CN112464488A - Reliability evaluation method, device, equipment and medium for subway traction power supply system - Google Patents
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Abstract
The invention discloses a reliability evaluation method, a device, equipment and a medium for a subway traction power supply system, wherein the method comprises the following steps: establishing a fault tree model of a subway traction power supply system; calculating the distribution parameters of the faults of the subway traction power supply equipment; based on the fault tree model and the distribution parameters, simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method, and calculating to obtain fault interval time, repair times and fault times; and calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times. According to the method, the fault tree model is established, the sequential Monte Carlo method is adopted to simulate the running states of the equipment complying with different fault distribution types, the applicability is high, the reliability evaluation time index of the mean repair time of the system fault and the mean interval time of the system fault can be calculated, and the obtained reliability information is richer and more applicable.
Description
Technical Field
The invention belongs to the technical field of subway traction power supply system evaluation, and particularly relates to a reliability evaluation method, device, equipment and medium for a subway traction power supply system.
Background
The subway traction power supply system is used as a heart and a blood vessel of the subway and is mainly responsible for providing continuous power for the train, and the safe and reliable operation of the traction power supply system is the guarantee of the safe and stable operation of the subway. If the subway traction power supply system cannot reliably operate, immeasurable economic loss and severe social influence can be caused, and even the life safety of crew and passengers can be endangered in severe cases.
In the prior art, the reliability of a traction power supply system is analyzed, the reliability law of the traction power supply system is qualitatively and quantitatively analyzed and evaluated by using mathematical methods such as probability theory and mathematical statistics, and the reliability evaluation methods of an urban rail transit traction power supply system, an electrified railway traction power supply system and a high-speed railway traction power supply system are respectively provided for three Chinese patents with application numbers of CN201810693308.7, CN201910438407.5 and CN 201610329344.6. However, because the fault rate of each traction power supply device in the system has a direct influence on the reliability evaluation result, the current reliability evaluation method does not consider the influence of the continuous variation of the fault rate of the device in the whole life operation cycle of the device, so that the modeling is not accurate.
The traditional reliability assessment method mostly adopts an analytic method (such as a fault enumeration method, a minimal cut set method and the like) to solve the reliability index of the system, has clear physical concept and high calculation precision, but increases exponentially with the increase of the scale of the system and the increase of the number of devices in the system, increases the number of states of the system, increases the calculated amount sharply, has single calculated reliability index, and is difficult to be applied to the assessment of the reliability of a large-scale complex system. The subway construction operation period is short, the number of statistical samples of traction power supply equipment faults or outage is small, a large amount of statistical data are needed for quantitative evaluation of a traction power supply system, and equipment fault data are difficult to obtain. How to fully mine historical fault data information of traction power supply equipment and accurately model the reliability of a subway traction power supply system under the condition of small samples is always a hot point problem and bottleneck in the reliability evaluation process of the traction power supply system.
Disclosure of Invention
In order to overcome the technical defects, the invention provides a method, a device, equipment and a medium for evaluating the reliability of a subway traction power supply system, which can realize the quantitative evaluation of the reliability of the subway traction power supply system by acquiring rich reliability indexes.
In order to solve the problems, the invention is realized according to the following technical scheme:
a reliability evaluation method for a subway traction power supply system comprises the following steps:
establishing a fault tree model of a subway traction power supply system;
calculating the distribution parameters of the faults of the subway traction power supply equipment;
based on the fault tree model and the distribution parameters, simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method, and calculating to obtain fault interval time, repair times and fault times;
and calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times.
As a further improvement of the present invention, the step of establishing the fault tree model of the metro traction power supply system includes the steps of:
determining a top event of a fault tree of a subway traction power supply system, acquiring all direct reasons causing the top event of the fault tree as intermediate events of the fault tree according to a topological structure and an operation mode of the subway traction power supply system based on a subway traction power supply system framework, and searching the direct reasons causing the intermediate events of the fault tree to have faults step by step from top to bottom until a bottom event of the fault tree is deduced;
and determining a logic gate of the fault tree model according to the logic relation between the bottom event of the fault tree and the middle event of the fault tree, and establishing the fault tree model according to the sequence of the top event of the fault tree, the logic gate, the middle event of the fault tree, the logic gate and the bottom event of the fault tree.
