CN113868821A - Distribution network loss reduction method based on marketing and distribution big data fusion and terminal - Google Patents
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Abstract
The invention provides a distribution network loss reduction method and a terminal based on marketing and distribution big data fusion, which are used for acquiring distribution network operation data and establishing a key relationship among the data; constructing distribution network topology logic structure data; fusing distribution network operation data and topological logic structure data; respectively calculating the fine-grained loss of a user and the fine-grained loss of a distribution network line; optimizing the reactive power of the power grid; after the topological logic data of the distribution network structure are established, the visualization of the distribution network structure data is realized according to the topological relation among the line levels, branch lines, leads, towers and distribution transformer users according to the multi-branch tree topology expression model of the distribution network lines. The method is based on fusion of topological logic data of the distribution network tower and operation data of the distribution transformer, and a current and reactive power superposition algorithm is innovated, so that fine-grained control of distribution network line loss and recommendation of a distribution network reactive power reduction optimization scheme are realized; and the dynamic monitoring and early warning function of the distribution network operation condition is realized by utilizing a structure visualization technology.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a distribution network loss reduction method and a terminal based on marketing and distribution big data fusion.
Background
At present, a great deal of research is carried out on how to reduce the line loss by power workers, wherein some innovations in management or technology are not lacked, but no loss reduction scheme which is widely popularized and applied is formed at present.
In the prior art, the mining depth of the basic data value of the distribution network is insufficient, due to the fact that the modeling technology of the topological structure of the distribution network line section is lacked, effective indexes are not available among a plurality of data systems, and accurate fusion of data cannot be achieved, so that the calculation of the distribution network section loss and the reactive power optimization of nodes cannot be carried out, and the fine-grained calculation of the distribution network structure data cannot be achieved.
The existing distribution network reactive compensation calculation is biased to centralized compensation, and due to the fact that data are incomplete and a segmented superposition calculation function is lacked, although the speed is high, the granularity is coarse, line loss causes cannot be accurately positioned to specific nodes or line segments, loss reduction work consumes a large amount of manpower and material resources, and the effect is poor.
The operation state fine monitoring tool of the distribution network line section is vacant, the reactive compensation under-compensation capacity of the distribution network line section and a client cannot be effectively fed back in time, and specific loss reduction and electricity saving decision service cannot be provided for the user.
Disclosure of Invention
The invention provides a distribution network loss reduction method based on operation and distribution big data fusion, which is based on fusion of distribution network tower topological logic data and distribution transformer operation data, and innovations of current and reactive power superposition algorithms are performed, so that distribution network line loss fine-grained management and control and distribution network loss reduction reactive power optimization scheme recommendation are realized; and the dynamic monitoring and early warning function of the distribution network operation condition is realized by utilizing a structure visualization technology.
The method comprises the following steps:
acquiring distribution network operation data and establishing a main key relationship among the data;
step two, constructing distribution network topology logic structure data;
step three, fusing distribution network operation data and topological logic structure data;
step four, calculating the fine-grained loss of the user and the fine-grained loss of the distribution network line respectively;
fifthly, optimizing the reactive power of the power grid;
and step six, after the topological logic data of the distribution network structure is established, according to the multi-branch tree topology expression model of the distribution network line, according to the line level and the topological relation among branch lines, conducting wires, towers and distribution transformer users, the wiring structure is drawn from the outlet starting point to the tail end of the distribution network of the transformer substation, and the visualization of the distribution network structure data is realized.
It should be further noted that the obtained distribution network operation data includes: the method comprises the steps that electric energy indicating value data, current data, transformer operation data, transformer substation outgoing line meter reading data, marketing and distribution run-through transformer data, marketing equipment short codes, distributed power supply file details, line data, wire data, photovoltaic power generation acquisition data and high-voltage user daily meter reading power factor data are read by a high-voltage user daily;
establishing a primary key relationship between the transformer substation table and the line table through the equipment name;
establishing a main key relationship between a branch line lead list and a line list through the large feeder line;
establishing a main key relationship between the tower table and the line table through the tower number;
establishing a main key relationship between the tower table and the marketing system transformer ledger table through a T-connection branch line pole;
establishing a primary key relation between a PMS transformer standing book table and a marketing system transformer standing book table through the equipment name;
establishing a main key relationship between the tower table and the public transformer table through the branch line;
establishing a main key relationship between the photovoltaic power generation household table and the marketing system transformer standing book table through the station area number;
establishing a primary key relation between the centralized reading data table and the marketing system transformer ledger table through the station area numbers;
establishing a main key relation between the wire resistance meter and the tower meter through the wire model;
and establishing a primary key relation between the power factor table and the marketing system transformer ledger table through the station area numbers.
