Method for evaluating reliability of power generation system based on aging failure of generating unit

Abstract

The present application relates to the field of power system reliability evaluation method, concretely relates to a power generation system reliability evaluation method based on unit aging failure, first, based on the time sequence state transition process analysis and state duration random variable refinement modeling of generator unit, a three-state reliability model of generator unit is constructed, which comprehensively considers aging failure and random failure mode, for describing the time sequence state transition process of generator unit in the system under the coupling influence of aging failure and random failure; secondly, a system time sequence state transition sequence generation method based on the three-state reliability model of generator unit and the sequential MCS technology is proposed; finally, the generated system states are analyzed one by one through the optimal load reduction model, and then the system probability, expectation and frequency class reliability indexes are obtained. The present application can realize the accurate and quantitative analysis of the reliability of power generation system.

Description

Technical Field

[0001] The present invention relates to the field of power system reliability evaluation methods, and in particular to a power generation system reliability evaluation method based on unit aging failure. Background Art

[0002] The power generation system is a crucial component of the power system. Accurately assessing its reliability (i.e., its ability to meet power demand) can provide accurate decision-making guidance for power generation companies in ensuring power supply. Some equipment in the power generation system has been in operation for a long time, with some units approaching 20 years. This has led to an increasing concern for unit aging. Power outages caused by aging and failure pose a serious threat to the reliability of the power generation system. Therefore, reliability assessments of power generation systems with aging and failure of generator sets are extremely important.

[0003] At present, the power system reliability assessment methods for unit aging failure include: power generation system reliability assessment methods based on state enumeration or non-sequential Monte Carlo sampling (MCS) method, and power generation system reliability assessment methods based on sequential MCS technology.

[0004] However, the existing power system reliability assessment methods have the following problems:

[0005] Reliability assessments based on state enumeration or non-sequential Monte Carlo sampling ignore the temporal correlation of the operating states of generator units and power generation systems, and therefore can only obtain probabilistic and expected reliability indicators. Furthermore, these methods cannot consider the impact of strategies with temporal characteristics, such as planned maintenance of units, limiting their application in engineering.

[0006] Reliability assessment based on the sequential MCS method usually uses a two-state reliability model for generator sets. First, this model assumes that a generator set that has undergone aging failure can be restored to its pre-failure state through repair operations, which is inconsistent with the irreparable characteristics of aging failure of the generator set. Second, this model obscures the impact of the unit's random failure mode on its state transition process. Therefore, there are inevitable deviations in the reliability assessment results. Summary of the Invention

[0007] The present invention aims to provide a method for evaluating the reliability of a power generation system based on unit aging failure, so as to solve the problem of deviation in evaluation results in existing reliability evaluation methods.

[0008] The power generation system reliability assessment method based on unit aging failure in this solution includes:

[0009] Step 1: Construct a three-state reliability model for the generator set that comprehensively considers random failures and aging failures;

[0010] Step 2: Based on the historical data records of the generator sets accumulated by the power generation enterprise, a refined modeling method for the state duration in the three-state reliability model of the generator set in step 1 is used. The state duration includes the duration TTF from the unit entering the normal operating state to the first random failure. ran , the duration from the occurrence of random failure of the unit to the completion of its maintenance TTR ran , the duration TTF from the unit entering normal operation to the first occurrence of aging failure aging , the duration from the occurrence of aging failure to the completion of replacement operation TFR aging ;

[0011] Step 3: Based on the three-state reliability model of the generator set established in Step 1, the component state duration sampling method in the sequential MCS method is used to generate the generator set state transition sequence considering aging failure. The state transition sequence of each generator set in the power generation system is combined with the system load time series curve to obtain the time series transition sequence of the power generation system state;

[0012] Step 4: Synchronously generate a state transition sequence for each unit in the power generation system, combine them, and further split the system state with multiple load levels to generate a state transition sequence for the entire power generation system that takes into account the load timing curve;

[0013] Step 5: Analyze each system state included in the evaluation period based on the optimal load reduction model;

[0014] Step 6: Based on the results obtained in step 5, the reliability level of the power generation system with aging and failure of computer groups is comprehensively quantified from three aspects: power outage probability, expectation, and frequency.

[0015] The beneficial effects of this program are:

[0016] A model is established to analyze the aging failure and random failure of the generator set, describe the time sequence state transition process under the influence of the two failure coupling, generate the system time sequence state transition sequence, perform the optimal solution, and obtain the evaluation indicators of the power generation system under the two failure modes. This realizes the quantitative evaluation of the reliability of the power generation system considering the aging failure of the unit, which can provide a useful reference for power generation companies to formulate the operation and maintenance strategy of the unit.

