A power distribution system operation reliability evaluation method considering time-varying characteristics of severe weather intensity

Abstract

The application discloses a power distribution system operation reliability evaluation method considering time variation of severe weather intensity, and relates to the field of power distribution system reliability evaluation, and specifically comprises the following steps: generating an accident state sequence and a duration sequence under extreme weather; calculating load reduction of all load nodes under each accident state; calculating system economic loss samples based on the load reduction and the accident duration, and obtaining a reliability index estimation of expected economic loss; further, the method evaluates the contribution value of each load node to the index, and finally generates a reinforcement priority ranking of overhead lines and load nodes. The application aims to improve the ability of the power distribution system to cope with extreme weather disasters by reinforcing overhead lines and load nodes, and reduce economic losses caused by line damage and load reduction.

Description

Technical Field

[0001] This invention relates to a method for assessing the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather, and belongs to the technical field of power distribution system reliability assessment. Background Technology

[0002] With the increasing frequency of extreme events (such as extreme weather, geographical disasters, and man-made events), the operational reliability of power distribution systems is seriously threatened. The most direct impact of extreme events is a significant increase in the failure rate of overhead lines, making them more prone to failure under extreme conditions than under normal conditions. Therefore, establishing reliability models for overhead lines and power distribution systems that can account for the impact of extreme events and incorporating them into reliability assessment systems has become a research hotspot in recent years.

[0003] However, current methods for assessing the reliability of distribution systems typically assume that weather intensity remains constant during extreme weather events. Therefore, homogeneous Poisson process models are commonly used to model the state changes of overhead lines and the system under extreme weather conditions. The premise of the homogeneous Poisson process model is that the intensity of the event is fixed, meaning the impact of extreme weather is considered to be static. In reality, the intensity of extreme weather fluctuates significantly over time, and the reliability parameters of overhead lines are highly sensitive to weather intensity. Intensity fluctuations not only affect the probability of line failure but also make models based on the constant intensity assumption difficult to accurately describe the actual state changes of lines under extreme weather conditions. This limitation in assumptions leads to low accuracy in existing assessment methods, making it difficult to effectively evaluate the true reliability of distribution systems under complex extreme events. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for evaluating the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather. Based on the evaluation results, weak overhead lines and load nodes of the power distribution network can be reinforced before a disaster occurs. The reinforcement of overhead lines and load nodes can effectively reduce economic losses caused by line damage and load reduction during extreme weather.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] This invention provides a method for assessing the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather, including: S1. Acquire power flow data of the power distribution system, reliability data of overhead lines, meteorological forecast data of extreme weather, and load forecast data during the duration of extreme weather. S2, Constructing the matrix sum matrix , Two lines containing only 0 elements A matrix of columns For an element that is all 0 OK A matrix of columns, where, Indicates the number of overhead lines in the power distribution system. Indicates the number of load nodes in the power distribution system; S3. Based on meteorological forecast data of extreme weather, load forecast data during the duration of extreme weather, and reliability data of overhead lines, obtain the curves of the failure rate and repair rate of overhead lines during extreme weather as a function of time. S4, let the first The overhead lines remain operational during extreme weather, and a sequence of accident states and their durations is randomly generated for the power distribution system. S5. Calculate all fault states in the fault state sequence of the power distribution system. The impact of load shedding at each load node; S6, according to Calculate the economic loss sample of the system based on the load shedding impact at each load node and the duration of the fault state of the power distribution system. S7. Calculate the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample. S8. Repeat S4~S8 until the variance coefficient of the expected economic loss reliability index estimate is less than the preset termination threshold. S9. Calculate the contribution of each load node to the estimated reliability index of expected economic loss based on the system economic loss sample. S10, the first The serial numbers of each overhead line and the estimated reliability index of expected economic loss are stored sequentially in a matrix. The Column, and the first The serial numbers of each overhead line and the contribution values ​​of each load node to the estimated reliability index of expected economic loss are stored in a matrix. The List; S11, Order Repeat S4~S11 until... ; S12, to The column according to The second row of data is sorted from left to right, and then... Sort the columns so that the elements in the first row are the same as... If the elements in the first row are identical, output the matrix. and ,in, It contains priority information for the reinforcement of overhead power lines. It contains the reinforcement priority ranking information for load nodes.