As a further improvement of the present invention, the step of calculating the distribution parameter of the fault of the subway traction power supply equipment includes the steps of:
selecting a plurality of historical fault times of a certain subway traction power supply device, and sorting according to the time;
describing equipment fault distribution by using a Weir distribution function according to the total number of the equipment of the subway traction power supply system;
calculating to obtain a scale parameter and a shape parameter of a Weibull distribution function according to historical fault time;
and acquiring the repair rate of each traction power supply device in the actual engineering, and describing the repair distribution of the devices by using an exponential distribution function.
As a further improvement of the present invention, the step of simulating a traction power supply device and a system state transition process by using a sequential monte carlo method based on the fault tree model and the distribution parameters, and calculating to obtain fault interval time, repair times and fault times includes the steps of:
setting the initial states of the traction power supply equipment and the subway traction power supply system to be normal based on the fault tree model and the distribution parameters, and setting the sampling times of Monte Carlo simulation of the system;
random sampling is carried out to generate random number samples of which the sampling times are all obeyed the distribution parameters, and the random number samples are arranged according to the size sequence to obtain a fault time period sample;
random sampling is carried out to generate random number samples with sampling times which are all subjected to exponential distribution, and the random number samples are arranged according to the size sequence to obtain a sample of the repair time period;
calculating to obtain failure time and repair time of the traction power supply equipment according to the fault interval time and the repair time, and arranging the failure time and the repair time according to the size sequence to obtain the working state of the traction power supply equipment in each time period;
and judging the states of a top event, a middle event and a bottom event in a Monte Carlo simulation sampling period according to the fault tree logic, and calculating the fault interval time, the repair times and the fault times according to the states of the top event, the middle event and the bottom event.
As a further improvement of the present invention, the step of calculating the reliability index of the metro traction power supply system according to the fault interval time, the repair times and the fault times includes the steps of:
calculating to obtain the average repair time of the system fault according to the repair time and the repair times;
calculating to obtain the average interval time of the system faults according to the fault interval time and the fault times;
calculating to obtain the steady-state availability of the system according to the average system fault repairing time and the average system fault interval time;
and calculating to obtain the system steady-state unavailability according to the system steady-state availability.
As a further improvement of the invention, the scale parameter and the shape parameter of the boolean distribution function are calculated according to historical failure time, specifically:
and calculating to obtain scale parameters and shape parameters of the Weibull distribution function by adopting a least square method according to historical fault time.
Compared with the prior art, the invention has the following beneficial effects:
1. the method considers different service performances of various devices of the subway traction power supply system, adopts a sequential Monte Carlo method to simulate the running states of the devices complying with different fault distribution types, can also consider the actual engineering conditions of maintenance arrangement, device maintenance and the like, and has flexible application and stronger adaptability;
2. according to the method, the duration and the state transition frequency of each state of subway traction power supply are simulated, the reliability evaluation probability indexes of the availability and the unavailability are obtained, meanwhile, the reliability evaluation time indexes of the system fault mean repair time and the system fault mean interval time can be calculated, and the obtained reliability information is richer and more applicable;
3. the method can be used for reliability evaluation of the subway traction power supply systems of different scales by establishing the fault tree model of the subway traction power supply system, has good compatibility, has no relation between the sampling times and the scale of the system in the evaluation process, and has more superiority in the reliability evaluation of the complex subway traction power supply system.
Drawings
Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
fig. 1 is a flowchart of a reliability evaluation method for a subway traction power supply system according to embodiment 1.
Fig. 2 is a structural diagram of the reliability evaluation device of the subway traction power supply system according to embodiment 2.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1
The embodiment provides a reliability evaluation method for a subway traction power supply system, as shown in fig. 1, comprising the steps of:
s1, establishing a fault tree model of the subway traction power supply system;
s2, calculating distribution parameters of faults of the subway traction power supply equipment;
s3, simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method based on the fault tree model and the distribution parameters, and calculating to obtain fault interval time, repair times and fault times;
and S4, calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times.
