CN109165873B - Reliability evaluation method for UMDs system - Google Patents

Reliability evaluation method for UMDs system Download PDF

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CN109165873B
CN109165873B CN201811100211.7A CN201811100211A CN109165873B CN 109165873 B CN109165873 B CN 109165873B CN 201811100211 A CN201811100211 A CN 201811100211A CN 109165873 B CN109165873 B CN 109165873B
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张勇
杨东升
王昕�
马占超
吕开钧
麻壮
周博文
林森
袁皓
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Design Branch Of China Petroleum Pipeline Engineering Corp
Shenyang Blower Works Group Corp
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Abstract

The invention provides a method for evaluating reliability of UMDs (unified Power systems), and relates to the technical field of UMDs uninterruptible power supplies. A method for evaluating reliability of a UMDs system comprises the steps of firstly establishing a reliability index of the UMDs system, and then establishing a power loss model of the UMDs system; and finally, carrying out reliability evaluation on the UMDs system by using an improved Monte Carlo method. The UMDs system reliability evaluation method provided by the invention can be used for quickly and accurately evaluating the continuous power supply capacity of the UMDs system, finding out weak links influencing the reliability level of the system so as to seek measures for improving the reliability level, and further improving the continuous power supply capacity of the UMDs system. The improved Monte Carlo method can remarkably reduce the sampling variance on the premise of ensuring the calculation precision, thereby improving the calculation efficiency, accelerating the convergence speed and reducing the hardware requirement.

Description

Reliability evaluation method for UMDs system
Technical Field
The invention relates to the technical field of UMDs uninterrupted power supplies, in particular to a method for evaluating the reliability of UMDs systems.
Background
The electricity generation and utilization conditions of the urban power network in China are complex, equipment is lagged behind, and management is not good, so that the quality of alternating current used by people is poor, the voltage fluctuation range is large, the alternating current is changed from 170-250V (320-450V) in a large range, electromagnetic and harmonic interference is serious, and meanwhile, due to the shortage of electric power, the power failure phenomenon occurs in many areas. The requirement of a load of high supply power quality. Sensitive loads such as precision machining, electronic manufacturing enterprises and data centers have extremely high requirements on the quality of electric energy, and for example, once a voltage sag or short-time voltage interruption occurs on a power supply bus of an integrated circuit manufacturing enterprise, a large quantity of chips of the enterprise are scrapped or precision process equipment is shut down, and even the whole line is shut down.
The UMD industrial drive uninterrupted power system is a power supply device for providing uninterrupted power supply guarantee for electric motor loads such as an oil pump, a water pump, a fan and the like. A large-scale compressor needs a plurality of water cooling and lubricating systems, once an unexpected power failure occurs, the water cooling and lubricating systems stop working, a bearing bush of the compressor can be broken, the loss is up to millions of yuan, and in addition, when the voltage of a power grid is greatly sunken or noise waves and lightning interference occur, the frequency converter of the oil delivery pump can be shut down or out of control, and great economic loss is caused. The UMDs power supply is adopted to successfully solve the problems, and the UMDs power supply has reliable operation and simple structure and is an economical and practical power supply device.
The UMDs system is a foundation stone for reliable operation of loads, so quantitative evaluation of the reliability of the UMDs system is very necessary, while current research on the reliability mainly aims at a power grid, and no effective method for performing the quantitative evaluation of the reliability of emergency power supplies such as the UMDs is available at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a reliability evaluation method for a UMDs system, which is used for quickly and accurately evaluating the continuous power supply capacity of the UMDs system, finding out weak links influencing the reliability level of the system so as to seek measures for improving the reliability level, and further improving the continuous power supply capacity of the system.