[0007] Furthermore, the reliability data of the power distribution system includes the failure rate and repair rate of overhead lines in the power distribution system under normal weather conditions.

[0008] Furthermore, the process of obtaining curves showing the changes in the failure rate and repair rate of overhead lines over time during extreme weather based on meteorological forecast data of extreme weather, load forecast data during the duration of extreme weather, and overhead line reliability data includes:

[0009] The curve of weather intensity changing over time is obtained based on meteorological forecast data of extreme weather.

[0010] Based on the curves of weather intensity changing over time, load forecast data during extreme weather periods, and the failure rate and repair rate of overhead lines under normal weather conditions, the curves of failure rate and repair rate of overhead lines during extreme weather periods changing over time are calculated, and their calculation expressions are as follows: ; ; in, This represents the wind speed at time t. This indicates the failure rate of overhead power lines under normal weather conditions. Indicates wind speed as The failure rate of overhead lines at that time This indicates the repair rate of overhead power lines under normal weather conditions. Indicates wind speed as Repair rate of overhead power lines.

[0011] Furthermore, the aforementioned first... The overhead lines remain operational during extreme weather events. A sequence of fault states and their durations is randomly generated for the power distribution system, including: a. When When the number is odd, the process starts at the beginning of the process. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of operation and the first outage of an overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the operation time. when When the number is even, the process starts at the beginning of the process. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of the outage process and the first repair of the overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the outage time. b. Round the sample values ​​to obtain... and use it as a row vector The One element; c. For row vectors Summing the elements in the array yields the result. ; d. Order , Repeat steps a through d until... Greater than or equal to ,in, The preset duration of extreme weather; e. Let row vectors The The elements equal to and output row vectors row vector The sequence of elements in is the first one. The sequence of operating status and outage status of an overhead line during extreme weather, as well as the sequence of duration of operating status and outage status; f. Combine the operating status and outage status sequences of all overhead lines during extreme weather with the load sequence to obtain the fault status sequence and fault status duration sequence of the power distribution system.

[0012] Furthermore, the distribution function of the random variable representing the time interval between the start of operation and the first outage of the overhead line is expressed as follows: ; in, The distribution function of a random variable representing the time interval from the start of operation of an overhead line to the first outage; A random variable representing the time interval from the start of operation of an overhead line to the first outage; This is the start time of the process; Indicates the length of time elapsed since the start of the process. The cumulative number of outages of the overhead lines; Indicates the length of time elapsed since the start of the process. The cumulative outage intensity function after that, ; Indicates time The failure rate of overhead lines;

[0013] The expression for the distribution function of the random variable representing the time interval between the start of the outage and the first repair of the overhead line is as follows: ; in, The distribution function of a random variable representing the time interval between the start of the shutdown process and the first repair of an overhead power line; A random variable representing the time interval between the start of the shutdown process and the first repair of an overhead line; Indicates the length of time elapsed since the start of the process. The cumulative number of repairs to the overhead power lines; Indicates the length of time elapsed since the start of the process. The cumulative repair strength function after that, ; Indicates time The overhead line repair rate.

[0014] Furthermore, all fault states in the fault state sequence of the power distribution system are... The impact of load shedding at each load node is obtained by analyzing the power flow data of the distribution system using a power flow analysis algorithm.

[0015] Furthermore, the aforementioned according to The expression for calculating the system economic loss sample based on the load shedding impact at each load node and the duration sequence of the fault state of the power distribution system is as follows: ; in, Represents a random variable indicating economic loss. The random variable represents the duration of accident state c during extreme weather. Indicates the duration of extreme weather. Indicates the number of load nodes in the power distribution system. This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load on the k-th load node.