[0017] Furthermore, the process of constructing the three-state reliability model of the generator set in step 1 includes the following sub-steps:

[0018] Sub-step 1.1: Compare and analyze the random failure mode and aging failure mode of the generator set from multiple dimensions of failure causes and failure characteristics;

[0019] Sub-step 1.2: decompose the impact of each failure mode on the generator set's sequential state transition process and characterize it as a combination of two different state durations;

[0020] In sub-step 1.3, different probability distributions are used to perform refined modeling on the random variables of each state duration.

[0021] The beneficial effects are: comparative analysis of failure modes from multiple dimensions of the unit, quantitative representation as a combination of durations, modeling based on probability distribution, quantification of the unit status under the failure model, and facilitation of accurate judgment and analysis in subsequent steps.

[0022] Furthermore, in step 2, the state duration TTF ran The exponential distribution is used for modeling, and its cumulative distribution function is expressed as:

[0023]

[0024] Where λ ran is the average random failure rate of the generator set obtained based on historical failure data statistics.

[0025] The beneficial effect is that the duration of random failures caused by unexpected factors is quantified through exponential distribution, making the modeling results more reliable.

[0026] Furthermore, in step 2, the state duration TTR ran The modeling is carried out using the lognormal distribution, and the cumulative distribution function of the lognormal distribution is expressed as:

[0027]

[0028] Where μ ran and σ ran They represent the mean and standard deviation parameters of the general normal distribution, and their specific values ​​are obtained by fitting the historical failure repair records of the generator set; erf(·) is the error function, which is defined as:

[0029]

[0030] The beneficial effect is that due to factors such as maintenance personnel efficiency, weather and environmental conditions, the duration TTR may be shortened. ran There are certain fluctuations, and modeling with lognormal distribution can accurately quantify the situation caused by uncertain factors.

[0031] Furthermore, in step 2, the state duration TTF aging The modeling is carried out using the conditional probability formula in statistics, and the cumulative distribution function of the conditional probability formula is expressed as:

[0032]

[0033] According to the analysis of historical statistical data, the cumulative aging failure distribution function F of the generator set l (t) is modeled by the three-parameter Weibull distribution, specifically expressed as:

[0034]

[0035] Where, α n and η n are the Weibull scale parameter and threshold parameter corresponding to the generator set; β is the Weibull shape parameter, α n ,η n The three parameters , β are obtained by performing least square fitting on the historical failure data of the unit.

[0036] The beneficial effect is: modeling duration TTF with conditional probability formula aging , which can fully consider the actual aging changes of the unit.

[0037] Furthermore, in step 2, the state duration TFR aging Includes the status duration corresponding to the first stage status required for order creation and unit production and the duration of the state corresponding to the second stage state required for unit transportation and installation The state duration TFR aging The modeling uses the lognormal distribution to independently calculate the duration of these two periods, which are expressed as:

[0038]

[0039] Where, and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the first stage state; and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the second stage state.

[0040] The beneficial effect is that the duration from aging failure to replacement can be accurately characterized by modeling the two stages separately.

[0041] Furthermore, step 3 includes the following sub-steps:

[0042] Sub-step 3.1: For any unit in the power generation system, first determine the various parameters in the unit's three-state reliability model, including the simulation period D total, the current age of the generator set T ser ;

[0043] Sub-step 3.2, initialization Assume that the unit is in operation at the initial moment, set the simulated time and assign 0 to TL sim , component operation status identification variable I state Assign a value of 1, and the aging failure indicator variable I occur Assign a value of 0; according to the current age of the crew T ser , according to the state duration TTF aging The cumulative distribution function formula is updated Tser (TTF aging );

[0044] Sub-step 3.3, determine the duration d of the unit from the current moment to the next state transition according to the preset sub-steps sim ;

[0045] Sub-step 3.4: Reset the aging failure occurrence indicator variable I occur Assign a value of 0; and determine whether the preset simulation time has been reached. If not, return to sub-step 3.3. If the preset simulation time has been reached, obtain its state transition process and state duration in time period D according to the state transition process and state duration. total The state transition sequence within.

[0046] The beneficial effect is that the state transition sequence of each generator set in the power generation system is combined with the system load timing curve to obtain the timing transition sequence of the power generation system state, which can dynamically analyze the changing state of the aging condition over time.

