CN110580387B - DC protection system mixed Weibull reliability evaluation method based on entropy weight method - Google Patents
DC protection system mixed Weibull reliability evaluation method based on entropy weight method Download PDFInfo
- Publication number
- CN110580387B CN110580387B CN201910794352.1A CN201910794352A CN110580387B CN 110580387 B CN110580387 B CN 110580387B CN 201910794352 A CN201910794352 A CN 201910794352A CN 110580387 B CN110580387 B CN 110580387B
- Authority
- CN
- China
- Prior art keywords
- protection system
- defect
- direct current
- reliability
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a DC protection system mixed Weibull reliability evaluation method based on an entropy weight method, which comprises the following steps: obtaining index data of the defects of the direct current protection system according to the historical data; checking the accumulation of the defect interval time to determine whether the Weibull distribution is met; determining the weight coefficient of the defect index by using an entropy weight method, and further obtaining the weight coefficient of various devices of the direct current protection system; and establishing a single Weibull model of each device based on the defect interval time accumulated data by using a maximum likelihood method, combining weight coefficients of each device in the defect data to obtain a mixed Weibull model of the defect data of the high-voltage direct-current transmission protection system, and analyzing the reliability of the high-voltage direct-current protection system by using the mixed Weibull model. The invention can evaluate the reliability of the direct current transmission protection system by utilizing the operation defect information of the high voltage direct current transmission protection equipment, and provides an important reference for the operation and maintenance work of the direct current transmission protection system equipment.
Description
Technical Field
The invention relates to the technical field of reliability research of power systems, in particular to a DC protection system hybrid Weibull reliability evaluation method based on an entropy weight method.
Background
The direct current transmission protection system is a first defense line for safe and reliable operation of the direct current transmission system, and is an important guarantee for safe and stable operation of direct current engineering. In recent years, the problem of shutdown or locking of a direct current system caused by the fault of the direct current transmission protection system occurs, wherein the measurement fault of the protection system, the fault of a board card and the fault of a protection host cause more than 10% of direct current locking, and the safe and stable operation of the direct current system is seriously influenced. Therefore, the reliability of the direct current transmission protection system is important.
The reliability evaluation index aspect of the current protection system mainly takes the correct protection action as an evaluation index, researches reliability evaluation indexes of a protection method, a protection configuration scheme and protection equipment, and has few action samples, the reliability of the current protection system is evaluated by adopting the indexes such as the existing correct action rate, the false action rate, the refusal action rate and the like, so that the reliability of an evaluation result is low, and meanwhile, the indexes are mainly used for evaluating the inherent reliability of the system and are difficult to evaluate the use reliability of the system; meanwhile, the reliability of the evaluation result of the traditional reliability evaluation method is low due to the fact that the number of direct current protection samples is small, and the practicability is poor. Therefore, the reliability evaluation is carried out on the defect data of the direct current protection system, the running state of the direct current protection system can be reflected, and an important reference basis is provided for the running maintenance work of the direct current protection system equipment.
Disclosure of Invention
The invention aims to provide a mixed Weibull reliability evaluation method of a direct current protection system based on an entropy weight method, which evaluates the reliability of the direct current transmission protection system by utilizing the operation defect information of high-voltage direct current transmission protection equipment, provides an important reference basis for the operation and maintenance work of the direct current protection system equipment, and is also suitable for evaluating the reliability of an alternating current/direct current system based on the defect data and fault information of the alternating current/direct current system.
The invention is realized by the following technical scheme:
a DC protection system mixed Weibull reliability evaluation method based on an entropy weight method comprises the following steps:
step one: classifying the direct current protection system according to different positions of the defects by combining the characteristics of the direct current protection system, collecting historical data of the defects of the direct current protection system, and obtaining index data of the defects of the direct current protection system according to the historical data;
step two: checking defect interval time accumulation of the high-voltage direct-current transmission protection system, and executing the third step after determining that the defect interval time accumulation accords with Weibull distribution;
step three: determining the weight coefficient of index data of each direct current protection system defect by using an entropy weight method, and further obtaining the weight coefficient of various devices of the direct current transmission protection system in the defect data;
step four: and establishing a single Weibull model of each device based on the defect interval time accumulated data by using a maximum likelihood method, combining weight coefficients of each device in the defect data to obtain a mixed Weibull model of the defect data of the high-voltage direct-current transmission protection system, and analyzing the reliability of the high-voltage direct-current protection system by using the mixed Weibull model.