[0016] Furthermore, the calculation of the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample includes:

[0017] The reliability index estimate of expected economic loss is calculated based on the system's economic loss sample, and its expression is as follows: ; in, This represents the reliability estimate of expected economic loss. This indicates the total number of samples. Represents a random variable indicating economic loss. express The One sample, The random variable represents the duration of accident state c during extreme weather. express The One sample, This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load on the k-th load node;

[0018] The variance coefficient of the expected economic loss reliability index estimate is calculated based on the expected economic loss reliability index estimate, and its expression is as follows: ; in, The variance coefficient represents the estimate of the reliability index of expected economic loss. This represents the variance of the estimate of the reliability index of expected economic loss. This represents the expected value of the reliability index estimate of expected economic losses.

[0019] Furthermore, the expression for calculating the contribution value of each load node to the estimated reliability index of expected economic loss based on the system economic loss sample is as follows: ; in, Indicates the k-th load node pair The contribution value, Let the random variable represent the economic loss of the k-th load node. express The One sample.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0021] This invention proposes a duration model for overhead lines and system states based on a non-homogeneous Poisson process. By considering the characteristics of overhead line reliability parameters changing with weather intensity during extreme weather events, the accuracy of reliability indices is significantly improved. Based on the expected loss reliability index, the reinforcement priorities for overhead lines and load nodes are ranked, thereby achieving efficient utilization of limited reinforcement resources. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a method for assessing the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather, according to one embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of an overhead line and system fault state duration model of a two-dimensional system in one embodiment of the present invention;

[0024] Figure 3This is a schematic diagram illustrating the modeling approach for the duration of overhead line status based on multiple simulations, as described in one embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather, comprising the following steps:

[0027] S1. Obtain power flow data, overhead line reliability data, extreme weather forecast data, and load forecast data during the duration of extreme weather in the power distribution system. Among them, the overhead line reliability data includes the failure rate and repair rate of overhead lines in the power distribution system under normal weather conditions.

[0028] S2, Constructing the matrix sum matrix , Two lines containing only 0 elements A matrix of columns For an element that is all 0 OK A matrix of columns, where, Indicates the number of overhead lines in the power distribution system. This indicates the number of load nodes in the power distribution system.

[0029] S3. Based on meteorological forecast data for extreme weather, load forecast data during the duration of extreme weather, and overhead line reliability data, obtain curves showing the changes in the failure rate and repair rate of overhead lines over time during extreme weather. Specifically:

[0030] The curve of weather intensity changing over time is obtained based on meteorological forecast data of extreme weather.

[0031] Based on the curves of weather intensity changing over time, load forecast data during extreme weather periods, and the failure rate and repair rate of overhead lines under normal weather conditions, the curves of failure rate and repair rate of overhead lines during extreme weather periods changing over time are calculated, and their calculation expressions are as follows: ; ; in, This represents the wind speed at time t. This indicates the failure rate of overhead power lines under normal weather conditions. Indicates wind speed as The failure rate of overhead lines at that time This indicates the repair rate of overhead power lines under normal weather conditions. Indicates wind speed as Repair rate of overhead power lines.

[0032] S4, let the first The overhead lines remain operational during extreme weather events, and a sequence of accident states and their durations is randomly generated for the power distribution system.

[0033] Because steady-state probability models and traditional state duration models (i.e., homogeneous Poisson process models) cannot account for changes in overhead line reliability parameters with weather intensity during extreme weather events, they can lead to significant errors in reliability indices. To address this issue, this embodiment proposes an overhead line and system state duration model based on a non-homogeneous Poisson process.

[0034] The following section will start with a simple example system to present the derivation of the model and gradually derive the mathematical expression of the model based on the principle of the Poisson process.

