CN113346504A - Active power distribution network voltage control method based on data knowledge driving - Google Patents

Active power distribution network voltage control method based on data knowledge driving Download PDF

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CN113346504A
CN113346504A CN202110672346.6A CN202110672346A CN113346504A CN 113346504 A CN113346504 A CN 113346504A CN 202110672346 A CN202110672346 A CN 202110672346A CN 113346504 A CN113346504 A CN 113346504A
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distribution network
power distribution
voltage
strategy
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CN113346504B (en
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李建国
高巍
侯富
吴敬强
王伟
卢文华
曹磊
张辉
谷凯凯
顾然
李穆
周倩雯
彭仲晗
吴启瑞
胡锦亮
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Chifeng Power Supply Co Of State Grid East Inner Mongolia Electric Power Co ltd
State Grid Corp of China SGCC
Wuhan NARI Ltd
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Chifeng Power Supply Co Of State Grid East Inner Mongolia Electric Power Co ltd
State Grid Corp of China SGCC
Wuhan NARI Ltd
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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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

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Abstract

The invention discloses an active power distribution network voltage control method based on data knowledge driving. Firstly, establishing an active power distribution network reactive power optimization model based on second-order cone optimal power flow; secondly, performing big data simulation based on the established reactive power optimization model to form a data expansion strategy, screening and combining historical data and solution expansion strategy constraints, establishing strategy constraints through the data model to form accumulative data knowledge, and establishing a reactive voltage control strategy knowledge map taking power distribution network data and a control strategy as a core; and finally, matching the control strategy and parameter results by matching similar states in the knowledge map according to the constructed reactive voltage control knowledge map of the power distribution network according to the current network state and adopting an improved time series similar data retrieval algorithm through similarity calculation, sectional verification and slope verification, performing safety verification and optimal solution, generating a control strategy and updating the state strategy in the knowledge map.

Description

Active power distribution network voltage control method based on data knowledge driving
Technical Field
The invention belongs to the power industry and relates to voltage control of a power distribution network, in particular to an active power distribution network voltage control method based on data knowledge driving.
Background
At present, the permeability of distributed power sources in a power distribution network is continuously increased, and in 2050, the consumption of non-fossil energy accounts for more than 50% of the consumption of primary energy in China, which provides new challenges for the voltage stability and the regulation and control operation complexity of a system. Meanwhile, ensuring higher standard electric energy quality is a necessary trend of power development and is an objective demand for promoting social stability development. The over-voltage and the low-voltage not only reduce the energy conversion efficiency, but also easily cause equipment inrush current and harmonic waves to damage user equipment, and even cause safety accidents. At present, a reactive power adjusting method is mainly adopted to control voltage within a designated range, and specifically, the reactive power adjusting method comprises means of changing the voltage of an on-load tap adjusting bus of a main transformer of a transformer substation, switching a capacitor bank to compensate reactive power and the like through cooperative control of reactive power adjusting equipment, so that the aims of improving the quality of electric energy, operating economy and the like are fulfilled. At present, the mathematical solution is mostly solved by adopting an optimization-based or reinforcement learning method. In the optimization-based method, due to the fact that the transformer tap is arranged, the shunt capacitors are discrete and the load flow equation is nonlinear, the problem of mixed integer nonlinear programming is generally solved, the second-order cone relaxation has high accuracy in solving the problem of reactive voltage control, but along with the increase of the distribution network scale and controllable equipment, the solving complexity is exponentially increased, the calculation time is long, and the real-time regulation and control requirements are difficult to meet. At the same time, there is a convergence problem, possibly falling into local optimality. The reactive power control method based on the reinforcement learning method can meet the real-time control requirement in speed, has certain inspiration and robustness, is insufficient in interpretability and easy to fall into dimension disaster, is easy to cause extreme strategy conditions in free action exploration during learning, and cannot guarantee safety.
Disclosure of Invention
The invention aims to provide a data knowledge-driven active power distribution network voltage control method, a knowledge graph-based reactive voltage control model can well represent data and strategies, and strategy constraints are established through the data model to form accumulative data knowledge. By improving the time sequence retrieval algorithm, the data retrieval requirement of the power distribution network can be better met.
