CN108681815B - Power distribution system operation reliability evaluation method based on rapid sequencing and block matrix - Google Patents
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Abstract
The invention relates to a power distribution system operation reliability evaluation method based on rapid sequencing and block matrixes, which comprises the following steps of: step S1: establishing a time-varying fault rate model, and respectively calculating the fault rates of the transformer, the line and the switch under the influence of the service life cycle of the equipment and weather factors; step S2: sorting the element fault rates in a descending order by using a quick sorting method, and screening out the first 80% of element fault rates; step S3: blocking the screened first 80% of element fault rate through a blocking algorithm, storing blocking information, and searching a blocking link matrix according to fault blocking; step S4: counting the load point and system reliability indexes of each simulation year, and calculating the mean value of the load point and system reliability indexes; the method effectively combines the advantages of rapidity of a rapid sequencing method and a blocking algorithm and the advantages of processing a complex system and time sequence by a sequential Monte Carlo simulation method, forms an effective reliability evaluation algorithm of the power distribution system, and can quickly and effectively calculate the reliability index of the power distribution system.
Description
Technical Field
The invention belongs to the technical field of operation reliability of power systems, and particularly relates to a power distribution system operation reliability evaluation method based on rapid sequencing and block matrixes.
Background
The Reliability Evaluation research of the Power distribution system is relatively early in the abroad, the management work on the Reliability of the Power distribution network is carried out in the United kingdom and Canada in the middle of the last 60 th century, the statistical analysis work on the Reliability of the Power distribution network is also fully carried out in the United states, Japan and European countries in the last 70 th century, the Reliability Evaluation of the Power system (Reliability Evaluation of Power Systems) is published by Roy Billton et al at the end of the 80 th century, a series of data and test results of a Reliability test system (RBTS) are published, and the research progress of the Reliability of the Power system is promoted. Today, power supply system reliability assessment is a routine task in distribution network planning decisions in many countries, developed countries such as the united states, japan, uk, canada, france, etc. have specialized research institutes responsible for collecting and collating the raw data required for reliability assessment, and have established sophisticated distribution network reliability assessment index systems, models and algorithms.
The reliability of the power distribution system is researched only from the beginning of the 80 th century in China, and the reliability of the power distribution system is still long-lived due to the lack of statistics and effective evaluation methods for necessary data. The statistical method of the power supply reliability of power customers of a power supply system is issued by the national power reliability management center in 1989, and the work of China on the aspect of power distribution system reliability management is not fully developed. In recent more than twenty years, China has made relatively deep research on the reliability of power distribution systems, programs and methods for counting distribution network data are worked out, an effective power distribution system reliability database and a management system are established, the statistical analysis work of the reliability of the power distribution systems is realized nationwide, and the improvement of the reliability of the power distribution systems is taken as one of the main work targets of power supply departments. Through research and development of twenty years, the reliability of a power distribution system in China achieves favorable results, and according to relevant data published by the national power reliability management center in 2005, the following results can be seen: the average level of the reliability of the power distribution system in China is improved from 99.646% in 1992 to 99.927% in 2004 without considering the factor of limiting the electricity, but a larger gap exists compared with the reliability of power supply in developed countries.
At present, the research on the reliability of the power distribution system in China mainly focuses on the research on theories and methods and the statistics and report forms on reliability parameters, a complete system for quantitatively evaluating the current situation of the reliability of the power distribution system by effectively utilizing the existing statistical data and a practical analysis and calculation method are lacked, and the research result of the reliability theory is not applied and popularized in actual engineering. In the aspect of a reliability evaluation model, a non-power component model adopts a constant failure rate which is too coarse, and time-varying factors such as the service life cycle of a component, weather and the like are not combined with a distributed power timing sequence model. In terms of a reliability evaluation method, as a modern power system becomes extremely complex, the number of components is large, reliability evaluation takes a long time, and the speed of a reliability evaluation algorithm needs to be improved, it is extremely important to develop a rapid and efficient operation reliability evaluation method.