[0035] The state duration model can fully describe the state transition process of overhead lines and systems. Figure 2 A graphical representation of the overhead lines and system state duration model of a two-dimensional system is presented. An overhead line state value of 1 indicates the overhead line is in operation; a state value of 0 indicates the overhead line is out of operation; a system state value of 0 indicates both overhead lines 1 and 2 are out of operation; a system state value of 1 indicates overhead line 1 is in operation while overhead line 2 is out of operation; a system state value of 2 indicates overhead line 1 is out of operation while overhead line 2 is in operation; and a system state value of 3 indicates both overhead lines 1 and 2 are in operation. For any system, the duration of each overhead line's operation or outage is a random variable. If the distribution of the random variable representing the overhead line's state duration can be obtained, then samples of the overhead line's state duration can be randomly generated according to this distribution, and these samples can be further combined to form a sample of the system's state duration.

[0036] According to the definition of a Poisson process, if all outage durations are removed and the durations of the remaining operating states are merged during the state transition of an overhead line, then the overhead line will start counting from the beginning of the time. Length of time period The cumulative number of service interruptions Let it be a random variable. When fixed, When taking values ​​not less than 0, the family of random variables This is a Poisson process. Similarly, if all normal state durations are removed and the remaining out-of-service durations are merged during the state transition process of an overhead line, then the overhead line will start from the time of timing... Length of time period Cumulative number of repairs Let be a random variable and the corresponding family of random variables Both are Poisson processes. The difference between the two Poisson processes mentioned above is that the former is a running process while the latter is a shutdown process (i.e., a repair process).

[0037] The following describes the operation process of overhead power lines. For example, the basic properties of a Poisson process are given below:

[0038] (1) ;

[0039] (2) It is an independent incremental process;

[0040] (3) For any time period , ;

[0041] (4) .

[0042] Of the above properties, This indicates that the cumulative number of overhead line outages is 0 after a time period of 0 hours from the start of the process. Indicates time The failure rate of overhead lines. For non-overhead lines, the failure rate of overhead lines is constant. In this case, the process... This is a homogeneous Poisson process. For overhead lines, the fault rate is a variable. Given a variable fault rate, the process... This is a non-homogeneous Poisson process. A homogeneous Poisson process can be considered a special case of a non-homogeneous Poisson process.

[0043] Based on the above four fundamental properties, a fifth property can be derived as follows: ; In the formula, It is a non-negative integer. Indicates the length of time elapsed since the start of the process. The cumulative outage intensity function can be expanded as follows:

[0044] ;

[0045] make A random variable representing the time interval between the first shutdown and the start of the operation, also known as the runtime random variable, is a variable that represents the time interval between the start of the operation and the first shutdown. Let be the starting point of the process. express The distribution function of the random event is then derived based on the equivalence of random events and the fifth property of the Poisson process. The formula is as follows: ; in, The distribution function of a random variable representing the time interval from the start of operation of an overhead line to the first outage; A random variable representing the time interval from the start of operation of an overhead line to the first outage; This is the start time of the process; Indicates the length of time elapsed since the start of the process. The cumulative number of outages of the overhead lines; Indicates the length of time elapsed since the start of the process. The cumulative outage intensity function after that, ; Indicates time The failure rate of overhead lines.

[0046] Similarly, during the shutdown of overhead power lines In the middle, order This represents the random variable indicating the time interval between the start of the shutdown process and the first repair, also known as the shutdown time random variable. Let... Represents the random variable of downtime The distribution function, then The formula is: ; in, The distribution function of a random variable representing the time interval between the start of the shutdown process and the first repair of an overhead power line; A random variable representing the time interval between the start of the shutdown process and the first repair of an overhead line; Indicates the length of time elapsed since the start of the process. The cumulative number of repairs to the overhead power lines; Indicates the length of time elapsed since the start of the process. The cumulative repair strength function after that, ; Indicates time The overhead line repair rate.

[0047] According to the above formula, given the overhead line state transition rate function and the initial time of the process, the distribution of the overhead line state duration can be obtained. However, overhead lines in a distribution system are in an alternating cycle of normal operation and shutdown. Specifically, the first shutdown time of an operation process is the end time of that process and the start time of the next shutdown process. Similarly, the first repair time of a shutdown process is the end time of that process and the start time of the next operation process. In this case, only the initial time of the first process is known. To address this problem, this embodiment uses multiple simulations to establish a state duration model of overhead lines during extreme weather conditions.