[0047] Furthermore, in the sub-step 3.3, the preset sub-step includes:

[0048] Sub-step 3.3.1, if I state =1, indicating that the unit is in normal operation at the current moment, according to the state duration TTF ran Cumulative distribution function and state duration TTF aging The cumulative distribution function of TTF ran and TTF aging Perform random sampling and determine the next component failure type; where:

[0049]

[0050] If TTF ran_sam >TTF aging_sam , indicating that the next failure mode is random failure, update d sim TTF aging_sam , Update I state is 0, Ioccur is 1; otherwise update d sim TTF ran_sam , Update I state 0I state ←0;

[0051] Sub-step 3.3.2, if I state =0, indicating that the unit is in a failure shutdown state at the current moment. If there is I occur =0, then update I state is 1, and according to the state duration TTR ran The cumulative distribution function update d sim TTR ran_sam ,in:

[0052]

[0053] Where Φ(·) is the standard normal distribution;

[0054] Sub-step 3.3.3, if I state =0, indicating that the unit is in a failure shutdown state at the current moment. If there is I occur =1, then update I state is 1, and according to the state duration TFR aging The cumulative distribution function update d sim TFR aging_sam ,in:

[0055]

[0056] Sub-step 3.3.4, according to the identification variable I state Determine the unit in the period [TL sim ,TL sim +d sim ] and update the simulated time TL sim For TL sim +d sim ; Then, update the component age T according to the following formula ser :

[0057]

[0058] The beneficial effect is that the calculation of the duration between different states can accurately obtain the corresponding timing.

[0059] Furthermore, in step 5, the objective function is to minimize the total load reduction of the power generation system. Based on the DC power flow calculation model, for a given system state s, the system component state vector and the system load level are defined as v and v respectively. s and L s, an optimal load reduction model for the power generation system considering unit aging failure is established, and the model is expressed as:

[0060] The objective function is the sum of the load reduction on each busbar of the power generation system LC s Minimum is the objective function of the model, which is expressed as:

[0061]

[0062] Where, P s,i,cut represents the load reduction of node i in system state s, Ψ bus Represents a collection of system nodes;

[0063] Constraints include:

[0064] The system power balance constraint is expressed as:

[0065]

[0066] Where, P s,g represents the output of the g-th generator set; Ψ g Represents the set of generator sets in the system; P i,peak represents the annual peak load of node i; L s ·P i,peak represents the load demand of node i in state s;

[0067] The node power balance constraint is expressed as:

[0068]

[0069] Where, g,i represents the set of generators connected to node i; P s,i,in represents the injected power of node i;

[0070] The node load reduction constraint is expressed as:

[0071]

[0072] The node injection power calculation is expressed as:

[0073]

[0074] Where n is the number of system nodes; θ s,j is the voltage phase angle of node j; b s,ij represents the element in the i-th row and j-th column of the node admittance matrix B;

[0075] The DC power flow equation of the transformer / line branch is expressed as:

[0076]

[0077] Where θ s,i and θ s,j Respectively represent the voltage phase angles of the first and last nodes i and j of branch r; x s,r Represents the reactance of branch r; PL s,r represents the power flow of branch r; branch Represents the system branch set;

[0078] The transformer / line branch transmission capacity constraint is expressed as:

[0079]

[0080] Where, Indicates the maximum transmission capacity of branch r; state identification variable υ s,r Used to describe the operating status of branch r;

[0081] The generator output constraint is expressed as:

[0082]

[0083] Where, Indicates the maximum output of the generator set g, which is taken as the rated capacity; s,g Used to describe the operating status of the generator set g: When the generator set g stops operating due to random failure or aging, υ s,g The value is 0, otherwise υ s,g is 1;

[0084] For the system state s, if LC s >0 means that the power generation system cannot fully meet the load supply in this state, and there is load reduction. In this case, the load reduction indicator variable J s Recorded as 1; if LC s =0, then J s is 0;

[0085] In the reliability assessment process based on sequential MCS, the LC of each system state is recorded. s and J s The calculation results are used in step 6 to calculate the reliability index of the power generation system.

[0086] The beneficial effect is that the setting of constraint conditions can make the operation of the overall power generation system more economical and safer while taking into account aging failures.

[0087] Furthermore, in step 6, the process of quantifying the reliability level of the power generation system due to aging failure of the computer group includes the following sub-steps:

[0088] Sub-step 6.1, calculate the load reduction probability LOLP index of the power generation system, the formula is:

[0089]

[0090] Where K sam Indicates the total number of evaluation cycles, which can usually be set to 5000 times; D total Represents the length of the reliability assessment cycle, usually 1 year; Θ k represents the set of all system states in the kth evaluation cycle; J k,s and d k,s They represent the load reduction identification variable and state duration corresponding to the system state s in the kth evaluation period respectively;

[0091] Sub-step 6.2, calculate the expected power shortage EENS index of the power generation system, which is expressed as:

[0092]

[0093] Where, LC k,s represents the load reduction per unit time of system state s in the kth evaluation period;

[0094] Sub-step 6.3, calculate the expected power shortage LOLF index of the power generation system, which is expressed as:

[0095]

[0096] Where, I k,s is a 0-1 flag variable. If the system state s has load shedding and its previous state does not have load shedding, then I k,s The value is 1, otherwise I k,s The value is 0.