It should be further noted that the second step further includes:
drawing a standard CAD drawing with line length, lead model, distribution transformer area number, transformer user name, transformer capacity and transformer model information, using openCV to perform CAD image preprocessing, automatically identifying characters and graphic elements in the drawing by matching with a machine learning technology, forming a line logic name in a topological relation by using a self-created logic name naming rule, and constructing distribution network topological logic structure data;
or acquiring tower data in the line table from the pms system in the data, and associating the tower data with the marketing system transformer account table to perfect T-connection branch line pole numbers and distribution network topology logic structure data in the marketing system transformer account table.
It should be further noted that, the fine-grained loss calculation method for the user in step four includes:
calculating the active and reactive values of the iron loss in 24 hours according to the operation data of the user;
and calculating the active copper loss value of 24 hours according to the operation data of the user.
It should be further noted that, in the fourth step, the calculation method of the fine-grained loss of the distribution network line includes:
the method comprises the steps that a PQ decomposition method is utilized, 24-hour positive active power which is lost by a distribution transformer user is allocated according to the proportion of the positive active power of the distribution transformer user to the positive active total power of a distribution transformer circuit, and then the 24-hour current value of the distribution transformer user is obtained through the relation between the power and the current;
when multiple power supplies are connected to the grid, determining the current iteration direction according to the reverse active and reverse reactive indication values, and determining an iteration strategy;
and based on the fine-grained topological structure data of the distribution transformer line and the current operation data of the distribution transformer user, the loss calculation of any line section is realized.
It is further noted that, in the fifth step
And obtaining the power factor of 24 hours according to a power factor calculation formula, wherein:
(1) calculating according to the high supply and high count and the reading number of the meter in 24 hours and combining a power factor calculation formula;
(2) according to the combination of a high-voltage power supply and low-voltage power supply meter and a public area, the 24-hour meter reading number and the active loss and the reactive loss of the transformer are calculated through a power factor calculation formula;
screening out the line sections and the users which are lower than a preset threshold value of the power factor, calculating the compensation capacity of the line sections and the users according to a compensation capacity calculation formula, and performing reactive compensation on the line sections and the users;
calculating the current density of each line section according to the distribution transformer user current operation data and the model of the lead;
screening out a line section of the neck tower according to the economic current density and the safe current density;
and setting the current increase amplitude, and screening out the line section needing to be modified after 1-N years through field investigation.
It is further noted that the method further comprises: constructing a distribution network topological relation based on a fuzzy matching algorithm;
the method specifically comprises the following steps: placing the pre-judged redundant information in a redundant information list, and traversing all character strings to remove the redundant information;
dynamically adjusting the precision of the threshold according to the data matching condition after redundancy removal, and determining that the two character strings are similar character strings when the coincidence rate is higher than the threshold;
based on the KMP algorithm, the matching degree is described by the longest common subsequence of two strings.
It is further noted that the method further comprises: a fine-grained distribution network current backtracking algorithm is used for solving the current density;
by utilizing a cross-linked list data structure, the current and power iteration relation between the logic name hierarchy of the line and the number of the starting and stopping rod is deeply dug, the split-phase current value and the reactive value of each line section are iterated from the tail end of the transformer in a backtracking manner, the current value and the reactive power of each line section are obtained through calculation, the economic current density of the line section is calculated by combining the type number and the length of a lead, the line section with a clamping neck is screened, and the calculation of the current and the power of the line section with a branching line is realized.