A UMDs system reliability assessment method includes the following steps:
step 1: establishing a reliability index of the UMDs system;
according to the characteristics of the UMDs system, referring to the reliability indexes of electronic equipment and a power distribution network, selecting the average system power supply availability SA as the reliability index of the UMDs system to describe the availability of the UMDs system, wherein the power supply availability refers to the steady-state probability that the system is in a normal working state;
step 2: establishing a power loss model of the UMDs system;
according to the characteristics of the UMDs system, the UMDs system is divided into 12 modules: the system comprises a commercial power U, a generator G, an automatic transfer switch ATS, an alternating current bus AB, a charging circuit CC, a battery pack BP, an inverter circuit IC, a monitoring control circuit MC, a static transfer switch STS, a direct current bus protection control PM, a direct current bus DB and an air cooling module WM; establishing a fault tree according to the working principle of the UMDs system to obtain an operation state set phi of each module when the system loses power;
the generator, the commercial power, the automatic change-over switch, the alternating current bus, the charging circuit, the battery pack, the inverter circuit, the monitoring control circuit, the static change-over switch, the direct current bus protection control, the direct current bus and the air cooling module all have two states: a normal state and a fault state; the mean time between failures and the mean time between repairs of each module in the UMDs system are respectively
Figure GDA0003156053600000021
And
Figure GDA0003156053600000022
the availability ratio of power supply is AiWherein i ═ 1,2, …,12 correspond to the different modules in the UMDs system;
mean time between failures
Figure GDA0003156053600000023
Average time to repair for expected average time to run of a module
Figure GDA0003156053600000024
The mean value of the repair time when the module is converted from the fault state to the working state;
and step 3: the reliability evaluation of the UMDs system is carried out by utilizing an improved Monte Carlo method, and the specific method comprises the following steps:
step 3-1: inputting basic information of the UMDs system;
step 3-1-1: inputting a state set phi of each module when the UMDs system loses power;
step 3-1-2: mean time between failures of individual modules of the input UMDs system
Figure GDA0003156053600000025
Mean time to repair
Figure GDA0003156053600000026
Discharge time T of UMDs system battery packBSFAnd charging time T of the battery packBSR；
Step 3-1-3: setting the maximum number of iterations CmaxInitial value of optimal multiplier k, number of iterative samples per time nmaxAnd a variance coefficient;
step 3-2: initializing the iteration times c to be 0, and starting the iteration;
step 3-3: the iteration number c is c + 1;
step 3-4: correcting a distribution function of the UMDs system state according to the optimal multiplier k value;
the random state of the system is composed of random states of 12 modules, and the random variables of the 12 modules are considered to be independent from each other, and the state probability distribution function of the ith module is defined as:
Figure GDA0003156053600000027
wherein, XiWith 0 representing the ith moduleThe state being a fault state, X i1 means that the state of the ith module is a normal state, foriFor the forced failure rate of the ith module, the following equation is shown:
Figure GDA0003156053600000028
step 3-5: initializing the sampling times n to be 0, starting sampling, and starting the iterative sampling;
step 3-5-1: generating a set of random number vectors;
the interval [0,1] is equally divided into h parts, and satisfies:
Figure GDA0003156053600000031
then in the a-th sub-interval, a is 1,2, …, h, the state X of the i-th moduleiaDetermined according to the following formula:
Figure GDA0003156053600000032
wherein x isiIs a state random number;
step 3-5-2: the sampling times n are n + 1;
step 3-5-3: judging the state of the UMDs system in each subinterval divided in the step 3-5-1, and if the state of the UMDs system is in a UMDs system power-off state set phi, testing function values of the a-th subinterval of the UMDs system at the nth sampling time
Figure GDA0003156053600000033
And taking 0, otherwise, taking 1, and taking the average value of the test function values of the h subintervals as the test function value of the nth sampling, wherein the test function value is shown in the following formula:
Figure GDA0003156053600000034
wherein，
Figure GDA0003156053600000035
The test function value of the UMDs system at the nth sampling;
step 3-5-4: judging whether the sampling times n are more than nmaxIf yes, continuing to execute the step 3-6, otherwise, returning to the step 3-5-1 to execute next sampling;
step 3-6: and updating the k value according to the calculation result of the c iteration, wherein the calculation process is as follows:
Figure GDA0003156053600000036
wherein N is0And N1Respectively determining the number of modules in fault state and the number of modules in normal state in the iteration process;
Figure GDA0003156053600000037
weighted mean of forced failure rates for all modules;
step 3-7: accumulating the reliability indexes and calculating a variance coefficient;
the estimated value of the reliability index SA is
Figure GDA0003156053600000041
As shown in the following equation:
Figure GDA0003156053600000042
the variance factor β is then expressed as:
Figure GDA0003156053600000043
wherein the content of the first and second substances,
Figure GDA0003156053600000044
is a vector
Figure GDA0003156053600000045
N-th sample value, variance
Figure GDA0003156053600000046
Is an estimated value
Figure GDA0003156053600000047
An error of (2);
step 3-8: judging whether the iteration number C is larger than the maximum iteration number CmaxOr whether the variance coefficient meets the calculation precision, if so, outputting a reliability index, and otherwise, returning to the step 3-3 to continue iterative calculation.
