Power distribution system operation reliability evaluation method considering severe weather intensity time variation

Abstract

The invention discloses a power distribution system operation reliability evaluation method considering severe weather intensity time-varying characteristics, and relates to the field of power distribution system reliability evaluation, and the method specifically comprises the steps: generating an accident state sequence and a duration time sequence in extreme weather; calculating the load cutting capacity of all load nodes in each accident state; based on the load cutting capacity and the accident duration, a system economic loss sample is calculated, the reliability index estimator of the expected economic loss is obtained accordingly, further, the contribution value of each load node to the index is evaluated by the method, and finally the reinforcement priority sequence of the overhead line and the load nodes is generated. The invention aims to improve the ability of a power distribution system to deal with extreme meteorological disasters and reduce economic loss caused by line damage and load reduction by reinforcing overhead lines and load nodes.

Description

Technical Field

[0001] The invention relates to a method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather, and belongs to the technical field of reliability evaluation of power distribution systems. Background Art

[0002] With the increasing frequency of extreme events (such as extreme weather, geographical disasters and man-made events), the operational reliability of distribution systems is seriously threatened. The most direct impact of extreme events is that the failure rate of overhead lines is significantly increased, making them more prone to failure under extreme conditions than under normal conditions. Based on this, establishing a reliability model for overhead lines and distribution systems that can take into account the impact of extreme events and incorporating it into the reliability assessment system has become a hot topic in recent years.

[0003] However, current distribution system reliability assessment methods usually assume that the weather intensity remains constant during extreme weather, so the homogeneous Poisson process model is generally used to model the state changes of overhead lines and systems under extreme weather. The premise of the homogeneous Poisson process model is that the intensity of the event is fixed, that is, the impact of extreme weather is considered to be non-fluctuating over time. In fact, the intensity of extreme weather can fluctuate significantly over time, and the reliability parameters of overhead lines are very sensitive to weather intensity. Intensity fluctuations not only affect the failure probability of the line, but also make it difficult for models based on the constant intensity assumption to accurately describe the actual state changes of the line under extreme weather. The limitations of this assumption lead to low accuracy of existing assessment methods, making it difficult to effectively assess the true reliability of distribution systems under complex extreme events. Summary of the invention

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

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather, comprising: S1. Obtain power flow data of the distribution system, overhead line reliability data, extreme weather forecast data, and load forecast data during the period of extreme weather; S2. Build a matrix and matrix , For two rows with all elements being 0 A matrix of columns, For an element that is all 0 OK A matrix of columns, where represents the number of overhead lines in the distribution system, Indicates the number of load nodes in the distribution system; S3, obtaining a curve showing the change of the failure rate and repair rate of the overhead line during the extreme weather period over time according to the meteorological forecast data of the extreme weather, the load forecast data during the extreme weather period, and the reliability data of the overhead line; S4, set The overhead lines remain in operation during extreme weather, and the accident state sequence and accident state duration sequence of the distribution system are randomly generated; S5. Calculate all the accident states in the accident state sequence of the distribution system. The impact of load shedding on each load node; S6. According to The load shedding effect on each load node and the accident state duration sequence of the distribution system are used to calculate the system economic loss samples; S7. Calculate the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample; S8, repeat S4 to S8 until the variance coefficient of the estimated reliability index of expected economic loss is less than the preset termination threshold; S9. Calculate the contribution value of each load node to the estimated value of the expected economic loss reliability index based on the system economic loss sample; S10, The serial numbers of the overhead lines and the estimated reliability index of expected economic loss are stored in the matrix No. Column, and The serial number of the overhead line and the contribution value of each load node to the estimated value of the expected economic loss reliability index are stored in the matrix No. List; S11. Order , repeat S4~S11 until ; S12, yes The columns are sorted by The second row of data is sorted from left to right, and Sort the columns so that the elements of the first row are The elements in the first row are consistent, and the output matrix and ,in, contains the reinforcement priority information for overhead lines, Contains the reinforcement priority sorting information of the 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 curves showing the changes of the failure rate and repair rate of overhead lines during extreme weather with time obtained based on the meteorological forecast data of extreme weather, the load forecast data during the extreme weather period, and the reliability data of overhead lines include:

