Reliability evaluation method for distribution network considering uncertainty of new energy generation and load

Abstract

The application discloses a kind of considering new energy power generation and load uncertainty distribution network reliability evaluation method, solve the deficiency of prior art, including the following steps: S1, determine the same parameter of distribution system, S2, establish line reliability model, specifically including establishing line reliability model, establishing new energy generator set output model and establishing load model;S3, the consequence of element failure is analyzed, and the reliability of distribution network system is determined.The application can quickly and efficiently analyze the distribution network containing new energy generator set by analytical method reliability evaluation, considering the randomness of new energy generator set output and the uncertainty of load, analyze the ability of distribution system to continuously supply power to users, reflect the improvement of new energy unit access to the reliability of distribution system, guide the relevant departments of power grid to access new energy unit for distribution system.

Description

Technical Field

[0001] This invention relates to the field of power distribution system technology, and in particular to a method for assessing the reliability of power distribution networks that takes into account the uncertainty of new energy power generation and load. Background Technology

[0002] In a power system, the distribution system is a crucial link between the transmission and consumption systems. Connected to the main grid at one end and to the load side at the other, it directly faces the users and is directly related to the continuity and reliability of power supply. The distribution network has a complex structure and numerous components; failures in these components have a significant impact on power outages. According to statistics, over 80% of power outages are caused by component failures in the distribution system. Reliability is a measure of a power system's ability to continuously supply electricity and energy to users according to acceptable quality standards and required quantities. A reliability assessment of the distribution system reflects its ability to continuously supply power to users.

[0003] Currently, China is continuously promoting the adjustment of its industrial and energy structures, vigorously developing new energy sources, and promoting the grid connection of new energy generators such as wind power and photovoltaics. The output characteristics of new energy units differ from those of conventional units. Due to the randomness of wind and solar power output, they cannot reliably provide users with the electricity they need. In the power distribution system, they are usually used as distributed power sources, which can reduce the output pressure on conventional units. When an upstream line fails, distributed power sources can also supply power to nearby power-deficient load nodes, reducing the severity of the fault's impact.

[0004] For power distribution systems, the uncertainty of load and the randomness of renewable energy output pose challenges to reliability assessment due to variations in user electricity demand across different times of day, number of days, and seasons. Existing reliability assessment methods for power distribution systems considering renewable energy output and load uncertainty mostly employ Monte Carlo simulation. This method generates random numbers to sample renewable energy unit output and user load, creating output and consumption scenarios. It then simulates system component failure scenarios, analyzes the system state, identifies the power outage load, and completes a sample assessment. By repeatedly performing this process, a large number of simulation scenarios are generated, and system reliability indicators under each scenario are analyzed and calculated until the indicators converge, completing the assessment. Because simulation requires generating a large number of scenarios and performing state analysis and indicator calculations for each scenario, the computational load is enormous, and the assessment is time-consuming. Therefore, improving the efficiency of distribution network reliability assessment considering renewable energy generation and load uncertainty is a worthwhile research topic. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies that mostly use Monte Carlo simulation to assess the reliability of power distribution systems, which results in huge computational loads, long assessment times, and low efficiency. This invention provides a power distribution network reliability assessment method that considers the uncertainty of new energy power generation and load.

[0006] The objective of this invention is achieved through the following technical solution: A distribution network reliability assessment method considering the uncertainty of renewable energy generation and load includes the following steps: S1, determine the relevant parameters of the power distribution system, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the installation location of the new energy generator in the system and the probability density distribution function of its output; S103, determine the load curves of each node; S104, determine the repair time of the faulty component in the system and the time required for the switch operation; S2, Establish a line reliability model, which specifically includes establishing a line reliability model, establishing a new energy generator output model, and establishing a load model; S3 analyzes the consequences of component failures to determine the reliability of the power distribution network system.

[0007] Preferably, in S2, the line reliability model is specifically a two-state model used to represent the line reliability model.