It is further noted that the method further comprises: a load increase prediction algorithm;
the algorithm takes the monthly load as modeling data and sets ytA load observed value for t months; gtTrend component of the t-month load observed value; htA periodic component of the t-month load observation; etThe random noise with the mean value of zero, including measurement noise and model error, can be described by the following product model for a load model with trend and seasonal change laws:
yt＝GtHtEt
let the monthly load sequence be y1,y2,...,yTWherein T is the length of the sequence, and the relation between the length of the sequence and the annual number N and the monthly load cycle number 12 of the observed data sequence is T-12N, and by using a median moving average method, a trend term without seasonal period fluctuation can be extracted
Wherein T is 1,2, …, T-12;
the expression represents 2-order symmetric moving average digital filtering taking 12 as a period and t as a center, and no periodic component exists after the digital filtering;
after the trend is removed from the observation data sequence, description and modeling are carried out, polynomial trend is taken according to trend item data and curve characteristics thereof, and then a season period item with noise is obtained
HtEt＝yt/Gt
And then, load prediction is carried out according to the support vector machine theory.
The invention also provides a terminal for realizing the distribution network loss reduction method based on the marketing and distribution big data fusion, which comprises the following steps:
the memory is used for storing a computer program and a distribution network loss reduction method based on marketing and distribution big data fusion;
and the processor is used for executing the computer program and the distribution network loss reduction method based on marketing and distribution big data fusion so as to realize the step of the distribution network loss reduction method based on marketing and distribution big data fusion.
According to the technical scheme, the invention has the following advantages:
according to the distribution network loss reduction method based on operation and distribution big data fusion, operation and distribution data of a distribution network are fused, artificial intelligent vision is applied to mining the data, line and tower codes are subjected to operation and distribution data fusion, fine-grained theoretical calculation is carried out through a big data algorithm, reactive compensation capacity and line sections are optimized and screened, and stable, economical and safe operation and loss reduction and electricity saving of a power system are achieved. The invention obtains the fine-grained loss of the distribution network line through data analysis, and plans the distribution network with pertinence and directionality. Obtain 10kV circuit shaft tower wire section line loss through data analysis, have the wire section accurate positioning of card neck to 10kV circuit, transformer overload provides and reforms transform promotion plan deposit detail, makes clear and definite distribution lines overhaul technical improvement project deposit direction, in time reforms transform the circuit that has the hidden danger, can carry out reactive compensation to the circuit simultaneously and calculate, improves the voltage qualification rate, reduces the line loss. The invention screens out the line sections and users which are lower than the power factor standard through system calculation, calculates the compensation capacity of the line sections and the users through a loss reduction node computer identification algorithm, excavates a compensation line section and a user reactive compensation scheme, provides a customer under-compensation list and avoids user complaints. The invention can serve customers, avoid force rate fines and provide an optimal scheme for local and centralized compensation.
The distribution network loss reduction method based on marketing and distribution big data fusion can greatly improve the early-stage reserve efficiency of distribution network planning, distribution and rural network projects and major repair technical improvement projects, improve the accuracy of 10kV line new construction planning and neck reconstruction of the distribution network, screen out the power grid construction requirements after 1-N years by setting the current increase range, realize lean intelligent project reserve and improve the power supply reliability of the power grid. The method comprises the steps of service marketing, service customers and guidance of power supply enterprises to make reactive power optimization and transformation, and greatly reduces the line loss rate of the distribution lines by improving the power factors of the distribution lines, reactive power compensation and other methods on the basis of reducing reactive current. Meanwhile, the voltage qualification rate of the distribution line can be improved, the compensation capacity of a user is calculated through big data analysis, the client is helped to determine the short plate of reactive compensation, the economic loss of the client is avoided, the power-saving awareness of the client is improved, the electric energy utilization rate is improved, and the development of the economic society is served.
The distribution network loss reduction method based on marketing and distribution big data fusion can greatly improve the accuracy of power grid neck line transformation planning. And the power supply enterprises are instructed to make reactive power optimization transformation, so that the power factor of the distribution line is improved and the line loss of the distribution line is reduced on the basis of reducing reactive current. Meanwhile, the voltage qualification rate of the distribution circuit can be improved, the customer is helped to determine the short plates of reactive compensation through big data analysis, the economic loss of the customer is avoided, the power-saving consciousness of the customer is improved, the electric energy utilization rate is improved, and the development of the economic society is served.