[0009] Based on the meteorological forecast data of extreme weather, a curve showing the change of weather intensity over time is obtained;

[0010] Based on the curve of weather intensity changing with time, the load forecast data during the extreme weather period, and the failure rate and repair rate of overhead lines in normal weather, the curve of the failure rate and repair rate of overhead lines changing with time during extreme weather is obtained. The calculation expressions are as follows: ; ; in, represents the wind speed at time t, represents the failure rate of overhead lines under normal weather conditions, Indicates wind speed The failure rate of overhead lines is Indicates the repair rate of overhead lines under normal weather conditions, Indicates wind speed The repair rate of overhead lines.

[0011] Furthermore, the said The overhead lines remain in operation during extreme weather, and the accident state sequence and accident state duration sequence of the distribution system are randomly generated, including: a. When When it is an odd number, the starting time of the process is Calculate the The distribution function of the random variable of the time interval from the start of the operation process to the first outage of the overhead line and its inverse function are used to randomly generate a random number and substitute it into the inverse function to obtain the sample value of the operation time; when When it is an even number, the starting time of the process is Calculate the The distribution function of the random variable of the time interval from the start of the outage process to the first repair of the overhead line and its inverse function are used to randomly 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 get , and use it as a row vector No. elements; c. For row vectors Sum the elements in ; d. Order , , repeat a~d until Greater than or equal to ,in, The duration of the expected extreme weather conditions; e. Let the row vector No. Elements equal to , and output the row vector , the row vector The sequence of elements in is the The sequence of operating and outage states of overhead lines during extreme weather, as well as the sequence of duration of operating and outage states; f. Combine the operating status and outage status sequences of all overhead lines during extreme weather, as well as the operating status and outage status duration sequences with the load sequence to obtain the accident status sequence and accident status duration sequence of the distribution system.

[0012] Furthermore, the calculation expression of the distribution function of the random variable of the interval time from the start of the operation process to the first outage of the overhead line is: ; in, The distribution function of the random variable representing the time interval from the start of the operation of the overhead line to the first outage; A random variable representing the time interval from the start of the operation of the overhead line to the first outage; is the starting time of the process; Indicates the length of the time period from the start of the process The cumulative number of outages of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative outage intensity function after ; Indicates time Overhead line failure rate;

[0013] The calculation expression of the distribution function of the random variable of the interval time from the start of the overhead line outage process to the first repair is: ; in, The distribution function of the random variable representing the time interval from the start of the overhead line outage process to the first repair; A random variable representing the time interval from the start of the overhead line outage process to the first repair; Indicates the length of the time period from the start of the process The cumulative number of repairs of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative repair strength function after ; Indicates time The repair rate of overhead lines.

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

[0015] Furthermore, according to The expression for calculating the system economic loss sample based on the load shedding effect on each load node and the accident state duration sequence of the distribution system is: ; in, represents the economic loss random variable, represents the duration random variable of the accident state c during the extreme weather period, Indicates the duration of extreme weather. represents the number of load nodes in the distribution system, represents the power reduction on the kth load node caused by the accident state c, Represents the cost parameter of the load on the kth load node.

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

[0017] The expected economic loss reliability index estimate is calculated based on the system economic loss sample, and its expression is: ; in, represents the expected economic loss reliability index estimate, represents the total number of samples, represents the economic loss random variable, express No. samples, represents the duration random variable of the accident state c during the extreme weather period, express No. samples, represents the power reduction on the kth load node caused by the accident state c, represents the cost parameter of the load on the kth 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: ; in, represents the variance coefficient of the expected economic loss reliability index estimate, represents the variance value of the expected economic loss reliability index estimate, Represents the expected value of the reliability index estimate of expected economic loss.