Further, the specific implementation process of the first step is as follows:
(a) The defects of the direct current protection system are divided into the following five types according to the difference of the positions of the defects by combining the characteristics of the direct current protection system: measuring equipment defects, interface device defects, direct current protection device defects, three-taking-two device defects, tripping outlet defects and secondary circuit defects, and independently calculating a host type, a device type and an independent type protection device as one type when the defect rate of the host type, the device type and the independent type protection device is high;
(b) Collecting historical data of defects of a DC protection system of a plurality of convertor stations in recent years;
(c) Three index data of the defects of the direct current protection system are obtained according to the historical data: component failure time rate, component average repair time rate, and component defect rate;
further, the specific implementation process of the second step is as follows:
(a) The defect interval time accumulation of the high-voltage direct-current transmission protection system is arranged in ascending order;
(b) Checking the defect interval time accumulation of the high-voltage direct-current transmission protection system, and judging and determining whether the defect interval time accumulation accords with Weibull distribution by KS (K-means) test:
the weibull probability distribution for both parameters is:
wherein t is time, beta is a shape parameter, and eta is a scale parameter;
assuming that the unitary linear regression equation is y=bx+a, the linear transformation of the two-parameter weibull function is obtained:
x i ＝lnt i (9)
in the formulas (7) - (9), i is the accumulated sequence number of the defect interval time, n is the accumulated sample number of the defect interval time of the high-voltage direct-current transmission protection system, and t i Protection for high voltage direct current transmissionSystem defect interval time accumulation;
by calculation, regression systems A and B in a unitary linear regression equation of y=BX+A are obtained, and then the shape parameter beta and the scale parameter eta are obtained:
β＝B (11)
judging the correctness of the defect distribution by KS test, and obtaining the maximum value of the absolute value of the difference value in all data by making the difference value of the values of the formula (6) and the formula (8), wherein the maximum value is the observed value D max It is compared with the empirical critical value D n Comparing, if the accumulated time is smaller than the experience critical value, determining that the accumulated time of the defects obeys the Weibull distribution, if the accumulated time of the defects does not accord with the Weibull distribution, re-judging the accumulated time data of the defects, possibly causing missing or misplugging of some defects, and judging after the data is improved again.
Further, the specific implementation process of the third step is as follows:
(a) Normalizing the defect index obtained in the step one, and obtaining a relation matrix (x ij ) m×n Is converted into a standard matrix, and the index data are all reverse indexes, so that polarization treatment is carried out according to a reverse index formula (13) to obtain a standard matrix (a) ij ) m×n M is the number of defect categories, n is the number of evaluation indexes;
in the formula (13), x max Representing the maximum value of each column, x min Representing the minimum value for each column;
(b) According to the standard matrix, the characteristic weight P of the j index of the i-th device is obtained ij ：
When P ij When=0, the calculated entropy is meaningless and thus is not valid for P ij Is modified and defined as
(c) Calculating the entropy of the jth index as
(d) Entropy weight of the j-th index is
(e) The weight coefficients of various elements of the direct current transmission protection system in the defect data are as follows:
(f) And according to the weight coefficients of various elements, further solving the weight coefficients of various devices of the direct current transmission protection system in the defect data.
Further, the specific implementation process of the fourth step is as follows:
(a) The defect interval time accumulation of five types of equipment of the high-voltage direct-current transmission protection system is arranged in ascending order, and the shape parameters beta and eta of various types of equipment are estimated by using a maximum likelihood estimation method respectively:
for a two-parameter weibull distribution, the construction likelihood function is:
partial derivatives are obtained for each parameter in the above formula (19), and a system of equations is obtained:
equation (20) is an overrun equation set, the parameters cannot be directly obtained, and Newton-Raphson iteration method is used on MATLAB to solve the parameters beta and eta of various devices, so as to obtain the Weibull reliability function R of various devices i (x)：
In the formula (21), beta i For the shape parameter of the i-th element, eta i Is the scale parameter of the i-th class element.
