CN106780127B - Evaluation method for distribution-containing photovoltaic power distribution network - Google Patents
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Abstract
The invention relates to an evaluation method of a distribution network containing distributed photovoltaic, which uses an AHP analysis method to establish a reliability model of the distribution network containing distributed photovoltaic; calculating three-layer indexes in a reliability model of the distribution-type photovoltaic power distribution network by using n collected region reliability data and an engineering statistical algorithm, adding a region total load value and a total user number to a calculation result of the engineering statistical algorithm to serve as a BP neural network training sample input value, grading the reliability indexes by using a fuzzy membership method to serve as a sample output value, and training to obtain a case base with three-layer index grading; determining the weight value between each layer of indexes in the reliability model by using a Satty rule and a relative weight method; and calculating the reliable total index of the target system according to the three-layer index engineering calculated value of the actual target system. The method can completely and comprehensively calculate the indexes of prearranged power failure reliability and economic reliability.
Description
The application has the following application numbers: 201510289086.9, the invention provides a 'reliability evaluation method for distribution-containing photovoltaic power distribution network', the application date is: divisional application of the invention patent application on 5/29/2015.
Technical Field
The invention relates to the technical field of reliability evaluation of power distribution networks, in particular to a reliability evaluation method for a power distribution network containing distributed photovoltaic power.
Background
With the rapid development of modern social economy and the wide popularization of high-tech products and highly information-based equipment, the output value of each degree of electricity of a user rises day by day, and the economic loss caused by unit power failure to the user and the society is larger and larger. Therefore, the demand of users for the reliability of power supply of the power system is also increasing. The reliability of a power distribution system is an important component of the reliability of a power system, a complete reliability evaluation model containing a distributed photovoltaic power distribution network does not exist in the current research on the reliability of the power distribution system, the existing theoretical analysis algorithm cannot well calculate the reliability index of the prearranged power failure, the theoretical analysis algorithm is suitable for the pre-evaluation of the reliability index of the power grid, and only the reliability index of the power failure and the power failure of the power grid can be calculated, but the reliability index of the prearranged power failure in the reliability model built by the project cannot be calculated.
Disclosure of Invention
The invention aims to provide a complete and comprehensive evaluation method for a distribution-type photovoltaic power distribution network, which can calculate indexes of prearranged power failure reliability and economic reliability.
One of the technical schemes for realizing the aim of the invention is to provide a power distribution network reliability evaluation method, wherein an AHP analysis method is used for establishing a reliability model of a distribution-type photovoltaic power distribution network; calculating three-layer indexes in a reliability model of the distribution-type photovoltaic power distribution network by using n collected region reliability data and an engineering statistical algorithm, adding a region total load value and a total user number to a calculation result of the engineering statistical algorithm to serve as a BP neural network training sample input value, grading the reliability indexes by using a fuzzy membership method to serve as a sample output value, and training to obtain a case base with three-layer index grading; determining the weight value between each layer of indexes in the reliability model by using a Satty rule and a relative weight method; and calculating the reliable total index of the target system according to the three-layer index engineering calculated value of the actual target system.
The second technical scheme for achieving the aim of the invention is to provide an evaluation method of a distribution network containing distributed photovoltaic, which comprises the following steps:
firstly, establishing a reliability evaluation model of a power distribution network containing distributed photovoltaic: the method adopts an AHP method to take the reliability of a power distribution network containing distributed photovoltaic as a total index, selects the conventional reliability, the economic reliability and the equipment performance as a primary index, selects the conventional reliability of fault and power failure, the conventional reliability of prearranged power failure, the economic reliability of fault and power failure, the economic reliability of prearranged power failure, the transformer performance and the line performance as secondary indexes, and selects the average fault and power failure times of a user, the average fault and power failure time of the user, the average prearranged power failure time of the user, the prearranged power failure reliability, the index of the prearranged power failure time, the average power failure number of the user, the average fault and power failure economic time of the, The average economic duration of the fault power failure, the economic reliability of the fault power supply, the average power shortage amount of the fault power failure, the average prearranged power failure economic times of the users, the average prearranged power failure economic time of the users, the average prearranged power failure economic duration of the power failure, the economic reliability of the prearranged power supply, the average power shortage amount of the prearranged power failure, the power failure rate of the line, the power failure rate of the transformer, the average power failure duration of the line and the average power failure duration of the transformer are three-level indexes; each level of index is in a subordinate relation;
