A condition inspection method, system and storage medium based on secondary equipment light intensity trend analysis

Abstract

The present invention discloses a state maintenance method, system and storage medium based on the trend analysis of light intensity of secondary equipment. The optical fiber interface provided by the secondary equipment is used to send light intensity data based on the existing SCADA platform and 61850 communication acquisition. The light intensity measurement data of the secondary equipment is entered into a historical database. The arithmetic mean and standard deviation of the light intensity are calculated in each given time interval, which are used to evaluate the aging degree and stability of the optical module respectively; the least square method is used to perform adaptive regression analysis on all the calculated mean values ​​and standard deviations, and two regression equations are obtained; the maintenance personnel give the light intensity maintenance threshold and the light intensity standard deviation maintenance threshold in real time through the SCADA monitoring background, and obtain the recommended maintenance time based on the regression equation. The present invention effectively improves the operation state prediction level of the process layer optical module, realizes the dynamic prediction of the maintenance time, realizes the optimal configuration of maintenance resources and promotes the improvement of maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the field of process layer status maintenance of intelligent substations, and in particular to a status maintenance method, system and storage medium based on secondary equipment light intensity trend analysis. Background Art

[0002] The relay protection system is an important part of the power system. It plays a key role in ensuring the safe and economical operation of the power system and preventing accidents from happening and expanding. At present, judging from the actual operation of the power system, there are still many accidents caused by power relay protection, which cause local power grid decoupling and voltage loss, bring a lot of economic losses, and pose a great threat to the safety of the power grid.

[0003] At present, the maintenance of relay protection is mainly carried out by manual regular inspection. The test methods and results of regular maintenance are authentic and reliable, and the test items are complete, which can find problems and eliminate safety hazards. However, this method of maintenance without considering the actual situation of the equipment and simply performing maintenance at prescribed time intervals will cause the loss of effective utilization time of the equipment, and even cause maintenance failures, and the failure of the device between two maintenances cannot be discovered in time.

[0004] Condition maintenance collects and evaluates the status of the equipment by making full use of the hardware monitoring function of the microcomputer protection device itself and the online monitoring function of some secondary circuits. The practice of some power companies at home and abroad in applying power equipment condition maintenance has proved that this equipment maintenance management strategy has obvious social and economic benefits, and it has the characteristics of high accuracy, low maintenance pressure and high power supply reliability.

[0005] In recent years, the engineering application of smart substations has provided a good basic condition for the condition inspection and intelligent diagnosis of relay protection systems. The traditional secondary cables in substations that cannot be detected online have been transformed into optical fiber networks that can be detected online. At the same time, the intelligence level of relay protection devices has been continuously improved, and the monitoring ability of key information of the devices themselves has become stronger and stronger, which provides a good foundation for online monitoring and condition inspection of process layer networks of smart substations. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the problems of large data volume, insufficient utilization, inability to accurately predict the working status of the optical module, and inability to provide effective information for maintenance personnel in the existing optical module monitoring technology.

[0007] In order to solve the above technical problems, the solution of the present invention is:

[0008] A condition maintenance method based on secondary equipment light intensity trend analysis includes the following steps:

[0009] 1) A light intensity data analysis module is set up in the monitoring system of the intelligent substation control layer, and the SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary equipment;

[0010] 2) The light intensity measurement data of the secondary equipment is entered into the history database, and the arithmetic mean and standard deviation of the light intensity are calculated at given time intervals;

[0011] 3) Use the least squares method to perform adaptive regression analysis on all the calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, providing a basis for equipment status maintenance.

[0012] A condition inspection system based on secondary equipment light intensity trend analysis includes the following program modules:

[0013] Light intensity data acquisition module: A light intensity data analysis module is set up in the monitoring system of the intelligent substation control layer, and the SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary equipment;

[0014] Light intensity calculation module: The light intensity measurement data of the secondary equipment is stored in the history library, and the arithmetic mean and standard deviation of the light intensity are calculated at a given time interval;

[0015] Maintenance time calculation module: Use the least squares method to perform adaptive regression analysis on all calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, providing a basis for equipment status maintenance.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the aforementioned condition maintenance method based on secondary equipment light intensity trend analysis.

[0017] The beneficial effects achieved by the present invention are:

[0018] The condition maintenance method and system based on secondary equipment light intensity trend analysis of the present invention have low consumption of existing system resources and strong operability, effectively improve the utilization efficiency of process layer monitoring data, effectively predict the time point when the process layer network needs maintenance, and support the formulation of a more optimized maintenance plan for the process layer network of the intelligent station. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a functional module design diagram of a condition maintenance method based on light intensity trend analysis of secondary equipment in the present invention;

[0020] Figure 2This is a statistical diagram of light intensity time series in a state maintenance method based on light intensity trend analysis of secondary equipment in the patent of this invention;

[0021] Figure 3 This is a statistical diagram of the time series of the light intensity average value in a condition maintenance method based on light intensity trend analysis of secondary equipment in the patent of this invention.