In the above embodiment, step S1 specifically includes the following steps:
s11, determining a top event of a fault tree of a subway traction power supply system, acquiring all direct reasons causing the top event of the fault tree as intermediate events of the fault tree based on a subway traction power supply system framework according to a topological structure and an operation mode of the subway traction power supply system, and gradually searching the direct reasons causing the intermediate events of the fault tree to have faults from top to bottom until a bottom event of the fault tree is deduced; in this embodiment, a "power supply equipment failure in the subway traction power supply system" is used as a fault tree top event, a "power supply equipment failure in the subway traction power supply system" is used as a fault tree bottom event, all direct reasons causing the "normal power supply of the subway traction power supply system to the overhead line system" are obtained as intermediate events of the fault tree based on a subway traction power supply system architecture according to a topological structure and an operation mode of the subway traction power supply system, and direct reasons causing the fault of the intermediate events of the fault tree are searched step by step from top to bottom until the bottom event of the fault tree is deduced.
And S12, determining a logic gate of the fault tree model according to the logic relation between the bottom event of the fault tree and the middle event of the fault tree, and establishing the fault tree model according to the sequence of the top event of the fault tree, the logic gate, the middle event of the fault tree, the logic gate and the bottom event of the fault tree.
Further, step S2 includes the following steps:
s21, selecting n historical fault times of a certain subway traction power supply device, and sorting according to the time: x is the number of1，x2，…，xb，…，xnWherein b is 1, 2, …, n;
s22, describing ith equipment fault distribution by using a Weir distribution function according to the total number N of the subway traction power supply system equipment;
s23, counting the historical fault time of the ith equipment according to the Weir distribution function, and calculating by adopting a least square method to obtain a scale parameter eta of the Weir distribution functioniAnd a shape parameter betai；
S23, obtaining the repair rate of each traction power supply device in the actual engineering, and describing the device repair distribution by using an exponential distribution function, wherein the repair rate of the ith traction power supply device is mui，i＝1，2，3，…，N。
In the above embodiment, step S3 includes the steps of:
s31, based on fault tree model and scale parameter etaiAnd a shape parameter betaiSetting the initial state of the traction power supply equipment and the subway traction power supply system to be normal, and setting the systemThe number of samples FT for the Monte Carlo simulation;
s32, randomly sampling to generate FT random number samples obeying distribution parameters, arranging the random number samples according to the size sequence to obtain FT fault time periods 0-ST of the ith traction power supply equipmenti1，0～STi2，…，0～STim，…，0～STiFTWherein i ═ 1, 2, 3, …, N;
s33, randomly sampling to generate sampling times with a plurality of compliance parameters of muiExponentially distributed random number samples are arranged according to the size sequence to obtain FT repair time periods 0-XT of the ith traction power supply devicei1，0～XTi2，…，0～XTim，…，0～XTiFTWherein i ═ 1, 2, 3, …, N; wherein m is the serial number of the random number, and m is 1, 2, 3, …, FT;
s34, calculating to obtain failure time and repair time of the traction power supply equipment according to the fault interval time and the repair time, arranging the failure time and the repair time according to the size sequence to obtain the working state of the traction power supply equipment in each time period, and firstly defining the failure time sequence of the ith traction power supply equipment as sti1，sti2，…，stim，…，stiFTDefining the repair time sequence of the ith traction power supply device as xti1，xti2，…，xtim，…，xtiFT(ii) a When m is 1, let sti1＝STi1，xti1＝STi1+XTi1When m is greater than or equal to 2, let stim＝xti(m-1)+STim，xtim＝stim+XTimArranging the failure time and the repair time of the ith traction power supply equipment according to the sequence of magnitude to obtain a time sequence sti1，xti1，sti2，xti2，…，stim，xtim，…，stiFT，xtiFT(ii) a Next, a variable w is definedimTo describe the state of the i-th traction power supply unit, m is 1, 2, 3, …, FT, according to the traction power supply unit failure time sequence stimAnd the sequence of repair times xtimJudging whether the ith traction power supply equipment is in a fault state under the time sequence, and when the ith traction power supply equipment is in a fault state, controlling wimWhen the ith traction power supply device does not have a fault, let wimThe state transition sequence of the ith traction power supply device is output as wi1，wi2，…，wiFTSo as to obtain the failure time sequence st of each equipmentimAnd the sequence of repair times xtimAnd (5) corresponding to the state transition sequence of the traction power supply equipment at the moment.