[0008] Preferably, the power output model of the new energy generator sets specifically includes wind turbine power output modeling and photovoltaic power output modeling, wherein the wind turbine power output modeling is specifically as follows: The output of a wind turbine is related to wind speed, which follows a two-parameter Weibull distribution with the following probability density function: Using historical wind speed data, the parameters a and b in the Weibull distribution are obtained using the maximum likelihood estimation method, and a Weibull distribution model of wind speed is fitted. The output of a wind turbine varies with wind speed. Wind turbines cannot operate when the wind speed is too low or too high. The relationship between wind turbine output and wind speed is expressed as follows: The expressions for A, B, and C are as follows: Where P is the actual output of the wind turbine, P r For rated power, V ci To cut off the wind speed, V r For the rated wind speed, V co To determine the wind speed, the probability density function of the wind turbine's output can be obtained. The specific modeling of the output of the photovoltaic generator is as follows: The output of the photoelectric generator is related to the light irradiance, which follows a beta distribution with the following probability density function: Where r is the irradiance (W / m²) 2 ), where a is the location parameter of the beta distribution, b is the shape parameter of the beta distribution, and the expression for the gamma function Γ(x) is: Using historical data, the parameters a and b of the beta distribution are determined using the method of moments, and a beta distribution model of irradiance is obtained by fitting the model. The relationship between photovoltaic unit output and solar irradiance is as follows: Where P represents the actual output of the photovoltaic unit, and A represents the area of ​​the photovoltaic cell (m²). 2 ), where η is the conversion efficiency of the photovoltaic cell, expressed as: η=η0[1-γ(T t -T0)] Where η0 is the photovoltaic cell conversion efficiency at the reference temperature, γ is the temperature coefficient of the photovoltaic cell, and T t T0 is the ambient temperature, and T0 is the reference temperature of 298K. Based on this, the probability density function of the photovoltaic unit's output can be obtained.

[0009] Preferably, the load model is represented by load curves at different stages of the year, or by monthly, daily, or hourly loads.

[0010] Preferably, S3 is specifically: S301. Load nodes are classified according to different power supply methods so as to determine the power outage status of the load node when a component fails. When the load node is outside the power supply range of the new energy generator, such a node is called an ordinary load node; when a new generator set is installed on the load node, such a node is called a new energy generator set installation node; when the load node itself does not have a new energy generator set installed, but is within the power supply range of a nearby new energy generator set, such a node is called a new energy generator set coverage node. S302. For new energy unit installation nodes and new energy unit coverage nodes, when a component on the power supply path of the substation fails, the new energy unit will provide power. Since the output of the new energy unit is random, when the output is less than the demand of the load node, it can only supply the electricity needs of some users, causing power outages for the remaining users. The annual average fault frequency, annual average outage amount, and annual average outage duration are used as node reliability indicators, calculated using the following formulas: Average annual failure frequency: Where, λ i Let be the failure rate of node i. Let F be the failure rate of line element j, and F be the set of lines on the power supply path of node i. Average annual power outage volume: Among them, EENS i Let L be the power outage amount of the i-th node. i Let P be the load size of the i-th node. ni Let T be the output that the nth renewable energy unit can provide to node i. r T represents the repair time for the faulty line. s This refers to the switching operation time for changing the power supply path. For new energy unit installation nodes, the second term in the above formula is zero. For ordinary load nodes, P... ni Zero; Average annual power outage duration: The system average power outage frequency, system average power outage duration, expected power shortage, and average power availability are selected as system reliability indicators, and the calculation formulas are as follows: Average system outage frequency: Where, N i It is the number of users on the i-th load node; Average system outage duration: Low battery expectation: Where S is the set of all load nodes in the system; Average power availability: S303. Considering the randomness of the output and load of new energy units, calculate the node reliability index using the following formula: Average annual failure frequency: For both ordinary load nodes and nodes covered by new energy units, faults in line components along the power supply path of the substation will cause short-term or long-term power outages at the nodes. Therefore, the calculation formula is as follows: For new energy unit installation nodes, when a line component on the power supply path of the substation fails, if the output of the new energy unit is greater than the load of the node, no power outage event will occur at that node. Therefore, the calculation formula is: The probability that the output of the new energy generating units is less than the load of the node is: Where f is the probability density function of the load at node i, and g is the probability density function of the output provided by the renewable energy unit to node i.

