faster and simpler way, and the training speed and the prediction accuracy of the neural network are improved.
Drawings
Fig. 1 is a flow chart of the SF6 plant gas pressure prediction method of the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
referring to fig. 1, the SF6 equipment gas pressure prediction method based on the Prophet-LSTM model of the present invention includes the following steps:
s1, acquiring SF6 air pressure time sequence data in the past year through an air pressure sensor of SF6 equipment;
s2, preprocessing the acquired SF6 historical air pressure data: the method comprises the steps of processing missing data, repeated and redundant data, abnormal or wrong data, further normalizing the data in consideration of the problem that the value range difference of the data in a training model is overlarge and possibly causes the wrong weight distribution of the model, and then dividing the data into a training set and a test set;
s3, training: respectively inputting the training set data into a Prophet model and an LSTM model optimized by Bayes for training, and simultaneously obtaining the optimal weight coefficient of the combined model;
s4, testing: after the training of the Prophet-LSTM combined model is finished, inputting the test set data into the Prophet-LSTM combined model for prediction, and taking the linear weighted value of the output results of the Prophet-LSTM combined model and the Prophet-LSTM combined model as the final test set prediction result;
and S5, testing the prediction effect of the combined model, if the error between the prediction result of the test set and the actual test set meets the requirement, keeping the structure and parameters of the current combined prediction model, and if not, returning to the training stage.
The step S2 is specifically implemented as follows:
s21, processing missing data by adopting a proximity value filling method, and replacing the missing value by the average value of the front and back adjacent position data;
s22, deleting repeated or redundant data;
s23, recognizing abnormal and error data by using a manually set range, and processing the abnormal and error data as a missing value;
s24, in order to accelerate the convergence rate and the prediction precision of the prediction model, the data is further processed by adopting a minimum-maximum (min-max) normalization method, the data is compressed to an interval [0,1], and meanwhile, the influence of different dimensions is eliminated, and the conversion formula is as follows:
Figure BDA0003364548840000061
wherein x is the actual value of the historical pressure data of SF6, max and min are the maximum value and the minimum value respectively, and x*The normalized data is represented.
The Prophet model in the step S3 can effectively analyze the time series characteristics of the data, such as periodicity, holiday effect, future trend, and the like, and the steps of training by using the Prophet model are as follows:
the Prophet model decomposes the equipment data into three parts, g (t) represents an aperiodic term in the data, h (t) represents a holiday term in the data, and s (t) represents a periodic term in the data, and the formula is as follows:
P＝g(t)+s(t)+(t)+εt
for data of non-periodic terms, modeling is carried out by using a logic growth model, and the formula is as follows:
Figure BDA0003364548840000062
where C represents the capacity of the prediction model, k is the trend growth rate, and n represents the offset parameter.
The periodic effect is simulated using a fourier series for the data of the holiday and periodic terms, the formula being:
Figure BDA0003364548840000063
and determining parameters in the final model through testing to obtain a Prophet model predicted value P.
The LSTM model in step S3 effectively alleviates the problems of gradient extinction and gradient explosion when the Recurrent Neural Network (RNN) processes long-sequence data by adding forgetting gates to the neuron portion, and is suitable for training long-distance time-series data, and the LSTM model is implemented as follows:
s41, initializing the weight and the offset parameter of the neural network, and then searching the optimal hyper-parameter combination of the LSTM model through a Bayesian optimization method, wherein the Bayesian optimization does not need derivation, thereby being suitable for adjusting the hyper-parameter of the neural network and finding a better hyper-parameter combination with fewer steps;
and S42, inputting training set data to train the LSTM neural network.
In step S3, the training set data are respectively input to the Prophet model and the LSTM model optimized by bayes, the obtained training set prediction results are respectively expressed as p (t) and l (t), the two are linearly weighted and then input to the optimal weight coefficient obtaining module together with the training set real data, and the training set prediction results obtained by combining the models are as follows:
R(t)＝ω1 *P(t)+ω2 *L(t)
in the formula, ω1 *+ω2 *When the training set prediction result r (t) is closest to the actual training set, the weight coefficient ω is 11 *And ω2 *The value of (a) is determined by the least squares method.
And step S4, inputting the test set data into the combined model, and obtaining a final test set prediction result according to the prediction results of the two models and the optimal combined weight.