[0019] Furthermore, the expression for calculating the contribution value of each load node to the estimated value of the expected economic loss reliability index based on the system economic loss sample is: ; in, represents the kth load node pair The contribution value of represents the economic loss random variable of the kth load node, express No. samples.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention proposes an overhead line and system state duration model based on a non-homogeneous Poisson process. By considering the characteristics of the overhead line reliability parameters changing with weather intensity during extreme weather, the accuracy of the reliability index is significantly improved. According to the expected loss reliability index, the reinforcement priority of the overhead line and load nodes is sorted, thereby achieving efficient utilization of limited reinforcement resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of a method for evaluating the reliability of power distribution system operation taking into account the time-varying intensity of severe weather in one embodiment of the present invention;

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

[0024] Figure 3The figure is a schematic diagram of a modeling concept of overhead line state duration based on multiple simulations in an embodiment of the present invention. DETAILED DESCRIPTION

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

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

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

[0028] S2. Build a matrix and matrix , For two rows with all elements being 0 A matrix of columns, For an element that is all 0 OK A matrix of columns, where represents the number of overhead lines in the distribution system, Represents the number of load nodes in the distribution system.

[0029] S3. Based on the meteorological forecast data of extreme weather, the load forecast data during the extreme weather period, and the overhead line reliability data, the curve of the failure rate and repair rate of the overhead line during the extreme weather period over time is obtained. Specifically:

[0030] Based on the meteorological forecast data of extreme weather, a curve showing the change of weather intensity over time is obtained;

[0031] Based on the curve of weather intensity changing with time, the load forecast data during the extreme weather period, and the failure rate and repair rate of overhead lines in normal weather, the curve of the failure rate and repair rate of overhead lines changing with time during extreme weather is obtained. The calculation expressions are as follows: ; ; in, represents the wind speed at time t, represents the failure rate of overhead lines under normal weather conditions, Indicates wind speed The failure rate of overhead lines is Indicates the repair rate of overhead lines under normal weather conditions, Indicates wind speed The repair rate of overhead lines.

[0032] S4, set The overhead lines remain in operation during extreme weather, and the accident state sequences and accident state duration sequences of the distribution system are randomly generated.

[0033] Since the steady-state probability model and the traditional state duration model (i.e., the homogeneous Poisson process model) cannot take into account the changes in the reliability parameters of the overhead line during extreme weather with the weather intensity, the reliability index will have a large error. To address this problem, this embodiment proposes an overhead line and system state duration model based on a non-homogeneous Poisson process.

[0034] The following will give the idea of ​​model derivation starting from a simple example system and gradually derive the mathematical expression of the model based on the principle of Poisson process.

[0035] The state duration model can completely describe the state transition process of overhead lines and systems. Figure 2 The overhead line and system state duration model of a two-dimensional system is presented in graphical form. The overhead line state value of 1 indicates that the overhead line is in operation; the overhead line state value of 0 indicates that the overhead line is in outage; the system state value of 0 indicates that both overhead lines 1 and 2 are in outage; the system state value of 1 indicates that overhead line 1 is in operation and overhead line 2 is outage; the system state value of 2 indicates that overhead line 1 is outage and overhead line 2 is in operation; the system state value of 3 indicates that both overhead lines 1 and 2 are in operation. For any system, the time that the overhead line is in operation or outage each time is a random variable. If the distribution of the overhead line state duration random variable can be obtained, the duration samples of the overhead line state can be randomly generated according to the distribution, and the state duration samples of all overhead lines can be further combined to form the duration samples of the system state.