S35, judging the states of the top event, the middle event and the bottom event in the Monte Carlo simulation sampling period according to the fault tree logic, and calculating the fault interval time, the repair times and the fault times according to the states of the top event, the middle event and the bottom event, wherein the method specifically comprises the following steps:
s351, when the logic gate of the fault tree is an OR gate of P input events, the system state sequence A is enabled at any timeam＝w1m+w2m+…+wpmJudgment AamIf not less than 1, let AamIf not, let AamWhen the value is equal to 0, a is the serial number of an OR gate in all logic gates of the system, and a is more than or equal to 0;
when the logic gate of the fault tree is an AND gate of Q input events, the system state sequence B is ordered at any timebm＝w1m·w2m·…·wQmB is the serial number of an AND gate in all logic gates of the system, and b is more than or equal to 0;
when the fault tree logic gate selects M voting gates of input events for K, the system state sequence C is ordered at any timecm＝w1m+w2m+…+wKmJudgment of CcmIf not less than M, let CcmIf not, then CcmC is the serial number of a voting gate in all logic gates of the system, and c is more than or equal to 0;
defining a variable vjmDescribing the state of the j-th event, state v of event jjmThe value of (c) is determined by the state of the system fault tree logic gate and the input event, when vjmWhen v occurs, event j occursjmWhen 0, event j does not occur;
s352, setting the initial value of m to be 1, and defining cjNumber of failures as event j, ejThe number of repairs for event j;
s353, judging vjm+vj(m+1)If it is equal to 1, if yes, go to step C7, if no, let m be m +1 and go to step S354;
s353, judging whether m is less than or equal to FT, if yes, returning to the step C5, and if not, entering the step S355;
s354, judgment vjmIf it is equal to 1, if so, the fault time st is setimFailure time st for event jjmAnd order ej＝ej+1, m ═ m + 1; if not, the repair time xt is orderedimRepairing time xt for event jjmAnd order cj＝cj+1, m ═ m + 1. Judging whether m is less than or equal to FT, if so, returning to the step C5, and if not, outputting a state transition sequence v of the fault tree event jj1，vj2，vj3，…，vjFTAnd proceeds to step S355;
s355, judging whether the event j is a top event, if so, recording a state transition sequence v of the event jj1，vj2，vj3，…，vjFTEvent j failure time stjmEvent j repair time xtjmNumber of failures cjRepair frequency ejCalculating the time interval ST between each failure of the event jjmAnd event j repair time XTjmLet XTjm＝xtjm-stjmWhen m is equal to 1, let STjm＝stjmWhen m is greater than or equal to 2, let STjm＝stjm-xtj(m-1)And entering the step D, if not, returning to the step S3, and repeating the steps S351-S353;
in the above embodiment, step S4 includes the steps of:
s41, calculating the Mean Time To Repair (MTTR) of the system fault,
s42, calculating the mean time between failure MTBF of the system,
s43, calculating the system steady-state availability A,
s44, calculating the system steady state unavailability U,
example 2
The embodiment provides a reliability evaluation device for a subway traction power supply system, as shown in fig. 2, including: the system comprises a fault tree model construction module 1, a distribution parameter calculation module 2, a sequential Monte Carlo method simulation module 3 and a reliability index calculation module 4, wherein the fault tree model construction module 1 is used for establishing a fault tree model of a subway traction power supply system; the distribution parameter calculation module 2 is used for calculating the distribution parameters of the faults of the subway traction power supply equipment; the sequential Monte Carlo method simulation module 3 is used for simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method based on a fault tree model and distribution parameters, and calculating to obtain fault interval time, repair times and fault times; and the reliability index calculation module 4 is used for calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times.
Please refer to embodiment 1 for a specific implementation process of this embodiment, which is not described herein again.
Example 3
The present embodiment provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for evaluating reliability of a subway traction power supply system according to embodiment 1.
Example 4
The present embodiment provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the reliability evaluation method of the subway traction power supply system according to embodiment 1
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (9)
1. A reliability evaluation method for a subway traction power supply system is characterized by comprising the following steps:
establishing a fault tree model of a subway traction power supply system;
calculating the distribution parameters of the faults of the subway traction power supply equipment;
based on the fault tree model and the distribution parameters, simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method, and calculating to obtain fault interval time, repair times and fault times;
and calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times.