In order to achieve the purpose, the active power distribution network voltage control method based on data knowledge driving comprises the following steps:
step 1: considering that discrete reactive voltage regulation equipment comprises an on-load tap changing transformer, a grouping switching capacitor and a voltage regulator, and establishing an active power distribution network reactive power optimization model based on second-order cone optimal power flow;
step 2: based on the active power distribution network reactive power optimization model established in the step 1, carrying out big data simulation by utilizing the existing load and photovoltaic data to form a data expansion strategy, combining the historical voltage regulation strategy of the power distribution network with the result of the data expansion strategy, establishing strategy constraints through the data model to form accumulative data knowledge, and establishing a power distribution network reactive voltage control strategy knowledge map taking power distribution network data and control strategies as the core;
and step 3: and (3) based on the power distribution network reactive voltage control knowledge graph constructed in the step (2), matching the similar state in the knowledge graph with the current network state, adopting an improved time series similar data retrieval algorithm, performing safety check and optimization solution through similarity calculation, segmentation check, slope check, matching control strategy and parameter results, generating a control strategy and updating the state strategy in the power distribution network reactive voltage control knowledge graph.
The invention has the beneficial effects that: the reactive voltage control model based on the knowledge graph can well represent data and strategies, and strategy constraints are established through the data model to form accumulative data knowledge. By improving the time sequence retrieval algorithm, the data retrieval requirements of the power distribution network can be better met, and the generated strategy has excellent performance on the reactive power optimization effect; compared with an optimization algorithm, the time consumption of strategy generation is greatly reduced, and the strategy interpretability is improved. The man-machine interaction generation strategy improves the regulation and control level of power personnel under different reactive voltage control scenes, is suitable for the rapid regulation and control of a power grid, and performs valuable exploration in knowledge data reasoning and interaction in the field of power systems.
Drawings
FIG. 1 is a data model diagram of a knowledge graph of a reactive voltage control strategy of a power distribution network according to the present invention;
FIG. 2 is a flow diagram of knowledge-graph based policy generation;
fig. 3 is an interaction diagram of a reactive voltage control test system of a power distribution network.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
a voltage control method of an active power distribution network based on data knowledge driving comprises the following steps:
step 1: considering that discrete reactive voltage regulation equipment comprises an on-load tap changing transformer, a grouping switching capacitor and a voltage regulator, and establishing an active power distribution network reactive power optimization model based on second-order cone optimal power flow;
step 2: based on the active power distribution network reactive power optimization model established in the step 1, carrying out big data simulation by utilizing the existing load and photovoltaic data to form a data expansion strategy, combining the historical voltage regulation strategy of the power distribution network with the result of the data expansion strategy, establishing strategy constraint through the data model to form accumulative data knowledge, and establishing a power distribution network reactive voltage control strategy knowledge map taking the power distribution network data and the control strategy as the core, wherein the data model is shown in figure 1;
and step 3: based on the power distribution network reactive voltage control knowledge graph constructed in the step 2, matching the similar state in the knowledge graph with the current network state, adopting an improved time series similar data retrieval algorithm, performing security check and optimization solution through similarity calculation, segmentation check, slope check, matching control strategy and parameter results, generating a control strategy and updating the state strategy in the power distribution network reactive voltage control knowledge graph, as shown in fig. 2.
In the above technical solution, the active power distribution network voltage control method based on data knowledge driving further includes the following steps:
and 4, step 4: human-computer interaction online decision is added, time scale, equipment action and key point voltage are controlled, and a power distribution network voltage control test system is developed based on the method so as to improve the convenience and interactivity of reactive voltage control operation.