[0036] According to the definition of Poisson process, if all outage durations are removed and the remaining operation durations are combined during the state transition of the overhead line, the overhead line will be Length of the elapsed time period The cumulative number of outages after is a random variable. fixed, When the value is not less than 0, the random variable family is a Poisson process. Similarly, if all normal state durations are removed and the remaining outage state durations are combined during the state transition of the overhead line, then the overhead line will be Length of the elapsed time period Cumulative number of repairs after is a random variable and the corresponding random variable family The difference between the two Poisson processes mentioned above is that the former is an operating process and the latter is an outage process (also known as a repair process).

[0037] The following is the operation process of the overhead line As an example, the basic properties of the Poisson process are given as follows:

[0038] (1) ;

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

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

[0041] (4) .

[0042] Among the above properties, It means that the cumulative outage times of the overhead line after the time period length 0 from the start of the process is 0; Indicates time For non-overhead lines, the overhead line failure rate is a constant. In this case, the process is a homogeneous Poisson process. For overhead lines, the overhead line failure rate is a variable. In the case of a variable failure rate, the process is a non-homogeneous Poisson process. The homogeneous Poisson process can be considered as a special case of the non-homogeneous Poisson process.

[0043] Based on the above four basic properties, the fifth property can be further deduced as follows: ; In the formula, A non-negative integer. Indicates the length of the time period from the start of the process The cumulative outage intensity function after , can be expanded as follows:

[0044] ;

[0045] make The random variable representing the time interval from the start of the operation process to the first shutdown is also called the running time random variable, where: is the starting time of the process. express The distribution function of is, based on the equivalence of random events and the fifth property of the Poisson process, we can deduce The formula is as follows: ; in, The distribution function of the random variable representing the time interval from the start of the operation of the overhead line to the first outage; A random variable representing the time interval from the start of the operation of the overhead line to the first outage; is the starting time of the process; Indicates the length of the time period from the start of the process The cumulative number of outages of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative outage intensity function after ; Indicates time Overhead line failure rate.

[0046] Similarly, during an overhead line outage In, order The random variable representing the time interval between the first repair and the start of the outage process is also called the outage time random variable. represents the random variable of downtime The distribution function of The formula is: ; in, The distribution function of the random variable representing the time interval from the start of the overhead line outage process to the first repair; A random variable representing the time interval from the start of the overhead line outage process to the first repair; Indicates the length of the time period from the start of the process The cumulative number of repairs of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative repair strength function after ; Indicates time The repair rate of overhead lines.

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

[0048] Figure 3 The idea of ​​modeling the duration of overhead line status based on multiple simulations is demonstrated. It is assumed that the overhead lines are in normal operation at the beginning of extreme weather. The sequence number of the process reflects the order in which the process occurs. The Poisson process with an odd sequence number is an operating process; the Poisson process with an even sequence number is an outage process. represents the running time random variable; represents the outage time random variable. It represents the starting time of a process. Its value can distinguish different running processes, shutdown processes, running time random variables and shutdown time random variables. If all and Assuming the starting time of process 1 is 0, the random variables of process 1 and its running time can be expressed as and .right Sampling is performed and the sample size is assumed to be In the case of , process 2 and its downtime random variables can be expressed as and Further Sampling is performed and the sample size is assumed to be In this case, process 3 and its running time random variable can be expressed as and By analogy, a complete state duration model of overhead lines during extreme weather can be obtained.

[0049] Therefore, let The overhead lines remain in operation during extreme weather, and the accident state sequence and accident state duration sequence of the distribution system are randomly generated, including:

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

[0051] when When it is an even number, the starting time of the process is Calculate the The distribution function of the random variable of the time interval from the start of the outage process to the first repair of the overhead line and its inverse function are used to randomly 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 get , and use it as a row vector No. elements;

[0053] c. For row vectors Sum the elements in ;

[0054] d. Order , , repeat a~d until Greater than or equal to ,in, The duration of the expected extreme weather conditions;

[0055] e. Let the row vector No. Elements equal to , and output the row vector , the row vector The sequence of elements in is the The sequence of operating and outage states of overhead lines during extreme weather, as well as the sequence of duration of operating and outage states;

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

[0057] S5, call the power flow analysis algorithm to analyze the power flow data of the distribution system, so as to obtain the state sequence of all the accident states in the distribution system. The load shedding effect on each load node is analyzed by the current mature algorithm, which will not be elaborated in detail here.