(b) Establishing a mixed Weibull model R (x) of defect data of the high-voltage direct-current transmission protection system to obtain a reliability curve of the high-voltage direct-current transmission protection system;
R(x)＝P 1 R 1 (x)+P 2 R 2 (x)+…+P n R n (x) (22)
P＝P 1 +P 2 +…+P n ＝1 (23)
where n is the number of device classes.
(c) Analysis of reliability of HVDC protection system
The independent variable of the mixed Weibull model is the accumulation of defect interval time, and can be regarded as the normal operation time of the equipment from the occurrence of the first defect, the larger the normal operation time is, the lower the reliability of the system is, the mixed Weibull model curve (namely the reliability curve of the high-voltage direct-current transmission protection system) can be regarded as the normal operation time of the equipment from the occurrence of the first defect, and under the condition that the operation time is as long as possible, the higher the reliability is, the better the reliability is, and the expected value of the reliability is set as a standard value R i Standard value R for the first time 1 ＝0.95，Obtaining corresponding running time T according to the mixed Weibull model curve 1 Standard value R of last time i ＝R i-1 X0.95, T is obtained from the curve i Thereby obtaining T in a period of time i The value, which can be considered as the optimal maintenance time for the dc protection system, is averaged. If the reliability of a certain type of equipment is to be analyzed independently, the reliability function of each type of equipment can be utilized to obtain the optimal maintenance time of each type of equipment by adopting the same method as the reliability analysis of the mixed Weibull model.
Comparing the average value T with the defect time interval of the high-voltage direct-current transmission protection system of each station, and when the defect time interval of the system is larger than the average value T, judging that the reliability of the system is lower, at the moment, adjusting the equipment maintenance time according to the average value T or adopting measures such as replacing part of equipment to ensure that the reliability of the system is at a higher level; combining the historical data of each station with the average value T of the mixed Weibull model, wherein the historical defect interval time average value is larger than the average value T of the mixed Weibull model, and the larger the historical defect interval time average value is, the reliability of the direct current protection system of the station is high, the time interval of the next defect of the system is long, and the number of defects in the same time period is small; the historical defect interval time average value is smaller than the mixed Weibull model average value T, and the smaller the historical defect interval time average value is, the reliability of the direct current protection system of the station is low, the time interval of the next defect occurrence of the system is short, and the number of defects occurring in the same time period is large.
The invention obtains the mixed Weibull model of the direct current protection system based on the entropy weight method, further obtains the optimal maintenance time of the system and the optimal maintenance time of various devices, evaluates the direct current protection system of each converter station in combination with the actual situation of each converter station, provides an important guiding function for the operation and maintenance of the direct current protection system, and can obtain the mixed Weibull model curve of the converter station for analyzing the reliability, defect occurrence rate and the like of the direct current protection system of the converter station under the condition that the defect sample of each station is more complete. The using effect of the invention can be obtained through a specific implementation scheme, the scheme verifies the effectiveness of the invention, the optimal operation and maintenance time of the direct current protection system of the converter station is provided, and an important operation and maintenance time basis is provided for equipment operation and maintenance units. The invention is also suitable for analyzing the defect or fault data of the AC/DC system.
Drawings
Fig. 1 is a diagram of the components of a hvdc transmission system according to the present invention;
FIG. 2 is a diagram showing a hybrid Weibull model R (x) and R for various types of devices in an embodiment of the invention i (x) Schematic diagram of the curve.
Detailed Description
The present invention will be further described with reference to specific examples to provide a full understanding of the objects, features and effects of the present invention.
In this embodiment, the data originates from defects of the hvdc transmission protection system in 2014-2018 of the Hubei power grid.
The method for evaluating the reliability of the mixed Weibull of the direct current protection system based on the entropy weight method in the embodiment comprises the following steps:
step one: the method comprises the steps of classifying the direct current protection system according to different positions of defects by combining the characteristics of the direct current protection system, collecting historical data of the defects of the direct current protection system, and obtaining index data of the defects of the direct current protection system according to the historical data: component failure time rate, component average repair time rate, and component defect rate.