According to the technical scheme, the invention has the beneficial effects that: according to the UMDs system reliability evaluation method provided by the invention, based on probability distribution of accidental faults of the UMDs system and consequence analysis thereof, the continuous power supply capacity of the UMDs system is quickly and accurately evaluated, weak links influencing the reliability level of the system are found out to seek measures for improving the reliability level, and further the continuous power supply capacity of the UMDs system is improved; the improved Monte Carlo method provided by the invention can obviously reduce the sampling variance on the premise of ensuring the calculation precision, thereby improving the calculation efficiency, accelerating the convergence speed and reducing the hardware requirement.
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FIG. 1 is a flowchart illustrating a method for evaluating reliability of UMDs systems according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a UMDs system provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fault tree of the UMDs system provided by the embodiment of the present invention;
fig. 4 is a flowchart of reliability evaluation based on the improved monte carlo method according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, a certain UMDs system is taken as an example, and the reliability evaluation method of the UMDs system of the present invention is used to perform reliability evaluation on the UMDs system.
A method for evaluating reliability of UMDs systems, as shown in fig. 1, comprising the steps of:
step 1: establishing a reliability index of the UMDs system;
the reliability index is the basis for quantitatively evaluating the reliability of the UMDs system, and according to the characteristics of the UMDs system, the reliability index of electronic equipment and a power distribution network is referred, and the average system power supply availability SA is selected as the reliability index of the UMDs system and used for describing the availability of the UMDs system, wherein the power supply availability refers to the steady-state probability that the system is in a normal working state;
step 2: establishing a power loss model of the UMDs system;
according to the characteristics of the UMDs system shown in fig. 2, the UMDs system is divided into 12 modules: the system comprises a commercial power U, a generator G, an automatic transfer switch ATS, an alternating current bus AB, a charging circuit CC, a battery pack BP, an inverter circuit IC, a monitoring control circuit MC, a static transfer switch STS, a direct current bus protection control PM, a direct current bus DB and an air cooling module WM; and establishing a fault tree according to the working principle of the UMDs system to obtain the running state set phi of each module when the system loses power.
The generator, the commercial power, the automatic change-over switch, the alternating current bus, the charging circuit, the battery pack, the inverter circuit, the monitoring control circuit, the static change-over switch, the direct current bus protection control, the direct current bus and the air cooling module have two states: a normal state and a fault state; the mean time between failures and the mean time between repairs of each module in the UMDs system are respectively
Figure GDA0003156053600000051
And
Figure GDA0003156053600000052
the availability ratio of power supply is AiWhere i ═ 1,2, …,12 correspond to the different modules in the UMDs system.
Mean time between failures
Figure GDA0003156053600000053
Average time to repair for expected average time to run of a module
Figure GDA0003156053600000054
The average value of the repair time when the module is changed from the fault state to the working state.
When the power grid normally supplies power or the standby generator can supply power when the power grid fails, the UMDs system does not participate in normal power supply, and the UMDs system is connected in parallel to a power supply loop and does not participate in the normal power supply process; when power failure or power failure occurs and the standby generator loses power supply capacity, the UMDs system can provide direct current output for the frequency converter without delay, continuous and stable operation of key loads is guaranteed, and meanwhile, alternating current can be output to be used by other loads.
In this embodiment, according to the working principle of the UMDs system, a UMDs system fault tree shown in fig. 3 is established, and an operation state set Φ of each module when the system loses power is obtained, as shown in table 1:
Figure GDA0003156053600000055
Figure GDA0003156053600000061
in table 1, 0 indicates that the module is in a failure state, 1 indicates that the module is in a normal state, and/indicates that the module is in an arbitrary state.