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

[0059] Considering the different importance of different load nodes and the randomness of the simulation of state duration, this embodiment proposes an expected economic loss (EEL) reliability index to quantify the possible loss of the distribution system during extreme weather. The importance of the load node is measured by the economic loss corresponding to the unit load reduction of different nodes. The basic idea of ​​calculating the EEL index is to treat the economic loss of the distribution system during extreme weather as a random variable. (unit is $), by finding The estimated value of the EEL reliability index can be obtained by using the sample mean method. It should be noted that Theoretically, it is a continuous variable. However, after rounding the samples in the previous section, It is actually a discrete random variable, and its calculation model is: ; in, represents the economic loss random variable, represents the duration random variable of the accident state c during the extreme weather period (in hours), and its sample value comes from the distribution system state duration sequence obtained by simulation. Indicates the duration of extreme weather, and its value depends on weather forecast information. represents the number of load nodes in the distribution system, represents the power reduction on the kth load node caused by the accident state c (in MW), Represents the cost parameter of the load at the kth load node (in $ / MWh). The cost parameter is set to distinguish 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 expected economic loss reliability index estimate is calculated based on the system economic loss sample, and its expression is: ; in, represents the expected economic loss reliability index estimate, represents the total number of samples, represents the economic loss random variable, express No. samples, represents the duration random variable of the accident state c during the extreme weather period, express No. samples, represents the power reduction on the kth load node caused by the accident state c, Represents the cost parameter of the load on the kth 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: ; in, represents the variance coefficient of the expected economic loss reliability index estimate, represents the variance value of the expected economic loss reliability index estimate, Represents the expected value of the reliability index estimate of expected economic loss.

[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 value of each load node to the estimated value of the expected economic loss reliability index based on the system economic loss sample. The expression is: ; in, represents the kth load node pair The contribution value of represents the economic loss random variable of the kth load node, express No. samples.

[0065] S10, The serial numbers of the overhead lines and the estimated reliability index of expected economic loss are stored in the matrix No. Column, and The serial number of the overhead line and the contribution value of each load node to the estimated value of the expected economic loss reliability index are stored in the matrix No. List;

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

[0067] S12, yes The columns are sorted by The second row of data is sorted from left to right, and Sort the columns so that the elements of the first row are The elements in the first row are consistent, and the output matrix and ,in, contains the reinforcement priority information for overhead lines, Contains the reinforcement priority sorting information of the load nodes.

[0068] This embodiment adopts the IEEE RBTS BUS6 test system. The IEEE RBTS BUS6 test system is a distribution system with 40 load buses and 52 branches. It is assumed that all 52 branches will be affected by severe weather. The annual peak load of the distribution system is 20MW. For specific power flow data and reliability data under normal weather conditions in this embodiment, please refer to the document 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 conventional severe weather, the failure rate of overhead lines during conventional severe weather is set to the failure rate under strong winds of 33m / s (i.e., the central wind speed of the 12-level typhoon "Mikra"); the repair rate is set to be consistent with the parameters under normal weather; the duration of conventional severe weather is set to two days (i.e., the duration of the impact of Typhoon "Mikra"); the load level during conventional severe weather is set to 100% peak load level; the load level during normal weather is set to multiple load levels. Each load level is 20%, 40%, 60%, 80% and 100% of the peak load level and has 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 14-level typhoon. The repair rate of overhead lines during extreme severe weather is set to 0. The cost parameters of all load nodes are 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 a non-homogeneous Poisson process