According to the technical scheme, 173 defects of the high-voltage direct-current transmission protection system of the Hubei power grid 2014-2018 are obtained based on historical data, wherein the number of defects of the measurement equipment is 56, the number of defects of the measurement interface is 43, the number of defects of the host type direct-current protection system is 58, the number of defects of the independent type direct-current protection system is 6, and the number of defects of the device type direct-current protection system is 10.
The index data of the defects of the direct current protection system are shown in the following table:
step two: checking defect interval time accumulation of a high-voltage direct-current transmission protection system, and determining whether the defect interval time accumulation accords with Weibull distribution, wherein the second step is as follows:
(a) The defect interval time accumulation of the high-voltage direct-current transmission protection system is arranged in ascending order;
(b) And checking the defect interval time accumulation of the HVDC protection system, and judging and determining whether the system accords with the Weibull distribution by KS test.
According to defect data of the high-voltage direct-current transmission protection system, defect interval time accumulated data are obtained, a unitary linear regression equation is obtained to be y= 1.1049x-7.4195, and then beta= 1.1049, eta=824.5367 and D are obtained max =0.0883, where β is the shape parameter, η is the scale parameter, D max For the maximum absolute value of the difference in all data, i.e. the observed value D max 。
Obtaining an empirical critical value according to the KS test table
n is the sample size 172, D is obtained n ＝0.1037，D max Below the empirical threshold, the defect interval time accumulation compliance with the weibull distribution may be determined.
Step three: and determining the weight coefficient of each defect index by using an entropy weight method, and further obtaining the weight coefficient of various devices of the direct current transmission protection system in the defect data. The third step is specifically as follows:
(a) The index is normalized, and the relation matrix (x ij ) m×n Converting into standard matrix, and performing polarization treatment according to the reverse index formula (1) to obtain standard matrix (a) because index data are reverse indexes ij ) m×n M is the number of defect categories, and n is the number of evaluation indexes.
In the formula (1), x max Representing the maximum value of each column, x min Representing the minimum value for each column.
(b) According to the standard matrix, the characteristic weight P of the j index of the i-th device is obtained ij ：
When P ij When=0, the calculated entropy is meaningless and thus is not valid for P ij Is modified and defined as
(c) Calculating the entropy of the jth index as
(d) Entropy weight of the j-th index is
(e) The weights of various elements of the direct current transmission protection system in the defect data are as follows:
(f) And according to the weights of various elements, further solving the weights of the direct current transmission protection system equipment.
The entropy weights of the three indexes obtained by the method are shown in the following table:
| index amount | Time to failure duty cycle | Average maintenance time duty cycle | Element defect ratio |
| Entropy weight of index quantity | 0.6188 | 0.1815 | 0.2067 |
The weights of various devices are obtained according to the formula (6) as shown in the following table:
| all kinds of equipment | Weight (P) |
| Measuring device | 0.1596 |
| Measurement interface | 0.2502 |
| Independent protection | 0.1829 |
| Host type protection | 0.3023 |
| Device type protection | 0.1049 |
Step four: and establishing a single Weibull model of each device based on the defect interval time accumulated data by using a maximum likelihood method, combining weight coefficients of each device in the defect data to obtain a mixed Weibull model of the defect data of the high-voltage direct-current transmission protection system, and analyzing the reliability of the high-voltage direct-current protection system by using the mixed Weibull model.
(a) And the defect interval time accumulation of five types of equipment of the high-voltage direct-current transmission protection system is arranged in an ascending order, and the parameters beta and eta of various types of equipment are estimated respectively by using a maximum likelihood estimation method.