[0048] Figure 3 This paper demonstrates a modeling approach for the duration of overhead line status based on multiple simulations. It assumes that overhead lines are in normal operation at the onset of extreme weather events. The process numbers indicate the chronological order of events. Odd-numbered Poisson processes represent operating processes; even-numbered Poisson processes represent shutdown processes. Represents a runtime random variable; This represents a random variable indicating the downtime. This represents the start time of a process, and its value can distinguish between different running processes, shutdown processes, running time random variables, and shutdown time random variables. If all... and By determining the starting time of process 1, we can sample the operating and shutdown times according to the distribution function to obtain the complete state transition process of the overhead line during extreme weather. Let the starting time of process 1 be 0. Then, process 1 and its operating time random variables can be represented as follows: and .right Sampling is performed and it is assumed that the sample is In this case, process 2 and its downtime random variables can be expressed as follows: and Further investigation Sampling is performed and it is assumed that the sample size is... In this case, process 3 and its running time random variable can be represented as follows: and By analogy, a complete model of the duration of overhead power lines during extreme weather can be obtained.

[0049] Therefore, let the first... The overhead power lines remain operational during extreme weather events. The randomly generated fault state sequence and fault state duration sequence for the power distribution system specifically include:

[0050] a. When When the number is odd, the process starts at the beginning of the process. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of operation and the first outage of an overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the operation time.

[0051] when When the number is even, the process starts at the beginning of the process. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of the outage process and the first repair of the overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the outage time.

[0052] b. Round the sample values ​​to obtain... and use it as a row vector The One element;

[0053] c. For row vectors Summing the elements in the array yields the result. ;

[0054] d. Order , Repeat steps a through d until... Greater than or equal to ,in, The preset duration of extreme weather;

[0055] e. Let row vectors The The elements equal to and output row vectors row vector The sequence of elements in is the first one. The sequence of operating status and outage status of an overhead line during extreme weather, as well as the sequence of duration of operating status and outage status;

[0056] f. Combine the operating status and outage status sequences of all overhead lines during extreme weather with the load sequence to obtain the fault status sequence and fault status duration sequence of the power distribution system.

[0057] S5. Analyze the power flow data of the distribution system using a power flow analysis algorithm to obtain the state sequence of all fault states in the distribution system. The load shedding impact on individual load nodes is analyzed using a mature power flow analysis algorithm, which will not be elaborated upon here.

[0058] S6, according to The system's economic loss samples are calculated based on the load shedding impact sequence and the accident state duration sequence at each load node.

[0059] Considering the varying importance of different load nodes and the randomness of the simulation duration, this embodiment proposes an Expected Economic Loss (EEL) reliability index to quantify the potential losses of the power distribution system during extreme weather events. The importance of load nodes is measured by the economic loss corresponding to a unit load shedding at each node. The basic idea behind calculating the EEL index is to treat the economic loss of the power distribution system during extreme weather events as a random variable. (Unit: $), by calculating The EEL reliability index can be estimated using the sample mean method. It is important to note that... Theoretically, it is a continuous variable. However, after rounding the sample as described in the previous section, It is actually a discrete random variable, and its calculation model is as follows: ; in, Represents a random variable indicating economic loss. The random variable (in hours) represents the duration of accident state c during extreme weather. Its sample values ​​are derived from the simulated power distribution system state duration sequence. This indicates the duration of extreme weather events, and its value depends on weather forecast information. Indicates the number of load nodes in the power distribution system. This represents the power reduction (in MW) at the k-th load node caused by accident state c. This represents the cost parameter (in $ / MWh) for the load at the k-th load node. The cost parameter is set to differentiate the importance of the load.

[0060] S7. Calculate the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample. Specifically:

[0061] First, the reliability index estimate of expected economic loss is calculated based on the system's economic loss sample, and its expression is as follows: ; in, This represents the reliability estimate of expected economic loss. This indicates the total number of samples. Represents a random variable indicating economic loss. express The One sample, The random variable represents the duration of accident state c during extreme weather. express The One sample, This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load on the k-th load node.

