CN115511216A - GIS weak discharge development trend prediction method based on ARMA model - Google Patents
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Abstract
The invention relates to a GIS weak discharge development trend prediction method based on an ARMA model, which comprises the following steps: weak discharge data in the gas insulated switchgear is collected and preprocessed, so that the data are stabilized, and a stable data sequence is obtained; performing white noise inspection on the stable data sequence, and discarding unqualified data; determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference elimination; constructing an ARMA model, carrying out order fixing on the model, and determining the value of an unknown parameter in the model; verifying the effectiveness of the model, if the model does not meet the condition, reconstructing and determining the model, and if the model meets the condition, obtaining an effective prediction model; predicting the development trend of GIS weak discharge by using an effective prediction model; the method can better predict the extreme value and the peak value of the weak discharge step parameter and the nonlinear parameter, and has greater reference significance for the prediction of the development trend of subsequent research.
Description
Technical Field
The invention belongs to the technical field of GIS discharge prediction, and particularly relates to a GIS weak discharge development trend prediction method based on an ARMA model.
Background
The most of the fault reasons in the gas insulated switchgear are weak discharge, and the avoidance of the weak discharge as much as possible is a key factor for prolonging the service life of the gas insulated switchgear. Since the gradual development of the weak discharge in the gas insulated switchgear to the insulation flashover breakdown is a process with a plurality of influence factors, the development process of the weak discharge is accurately grasped through state monitoring data and the development trend is predicted at present, so that the improvement of the high efficiency and the real-time performance of state maintenance is a difficult problem to be solved urgently; ARMA is a common random time series model which is widely used in various fields to solve practical problems; therefore, the method for effectively predicting the development trend of the weak discharge in the GIS by constructing the ARMA prediction model has important significance.
Disclosure of Invention
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the GIS weak discharge development trend prediction method based on the ARMA model is characterized by comprising the following steps of:
weak discharge data in the gas insulated switchgear is collected and preprocessed, so that the data are stabilized, and a stable data sequence is obtained;
performing white noise inspection on the stable data sequence, and discarding unqualified data;
determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference elimination;
constructing an ARMA model, fixing the order of the model, and determining the value of an unknown parameter in the model;
verifying the effectiveness of the model, if the model does not meet the conditions, reconstructing and determining the model, and if the model meets the conditions, obtaining an effective prediction model;
and predicting the development trend of the GIS weak discharge by using the effective prediction model.
Further, the weak discharge data is preprocessed, so that the stability of the data needs to be judged in the process of data stabilization, and the method for judging the stability is to use a difference method or a correlation coefficient diagram.
Further, the method for determining the autocorrelation of the collected data and the autocorrelation of the collected data after interference removal comprises the following steps:
the formula for obtaining the autocorrelation coefficient of the weak discharge parameter is as follows:
in the formula, n is a number series dimension, k is a number series lag number, x is sample data (such as the average discharge times per minute of a linear characteristic parameter suspension discharge model, a step parameter insulator metal foreign matter discharge normalized amplitude and nonlinear characteristic quantity insulator surface discharge amplitude information entropy), and x is the average value of the data population; the autocorrelation coefficient can express the correlation of data before and after a group of data;
the weak discharge parameter partial autocorrelation coefficient is obtained by the following formula:
in the formula (I), the compound is shown in the specification,
the partial autocorrelation coefficients are obtained by eliminating k-1 variable interferences and then checking the correlation of other data.
Further, the model is ranked by adopting an AIC criterion; the AIC criterion can be expressed as: AIC (p) =2p-2ln (L), wherein p is weak discharge parameter quantity, and L is a likelihood function; let N be the number of observations,
being the sum of the squared residuals, the above equation becomes:
the optimal autoregressive model order refers to the p-value at which AIC (p) is minimized.
Further, the method for determining the value of the unknown parameter in the model comprises the following steps:
after the order of the model is determined, finding out a likelihood function L of the sample, and solving a parameter value which enables the function to reach the maximum;
is recorded as:
suppose that
Obeying multivariate normal distribution
The likelihood function is then:
to find the maximum value
Further, the method for verifying the validity of the model comprises the following steps: the model is valid if it extracts enough information from the data and the residual is a white noise sequence.
GIS weak discharge development trend prediction device based on ARMA model includes:
the stable data sequence acquisition module is used for acquiring weak discharge data in the gas insulated switchgear and preprocessing the weak discharge data to stabilize the data and acquire a stable data sequence;
the unqualified data eliminating module is used for carrying out white noise inspection on the stable data sequence and abandoning unqualified data;
the autocorrelation data acquisition module is used for determining autocorrelation of the acquired data and autocorrelation of the acquired data after interference removal;
the ARMA model building module is used for building an ARMA model, carrying out order fixing on the model and determining the value of an unknown parameter in the model;
the effective prediction model acquisition module is used for verifying the effectiveness of the model, reconstructing and determining the model if the model does not meet the condition, and obtaining an effective prediction model if the model meets the condition;
and the GIS weak discharge development trend prediction module is used for predicting the GIS weak discharge development trend by utilizing the effective prediction model.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
when the one or more programs are executed by the one or more processing units, the one or more processing units are enabled to execute the ARMA model-based GIS weak discharge trend prediction method.