The step S5 is specifically implemented as follows:
the two indexes of the Mean Absolute Percentage (MAPE) and the Root Mean Square Error (RMSE) are adopted to test the prediction effect of the combined model, and the formulas are respectively as follows:
Figure BDA0003364548840000071
Figure BDA0003364548840000072
wherein p isiAnd aiAnd respectively representing the prediction result of the combined model at the moment i on the test data and the true value of the test data, if the MAPE and RMSE indexes meet the requirements, storing the structure and parameters of the current combined model, and otherwise, continuing to train the model.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

Claims (7)

1. A gas pressure prediction method of SF6 equipment based on a Prophet-LSTM model is characterized by comprising the following steps:
s1, acquiring SF6 air pressure time sequence data in the past year through an air pressure sensor of SF6 equipment;
s2, preprocessing the acquired SF6 historical air pressure data: the method comprises the steps of processing missing data, repeated and redundant data, abnormal or wrong data, further normalizing the data in consideration of the problem that the value range difference of the data in a training model is overlarge and possibly causes the wrong weight distribution of the model, and then dividing the data into a training set and a test set;
s3, training: respectively inputting the training set data into a Prophet model and an LSTM model optimized by Bayes for training, and simultaneously obtaining the optimal weight coefficient of the combined model;
s4, testing: after the training of the Prophet-LSTM combined model is finished, inputting the test set data into the Prophet-LSTM combined model for prediction, and taking the linear weighted value of the output results of the Prophet-LSTM combined model and the Prophet-LSTM combined model as the final test set prediction result;
and S5, testing the prediction effect of the combined model, if the error between the prediction result of the test set and the actual test set meets the requirement, keeping the structure and parameters of the current combined prediction model, and if not, returning to the training stage.
2. The method for predicting SF6 plant gas pressure based on Prophet-LSTM model as claimed in claim 1, wherein said step S2 specifically comprises:
s21, processing missing data by adopting a proximity value filling method, and replacing the missing value by the average value of the front and back adjacent position data;
s22, deleting repeated or redundant data;
s23, recognizing abnormal and error data by using a manually set range, and processing the abnormal and error data as a missing value;
s24, in order to accelerate the convergence rate and the prediction precision of the prediction model, the data is further processed by adopting a minimum-maximum (min-max) normalization method, the data is compressed to an interval [0,1], and meanwhile, the influence of different dimensions is eliminated, and the conversion formula is as follows:
Figure FDA0003364548830000011
wherein x is the actual value of the historical pressure data of SF6, max and min are the maximum value and the minimum value respectively, and x*The normalized data is represented.
3. The SF6 facility gas pressure prediction method based on Prophet-LSTM model as claimed in claim 1, wherein the Prophet model is capable of analyzing time series characteristics of periodicity, holiday effect and future trend of data effectively in step S3, and the step of training with Prophet model is as follows:
the Prophet model decomposes the equipment data into three parts, g (t) represents an aperiodic term in the data, h (t) represents a holiday term in the data, and s (t) represents a periodic term in the data, and the formula is as follows:
P＝g(t)+s(t)+h(t)+εt
for data of non-periodic terms, modeling is carried out by using a logic growth model, and the formula is as follows:
Figure FDA0003364548830000021
wherein C represents the capacity of the prediction model, k is the trend growth rate, and n represents the offset parameter;
the periodic effect is simulated using a fourier series for the data of the holiday and periodic terms, the formula being:
Figure FDA0003364548830000022
and determining parameters in the final model through testing to obtain a Prophet model predicted value P.
4. The method for predicting SF6 facility gas pressure based on Prophet-LSTM model in claim 1, wherein the LSTM model in step S3 is adapted to train long distance time series data by adding forgetting gate in the neuron part to solve the problem of gradient extinction and gradient explosion when the recurrent neural network processes long series data, and is implemented as follows:
s41, initializing the weight and the offset parameter of the neural network, and then searching the optimal hyper-parameter combination of the LSTM model through a Bayesian optimization method, wherein the Bayesian optimization does not need derivation, thereby being suitable for adjusting the hyper-parameter of the neural network and finding a better hyper-parameter combination with fewer steps;
and S42, inputting training set data to train the LSTM neural network.
5. The method for predicting the gas pressure of the SF6 device based on the Prophet-LSTM model as claimed in claim 1, wherein in step S3, the training set data are respectively input to the Prophet model and the LSTM model optimized by Bayesian optimization, the obtained training set prediction results are respectively expressed as P (t) and L (t), the training set prediction results and the training set real data are input to the optimal weight coefficient obtaining module after linear weighting, and the training set prediction results obtained by the combined model are as follows:
R(t)＝ω1 *P(t)+ω2 *L(t)
in the formula, ω1 *+ω2 *When the training set prediction result r (t) is closest to the actual training set, the weight coefficient ω is 11 *And ω2 *The value of (a) is determined by the least squares method.
6. The method of claim 1, wherein the step S4 inputs test set data into the combined model, and obtains a final test set prediction result according to the prediction results of the two models and the optimal combined weight.
7. The method for predicting the gas pressure of the SF6 plant based on the Prophet-LSTM model as claimed in claim 1, wherein the step S5 is implemented as follows:
the two indexes of average absolute percentage and root mean square error are adopted to test the prediction effect of the combined model, and the formulas are respectively as follows:
Figure FDA0003364548830000031
Figure FDA0003364548830000032
wherein p isiAnd aiRespectively representing the prediction result of the time i combination model to the test data and the true value of the test data, if the MAPE and RMSE indexes meet the requirements, saving the current groupAnd combining the structure and the parameters of the model, otherwise, continuing to train the model.
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