For a two-parameter weibull distribution, the construction likelihood function is:
partial derivatives are obtained for each parameter in the above formula (7), and an equation set is obtained:
equation (8) is an transcendental equation system and cannot directly calculate the parameters. Solving parameters beta and eta of various devices on MATLAB by using Newton-Raphson iteration method, and further obtaining F of various devices i (x)。
And obtaining parameters beta and eta of various devices by using MATLAB to the five types of devices by adopting a maximum likelihood estimation method, wherein the parameters beta and eta are shown in the following table:
| all kinds of equipment | Parameter beta | Parameter eta |
| Measuring device | 1.4017 | 912.6431 |
| Measurement interface | 1.1336 | 720.7997 |
| Independent protection | 2.0353 | 1131.0011 |
| Host type protection | 1.0240 | 595.8380 |
| Device type protection | 2.8301 | 1416.5968 |
(b) Establishing a mixed Weibull model R (x) of defect data of the high-voltage direct-current transmission protection system to obtain a reliability curve of the high-voltage direct-current transmission protection system;
R(x)＝P 1 R 1 (x)+P 2 R 2 (x)+…+P n R n (x) (9)
P＝P 1 +P 2 +…+P n ＝1 (10)
the mixed weibull model is built by formulas (9) and (10) as follows:
R(x)＝P 1 R 1 (x)+P 2 R 2 (x)+…+P n R n (x) The mixed Weibull model is used for protecting the defect data of the system for high-voltage direct-current transmission. R is R 1 (x) R is a reliability function of the measuring equipment 2 (x) To measure the reliability function of the interface, R 3 (x) As a reliability function of independent protection, R 4 (x) As a reliability function of the host type protection, R 5 (x) The Weibull model R (x) and R of various devices are mixed as a reliability function of device type protection i (x) The curve is shown in fig. 2:
(c) Analysis of reliability of HVDC protection system
The mixed Weibull model curve can be regarded as the normal running time of the equipment from the first defect occurrence, and under the condition that the running time is as long as possible, the higher the reliability is, the better, and the expected value of the reliability is set as a standard value R i Standard value R for the first time 1 =0.95, the corresponding run time T is obtained from a hybrid weibull model curve (i.e. a hvdc transmission protection system reliability curve) 1 Standard value R of last time i ＝R i-1 X 0.95 rootFrom the curve, T is obtained i Thereby obtaining T in a period of time i The value was found to have an average T of 31.97 days. Average values of various devices can be obtained in the same way, and specific data are shown in the following table:
| device or system name | Average value T |
| Measuring device | 32.75 |
| Measurement interface | 31.79 |
| Independent protection | 28.08 |
| Host type protection | 29.62 |
| Device type protection | 29.28 |
| DC protection system | 31.97 |
From the historical defect data for each converter station, the historical defect interval time average for each station 2014 to 2018 can be derived as follows:
it is clear from the table that the defect interval time of the Longquan converter station is short, and the equipment maintenance work of the Longquan converter station is suggested to be enhanced by combining the average value T of the mixed Weibull model. According to the combination of the historical data of each station and the average value T of the mixed Weibull model, the historical defect interval time average value of each Ge Zhouba converter station, each Jiangling converter station, each group forest converter station and each suitable converter station is larger than the average value T of the mixed Weibull model, but the historical defect interval time average value of each Jiangling converter station is not much different from the average value T of the mixed Weibull model, and the maintenance work of the equipment of the Jiangling converter station is also enhanced. The historical defect interval time average value of the Longquan converter station is smaller than the mixed Weibull model average value T, so that the reliability of the direct current protection system of the station is low, the time interval of the next defect of the system is short, and the number of defects in the same time period is large.
According to the analysis, the number of defects of the Longquan converter station is the largest, the defect interval time is the shortest, the difference between the historical defect interval time average value of the Longquan converter station and the average value of the measuring equipment and the measuring interface equipment is the largest, the number of defects can be increased, in order to verify the effectiveness of the invention, the actual defect data of each converter station in 2019 is combined, the actual defects of the Longquan converter station in 2019 are the measuring equipment and the measuring interface equipment, the number of defects is twice the number of defects of other stations, and the effectiveness of the invention is verified.
The invention obtains the mixed Weibull model of the direct current protection system based on the entropy weight method, further obtains the optimal maintenance time of the system and the optimal maintenance time of various devices, evaluates the direct current protection system of each converter station in combination with the actual situation of each converter station, provides an important guiding function for the operation and maintenance of the direct current protection system, and can obtain the mixed Weibull model curve of the converter station for analyzing the reliability, defect occurrence rate and the like of the direct current protection system of the converter station under the condition that the defect sample of each station is more complete.
