CN103593707A - Method and device for evaluating reliability of power distribution network - Google Patents

Abstract

The invention provides a method for evaluating the reliability of a power distribution network. The method for evaluating the reliability of the power distribution network comprises the steps that a composition structure of the power distribution network is divided into at least two blocks according to a preset division rule, so that an element layer, a block layer and a load point layer of a Bayesian network are obtained; according to the preset reliability parameters of elements, the element layer and the block layer, reliability index data of each load point of the load point layer in the power distribution network are obtained through calculation, and a weak link of each load point is analyzed; according to the reliability index data of each load point in the power distribution network, reliability index data of the power distribution network are obtained through calculation and a weak link of the power distribution network is analyzed. According to the method for evaluating the reliability of the power distribution network, the power distribution network is divided into the multiple blocks, so that the Bayesian network is established; the reliability index data of each block in the Bayesian network and the reliability index data of each load point of the power distribution network are calculated according to the reliability parameters of the elements, so that the weak link of the power distribution network is obtained; the calculation method is simple, quantitative analysis and identification are conducted on the obtained weak links of the power distribution network, data are abundant, and the accuracy degree is high.

Description

Method and device for evaluating reliability of power distribution network

Technical Field

The invention belongs to the field of power distribution networks, and particularly relates to a method and a device for evaluating reliability of a power distribution network.

Background

The power distribution network is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive compensation capacitors, accessory facilities and the like, and plays a role in distributing electric energy in the power network.

With the increase of national economy, the power consumption demand of users is continuously increased, and the power distribution system is used as a link directly facing the users in the power system, and has the most direct influence on the power supply quality and the power supply reliability of the users. Statistics show that. Nearly 80% of consumer outage failures are caused by failures in the distribution network. Therefore, analyzing the reliability of the power distribution network and identifying network weak links have very important significance for providing power supply reliability of the power distribution network, improving power supply quality, reducing network line loss, reducing system power failure loss, promoting and improving power industry production technology and management, and improving economic and social benefits.

The current method for evaluating the reliability of the power distribution network mainly comprises the following steps: failure Mode and Effects Analysis, recursive algorithms, minimum-path-based Analysis, minimum cut-set-based Analysis, and the like.

The FMEA method is used for enumerating element faults and analyzing and forming a fault mode consequence influence table from the element angle, and when the structure of a power distribution network is complex, the method is large in calculation amount and low in calculation speed. The recursive algorithm, the minimum path-based analysis method and the minimum cut set-based analysis method are from the viewpoint of load points, can calculate the reliability of the power distribution network with higher complexity, but have lower result precision. Moreover, the algorithms evaluate the reliability of the power distribution network from the perspective of the whole or elements, the reliability weak link result cannot be directly obtained, and the weak link of the power distribution network is difficult to identify from the perspective of quantitative analysis.

Disclosure of Invention

In view of the above, the present invention aims to provide a method and an apparatus for power distribution network reliability evaluation, which establish a bayesian network of a power distribution network based on partitions of power distribution network reliability evaluation, and calculate reliability index data of each partition in the bayesian network and a load point of the power distribution network through element reliability parameters, so as to obtain a weak link of the power distribution network.

A method for reliability assessment of a power distribution network comprises the following steps:

dividing the composition structure of the power distribution network into at least two blocks according to a preset division rule to obtain an element layer, a block layer and a load point layer of the Bayesian network;

according to the preset element reliability parameters, the element layer and the block layer, reliability index data of each load point in a load point layer in the power distribution network are obtained through calculation respectively, and weak links of the load points are analyzed;

and calculating to obtain the reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network.

Preferably, in the method, the dividing the composition structure of the power distribution network into at least two blocks according to a preset dividing rule to obtain an element layer, a block layer, and a load point layer of a bayesian network includes:

dividing a composition structure of the power distribution network into at least two blocks by taking a switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network;

establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network;

and setting a load point layer, and associating each load point in the load point layer with a partitioning layer.

In the above method, preferably, if the block layer further includes a switch element, the setting of the load point layer, where each load point in the load point layer is associated with each block in the block layer, includes:

determining the node type of the load point according to the relationship between the operation state of the block and the operation state of any load point or the relationship between the operation state of the switch element and the operation state of the load point;

judging whether the node type of the load point is a first type or not;

if yes, establishing an association relationship between the load point and the block or between the load point and the switch element.

In the above method, preferably, the blocking layer includes a blocking element and a switching element, the reliability index data of each load point in the load point layer in the power distribution network is obtained by calculation according to a preset element reliability parameter and the element layer and the blocking layer, and analyzing the weak link of the load point includes:

respectively calculating reliability index data of each block in the Bayesian network according to reliability parameters of preset elements;

retrieving and acquiring the reliability parameters of the switching element from preset element reliability parameters;

according to the reliability index data of each block, the reliability parameters of the switch elements and the node types of the load points, respectively calculating the reliability index data of each load point in the power distribution network;

and analyzing the weak link of the load point according to the reliability index data of the load point.

In the above method, preferably, the reliability index data of the load point includes: load point failure rate, average repair time.

In the foregoing method, preferably, the reliability index of the power distribution network includes: average outage rate of the system SAIFI, average outage duration of the system SAIDI, average outage duration of the user CAIDI, average system power availability rate ASAI, and/or expected outage amount ENS.

An apparatus for reliability assessment of a power distribution network, comprising:

the dividing module is used for dividing the composition structure of the power distribution network into at least two blocks according to a preset dividing rule to obtain an element layer, a block layer and a load point layer of the Bayesian network;

the first calculation module is used for respectively calculating reliability index data of each load point in a load point layer in the power distribution network according to the preset element reliability parameters, the element layer and the block layer, and analyzing weak links of the load points;

and the second calculation module is used for calculating reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network.

The above apparatus, preferably, the dividing module includes:

the dividing unit is used for dividing the composition structure of the power distribution network into at least two blocks by taking the switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to the network structure of the power distribution network;

the network forming unit is used for establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form the element layer and the blocking layer of the Bayesian network;

and the load point association unit is used for setting a load point layer and associating each load point in the load point layer with the partitioning layer.

In the above apparatus, preferably, the load point associating unit includes:

the first judgment subunit is used for determining the node type of the load point according to the relationship between the operation state of the block and the operation state of any load point or the relationship between the operation state of the switch element and the operation state of the load point;

the second judging subunit is used for judging whether the node type of the load point is the first type;

and the association subunit is used for establishing the association relationship between the load point and the block or between the load point and the switch element when the node type of the load point is judged to be the first type.

In the above apparatus, preferably, the partitioning layer includes partitions and switching elements, and the first calculating module includes:

the first calculation unit is used for calculating reliability index data of each block in the Bayesian network according to the reliability parameters of preset elements;

the acquisition unit is used for retrieving and acquiring the reliability parameters of the switching element from preset element reliability parameters;

the second calculation unit is used for calculating the reliability index data of each load point in the power distribution network according to the reliability index data of each block, the reliability parameters of the switch elements and the node type of the load point;

and the analysis unit is used for analyzing the weak link of the load point according to the reliability index data of the load point.

The invention provides a method for evaluating reliability of a power distribution network, which comprises the following steps: dividing the composition structure of the power distribution network into at least two blocks according to a preset division rule to obtain an element layer, a block layer and a load point layer of the Bayesian network; according to the preset element reliability parameters, the element layer and the block layer, reliability index data of each load point in a load point layer in the power distribution network are obtained through calculation respectively, and weak links of the load points are analyzed; and calculating to obtain the reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network. The power distribution network is divided into a plurality of blocks, the Bayesian network is established, reliability index data of the blocks in each Bayesian network and load points of the power distribution network are calculated through element reliability parameters, weak links of the power distribution network can be obtained, the calculation method is simple, the obtained weak links of the power distribution network are subjected to quantitative analysis and identification, data are abundant, and accuracy is high.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a flowchart of an embodiment 1 of a method for reliability evaluation of a power distribution network provided in the present application;

fig. 2 is a flowchart of an embodiment 2 of a method for reliability evaluation of a power distribution network provided by the present application;

fig. 3 is a detailed flowchart of step S1013 of the method for reliability evaluation of a power distribution network according to the embodiment 2 of the present application;

fig. 4 is a flowchart of embodiment 3 of a method for reliability evaluation of a power distribution network provided by the present application;

fig. 5 is a schematic structural diagram of an embodiment 1 of an apparatus for reliability evaluation of a power distribution network provided by the present application;

fig. 6 is a schematic structural diagram of an apparatus for reliability evaluation of a power distribution network according to embodiment 2 provided by the present application;

fig. 7 is a schematic structural diagram of a load point association unit 13 in an embodiment 2 of an apparatus for reliability assessment of a power distribution network provided by the present application;

fig. 8 is a schematic structural diagram of an embodiment 3 of an apparatus for reliability evaluation of a power distribution network provided by the present application;

fig. 9 is a schematic structural diagram of a simple medium-voltage distribution network in an application scenario of the method for reliability evaluation of a distribution network provided in the present application;

fig. 10 is a flowchart in an application scenario of a method for reliability assessment of a power distribution network according to the present application;

fig. 11 is an equivalent block network diagram in an application scenario of the method for evaluating reliability of a power distribution network provided by the present application;

fig. 12 is a relationship diagram of an element layer and a block layer in an application scenario of the method for reliability assessment of a power distribution network provided by the present application;

fig. 13 is a relational diagram of a block layer and a load point in an application scenario of the method for evaluating reliability of a power distribution network provided by the present application;

fig. 14 is a bayesian network diagram of a power distribution network system in an application scenario of the method for evaluating reliability of a power distribution network provided by the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The Bayesian network is one of the most effective theoretical models in the field of uncertain knowledge expression and reasoning at present. A bayesian network is a directed acyclic graph consisting of nodes representing variables and directed edges connecting these nodes. The nodes represent random variables, the directed edges among the nodes represent the mutual relations among the nodes (the father nodes point to the child nodes), the relation strength is expressed by using the conditional probability, and the prior probability is used for expressing information without the father nodes.

Bayesian equations are used to describe the relationship between two conditional probabilities, such as P (A | B) and P (B | A). According to the multiplication rule: p (a ≠ B) = P (a) = P (B | a) = P (B) × P (a | B), which may also be modified:

P(B|A)=P(A|B)*P(B)/P(A) （*）

the reliability of a power distribution network system generally has the following reliability indexes: SAIFI (system average power failure frequency), SAIDI (system average power failure duration index), CAIDI (user average power failure duration index), ASAI (average power availability index), ENS (expected not-available power supply), and the like. The system reliability is determined according to the index values of the SAIFI, the SAIDI, the CAIDI and the ENS, wherein the SAIFI, the SAIDI, the CAIDI and the ENS are in reverse correlation with the system reliability, and the system reliability is lower when the four index values are larger; and the ASAI is in positive correlation with the system reliability, and the larger the index value is, the higher the system reliability is.

Example 1

Referring to fig. 1, a flowchart of an embodiment 1 of a method for reliability evaluation of a power distribution network provided by the present application is shown, including:

step S101: dividing the composition structure of the power distribution network into at least two blocks according to a preset division rule to obtain an element layer, a block layer and a load point layer of the Bayesian network;

there are a large number of components in the distribution network, such as circuit breakers, disconnectors, fuses, tie switches, lines, transformers, etc.

Presetting a division rule, and dividing the composition structure of the power distribution network into at least two blocks.

And forming each component element in the power distribution network into an element layer, forming each block into a block layer, and forming each load point of the power distribution network into a load point layer.

Step S102: according to the preset element reliability parameters, the element layer and the block layer, reliability index data of each load point in a load point layer in the power distribution network are obtained through calculation respectively, and weak links of the load points are analyzed;

since the reliability parameters of the individual component elements in the distribution network are known, the reliability parameters of the individual component elements are listed.

The element layer in the Bayesian network is associated with the block layer, the block layer is associated with the load point layer, and the reliability index data of each block can be calculated in a block manner according to the preset element reliability parameters, so that the reliability index data of each load point can be calculated.

According to the reliability parameters of the preset elements in the power distribution network and the structure of the power distribution network, weak links of load points can be analyzed.

Step S103: and calculating to obtain the reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network.

And the load point layer in the Bayesian network is associated with the overall reliability of the power distribution network, and the reliability index data of the power distribution network can be calculated according to the reliability index data of each load point in the load point layer.

And quantitatively analyzing and identifying according to the reliability index data of the power distribution network, the reliability index data of each load point and the reliability index data of the blocks to obtain the weak links of the power distribution network.

In summary, according to the method for evaluating the reliability of the power distribution network provided in embodiment 1 of the present application, the power distribution network is divided into a plurality of blocks, a bayesian network is established, and reliability index data of the blocks and load points of the power distribution network in each bayesian network is calculated through element reliability parameters, so that weak links of the power distribution network can be obtained.

Example 2

Referring to fig. 2, a flowchart of embodiment 2 of a method for evaluating reliability of a power distribution network provided by the present application is shown, and in the flowchart shown in fig. 1, step S101 includes:

step S1011: dividing a composition structure of the power distribution network into at least two blocks by taking a switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network;

the switching element has the function of being able to connect and disconnect lines in the distribution network, and the distribution network is divided into at least two blocks by blocking the switching element as a boundary.

According to the network structure of the power distribution network, an equivalent block network diagram formed by blocks is drawn, and the equivalent block network diagram comprises all blocks of the power distribution network and the connection relation of the blocks.

Step S1012: establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network;

and combining the blocks to obtain a block layer, wherein elements in the power distribution network form an element layer. According to the equivalent block network diagram, a relationship between an element layer and a block layer is established, wherein each block in the block layer corresponds to one or more elements. And establishing association relationship between the elements in the element layer and the unique blocks.

Step S1013: and setting a load point layer, wherein each load point in the load point layer is associated with the partitioning layer.

According to the structure of the power distribution network, obtaining each load point in the power distribution network, wherein the load points form a load point layer, and each load point in the load point layer is associated with a block layer.

After any fault event occurs, the nodes can be classified into 4 types according to the difference of fault time.

A type: a normal node, namely a node which is not influenced by the fault when the correct action of the switch is carried out after the fault event occurs;

b type: the fault time is the node of the isolation operation time;

class C: the fault time is the node of the isolation operation plus the switching operation time;

and D type: the failure time is the node of the element repair time.

In the present application, the node type of the load point is determined according to the node classification rule.

Referring to fig. 3, a detailed flowchart of step S1013 of embodiment 2 of the method for reliability evaluation of a power distribution network provided by the present application is shown, where the method includes:

step S10131: determining the node type of the load point according to the relationship between the operation state of the block and the operation state of any load point or the relationship between the operation state of the switch element and the operation state of the load point;

because the switching element is used as a single block in this embodiment due to its dividing function, the switching element needs to be considered when determining the structure type of the load point according to the composition content of the block layer.

And respectively determining the node type of the load point according to the relation between the operation states of each block and the switching element in the block layer and the operation state of the load point. Since the operating state of the load point is not affected by the failure of the blocking layer when the node type is class a, and the operating state of the load point is affected by the failure of the blocking layer when the node type is class B, class C, or class D, in the present embodiment, three types B, C, D are summarized as the first type.

Step S10132: judging whether the node type of the load point is a first type or not;

when the node type of the load point satisfies the first type, the working state of the load point is affected by the fault of the block layer, and when the node type of the load point does not satisfy the first type, the working state of the load point is not affected by the fault of the block layer.

When any structure in the block layer, such as a block S, has a fault, the working state of a certain load point is influenced, and the load point is of a first type relative to the node type of the block S; and when other structures in the block layer, such as the block Z, have faults, the working state of the load point is not affected, and the node type of the load point relative to the block Z is not the first type.

And respectively carrying out node type judgment on each component in the partitioning layer and each load point in the load point layer to obtain the node types of a plurality of groups of load points.

Step S10133: if yes, establishing an association relationship between the load point and the block or between the load point and the switch element.

When the node type of the load point meets the first type, the working state of the load point is affected by the fault of the block layer, and the association of the negative charge and the structure in the block layer related to the first type is established.

For example, when the node type of the load point relative to the block S is the first type, the load point and the block S establish an association relationship; when the node type of the load point relative to the block Z is not the first type, the load point and the block Z do not establish an association relationship.

The incidence relation is used for the subsequent calculation of the reliability index.

In summary, an embodiment 2 of the present application provides a method for evaluating reliability of a power distribution network, and in this embodiment, a process of establishing a bayesian network of the power distribution network is provided, where the process includes: dividing a composition structure of the power distribution network into at least two blocks by taking a switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network; establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network; and setting a load point layer, wherein each load point in the load point layer is associated with each block in the block layer. And a basis is provided for obtaining the weak link of the power distribution network through subsequent calculation according to each block and load point.

Example 3

Referring to fig. 4, a flowchart of embodiment 3 of the method for evaluating reliability of a power distribution network provided by the present application is shown, and in the flowchart shown in fig. 2, step S102 includes:

step S1021: respectively calculating reliability index data of each block in the Bayesian network according to reliability parameters of preset elements;

since the reliability parameters of the individual component elements in the distribution network are known, the reliability parameters of the individual component elements are listed.

The element layer in the Bayesian network is associated with the block layer, and the reliability index data of each block can be calculated in a block mode according to the preset element reliability parameters.

In practical implementation, the element reliability parameters include: the failure rate and the average repair time of the elements, and the reliability indexes of the blocks comprise: the block equivalent fault rate and the equivalent fault time, and the reliability index data of the load point comprises: load point failure rate, average repair time.

The reliability index calculation formula of the blocks is as follows:

<math> <mrow> <msub> <mi>&lambda;</mi> <mi>sub</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>&gamma;</mi> <mi>sub</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <mo>/</mo> <msub> <mi>&lambda;</mi> <mi>sub</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein sub is a block to be analyzed, lambda_iIs the failure rate of element i, γ_iIs the mean repair time of element i, λ_subFor blocking equivalent failure rate, gamma_subFor blocking equivalent fault times, N_sIs the number of elements contained in a block.

And calculating each block by adopting the calculation mode to obtain the equivalent fault rate and the equivalent fault time of the corresponding block, namely reliability index data.

Step S1022: retrieving and acquiring the reliability parameters of the switching element from preset element reliability parameters;

the switching element is used as a component in the block layer, and after the reliability index of each block in the block layer is calculated, the reliability parameter of the switching element needs to be acquired for acquiring, so as to be used for calculating the reliability index data of each load point of the load point layer according to the reliability index and the reliability parameter in the block layer.

Step S1023: and respectively calculating the reliability index data of each load point in the power distribution network according to the reliability index data of each block, the reliability parameters of the switch element and the node type of the load point, and analyzing the weak link of the load point.

Since the operation state of the load point is affected by the components in the block layer as described in step S1013, the reliability index data of the load point is calculated in accordance with the node type of the load point based on the reliability index data of each block in the block layer and the reliability index parameter of the switching element. The reliability index data of the load point includes: load point failure rate, average repair time.

The reliability index calculation formula of the load point is as follows:

<math> <mrow> <msub> <mi>&lambda;</mi> <mi>LP</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>b</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mi>j</mi> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>&gamma;</mi> <mi>LP</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>b</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <msub> <mi>&gamma;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mi>j</mi> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <msub> <mi>&gamma;</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>&lambda;</mi> <mi>LP</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein LP is the load point to be analyzed, λ_subiAnd gamma_subiFor the failure rate and failure time, lambda, of the sub-block i having an effect on the load point after the failure_iAnd gamma_jRespectively, the failure rate and failure time, lambda, of a switch j having an effect on the supply of power to the load point after a failure_LPAnd gamma_LPFailure rate and failure time, N, respectively, of load points_bNumber of blocks, N, having an influence on the load point_sThe number of switching elements that have an effect on the load point.

And calculating each load point by adopting the calculation mode to obtain the corresponding load point fault rate and fault time, namely reliability index data.

When the load point faults are calculated respectively, the probability of the faults caused by each component in the power distribution network is calculated, and the component is easier to be a weak link of the load point when the calculated numerical value is larger.

And the reliability index data of each load point obtained by calculation can be used for calculating the reliability of the whole power distribution network.

The reliability indexes of the power distribution network comprise: SAIFI, SAIDI, CAIDI, ASAI, and ENS.

The reliability index calculation formula of the power distribution network is as follows:

<math> <mrow> <mi>SAIFI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>SAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>ASAI</mi> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mn>8760</mn> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>CAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula, λ_iIs the failure rate of load point i, U_iTime of failure, N, for load point i_sThe number of users at load point i.

Calculating ENS shared by the jth element in the power distribution network, wherein the calculation formula is as follows:

<math> <mrow> <msub> <mi>ENS</mi> <mi>j</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>LP</mi> </msub> </munderover> <msub> <mi>L</mi> <mi>ai</mi> </msub> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <msub> <mi>&gamma;</mi> <mi>ij</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula, L_aiIs the magnitude of the load power, λ, of the ith load point_jIs the jth element failure rate, γ_ijThe failure time of the ith load point when the jth element fails.

According to the calculated ENS of each element in the power distribution network, the larger the calculated value is, the easier the component is to be a weak link of a load point, and the ENS value of the power distribution network can be further calculated.

ENS=∑ENS_j （1-10）

In the formula, ENS_jIs the power shortage at the jth load point.

In this embodiment, the reliability index data of each block is calculated first, and then the reliability parameter of the switching element is acquired, but the order of the two steps is not limited to this, and in actual implementation, the reliability parameter of the switching element may be acquired first, or the two steps may be performed simultaneously, and the setting may be performed according to actual circumstances.

In summary, the method for evaluating the reliability of the power distribution network provided in embodiment 3 of the present application describes a process of calculating reliability index data of each load point in a load point layer in the power distribution network, the calculation process is based on preset element reliability parameters and the element layer and the block layer, the calculation method is simple, the reliability of each load point is quantitatively analyzed, and the method has abundant data and high accuracy.

Corresponding to the method embodiment for power distribution network reliability evaluation provided by the application, the application specification further provides an apparatus embodiment for power distribution network reliability evaluation.

Example 1

Referring to fig. 5, a schematic structural diagram of an embodiment 1 of the device for reliability evaluation of a power distribution network provided by the present application is shown, including: the system comprises a dividing module 1, a first calculating module 2 and a second calculating module 3;

the dividing module 1 is configured to divide the composition structure of the power distribution network into at least two blocks according to a preset dividing rule, so as to obtain an element layer, a block layer and a load point layer of a bayesian network;

there are a large number of components in the distribution network, such as circuit breakers, disconnectors, fuses, tie switches, lines, transformers, etc.

The dividing module 1 divides the composition structure of the power distribution network into at least two blocks according to a preset dividing rule, each composition element in the power distribution network forms an element layer, each block group forms a block layer, and each load point of the power distribution network forms a load point layer.

The first calculation module 2 is configured to calculate reliability index data of each load point in a load point layer in the power distribution network according to a preset element reliability parameter, the element layer and the block layer, and analyze a weak link of the load point;

since the reliability parameters of the individual component elements in the distribution network are known, the reliability parameters of the individual component elements are listed.

The element layer and the block layer in the Bayesian network are associated, the block layer is associated with the load point layer, the first calculating module 2 can calculate the reliability index data of each block in a block manner according to the preset element reliability parameters, and then the reliability index data of each load point can be calculated.

According to the reliability parameters of the preset elements in the power distribution network and the structure of the power distribution network, weak links of load points can be analyzed.

The second calculating module 3 is configured to calculate reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyze weak links in the power distribution network.

The load point layer in the bayesian network is associated with the overall reliability of the power distribution network, and the second calculation module 3 can calculate the reliability index data of the power distribution network according to the reliability index data of each load point in the load point layer.

And quantitatively analyzing and identifying according to the reliability index data of the power distribution network, the reliability index data of each load point and the reliability index data of the blocks to obtain the weak links of the power distribution network.

To sum up, the device for evaluating the reliability of the power distribution network provided in embodiment 1 of the present application establishes the bayesian network by dividing the power distribution network into a plurality of blocks, calculates reliability index data of the load points of the power distribution network and the blocks in each bayesian network through the element reliability parameters, and further can obtain the weak links of the power distribution network.

Example 2

Referring to fig. 6, a schematic structural diagram of an embodiment 2 of the device for power distribution network reliability evaluation provided by the present application is shown, and in the structure shown in fig. 5, the partitioning module 1 includes: a dividing unit 11, a network forming unit 12, and a load point associating unit 13;

the dividing unit 11 is configured to divide a composition structure of the power distribution network into at least two blocks by using a switching element as a boundary, and draw an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network;

the switching element has a function of connecting and disconnecting a line in the distribution network, and the dividing unit 11 divides the distribution network into at least two blocks by partitioning the distribution network according to the switching element as a boundary.

The dividing unit 11 draws an equivalent block network diagram composed of blocks according to the network structure of the power distribution network, wherein the equivalent block network diagram includes each block of the power distribution network and the connection relationship of the blocks.

The network forming unit 12 is configured to establish a relationship between an element layer and a blocking layer according to the equivalent blocking network diagram, and form an element layer and a blocking layer of a bayesian network;

and combining the blocks to obtain a block layer, wherein elements in the power distribution network form an element layer. The network forming unit 12 establishes a relationship between the component layer and the block layer according to the equivalent block network diagram, wherein each block in the block layer corresponds to one or more components. And establishing association relationship between the elements in the element layer and the unique blocks.

The load point associating unit 13 is configured to set a load point layer, and associate each load point in the load point layer with a block layer.

According to the structure of the distribution network, each load point in the distribution network is obtained, the load points form a load point layer, and the load point association unit 13 associates each load point in the load point layer with each block layer.

After any fault event occurs, the nodes can be classified into 4 types according to the difference of fault time.

A type: a normal node, namely a node which is not influenced by the fault when the correct action of the switch is carried out after the fault event occurs;

b type: the fault time is the node of the isolation operation time;

class C: the fault time is the node of the isolation operation plus the switching operation time;

and D type: the failure time is the node of the element repair time.

In the present application, the node type of the load point is determined according to the node classification rule.

Referring to fig. 7, a specific structural diagram of a load point association unit 13 in an embodiment 2 of the apparatus for reliability assessment of a power distribution network provided by the present application is shown, including: a first judgment subunit 131, a second judgment subunit 132, and an association subunit 133;

the first determining subunit 131 is configured to determine a node type of a load point according to a relationship between an operation state of a block and an operation state of any load point, or a relationship between an operation state of a switching element and an operation state of the load point;

because the switching element is used as a single block in this embodiment due to its dividing function, the switching element needs to be considered when determining the structure type of the load point according to the composition content of the block layer.

The first judging subunit 131 determines the node type of the load point according to the relationship between the operating states of the blocks and the switching elements in the block layer and the operating state of the load point. Since the operating state of the load point is not affected by the failure of the blocking layer when the node type is class a, and the operating state of the load point is affected by the failure of the blocking layer when the node type is class B, class C, or class D, in the present embodiment, three types B, C, D are summarized as the first type.

The second determining subunit 132 is configured to determine whether the node type of the load point is the first type;

when the node type of the load point satisfies the first type, the working state of the load point is affected by the fault of the block layer, and when the node type of the load point does not satisfy the first type, the working state of the load point is not affected by the fault of the block layer.

When any structure in the block layer, such as a block S, has a fault, the working state of a certain load point is influenced, and the load point is of a first type relative to the node type of the block S; and when other structures in the block layer, such as the block Z, have faults, the working state of the load point is not affected, and the node type of the load point relative to the block Z is not the first type.

The second judging subunit 132 performs node type judgment on each component in the partitioning layer and each load point in the load point layer, so as to obtain node types of multiple groups of load points.

Wherein the association subunit 133 is configured to, when it is determined that the node type of the load point is the first type, establish an association relationship between the load point and the block or between the load point and the switch element.

When the node type of the load point satisfies the first type, the operating state of the load point is affected by the fault of the block layer, and the association subunit 133 establishes an association of the negative charge with the structure in the block layer to which the first type is related.

For example, when the node type of the load point relative to the block S is the first type, the load point and the block S establish an association relationship; when the node type of the load point relative to the block Z is not the first type, the load point and the block Z do not establish an association relationship.

The incidence relation is used for the subsequent calculation of the reliability index.

To sum up, the device that provides a distribution network reliability evaluation in this application embodiment 2, in this embodiment, provides the process of establishing the bayesian network of distribution network, includes: dividing a composition structure of the power distribution network into at least two blocks by taking a switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network; establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network; and setting a load point layer, wherein each load point in the load point layer is associated with each block in the block layer. And a basis is provided for obtaining the weak link of the power distribution network through subsequent calculation according to each block and load point.

Example 3

Referring to fig. 8, a schematic structural diagram of an embodiment 2 of the device for power distribution network reliability evaluation provided by the present application is shown, and in the structure shown in fig. 6, the first computing module 2 includes: a first calculation unit 21, an acquisition unit 22, and a second calculation unit 23;

the first calculating unit 21 is configured to calculate reliability index data of each block in the bayesian network according to reliability parameters of preset elements;

since the reliability parameters of the individual component elements in the distribution network are known, the reliability parameters of the individual component elements are listed.

The first calculating unit 21 may calculate the reliability index data of each block according to the preset element reliability parameter, where the element layer and the block layer in the bayesian network are associated.

In practical implementation, the element reliability parameters include: the failure rate and the average repair time of the elements, and the reliability indexes of the blocks comprise: the block equivalent fault rate and the equivalent fault time, and the reliability index data of the load point comprises: load point failure rate, average repair time.

The reliability index calculation formula of the blocks is as follows:

<math> <mrow> <msub> <mi>&lambda;</mi> <mi>sub</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>&gamma;</mi> <mi>sub</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>s</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <mo>/</mo> <msub> <mi>&lambda;</mi> <mi>sub</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein sub is a block to be analyzed, lambda_iIs the failure rate of element i, γ_iIs the mean repair time of element i, λ_subFor blocking equivalent failure rate, gamma_subFor blocking equivalent fault times, N_sIs the number of elements contained in a block.

The first calculating unit 21 calculates each block by using the above calculating method to obtain the equivalent fault rate and the equivalent fault time of the corresponding block, that is, the reliability index data.

The obtaining unit 22 is configured to retrieve and obtain the reliability parameter of the switching element from preset element reliability parameters;

the switching element is used as a component in the block layer, and after the reliability index of each block in the block layer is calculated, the obtaining unit 22 is further required to obtain the reliability parameter of the switching element, so as to be used for subsequently calculating the reliability index data of each load point in the load point layer according to the reliability index and the reliability parameter in the block layer.

The second calculating unit 23 is configured to calculate reliability index data of each load point in the power distribution network according to the reliability index data of each partition, the reliability parameter of the switching element, and the node type of the load point, and analyze a weak link of the load point.

Since the operating state of the load point is affected by the components in the block layer, the second calculation unit 23 calculates the reliability index data of the load point in accordance with the node type of the load point, based on the reliability index data of each block in the block layer and the reliability index parameter of the switching element. The reliability index data of the load point includes: load point failure rate, average repair time.

The reliability index calculation formula of the load point is as follows:

<math> <mrow> <msub> <mi>&lambda;</mi> <mi>LP</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>b</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mi>j</mi> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>&gamma;</mi> <mi>LP</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>b</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <msub> <mi>&gamma;</mi> <msub> <mi>sub</mi> <mi>i</mi> </msub> </msub> <mo>+</mo> <munderover> <mi>&Sigma;</mi> <mi>j</mi> <msub> <mi>N</mi> <mi>q</mi> </msub> </munderover> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <msub> <mi>&gamma;</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>&lambda;</mi> <mi>LP</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein LP is the load point to be analyzed, λ_subiAnd gamma_subiTo shadow the load point after a faultFailure rate and failure time, λ, of a loud chunk i_iAnd gamma_jRespectively, the failure rate and failure time, lambda, of a switch j having an effect on the supply of power to the load point after a failure_LPAnd gamma_LPFailure rate and failure time, N, respectively, of load points_bNumber of blocks, N, having an influence on the load point_sThe number of switching elements that have an effect on the load point.

The second calculating unit 23 calculates each load point by using the above calculating method, and obtains the corresponding load point failure rate and failure time, that is, the reliability index data.

The second calculating module 3 can be used for calculating the reliability of the whole power distribution network according to the reliability index data of each load point calculated by the first calculating module 2.

The reliability indexes of the power distribution network comprise: SAIFI, SAIDI, CAIDI, ASAI, and ENS.

The reliability index calculation formula of the power distribution network is as follows:

<math> <mrow> <mi>SAIFI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>SAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>ASAI</mi> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mn>8760</mn> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <mi>CAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula, λ_iIs the failure rate of load point i, U_iTime of failure, N, for load point i_sThe number of users at load point i.

Calculating ENS shared by the jth element in the power distribution network, wherein the calculation formula is as follows:

<math> <mrow> <msub> <mi>ENS</mi> <mi>j</mi> </msub> <mo>=</mo> <munderover> <mi>&Sigma;</mi> <mi>i</mi> <msub> <mi>N</mi> <mi>LP</mi> </msub> </munderover> <msub> <mi>L</mi> <mi>ai</mi> </msub> <msub> <mi>&lambda;</mi> <mi>j</mi> </msub> <msub> <mi>&gamma;</mi> <mi>ij</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow> </math>

in the formula, L_aiIs the magnitude of the load power, λ, of the ith load point_jIs the jth element failure rate, γ_ijThe failure time of the ith load point when the jth element fails.

The second calculating module 3 sums the ENS of all the load points in an accumulated manner according to the calculated ENS of the distribution network in which each element is allocated, and can further calculate the ENS value of the distribution network.

In summary, the device for evaluating the reliability of the power distribution network provided in embodiment 3 of the present application describes a process of calculating reliability index data of each load point in a load point layer in the power distribution network in this embodiment, the calculation process is based on preset element reliability parameters and the element layer and the block layer, the calculation method is simple, the reliability of each load point is quantitatively analyzed, the data is rich, and the accuracy is high.

The application provides a specific application scenario of a method for evaluating reliability of a power distribution network.

As shown in fig. 9, the simple medium voltage distribution network includes: lines L1, L2, L3, L4, L5, L6, L7 and L8, switches S1, S2, b1 and C1, load points LP1, LP2, LP3 and LP4, transformers T1, T2, T3 and T4, and a resistor r 1.

The reliability parameters of the various components in the distribution network are listed in table 1 below.

Name of component	Failure rate lambda (times/year)	Mean time to repair gamma (hours/times)
			L1	0.1	5
L2	0.15	5
			L3	0.05	5
L4	0.12	5
			L5	0.14	5
L6	0.16	5
			L7	0.05	5
L8	0.05	5
			T1	0.02	8
T2	0.02	8
			T3	0.02	8
T4	0.02	8
			b1	0.02	24
r1	0.02	12
			S1	0.01	16
S2	0.01	16

TABLE 1

The specific flow of the application scenario is shown in fig. 10.

Step S201: dividing the composition structure of the power distribution network into 4 blocks by taking the switch element as a boundary;

the components (lines, transformers) and load points contained in the blocks are as follows:

partitioning 1: l1;

and (3) partitioning 2: l2, L3, L6, L7, T2, T3, LP2, LP 3;

and (3) partitioning: l4, L8, T4, LP 4;

and (4) partitioning: l5, T1, LP 1.

Step S202: drawing an equivalent block network diagram formed by blocks according to the network structure of the power distribution network;

the equivalent block network diagram shown in fig. 11 is a block 1, a block 2, a block 3, a block 4, S1, S2, b1, C1, and r 1.

Step S203: establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network;

as shown in fig. 12, the relationship between the component layer and the tile layer, each tile is connected to a component contained therein, and the direction of the arrow points to the tile.

L1 points to partition 1: l2, L3, L6, L7, T2, T3, LP2 and LP3 point to partition 2; l4, L8, T4 and LP4 point to partition 3; l5, T1 and LP1 point to partition 4.

Step S204: determining and listing the node types of the load points;

and determining the node type of each load point according to the relation among the operation states of each block, the switch element and the load point, and listing.

The node types for each load point are listed in table 2 below.

TABLE 2

Step S205: establishing a relation between the load points and the blocks of the Bayesian network according to the node types of the load points;

as shown in fig. 13, the relationship between the block layer and the load points, each load point is connected to the block layer, and the direction of the arrow points to the load point.

When the node type of the load point is B, C or D, the load point is connected to the corresponding block or switching element of the block layer, and when the node type of the load point is a, the load point is not connected to the structure of the block layer.

The block 1 is respectively connected with LP1, LP2, LP3 and LP 4; partition 2 is connected with LP1, LP2, LP3 and LP4 respectively; the block 3 is respectively connected with LP1, LP2, LP3 and LP 4; block 4 is connected to LP 1; b1 is respectively connected with LP1, LP2, LP3 and LP 4; r1 is respectively connected with LP1, LP2, LP3 and LP 4; s1 is respectively connected with LP1, LP2, LP3 and LP 4; s2 is connected with LP1, LP2, LP3 and LP4 respectively.

Step S206: calculating the fault rate, the equivalent fault rate and the equivalent fault time of each block;

the block or element failure is represented by "1" and the normality is represented by "0".

The failure probability of partition 3 is:

p (block 3=1) =1-P (L4=0) P (L8=0) P (T4=0)

The failure probabilities of block 1, block 2 and block 4 are calculated in sequence.

S1, S2, b1, C1, and r1 may be obtained from the queries in Table 1.

The calculated results are shown in table 3, the failure rate of each block.

	P this block =1 (fault)	P this partition =0 (normal)
			Block 1	0.0000570744	0.9999429256
Block 2	0.0002705024	0.9997294976
			Block 3	0.0001152872	0.9998847128
Block 4	0.0000981653	0.9999018347

TABLE 3

Equivalence of partition 3The failure rate is: lambda [ alpha ]_sub3=λ_L4+λ_L8+λ_T4=0.12+0.05+0.02=0.19 times/year

The equivalent failure time for partition 3 is: gamma ray_sub3=(λ_L4γ_L4+λ_L8γ_L8+λ_T4γ_T4)/γ_sub3=5.31 hours/time

And sequentially calculating the equivalent fault rate and the equivalent fault time of the block 1, the block 2 and the block 4.

The calculated results are shown in table 4, equivalent failure rate and equivalent failure time for each block.

	Equivalent failure rate lambda (times/year)	Equivalent failure time gamma (hour/time)
			Block 1	0.1	5
Block 2	0.45	5.27
			Block 3	0.19	5.31
Block 4	0.16	5.38

TABLE 4

Step S207: calculating the failure rate, the average repair time and the outage probability of each load point;

the failure rate at load point LP1 is:

λ_LP1=λ_sub1+λ_sub2+λ_sub3+λ_sub4+λ_b1+λ_r1+λ_S1+λ_S2=0.96 times/year

The average repair time for load point LP1 is:

γ_LP1=(λ_sub1γ_sub1+λ_sub2γ_sub2+λ_sub3γ_sub3+λ_sub4γ_sub4+λ_b1γ_b1+λ_r1γ_r1+λ_S1γ_S1+λ_S2γ_S1)/γ_LP1

=2.60937 hours/times

The failure rate and the average repair time of the load point LP2, the load point LP3, and the load point LP4 are calculated in this order.

The results of the calculation are shown in table 5, the failure rate and the average repair time at each load point.

Load point	Failure rate lambda (times/year)	Mean time to repair gamma (hours/times)
			LP1	0.96	2.60937
LP2	0.8	3.61865
			LP3	0.8	3.61865
LP4	0.8	2.025

TABLE 5

And calculating the outage probability of the load point according to the relationship between the load point and the blocks and the switching elements.

P (LP1=1) =1-P (partition 1=0) P (partition 2=0) P (partition 3=0) P (partition 4=0) P (b1=0) P (r1=0)

P(S1=0)P(S2=0)=0.0006595806

The outage probabilities of the load point LP2, the load point LP3, and the load point LP4 are calculated in this order.

The calculated results are shown in table 6 as the outage probability for each load point.

Load point	Probability of outage
		LP1	0.0006595806
LP2	0.0005614704
		LP3	0.0005614704
LP4	0.0005614704

TABLE 6

Step S208: analyzing weak links of load points;

if the load point LP1 fails, analyzing the probability caused by the failure of the line L1 as follows:

<math> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>L</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>|</mo> <mi>LP</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>LP</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>|</mo> <mi>L</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&times;</mo> <mi>P</mi> <mrow> <mo>(</mo> <mi>L</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>LP</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow> </math>

p (LP =1| L1=1) can be determined from the bayesian network structure that the line L1 belongs to partition 1, so that P (LP =1| L1=1) =1

$P(\mathrm{<figure-callout\; id="1"\; label="L"\; filenames="HDA0000423636010000052.png,HDA0000423636010000083.png"\; state="\{\{state\}\}">L</figure-callout>}1=1|\mathrm{<figure-callout\; id="1"\; label="LP"\; filenames="HDA0000423636010000052.png,HDA0000423636010000083.png"\; state="\{\{state\}\}">LP</figure-callout>}1=1)=\frac{P(\mathrm{<figure-callout\; id="1"\; label="L"\; filenames="HDA0000423636010000052.png,HDA0000423636010000083.png"\; state="\{\{state\}\}">L</figure-callout>}1=1)}{P(\mathrm{<figure-callout\; id="1"\; label="LP"\; filenames="HDA0000423636010000052.png,HDA0000423636010000083.png"\; state="\{\{state\}\}">LP</figure-callout>}1=1)}$

Wherein, <math> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>L</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&lambda;</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> <msub> <mi>&gamma;</mi> <mrow> <mi>L</mi> <mn>1</mn> </mrow> </msub> </mrow> <mn>8760</mn> </mfrac> </mrow> </math>

can be calculated to obtain <math> <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>L</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>|</mo> <mi>LP</mi> <mn>1</mn> <mo>=</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mn>0.1</mn> <mo>&times;</mo> <mn>5</mn> </mrow> <mn>8760</mn> </mfrac> <mo>=</mo> <mn>0.000057</mn> </mrow> </math>

When the load point faults are calculated respectively, the probability of faults caused by each component in the power distribution network is calculated, the component is easy to be a weak link of the load point when the calculated numerical value is larger, and the component with the largest numerical value is the weakest link of the load point.

Step S209: and calculating reliability index data of the power distribution network, and analyzing weak links in the power distribution network.

As shown in fig. 14, the distribution grid system bayesian network diagram, in conjunction with the structures of fig. 12 and 13, has a load point layer connected to the system layer, with the direction of the arrows pointing to the system layer.

The index calculation formula is as follows:

<math> <mrow> <mi>SAIFI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> <math> <mrow> <mi>SAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> <math> <mrow> <mi>ASAI</mi> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mn>8760</mn> <mi>&Sigma;</mi> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> <math> <mrow> <mi>CAIDI</mi> <mo>=</mo> <mfrac> <mrow> <mi>&Sigma;</mi> <msub> <mi>U</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> <mrow> <mi>&Sigma;</mi> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </math> and calculating to obtain the numerical value of each index by combining the number of users of each load point of the power distribution network.

And calculating the expected power shortage amount apportioned by each component in the power distribution network.

The expected amount of power starved apportioned by line L3 is:

ENS_L3=L_a1λ_L3γ_{1 L3}+L_a2λ_L3γ_{2 L3}+L_a3λ_L3γ_{3 L3}+L_a4λ_L3γ_{4 L3}

=200×0.05×0.5+300×0.05×5+400×0.05×5+300×0.05×(0.5+0.25)

=191.25(kWh)·a^-1

the expected power shortage amount apportioned by other components in the power distribution network is calculated respectively, the component is the weakest link of the load point more easily the larger the calculated value is, and the component with the largest value is the weakest link of the power distribution network.

And summing the expected power shortage amounts of the various parts to calculate the expected power shortage amount of the power distribution network.

ENS=∑ENS_j

In the formula, ENS_jIs the power shortage at the jth load point.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A method for reliability assessment of a power distribution network is characterized by comprising the following steps:

dividing the composition structure of the power distribution network into at least two blocks according to a preset division rule to obtain an element layer, a block layer and a load point layer of the Bayesian network;

according to the preset element reliability parameters, the element layer and the block layer, reliability index data of each load point in a load point layer in the power distribution network are obtained through calculation respectively, and weak links of the load points are analyzed;

and calculating to obtain the reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network.

2. The method according to claim 1, wherein the dividing the composition structure of the distribution network into at least two blocks according to a preset dividing rule to obtain an element layer, a block layer and a load point layer of a bayesian network comprises:

dividing a composition structure of the power distribution network into at least two blocks by taking a switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to a network structure of the power distribution network;

establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form an element layer and a blocking layer of the Bayesian network;

and setting a load point layer, and associating each load point in the load point layer with a partitioning layer.

3. The method of claim 2, wherein the block layer further comprises switching elements, and the setting a load point layer, each load point in the load point layer being associated with each block in the block layer, comprises:

determining the node type of the load point according to the relationship between the operation state of the block and the operation state of any load point or the relationship between the operation state of the switch element and the operation state of the load point;

judging whether the node type of the load point is a first type or not;

if yes, establishing an association relationship between the load point and the block or between the load point and the switch element.

4. The method according to claim 3, wherein the block layer comprises a block and a switch element, the reliability index data of each load point in the load point layer in the distribution network is obtained by calculation according to the preset element reliability parameters and the element layer and the block layer, and the analyzing the weak link of the load point comprises:

respectively calculating reliability index data of each block in the Bayesian network according to reliability parameters of preset elements;

retrieving and acquiring the reliability parameters of the switching element from preset element reliability parameters;

according to the reliability index data of each block, the reliability parameters of the switch elements and the node types of the load points, respectively calculating the reliability index data of each load point in the power distribution network;

and analyzing the weak link of the load point according to the reliability index data of the load point.

5. The method according to any of claims 1-4, wherein the reliability indicator data of the load points comprises: load point failure rate, average repair time.

6. The method of any of claims 1-4, wherein the reliability indicators for the power distribution network comprise: average outage rate of the system SAIFI, average outage duration of the system SAIDI, average outage duration of the user CAIDI, average system power availability rate ASAI, and/or expected outage amount ENS.

7. An apparatus for reliability assessment of a power distribution network, comprising:

the dividing module is used for dividing the composition structure of the power distribution network into at least two blocks according to a preset dividing rule to obtain an element layer, a block layer and a load point layer of the Bayesian network;

the first calculation module is used for respectively calculating reliability index data of each load point in a load point layer in the power distribution network according to the preset element reliability parameters, the element layer and the block layer, and analyzing weak links of the load points;

and the second calculation module is used for calculating reliability index data of the power distribution network according to the reliability index data of each load point in the power distribution network, and analyzing weak links in the power distribution network.

8. The apparatus of claim 7, wherein the partitioning module comprises:

the dividing unit is used for dividing the composition structure of the power distribution network into at least two blocks by taking the switch element as a boundary, and drawing an equivalent block network diagram formed by the blocks according to the network structure of the power distribution network;

the network forming unit is used for establishing a relation between an element layer and a blocking layer according to the equivalent blocking network diagram to form the element layer and the blocking layer of the Bayesian network;

and the load point association unit is used for setting a load point layer and associating each load point in the load point layer with the partitioning layer.

9. The apparatus of claim 8, wherein the load point associating unit comprises:

the first judgment subunit is used for determining the node type of the load point according to the relationship between the operation state of the block and the operation state of any load point or the relationship between the operation state of the switch element and the operation state of the load point;

the second judging subunit is used for judging whether the node type of the load point is the first type;

and the association subunit is used for establishing the association relationship between the load point and the block or between the load point and the switch element when the node type of the load point is judged to be the first type.

10. The apparatus of claim 9, wherein the partition layer comprises partitions and switching elements, and wherein the first computing module comprises:

the first calculation unit is used for calculating reliability index data of each block in the Bayesian network according to the reliability parameters of preset elements;

the acquisition unit is used for retrieving and acquiring the reliability parameters of the switching element from preset element reliability parameters;

the second calculation unit is used for calculating the reliability index data of each load point in the power distribution network according to the reliability index data of each block, the reliability parameters of the switch elements and the node type of the load point;

and the analysis unit is used for analyzing the weak link of the load point according to the reliability index data of the load point.

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