In the above technical solution, the active power distribution network reactive power optimization model based on the second-order cone optimal power flow is as follows: the method comprises the steps that the goal of minimizing the total operation cost is achieved on the premise that voltage constraints are met, the voltage constraints comprise power flow balance constraints, ohm law corresponding constraints, node voltage constraints and apparent power equality constraints, and the total operation cost comprises line loss, on-load tap changer switching loss, voltage regulator switching loss and grouping switching capacitor switching loss;
the expression for minimizing the total operation cost is as follows:
Figure BDA0003119872540000041
wherein the content of the first and second substances,
Figure BDA0003119872540000042
representing the active loss of the line, C, operating during time tpIs composed of
Figure BDA0003119872540000043
Switching loss factor of loss, COLTCFor the switching loss factor of on-load tap changers, CCBSwitching loss factor, C, for switched capacitors in groupsVRIs the switching loss coefficient of the voltage regulator, wherein N is the proportional switching loss of the reactive voltage regulating equipment of the power distribution networkOLTCNumber of on-load tap changers, NCBNumber of capacitors switched for groups, NVRIn order to count the number of the voltage regulators,
Figure BDA0003119872540000044
for the nth on-load tap changer gear at time tth,
Figure BDA0003119872540000045
the capacitor gear is switched for the nth group at time tth,
Figure BDA0003119872540000046
the gear of the nth voltage regulator is the time T, and the T is the operation time range of reactive voltage control of the power distribution network;
the power flow balance constraint expression is as follows:
Figure BDA0003119872540000047
the ohm law corresponding constraint expression is as follows:
Figure BDA0003119872540000048
wherein PGtRepresenting the active power of the line, QG, at time ttRepresenting the active power flow of the line at time t, QDtRepresenting the reactive power of the line, u, at time ttRepresenting the square of the magnitude of the voltage at all nodes at time t
Figure BDA0003119872540000049
The vector of the composition is then calculated,
Figure BDA00031198725400000410
representing the square of the magnitude of the voltage at node i at time t, ltRepresenting the square of the magnitude of all line currents at time t
Figure BDA00031198725400000411
The vector of the composition is then calculated,
Figure BDA00031198725400000412
representing the square of the amplitude of the current on the line linking node i and node j at time t, TptVector P representing gear data of all reactive voltage regulating equipment of power distribution networktRepresenting the active demand of the line at time t, QtRepresenting the reactive power flow of the line at time t, PijAnd QijRepresenting the active and reactive power flows on the line connecting node i and node j respectively,
Figure BDA0003119872540000051
represents an arbitrary time t;
the node voltage constraint expression is:
Figure BDA0003119872540000052
where u represents the upper limit of the square of the voltage amplitude,
Figure BDA0003119872540000053
representing the lower limit of the square of the voltage amplitude, N is the set of nodes,
Figure BDA0003119872540000054
represents an arbitrary node;
the quadratic equation of the apparent power equality constraint model can be relaxed into inequality constraint, and the expression is as follows:
Figure BDA0003119872540000055
wherein the content of the first and second substances,
Figure BDA0003119872540000056
representing the active power on the line linking node i and node j at time t,
Figure BDA0003119872540000057
representing the reactive power, l, on the line linking node i and node j at time tijRepresenting the square of the magnitude of the current, u, on a line linking node i and node jiRepresents the square of the voltage magnitude at node i;
the service life of the on-load tap changer and the grouping switching capacitor is influenced by frequent actions, so the on-load tap changer and the grouping switching capacitor are subjected to the action in the dispatching period TDThe number of switching times and the gear position in the gear are limited. Solving the reactive voltage regulation model becomes a mixed integer nonlinear problem, and big data simulation can be carried out by utilizing the existing load and photovoltaic data, so that a stage control strategy is obtained.
In the above technical solution, the specific implementation method of step 2 is:
based on the active power distribution network reactive power optimization model established in the step 1, the historical voltage regulation strategy of the power distribution network and the data expansion strategy constraint are screened out and combined, and a reactive voltage control strategy knowledge graph taking power distribution network data and a power distribution network reactive voltage control strategy as a core is established. The traditional reactive voltage control model needs complex solving calculation, has high requirements on hardware computing power and long time consumption for strategy generation, and is usually repeated calculation on similar data of the same model, so that the resource utilization rate is low and the historical control strategy is not effectively summarized and applied.
Constructing a power distribution network reactive voltage control strategy knowledge graph based on power distribution network system data, wherein the power distribution network system data comprises active data, reactive data, distributed power supplies, load prediction time sequence data and optimal gears of voltage regulating equipment in power distribution network historical data and simulation data; the knowledge graph comprises entities, entity relations and attributes, the entities comprise the states of the reactive power voltage regulation equipment of the power distribution network, system data of the power distribution network, action strategies and strategy results, and the entity relations are the states of the voltage regulation equipment, the system data space, the control strategies and the power grid parameter results which are sequentially connected, as shown in figure 1.