Disclosure of Invention
In view of the above, the invention effectively combines the advantages of the rapidity of the rapid ordering method and the blocking algorithm and the rapidity of the sequential monte carlo simulation method for processing the complex system and the time sequence to form an effective power distribution system reliability evaluation algorithm, and can quickly and effectively calculate the reliability index of the power distribution system.
The purpose of the invention is realized by the following technical scheme:
the operation reliability evaluation method based on the rapid sequencing and the block matrix comprises the following steps:
step S1: establishing a time-varying fault rate model of each element along with the service life cycle of the equipment and weather factors, and respectively calculating the fault rates of the transformer, the line and the switch under the influence of the service life cycle of the equipment and the weather factors by using the average fault rate under the condition of lacking statistics on the fault rate data of the elements;
step S2: sorting the calculated element fault rates in a descending order by using a quick sorting method, and screening the first eighty percent of element fault rates;
step S3: blocking the fault rate of the first eighty percent of the screened elements by a blocking algorithm, and storing blocking information; searching a block link matrix according to the fault block;
step S4: and (4) counting the load point and system reliability indexes of each simulation year, and calculating the mean value of the load point and system reliability indexes.
Specifically, the step S1 includes the steps of:
step S11: improving a Weibull distribution function, calculating the time-varying fault rate considering the life cycle influence of the equipment by using the average fault rate, and establishing a time-varying fault rate model considering the life cycle influence of the equipment:
the Weibull distribution function describes the expression of the equipment fault rate as follows:
λ(t)＝ktβ-1；
in the formula, k is a scale parameter, and beta is a shape parameter;
the time-varying fault rate model expression considering the effect of the life cycle of the equipment is as follows:
λ(t)＝λavgeβT；
in the formula: lambda [ alpha ]avgIs the mean failure rate;
step S12: on the basis of the step S11, establishing a time-varying fault rate model which simultaneously takes the component service life and the climate influence into account, namely introducing a weather factor influence factor on the basis of considering the time-varying fault rate influenced by the equipment service life;
the time-varying fault rate model expression taking into account the component life and climate effects is as follows:
λ(t)＝[λ(T)]θ(t)；
in the formula, lambda (t) is a time-varying fault rate, and theta (t) is a weather influence factor;
step S13: and calculating the failure rate of the element according to the time-varying failure rate model.
Specifically, the step S2 includes the steps of:
step S21: sorting the calculated element fault rates in a descending order by using a quick sorting method;
step S22: and after the sorting is finished, screening out the fault elements with the fault rate of eighty percent.
Specifically, the step S3 includes the steps of:
step S31: partitioning a system by a fault element with the fault rate of eighty percent after being screened by a rapid sorting method through a partitioning algorithm, storing partitioning information, then performing fault enumeration by taking a block as a unit, and determining the sequence and specific time of the fault of the partition by using a sequential Monte Carlo simulation method;
step S32: for a specific block fault, searching a fault influence range through a block connection matrix, analyzing the influence of the fault influence range on load points in the system, continuously advancing simulation time, and storing the influence of each fault on the load points one by one until the simulation time is reached;
step S33: calculating equivalent reliability parameters of each block, wherein the equivalent reliability parameters, the block equivalent failure rate lambda and the failure repair time mu of each block are calculated by a multi-element series formula for the reliability of each block as the same switching element action is caused after any element in each block fails and the influence on the power failure of a load point is the same;
wherein: m is the number of elements in the block; lambda [ alpha ]iFailure rate of the ith element; mu.siTime to fail repair of ith element.