2. The subway traction power supply system reliability evaluation method according to claim 1, wherein said step of establishing a fault tree model of the subway traction power supply system comprises the steps of:
determining a top event of a fault tree of a subway traction power supply system, acquiring all direct reasons causing the top event of the fault tree as intermediate events of the fault tree according to a topological structure and an operation mode of the subway traction power supply system based on a subway traction power supply system framework, and searching the direct reasons causing the intermediate events of the fault tree to have faults step by step from top to bottom until a bottom event of the fault tree is deduced;
and determining a logic gate of the fault tree model according to the logic relation between the bottom event of the fault tree and the middle event of the fault tree, and establishing the fault tree model according to the sequence of the top event of the fault tree, the logic gate, the middle event of the fault tree, the logic gate and the bottom event of the fault tree.
3. The subway traction power supply system reliability evaluation method according to claim 1, wherein said step of calculating distribution parameters of subway traction power supply equipment faults comprises the steps of:
selecting a plurality of historical fault times of a certain subway traction power supply device, and sorting according to the time;
describing equipment fault distribution by using a Weir distribution function according to the total number of the equipment of the subway traction power supply system;
calculating to obtain a scale parameter and a shape parameter of a Weibull distribution function according to historical fault time;
and acquiring the repair rate of each traction power supply device in the actual engineering, and describing the repair distribution of the devices by using an exponential distribution function.
4. A method according to claim 3, wherein the step of calculating the fault interval time, repair times and fault times by simulating the traction power supply equipment and the system state transition process using a sequential monte carlo method based on the fault tree model and the distribution parameters comprises the steps of:
setting the initial states of the traction power supply equipment and the subway traction power supply system to be normal based on the fault tree model and the distribution parameters, and setting the sampling times of Monte Carlo simulation of the system;
random sampling is carried out to generate random number samples of which the sampling times are all obeyed the distribution parameters, and the random number samples are arranged according to the size sequence to obtain a fault time period sample;
random sampling is carried out to generate random number samples with sampling times which are all subjected to exponential distribution, and the random number samples are arranged according to the size sequence to obtain a sample of the repair time period;
calculating to obtain failure time and repair time of the traction power supply equipment according to the fault interval time and the repair time, and arranging the failure time and the repair time according to the size sequence to obtain the working state of the traction power supply equipment in each time period;
and judging the states of a top event, a middle event and a bottom event in a Monte Carlo simulation sampling period according to the fault tree logic, and calculating the fault interval time, the repair times and the fault times according to the states of the top event, the middle event and the bottom event.
5. A method according to claim 1, wherein the step of calculating the reliability index of the metro tractive power supply system according to the fault interval time, the repair times and the fault times comprises the steps of:
calculating to obtain the average repair time of the system fault according to the repair time and the repair times;
calculating to obtain the average interval time of the system faults according to the fault interval time and the fault times;
calculating to obtain the steady-state availability of the system according to the average system fault repairing time and the average system fault interval time;
and calculating to obtain the system steady-state unavailability according to the system steady-state availability.
6. A method for evaluating reliability of a subway traction power supply system according to claim 3, wherein the scale parameter and the shape parameter of the weil distribution function are calculated according to historical failure time, specifically:
and calculating to obtain scale parameters and shape parameters of the Weibull distribution function by adopting a least square method according to historical fault time.
7. A reliability evaluation device for a subway traction power supply system is characterized by comprising:
the fault tree model construction module is used for establishing a fault tree model of the subway traction power supply system;
the distribution parameter calculation module is used for calculating the distribution parameters of the faults of the subway traction power supply equipment;
the sequential Monte Carlo method simulation module is used for simulating a traction power supply device and a system state transfer process by adopting a sequential Monte Carlo method based on the fault tree model and the distribution parameters, and calculating to obtain fault interval time, repair times and fault times;
and the reliability index calculation module is used for calculating the reliability index of the subway traction power supply system according to the fault interval time, the repair times and the fault times.
8. A computer device, characterized in that the computer device comprises a processor and a memory, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the memory, and the at least one instruction, at least one program, a set of codes, or a set of instructions is loaded and executed by the processor to implement the method for evaluating reliability of a subway traction power supply system according to any one of claims 1 to 6.
9. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the computer-readable storage medium, and the at least one instruction, at least one program, a set of codes, or a set of instructions is loaded and executed by a processor to implement the method for evaluating reliability of a tractive power supply system of a subway according to any one of claims 1 to 6.
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