A computer readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of the ARMA model-based GIS weak discharge development trend prediction method.
The invention has the advantages and positive effects that:
the method can accurately predict the development trend of the weak discharge linear parameter to obtain a better effect, and the overall prediction error values are all below 10%; the extreme value and the peak value of the weak discharge step parameter and the nonlinear parameter can be well predicted, and the method has great reference significance for the prediction of the development trend of the follow-up research.
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The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and examples, but it should be understood that these drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein and are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of a GIS weak discharge development trend prediction method based on an ARMA model according to an embodiment of the present invention;
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any single feature described or implicit in any embodiment or any single feature shown or implicit in any drawing may still be combined or subtracted between any of the features (or equivalents thereof) to obtain still further embodiments of the invention that may not be directly mentioned herein. In addition, for the sake of simplicity, the same or similar features may be indicated in only one place in the same drawing.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
As shown in fig. 1, the method for predicting the development trend of the weak discharge of the GIS based on the ARMA model provided in this embodiment includes the following steps:
weak discharge data in the gas insulated switchgear is collected and preprocessed, so that the data are stabilized, and a stable data sequence is obtained;
white noise detection is carried out on the stable data sequence, and unqualified data are abandoned;
determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference elimination;
constructing an ARMA model, carrying out order fixing on the model, and determining the value of an unknown parameter in the model;
verifying the effectiveness of the model, if the model does not meet the condition, reconstructing and determining the model, and if the model meets the condition, obtaining an effective prediction model;
and predicting the development trend of the GIS weak discharge by using an effective prediction model.
Specifically, the weak discharge data is preprocessed, so that the stability of the data needs to be judged in the process of data stabilization, and the method for judging the stability is to use a difference method or a correlation coefficient diagram.
The method for determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference elimination comprises the following steps of, wherein the acquired data is an acquired stable data sequence, and the acquired data after interference elimination is a stable data sequence with unqualified data discarded;
the formula for obtaining the autocorrelation coefficient of the weak discharge parameter is as follows:
in the formula, n is a number series dimension, k is a number series lag number, x is sample data (such as the average discharge times per minute of a linear characteristic parameter suspension discharge model, a step parameter insulator metal foreign matter discharge normalized amplitude and nonlinear characteristic quantity insulator surface discharge amplitude information entropy), and x is the average value of the data population; the autocorrelation coefficient can express the correlation of data before and after a group of data;
the formula for obtaining the weak discharge parameter partial autocorrelation coefficient is as follows:
in the formula (I), the compound is shown in the specification,
the partial autocorrelation coefficients are obtained by eliminating k-1 variable interferences and then checking the correlation of other data.
Determining the order of the model by adopting an AIC criterion; the AIC criterion can be expressed as: AIC (p) =2p-2ln (L), wherein p is weak discharge parameter quantity, and L is a likelihood function; let N be the number of observations,
being the sum of the squared residuals, the above equation becomes:
the optimal autoregressive model order refers to the p-value at which AIC (p) is minimized.
The method for determining the value of the unknown parameter in the model comprises the following steps:
after the order of the model is determined, finding out a likelihood function L of the sample, and solving a parameter value which enables the function to reach the maximum;
is recorded as:
suppose that
Subject to multivariate normal distribution
The likelihood function is then:
to find the maximum value
The method for verifying the validity of the model comprises the following steps: the model is valid if it extracts enough information from the data and the residual is a white noise sequence.
GIS weak discharge development trend prediction device based on ARMA model includes:
the stable data sequence acquisition module is used for acquiring weak discharge data in the gas insulated switchgear and preprocessing the weak discharge data to stabilize the data and acquire a stable data sequence;
the unqualified data eliminating module is used for carrying out white noise inspection on the stable data sequence and abandoning unqualified data;
the autocorrelation data acquisition module is used for determining autocorrelation of the acquired data and autocorrelation of the acquired data after interference removal;
the ARMA model building module is used for building an ARMA model, carrying out order fixing on the model and determining the value of an unknown parameter in the model;
the effective prediction model obtaining module is used for verifying the effectiveness of the model, reconstructing and determining the model if the model does not meet the condition, and obtaining an effective prediction model if the model meets the condition;
and the GIS weak discharge development trend prediction module is used for predicting the GIS weak discharge development trend by utilizing the effective prediction model.