Specifically, the step S4 includes the steps of:
step S41: calculating a system reliability index through a power distribution system reliability algorithm based on an improved block-sequential Monte Carlo simulation method;
step S42: firstly, single fault simulation is carried out, the influence of a fault on each load point in a system is determined by utilizing a block connection matrix, and corresponding power failure information is stored, wherein the power failure information comprises power failure times, power failure time and power shortage amount;
step S43: and then carrying out system simulation, simulating the block fault sequence and the fault time, and calculating the system reliability index value by utilizing the power failure information of each load point in single fault simulation, wherein the method comprises the following steps: average system power failure frequency SAIFI, average system power failure duration SAIDI, average user power failure duration CAIDI, average system power supply availability ASAI and expected insufficient electric quantity EENS;
the system reliability index calculation formula is as follows:
average power failure frequency of the system:
wherein: lambda [ alpha ]iIs the failure rate of load point i; n is a radical ofiThe number of users at the load point i;
average outage duration of the system:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at the load point i;
average power outage duration for user:
wherein U isiAverage outage duration, N, for load point iiIs the number of users, λ, at load point iiIs the failure rate of load point i;
the average power supply availability ratio of the system is as follows:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at the load point i;
electric quantity shortage expectation: EENS ═ Σ LiUi；
Wherein: u shapeiAverage outage duration, L, for load point iiIs the load connected at load point i.
The invention has the beneficial effects that:
aiming at the defects existing in the research of the reliability of the power distribution system in China at present, the method effectively combines the advantages of the rapidity of a rapid sequencing method and a blocking algorithm and the rapidity of a sequential Monte Carlo simulation method for processing a complex system and the time sequence, forms an effective power distribution system reliability evaluation algorithm, and can quickly and effectively calculate the reliability index of the power distribution system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of an algorithm according to the present invention;
FIG. 2 is a wiring diagram of the RBTS BUS 6F 4 feeder system.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
Referring to fig. 1, the present invention will be further described with reference to the accompanying drawings and examples.
The present research provides a power distribution system operation reliability evaluation method based on a fast sorting and blocking matrix, as shown in fig. 1, specifically including the following steps:
step S1: and establishing a time-varying fault rate model of each element along with the service life cycle of the equipment and weather factors, and respectively calculating the fault rates of the transformer, the line and the switch under the influence of the service life cycle of the equipment and the weather factors by using the average fault rate under the condition of lacking statistics on the fault rate data of the elements. Specifically, the method comprises the following substeps:
step S11: and improving the Weibull distribution function, calculating the time-varying fault rate considering the life cycle influence of the equipment by using the average fault rate, and establishing a time-varying fault rate model considering the life cycle influence of the equipment.
The Weibull distribution function describes the expression of the equipment fault rate as follows:
λ(t)＝ktβ-1
where k is a scale parameter and β is a shape parameter.
The time-varying fault rate model expression considering the effect of the life cycle of the equipment is as follows:
λ(T)＝λavgeβT
in the formula: lambda [ alpha ]avgThe mean failure rate.
Step S12: on the basis of the step S11, a time-varying fault rate model is established that takes into account both the component lifetime and the climate influence, i.e. a weather factor influencing factor is introduced on the basis of the time-varying fault rate that takes into account the equipment lifetime influence.
The time-varying fault rate model expression taking into account the component life and climate effects is as follows:
λ(t)＝[λ(T)]θ(t)
in the formula, λ (t) is a time-varying failure rate, and θ (t) is a weather influence factor.
Step S13: and calculating the failure rate of the element according to the time-varying failure rate model.
Step S2: sorting the calculated component failure rates in a descending order by using a quick sorting method, and screening the first eighty percent of component failure rates, wherein the step S2 further comprises the following steps:
step S21: and sorting the calculated element fault rates in a descending order by using a quick sorting method.
Step S22: and after the sorting is finished, screening out the fault elements with the fault rate of eighty percent.
Step S3: blocking the fault rate of the first eighty percent of the screened elements by a blocking algorithm, and storing blocking information; searching the block link matrix according to the failure block, specifically, step S3 includes the following sub-steps:
step S31: and partitioning the system by a fault element with the fault rate of eighty percent after screening by using a rapid sorting method through a partitioning algorithm, storing partitioning information, performing fault enumeration by taking a block as a unit, and determining the sequence and the specific time of the fault of the partition by using a sequential Monte Carlo simulation method. Taking RBTS BUS6 system F4 main feeder as an example, the block number and branch information of the F feeder system are shown in table 1.