The distribution network loss reduction method based on marketing and distribution big data fusion is developed and applied based on a big data platform, is formed by visualizing a line topological structure, displays units, substations and lines, reduces loss and saves electricity such as reactive compensation under-capacity, neck sub-line sections and the like, can simultaneously realize the functions of information push and short message reminding of a mobile terminal, and ensures that distribution network operation and inspection, marketing and customer detection results can receive loss reduction measures in time. The invention highlights the algorithm results of whether the automatic compensation function is normal, the compensation requirement prompt, the neck circuit adjustment record and the like in various expression forms of an electronic map, a power grid topological graph, an analysis chart, a download loss reduction list, a short message prompt and the like. And whether the capacitance capacity of the reactive compensation of the user meets the requirement or not, adjustment suggestions and result comparison of distribution network parameters before and after adjustment can be calculated and displayed.
The data source in the method is reliable and accurate, the data acquisition method is simple and easy to operate, no manpower is needed in the calculation and screening process, the reactive power compensation modification suggestion of the user can be given, and a convenient visualization function is provided for the user.
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In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flow chart of a distribution network loss reduction method based on marketing and distribution big data fusion.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 units and algorithm steps of each example described in the embodiment disclosed in the distribution network loss reduction method based on marketing and distribution big data fusion provided by the invention can be realized by electronic hardware, computer software or a combination of the two, and in order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the distribution network loss reduction method based on marketing and distribution big data fusion provided by the invention, it should be understood that the disclosed system, device and method can be realized in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The distribution network loss reduction method based on marketing and distribution big data fusion provided by the invention comprises the following steps:
s101, acquiring distribution network operation data and establishing a main key relationship among the data;
the obtained distribution network operation data comprises: the method comprises the steps of daily meter reading electric energy indicating data (the data types comprise forward active power, forward reactive power, reverse active power and reverse reactive power), daily meter reading current data (the data types comprise A phase, B phase and C phase), transformer detail, outgoing line meter reading data (the data types comprise forward active power, forward reactive power, reverse active power and reverse reactive power) of a transformer substation, marketing and distribution run-through transformer data, short codes of marketing equipment, distributed power supply file detail, line detail data, wire detail data, photovoltaic power generation acquisition data and daily meter reading power factor data (the data types comprise A phase, B phase and C phase).
The method comprises the steps of obtaining relevant data of a transformer substation, a line, a branch line wire, a tower, a transformer account and a public transformer from a PMS (power distribution system), obtaining relevant data of the transformer account and a photovoltaic power generation user from a power marketing business application system, obtaining relevant data of meter reading (including reading types such as active power, reactive power, power factors and current) from a power user electricity consumption information acquisition system, deriving relevant data of wire resistance from an integrated line loss system, and respectively establishing corresponding tables.
Establishing a primary key relationship between the transformer substation table and the line table through the equipment name; establishing a main key relationship between a branch line lead list and a line list through the large feeder line; establishing a main key relationship between the tower table and the line table through the tower number; establishing a main key relationship between the tower table and the marketing system transformer account table through T-connection branch poles (logic names); establishing a primary key relation between a PMS transformer standing book table and a marketing system transformer standing book table through the equipment name; establishing a main key relationship between the tower table and the public transformer table through the branch line; establishing a main key relationship between the photovoltaic power generation household table and the marketing system transformer standing book table through the station area number; establishing a primary key relation between the centralized reading data table and the marketing system transformer ledger table through the station area numbers; establishing a main key relation between the wire resistance meter and the tower meter through the wire model; and establishing a primary key relation between the power factor table and the marketing system transformer ledger table through the station area numbers.
After the main key relation is established, data taken from a branch line lead meter and a lead resistance meter are used for perfecting missing items in a tower meter; perfecting missing items in a transformer ledger of a marketing system according to loss parameters in a PMS transformer ledger table; and perfecting the missing items in the transformer standing book table of the marketing system by using the meter reading data taken from the centralized meter reading data table. The loss parameters include rated capacity, impedance voltage, no-load current, short-circuit loss, no-load loss, voltage ratio, rated current (high voltage), rated current (low voltage).