State s of reactive voltage regulation equipment of power distribution networktThe state combination set of all the reactive power voltage regulation equipment of the power distribution network is S, and the S contains the gear conditions of all the reactive power voltage regulation equipment; the expression mode of the data space of the power distribution network system is
Figure BDA0003119872540000061
Wherein n isdIs a system data sequence, d is a controllable voltage regulating equipment ordinal number, NsThe number of the pressure regulating devices can be controlled;
from stThe result of the state of the voltage regulating equipment is s based on the matching of the data space path to the next dispatching cyclet+1Namely, the action state strategy pi is generated according to the gear state change situation of the equipment before and after the actioniAnd the state conditions of the voltage regulating devices are combined into a finite state set.
Because the path from the system state to the action state strategy is obtained by solving based on the second-order cone optimal power flow algorithm, and the result already restrains active, reactive and voltage regulating equipment and the like, the action strategy linked with the initial state of the knowledge graph is a result with restraint. Meanwhile, in order to completely represent the action strategy effect, the network parameters of the power distribution network after action are constructed into a strategy result entity, so that the strategy result is evaluated, and strategy selection constraint is further realized through interaction.
In the above technical solution, the concrete implementation method of the similarity calculation in step 3 is as follows:
matching the initial state of the power distribution network system according to the current timestamp t to obtain the entity initial state s meeting the conditions in the knowledge graphtGiven a new distribution network system data sequence nsAnd retrieving the data sequence n of the most similar system data in the data spacedObtaining the state and action strategy s of the voltage regulating equipment in the next dispatching cyclet+1；
The data space comprises an active sequence, a reactive sequence, a distributed power output sequence and a load output sequence, and the data sequence data n of the most similar systemdWith a given system data sequence nsThe sampling period and the number of the sampling periods are the same.
In the above technical solution, the retrieving of the data sequence n of the most similar system data in the data spacedThe method comprises the following steps of measuring the similarity by adopting an Euclidean distance function, wherein the expression is as follows:
Figure BDA0003119872540000071
wherein n isdFor the most similar system data sequence data in data space, nsFor distribution network system data sequences, wiFor data importance weight, ε is similarity threshold, and sequences n satisfying the formula are retaineddThe adjacent control strategy entity is a reactive voltage control strategy obtained under the condition of a similarity threshold epsilon, and the expression is as follows:
dist(nd,ns)≤ε
in the above technical solution, the similarity calculation and matching in step 3 further adopts a method of segment check and slope check, and the specific implementation process is as follows:
the specific implementation method of the segmented verification comprises the following steps: defining a sliding window
Figure BDA0003119872540000072
Window sw combines data sequence ndCutting the data into q sections, fixing the length of each section as p, performing section matching calculation by adopting an Euclidean distance function, reserving a data sequence meeting a distance condition, and stopping calculation if the similarity of the section data sequence is greater than a similarity threshold value; and accumulating and counting the Euclidean distance of the finally reserved data sequence.
The specific implementation method of the slope verification comprises the following steps: adding slope filtering on the basis of linear accumulation of calculation results of Euclidean distance functions, and sliding matching n of window swsAnd ndData sequence, characteristic point sequence section obtained by sliding matching
Figure BDA0003119872540000073
And
Figure BDA0003119872540000074
n in the window with unit sliding window lengthsAnd ndWhen the difference product of the data sequence segments is negative and less than kappa, wherein kappa is a negative slope trend threshold, n is indicatedsAnd ndThe slope trend of the data sequence segment is opposite and the difference value is larger, and the data sequence segment is removed from the alternative similar data sequence, so that the retrieval precision can be improved, and the expression is as follows:
Figure BDA0003119872540000075
obtaining m most similar system data sequence data through segmented verification and slope filtering, wherein the corresponding number of s is not more than mt+1Action strategy, if m is 1, then there is only one kind of st+1Movement ofStrategy, then st+1Is the corresponding matching item; if m>1, according to the reactive voltage regulation operation cost pair st+1Sorting in ascending order and selecting the action strategy with the lowest operation cost; and if m is 0, performing real-time optimization simulation based on the active power distribution network reactive power optimization model established in the step 1, and storing corresponding data into a knowledge graph.
In the above technical solution, the security check in step 3 is:
and verifying that the power flow of the action strategy is converged and the voltage is not out of limit for the action strategy result obtained under the condition that the number m of the most similar system data sequence data is not 0.