determining a calculation expression of three-level indexes in the reliability evaluation model: the three-level index calculation method adopts an engineering statistical algorithm;
establishing a third-level index scoring standard: establishing a three-level index scoring standard by adopting a fuzzy membership method;
establishing a three-level index scoring case library: selecting reliability statistical parameters of n regions, wherein n is greater than 20, obtaining engineering calculation values of three-level indexes through an engineering algorithm, and obtaining scoring values through scoring standards;
calculating the three-level index of the target system: calculating data required by the target system by adopting an engineering statistical algorithm to calculate the three-level index, and calculating the three-level index value of the target system according to the expression in the table 1 obtained in the step II;
sixthly, scoring the three-level indexes of the target system: outputting a three-level index rating value of the target system according to the calculated value of the three-level index of the target system, the total load value of the area, the total number of users of the area and the total number of equipment and according to a three-level index rating case library;
determining the weight between indexes in the reliability evaluation model: establishing each layer judgment matrix by utilizing a 1-9 scale method of Satty, and determining the weight of each index on the same layer by adopting a relative weight method;
and eighthly, evaluating the reliability of the target system: adopting an AHP method, and calculating layer by layer upwards according to a formula (1) to obtain a total reliability index comprehensive score value of a target system:
in the formula: s' R represents the score of any non-underlying indicator; s'iA score representing the lower index i; wiRepresents the weight of the lower index i; b represents the number of lower-layer indexes of the index S' R; calculating from the basic level index score and the weighted sum of the weights layer by layer upwards, wherein the highest level S' R value is the total index comprehensive score value; and evaluating the reliability of the target system according to the total reliability index score value.
Further, in the second step, the third-level index calculation method adopts an engineering statistical algorithm, specifically: known parameters are set for a certain power distribution system as: the total number N of users is a unit of a user, the total assembly variable capacity S is a unit of MVA, the total line length L is a unit of km, and the total number T of transformers is a unit of a transformer; the method for calculating the data to be counted, which contains the three-level reliability index of the distributed photovoltaic power distribution network, by adopting an engineering statistical algorithm comprises the following steps: failure power failure times M in absence of distributed photovoltaicF(MS) Time of power failure in unit of time and each time of failure HFi(HSi) The unit is h, each timeNumber of fault power failure users NFi(NSi) Unit is household, each fault power failure load capacity PFi(PSi) Unit MW h and packing capacity SFi(SSi) MVA, and the failure times of the line and the transformer are respectively MFL、MFTNeglecting the fault condition of the switch and the breaker, MFL+MFT＝MFThe total time of the line and the transformer out of service is HFL、HFT，
When distributed photovoltaic exists, the power failure frequency M of the faults that the power failure load is totally outside the island range, the power failure load part is in the island range and the power failure load is totally in the island rangeFa、MFbAnd MFc(MFa+MFb+MFc＝MF；MFaCorresponding to HFi、NFi、PFi、SFiThe value is unchanged; mFcCorresponding to HFi、NFi、PFi、SFiA value of 0; mFbCorresponding to HFiConstant value, NFi、PFi、SFiBecomes N'Fi、PF'i、S'Fi) (ii) a The expression of the three-level index engineering statistical algorithm of the reliability of the power distribution network containing the distributed photovoltaic is as follows:
average fault power failure frequency AFTC' of users:
average fault power failure time AIHC-F' of user:
mean duration of fault power failure MID-F:
fault power supply reliability RS-F':
fault power shortage indicator ENS-F':
mean number of users MIC-F in power failure:
average prearranged blackout times ASTC of users:
average prearranged power failure time AIHC-S of users:
pre-scheduled average duration of outage MID-S:
prearranged power reliability RS-S:
pre-arrangement electric quantity shortage index ENS-S:
presetting average number of users MIC-S for power failure:
user average failure power failure economic times AFETC':
user average fault power failure economic time AIEHC-F':
mean economic duration of power failure MID-F:
fault power supply economic reliability ERS-F:
average power shortage amount AENT-F in fault and power failure:
average prearranged power failure economic times ASETC of users:
the average user prearranged power failure economic time AIEHC-S:
pre-scheduling average economic duration of blackout, MIED-S:
prearranged power economic reliability ERS-S:
prearranged average power shortage amount AENT-S:
line fault outage rate RIFI-L:
and (3) the fault outage rate RTFI-T of the transformer:
line fault outage average duration MDLOI-L:
average power failure duration of transformer fault MDTOI-T:
further, the process of establishing the three-level index scoring standard by adopting a fuzzy membership method specifically comprises the following steps: firstly, determining a typical score point, then quantizing the three-level index values in a certain range into the typical score point, and gradually forming each three-level index score standard.