[0072] Table 1 shows the performance of the system state duration model based on the non-homogeneous Poisson process proposed in this embodiment by comparing the influence of different system reliability models on the calculation efficiency and accuracy of the EEL index. In order to eliminate the interference caused by the state impact database, the data in the table are obtained when the state impact database is shielded. NSD represents the system state duration model based on the non-homogeneous Poisson process proposed in this embodiment; SD-A represents the traditional state duration model based on the average wind speed during the typhoon disaster; SD-H represents the traditional state duration model based on the highest wind speed during the typhoon disaster; SD-L represents the traditional state duration model based on the lowest wind speed during the typhoon disaster. Compared with the three SD models, the NSD model is more in line with the actual situation because it takes into account the 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 data of the EEL index obtained by SD-L is very small. This is because the critical wind speed set in the example is 25m / s; and the lowest wind speed in the typhoon forecast data used is 18m / s. In this case, the component failure rate is equal to the failure rate under normal weather. According to the reliability index calculation time data, it can be found that the calculation time of the SD-L model is significantly higher than that of other models. This is because the calculation efficiency of the EEL index depends not only on the model but also on the system reliability level. When the system reliability level is high, the simulation method takes longer to converge.

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

[0074] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the operational reliability of a power distribution system taking into account the time-varying intensity of severe weather, characterized in that: include: S1. Obtain power flow data of the distribution system, overhead line reliability data, extreme weather forecast data, and load forecast data during the period of extreme weather; S2. Build a matrix and matrix , For two rows with all elements being 0 A matrix of columns, For an element that is all 0 OK A matrix of columns, where represents the number of overhead lines in the distribution system, Indicates the number of load nodes in the distribution system; S3, obtaining a curve showing the change of the failure rate and repair rate of the overhead line during the extreme weather period over time according to the meteorological forecast data of the extreme weather, the load forecast data during the extreme weather period, and the reliability data of the overhead line; S4, set The overhead lines remain in operation during extreme weather, and the accident state sequence and accident state duration sequence of the distribution system are randomly generated; S5. Calculate all the accident states in the accident state sequence of the distribution system. The impact of load shedding on each load node; S6. According to The load shedding effect on each load node and the accident state duration sequence of the distribution system are used to calculate the system economic loss samples; S7. Calculate the variance coefficient of the expected economic loss reliability index estimate based on the system economic loss sample; S8, repeat S4 to S8 until the variance coefficient of the estimated reliability index of expected economic loss is less than the preset termination threshold; S9. Calculate the contribution value of each load node to the estimated value of the expected economic loss reliability index based on the system economic loss sample; S10, The serial numbers of the overhead lines and the estimated reliability index of expected economic loss are stored in the matrix No. Column, and The serial number of the overhead line and the contribution value of each load node to the estimated value of the expected economic loss reliability index are stored in the matrix No. List; S11. Order , repeat S4~S11 until ; S12, yes The columns are sorted by The second row of data is sorted from left to right, and Sort the columns so that the elements of the first row are The elements in the first row are consistent, and the output matrix and ,in, contains the reinforcement priority information for overhead lines, Contains the reinforcement priority sorting information of the load nodes.

2. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The reliability data of the power distribution system includes the failure rate and repair rate of the overhead lines in the power distribution system under normal weather conditions.

3. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The curve of the failure rate and repair rate of overhead lines during extreme weather obtained based on the meteorological forecast data of extreme weather, the load forecast data during the extreme weather period, and the reliability data of overhead lines over time includes: Based on the meteorological forecast data of extreme weather, a curve showing the change of weather intensity over time is obtained; Based on the curve of weather intensity changing with time, the load forecast data during the extreme weather period, and the failure rate and repair rate of overhead lines in normal weather, the curve of the failure rate and repair rate of overhead lines changing with time during extreme weather is obtained. The calculation expressions are as follows: ; ; in, represents the wind speed at time t, represents the failure rate of overhead lines under normal weather conditions, Indicates wind speed The failure rate of overhead lines is Indicates the repair rate of overhead lines under normal weather conditions, Indicates wind speed The repair rate of overhead lines.

4. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The said The overhead lines remain in operation during extreme weather, and the accident state sequence and accident state duration sequence of the distribution system are randomly generated, including: a. When When it is an odd number, the starting time of the process is Calculate the The distribution function of the random variable of the time interval from the start of the operation process to the first outage of the overhead line and its inverse function are used to randomly generate a random number and substitute it into the inverse function to obtain the sample value of the operation time; when When it is an even number, the starting time of the process is Calculate the The distribution function of the random variable of the time interval from the start of the outage process to the first repair of the overhead line and its inverse function are used to randomly 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 get , and use it as a row vector No. elements; c. For row vectors Sum the elements in ; d. Order , , repeat a~d until Greater than or equal to ,in, The duration of the expected extreme weather conditions; e. Let the row vector No. Elements equal to , and output the row vector , the row vector The sequence of elements in is the The sequence of operating and outage states of overhead lines during extreme weather, as well as the sequence of duration of operating and outage states; f. Combine the operating status and outage status sequences of all overhead lines during extreme weather, as well as the operating status and outage status duration sequences with the load sequence to obtain the accident status sequence and accident status duration sequence of the distribution system.

5. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 4, characterized in that: The calculation expression of the distribution function of the random variable of the interval time from the start of the operation process to the first outage of the overhead line is: ; in, The distribution function of the random variable representing the time interval from the start of the operation of the overhead line to the first outage; A random variable representing the time interval from the start of the operation of the overhead line to the first outage; is the starting time of the process; Indicates the length of the time period from the start of the process The cumulative number of outages of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative outage intensity function after ; Indicates time Overhead line failure rate; The calculation expression of the distribution function of the random variable of the interval time from the start of the overhead line outage process to the first repair is: ; in, The distribution function of the random variable representing the time interval from the start of the overhead line outage process to the first repair; A random variable representing the time interval from the start of the overhead line outage process to the first repair; Indicates the length of the time period from the start of the process The cumulative number of repairs of the subsequent overhead lines; Indicates the length of the time period from the start of the process The cumulative repair strength function after ; Indicates time The repair rate of overhead lines.

6. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: All the accident states in the accident state sequence of the distribution system are The load shedding impact on each load node is obtained by analyzing the power flow data of the distribution system by calling the power flow analysis algorithm.

7. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The basis The expression for calculating the system economic loss sample based on the load shedding effect on each load node and the accident state duration sequence of the distribution system is: ; in, represents the economic loss random variable, represents the duration random variable of the accident state c during the extreme weather period, Indicates the duration of extreme weather. represents the number of load nodes in the distribution system, represents the power reduction on the kth load node caused by the accident state c, Represents the cost parameter of the load on the kth load node.

8. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The variance coefficient of the estimated amount of the reliability index of expected economic loss is calculated based on the system economic loss sample, including: The expected economic loss reliability index estimate is calculated based on the system economic loss sample, and its expression is: ; in, represents the expected economic loss reliability index estimate, represents the total number of samples, represents the economic loss random variable, express No. samples, represents the duration random variable of the accident state c during the extreme weather period, express No. samples, represents the power reduction on the kth load node caused by the accident state c, represents the cost parameter of the load on the kth load node; 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: ; in, represents the variance coefficient of the expected economic loss reliability index estimate, represents the variance value of the expected economic loss reliability index estimate, Represents the expected value of the reliability index estimate of expected economic loss.

9. The method for evaluating the operation reliability of a power distribution system taking into account the time-varying intensity of severe weather according to claim 1, characterized in that: The expression for calculating the contribution value of each load node to the estimated value of the expected economic loss reliability index based on the system economic loss sample is: ; in, represents the kth load node pair The contribution value of represents the economic loss random variable of the kth load node, express No. samples.

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