S102, constructing distribution network topology logic structure data; there are two ways to construct the distribution network topology logic structure data.
One is to draw a standard CAD drawing with information of line length, wire model, distribution transformer area number, transformer user name, transformer capacity and transformer model, to use openCV to preprocess CAD images, to automatically identify characters and graphic elements in the drawing by matching with machine learning technology, to form a line logic name with topological relation by using self-created logic name naming rules, to construct distribution network topology logic structure data;
and the other method is to acquire tower data in the line table from the pms system in the data, and correlate the tower data with the transformer account table of the marketing system, so as to improve the number of T-connection branch lines in the transformer account table of the marketing system and the data of the topological structure of the distribution network.
As an embodiment of the invention, the distribution network topological relation can be constructed based on a fuzzy matching algorithm.
Specifically, after fine-grained power distribution network data is obtained, the description and expression problems of the power distribution network grid structure need to be considered. And constructing a detailed distribution network topological relation by decomposing the branch lines of the fuzzy expression, performing fuzzy matching on the two character strings and calculating the matching degree of the two character strings.
The fuzzy matching algorithm comprises three parts: redundant data screening, threshold setting, longest common subsequence matching. Firstly, pre-judging redundant information is placed in a redundant information list, and all character strings are traversed to remove the redundant information; dynamically adjusting the precision of the threshold according to the data matching condition after the redundancy is removed, and determining that the two character strings are similar character strings when the coincidence rate is higher than the threshold; based on the KMP algorithm, the longest common subsequence of two strings is used to describe the degree of matching, and a subsequence of a string refers to a new string: it is a new character string composed of the original character string without changing the relative sequence of the characters after deleting some characters (or deleting any character).
For example, "ace" is a subsequence of "abcde," but "aec" is not a subsequence of "abcde. The "common subsequence" of two strings is a subsequence that both strings have in common.
S103, fusing distribution network operation data and topological logic structure data;
and performing deep fusion on the processed distribution network operation data and distribution network topology logic structure data by using the logic structure data of the pole number of the T-pole of the tower.
S104, respectively calculating user fine-grained loss and distribution network line fine-grained loss;
the fine-grained loss of the user is calculated as follows:
calculating the active and reactive values of the iron loss in 24 hours according to the operation data of the user;
and calculating the active copper loss value of 24 hours according to the operation data of the user.
The distribution network line fine-grained loss calculation mode comprises the following steps:
the method comprises the steps that a PQ decomposition method is utilized, 24-hour positive active power which is lost by a distribution transformer user is allocated according to the proportion of the positive active power of the distribution transformer user to the positive active total power of a distribution transformer circuit, and then the 24-hour current value of the distribution transformer user is obtained through the relation between the power and the current;
when multiple power supplies are connected to the grid, determining a current iteration direction and an iteration strategy according to the reverse active and reverse reactive index values;
and based on the fine-grained topological structure data of the distribution transformer line and the current operation data of the distribution transformer user, the loss calculation of any line section is realized.
S105, optimizing reactive power of the power grid;
and obtaining the power factor of 24 hours according to a power factor calculation formula, wherein:
(1) calculating according to the high supply and high count and the reading number of the meter in 24 hours and combining a power factor calculation formula;
(2) according to the combination of a high-voltage power supply and low-voltage power supply meter and a public area, the 24-hour meter reading number and the active loss and the reactive loss of the transformer are calculated through a power factor calculation formula;
screening out the line sections and the users which are lower than a preset threshold value of the power factor, calculating the compensation capacity of the line sections and the users according to a compensation capacity calculation formula, and performing reactive compensation on the line sections and the users;
calculating the current density of each line section according to the distribution transformer user current operation data and the model of the lead;
screening out a line section of the neck tower according to the economic current density and the safe current density;
and setting the current increase amplitude, and screening out the line section needing to be modified after 1-N years through field investigation.