In addition, the present embodiment also provides a computing device, including:
one or more processing units;
a storage unit for storing one or more programs,
when the one or more programs are executed by the one or more processing units, the one or more processing units are enabled to execute the GIS weak discharge trend prediction method based on the ARMA model; it is noted that the computing device may include, but is not limited to, a processing unit, a storage unit; those skilled in the art will appreciate that the computing device includes processing units, memory units, and not limitation of the computing device, and may include more components, or combine certain components, or different components, e.g., the computing device may also include input output devices, network access devices, buses, and the like.
There is also provided a computer readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of the aforementioned ARMA model-based GIS weak discharge trend prediction method; it should be noted that the readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof; the program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
The present invention has been described in detail with reference to the above examples, but the above description is only for the purpose of describing the preferred embodiments of the present invention, and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (9)
1. The GIS weak discharge development trend prediction method based on the ARMA model is characterized by comprising the following steps of:
weak discharge data in the gas insulated switchgear is collected and preprocessed, so that the data are stabilized, and a stable data sequence is obtained;
performing white noise inspection on the stable data sequence, and discarding unqualified data;
determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference elimination;
constructing an ARMA model, carrying out order fixing on the model, and determining the value of an unknown parameter in the model;
verifying the effectiveness of the model, if the model does not meet the condition, reconstructing and determining the model, and if the model meets the condition, obtaining an effective prediction model;
and predicting the development trend of the GIS weak discharge by using an effective prediction model.
2. The ARMA model-based GIS weak discharge trend prediction method according to claim 1, wherein: and preprocessing weak discharge data to judge the stability of the data in the process of stabilizing the data, wherein a difference method or a correlation coefficient diagram is used for judging the stability.
3. The ARMA model-based GIS weak discharge trend prediction method according to claim 1, wherein: the method for determining the autocorrelation of the acquired data and the autocorrelation of the acquired data after interference removal comprises the following steps:
the formula for obtaining the autocorrelation coefficient of the weak discharge parameter is as follows:
wherein n is a number series dimension, k is a number series lag number, x is sample data (such as the average discharge times per minute of a linear characteristic parameter suspension discharge model, the discharge normalized amplitude of metallic foreign bodies of a step parameter insulator, and the information entropy of the surface discharge amplitude of the nonlinear characteristic quantity insulator),
average value of data population; the autocorrelation coefficient can express the correlation of data before and after a group of data;
the weak discharge parameter partial autocorrelation coefficient is obtained by the following formula:
in the formula (I), the compound is shown in the specification,
the partial autocorrelation coefficients are obtained by eliminating k-1 variable interferences and then checking the correlation of other data.
4. The ARMA model-based GIS weak discharge trend prediction method according to claim 1, wherein: determining the order of the model by adopting an AIC criterion; the AIC criterion can be expressed as: AIC (p) =2p-2ln (L), wherein p is weak discharge parameter quantity, and L is a likelihood function; let N be the number of observations,
being the sum of the squared residuals, the above equation becomes:
the optimal autoregressive model order refers to the p-value at which AIC (p) is minimized.
5. The ARMA model-based GIS weak discharge trend prediction method according to claim 1, wherein: the method for determining the value of the unknown parameter in the model comprises the following steps:
after the order of the model is determined, finding out a likelihood function L of the sample, and solving a parameter value which enables the function to reach the maximum;
is recorded as:
suppose that
Obeying multivariate normal distribution
The likelihood function is then:
to find the maximum value
6. The ARMA model-based GIS weak discharge trend prediction method according to claim 1, wherein: the method for verifying the validity of the model comprises the following steps: the model is valid if it extracts enough information from the data and the residual is a white noise sequence.
7. GIS weak discharge development trend prediction device based on ARMA model, its characterized in that includes:
the stable data sequence acquisition module is used for acquiring weak discharge data in the gas insulated switchgear and preprocessing the weak discharge data to stabilize the data and acquire a stable data sequence;
the unqualified data eliminating module is used for carrying out white noise inspection on the stable data sequence and abandoning unqualified data;
the autocorrelation data acquisition module is used for determining autocorrelation of the acquired data and autocorrelation of the acquired data after interference removal;
the ARMA model building module is used for building an ARMA model, carrying out order fixing on the model and determining the value of an unknown parameter in the model;
the effective prediction model obtaining module is used for verifying the effectiveness of the model, reconstructing and determining the model if the model does not meet the condition, and obtaining an effective prediction model if the model meets the condition;
and the GIS weak discharge development trend prediction module is used for predicting the GIS weak discharge development trend by utilizing the effective prediction model.
8. A computing device, characterized by: the method comprises the following steps:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-6.
9. A computer-readable storage medium with non-volatile program code executable by a processor, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 6 when executed by the processor.
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