In the above technical solution, the step 3 retrieves the corresponding result tail entity data in the data space as the reactive power voltage regulation equipment action strategy, and based on the constructed reactive power voltage regulation strategy knowledge graph, the data space is limited by the knowledge graph matching action strategy, so as to accurately restrict part of equipment action limits and times, reduce the data retrieval range, and implement scene-adaptive and interactive strategy generation.
In the above technical solution, the specific implementation method of step 4 is as follows:
changing retrieval data scheduling period TDGenerating three time scale control schemes of a daily level, an hour level and a minute level according to the operation time range T of the reactive voltage control of the power distribution network, and carrying out adaptive adjustment according to the output and load states and real-time requirements;
the adaptability adjustment is as follows: the day-level control is adopted for a power grid with unobvious long-time output and load change, the hour-level control is adopted under the condition of normal fluctuation, and the minute-level control is adopted in an emergency state, so that the method is generally not adopted due to higher cost and frequent action.
In the reactive voltage regulation process, the voltage fluctuation condition of an important load node is the key for influencing the quality of electric energy. According to different scenes, important load points also change, so that in the data space retrieval process, corresponding state results of strategies are constrained, and therefore in the strategy generation process, key node selection can be adjusted through real-time interaction, and the voltage fluctuation range of important nodes is accurately limited.
In the technical scheme, the power distribution network voltage control test system realizes data storage, retrieval and interaction, realizes data storage and management based on the most popular graph database Neo4, and adopts an artificial intelligence markup language technology to realize man-machine interaction, as shown in fig. 3. The reactive voltage control knowledge map of the power distribution network is stored in a map database Neo4j, a regulation and control person inputs the data in Chinese characters, the system analyzes input meanings by using AIML, generates instructions according to analysis results to complete operations such as map query search, updating and management, and completes interaction according to question and answer logic, so that scene random fluctuation and control target complex change in the reactive voltage control process of the power distribution network are realized, multiple schemes are provided for selection, and time scales, equipment action conditions and special scene voltage regulation requirements are considered, so that interactivity and applicability are improved. In the inquiry and recommendation process, interactive results are presented in various forms such as character visualization, figure visualization, data tables and the like, so that the recommendation information can be intuitively mastered from multiple angles.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.

Claims (9)

1. A voltage control method of an active power distribution network based on data knowledge driving is characterized by comprising the following steps: it comprises the following steps:
step 1: considering that discrete reactive voltage regulation equipment comprises an on-load tap changing transformer, a grouping switching capacitor and a voltage regulator, and establishing an active power distribution network reactive power optimization model based on second-order cone optimal power flow;
step 2: based on the active power distribution network reactive power optimization model established in the step 1, carrying out big data simulation by utilizing the existing load and photovoltaic data to form a data expansion strategy, combining the historical voltage regulation strategy of the power distribution network with the result of the data expansion strategy, establishing strategy constraints through the data model to form accumulative data knowledge, and establishing a power distribution network reactive voltage control strategy knowledge map taking power distribution network data and control strategies as the core;
and step 3: and (3) based on the power distribution network reactive voltage control knowledge graph constructed in the step (2), matching the similar state in the knowledge graph with the current network state, adopting an improved time series similar data retrieval algorithm, performing safety check and optimization solution through similarity calculation, segmentation check, slope check, matching control strategy and parameter results, generating a control strategy and updating the state strategy in the power distribution network reactive voltage control knowledge graph.
2. The active power distribution network voltage control method based on data knowledge driving of claim 1, characterized in that: it also includes the following steps:
and 4, step 4: and man-machine interaction online decision is added, and time scale, equipment action and key point voltage are controlled.