FIG. 2 is a wiring diagram of the RBTS BUS 6F 4 feeder system.
TABLE 1F 4 feeder system Block number and Branch outline information
Step S32: for a specific block fault, searching a fault influence range through a block connection matrix, analyzing the influence of the fault influence range on load points in the system, continuously advancing the simulation time, and storing the influence of each fault on the load points one by one until the simulation time is reached.
Step S33: and calculating equivalent reliability parameters of each block. Because the same switch element action is caused after any element in the blocks fails and the influence on the power failure of a load point is the same, the equivalent reliability parameter of each block can be calculated by a multi-element series formula for the reliability of each block: the equivalent fault rate lambda of the block and the fault repair time mu.
Wherein: m is the number of elements in the block; lambda [ alpha ]iFailure rate of the ith element; mu.siTime to fail repair of ith element.
Step S4: taking RBTS BUS6 system F4 main feeder as an example, load point and system reliability indexes of each simulation year are counted, and the average value is the reliability index value of the load point and the system. Specifically, step S4 includes the steps of:
step S41: taking an RBTS BUS6 system F4 main feeder as an example, calculating a system reliability index through a power distribution system reliability algorithm based on an improved block-sequential Monte Carlo simulation method;
step S42: firstly, single fault simulation is carried out, the influence of the fault on each load point in the system is determined by utilizing the block connection matrix, and corresponding power failure information (power failure times, power failure time and power shortage amount) is stored.
Step S43: and then carrying out system simulation, simulating the block fault sequence and the fault time, and calculating the system reliability index value by utilizing the power failure information of each load point in single fault simulation, wherein the method comprises the following steps: the system average power failure frequency (SAIFI), the system average power failure duration (SAIDI), the user average power failure duration (CAIDI), the system average power supply availability (ASAI) and the insufficient Electricity Expectation (EENS) can accurately evaluate the reliability of the power distribution system through the reliability index values. The element reliability parameters used herein are derived from the safety and compliance power grid, and the system reliability index calculation results are shown in table 2.
The system reliability index calculation formula is as follows:
average power failure frequency of the system:
wherein: lambda [ alpha ]iIs the failure rate of load point i; n is a radical ofiThe number of users at load point i.
Average outage duration of the system:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at load point i.
Average power outage duration for user:
wherein U isiAverage outage duration, N, for load point iiIs the number of users, λ, at load point iiIs the failure rate of load point i.
The average power supply availability ratio of the system is as follows:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at load point i.
Electric quantity shortage expectation: EENS ═ Σ LiUi
Wherein: u shapeiAverage outage duration, L, for load point iiIs the load connected at load point i.
TABLE 2 System reliability index calculation results
In particular, the statistical data used in the present example is based on the data related to the Guizhou Power grid Anshu Power supply bureau.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (3)
1. A power distribution system operation reliability assessment method based on rapid sequencing and block matrixes is characterized in that: the method comprises the following steps:
step S1: establishing a time-varying fault rate model of each element along with the service life cycle of the equipment and weather factors, and respectively calculating the fault rates of the transformer, the line and the switch under the influence of the service life cycle of the equipment and the weather factors by using the average fault rate under the condition of lacking statistics on the fault rate data of the elements;
the step S1 includes the steps of:
step S11: improving a Weibull distribution function, calculating the time-varying fault rate considering the life cycle influence of the equipment by using the average fault rate, and establishing a time-varying fault rate model considering the life cycle influence of the equipment:
the Weibull distribution function describes the expression of the equipment fault rate as follows:
λ(t)＝ktβ-1；
in the formula, k is a scale parameter, and beta is a shape parameter;
the time-varying fault rate model expression considering the effect of the life cycle of the equipment is as follows:
λ(T)＝λavgeβT；
in the formula: lambda [ alpha ]avgIs the mean failure rate;
step S12: on the basis of the step S11, establishing a time-varying fault rate model which simultaneously takes the component service life and the climate influence into account, namely introducing a weather factor influence factor on the basis of considering the time-varying fault rate influenced by the equipment service life;