[0062] Then, the variance coefficient of the expected economic loss reliability index estimate is calculated based on the expected economic loss reliability index estimate, and its expression is as follows: ; in, The variance coefficient represents the estimate of the reliability index of expected economic loss. This represents the variance of the estimate of the reliability index of expected economic loss. This represents the expected value of the reliability index estimate of expected economic losses.

[0063] S8. Repeat S4~S8 until the variance coefficient of the expected economic loss reliability index estimate is less than the termination threshold. .

[0064] S9. Calculate the contribution of each load node to the estimated reliability index of expected economic loss based on the system economic loss sample. The expression is as follows: ; in, Indicates the k-th load node pair The contribution value, Let the random variable represent the economic loss of the k-th load node. express The One sample.

[0065] S10, the first The serial numbers of each overhead line and the estimated reliability index of expected economic loss are stored sequentially in a matrix. The Column, and the first The serial numbers of each overhead line and the contribution values ​​of each load node to the estimated reliability index of expected economic loss are stored in a matrix. The List;

[0066] S11, Order Repeat S4~S11 until... ;

[0067] S12, to The column according to The second row of data is sorted from left to right, and then... Sort the columns so that the elements in the first row are the same as... If the elements in the first row are identical, output the matrix. and ,in, It contains priority information for the reinforcement of overhead power lines. It contains the reinforcement priority ranking information for load nodes.

[0068] This embodiment uses the IEEE RBTS BUS6 test system, which is a power distribution system with 40 load buses and 52 branches. It is assumed that all 52 branches will be affected by severe weather, and the annual peak load of the power distribution system is 20MW. For specific power flow data and reliability data under normal weather conditions in this embodiment, please refer to the literature R. Billinton and S. Jonnavithula. A test system for teaching overall power system reliability assessment[J]. IEEE Transactions on Power Systems, 1996, 4(4): 1670-1676.

[0069] For data related to regular severe weather, the overhead line failure rate during regular severe weather periods is set to the failure rate under strong winds of 33 m / s (i.e., the center wind speed of Typhoon Mikra, a Category 12 typhoon); the repair rate is set to be consistent with the parameters under normal weather conditions; the duration of regular severe weather is set to two days (i.e., the duration of the impact of Typhoon Mikra); the load level during regular severe weather periods is set to 100% peak load level; and the load level during normal weather periods is set to multi-level load levels. Each load level is, in turn, 20%, 40%, 60%, 80%, and 100% of the peak load level, with the same duration. For data related to extreme severe weather, the meteorological forecast data for extreme severe weather is set to the 24-hour wind speed data of a Category 14 typhoon. The overhead line repair rate during extreme severe weather periods is set to 0. The cost parameter for all load nodes is set to $103 / MWh.

[0070] The performance of the system state duration model based on the non-homogeneous Poisson process is shown in Table 1.

[0071] Table 1: Performance of the system state duration model for nonhomogeneous Poisson processes

[0072] Table 1 illustrates the performance of the proposed system state duration model based on a non-homogeneous Poisson process in this embodiment by comparing the impact of different system reliability models on the calculation efficiency and accuracy of the EEL index. To eliminate interference from the state influence database, the data in the table were obtained with the state influence database masked. NSD represents the system state duration model based on a non-homogeneous Poisson process proposed in this embodiment; SD-A represents the traditional state duration model based on the average wind speed during typhoon disasters; SD-H represents the traditional state duration model based on the highest wind speed during typhoon disasters; and SD-L represents the traditional state duration model based on the lowest wind speed during typhoon disasters. Compared to the three SD models, the NSD model is more consistent with reality because it considers changes in weather intensity. The EEL index value obtained by the NSD model is used as the benchmark value. According to the reliability index data, the results of the three SD models all have certain errors. Among them, the EEL index data obtained by SD-L is very small. This is because the critical wind speed set in the example is 25 m / s; while the lowest wind speed in the typhoon forecast data used is 18 m / s. In this case, the component failure rate is equal to the failure rate under normal weather conditions. Based on the reliability index calculation time data, it can be found that the SD-L model has a significantly longer computation time than other models. This is because the computational efficiency of the EEL index depends not only on the model but also on the system's reliability level. When the system's reliability level is high, simulation methods require a longer convergence time.