Claims (5)
1. The method for evaluating the reliability of the mixed Weibull of the direct current protection system based on the entropy weight method is characterized by comprising the following steps of: the method comprises the following steps:
step one: classifying the direct current protection system according to different positions of the defects by combining the characteristics of the direct current protection system, collecting historical data of the defects of the direct current protection system, and obtaining index data of the defects of the direct current protection system according to the historical data;
step two: checking defect interval time accumulation of the high-voltage direct-current transmission protection system, and executing the third step after determining that the defect interval time accumulation accords with Weibull distribution;
step three: determining the weight coefficient of index data of each direct current protection system defect by using an entropy weight method, and further obtaining the weight coefficient of various devices of the direct current transmission protection system in the defect data;
step four: and establishing a single Weibull model of each device based on the defect interval time accumulated data by using a maximum likelihood method, combining weight coefficients of each device in the defect data to obtain a mixed Weibull model of the defect data of the high-voltage direct-current transmission protection system, and analyzing the reliability of the high-voltage direct-current protection system by using the mixed Weibull model.
2. The method for evaluating the reliability of the mixed weibull of the direct current protection system based on the entropy weight method according to claim 1 is characterized in that: the specific implementation process of the first step is as follows:
(a) The defects of the direct current protection system are divided into the following five types according to the difference of the positions of the defects by combining the characteristics of the direct current protection system: measuring equipment defects, interface device defects, direct current protection device defects, three-taking-two device defects, tripping outlet defects and secondary circuit defects, and independently calculating a host type, a device type and an independent type protection device as one type when the defect rate of the host type, the device type and the independent type protection device is high;
(b) Collecting historical data of defects of a DC protection system of a plurality of convertor stations in recent years;
(c) Three index data of the defects of the direct current protection system are obtained according to the historical data: component failure time rate, component average repair time rate, and component defect rate;
3. the method for evaluating the reliability of the mixed weibull of the direct current protection system based on the entropy weight method according to claim 1 is characterized in that: the specific implementation process of the second step is as follows:
(a) The defect interval time accumulation of the high-voltage direct-current transmission protection system is arranged in ascending order;
(b) Checking the defect interval time accumulation of the high-voltage direct-current transmission protection system, and judging and determining whether the defect interval time accumulation accords with Weibull distribution by KS (K-means) test:
the weibull probability distribution for both parameters is:
/>
wherein t is time, beta is a shape parameter, and eta is a scale parameter;
assuming that the unitary linear regression equation is y=bx+a, the linear transformation is performed on the two-parameter weibull function to obtain:
x i ＝lnt i (9)
in the formulas (7) - (9), i is the accumulated sequence number of the defect interval time, n is the accumulated sample number of the defect interval time of the high-voltage direct-current transmission protection system, and t i Accumulating the defect interval time of the high-voltage direct-current transmission protection system;
by calculation, regression systems A and B in a unitary linear regression equation of y=BX+A are obtained, and then the shape parameter beta and the scale parameter eta are obtained:
β＝B (11)
judging the correctness of the defect distribution by KS test, and obtaining the maximum value of the absolute value of the difference value in all data by making the difference value of the values of the formula (6) and the formula (8), wherein the maximum value is the observed value D max It is compared with the empirical critical value D n Comparing, if the accumulated time is smaller than the experience critical value, determining that the accumulated time of the defects obeys the Weibull distribution, if the accumulated time of the defects does not accord with the Weibull distribution, re-judging the accumulated time data of the defects, possibly causing missing or misplugging of some defects, and judging after the data is improved again.
4. The method for evaluating the reliability of the mixed weibull of the direct current protection system based on the entropy weight method according to claim 1 or 2, which is characterized in that: the specific implementation process of the third step is as follows:
(a) Normalizing the defect index obtained in the step one, and obtaining a relation matrix (x ij ) m×n Is converted into a standard matrix, and the index data are all reverse indexes, so that polarization treatment is carried out according to a reverse index formula (13) to obtain a standard matrix (a) ij ) m×n M is the number of defect categories, n is the number of evaluation indexes;
in the formula (13), x max Representing the maximum value of each column, x min Representing the minimum value for each column;
(b) According to the standard matrix, the characteristic weight P of the j index of the i-th device is obtained ij ：
When P ij When=0, the calculated entropy is meaningless and thus is not valid for P ij Is modified and defined as
(c) Calculating the entropy of the jth index as
(d) Entropy weight of the j-th index is
(e) The weight coefficients of various elements of the direct current transmission protection system in the defect data are as follows:
(f) And according to the weight coefficients of various elements, further solving the weight coefficients of various devices of the direct current transmission protection system in the defect data.