[0097] The beneficial effect is that the calculation of various quantitative indicators can evaluate the reliability of the power generation system from multiple aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 This is a flowchart of an embodiment of a method for evaluating the reliability of a power generation system based on unit aging failure according to the present invention;

[0099] Figure 2 This is a reliability assessment flow chart of an embodiment of a method for assessing reliability of a power generation system based on unit aging failure according to the present invention;

[0100] Figure 3 This is a diagram showing a time sequence state transition process of a generator set in an embodiment of a method for evaluating the reliability of a power generation system based on aging failure of a generator set according to the present invention;

[0101] Figure 4A three-state reliability model of a generator set considering aging failure in an embodiment of the method for evaluating reliability of a power generation system based on aging failure of a generator set according to the present invention;

[0102] Figure 5 This is a diagram of a power generation system state transition sequence generation process based on sequential MCS in an embodiment of the power generation system reliability assessment method based on unit aging failure of the present invention. DETAILED DESCRIPTION

[0103] The following is further explained in detail through specific implementation methods.

[0104] Example

[0105] Reliability assessment methods for power generation systems based on unit aging failure, such as Figure 1 and Figure 2 Shown, including:

[0106] Step 1: Construct a three-state reliability model for the generator set that comprehensively considers random failures and aging failures. The process of constructing the three-state reliability model for the generator set includes the following sub-steps:

[0107] Substep 1.1 compares and analyzes the random failure modes and aging failure modes of the generator set from multiple perspectives, including failure causes and characteristics. Any failure mode will cause the unit to transition from normal operation to a failed shutdown state. To quantitatively characterize the differential impact of these two failure modes on the generator set's sequential state transitions, we first conducted a comparative analysis of the causes, failure rates, and failure frequencies of these two failure modes. The specific analysis results are shown in Table 1.

[0108] Table 1 Comparison of aging failure modes and random failure modes of generator sets

[0109]

[0110] As can be seen from Table 1, the essential difference between random failure and aging failure modes is that random failure is usually repairable and has nothing to do with the unit's operating time, while aging failure is related to the unit's operating time and cannot be repaired.

[0111] Sub-step 1.2: As can be seen from sub-step 1.1, when a generator set fails due to aging, it needs to be replaced with a new one to restore the system function. Before that, the generator set will alternate between normal operation and random failure. Therefore, the schematic diagram of the generator set timing state transition process considering aging failure is as follows: Figure 3 The influence of each failure mode on the generator set timing state transition process is decomposed and characterized as a combination of two different state durations.

[0112] Sub-step 1.3, from sub-step 1.2, we know that the timing state transition process of the generator set can be regarded as a timing combination of two operating cycles corresponding to the random failure mode and the aging failure mode. Among them, the operating cycle corresponding to the random failure mode can be decomposed into TTF ran and TTR ran Two state duration random variables; during this operating cycle, the unit first experiences a random failure after running for a period of time, causing it to switch from the operating state to the outage state; after the maintenance operation is completed, the unit will return to the normal operating state, and the operating time continues to accumulate. Accordingly, the operating cycle corresponding to the aging failure mode can be regarded as the state duration random variable TTF aging and TFR aging The unit will age and fail after a period of normal operation during the operation cycle, and then transfer to the shutdown state; after the replacement operation is completed, the unit will transfer back to the normal operation state. Different probability distributions are used to fine-tune the random variables of each state duration to construct a three-state reliability model as follows: Figure 4 shown.

[0113] Step 2: Based on the historical data records of the generator sets accumulated by the power generation enterprise, a refined modeling method for the state duration in the three-state reliability model of the generator set in step 1 is used. The state duration includes the duration TTF from the unit entering the normal operating state to the first random failure. ran , the duration from the occurrence of random failure of the unit to the completion of its maintenance TTR ran , the duration TTF from the unit entering normal operation to the first occurrence of aging failure aging , the duration from the occurrence of aging failure to the completion of replacement operation TFR aging .

[0114] Random failure of generator sets is usually caused by unexpected factors, and the state duration TTF ran The exponential distribution is used for modeling, and its cumulative distribution function is expressed as:

[0115]

[0116] Where λ ran is the average random failure rate of the generator set obtained based on historical failure data statistics.