S106, after the topological logic data of the distribution network structure are established, according to a multi-branch tree topology expression model of the distribution network line, according to the line level and the topological relation between branch lines, wires, towers and distribution transformer users, a wiring structure is sequentially drawn from the distribution network outlet starting point to the tail end of a transformer substation by using canvas of H5, the visualization of the distribution network structure data is realized, the distribution network operation data is fused through the pole number of the tower and the T connecting rod number of the distribution transformer user number, and the realization can be realized by matching with a current and reactive iteration algorithm: monitoring operation information such as current density, reactive under-compensation or over-compensation condition, positive and negative current, loss and the like of a line section and a node of any tower in a distribution network topological structure schematic diagram; the effects before and after the technology loss reduction transformation can be compared.
As an embodiment of the invention, the method further comprises: a fine-grained distribution network current backtracking algorithm is used for solving the current density;
by utilizing a cross-linked list data structure, the current and power iteration relation between the logic name hierarchy of the line and the starting and stopping rod numbers (nodes) is deeply dug, the split-phase current value and the reactive value of each line section are iterated from the tail end of the transformer in a backtracking manner, the current value and the reactive power of each line section are obtained through calculation, the economic current density of the line section is calculated by combining the model number and the length of a lead, the line section with a clamping neck is screened, and the accurate calculation of the line current and power branch sections is realized.
The method provided by the invention further comprises the following steps: a load increase prediction algorithm;
the algorithm is based on monthly load as modeling data, and the power load has an obvious nonlinear process and a trend change rule with a period of 12 months. Let ytA load observed value for t months; gtTrend component of the t-month load observed value; htA periodic component of the t-month load observation; etThe random noise with the mean value of zero, including measurement noise and model error, can be described by the following product model for a load model with trend and seasonal change laws:
yt＝GtHtEt
let the monthly load sequence be y1,y2,...,yTWherein T is the length of the sequence, the relation between the length of the sequence and the annual number N and the monthly load cycle number 12 of the observed data sequence is T-12N, and a trend term without seasonal period fluctuation can be extracted by a median moving average method
Wherein T is 1,2, …, T-12. The expression shows that the 2 nd order symmetric moving average digital filtering taking 12 as a period and t as a center does not contain periodic components after digital filtering. After the trend is separated from the observation data sequence, the trend can be described and modeled. And according to the trend item data and its curve characteristics taking polynomial trend and then obtaining the seasonal period item with noise
HtEt＝yt/Gt
And then, load prediction is carried out according to the support vector machine theory.
According to the distribution network loss reduction method based on operation and distribution big data fusion, operation and distribution data of a distribution network are fused, artificial intelligent vision is applied to mining the data, line and tower codes are subjected to operation and distribution data fusion, fine-grained theoretical calculation is carried out through a big data algorithm, reactive compensation capacity and line sections are optimized and screened, and the purposes of stable, economical and safe operation of a power system and loss reduction and electricity saving are achieved. The invention obtains the fine-grained loss of the distribution network line through data analysis, and plans the distribution network with pertinence and directionality. Obtain 10kV circuit shaft tower wire section line loss through data analysis, have the wire section accurate positioning of card neck to 10kV circuit, transformer overload provides and reforms transform promotion plan deposit detail, makes clear and definite distribution lines overhaul technical improvement project deposit direction, in time reforms transform the circuit that has the hidden danger, can carry out reactive compensation to the circuit simultaneously and calculate, improves the voltage qualification rate, reduces the line loss. The invention screens out the line section and the user which are lower than the power factor standard through system calculation, calculates the compensation capacity of the line section and the user through a loss reduction and electricity saving computer identification algorithm, excavates a compensation line section and a user reactive compensation scheme, provides a customer under-compensation list and avoids user complaints. The invention can serve customers, avoid force rate fines and provide an optimal scheme for local and centralized compensation.
The distribution network loss reduction method based on marketing and distribution big data fusion can greatly improve the early-stage reserve efficiency of distribution network planning, distribution and rural network projects and major repair technical improvement projects, improve the accuracy of 10kV line new construction planning and neck reconstruction of the distribution network, screen out the power grid construction requirements after 1-N years by setting the current increase range, realize lean intelligent project reserve and improve the power supply reliability of the power grid. The method comprises the steps of service marketing, service customers and guidance of power supply enterprises to make reactive power optimization and transformation, and greatly reduces the line loss rate of the distribution lines by improving the power factors of the distribution lines, reactive power compensation and other methods on the basis of reducing reactive current. Meanwhile, the voltage qualification rate of the distribution line can be improved, the compensation capacity of a user is calculated through big data analysis, the client is helped to determine the short plate of reactive compensation, the economic loss of the client is avoided, the power-saving awareness of the client is improved, the electric energy utilization rate is improved, and the development of the economic society is served.