[0011] Average annual power outage volume: Where E is the power outage amount after component j fails, expressed as: h(E) is the probability density function of E, and the formula for calculating ∫Eh(E)dE is: Average annual power outage duration: Where T is the power outage time after component j fails, and the formula for calculating ∫Ts(T)dT is: s(T) is the probability density distribution function of T, expressed as: The formula for calculating the system reliability index remains unchanged.

[0012] The beneficial effects of this invention are as follows: This invention can quickly and efficiently perform analytical reliability assessments on distribution networks containing renewable energy generator units. It also considers the randomness of renewable energy generator unit output and the uncertainty of load, analyzes the distribution system's ability to continuously supply power to users, reflects the improvement in distribution system reliability due to the integration of renewable energy units, and guides relevant power grid departments in integrating renewable energy units into the distribution system. The first step of this invention establishes an analytical method for reliability assessment of distribution systems considering the uncertainty of renewable energy generation and load, clarifying the data and steps required for the assessment. The second step establishes component models, modeling transmission lines, renewable energy unit output, and load, characterizing the randomness of renewable energy unit output and the uncertainty of load. The third step proposes a system state analysis method, classifying load nodes according to the presence or absence of renewable energy unit power supply, considering the impact of different line faults under the support of renewable energy units, and calculating reliability indicators based on probability density distribution functions to complete the reliability assessment. This method uses an analytical approach, which significantly improves computational efficiency compared to existing Monte Carlo simulation methods. Attached Figure Description

[0013] Figure 1 This is a flowchart of the present invention; Figure 2 This is a two-state model diagram of a power transmission line; Figure 3 This is a graph showing the relationship between wind turbine output and wind speed; Figure 4 This is a schematic diagram of the load curve; Figure 5 This is a schematic diagram of load node classification; Figure 6 This is the modified IEEE 37-node system architecture diagram; Figure 7 This is the wind speed probability density distribution map used in the example; Figure 8 This is the probability density distribution of light irradiance used in the example. Detailed Implementation

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Example: Distribution network reliability assessment methods that consider the uncertainties of renewable energy generation and load, such as Figure 1 As shown, it includes the following steps: S1, determine the relevant parameters of the power distribution system, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the installation location of the new energy generator in the system and the probability density distribution function of its output; S103, determine the load curves of each node; S104, determine the repair time of the faulty component in the system and the time required for the switch operation; S2, Establish a line reliability model, which specifically includes establishing a line reliability model, establishing a new energy generator output model, and establishing a load model; S3 analyzes the consequences of component failures to determine the reliability of the power distribution network system.

[0016] In S2, the line reliability model specifically uses a two-state model to represent the line reliability model. For example... Figure 2 As shown, the failure rate of a component under normal operating conditions is the frequency of failures per unit time, and the repair rate of a component under fault conditions is the repair frequency per unit time.

[0017] The aforementioned new energy generator output model specifically includes wind turbine output modeling and photovoltaic generator output modeling. The wind turbine output modeling specifically includes: The output of a wind turbine is related to wind speed, which follows a two-parameter Weibull distribution with the following probability density function: Using historical wind speed data, the parameters a and b in the Weibull distribution are obtained using the maximum likelihood estimation method, and a Weibull distribution model of wind speed is fitted. The output of a wind turbine varies with wind speed. If the wind speed is too low or too high, the wind turbine will not operate. The relationship between wind turbine output and wind speed is as follows: Figure 3 As shown, the expression is as follows: The expressions for A, B, and C are as follows: Where P is the actual output of the wind turbine, P r For rated power, V ci To cut off the wind speed, V r For the rated wind speed, V co To determine the wind speed, the probability density function of the wind turbine's output can be obtained. The specific modeling of the output of the photovoltaic generator is as follows: The output of the photoelectric generator is related to the light irradiance, which follows a beta distribution with the following probability density function: Where r is the irradiance (W / m²) 2 ), where a is the location parameter of the beta distribution, b is the shape parameter of the beta distribution, and the expression for the gamma function Γ(x) is: Using historical data, the parameters a and b of the beta distribution are determined using the method of moments, and a beta distribution model of irradiance is obtained by fitting the model. The relationship between photovoltaic unit output and solar irradiance is as follows: Where P represents the actual output of the photovoltaic unit, and A represents the area of ​​the photovoltaic cell (m²). 2 ), where η is the conversion efficiency of the photovoltaic cell, expressed as: η=η0[1-γ(T t -T0)] Where η0 is the photovoltaic cell conversion efficiency at the reference temperature, γ is the temperature coefficient of the photovoltaic cell, and T t T0 is the ambient temperature, and T0 is the reference temperature of 298K. Based on this, the probability density function of the photovoltaic unit's output can be obtained.