[0117] Repair Time TFR aging Due to factors such as maintenance personnel efficiency, weather and environmental conditions, there may be certain fluctuations. Status duration TTR ran The modeling is carried out using the lognormal distribution, and the cumulative distribution function of the lognormal distribution is expressed as:

[0118]

[0119] Where μ ran and σ ran They represent the mean and standard deviation parameters of the general normal distribution, and their specific values ​​are obtained by fitting the historical failure repair records of the generator set; erf(·) is the error function, which is defined as:

[0120]

[0121] Different from TTF aging and TFR agingn , state duration TTF aging TTF related to the running time of the generator set aging The cumulative distribution function of will change dynamically with the running time of the generator set. Assume that the cumulative aging failure distribution function of the generator set is F l (t), then its running time is T ser Under the condition of aging The modeling is carried out using the conditional probability formula in statistics, and the cumulative distribution function of the conditional probability formula is expressed as:

[0122]

[0123] According to the analysis of historical statistical data, the cumulative aging failure distribution function F of the generator set l (t) is modeled by the three-parameter Weibull distribution, specifically expressed as:

[0124]

[0125] Where, α n and η n are the Weibull scale parameter and threshold parameter corresponding to the generator set; β is the Weibull shape parameter, α n ,η n The three parameters , β are obtained by performing least square fitting on the historical failure data of the unit.

[0126] Generator set replacement time TFR aging It will be affected by many factors such as the manufacturer's manufacturing progress, transportation and on-site installation conditions. Therefore, the state duration TFR aging Includes the status duration corresponding to the first stage status required for order creation and unit production and the duration of the state corresponding to the second stage state required for unit transportation and installation Total replacement time TFR aging It can be expressed as The state duration TFRaging The modeling uses the lognormal distribution to independently calculate the duration of these two periods, which are expressed as:

[0127]

[0128] Where, and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the first stage state; and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the second stage state.

[0129] Step 3: Based on the three-state reliability model of the generator set established in Step 2, the component state duration sampling method in the sequential MCS method is used to generate the generator set state transition sequence taking into account aging failures. The state transition sequence of each generator set in the power generation system is combined with the system load timing curve to obtain the time sequence transition sequence of the power generation system state. Specifically, it includes the following sub-steps:

[0130] Sub-step 3.1: For any unit in the power generation system, first determine the various parameters in the unit's three-state reliability model, including the simulation period D total , the current age of the generator set T ser ;

[0131] Sub-step 3.2, initialization assumes that the unit is in operation at the initial moment and sets the simulated time TL sim ←0, the arrow in this article represents the assignment, that is, to TL sim Assign value 0, component operation status identification variable I state ←1, that is, to I state Assign value 1, aging failure occurrence identification variable I occur ←0, that is, to I occur Assign value 0; according to the current age T of the crew ser , according to the state duration TTF aging The cumulative distribution function formula is updated Tser (TTF aging );

[0132] Sub-step 3.3, determine the duration d of the unit from the current moment to the next state transition according to the preset sub-steps sim , the preset sub-steps include:

[0133] Sub-step 3.3.1, if I state =1, indicating that the unit is in normal operation at the current moment, according to the state duration TTF ranCumulative distribution function and state duration TTF aging The cumulative distribution function of TTF ran and TTF aging Perform random sampling and determine the next component failure type; where:

[0134]

[0135] If TTF ran_sam >TTF aging_sam , indicating that the next failure mode is random failure, update d sim ←TTF aging_sam , that is, to d sim Assign TTF aging_sam , Update I state ←0,I occur ←1, that is, to I state Assign 0 to I occur Assign 1; otherwise update d sim ←TTF ran_sam , that is, to d sim Assign TTF ran_sam , Update I state ←0, that is, to I state Assign value 0;

[0136] Sub-step 3.3.2, if I state =0, indicating that the unit is in a failure shutdown state at the current moment. If there is I occur =0, then update I state ←1, that is, to I state Assign a value of 1 and set the state duration TTR ran The cumulative distribution function update d sim ←TTR ran_sam , that is, to d sim Assign TTR ran_sam ,in:

[0137]

[0138] Where Φ(·) is the standard normal distribution;

[0139] Sub-step 3.3.3, if I state =0, indicating that the unit is in a failure shutdown state at the current moment. If there is I occur =1, then update I state ←1, that is, to I state Assign a value of 1 and calculate the duration of the state TFR aging The cumulative distribution function update d sim ←TFR aging_sam , that is, to d sim Assign TFRaging_sam ,in:

[0140]

[0141] Sub-step 3.3.4, according to the identification variable I state Determine the unit in the period [TL sim ,TL sim +d sim ] and update the simulated time TL sim ←TL sim +d sim ; Then, update the component age T according to the following formula ser :

[0142]

[0143] In the formulas in this article, unexplained parameters represent intermediate operational variables and have no actual meaning;

[0144] Sub-step 3.4: Reset the aging failure occurrence indicator variable I occur ←0; and determine whether the preset simulation time has been reached. If not, return to sub-step 3.3. If the preset simulation time has been reached, obtain its state transition process and state duration in time period D according to the state transition process and state duration. total The state transition sequence within.