Based on the method, the invention also provides a terminal for realizing the distribution network loss reduction method based on the marketing and distribution big data fusion, which comprises the following steps: the memory is used for storing a computer program and a distribution network loss reduction method based on marketing and distribution big data fusion; and the processor is used for executing the computer program and the distribution network loss reduction method based on marketing and distribution big data fusion so as to realize the step of the distribution network loss reduction method based on marketing and distribution big data fusion.
The terminal implementing the distribution network loss reduction method based on operation and distribution big data fusion is a unit and algorithm steps of each example described in connection with the embodiments disclosed herein, and can be implemented by electronic hardware, computer software, or a combination of the two. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Those skilled in the art will appreciate that various aspects of a distribution network loss reduction method based on marketing and distribution big data fusion may be implemented as a system, method, or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A distribution network loss reduction method based on marketing and distribution big data fusion is characterized by comprising the following steps:
acquiring distribution network operation data and establishing a main key relationship among the data;
step two, constructing distribution network topology logic structure data;
step three, fusing distribution network operation data and topological logic structure data;
step four, calculating the fine-grained loss of the user and the fine-grained loss of the distribution network line respectively;
fifthly, optimizing the reactive power of the power grid;
and step six, after the topological logic data of the distribution network structure is established, according to the multi-branch tree topology expression model of the distribution network line, according to the line level and the topological relation among branch lines, conducting wires, towers and distribution transformer users, the wiring structure is drawn from the outlet starting point to the tail end of the distribution network of the transformer substation, and the visualization of the distribution network structure data is realized.
2. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the obtained distribution network operation data comprises: the method comprises the steps that electric energy indicating value data, current data, transformer operation data, transformer substation outgoing line meter reading data, marketing and distribution run-through transformer data, marketing equipment short codes, distributed power supply file details, line data, wire data, photovoltaic power generation acquisition data and high-voltage user daily meter reading power factor data are read by a high-voltage user daily;
establishing a primary key relationship between the transformer substation table and the line table through the equipment name;
establishing a main key relationship between a branch line lead list and a line list through the large feeder line;
establishing a main key relationship between the tower table and the line table through the tower number;
establishing a main key relationship between the tower table and the marketing system transformer ledger table through a T-connection branch line pole;
establishing a primary key relation between a PMS transformer standing book table and a marketing system transformer standing book table through the equipment name;
establishing a main key relationship between the tower table and the public transformer table through the branch line;
establishing a main key relationship between the photovoltaic power generation household table and the marketing system transformer standing book table through the station area number;
establishing a primary key relation between the centralized reading data table and the marketing system transformer ledger table through the station area numbers;
establishing a main key relation between the wire resistance meter and the tower meter through the wire model;
and establishing a primary key relation between the power factor table and the marketing system transformer ledger table through the station area numbers.
3. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the second step further comprises:
drawing a standard CAD drawing with line length, lead model, distribution transformer area number, transformer user name, transformer capacity and transformer model information, using openCV to perform CAD image preprocessing, automatically identifying characters and graphic elements in the drawing by matching with a machine learning technology, forming a line logic name in a topological relation by using a self-created logic name naming rule, and constructing distribution network topological logic structure data;
or acquiring tower data in the line table from the pms system in the data, and associating the tower data with the marketing system transformer account table to perfect T-connection branch line pole numbers and distribution network topology logic structure data in the marketing system transformer account table.
4. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the fine-grained loss calculation method of the user in the fourth step comprises the following steps:
calculating the active and reactive values of the iron loss in 24 hours according to the operation data of the user;
and calculating the active copper loss value of 24 hours according to the operation data of the user.
5. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the fine-grained loss calculation method of the distribution network line in the fourth step comprises the following steps:
the method comprises the steps that a PQ decomposition method is utilized, 24-hour positive active power which is lost by a distribution transformer user is allocated according to the proportion of the positive active power of the distribution transformer user to the positive active total power of a distribution transformer circuit, and then the 24-hour current value of the distribution transformer user is obtained through the relation between the power and the current;
when multiple power supplies are connected to the grid, determining the current iteration direction according to the reverse active and reverse reactive indication values, and determining an iteration strategy;
and based on the fine-grained topological structure data of the distribution transformer line and the current operation data of the distribution transformer user, the loss calculation of any line section is realized.
6. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
in step five
And obtaining the power factor of 24 hours according to a power factor calculation formula, wherein:
(1) calculating according to the high supply and high count and the reading number of the meter in 24 hours and combining a power factor calculation formula;
(2) according to the combination of a high-voltage power supply and low-voltage power supply meter and a public area, the 24-hour meter reading number and the active loss and the reactive loss of the transformer are calculated through a power factor calculation formula;
screening out the line sections and the users which are lower than a preset threshold value of the power factor, calculating the compensation capacity of the line sections and the users according to a compensation capacity calculation formula, and performing reactive compensation on the line sections and the users;
calculating the current density of each line section according to the distribution transformer user current operation data and the model of the lead;
screening out a line section of the neck tower according to the economic current density and the safe current density;
and setting the current increase amplitude, and screening out the line section needing to be modified after 1-N years through field investigation.
7. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the method further comprises the following steps: constructing a distribution network topological relation based on a fuzzy matching algorithm;
the method specifically comprises the following steps: placing the pre-judged redundant information in a redundant information list, and traversing all character strings to remove the redundant information;
dynamically adjusting the precision of the threshold according to the data matching condition after removing redundancy, and determining that the two character strings are similar character strings when the coincidence rate is higher than the threshold;
based on the KMP algorithm, the matching degree is described by the longest common subsequence of two strings.
8. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the method further comprises the following steps: a fine-grained distribution network current backtracking algorithm is used for solving the current density;
by utilizing a cross-linked list data structure, the current and power iteration relation between the logic name hierarchy of the line and the number of the starting and stopping rod is deeply dug, the split-phase current value and the reactive value of each line section are iterated from the tail end of the transformer in a backtracking manner, the current value and the reactive power of each line section are obtained through calculation, the economic current density of the line section is calculated by combining the type number and the length of a lead, the line section with a clamping neck is screened, and the calculation of the current and the power of the line section with a branching line is realized.
9. The distribution network loss reduction method based on marketing and distribution big data fusion as claimed in claim 1,
the method further comprises the following steps: a load increase prediction algorithm;
the algorithm takes the monthly load as modeling data and sets ytA load observed value for t months; gtTrend component of the t-month load observed value; htA periodic component of the t-month load observation; etThe random noise with the mean value of zero, including measurement noise and model error, can be described by the following product model for a load model with trend and seasonal change laws:
yt＝GtHtEt
let the monthly load sequence be y1,y2,...,yTWherein T is the length of the sequence, and the relation between the length of the sequence and the annual number N and the monthly load cycle number 12 of the observed data sequence is T-12N, and by using a median moving average method, a trend term without seasonal period fluctuation can be extracted
Wherein T is 1,2, …, T-12;
the expression represents 2-order symmetric moving average digital filtering taking 12 as a period and t as a center, and no periodic component exists after the digital filtering;
after the trend is removed from the observation data sequence, description and modeling are carried out, polynomial trend is taken according to trend item data and curve characteristics thereof, and then a season period item with noise is obtained
HtEt＝yt/Gt
And then, load prediction is carried out according to the support vector machine theory.
10. A terminal for realizing a distribution network loss reduction method based on marketing and distribution big data fusion is characterized by comprising the following steps:
the memory is used for storing a computer program and a distribution network loss reduction method based on marketing and distribution big data fusion;
a processor, configured to execute the computer program and the distribution network loss reduction method based on marketing and distribution big data fusion, so as to implement the steps of the distribution network loss reduction method based on marketing and distribution big data fusion according to any one of claims 1 to 9.
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