[0018] The load model is represented by load curves at different stages of the year, or by monthly, daily, or hourly loads. This method obtains the probability density function of the load distribution by statistically analyzing the probability of different load magnitudes occurring, such as... Figure 4 As shown.

[0019] Specifically, S3 is: S301. Load nodes are classified according to different power supply methods to determine the power outage status of a load node in the event of a component failure. When a load node is outside the power supply range of the new energy generator, such a node is called a normal load node. Figure 5 Node 6 in the diagram is solely powered by an upstream low-voltage substation. When a line component on the power supply path fails, the load node experiences a power outage, the duration of which is equal to the repair time of the faulty component. When a new generator set is installed at the load node, this type of node is called a new energy unit installation node, such as... Figure 5 Node 3 in the diagram can be powered simultaneously by an upstream low-voltage substation and the renewable energy generating units on that node. If a component in the power supply path of the low-voltage substation fails, the renewable energy generating units can continue to supply power without causing a power outage, or at least ensure that some loads remain powered. When a load node itself does not have a renewable energy generating unit installed, but is within the power supply range of a nearby renewable energy generating unit, this type of node is called a renewable energy generating unit coverage node, such as... Figure 5Node 2 in the diagram can be simultaneously powered by both the upstream low-voltage substation and the renewable energy units on this node. When a component on the substation's power supply path fails, while the renewable energy unit's power supply path remains intact, the power supply path can be switched via a switch operation. Based on the output level of the renewable energy unit, it can be determined whether the node will have its power supply fully or partially restored. S302. For renewable energy unit installation nodes and renewable energy unit coverage nodes, when a component on the substation's power supply path fails, the renewable energy unit will provide power. Since the output of the renewable energy unit is random, when its output is less than the load node's demand, it can only supply the electricity needs of some users, resulting in power outages for the remaining users. The annual average fault frequency, annual average outage amount, and annual average outage time are used as node reliability indicators, calculated using the following formulas: Average annual failure frequency: Where, λ i Let be the failure rate of node i. Let F be the failure rate of line element j, and F be the set of lines on the power supply path of node i. Average annual power outage volume: Among them, EENS i Let L be the power outage amount of the i-th node. i Let P be the load size of the i-th node. ni Let T be the output that the nth renewable energy unit can provide to node i. r T represents the repair time for the faulty line. s This refers to the switching operation time for changing the power supply path. For new energy unit installation nodes, the second term in the above formula is zero. For ordinary load nodes, P... ni Zero; Average annual power outage duration: The system average power outage frequency, system average power outage duration, expected power shortage, and average power availability are selected as system reliability indicators, and the calculation formulas are as follows: Average system outage frequency: Where, N i It is the number of users on the i-th load node; Average system outage duration: Low battery expectation: Where S is the set of all load nodes in the system; Average power availability: S303. Considering the randomness of the output and load of new energy units, calculate the node reliability index using the following formula: Average annual failure frequency: For both ordinary load nodes and nodes covered by new energy units, faults in line components along the power supply path of the substation will cause short-term or long-term power outages at the nodes. Therefore, the calculation formula is as follows: For new energy unit installation nodes, when a line component on the power supply path of the substation fails, if the output of the new energy unit is greater than the load of the node, no power outage event will occur at that node. Therefore, the calculation formula is: The probability that the output of the new energy generating units is less than the load of the node is: Where f is the probability density function of the load at node i, and g is the probability density function of the output provided by the renewable energy unit to node i.

[0020] Average annual power outage volume: Where E is the power outage amount after component j fails, expressed as: h(E) is the probability density function of E, and the formula for calculating ∫Eh(E)dE is: Average annual power outage duration: Where T is the power outage time after component j fails, and the formula for calculating ∫Ts(T)dT is: s(T) is the probability density distribution function of T, expressed as: The formula for calculating the system reliability index remains unchanged.