[0145] Step 4: Synchronously generate the state transition sequence of each unit in the power generation system, combine them and further split the system state with multiple load levels to generate the state transition sequence of the entire power generation system considering the load timing curve. Figure 5 Figure 2 shows the generation process of the system state transition sequence S2 for three components (C1-C3) and three load levels (Ls: L1-L3). In this figure, the indexes 1-15 represent 15 independent system states, and each system is connected to its adjacent states in chronological order.

[0146] Step 5: Analyze each system state included in the evaluation period based on the optimal load reduction model. Specifically, in the process of evaluating the reliability of the power generation system, it is assumed that the power lines, transformers and other equipment in the power system are completely reliable. Taking the minimum total load reduction of the power generation system as the objective function, based on the DC power flow calculation model, for a given system state s, the system component state vector and system load level are defined as v, ... s and L s , an optimal load reduction model for the power generation system considering unit aging failure is established, and the model is expressed as:

[0147] The objective function is the sum of the load reduction on each bus (node) of the power generation system LCs Minimum is the objective function of the model, which is expressed as:

[0148]

[0149] Where, P s,i,cut represents the load reduction of node i in system state s, Ψ bus Represents a collection of system nodes;

[0150] Constraints include:

[0151] The system power balance constraint is expressed as:

[0152]

[0153] Where, P s,g represents the output of the g-th generator set; Ψ g Represents the set of generator sets in the system; P i,peak represents the annual peak load of node i; L s ·P i,peak represents the load demand of node i in state s.

[0154] The node power balance constraint is expressed as:

[0155]

[0156] Where, g,i represents the set of generators connected to node i; P s,i,in represents the injected power of node i.

[0157] The node load reduction constraint is expressed as:

[0158]

[0159] The node injection power calculation is expressed as:

[0160]

[0161] Where n is the number of system nodes; θ s,j is the voltage phase angle of node j; b s,ij Represents the element in the i-th row and j-th column of the node admittance matrix B.

[0162] The DC power flow equation of the transformer / line branch is expressed as:

[0163]

[0164] Where θ s,i and θ s,j Respectively represent the voltage phase angles of the first and last nodes i and j of branch r; x s,rRepresents the reactance of branch r; PL s,r represents the power flow of branch r; branch Indicates a set of system branches.

[0165] The transformer / line branch transmission capacity constraint is expressed as:

[0166]

[0167] Where, Indicates the maximum transmission capacity of branch r; state identification variable υ s,r Used to describe the operating status of branch r. Since it is completely reliable, the value is 1.

[0168] The generator output constraint is expressed as:

[0169]

[0170] Where, Indicates the maximum output of the generator set g, which is taken as the rated capacity; s,g Used to describe the operating status of the generator set g: When the generator set g stops operating due to random failure or aging, υ s,g The value is 0, otherwise υ s,g is 1.

[0171] For the system state s, if LC s >0 means that the power generation system cannot fully meet the load supply in this state, and there is load reduction. In this case, the load reduction indicator variable J s Recorded as 1; if LC s =0, then J s is 0.

[0172] In the reliability assessment process based on sequential MCS, the LC of each system state is recorded. s and J s The calculation results are used in step 6 to calculate the reliability index of the power generation system.

[0173] Step 6: Based on the results obtained in step 5, comprehensively quantify the reliability level of the power generation system due to aging and failure of computer groups from three aspects: power outage probability, expectation, and frequency. Specifically, the process of quantifying the reliability level of the power generation system due to aging and failure of computer groups includes the following sub-steps:

[0174] Sub-step 6.1, calculate the load reduction probability LOLP index of the power generation system, the formula is:

[0175]

[0176] Where K sam Indicates the total number of evaluation cycles, usually set to 5000 times; Dtotal Represents the length of the reliability assessment cycle, usually 1 year; Θ k represents the set of all system states in the kth evaluation cycle; J k,s and d k,s They represent the load reduction identification variable and state duration corresponding to the system state s in the kth evaluation period respectively;

[0177] Sub-step 6.2, calculate the expected power shortage EENS index of the power generation system, which is expressed as:

[0178]

[0179] Where, LC k,s represents the load reduction per unit time of system state s in the kth evaluation period;

[0180] Sub-step 6.3, calculate the expected power shortage LOLF index of the power generation system, which is expressed as:

[0181]

[0182] Where, I k,s is a 0-1 flag variable. If the system state s has load shedding and its previous state does not have load shedding, then I k,s The value is 1, otherwise I k,s The value is 0.