[0073] In summary, this embodiment provides a priority ranking for system hardening work before the arrival of extreme weather, and presents a system state duration model based on a non-homogeneous Poisson process to improve assessment efficiency while ensuring the accuracy of reliability assessment.

[0074] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the operational reliability of a power distribution system that takes into account the time-varying intensity of severe weather, characterized in that, include: S1. Acquire power flow data of the power distribution system, reliability data of overhead lines, meteorological forecast data of extreme weather, and load forecast data during the duration of extreme weather. S2, Constructing the matrix sum matrix , Two lines containing only 0 elements A matrix of columns For an element that is all 0 OK A matrix of columns, where, Indicates the number of overhead lines in the power distribution system. Indicates the number of load nodes in the power distribution system; S3. Based on meteorological forecast data of extreme weather, load forecast data during the duration of extreme weather, and reliability data of overhead lines, obtain the curves showing the changes in failure rate and repair rate of overhead lines over time during the duration of extreme weather. S4, let the first The overhead lines remain operational during periods of extreme weather, and a sequence of accident states and their durations is randomly generated for the power distribution system. S5. Calculate all fault states in the fault state sequence of the power distribution system. The impact of load shedding at each load node; S6, according to Calculate the economic loss sample of the system based on the load shedding impact at each load node and the duration of the fault state of the power distribution system. S7. Calculate the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample. S8. Repeat S4~S8 until the variance coefficient of the expected economic loss reliability index estimate is less than the preset termination threshold. S9. Calculate the contribution of each load node to the estimated reliability index of expected economic loss based on the system economic loss sample. S10, the first The serial numbers of each overhead line and the estimated reliability index of expected economic loss are stored sequentially in a matrix. The Column, and the first The serial numbers of each overhead line and the contribution values ​​of each load node to the estimated reliability index of expected economic loss are stored in a matrix. The List; S11, Order Repeat S4~S11 until... ; S12, to The column according to The second row of data is sorted from left to right, and then... Sort the columns so that the elements in the first row are the same as... If the elements in the first row are identical, output the matrix. and ,in, It contains priority information for the reinforcement of overhead power lines. It contains the reinforcement priority ranking information for load nodes.

2. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather, as described in claim 1, is characterized in that... The overhead line reliability data includes the failure rate and repair rate of overhead lines in the power distribution system under normal weather conditions.

3. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather, as described in claim 1, is characterized in that... The curves showing the changes in the failure rate and repair rate of overhead lines over time during the extreme weather period, obtained from meteorological forecast data of extreme weather, load forecast data during the duration of extreme weather, and overhead line reliability data, include: The curve of weather intensity changing over time is obtained based on meteorological forecast data of extreme weather. Based on the curves of weather intensity changing over time, load forecast data during extreme weather periods, and the failure rate and repair rate of overhead lines under normal weather conditions, the curves of failure rate and repair rate of overhead lines changing over time during extreme weather periods are calculated, and their calculation expressions are as follows: ； ； in, This represents the wind speed at time t. This indicates the failure rate of overhead power lines under normal weather conditions. Indicates wind speed as The failure rate of overhead lines at that time This indicates the repair rate of overhead power lines under normal weather conditions. Indicates wind speed as Repair rate of overhead power lines.

4. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather as described in claim 1, characterized in that, The first The overhead lines remain operational during periods of extreme weather. A sequence of fault states and their durations is randomly generated for the power distribution system, including: a. When the When the number of processes is odd, the process starts at the time specified in the process start time. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of operation and the first outage of an overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the operation time. When the When the number of processes is even, the process starts at the time specified in the process start time. Calculate the first The distribution function and its inverse function of the random variable representing the time interval between the start of the outage process and the first repair of the overhead line are used to generate a random number and substitute it into the inverse function to obtain the sample value of the outage time. b. Round the sample values ​​to obtain... and use it as a row vector The One element; c. For row vectors Summing the elements in the array yields the result. ; d. Order , Repeat steps a through d until... Greater than or equal to ,in, The preset duration of extreme weather; e. Setting row vectors The 1 element, and output row vector row vector The sequence of elements in is the first one. The sequence of operating status and outage status of an overhead line during the period of extreme weather, as well as the sequence of duration of operating status and outage status; f. Combine the operating status and outage status sequences of all overhead lines during the period of extreme weather with the load sequence to obtain the fault status sequence and fault status duration sequence of the power distribution system.

5. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather, as described in claim 4, is characterized in that... The expression for the distribution function of the random variable representing the time interval between the start of operation and the first outage of the overhead line is as follows: ； in, The distribution function of a random variable representing the time interval from the start of operation of an overhead line to the first outage; A random variable representing the time interval from the start of operation of an overhead line to the first outage; This is the start time of the process; Indicates the length of time elapsed since the start of the process. The cumulative number of outages of the overhead lines; Indicates the length of time elapsed since the start of the process. The cumulative outage intensity function after that, ; Indicates time The failure rate of overhead lines; The expression for the distribution function of the random variable representing the time interval between the start of the outage and the first repair of the overhead line is as follows: ； in, The distribution function of a random variable representing the time interval between the start of the shutdown process and the first repair of an overhead power line; A random variable representing the time interval between the start of the shutdown process and the first repair of an overhead line; Indicates the length of time elapsed since the start of the process. The cumulative number of repairs to the overhead power lines; Indicates the length of time elapsed since the start of the process. The cumulative repair strength function after that, ; Indicates time The overhead line repair rate, where P represents the probability of the event occurring.

6. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather as described in claim 1, characterized in that, All fault states in the fault state sequence of the power distribution system are The impact of load shedding at each load node is obtained by analyzing the power flow data of the distribution system using a power flow analysis algorithm.

7. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather as described in claim 1, characterized in that, According to The expression for calculating the system economic loss sample based on the load shedding impact at each load node and the duration sequence of the fault state of the power distribution system is as follows: ； in, Represents a random variable indicating economic loss. The random variable represents the duration of accident state c during the extreme weather event. Indicates the duration of extreme weather. Indicates the number of load nodes in the power distribution system. This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load on the k-th load node. This represents the set of accident states.

8. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather, as described in claim 1, is characterized in that... The calculation of the variance coefficient of the reliability index estimate of expected economic loss based on the system economic loss sample includes: The reliability index estimate of expected economic loss is calculated based on the system's economic loss sample, and its expression is as follows: ； in, This represents the reliability estimate of expected economic loss. This indicates the total number of samples. Represents a random variable indicating economic loss. express The One sample, The random variable represents the duration of accident state c during the extreme weather event. express The One sample, This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load at the k-th load node, where E represents the expected value. Represents a set of accident states; The variance coefficient of the expected economic loss reliability index estimate is calculated based on the expected economic loss reliability index estimate, and its expression is as follows: ； in, The variance coefficient represents the estimate of the reliability index of expected economic loss. This represents the variance of the estimate of the reliability index of expected economic loss. This represents the expected value of the reliability index estimate of expected economic losses. This represents the reliability estimate of the expected economic loss.

9. The method for assessing the operational reliability of a power distribution system considering the time-varying intensity of severe weather as described in claim 1, characterized in that, The expression for calculating the contribution of each load node to the estimated reliability index of expected economic loss based on the system economic loss sample is as follows: ； in, Indicates the k-th load node pair The contribution value, This represents the reliability estimate of expected economic loss. Let the random variable represent the economic loss of the k-th load node. express The There are 10 samples, and E represents the expected value. This indicates the total number of samples. Represents the set of accident states. express The i-th sample, The random variable represents the duration of accident state c during the extreme weather event. This represents the power reduction at the k-th load node caused by accident state c. This represents the cost parameter of the load on the k-th load node.

Images