5. The method for evaluating the reliability of the mixed weibull of the direct current protection system based on the entropy weight method according to claim 1 is characterized in that: the specific implementation process of the fourth step is as follows:
(a) The defect interval time accumulation of five types of equipment of the high-voltage direct-current transmission protection system is arranged in ascending order, and the shape parameters beta and eta of various types of equipment are estimated by using a maximum likelihood estimation method respectively:
for a two-parameter weibull distribution, the construction likelihood function is:
partial derivatives are obtained for each parameter in the above formula (19), and a system of equations is obtained:
equation (20) is an overrun equation set, the parameters cannot be directly obtained, and Newton-Raphson iteration method is used on MATLAB to solve the parameters beta and eta of various devices, so as to obtain the Weibull reliability function R of various devices i (x)：
In the formula (21), beta i For the shape parameter of the i-th element, eta i Is the scale parameter of the i-th element;
(b) Establishing a mixed Weibull model R (x) of defect data of the high-voltage direct-current transmission protection system to obtain a reliability curve of the high-voltage direct-current transmission protection system;
R(x)＝P 1 R 1 (x)+P 2 R 2 (x)+…+P n R n (x) (22)
P＝P 1 +P 2 +…+P n ＝1 (23)
wherein n is the number of device classes;
(c) Analysis of reliability of HVDC protection system
The independent variable of the mixed Weibull model is the accumulation of defect interval time, the time is considered as the normal operation time of the equipment from the occurrence of the first defect, the larger the normal operation time is, the lower the reliability of the system is, the mixed Weibull model curve is considered as the normal operation time of the equipment from the occurrence of the first defect, the higher the reliability is considered as the better the expected value of the reliability is the standard value R under the condition that the operation time is as long as possible i Standard value R for the first time 1 =0.95, the corresponding run time T is derived from the hybrid weibull model curve 1 Standard value R of last time i ＝R i-1 X0.95, T is obtained from the curve i Thereby obtaining T over a period of time i Calculating an average value T of the values, wherein the average value T is considered as the optimal maintenance time of the direct current protection system; if the reliability of a certain type of equipment is to be analyzed independently, the reliability function of each type of equipment is utilized to obtain the optimal maintenance time of each type of equipment by adopting the same method as the reliability analysis of the mixed Weibull model;
comparing the average value T with the defect time interval of each station high-voltage direct-current transmission protection system, and when the defect time interval of the system is larger than the average value T, judging that the reliability of the system is lower, at the moment, adjusting the equipment maintenance time according to the average value T or adopting measures for replacing part of equipment to ensure that the reliability of the system is at a higher level; combining the historical data of each station with the average value T of the mixed Weibull model, wherein the historical defect interval time average value is larger than the average value T of the mixed Weibull model, and the larger the historical defect interval time average value is, the reliability of the direct current protection system of the station is high, the time interval of the next defect of the system is long, and the number of defects in the same time period is small; the historical defect interval time average value is smaller than the mixed Weibull model average value T, and the smaller the historical defect interval time average value is, the reliability of the direct current protection system of the station is low, the time interval of the next defect occurrence of the system is short, and the number of defects occurring in the same time period is large.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910794352.1A CN110580387B (en) | 2019-08-27 | 2019-08-27 | DC protection system mixed Weibull reliability evaluation method based on entropy weight method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910794352.1A CN110580387B (en) | 2019-08-27 | 2019-08-27 | DC protection system mixed Weibull reliability evaluation method based on entropy weight method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110580387A CN110580387A (en) | 2019-12-17 |
| CN110580387B true CN110580387B (en) | 2023-05-26 |