[0021] The method of the present invention will be further illustrated below with specific examples.

[0022] A modified IEEE-37 node test system is used as a case study, such as... Figure 6As shown, the original system had 37 load nodes, 35 lines, and 1 transformer. To facilitate the demonstration of the method proposed in this paper, the following modifications were made: load node 799 was changed to a low-voltage substation node, i.e., the power supply node of the distribution network; the transformer between load nodes 709 and 775 was replaced with line L35 with a length of 500m; wind turbines and photovoltaic units were installed at nodes 734 and 720, respectively, and the power supply range was the nodes within the two lines. The parameters of the wind turbines and photovoltaic units are shown in Table 1 and Table 2, respectively.

[0023] Table 1 Cut-in wind speed 3m / s Cut off the wind speed 30m / s Rated wind speed 10m / s Rated power 30kW Table 2 Conversion efficiency 15％ The wind speed and photovoltaic illuminance distributions were obtained by fitting minute-by-minute data collected by the National Renewable Energy Laboratory M2 in the first half of 2022, as shown in the figure. Figure 7 and Figure 8 As shown.

[0024] The load probability density distribution adopts a Gaussian distribution, and the mean load of each node is shown in Table 3.

[0025] Table 3 701 30.4 85 713 37.61 88 732 48.5 130 702 18.61 48 714 31.51 74 733 5.98 10 703 38.84 103 718 18.89 50 734 7.49 15 704 26.39 65 720 12.17 30 735 12.45 23 705 12.58 30 722 15.36 42 736 35.12 58 706 29.58 72 724 29.37 70 737 11.03 18 707 31.09 75 725 33.82 80 738 46.84 145 708 22.57 20 727 43.9 125 740 7.41 12 709 43.08 128 728 41.18 110 741 31.86 90 710 12.57 23 729 35.71 89 742 12.73 32 711 48.23 140 730 43.98 114 744 19.04 54 712 21.06 52 731 24.38 56 775 40.6 108 The reliability indices of each node were calculated using this method, as shown in Table 4. Comparing the two cases of having new energy sources and not having new energy sources in the system, it can be seen that for ordinary load nodes, the reliability indices are the same in both cases; for nodes with new energy units installed, the reliability indices of the nodes improve when new energy units are installed; for nodes covered by new energy units, the failure frequency is the same in both cases, but with the supply of new energy units, the average outage time and outage amount are reduced, and the reliability is improved.

[0026] Table 4 The system reliability indicators are shown in Table 5. Comparing the cases with and without new energy generating units, the analysis shows that installing distributed new energy generating units in the power distribution system can improve the reliability of the power distribution system.

[0027] Table 5 SAIFI (times / household) 0.3313 0.3359 SAIDI (hours / year) 1.6031 1.6796 EENS (kW) 157.5383 165.7082 ASAI 99.9817％ 99.9808％ The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A distribution network reliability assessment method considering the uncertainty of new energy power generation and load, characterized by: Includes the following steps: S1, determine the relevant parameters of the power distribution system, specifically: S101, Determine the network topology parameters of the power distribution system, including: branch information, user information and power supply information; S102, determine the installation location of the new energy generator in the system and the probability density distribution function of its output; S103, determine the load curves of each node; S104, determine the repair time of the faulty component in the system and the time required for the switch operation; S2, Establish a line reliability model, which specifically includes establishing a line reliability model, establishing a new energy generator output model, and establishing a load model; S3, analyze the consequences of component failures to determine the reliability of the power distribution network system; Specifically, S3 is: S301. Load nodes are classified according to different power supply methods so as to determine the power outage status of the load node when a component fails. When the load node is outside the power supply range of the new energy generator, such a node is called an ordinary load node; when a new generator set is installed on the load node, such a node is called a new energy generator set installation node; when the load node itself does not have a new energy generator set installed, but is within the power supply range of a nearby new energy generator set, such a node is called a new energy generator set coverage node. S302. For new energy unit installation nodes and new energy unit coverage nodes, when a component on the power supply path of the substation fails, the new energy unit shall provide power. The annual average fault frequency, annual average power outage amount and annual power outage time shall be used as node reliability indicators. S303. Considering the randomness of the output and load of new energy units, calculate the node reliability index.