And step 3: the reliability evaluation of the UMDs system is performed by using an improved monte carlo method, as shown in fig. 4, the specific method is as follows:
step 3-1: inputting basic information of the UMDs system;
step 3-1-1: inputting a state set phi of each module when the UMDs system loses power;
step 3-1-2: mean time between failures of individual modules of the input UMDs system
Figure GDA0003156053600000062
Mean time to repair
Figure GDA0003156053600000063
Discharge time T of UMDs system battery packBSFAnd charging time T of the battery packBSR；
Step 3-1-3: setting the maximum number of iterations CmaxInitial value of optimal multiplier k, number of iterative samples per time nmaxAnd a variance coefficient;
step 3-2: initializing the iteration times c to be 0, and starting the iteration;
step 3-3: the iteration number c is c + 1;
step 3-4: correcting a distribution function of the UMDs system state according to the optimal multiplier k value;
the random state of the system is composed of random states of 12 modules, and the random variables of the 12 modules are considered to be independent from each other, and the state probability distribution function of the ith module is defined as:
Figure GDA0003156053600000071
wherein, X i0 indicates that the state of the ith module is a fault state, X i1 means that the state of the ith module is a normal state, foriFor the forced failure rate of the ith module, the following equation is shown:
Figure GDA0003156053600000072
step 3-5: initializing the sampling times n to be 0, starting sampling, and starting the iterative sampling;
step 3-5-1: generating a set of random number vectors;
the interval [0,1] is equally divided into h parts, and satisfies:
Figure GDA0003156053600000073
then in the a-th sub-interval, a is 1,2, …, h, the state X of the i-th moduleiaDetermined according to the following formula:
Figure GDA0003156053600000074
wherein x isiIs a state random number;
step 3-5-2: the sampling times n are n + 1;
step 3-5-3: judging the state of the UMDs system in each subinterval divided in the step 3-5-1, and if the state of the UMDs system is in a UMDs system power-off state set phi, testing function values of the a-th subinterval of the UMDs system at the nth sampling time
Figure GDA0003156053600000075
And taking 0, otherwise, taking 1, and taking the average value of the test function values of the h subintervals as the test function value of the nth sampling, wherein the test function value is shown in the following formula:
Figure GDA0003156053600000076
wherein the content of the first and second substances,
Figure GDA0003156053600000077
the test function value of the UMDs system at the nth sampling;
step 3-5-4: judging whether the sampling times n are more than nmaxIf yes, continuing to execute the step 3-6, otherwise returning to the step 3-5-1 to execute the next sampling.
Step 3-6: updating the k value according to the iteration calculation result, wherein the calculation process is shown as the following formula:
Figure GDA0003156053600000081
wherein N is0And N1The number of modules respectively being fault states in the iteration processQuantity and number of modules in normal state;
Figure GDA0003156053600000082
weighted mean of forced failure rates for all modules;
step 3-7: accumulating the reliability indexes and calculating a variance coefficient;
the estimated value of the reliability index SA is
Figure GDA0003156053600000083
As shown in the following equation:
Figure GDA0003156053600000084
the variance factor β is then expressed as:
Figure GDA0003156053600000085
wherein the content of the first and second substances,
Figure GDA0003156053600000086
is a vector
Figure GDA0003156053600000087
N-th sample value, variance
Figure GDA0003156053600000088
Is an estimated value
Figure GDA0003156053600000089
An error of (2);
in the reliability evaluation, the variance coefficient β is generally used as a criterion for calculating convergence.
Step 3-8: judging whether the iteration number C is larger than the maximum iteration number CmaxOr whether the variance coefficient meets the calculation precision, if so, outputting a reliability index, and otherwise, returning to the step 3-3 to continue iterative calculation.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (3)

1. A UMDs system reliability assessment method is characterized in that: the method comprises the following steps:
step 1: establishing a reliability index of the UMDs system;
according to the characteristics of the UMDs system, referring to the reliability indexes of electronic equipment and a power distribution network, selecting the average system power supply availability SA as the reliability index of the UMDs system to describe the availability of the UMDs system, wherein the power supply availability refers to the steady-state probability that the system is in a normal working state;
step 2: establishing a power loss model of the UMDs system;
according to the characteristics of the UMDs system, the UMDs system is divided into 12 modules: the system comprises a commercial power U, a generator G, an automatic transfer switch ATS, an alternating current bus AB, a charging circuit CC, a battery pack BP, an inverter circuit IC, a monitoring control circuit MC, a static transfer switch STS, a direct current bus protection control PM, a direct current bus DB and an air cooling module WM; establishing a fault tree according to the working principle of the UMDs system to obtain an operation state set phi of each module when the system loses power;