[0022] Figure 4 This is a statistical diagram of the time series of light intensity standard deviation in a condition maintenance method based on light intensity trend analysis of secondary equipment in the patent of this invention.

[0023] Figure 5 This is a schematic diagram of an adaptive linear regression analysis algorithm in a condition maintenance method based on light intensity trend analysis of secondary equipment in the patent of the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical solution and features of the patent of the present invention clearer, the patent of the present invention is further elaborated below.

[0025] A condition maintenance method based on secondary equipment light intensity trend analysis includes the following steps:

[0026] 1) A light intensity data analysis module is set up in the monitoring system of the intelligent substation control layer, and the SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary equipment;

[0027] 2) The light intensity measurement data of the secondary equipment is entered into the historical database, and the arithmetic mean and standard deviation of the light intensity are calculated at given time intervals;

[0028] 3) Use the least squares method to perform adaptive regression analysis on all the calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, providing a basis for equipment status maintenance.

[0029] Further, in step 1), optical fiber interface light intensity data provided by secondary equipment is collected based on the existing SCADA platform and 61850 communication protocol;

[0030] Furthermore, in step 1), the optical fiber interface light intensity data provided by the secondary equipment is collected according to the set collection period t0, and the collection period t0 can be set by the maintenance personnel according to the actual situation.

[0031] Further, in step 1), the optical fiber interface light intensity data provided by the collected secondary equipment is recorded in time sequence, and the time sequence of the light intensity data is recorded as S (x,t) :(x1,t1),(x2,t2),…,(x i ,t i),…,(x n ,t n ).

[0032] Further, in step 2), the light intensity timing sequence S (x,t) Calculate the arithmetic mean of the light intensity according to the set calculation interval T0 and standard deviation

[0033] The coefficient

[0034] Get the light intensity mean time series

[0035] and standard deviation time series S (σ,T) :(σ1,T1),(σ2,T2),…,(σ i ,T i ),…,(σ n ,T n ),

[0036] The average light intensity in each calculation cycle;

[0037] σ1, σ2, …, σ n is the standard deviation of light intensity in each calculation cycle;

[0038] T1, T2, …, T n for each sampling period.

[0039] Further, in step 3), the light intensity mean time series is calculat and standard deviation time series S (σ,T) Perform adaptive univariate linear regression calculations to obtain the light intensity mean regression equation x T =a x +b x T and light intensity standard deviation regression equation σ T =a σ +b σ T,

[0040] where a x , b x are the parameters 1 and 2 of the linear regression of the light intensity mean, T is the sampling period, a σ , b σ They are parameter one and parameter two of the standard deviation regression equation respectively.

[0041] Further, in step 3), the light intensity mean regression equation x T =a x +b x T and light intensity standard deviation regression equation σ T=a σ +b σ T is subjected to correlation analysis and variance analysis. If the results obtained do not meet the conditions of correlation analysis and variance analysis, the light intensity mean time series is respectively and standard deviation time series S (σ,T) Perform linear variable substitution to obtain a new sequence:

[0042]

[0043]

[0044] Re-perform regression analysis on the new series and obtain a new regression equation

[0045] f x (x) = a x +b x f Tx (T) and f σ (σ)=a σ +b σ f Tσ (T),

[0046] They are linear transformation functions with the light intensity mean and time as variables respectively;

[0047] f σ (σ),f Tσ (T) are linear transformation functions with standard deviation and time as variables respectively.

[0048] is the light intensity mean linear transformation function 1 and the light intensity mean linear transformation function 2; is the standard deviation mean linear transformation function 1 and the standard deviation mean linear transformation function 2. Further, in step 3), the light intensity inspection threshold x is set through the SCADA monitoring background. limit And the light intensity standard deviation inspection threshold σ limit .

[0049] Further, in step 3), the inspection threshold x limit and σ limit Substitute them into the regression equation to get the recommended maintenance time for light intensity or and light intensity standard deviation recommended maintenance time or They are transformation functions f Tx (T), transformation function f Tσ The corresponding inverse function of (T).

[0050] Further, in step 3), the light intensity recommended maintenance time T x_s And light intensity standard deviation recommended maintenance time Tσ_s Take the minimum value to get the recommended maintenance time T for the optical module of the secondary equipment of the smart station s =Min(T x_s ,T σ_s ).

[0051] Further, in step 3), the recommended maintenance time T of the optical module of the secondary equipment of the intelligent station is s It is sent to maintenance personnel through the SCADA monitoring system to provide a basis for equipment status maintenance.