2. The distribution network reliability assessment method considering the uncertainty of new energy power generation and load as described in claim 1, characterized in that, In S2, the line reliability model is specifically a two-state model used to represent the line reliability model.

3. The distribution network reliability assessment method considering the uncertainty of new energy power generation and load as described in claim 1, characterized in that, The aforementioned new energy generator output model specifically includes wind turbine output modeling and photovoltaic generator output modeling. The wind turbine output modeling specifically includes: The output of a wind turbine is related to wind speed. It follows a two-parameter Weibull distribution, with the following probability density function: Using historical wind speed data, the parameters in the Weibull distribution are determined using the maximum likelihood estimation method. and The Weibull distribution model of wind speed was obtained by fitting the model. The output of a wind turbine varies with wind speed. Wind turbines cannot operate when the wind speed is too low or too high. The relationship between wind turbine output and wind speed is expressed as follows: in, , , The expression is as follows: in, For the actual output of the wind turbine unit Rated power, To cut into wind speed, Rated wind speed, To determine the wind speed, the probability density function of the wind turbine's output can be obtained. The specific modeling of the output of the photovoltaic generator is as follows: The output of the photoelectric generator is related to the light irradiance, which follows a beta distribution with the following probability density function: in, Light irradiance , These are the location parameters of the beta distribution. It is the shape parameter of the beta distribution, the gamma function. The expression is: Using historical data, the parameters of the beta distribution are determined using the method of moments. and The beta distribution model of light irradiance was obtained by fitting. The relationship between photovoltaic unit output and solar irradiance is as follows: in, To provide actual power to photovoltaic units, For the area of ​​photovoltaic cells , The conversion efficiency of a photovoltaic cell is expressed as: in, Photovoltaic cell conversion efficiency at reference temperature The temperature coefficient of a photovoltaic cell. It is the ambient temperature. The reference temperature is 298 degrees Celsius. Based on this, the probability density function of the photovoltaic unit's output can be obtained.

4. The distribution network reliability assessment method considering the uncertainty of new energy power generation and load as described in claim 1, characterized in that, The load model is represented by load curves at different stages of the year, or by monthly, daily, or hourly loads.

5. The distribution network reliability assessment method considering the uncertainty of new energy power generation and load as described in claim 1, characterized in that, S302. For new energy unit installation nodes and new energy unit coverage nodes, when a component on the power supply path of the substation fails, the new energy unit shall provide power. The annual average fault frequency, annual average power outage amount, and annual power outage time shall be used as node reliability indicators, and the calculation formula is as follows: Average annual failure frequency: in, For nodes Failure rate, For circuit components Failure rate, For nodes A collection of lines along the power supply path; Average annual power outage volume: in, For the first Power outage amount at each node, For the first The load size of each node, For the first A new energy unit to the node The effort provided The repair time for the faulty line. This refers to the switching operation time for changing the power supply path; for new energy unit installation nodes, the second term in the above formula is zero, and for ordinary load nodes, Zero; Average annual power outage duration: The system average power outage frequency, system average power outage duration, expected power shortage, and average power availability are selected as system reliability indicators, and the calculation formulas are as follows: Average system outage frequency: in, It is the first i Number of users per load node; Average system outage duration: Low battery expectation: in, It is the set of all load nodes in the system; Average power availability: S303. Considering the randomness of the output and load of new energy units, calculate the node reliability index using the following formula: Average annual failure frequency: For both ordinary load nodes and nodes covered by new energy units, faults in line components along the power supply path of the substation will cause short-term or long-term power outages at the nodes. Therefore, the calculation formula is as follows: For new energy unit installation nodes, when a line component on the power supply path of the substation fails, if the output of the new energy unit is greater than the load of the node, no power outage event will occur at that node. Therefore, the calculation formula is: The probability that the output of the new energy generating units is less than the load of the node is: in, For nodes The probability density function of the load. For new energy units, provide nodes The probability density function of the output force provided; Average annual power outage volume: in, For components The power outage amount after a fault is expressed as: for The probability density distribution function, The calculation formula is: Average annual power outage duration: in, For components Power outage time after the fault The calculation formula is: for The probability density distribution function is expressed as: The formula for calculating the system reliability index remains unchanged.

Images