[0183] In this embodiment, the proposed three-state reliability model accurately simulates the sequential state transition process and failure behavior of a generator set under the combined influence of aging and random failure modes. Secondly, the proposed reliability assessment method based on sequential MCS accurately quantifies the reliability level of a power generation system considering unit aging failures from three dimensions: outage probability, expectation, and frequency. Finally, the proposed assessment method is also applicable to operation and maintenance strategy decisions with time-series characteristics, such as planned maintenance analysis of generator sets, making it more suitable for engineering applications than traditional methods.

[0184] By combining the aging and random failure models of generator sets, this approach describes the sequential state transition process under the influence of the coupling of these two failure modes, generates a system sequential state transition sequence, and performs an optimal solution to obtain evaluation indicators for the power generation system under these two failure modes. This approach enables a quantitative assessment of the reliability of power generation systems that consider aging failures. This approach provides a comprehensive understanding of the operating status of power generation systems, providing guidance for making reasonable operational and planning decisions and preventing major power outages. It can also serve as a useful reference for power generation companies in formulating unit operation and maintenance strategies.

[0185] The above is only an embodiment of the present invention, and the common knowledge such as the specific structure and characteristics of the scheme is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for evaluating the reliability of a power generation system based on unit aging failure, characterized by: include: Step 1: Construct a three-state reliability model for the generator set that comprehensively considers random failures and aging failures; Step 2: Based on the historical data records of the generator sets accumulated by the power generation enterprise, the state duration in the three-state reliability model of the generator set in step 1 is refined and modeled. The state duration includes the duration from the unit entering the normal operating state to the first random failure. , The duration from the occurrence of random failure of the unit to the completion of its maintenance , The duration from the unit entering normal operation to the first occurrence of aging failure 2. Duration from unit aging failure to replacement operation completion ; Duration of the state The modeling is carried out using the conditional probability formula in statistics, and the cumulative distribution function of the conditional probability formula is expressed as: ； According to the analysis of historical statistical data, the cumulative aging failure distribution function of the generator set The model is modeled by the three-parameter Weibull distribution, which is specifically expressed as: ； Where, and are the Weibull scale parameter and threshold parameter corresponding to the generator set respectively; β is the Weibull shape parameter, 、 、 β The three parameters are obtained by least square fitting of the historical failure data of the unit; Step 3: Based on the three-state reliability model of the generator set established in step 2, the component state duration sampling method in the sequential MCS method is used to generate the generator set state transition sequence considering aging failure. The state transition sequence of each generator set in the power generation system is combined with the system load timing curve to obtain the time sequence transition sequence of the power generation system state. Step 4: Synchronously generate a state transition sequence for each unit in the power generation system, combine them, and further split the system state with multiple load levels to generate a state transition sequence for the entire power generation system that takes into account the load timing curve; Step 5: Analyze each system state included in the evaluation period based on the optimal load reduction model; Step 6: Based on the results obtained in step 5, the reliability level of the power generation system with aging and failure of computer groups is comprehensively quantified from three aspects: power outage probability, expectation, and frequency.

2. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 1, characterized in that: The process of constructing the three-state reliability model of the generator set in step 1 includes the following sub-steps: Sub-step 1.1: Compare and analyze the random failure mode and aging failure mode of the generator set from multiple dimensions of failure causes and failure characteristics; Sub-step 1.2: decompose the impact of each failure mode on the generator set's sequential state transition process and characterize it as a combination of two different state durations; In sub-step 1.3, different probability distributions are used to perform refined modeling on the random variables of each state duration.

3. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 1, characterized in that: In step 2, the state duration The exponential distribution is used for modeling, and its cumulative distribution function is expressed as: ； Where, is the average random failure rate of the generator set obtained based on historical failure data statistics.

4. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 1, characterized in that: In step 2, the state duration The modeling is carried out using the lognormal distribution, and the cumulative distribution function of the lognormal distribution is expressed as: ； Where, and They represent the mean and standard deviation parameters of the general normal distribution, and their specific values ​​are obtained by fitting the historical failure repair records of the generator sets; is the error function, defined as: 。 5. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 4 is characterized in that: In step 2, the state duration Includes the status duration corresponding to the first stage status required for order creation and unit production and the duration of the state corresponding to the second stage state required for unit transportation and installation , the duration of the state The modeling uses the lognormal distribution to independently calculate the duration of these two periods, which are expressed as: ； ； Where, and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the first stage state; and They represent the mean and standard deviation parameters of the general normal distribution function corresponding to the state duration of the second stage state.

6. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 5, characterized in that: Step 3 includes the following sub-steps: Sub-step 3.1: For any unit in the power generation system, first determine the parameters of the unit's three-state reliability model, including the simulation period , Current age of the generator set ; Sub-step 3.2, initialization assumes that the unit is in operation at the initial moment and sets the simulated time , assign 0 to , component operation status identification variable Assign a value of 1 to indicate that an aging failure has occurred. Assign a value of 0; according to the current age of the crew , according to the state duration The cumulative distribution function formula is updated ; Sub-step 3.3, determine the duration of the unit from the current moment to the next state transition according to the preset sub-steps ; Sub-step 3.4: Reset the aging failure occurrence indicator variable Assign a value of 0; and determine whether the preset simulation time has been reached. If not, return to sub-step 3.

3. If the preset simulation time has been reached, obtain its state transition process and state duration in the time period according to the component state transition process and state duration. The state transition sequence within.

7. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 6, characterized in that: In the sub-step 3.3, the preset sub-step includes: Sub-step 3.3.1, if , indicating that the unit is in normal operation at the current moment, according to the state duration Cumulative distribution function and state duration of The cumulative distribution functions of and Perform random sampling and determine the next unit failure type; where: ； ； if , indicating that the next failure mode is random failure, update for ,renew is 0, is 1; otherwise update for ,renew is 0; Sub-step 3.3.2, if , indicating that the unit is in a failure shutdown state at the current moment. , then update is 1, and according to the state duration Cumulative distribution function update for ,in: ； Where, is a standard normal distribution; Sub-step 3.3.3, if , indicating that the unit is in a failure shutdown state at the current moment. , then update is 1, and according to the state duration Cumulative distribution function update for ,in: ； Sub-step 3.3.4, according to the identification variable Determine the unit's time period The running status within the time and update the simulation time for ; Then, update the unit age according to the following formula : 。 8. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 7, characterized in that: In step 5, the objective function is to minimize the total load reduction of the power generation system, based on the DC power flow calculation model, for a given system state s , define the system component state vector and system load level as and , an optimal load reduction model for the power generation system considering unit aging failure is established, and the model is expressed as: The objective function is the sum of the load reduction on each busbar of the power generation system Minimum is the objective function of the model, which is expressed as: ； Where, Indicates the system status s Next node i The load reduction, Represents a collection of system nodes; Constraints include: The system power balance constraint is expressed as: ； Where, Indicates the g Output of generator sets; Represents the set of generators in the system; Representation node i Annual peak load; Indicates status s Next node i load demand; The node power balance constraint is expressed as: ； Where, Indicates connection at node i A collection of generator sets; Representation node i The injection power; The node load reduction constraint is expressed as: ； The node injection power calculation is expressed as: ； Where, n is the number of system nodes; For nodes j The voltage phase angle; represents the node admittance matrix B No. i Rank j Elements of the column; The DC power flow equation of the transformer / line branch is expressed as: ； Where, and Respectively represent branches r First and last nodes i and j The voltage phase angle; Indicates a branch r reactance; Indicates a branch r The trend; Represents the system branch set; The transformer / line branch transmission capacity constraint is expressed as: ； Where, Indicates a branch r Maximum transmission capacity; status identification variable Used to describe a branch r The operating status of The generator output constraint is expressed as: ； Where, Indicates a generator set g The maximum output is the rated capacity; Used to describe a generator set g Operating status: When the unit g Outage due to random or aging failure, The value is 0, otherwise is 1; For system status s ,like It means that the power generation system cannot fully meet the load supply in this state, and there is load reduction. At this time, the load reduction indicator variable needs to be Recorded as 1; if ,but is 0; In the reliability assessment process based on sequential MCS, the status of each system is recorded. and The calculation results are used in step 6 to calculate the reliability index of the power generation system.

9. The method for evaluating the reliability of a power generation system based on unit aging failure according to claim 8, characterized in that: In step 6, the process of quantifying the reliability level of the power generation system due to aging failure of the computer group includes the following sub-steps: Sub-step 6.1, calculate the probability of load reduction in the power generation system LOLP Indicator, the formula is: ； Where, Indicates the total number of evaluation cycles; Indicates the length of the reliability assessment cycle; Indicates the k The set of all system states in an evaluation cycle; and Respectively expressed in k System status during the evaluation cycle s Corresponding load shedding identification variable and state duration; Sub-step 6.2: Calculate the expected power shortage of the power generation system EENS Indicators, expressed as: ； Where, Indicates in k System status during the evaluation cycle s The amount of load reduction per unit time; Sub-step 6.3, calculate the expected power shortage LOLF index of the power generation system, which is expressed as: ； Where, Is a 0-1 identification variable. If the system status s If there is load shedding and there is no load shedding in the previous state, then The value is 1, otherwise The value is 0.

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