Further, specifically, in step (iv), the training sample of the BP neural network is composed of: the three-level index engineering calculation value, the area total load value and the area total user number form an input value of a BP neural network sample, a result obtained by grading the reliability index by using a fuzzy membership method is used as an output value of a training sample, and a format is given as follows: a sample input value; a sample output value;
(1) number of BP neural networks:
aiming at the conventional reliability, the economic reliability and the equipment reliability, different BP networks are respectively adopted; three types of BP neural networks are formed;
(2) number of layers of BP neural network:
the BP neural network adopts a 3-layer structure: an input layer + an intermediate layer + an output layer;
(3) the number of nodes in each layer is as follows:
the number of nodes of each layer of the three types of BP neural networks is shown in a table 2,
TABLE 2 number of nodes in each layer of three BP neural networks
| BP neural network type | Number of input layer points | Number of hidden layer nodes | Number of output layer nodes |
| Conventional reliability | 14 | 14 | 12 |
| Economic reliability | 12 | 12 | 10 |
| Device performance | 5 | 5 | 4 |
Conventional reliability BP network sample input values: corresponding three-level indexes + total area load value + total area user number; sample output value: the fuzzy membership method grade value of the corresponding three-level index;
economic reliability BP network sample input values: corresponding three-level indexes + total area load value + total area user number; sample output value: the fuzzy membership method grade value of the corresponding three-level index;
device performance BP network sample input values: corresponding three-level indexes plus the total number of equipment; sample output value: the fuzzy membership method grade value of the corresponding three-level index;
(4) the excitation function Sigmoid f (x) is 1/(1+ e-x).
Furthermore, in the step IV, the grade value of the third-level index is in the interval of 0,100.
Further, the specific steps of step (c) are as follows: (1) when the number of indexes is less than 3, the weight of the indexes is directly determined by experts; the number of the indexes is equal to or more than 3, and the indexes on the same layer are compared pairwise to obtain a judgment matrix which adopts 1-9 scales of Saaty to express the relative importance of each index;
(2) calculating the consistency index CR of the judgment matrix, and checking the consistency degree of the judgment matrix
(3) When CR is less than 0.10, judging that the matrix consistency is qualified through inspection, calculating the maximum characteristic root of the judgment matrix and the corresponding characteristic vector, wherein the characteristic vector after normalization processing is the weight w of each index; if the consistency is unqualified, readjusting and determining part of elements in the judgment matrix until the consistency meets the requirement.
Furthermore, in step (c), (2) calculating a consistency index CR of the judgment matrix, and checking the consistency degree of the judgment matrix as follows: 1) calculating a consistency index CI:
in the formula: lambda [ alpha ]maxRepresenting the maximum characteristic root value of the judgment matrix; a represents the number of indexes;
2) determining an average random consistency indicator RI:
the corresponding RI is found according to the average random consistency index value given by Saaty, as shown in table 3,
TABLE 3 average random consistency index values given by Saaty
| a | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
3) Calculating the consistency ratio CR:
further, in the step viii, the reliability quality corresponding to the total index score of reliability is shown in table 4:
TABLE 4 reliability level corresponding to the total index score value of reliability
The invention has the positive effects that: (1) compared with the existing method which only introduces and calculates individual fault reliability indexes, the evaluation method of the distribution type photovoltaic power distribution network can comprehensively summarize and count each reliability index. Although most basic indexes are clearly defined in the reliability index standard, no one has comprehensively evaluated the reliability of the power grid according to the indexes, and the reliability model established in the method can be used for reliability evaluation of the distribution-type photovoltaic power distribution network.
(2) According to the evaluation method of the distribution-type photovoltaic power distribution network, indexes of all aspects are distinguished and organized in detail, so that the deviation of three-level indexes and the preference of the three-level indexes are known through the evaluation method, and the indexes of the deviation part can be improved in a key manner in the future. The model established by the invention is relatively complete and comprehensive, and has feasibility and reference significance in comprehensive evaluation research and engineering application of the reliability of the power distribution network.
(3) The evaluation method of the distribution-containing photovoltaic power distribution network adopts an engineering statistical algorithm to calculate the three-level bidding of the reliability of the distribution-containing photovoltaic power distribution network, and the literature research in the aspect of reliability index calculation generally adopts a theoretical analysis algorithm, but the theoretical analysis algorithm is suitable for the pre-evaluation of the reliability index of the power grid, and can only calculate the power grid fault power failure reliability index and can not calculate the pre-power failure reliability index in the reliability model established by the project. The reliability index value is calculated by the engineering statistical algorithm through statistics of the actual power failure data of the power grid, the method has the advantages of being simple in calculation, wide in practicability, accurate in result, capable of calculating the prearranged power failure reliability and economic reliability index and the like, and is suitable for comprehensive reliability evaluation of the distribution-type photovoltaic power distribution network.