the time-varying fault rate model expression taking into account the component life and climate effects is as follows:
λ(t)＝[λ(T)]θ(t)；
in the formula, lambda (t) is a time-varying fault rate, and theta (t) is a weather influence factor;
step S13: calculating the failure rate of the element according to the time-varying failure rate model;
s2: sorting the calculated element fault rates in a descending order by using a quick sorting method, and screening out the first 80% of element fault rates;
step S3: blocking the screened first 80% of element fault rate through a blocking algorithm, and storing blocking information; searching a block link matrix according to the fault block;
the step S3 includes the steps of:
step S31: partitioning a system by a fault element with the fault rate of eighty percent after being screened by a rapid sorting method through a partitioning algorithm, storing partitioning information, then performing fault enumeration by taking a block as a unit, and determining the sequence and specific time of the fault of the partition by using a sequential Monte Carlo simulation method;
step S32: for a specific block fault, searching a fault influence range through a block connection matrix, analyzing the influence of the fault influence range on load points in the system, continuously advancing simulation time, and storing the influence of each fault on the load points one by one until the simulation time is reached;
step S33: calculating equivalent reliability parameters of each block, wherein the equivalent reliability parameters, the block equivalent failure rate lambda and the failure repair time mu of each block are calculated by a multi-element series formula for the reliability of each block as the same switching element action is caused after any element in each block fails and the influence on the power failure of a load point is the same;
wherein: m is the number of elements in the block; lambda [ alpha ]iFailure rate of the ith element; mu.siA fault repairing time step of the ith element;
s4: and (4) counting the load point and system reliability indexes of each simulation year, and calculating the mean value of the load point and system reliability indexes.
2. The method for evaluating the operational reliability of the power distribution system based on the rapid sequencing and the block matrix according to claim 1, wherein the method comprises the following steps: the step S2 includes the steps of:
step S21: sorting the calculated element fault rates in a descending order by using a quick sorting method;
step S22: and after the sorting is finished, screening out the fault elements with the fault rate of eighty percent.
3. The method for evaluating the operational reliability of the power distribution system based on the rapid sequencing and the block matrix according to claim 1, wherein the method comprises the following steps: the step S4 includes the steps of:
step S41: calculating a system reliability index through a power distribution system reliability algorithm based on an improved block-sequential Monte Carlo simulation method;
step S42: firstly, single fault simulation is carried out, the influence of a fault on each load point in a system is determined by utilizing a block connection matrix, and corresponding power failure information is stored, wherein the power failure information comprises power failure times, power failure time and power shortage amount;
step S43: and then carrying out system simulation, simulating the block fault sequence and the fault time, and calculating the system reliability index value by utilizing the power failure information of each load point in single fault simulation, wherein the method comprises the following steps: average system power failure frequency SAIFI, average system power failure duration SAIDI, average user power failure duration CAIDI, average system power supply availability ASAI and expected insufficient electric quantity EENS;
the system reliability index calculation formula is as follows:
average power failure frequency of the system:
wherein: lambda [ alpha ]iIs the failure rate of load point i; n is a radical ofiThe number of users at the load point i;
average outage duration of the system:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at the load point i;
average power outage duration for user:
wherein: u shapeiAverage outage duration, N, for load point iiIs the number of users, λ, at load point iiIs the failure rate of load point i;
the average power supply availability ratio of the system is as follows:
wherein: u shapeiAverage outage duration, N, for load point iiThe number of users at the load point i;
electric quantity shortage expectation: EENS ═ Σ LiUi；
Wherein: u shapeiAverage outage duration, L, for load point iiIs the load connected at load point i.
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| CN111798111A (en) * | 2020-06-27 | 2020-10-20 | 上海交通大学 | Comprehensive energy system energy supply reliability assessment method and computer system |
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