3. The active power distribution network voltage control method based on data knowledge driving of claim 1, characterized in that: the active power distribution network reactive power optimization model based on the second-order cone optimal power flow is as follows: the method comprises the steps that the goal of minimizing the total operation cost is achieved on the premise that voltage constraints are met, the voltage constraints comprise power flow balance constraints, ohm law corresponding constraints, node voltage constraints and apparent power equality constraints, and the total operation cost comprises line loss, on-load tap changer switching loss, voltage regulator switching loss and grouping switching capacitor switching loss;
the expression for minimizing the total operation cost is as follows:
Figure FDA0003119872530000021
wherein the content of the first and second substances,
Figure FDA0003119872530000022
representing the active loss of the line, C, operating during time tpIs composed of
Figure FDA0003119872530000023
Switching loss factor of loss, COLTCIs provided withSwitching loss factor, C, of a step-up transformerCBSwitching loss factor, C, for switched capacitors in groupsVRIs the switching loss coefficient of the voltage regulator, wherein N is the proportional switching loss of the reactive voltage regulating equipment of the power distribution networkOLTCNumber of on-load tap changers, NCBNumber of capacitors switched for groups, NVRIn order to count the number of the voltage regulators,
Figure FDA0003119872530000024
for the nth on-load tap changer gear at time tth,
Figure FDA0003119872530000025
the capacitor gear is switched for the nth group at time tth,
Figure FDA0003119872530000026
the gear of the nth voltage regulator is the time T, and the T is the operation time range of reactive voltage control of the power distribution network;
the power flow balance constraint expression is as follows:
Figure FDA0003119872530000027
the ohm law corresponding constraint expression is as follows:
Figure FDA0003119872530000028
wherein PGtRepresenting the active power of the line, QG, at time ttRepresenting the active power flow of the line at time t, QDtRepresenting the reactive power of the line, u, at time ttRepresenting the square of the magnitude of the voltage at all nodes at time t
Figure FDA0003119872530000029
The vector of the composition is then calculated,
Figure FDA00031198725300000210
representing the square of the magnitude of the voltage at node i at time t, ltRepresenting the square of the magnitude of all line currents at time t
Figure FDA00031198725300000211
The vector of the composition is then calculated,
Figure FDA00031198725300000212
representing the square of the amplitude of the current on the line linking node i and node j at time t, TptVector P representing gear data of all reactive voltage regulating equipment of power distribution networktRepresenting the active demand of the line at time t, QtRepresenting the reactive power flow of the line at time t, PijAnd QijRepresenting the active and reactive power flows on the line connecting node i and node j respectively,
Figure FDA00031198725300000213
represents an arbitrary time t;
the node voltage constraint expression is:
Figure FDA00031198725300000214
wherein the content of the first and second substances,
Figure FDA00031198725300000215
represents the upper limit of the square of the voltage amplitude,
Figure FDA00031198725300000216
representing the lower limit of the square of the voltage amplitude, N is the set of nodes,
Figure FDA00031198725300000217
represents an arbitrary node;
the quadratic equality relaxation of the apparent power equality constraint model is inequality constraint, and the expression is as follows:
Figure FDA0003119872530000031
Figure FDA0003119872530000032
wherein the content of the first and second substances,
Figure FDA0003119872530000033
representing the active power on the line linking node i and node j at time t,
Figure FDA0003119872530000034
representing the reactive power, l, on the line linking node i and node j at time tijRepresenting the square of the magnitude of the current, u, on a line linking node i and node jiRepresents the square of the voltage magnitude at node i;
the service life of the on-load tap changer and the grouping switching capacitor is influenced by frequent actions, so the on-load tap changer and the grouping switching capacitor are subjected to the action in the dispatching period TDThe number of switching times and gears in the system are limited, the solution of the reactive voltage regulation model is changed into a mixed integer nonlinear problem, and existing load and photovoltaic data can be used for big data simulation, so that a stage control strategy is obtained.
4. The active power distribution network voltage control method based on data knowledge driving of claim 1 is characterized in that: the specific implementation method of the step 2 comprises the following steps:
constructing a power distribution network reactive voltage control strategy knowledge graph based on power distribution network system data, wherein the power distribution network system data comprises active data, reactive data, distributed power supplies, load prediction time sequence data and optimal gears of voltage regulating equipment in power distribution network historical data and simulation data; the knowledge map comprises entities, entity relations and attributes, the entities comprise the reactive power voltage regulation equipment state of the power distribution network, system data of the power distribution network, action strategies and strategy results, and the entity relations are that the voltage regulation equipment state, the system data space, the control strategies and the power grid parameter results are sequentially connected;
state s of reactive voltage regulation equipment of power distribution networktThe state combination set of all the reactive power voltage regulation equipment of the power distribution network is S, and the S contains the gear conditions of all the reactive power voltage regulation equipment; the expression mode of the data space of the power distribution network system is
Figure FDA0003119872530000035
Wherein n isdIs a system data sequence, d is a controllable voltage regulating equipment ordinal number, NsThe number of the pressure regulating devices can be controlled;
from stThe result of the state of the voltage regulating equipment is s based on the matching of the data space path to the next dispatching cyclet+1Namely, the action state strategy pi is generated according to the gear state change situation of the equipment before and after the actioniAnd the state conditions of the voltage regulating devices are combined into a finite state set.