Drawings
Fig. 1 is a flowchart of an evaluation method of a distribution-containing photovoltaic power distribution network according to the present invention.
Fig. 2 is a model diagram of reliability evaluation of a distribution network including distributed photovoltaic.
Detailed Description
(example 1)
Referring to fig. 1, the general idea of the evaluation method for the distribution-containing photovoltaic power distribution network in the embodiment is to establish a reliability model of the distribution-containing photovoltaic power distribution network by using an AHP analysis method. The method comprises the steps of calculating three-layer indexes (an improved index calculation method) in a reliability model of the distribution-type photovoltaic power distribution network by using collected n regions of reliability data and adopting an engineering statistical algorithm, taking a total load value and a total user number of the regions, which are calculated by the engineering statistical algorithm, as an input value of a BP neural network training sample, scoring the reliability indexes by using a fuzzy membership method as a sample output value, and training to obtain a case base with three-layer index scoring. And determining the weight value between each layer of indexes in the reliability model by using a Satty rule and a relative weight method. And calculating the reliable total index of the target system according to the three-layer index engineering calculated value of the actual target system. The method specifically comprises the following steps:
firstly, establishing a reliability evaluation model of a power distribution network containing distributed photovoltaic: the reliability evaluation model of the distribution network containing the distributed photovoltaic is shown in fig. 2, and the construction method of the evaluation model is as follows: the method adopts an AHP method to take the reliability of a power distribution network containing distributed photovoltaic as a total index, selects the conventional reliability, the economic reliability and the equipment performance as a primary index, selects the conventional reliability of fault and power failure, the conventional reliability of prearranged power failure, the economic reliability of fault and power failure, the economic reliability of prearranged power failure, the transformer performance and the line performance as secondary indexes, and selects the average fault and power failure times of a user, the average fault and power failure time of the user, the average prearranged power failure time of the user, the prearranged power failure reliability, the index of the prearranged power failure time, the average power failure number of the user, the average fault and power failure economic time of the, The average economic duration of the fault power failure, the economic reliability of the fault power supply, the average power shortage amount of the fault power failure, the average prescheduled power failure economic times of the user, the average prescheduled power failure economic time of the user, the average prescheduled power failure economic duration of the power failure, the economic reliability of the prescheduled power supply, the average power shortage amount of the prescheduled power failure, the power failure rate of the line, the power failure rate of the transformer, the average power failure duration of the line and the average power failure duration of the transformer are three-level indexes. The indexes of each level are in a dependent relationship.
Determining a calculation expression of three-level indexes in the reliability evaluation model: the three-level index calculation method adopts an engineering statistical algorithm. Example of engineering statistical algorithm: known parameters are set for a certain power distribution system as: the total number of users N (household), the total assembly variable capacity S (MVA), the total line length L (km) and the total number of transformers T (household). Calculating data to be counted, which contains the three-level reliability index of the distributed photovoltaic power distribution network, by adopting an engineering statistical algorithm, wherein the data comprises (taking one year as a unit): number of failed (prearranged) blackouts M without distributed photovoltaicsF(MS) (Next), each time of failure (prearranged) power off time HFi(HSi) (h) number of power outage subscribers N per fault (prearranged)Fi(NSi) (household), each fault (prearrangement) outage load capacity PFi(PSi) (MW · h) and packing capacity SFi(SSi) (MVA), the number of line and transformer faults is MFL、MFT(ignoring switch, breaker failure conditions, MFL+MFT＝MF) The total time of the line and the transformer out of service is HFL、HFT 
When distributed photovoltaic exists, the power failure frequency M of the faults that the power failure load is totally outside the island range, the power failure load part is in the island range and the power failure load is totally in the island rangeFa、MFbAnd MFc(MFa+MFb+MFc＝MF。MFaCorresponding to HFi、NFi、PFi、SFiThe value is unchanged; mFcCorresponding to HFi、NFi、PFi、SFiA value of 0; mFbCorresponding to HFiConstant value, NFi、PFi、SFiBecomes N'Fi、PF'i、S'Fi). The expression of the three-level index engineering statistical algorithm of the reliability of the distribution network containing the distributed photovoltaic is shown in table 1:
TABLE 1 distribution network reliability three-level index engineering statistical algorithm expression containing distributed photovoltaic
Namely: average fault power failure frequency AFTC' of users:
average fault power failure time AIHC-F' of user:
mean duration of fault power failure MID-F:
fault power supply reliability RS-F':
fault power shortage indicator ENS-F':
mean number of users MIC-F in power failure:
average prearranged blackout times ASTC of users:
average prearranged power failure time AIHC-S of users:
pre-scheduled average duration of outage MID-S:
prearranged power reliability RS-S:
pre-arrangement electric quantity shortage index ENS-S:
presetting average number of users MIC-S for power failure:
user mean time failureElectricity economy number AFETC':
user average fault power failure economic time AIEHC-F':
mean economic duration of power failure MID-F:
fault power supply economic reliability ERS-F:
average power shortage amount AENT-F in fault and power failure:
average prearranged power failure economic times ASETC of users:
the average user prearranged power failure economic time AIEHC-S:
pre-scheduling average economic duration of blackout, MIED-S:
prearranged power economic reliability ERS-S:
prearranged average power shortage amount AENT-S:
line fault outage rate RIFI-L:
and (3) the fault outage rate RTFI-T of the transformer:
line fault outage average duration MDLOI-L:
average power failure duration of transformer fault MDTOI-T:
establishing a third-level index scoring standard: and establishing a three-level index scoring standard by adopting a fuzzy membership method. Firstly, determining a typical score point, then quantizing the three-level index values in a certain range into the typical score point, and gradually forming each three-level index score standard.