Family
ID=68812218
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910794352.1A Active CN110580387B (en) | 2019-08-27 | 2019-08-27 | DC protection system mixed Weibull reliability evaluation method based on entropy weight method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110580387B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112183995A (en) * | 2020-09-23 | 2021-01-05 | 国网湖北省电力有限公司电力科学研究院 | Direct-current protection system reliability evaluation method based on inter-zone analytic method and inter-zone entropy weight mixed weighting |
| CN113435652B (en) * | 2021-07-01 | 2023-01-24 | 贵州电网有限责任公司 | Primary equipment defect diagnosis and prediction method |
| CN113377759B (en) * | 2021-07-01 | 2023-05-16 | 贵州电网有限责任公司 | Defect filling data management method based on expert system algorithm |
| CN113792939B (en) * | 2021-09-30 | 2024-02-27 | 国网湖南省电力有限公司 | Electric energy meter reliability prediction method and device based on mixed Weibull distribution |
| CN116628619B (en) * | 2023-07-26 | 2023-10-20 | 西南交通大学 | Unknown abnormal electrical phenomenon identification method based on vehicle network coupling |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3631472B2 (en) * | 2001-06-07 | 2005-03-23 | 松下電器産業株式会社 | Semiconductor device evaluation method, semiconductor device manufacturing method, and semiconductor device evaluation apparatus and evaluation program |
| JP2012173096A (en) * | 2011-02-21 | 2012-09-10 | Tokyo Electric Power Co Inc:The | Maintenance system, maintenance program and maintenance method for power apparatus |
| CN103971024A (en) * | 2014-05-26 | 2014-08-06 | 华北电力大学（保定） | Method for evaluating reliability of relaying protection systems under small sample failure data |
| CN108596514A (en) * | 2018-05-11 | 2018-09-28 | 贵州电网有限责任公司 | Power equipment mixing Weibull Reliability Modeling based on fuzzy genetic algorithm |
-
2019
- 2019-08-27 CN CN201910794352.1A patent/CN110580387B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110580387A (en) | 2019-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110580387B (en) | DC protection system mixed Weibull reliability evaluation method based on entropy weight method | |
| CN109598435B (en) | Power distribution network cable running state evaluation method and system | |
| CN106505557B (en) | Remote measurement error identification method and device | |
| CN106529791A (en) | Evaluation method for evaluating branch importance of power system | |
| CN108649892B (en) | Defect diagnosis method and device for photovoltaic power station | |
| CN107153150A (en) | A kind of power distribution network over-voltage fault type recognition method and device | |
| CN104616090A (en) | Risk evaluation based cable overhaul strategy method | |
| CN104794206A (en) | Transformer substation data quality evaluation system and method | |
| CN104537271B (en) | A kind of power distribution network bad data recognition method based on quality tab | |
| CN111797365B (en) | Converter transformer temperature abnormity judgment method and system | |
| CN109283293B (en) | Power transformer fault diagnosis method based on coefficient of variation and TOPSIS method | |
| CN113484819B (en) | Method for diagnosing metering faults of electric energy meter in limited range based on high-frequency current sampling | |
| CN112362987A (en) | Lightning arrester fault diagnosis method based on robust estimation | |
| CN112418687B (en) | User electricity utilization abnormity identification method and device based on electricity utilization characteristics and storage medium | |
| CN106295240A (en) | A kind of Condition Assessment for Power Transformer method based on fuzzy mathematics | |
| CN111458601A (en) | Fault detection method and device | |
| CN114966295A (en) | Super capacitor operation power frequency modulation detection system based on big data | |
| CN114034997A (en) | Insulator degradation degree prediction method and system based on multiple parameters | |
| CN116502160A (en) | Automatic electric quantity data acquisition system | |
| CN115640950A (en) | Method for diagnosing abnormal line loss of distribution network line in active area based on factor analysis | |
| CN105488572A (en) | Health state evaluation method of power distribution equipment | |
| CN117391357B (en) | Scheduling self-checking system for power grid scheduling operation management based on big data | |
| CN112508254B (en) | Method for determining investment prediction data of transformer substation engineering project | |
| CN103020346B (en) | Test method for physical design similarity of circuit | |
| CN117494950A (en) | Optical storage, filling and inspection micro-grid integrated station operation safety evaluation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |