CN115241875A - Distribution network automatic switch distribution point optimization method based on dispersion - Google Patents
Distribution network automatic switch distribution point optimization method based on dispersion Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention relates to the field of power distribution networks, in particular to a distribution network automation switch distribution point optimization method based on dispersion, which comprises the steps of obtaining all switches and switch attributes in a large feeder of a power distribution network, and obtaining the number of users with switch influence by dividing with a minimum power failure unit according to a single line diagram of the large feeder of the power distribution network; acquiring historical switch fault power failure data; calculating the fault rate and the fault duration of the switch under each attribute factor based on historical switch fault power failure data and each attribute factor of switch attributes, and constructing a fault standard table; constructing a reliability simulation model according to the fault standard table; comparing the reliability simulation value with a preset reliability theoretical target value, and calculating the line reconstruction weight of each line by combining a grid ledger; and calculating the switch transformation weight according to the line transformation weight and the switch attribute, so as to optimize the switch with the large switch transformation weight. The invention has high accuracy and realizes accurate operation and maintenance.
Description
Technical Field
The invention relates to the field of power distribution networks, in particular to a distribution point optimization method of an automatic switch of a power distribution network based on dispersion.
Background
The three remote terminals of present distribution network's arrangement is not enough, leads to executable remote control's equipment volume little, and distribution automation practicality level is difficult to promote by a wide margin. However, business requirements such as terminal distribution and the like depend on manual offline carding statistics, so that efficiency is low, accuracy is low, instantaneity is insufficient, power distribution automation construction and operation and maintenance lack of accurate overall planning, and regional application success is not obvious.
Disclosure of Invention
The invention aims to provide an automatic switch distribution optimization method based on dispersion for a power distribution network, which is used for simulating switches based on dispersion events, automatically obtaining the switches to be modified, having high accuracy and realizing accurate operation and maintenance.
In order to solve the technical problems, the technical scheme of the invention is as follows: a distribution point optimization method based on discrete distribution network automation switch comprises the following steps:
step 1: acquiring each switch and switch attribute in a large feeder of the power distribution network, and dividing the large feeder of the power distribution network by a minimum power failure unit according to a single line diagram of the large feeder of the power distribution network to acquire the number of users influenced by the switches;
the switch attributes comprise a switch position, a switch type and a switch area grade; the method specifically comprises the following steps:
identifying the switch position of a switch on each line according to a large feeder line single line diagram of the power distribution network;
classifying the switches based on the automatic switch ledger and the switch positions to obtain switch types;
identifying the switch area grade of the switch according to the grid ledger;
step 2: acquiring historical switch fault power failure data to obtain power failure times and power failure duration;
and step 3: calculating the fault rate and the fault duration of the switch under each attribute factor based on historical switch fault power failure data and each attribute factor of the switch attribute in the step 1, and constructing a fault standard table;
and 4, step 4: and (3) constructing a reliability simulation model according to the fault standard table: according to the switch obtained in the step 1, matching a fault standard table and combining the influence of the switch in the step 1 on the number of users, calculating the number of households per year of power failure of each switch under each line and the total number of households per year of power failure of each line so as to obtain an annual average power failure event, and calculating a reliability simulation value of a single line;
and 5: comparing the reliability simulation value with a preset reliability theoretical target value, and calculating the line reconstruction weight of each line by combining a grid ledger;
step 6: and calculating the switch transformation weight according to the line transformation weight and the switch attribute, thereby optimizing the switch with the large switch transformation weight.
Further, in step 1, the switch positions include tie, segment, branch and demarcation; the method for identifying the position of the switch comprises the following steps: grading a feeder trunk and branches according to a line topology path of a large feeder single line diagram of the power distribution network, wherein switches belonging to trunk lines are section switches, and if the section switches are connected to other trunk lines, the section switches are interconnection switches; the switch belonging to the branch line is a branch switch, and if the branch switch is the final stage, namely the branch switch is arranged on the front side of a user, the branch switch is a boundary switch;
in the step 1, the switch types comprise a common switch, a two-remote switch and a three-remote switch; the method for classifying the switch types comprises the following steps: extracting a switch set, matching with a distribution network automation switch ledger, and performing label definition on switch types, wherein unmatched switches are ordinary switches, and other switches are two-remote or three-remote switches according to the distribution network automation switch ledger;
in the step 1, the switch area grades comprise a grade A, a grade B and a grade C; the method for identifying the switch area grade comprises the following steps: the grid ledger is a grid grading method comprising switch belonging line grading and line belonging grid, and the switch area grade is divided into A grade, B grade and C grade according to the grid grading.
Further, in step 1, a specific method for acquiring the number of users influenced by the switch is as follows: according to the topology of the line, recursion is carried out in the current direction of the normal operation side of the power grid, the topology is divided into N power supply minimum units according to the section switches or the branch switches, power failure of each switch is simulated respectively, the number of corresponding influencing users is obtained, and the number of the influencing users is the number of the switching influencing users.
Further, in step 1, a specific method for acquiring the number of users influenced by the switch is as follows:
according to the topology of the line, firstly recursion is carried out in the current direction of the normal operation side of the power grid, the topology is divided into N power supply minimum units according to the section switch or the branch switch, power failure of each switch is simulated respectively, and the number of corresponding influencing users is obtained and is the number of the influencing users of the first switch;
meanwhile, the situation of interconnection power supply is considered, recursion is carried out according to the current direction of an interconnection operator, and the number of the corresponding influencing users is obtained in the same way and is the number of the influencing users of the second switch;
setting a normal carrier influence coefficient and a contact carrier influence coefficient;
calculating the number of users influenced by the switch: switch influence number of users = first switch influence number of users normal operation influence coefficient + second switch influence number of users interconnection operation influence coefficient.
Further, the step 2 specifically includes: acquiring historical power failure pool information, namely historical switch fault power failure data, wherein the historical power failure pool information comprises a main table and a sub table, and the main table comprises a power failure line, line power failure starting time, line power failure ending time and power failure reasons; the sub-table comprises power failure equipment, equipment power failure starting time and equipment power failure ending time;
acquiring specific power failure equipment and equipment power failure time length corresponding to the sub-table according to the power failure reason, wherein the equipment power failure time length is the difference value between the equipment power failure ending time and the equipment power failure starting time; and dividing a minimum power failure unit according to power failure equipment, removing single distribution transformer fault data, and taking the power failure time length of the maximum equipment recorded by a single record as the power failure time length of the switch.
Further, the step 3 specifically includes:
step 3.1: calculating the failure times Cs and the failure duration Sc of each type of switch according to each attribute factor of the switch attribute in the step 1;
step 3.2: summarizing the power failure times and the power failure duration of all switches to obtain the total power failure times and the total power failure duration of each type, and calculating the annual average failure rate and the average failure duration;
wherein the average annual fault rate = total number of blackouts/N (years) total number of switches; n represents that the selected historical switch fault power failure data is data of N years;
wherein the average fault duration = total outage duration/total outage times;
step 3.3: and constructing a fault standard table based on the average annual fault rate and the average annual fault duration of the switches under each attribute factor.
Further, the step 4 specifically includes:
step 4.1: matching the switches obtained in the step 1 with a fault standard table to obtain the average annual fault rate and the average power failure duration of each switch;
step 4.2: circularly traversing each large feeder line, and calculating the number of households per year in power failure of each switch under each feeder line by combining the number of users influenced by the switches;
the number of the users during annual power failure of each switch = annual average failure rate of each switch and the influence of the switches on the number of the users and the average failure duration;
step 4.3: summing according to the hierarchical relationship between the lines and the switches in the line topology path to obtain the total number of the users in power failure of each line;
step 4.4: calculating the annual average power failure event so as to calculate and obtain a reliability simulation value of each line;
the average power failure event per year = the number of users/total number of users in total power failure;
wherein, the reliability simulation value =1- (annual average power outage event/365 × 24).
Further, step 5 specifically comprises:
step 5.1: presetting a theoretical target value of reliability;
and step 5.2: calculating the target weight of each line; each line target weight = reliability theoretical target value-reliability simulation value;
step 5.3: calculating a line reconstruction weight according to the line region level weight and the line target weight; the line area grade weight is a preset value preset according to the line area grade in the grid ledger.
Further, step 6 specifically includes:
step 6.1: acquiring all common switches and two-remote switches of each line under each line weight coefficient;
step 6.2: calculating a switch weight coefficient of each obtained switch, wherein the switch weight coefficient = the number of users during the power failure of the switch/the number of users during the total power failure of the current line × the line reconstruction weight;
step 6.3: calculating a switch reconstruction weight according to the switch position weight and the switch weight coefficient; the switch position weight is a preset value preset according to the switch position in the grid ledger.
Further, the optimization method further comprises the following steps:
and 7: and constructing constraint conditions to modify the switch with large switch modification weight under the constraint conditions of limited modification cost.
The invention has the following beneficial effects:
1. the method comprises the steps of obtaining a switch and switch attributes of the switch, constructing a fault standard table according to historical switch fault power failure data and switch data, constructing a reliability simulation model based on the fault standard table, and realizing simulation calculation of the reliability of the line switch;
2. the invention finds out the switch on the line which needs to be preferentially transformed by three remote distances by simulating and calculating the reliability of the line and the switch, calculates the most urgent switch or line to be transformed by taking the maximum improvement of the power supply reliability as a target under the limited economic investment, and has the advantages of high efficiency, high accuracy, strong real-time property, realization of accurate operation and maintenance and obvious regional application effect.
Drawings
FIG. 1 is an overall flow diagram of the process of the present invention;
fig. 2 is a topological diagram of the switch influencing the number of users in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, the present invention is a method for optimizing distribution points of an automatic switch of a distribution network based on dispersion, which includes:
step 1: acquiring each switch and switch attribute in a large feeder of the power distribution network, and dividing the large feeder of the power distribution network by a minimum power failure unit according to a single line diagram of the large feeder of the power distribution network to acquire the number of users influenced by the switches;
the switch attributes comprise switch positions, switch types and switch area grades; the method specifically comprises the following steps:
identifying the switch position of a switch on each line according to a large feeder single line diagram of the power distribution network; switch positions include tie, segment, branch and demarcation; the method for identifying the position of the switch comprises the following steps: grading a feeder trunk and branches according to a line topology path of a large feeder single line diagram of the power distribution network, wherein switches belonging to trunk lines are section switches, and if the section switches are connected to other trunk lines, the section switches are interconnection switches; the switch belonging to the branch line is a branch switch, and if the branch switch is the final stage, namely the branch switch is arranged on the front side of a user, the branch switch is a boundary switch;
classifying the switches based on the automatic switch ledger and the switch positions to obtain switch types; the switch types comprise a common switch, a two-remote switch and a three-remote switch; the method for classifying the switch types comprises the following steps: extracting a switch set, matching with a distribution network automation switch ledger, and performing label definition on switch types, wherein unmatched switches are ordinary switches, and other switches are two-remote or three-remote switches according to the distribution network automation switch ledger;
identifying the switch area grade of the switch according to the grid ledger; the switch area grades comprise a grade A, a grade B and a grade C; the method for identifying the switch area grade comprises the following steps: the grid ledger is a grid grading method comprising switch belonging line grading and line belonging grid, and the switch area grade is divided into A grade, B grade and C grade according to the grid grading.
The specific method for acquiring the number of the users influenced by the switch comprises the following steps: the users in the embodiment refer to distribution transformers or medium-voltage users, and the number of the influencing users is calculated according to the flow direction; in this embodiment, referring to fig. 2, according to the topology of the line, recursion is performed in the current direction of the normal operation side of the power grid, the topology is divided into N minimum power supply units (N in fig. 2 is 5) according to the section switch or the branch switch of the large feeder, power failure of each switch is simulated, and the number of the corresponding influencing users is obtained and is the number of the influencing users of the switches; in fig. 2, for example, if 5 users hang under the switch K1, the number of users influenced by the switch is 5, and if 2 users hang under the switch T2, the number of users influenced by the switch is 2; the number of the users is influenced by obtaining the minimum power failure unit according to the switch, namely, if the switch is disconnected, all the users connected below are powered off.
In some other embodiments, the power grid is normally in a normal operation mode, but when the power grid is in an emergency repair state, the operation mode is a connection operation mode, and in order to improve the accuracy of obtaining the number of users influenced by the switch, in other embodiments, the specific method for obtaining the number of users influenced by the switch is as follows:
according to the topology of the line, firstly recursion is carried out in the current direction of the normal operation side of the power grid, the topology is divided into N power supply minimum units according to the section switch or the branch switch, power failure of each switch is simulated respectively, and the number of corresponding influencing users is obtained and is the number of the influencing users of the first switch;
meanwhile, considering the condition of interconnection power supply, carrying out recursion according to the current direction of an interconnection operator, and acquiring the number of the corresponding influencing users in the same way, wherein the number of the influencing users is the number of the influencing users of the second switch;
setting a normal carrier influence coefficient and a contact carrier influence coefficient; the normal carrier impact coefficient can be set to 80%, and the contact carrier impact coefficient is set to 20%;
calculating the number of the users influenced by the switch: switch influence number of users = first switch influence number of users normal operator influence coefficient + second switch influence number of users tie operator influence coefficient.
The number of users of the normal operation party and the interconnection operation party is multiplied by the corresponding percentage respectively, the number of the users influenced by each switch in each operation mode is solved, and finally the number of the users influenced by each switch is obtained through summation.
Step 2: acquiring historical switch fault power failure data to obtain power failure times and power failure duration; in this embodiment, step 2 specifically includes: acquiring medium-voltage power-off information of nearly 3 years in the whole city, namely historical switch fault power failure data of nearly 3 years, by a distribution network reliability system, wherein the historical power failure battery information comprises a main table and a sub table, and the main table comprises a power failure line, line power failure starting time, line power failure ending time and power failure reasons; the sub-meter comprises power failure equipment, equipment power failure starting time and equipment power failure ending time;
acquiring specific power failure equipment and equipment power failure time length corresponding to the sub-table according to the power failure reason, wherein the equipment power failure time length is the difference between the equipment power failure ending time and the equipment power failure starting time and is measured in minutes; and dividing a minimum power failure unit according to power failure equipment, removing single distribution transformer fault data, and taking the power failure time length of the maximum equipment recorded by a single record as the power failure time length of the switch.
And step 3: calculating the fault rate and the fault duration of the switch under each attribute factor based on historical switch fault power failure data and each attribute factor of the switch attribute in the step 1, and constructing a fault standard table; the step 3 specifically comprises the following steps:
step 3.1: calculating the failure times Cs and the failure duration Sc of each type of switch according to each attribute factor of the switch attribute in the step 1; the method comprises the steps that according to switch attributes, total power failure data of nearly three years in the whole city are obtained through a distribution network automation system, then classification is carried out according to the switch attributes, namely switch positions, switch types and power supply region grades, and the power failure times and power failure duration of all switches are obtained;
step 3.2: summarizing the power failure times and the power failure duration of all switches in nearly three years in the whole city according to types to obtain the total power failure times and the total power failure duration of each type, and calculating the annual average fault rate and the average fault duration;
wherein the average annual fault rate = total number of blackouts/N (years) total number of switches; n represents that the selected historical switch fault power failure data is data of N years; in this embodiment, N =3;
wherein the average fault duration = total outage duration/total outage times;
step 3.3: and constructing a fault standard table based on the average annual fault rate and the average annual fault duration of the switches under each attribute factor to form a maximum permutation and combination table as follows.
And 4, step 4: and (3) constructing a reliability simulation model according to the fault standard table: according to the switch obtained in the step 1, matching a fault standard table and combining the influence of the switch in the step 1 on the number of users, calculating the number of households per year of power failure of each switch under each line and the total number of households per year of power failure of each line so as to obtain an annual average power failure event, and calculating a reliability simulation value of a single line; in this embodiment, the simulation calculation performed by using a machine learning method is highly consistent with the historical data, and machine learning is an existing data processing method and is not described herein. The step 4 specifically comprises the following steps:
step 4.1: matching the switches obtained in the step 1 with a fault standard table to obtain the average annual fault rate and the average power failure duration of each switch;
and 4.2: circularly traversing each large feeder line, and calculating the number of households per year in power failure of each switch under each feeder line by combining the number of users influenced by the switches;
the number of the users per year of power failure of each switch = the average failure rate per year of each switch, and the influence of the switches on the number of the users and the average failure duration (unit is converted into hour);
step 4.3: summing according to the hierarchical relationship between the lines and the switches in the line topology path to obtain the total number of the users in power failure of each line;
step 4.4: calculating the annual average power failure event so as to calculate and obtain the reliability simulation value of each line;
the average annual outage event = the number of users/total number of users in total outage;
the reliability simulation value =1- (annual average blackout event/365 × 24).
And 5: comparing the reliability simulation value with a preset reliability theoretical target value, and calculating the line reconstruction weight of each line by combining a grid ledger; the step 5 specifically comprises the following steps:
step 5.1: presetting a theoretical target value of reliability; in this example, the theoretical target value of reliability is set to 99.9%;
step 5.2: calculating the target weight of each line; each line target weight = reliability theoretical target value-reliability simulation value;
step 5.3: calculating a line reconstruction weight according to the line region level weight and the line target weight; in this embodiment, the level area a is set to 10, the level area b is set to 8, and the level area c is set to 5.
Step 6: calculating switch transformation weight according to the line transformation weight and the switch attribute, so as to optimize the switch with large switch transformation weight; the step 6 specifically comprises the following steps:
step 6.1: acquiring all common switches and two-remote switches of each line under each line weight coefficient;
step 6.2: calculating the obtained switch weight coefficient of each switch, wherein the switch weight coefficient = the number of users in the power failure of the switch/the number of users in the total power failure of the current line and the line reconstruction weight;
step 6.3: calculating a switch reconstruction weight according to the switch position weight and the switch weight coefficient; the switch position weight is a preset value preset according to the switch position in the grid ledger; in this embodiment, the communication is set to 10, the segment is set to 6, the branch is set to 3, and the boundary is set to 1 in the switch position.
And 7: and (4) constructing constraint conditions, and transforming the switch with large switch transformation weight under the constraint condition of limited transformation cost according to the annual input amount/switch transformation unit price, so that the reliability of the whole unit power supply is improved to the maximum extent under the limited investment.
The invention fully considers the power supply index of reliability during the stationing planning, and uses the discrete mathematical algorithm to simulate and calculate the reliability of the line. The discrete event means that the state change only occurs randomly at discrete time, and the power grid is understood to be a fault which occurs randomly under normal operation. The discrete event comprises three characteristics of an entity, an attribute and an activity. Entity-the specific objects (including objects, elements of research) that make up the grid, the entity in the present invention is a switch; attribute-characteristics of the entity (reflecting the state and parameters of the entity), the characteristics in the invention are the switch attribute of the switch and the influence of the switch on the number of users; activity- -the change of state of an entity over time (the process of changing two states in succession), an activity in the present invention is a power outage event for a switch. The invention constructs a reliability simulation model according to a fault standard table to generate discrete events, obtains the switch under the condition that the state of the simulation circuit changes, finds out the switch on the circuit which preferentially needs to be transformed by three remote distances through simulating and calculating the reliability of the circuit and the switch, and calculates the switch or the circuit which needs to be transformed most urgently by taking the maximum improvement of the power supply reliability as a target under the limited economic investment, thereby having high efficiency and high accuracy.
The non-related parts of the present invention are the same as or implemented using the prior art.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, numerous simple deductions or substitutions may be made without departing from the spirit of the invention, which shall be deemed to belong to the scope of the invention.
Claims (10)
1. A distribution network automatic switch distribution point optimization method based on dispersion is characterized by comprising the following steps: comprises that
Step 1: acquiring each switch and switch attribute in a large feeder of the power distribution network, and dividing by a minimum power failure unit according to a single line diagram of the large feeder of the power distribution network to acquire the number of users with switch influence;
the switch attributes comprise switch positions, switch types and switch area grades; the method comprises the following specific steps:
identifying the switch position of a switch on each line according to a large feeder single line diagram of the power distribution network;
classifying the switches based on the automatic switch ledger and the switch positions to obtain switch types;
identifying the switch area grade of the switch according to the grid standing book;
step 2: acquiring historical switch fault power failure data to obtain power failure times and power failure duration;
and step 3: calculating the fault rate and the fault duration of the switch under each attribute factor based on historical switch fault power failure data and each attribute factor of the switch attribute in the step 1, and constructing a fault standard table;
and 4, step 4: and (3) constructing a reliability simulation model according to the fault standard table: according to the switches obtained in the step 1, matching a fault standard table and combining the influence of the switches on the number of users in the step 1, calculating the number of households per year in power failure of each switch under each line and the number of households per total power failure of each line so as to obtain an average annual power failure event, and calculating a reliability simulation value of a single line;
and 5: comparing the reliability simulation value with a preset reliability theoretical target value, and calculating the line reconstruction weight of each line by combining a grid ledger;
and 6: and calculating the switch transformation weight according to the line transformation weight and the switch attribute, so as to optimize the switch with the large switch transformation weight.
2. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: in step 1, the switch positions comprise contact, segmentation, branching and demarcation; the method for identifying the switch position comprises the following steps: grading a feeder trunk and branches according to a line topology path of a large feeder single line diagram of the power distribution network, wherein switches belonging to trunk lines are section switches, and if the section switches are connected to other trunk lines, the section switches are interconnection switches; the switch belonging to the branch line is a branch switch, and if the branch switch is the final stage, namely the branch switch is arranged on the front side of a user, the branch switch is a boundary switch;
in the step 1, the switch types comprise a common switch, a two-remote switch and a three-remote switch; the method for classifying the switch types comprises the following steps: extracting a switch set, matching with a distribution network automation switch ledger, and performing label definition on switch types, wherein unmatched switches are ordinary switches, and other switches are two-remote or three-remote switches according to the distribution network automation switch ledger;
in the step 1, the switch area grades comprise a grade A, a grade B and a grade C; the method for identifying the switch area grade comprises the following steps: the grid ledger is a grid grading method comprising switch belonging line grading and line belonging grid, and the switch area grade is divided into A grade, B grade and C grade according to the grid grading.
3. The discrete-based power distribution network automation switch placement optimization method of claim 2, characterized in that: in step 1, the specific method for acquiring the number of users influenced by the switch comprises the following steps: according to the topology of the line, recursion is carried out in the current direction of the normal operation side of the power grid, the topology is divided into N power supply minimum units according to the section switches or the branch switches, power failure of each switch is simulated respectively, the number of corresponding influencing users is obtained, and the number of the influencing users is the number of the switching influencing users.
4. The discrete-based power distribution network automation switch placement optimization method of claim 2, characterized in that: in step 1, the specific method for acquiring the number of users influenced by the switch comprises the following steps:
according to the topology of the line, firstly, recursion is carried out in the current direction of the normal operation side of a power grid, the topology is divided into N power supply minimum units according to section switches or branch switches, power failure of each switch is simulated respectively, and the number of corresponding influence users is obtained and is the number of the influence users of the first switch;
meanwhile, considering the condition of interconnection power supply, carrying out recursion according to the current direction of an interconnection operator, and acquiring the number of the corresponding influencing users in the same way, wherein the number of the influencing users is the number of the influencing users of the second switch;
setting a normal carrier influence coefficient and a contact carrier influence coefficient;
calculating the number of users influenced by the switch: switch influence number of users = first switch influence number of users normal operator influence coefficient + second switch influence number of users tie operator influence coefficient.
5. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the step 2 specifically comprises the following steps: acquiring historical power failure pool information, namely historical switch fault power failure data, wherein the historical power failure pool information comprises a main table and a sub table, and the main table comprises a power failure line, line power failure starting time, line power failure ending time and power failure reasons; the sub-meter comprises power failure equipment, equipment power failure starting time and equipment power failure ending time;
acquiring specific power failure equipment and equipment power failure time length corresponding to the sub-table according to the power failure reason, wherein the equipment power failure time length is the difference value between the equipment power failure ending time and the equipment power failure starting time; and dividing a minimum power failure unit according to power failure equipment, removing single distribution transformer fault data, and taking the power failure time length of the maximum equipment recorded by a single record as the power failure time length of the switch.
6. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the step 3 specifically comprises the following steps:
step 3.1: calculating the failure times Cs and the failure duration Sc of each type of switch according to each attribute factor of the switch attribute in the step 1;
step 3.2: summarizing the power failure times and the power failure duration of all switches to obtain the total power failure times and the total power failure duration of each type, and calculating the annual average failure rate and the average failure duration;
wherein the average annual fault rate = total number of blackouts/N (years) total number of switches; n represents that the selected historical switch fault power failure data is data of N years;
wherein the average fault duration = total outage duration/total outage times;
step 3.3: and constructing a fault standard table based on the average annual fault rate and the average annual fault duration of the switches under each attribute factor.
7. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the step 4 specifically comprises the following steps:
step 4.1: matching the switches obtained in the step 1 with a fault standard table to obtain the average annual fault rate and the average power failure duration of each switch;
step 4.2: circularly traversing each large feeder line, and calculating the number of households per year of power failure of each switch under each feeder line by combining the number of users influenced by the switches;
the number of the users during annual power failure of each switch = annual average failure rate of each switch and the influence of the switches on the number of the users and the average failure duration;
step 4.3: summing according to the hierarchical relationship between the lines and the switches in the line topology path to obtain the total number of the users in power failure of each line;
step 4.4: calculating the annual average power failure event so as to calculate and obtain the reliability simulation value of each line;
the average annual outage event = the number of users/total number of users in total outage;
the reliability simulation value =1- (annual average blackout event/365 × 24).
8. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the step 5 specifically comprises the following steps:
step 5.1: presetting a theoretical target value of reliability;
step 5.2: calculating the target weight of each line; each line target weight = reliability theoretical target value-reliability simulation value; step 5.3: calculating a line reconstruction weight according to the line region level weight and the line target weight; the line area grade weight is a preset value preset according to the line area grade in the grid ledger.
9. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the step 6 specifically comprises the following steps:
step 6.1: acquiring all common switches and two-remote switches of each line under each line weight coefficient;
step 6.2: calculating the obtained switch weight coefficient of each switch, wherein the switch weight coefficient = the number of users in the power failure of the switch/the number of users in the total power failure of the current line and the line reconstruction weight;
step 6.3: calculating a switch reconstruction weight according to the switch position weight and the switch weight coefficient; the switch position weight is a preset value preset according to the switch position in the grid ledger.
10. The discrete-based power distribution network automation switch placement optimization method of claim 1, characterized in that: the optimization method further comprises the following steps:
and 7: and constructing constraint conditions to modify the switch with large switch modification weight under the constraint conditions of limited modification cost.
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| CN116073381A (en) * | 2023-03-21 | 2023-05-05 | 国网湖北省电力有限公司武汉供电公司 | Automatic equipment point distribution decision method considering reliability of power distribution network |
| CN116109108A (en) * | 2023-04-06 | 2023-05-12 | 广东电网有限责任公司佛山供电局 | Distribution network automatic switch position distribution generation method, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116073381A (en) * | 2023-03-21 | 2023-05-05 | 国网湖北省电力有限公司武汉供电公司 | Automatic equipment point distribution decision method considering reliability of power distribution network |
| CN116109108A (en) * | 2023-04-06 | 2023-05-12 | 广东电网有限责任公司佛山供电局 | Distribution network automatic switch position distribution generation method, device and storage medium |
| CN116109108B (en) * | 2023-04-06 | 2023-06-13 | 广东电网有限责任公司佛山供电局 | Distribution network automatic switch position distribution generation method, device and storage medium |
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