[0052] A condition inspection system based on secondary equipment light intensity trend analysis includes the following program modules:

[0053] Light intensity data acquisition module: A light intensity data analysis module is set up in the monitoring system of the intelligent substation control layer, and the SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary equipment;

[0054] Light intensity calculation module: The light intensity measurement data of the secondary equipment is stored in the history library, and the arithmetic mean and standard deviation of the light intensity are calculated at a given time interval;

[0055] Maintenance time calculation module: Use the least squares method to perform adaptive regression analysis on all calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, providing a basis for equipment status maintenance.

[0056] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the aforementioned condition maintenance method based on secondary equipment light intensity trend analysis.

[0057] Example 1

[0058] Add a light intensity data analysis module to the intelligent substation control layer monitoring system, as shown in the attached Figure 1 shown.

[0059] Based on the existing SCADA platform and 61850 communication protocol, the light intensity data of a certain optical fiber interface provided by the process layer switch equipment of the intelligent station is collected according to the set collection period t0 = 5min. The light intensity measurement data of the secondary equipment is entered into the historical database to form a time series record of the light intensity data as S (x,t) :(x1,t1),(x2,t2),…,(x i ,t i ),…,(x n ,t n ), as attached Figure 2 shown.

[0060] For the light intensity timing sequence S(x,t) Calculate the arithmetic mean of the light intensity according to the set calculation interval T0 = 168 hours and standard deviation in Get the light intensity mean time series As attached Figure 3 As shown, and the standard deviation time series S (σ,T) :(σ1,T1),(σ2,T2),…,(σ i ,T i ),…,σ n ,T n ), as attached Figure 4 shown.

[0061] Furthermore, the least squares method is used to estimate the light intensity mean time series. and standard deviation time series S (σ,T) Perform adaptive univariate linear regression calculations respectively, as shown in the attached Figure 5 As shown, the light intensity mean regression equation x is obtained T =a x +b x T, as attached Figure 3 As shown, the regression equation of light intensity standard deviation σ T =a σ +b σ T, as attached Figure 4 shown.

[0062] Set the light intensity inspection threshold x through the SCADA monitoring background limit =-20dBm and light intensity standard deviation inspection threshold σ limit =0.2, the inspection threshold x limit and σ limit Substitute them into the regression equation to get the recommended maintenance time for light intensity and light intensity standard deviation recommended maintenance time Take the minimum value of the two to get the recommended maintenance time of the optical module of the secondary equipment of the smart station And send it to maintenance personnel through the SCADA monitoring system to provide a basis for equipment status maintenance.

[0063] The above embodiments are only for illustrating the technical idea of ​​the patent of the present invention, and cannot be used to limit the protection scope of the patent of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the patent of the present invention shall fall within the protection scope of the patent of the present invention.

Claims

1. A condition maintenance method based on secondary equipment light intensity trend analysis, characterized in that: The following steps are involved: 1) The SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary device, specifically: the optical fiber interface light intensity data provided by the secondary device is collected according to the set collection period t0, and the collected optical fiber interface light intensity data provided by the secondary device is recorded in time sequence, and the time sequence of the light intensity data is recorded as S (x,t) :(x1,t1),(x2,t2),…,(x i ,t i ),…,(x n ,t n ); 2) The light intensity measurement data of the secondary equipment is stored in the history database. Given a time interval, the arithmetic mean and standard deviation of the light intensity are calculated respectively, specifically: For the light intensity timing sequence S (x,t) Calculate the arithmetic mean of the light intensity according to the set calculation interval T0 and standard deviation The coefficient Get the time series of the mean light intensity: And the standard deviation time series: S (σ,T) :(σ1,T1) , (σ2,T2),…,(σ i ,T i ),…,(s n ,T n ), The arithmetic mean value of light intensity in each calculation cycle; σ1, σ2, ..., σ n is the standard deviation of light intensity in each calculation cycle; T1, T2, ...., T n For each calculation cycle; t0 is the acquisition period; 3) Use the least squares method to perform adaptive regression analysis on all the calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, to provide a basis for equipment status maintenance, specifically: The least square method is used to calculate the light intensity mean time series and standard deviation time series S (σ,T) Perform adaptive univariate linear regression calculations to obtain the light intensity mean regression equation and the light intensity standard deviation regression equation σ T =a σ +b σ T, where a x , b x are the parameters 1 and 2 of the linear regression of the light intensity mean, T is the calculation period, a σ , b σ They are parameter one and parameter two of the standard deviation regression equation respectively; Light intensity mean regression equation and the light intensity standard deviation regression equation σ T =a σ +b σ T is subjected to correlation analysis and variance analysis. If the results obtained do not meet the conditions of correlation analysis and variance analysis, the light intensity mean time series is respectively and standard deviation time series S (σ,T) Perform linear variable substitution to obtain a new sequence: Re-perform regression analysis on the new series and obtain a new regression equation f Tx (T) are linear transformation functions with light intensity mean and time as variables; f σ (σ),f Tσ (T) are linear transformation functions with standard deviation and time as variables; Set the light intensity inspection threshold x through the SCADA monitoring background limit And the light intensity standard deviation inspection threshold σ limit ; Set the maintenance threshold x limit and σ limit Substitute them into the regression equation to get the recommended maintenance time for light intensity or and light intensity standard deviation recommended maintenance time or They are transformation functions f Tx (T), transformation function f Tσ The corresponding inverse function of (T); The light intensity is recommended for maintenance time Tx_s And light intensity standard deviation recommended maintenance time T σ_s Take the minimum value to get the recommended maintenance time T for the optical module of the secondary equipment of the smart station s =Min( Tx_s ,T σ_s ).