Establishing a three-level index scoring case library: reliability statistical parameters of n (n is more than 20) regions are selected, engineering calculation values of three-level indexes are obtained through an engineering algorithm, and scoring values are obtained through scoring standards. The grade value of the third-level index is in the interval of 0,100.
The training sample of the BP neural network consists of the following parts: the three-level index engineering calculation value, the area total load value and the area total user number form an input value of a BP neural network sample, a result obtained by grading the reliability index by using a fuzzy membership method is used as an output value of a training sample, and a format is given as follows: a sample input value; the sample output value.
(1) Number of BP neural networks:
different BP networks are respectively adopted for conventional reliability, economic reliability and equipment reliability. I.e. three types of BP neural networks are formed.
(2) Number of layers of BP neural network:
the BP neural network adopts a 3-layer structure: input layer + intermediate layer + output layer.
(3) The number of nodes in each layer is as follows:
the number of nodes in each layer of the three types of BP neural networks is shown in a table 2.
TABLE 2 number of nodes in each layer of three BP neural networks
| BP neural network type | Number of input layer points | Number of hidden layer nodes | Number of output layer nodes |
| Conventional reliability | 14 | 14 | 12 |
| Economic reliability | 12 | 12 | 10 |
| Device performance | 5 | 5 | 4 |
Conventional reliability BP network sample input values: the corresponding three-level index + total area load value + total area user number in fig. 1; sample output value: the fuzzy membership method scores of the corresponding three-level indicators in FIG. 1.
Economic reliability BP network sample input values: the corresponding three-level index + total area load value + total area user number in fig. 1; sample output value: the fuzzy membership method scores of the corresponding three-level indicators in FIG. 1.
Device performance BP network sample input values: the corresponding tertiary index + total number of devices in fig. 1; sample output value: the fuzzy membership method scores of the corresponding three-level indicators in FIG. 1.
(4) The excitation function Sigmoid f (x) is 1/(1+ e-x).
Calculating the three-level index of the target system: and (4) calculating data required by the three-level index by the target system by adopting an engineering statistical algorithm, and calculating the three-level index value of the target system according to the expression in the table 1 obtained in the step (II).
Sixthly, scoring the three-level indexes of the target system: and outputting a three-level index scoring value of the target system according to the three-level index calculation value of the target system, the total load value of the area, the total number of users of the area and the total number of equipment and according to a three-level index scoring case library.
Determining the weight between indexes in the reliability evaluation model: establishing each layer judgment matrix by utilizing a 1-9 scale method of Satty, and determining the weight of each index in the same layer by adopting a relative weight method, wherein the specific steps are as follows:
(1) when the number of indexes is less than 3, the weight of the indexes is directly determined by experts; the number of the indexes is equal to or more than 3, and the indexes on the same layer are compared pairwise to obtain a judgment matrix which adopts 1-9 scales of Saaty to express the relative importance of each index.
(2) Calculating a consistency index CR of the judgment matrix, and checking the consistency degree of the judgment matrix, wherein the steps are as follows:
1) calculating a consistency index CI:
in the formula: lambda [ alpha ]maxRepresenting the maximum characteristic root value of the judgment matrix; a represents the number of indices.