and step 3: the reliability evaluation of the UMDs system is carried out by utilizing an improved Monte Carlo method, and the specific method comprises the following steps:
step 3-1: inputting basic information of the UMDs system;
step 3-2: initializing the iteration times c to be 0, and starting the iteration;
step 3-3: the iteration number c is c + 1;
step 3-4: correcting a distribution function of the UMDs system state according to the optimal multiplier k value;
step 3-5: initializing the sampling times n to be 0, starting sampling, and starting the iterative sampling;
step 3-6: updating the k value according to the calculation result of the nth iteration;
step 3-7: accumulating the reliability indexes and calculating a variance coefficient;
step 3-8: judging whether the iteration number c is larger than the maximum iteration number or whether the variance coefficient meets the calculation precision, if so, outputting a reliability index, and otherwise, returning to the step 3-3 to continue iterative calculation;
step 2, the generator, the commercial power, the automatic transfer switch, the alternating current bus, the charging circuit, the battery pack, the inverter circuit, the monitoring control circuit, the static transfer switch, the direct current bus protection control, the direct current bus and the air cooling module are all in two states: a normal state and a fault state; the mean time between failures and the mean time between repairs of each module in the UMDs system are respectively
Figure FDA0003156053590000011
And
Figure FDA0003156053590000012
the availability ratio of power supply is AiWherein i ═ 1,2, …,12 correspond to the different modules in the UMDs system;
mean time between failures
Figure FDA0003156053590000013
Average time to repair for expected average time to run of a module
Figure FDA0003156053590000014
The mean value of the repair time when the module is converted from the fault state to the working state;
the specific method of the step 3-1 comprises the following steps:
step 3-1-1: inputting a state set phi of each module when the UMDs system loses power;
step 3-1-2: mean time between failures of individual modules of the input UMDs system
Figure FDA0003156053590000015
Mean time to repair
Figure FDA0003156053590000016
Discharge time T of UMDs system battery packBSFAnd charging time T of the battery packBSR；
Step 3-1-3: setting the maximum number of iterations CmaxInitial value of optimal multiplier k, number of iterative samples per time nmaxAnd a variance coefficient;
the specific method of the step 3-4 comprises the following steps:
the random state of the system is composed of random states of 12 modules, and the random variables of the 12 modules are considered to be independent from each other, and the state probability distribution function of the ith module is defined as:
Figure FDA0003156053590000021
wherein, Xi0 indicates that the state of the ith module is a fault state, Xi1 means that the state of the ith module is a normal state, foriFor the forced failure rate of the ith module, the following equation is shown:
Figure FDA0003156053590000022
the specific method of the step 3-5 is as follows:
step 3-5-1: generating a set of random number vectors;
the interval [0,1] is equally divided into h parts, and satisfies:
Figure FDA0003156053590000023
then in the a-th sub-interval, a is 1,2, …, h, the state X of the i-th moduleiaDetermined according to the following formula:
Figure FDA0003156053590000024
wherein x isiIs a state random number;
step 3-5-2: the sampling times n are n + 1;
step 3-5-3: judging the state of the UMDs system in each subinterval divided in the step 3-5-1, and if the state of the UMDs system is in a UMDs system power-off state set phi, testing function values of the a-th subinterval of the UMDs system at the nth sampling time
Figure FDA0003156053590000025
And taking 0, otherwise, taking 1, and taking the average value of the test function values of the h subintervals as the test function value of the nth sampling, wherein the test function value is shown in the following formula:
Figure FDA0003156053590000026
wherein the content of the first and second substances,
Figure FDA0003156053590000031
the test function value of the UMDs system at the nth sampling;
step 3-5-4: judging whether the sampling times n are more than nmaxIf yes, continuing to execute the step 3-6, otherwise returning to the step 3-5-1 to execute the next sampling.
2. The method of claim 1 for evaluating reliability of UMDs systems, wherein: the calculation process of the steps 3-6 is shown as the following formula:
Figure FDA0003156053590000032
wherein N is0And N1Respectively determining the number of modules in fault state and the number of modules in normal state in the iteration process;
Figure FDA0003156053590000033
is a weighted average of the forced failure rates of all modules.
3. The method of claim 2 for evaluating reliability of UMDs systems, wherein: the specific method of the steps 3-7 is as follows:
the estimated value of the reliability index SA is
Figure FDA0003156053590000034
As shown in the following equation:
Figure FDA0003156053590000035
the variance factor β is then expressed as:
Figure FDA0003156053590000036
wherein the content of the first and second substances,
Figure FDA0003156053590000037
is a vector
Figure FDA0003156053590000038
N-th sample value, variance
Figure FDA0003156053590000039
Is an estimated value
Figure FDA00031560535900000310
The error of (2).
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