2. The condition inspection method based on secondary equipment light intensity trend analysis according to claim 1 is characterized in that: In step 1), the optical fiber interface light intensity data provided by the secondary equipment is collected based on the existing SCADA platform and 61850 communication protocol.

3. A condition inspection system based on secondary equipment light intensity trend analysis, characterized in that: Includes the following program modules: Light intensity data acquisition module: The SCADA platform collects the light intensity data sent by the optical fiber interface provided by the secondary equipment. Specifically, the light intensity data of the optical fiber interface provided by the secondary equipment is collected according to the set collection period t0, and the collected light intensity data of the optical fiber interface provided by the secondary equipment is recorded in time sequence. The time sequence of the light intensity data is recorded as S (x,t) :(x1,t1),(x2,t2),…,(x i ,t i ),…,(x n ,t n ); Light intensity calculation module: The light intensity measurement data of the secondary equipment is stored in the history library. At a given time interval, the arithmetic mean and standard deviation of the light intensity are calculated respectively, as follows: For the light intensity timing sequence S (x,t) Calculate the arithmetic mean of the light intensity according to the set calculation interval T0 and standard deviation The coefficient Get the time series of the mean light intensity: And the standard deviation time series: S (σ,T) :(σ1,T1),(σ2,T2),…,(σ i ,T i ),…,(s n ,T n ), The arithmetic mean value of light intensity in each calculation cycle; σ1, σ2, ..., σ n is the standard deviation of light intensity in each calculation cycle; T1, T2, ...., T n For each calculation cycle; t0 is the acquisition period; Maintenance time calculation module: Use the least squares method to perform adaptive regression analysis on all calculated mean values ​​and standard deviations, and calculate the recommended maintenance time based on the regression equation according to the light intensity maintenance threshold and light intensity standard deviation maintenance threshold given in real time by the SCADA monitoring background, providing a basis for equipment status maintenance, specifically: The least square method is used to calculate the light intensity mean time series and standard deviation time series S (σ,T) Perform adaptive univariate linear regression calculations to obtain the light intensity mean regression equation and the light intensity standard deviation regression equation σ T =a σ +b σ T, where a x , b x are the parameters 1 and 2 of the linear regression of the light intensity mean, T is the calculation period, a σ , b σ They are parameter one and parameter two of the standard deviation regression equation respectively; Light intensity mean regression equation and the light intensity standard deviation regression equation σ T =a σ +b σ T is subjected to correlation analysis and variance analysis. If the results obtained do not meet the conditions of correlation analysis and variance analysis, the light intensity mean time series is respectively and standard deviation time series S (σ,T) Perform linear variable substitution to obtain a new sequence: Re-perform regression analysis on the new series and obtain a new regression equation and f σ (σ) = a σ + b σ f Tσ (T), f Tx (T) are linear transformation functions with light intensity mean and time as variables; f σ (σ),f Tσ (T) are linear transformation functions with standard deviation and time as variables; Set the light intensity inspection threshold x through the SCADA monitoring background limit And the light intensity standard deviation inspection threshold σ limit ; Set the maintenance threshold x limit and σ limit Substitute them into the regression equation to get the recommended maintenance time for light intensity or and light intensity standard deviation recommended maintenance time or They are transformation functions f Tx (T), transformation function f Tσ The corresponding inverse function of (T); The light intensity is recommended for maintenance time Tx_s And light intensity standard deviation recommended maintenance time T σ_s Take the minimum value to get the recommended maintenance time T for the optical module of the secondary equipment of the smart station s =Min( Tx_s ,T σ_s ).

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the condition inspection method based on secondary equipment light intensity trend analysis as described in any one of claims 1-2 are implemented.

Images