2) Determining an average random consistency indicator RI:
the corresponding RI is found according to the average random consistency index value given by Saaty, as shown in table 3,
TABLE 3 average random consistency index values given by Saaty
| a | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
3) Calculating the consistency ratio CR:
(3) when CR is less than 0.10, judging that the matrix consistency is qualified through inspection, calculating the maximum characteristic root of the judgment matrix and the corresponding characteristic vector, wherein the characteristic vector after normalization processing is the weight w of each index; if the consistency is unqualified, readjusting and determining part of elements in the judgment matrix until the consistency meets the requirement.
And eighthly, evaluating the reliability of the target system: adopting an AHP method, and calculating layer by layer upwards according to a formula (1) to obtain a total reliability index comprehensive score value of a target system:
in the formula: s' R represents the score of any non-underlying indicator; s'iA score representing the lower index i; wiRepresents the weight of the lower index i; b represents the number of lower-layer indexes of the index S' R; and calculating from the basic level index score and the weighted sum of the weights layer by layer upwards, wherein the highest level S' R value is the total index comprehensive score value.
And evaluating the reliability of the target system according to the total reliability index score value.
The reliability quality corresponding to the total index score value of reliability is shown in table 4:
TABLE 4 reliability level corresponding to the total index score value of reliability
| Value of credit | 100～90 | 90～80 | 80～70 | 70～60 | <60 |
| Quality of reliability | Superior food | Good wine | In | Passing and lattice | Difference (D) |
It should be understood that the above examples are only for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And such obvious variations or modifications which fall within the spirit of the invention are intended to be covered by the scope of the present invention.
Claims (1)
1. The evaluation method of the distribution-type photovoltaic power distribution network is characterized by comprising the following steps:
firstly, establishing a reliability evaluation model of a power distribution network containing distributed photovoltaic: the method adopts an AHP method to take the reliability of a power distribution network containing distributed photovoltaic as a total index, selects the conventional reliability, the economic reliability and the equipment performance as a primary index, selects the conventional reliability of fault power failure, the conventional reliability of prearranged power failure, the economic reliability of fault power failure, the economic reliability of prearranged power failure, the transformer performance and the line performance as secondary indexes, and selects the average fault power failure times of users, the average fault power failure time of users, the average fault power failure duration of users, the power supply reliability of prearranged power failure rate, the power supply reliability of prearranged power failure index, the average power failure number of users, the average fault power failure economic times of users, the average fault power failure economic time of users, the average prearranged power failure time of users, the average, The average economic duration of the fault power failure, the economic reliability of the fault power supply, the average power shortage amount of the fault power failure, the average prearranged power failure economic times of the users, the average prearranged power failure economic time of the users, the average prearranged power failure economic duration of the power failure, the economic reliability of the prearranged power supply, the average power shortage amount of the prearranged power failure, the power failure rate of the line, the power failure rate of the transformer, the average power failure duration of the line and the average power failure duration of the transformer are three-level indexes; each level of index is in a subordinate relation;
determining a calculation expression of three-level indexes in the reliability evaluation model: the three-level index calculation method adopts an engineering statistical algorithm;
establishing a third-level index scoring standard: establishing a three-level index scoring standard by adopting a fuzzy membership method;
establishing a three-level index scoring case library: selecting reliability statistical parameters of n regions, wherein n is greater than 20, obtaining engineering calculation values of three-level indexes through an engineering algorithm, and obtaining scoring values through scoring standards;
calculating the three-level index of the target system: calculating the data required by the third-level index by the statistical target system by adopting an engineering statistical algorithm, and calculating the third-level index value of the target system according to the expression obtained in the step II;
sixthly, scoring the three-level indexes of the target system: outputting a three-level index rating value of the target system according to the calculated value of the three-level index of the target system, the total load value of the area, the total number of users of the area and the total number of equipment and according to a three-level index rating case library;
determining the weight between indexes in the reliability evaluation model: establishing each layer judgment matrix by utilizing a 1-9 scale method of Satty, and determining the weight of each index on the same layer by adopting a relative weight method;
and eighthly, evaluating the reliability of the target system: adopting an AHP method, and calculating layer by layer upwards according to a formula (1) to obtain a total reliability index comprehensive score value of a target system:
in the formula: s' R represents the score of any non-underlying indicator; s'iA score representing the lower index i; wiRepresents the weight of the lower index i; b represents the number of lower-layer indexes of the index S' R; calculating from the basic level index score and the weighted sum of the weights layer by layer upwards, wherein the highest level S' R value is the total index comprehensive score value; evaluating the reliability of the target system according to the reliability total index score value;
and in the second step, the third-level index calculation method adopts an engineering statistical algorithm, specifically: known parameters are set for a certain power distribution system as: the total number N of users is a unit of a user, the total assembly variable capacity S is a unit of MVA, the total line length L is a unit of km, and the total number T of transformers is a unit of a transformer; the method for calculating the data to be counted, which contains the three-level reliability index of the distributed photovoltaic power distribution network, by adopting an engineering statistical algorithm comprises the following steps: failure power failure times M in absence of distributed photovoltaicFTime of power failure in unit of time and each time of failure HFiThe unit is h, and the number of users N in each fault power failureFiUnit is household, each fault power failure load capacity PFiUnit MW h and packing capacity SFiMVA, and the failure times of the line and the transformer are respectively MFL、MFTNeglect ofFailure condition of neutral switch, breaker, MFL+MFT＝MFThe total time of the line and the transformer out of service is HFL、HFT，
When distributed photovoltaic exists, the power failure frequency M of the faults that the power failure load is totally outside the island range, the power failure load part is in the island range and the power failure load is totally in the island rangeFa、MFbAnd MFc，MFa+MFb+MFc＝MF；MFaFault outage time H for corresponding outage loads all outside of island rangeFiNumber N of fault power failure users with power failure loads all outside island rangeFiAnd the power failure load capacity P of the fault with the power failure load all outside the island rangeFiCapacity S for loading power failure load out of island rangeFiThe value is unchanged; mFcFault outage time H for all corresponding outage loads within island rangeFiAnd the number of the fault power failure users with the power failure loads all within the island rangeFiAnd the power failure load capacity P of the fault with the power failure load all in the island rangeFiCapacity S of all load in islandFiA value of 0; mFbFault outage time H for corresponding outage load part in islanding rangeFiThe number N of the users with power failure in the island range of the power failure load part is not changedFiAnd the power failure load capacity P of the power failure load part in the island rangeFiCapacity S of power failure load part in island rangeFiBecomes N'Fi、P′Fi、S'Fi(ii) a The expression of the three-level index engineering statistical algorithm of the reliability of the power distribution network containing the distributed photovoltaic is as follows: wherein M isSIn order to pre-arrange the number of power failure, the unit is times HSiFor prearranged power-off time, the unit is h, NSiThe number of the power failure users is prearranged every time, the unit is a household, PSiFor each prearrangement of blackout load capacity, SSiCapacity is filled for each prearranged power cut;
average fault power failure frequency AFTC' of users:
average fault power failure time AIHC-F' of user:
mean duration of power failure MID-F':
fault power supply reliability RS-F':
fault power shortage indicator ENS-F':
mean number of users MIC-F' in power failure:
average prearranged blackout times ASTC of users:
average prearranged power failure time AIHC-S of users:
pre-scheduled average duration of outage MID-S:
prearrangementPower supply reliability RS-S:
pre-arrangement electric quantity shortage index ENS-S:
presetting average number of users MIC-S for power failure:
user average failure power failure economic times AFETC':
user average fault power failure economic time AIEHC-F':
mean economic duration of fault outage MIED-F':
fault power supply economic reliability ERS-F':
average power shortage amount AENT-F' during fault power failure:
average prearranged power failure economic times ASETC of users:
user average pre-schedulingThe power failure economic time AIEHC-S:
pre-scheduling average economic duration of blackout, MIED-S:
prearranged power economic reliability ERS-S:
prearranged average power shortage amount AENT-S:
line fault outage rate RIFI-L:
and (3) the fault outage rate RTFI-T of the transformer:
line fault outage average duration MDLOI-L:
average power failure duration of transformer fault MDTOI-T:
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| CN106845711A (en) * | 2017-01-22 | 2017-06-13 | 南方电网科学研究院有限责任公司 | The processing method and processing unit of power supply reliability data |
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| CN108288122B (en) * | 2018-01-12 | 2021-10-12 | 南方电网科学研究院有限责任公司 | Assessment method and device of multi-region interconnection system |
| CN110348652A (en) * | 2018-04-03 | 2019-10-18 | 普华讯光（北京）科技有限公司 | The quantitative estimation method of switchgear house operation health degree |
| CN109636232A (en) * | 2018-12-26 | 2019-04-16 | 天津大学 | A kind of distribution key element recognition methods based on user's sensing reliability |
| CN111339215A (en) * | 2019-05-31 | 2020-06-26 | 北京东方融信达软件技术有限公司 | Structured data set quality evaluation model generation method, evaluation method and device |
| CN110264068A (en) * | 2019-06-18 | 2019-09-20 | 国网北京市电力公司 | The treating method and apparatus of electric power data |
| CN110503305B (en) * | 2019-07-25 | 2022-02-01 | 西安理工大学 | Transformer performance evaluation method |
| CN110490471B (en) * | 2019-08-23 | 2023-07-11 | 广西电网有限责任公司电力科学研究院 | Transformation method for power distribution network power supply reliability differentiation |
| CN110570118A (en) * | 2019-09-06 | 2019-12-13 | 云南电网有限责任公司电力科学研究院 | Power distribution network fault management scheme evaluation method and evaluation device |
| CN111582626A (en) * | 2020-03-17 | 2020-08-25 | 上海博英信息科技有限公司 | Power grid planning adaptability method based on big data |
| CN111738546A (en) * | 2020-05-19 | 2020-10-02 | 无锡融合大数据创新中心有限公司 | Power equipment state evaluation system |
| CN112950027A (en) * | 2021-03-02 | 2021-06-11 | 国网河北省电力有限公司保定供电分公司 | Power grid working method and device and power grid working system |
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| CN113361941B (en) * | 2021-06-18 | 2023-11-21 | 国网江苏省电力有限公司信息通信分公司 | Reliability evaluation method and system for power communication network |
| CN114091947A (en) * | 2021-11-29 | 2022-02-25 | 深圳供电局有限公司 | User regional characteristic evaluation method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222908A (en) * | 2011-06-09 | 2011-10-19 | 重庆大学 | Distribution network reliability estimation method considering prearranged stoppage |
| CN102542347A (en) * | 2011-12-28 | 2012-07-04 | 东南大学 | Method for comprehensively evaluating electric energy quality |
| CN102611101A (en) * | 2012-03-08 | 2012-07-25 | 湖北省电力公司 | Method for evaluating operational safety of interconnected power grid |
| CN104008302A (en) * | 2014-06-09 | 2014-08-27 | 上海电力学院 | Power distribution network reliability evaluation method based on combinational weighting and fuzzy scoring |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473715A (en) * | 2013-09-09 | 2013-12-25 | 国家电网公司 | Method for evaluating reliability of power distribution network provided with distributed photovoltaic system |
| CN104318374A (en) * | 2014-10-21 | 2015-01-28 | 国网重庆市电力公司电力科学研究院 | Method for assessing reliability of medium voltage distribution network for calculating upstream power restoration operation time |
| CN104376504B (en) * | 2014-11-06 | 2017-10-27 | 国家电网公司 | A kind of distribution system probabilistic reliability appraisal procedure based on analytic method |
| CN104636988A (en) * | 2015-02-11 | 2015-05-20 | 国家电网公司 | Active power distribution network assessment method |
-
2015
- 2015-05-29 CN CN201611131325.9A patent/CN106780129B/en active Active
- 2015-05-29 CN CN201510289086.9A patent/CN105023195B/en active Active
- 2015-05-29 CN CN201611130237.7A patent/CN106780127B/en active Active
- 2015-05-29 CN CN201611130228.8A patent/CN106600139B/en active Active
- 2015-05-29 CN CN201611131310.2A patent/CN106780128B/en active Active
- 2015-05-29 CN CN201611131354.5A patent/CN106780130B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222908A (en) * | 2011-06-09 | 2011-10-19 | 重庆大学 | Distribution network reliability estimation method considering prearranged stoppage |
| CN102542347A (en) * | 2011-12-28 | 2012-07-04 | 东南大学 | Method for comprehensively evaluating electric energy quality |
| CN102611101A (en) * | 2012-03-08 | 2012-07-25 | 湖北省电力公司 | Method for evaluating operational safety of interconnected power grid |
| CN104008302A (en) * | 2014-06-09 | 2014-08-27 | 上海电力学院 | Power distribution network reliability evaluation method based on combinational weighting and fuzzy scoring |
Non-Patent Citations (4)
| Title |
|---|
| Reliability-network-equivalent approach to distribution-system-re1iability evaluation;R.Billinton等;《IEE Proceedings-Generation，Transmission and Distribution》;19980331;第145卷(第2期);第149-153页 * |
| 一项对配电网络进行可靠性评估的新指标;张新勇等;《继电器》;20040116;第32卷(第2期);第19-22页 * |
| 基于AHP的北京城区电网可靠性评价体系研究与应用;袁昕;《中国优秀硕士学位论文全文数据库(电子期刊)工程科技II辑》;20140115;C042-501 * |
| 大电网可靠性评估的指标体系探讨;王超等;《电力系统及其自动化学报》;20070228;第19卷(第1期);第42-48页 * |
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