5. The active power distribution network voltage control method based on data knowledge driving of claim 1, characterized in that: the concrete implementation method of similarity calculation in the step 3 is as follows:
matching the initial state of the power distribution network system according to the current timestamp t to obtain the entity initial state s meeting the conditions in the knowledge graphtGiven a new distribution network system data sequence nsAnd retrieving the most similar system data sequence n in the data spacedObtaining the state and action strategy s of the voltage regulating equipment in the next dispatching cyclet+1；
The data space comprises an active sequence, a reactive sequence, a distributed power output sequence and a load output sequence, and the data sequence data n of the most similar systemdWith a given system data sequence nsThe sampling period and the number of the sampling periods are the same.
6. The active power distribution network voltage control method based on data knowledge driving of claim 5 is characterized in that: said is atRetrieving data sequence n of most similar system data in data spacedThe method comprises the following steps of measuring the similarity by adopting an Euclidean distance function, wherein the expression is as follows:
Figure FDA0003119872530000041
wherein n isdFor the most similar system data sequence data in data space, nsFor distribution network system data sequences, wiFor data importance weight, ε is similarity threshold, and sequences n satisfying the formula are retaineddThe adjacent control strategy entity is a reactive voltage control strategy obtained under the condition of a similarity threshold epsilon, and the expression is as follows:
dist(nd,ns)≤ε。
7. the active power distribution network voltage control method based on data knowledge driving of claim 1, characterized in that: the similarity calculation and matching in the step 3 also adopts a method of segment check and slope check, and the specific implementation process is as follows:
the specific implementation method of the segmented verification comprises the following steps: defining a sliding window
Figure FDA0003119872530000042
Window sw combines data sequence ndCutting the data into q sections, fixing the length of each section as p, performing section matching calculation by adopting an Euclidean distance function, reserving a data sequence meeting a distance condition, and stopping calculation if the similarity of the section data sequence is greater than a similarity threshold value; accumulating and counting the Euclidean distance of the finally reserved data sequence;
the specific implementation method of the slope verification comprises the following steps: adding slope filtering on the basis of linear accumulation of calculation results of Euclidean distance functions, and sliding matching n of window swsAnd ndData sequence, characteristic point sequence section obtained by sliding matching
Figure FDA0003119872530000051
And
Figure FDA0003119872530000052
n in the window with unit sliding window lengthsAnd ndWhen the difference product of the data sequence segments is negative and less than kappa, wherein kappa is a negative slope trend threshold, n is indicatedsAnd ndThe slope trend of the data sequence segment is opposite and the difference value is larger, and the data sequence segment is removed from the alternative similar data sequence, so that the retrieval precision can be improved, and the expression is as follows:
Figure FDA0003119872530000053
obtaining m most similar system data sequence data through segmented verification and slope filtering, wherein the corresponding number of s is not more than mt+1Action strategy, if m is 1, then there is only one kind of st+1Action policy, then st+1Is the corresponding matching item; if m>1, according to the reactive voltage regulation operation cost pair st+1Sorting in ascending order and selecting the action strategy with the lowest operation cost; and if m is 0, performing real-time optimization simulation based on the active power distribution network reactive power optimization model established in the step 1, and storing corresponding data into a knowledge graph.
8. The active power distribution network voltage control method based on data knowledge driving of claim 1, characterized in that: the safety check in the step 3 is as follows:
and verifying that the power flow of the action strategy is converged and the voltage is not out of limit for the action strategy result obtained under the condition that the number m of the most similar system data sequence data is not 0.
9. The active power distribution network voltage control method based on data knowledge driving of claim 2 is characterized in that: the specific implementation method of the step 4 comprises the following steps:
changing retrieval data scheduling period TDAnd distribution networkGenerating three time scale control schemes of a day level, an hour level and a minute level within the operation time range T of reactive voltage control, and carrying out adaptive adjustment according to output and load states and real-time requirements;
the adaptability adjustment is as follows: the day-level control is adopted for the power grid with unobvious long-time output and load change, the hour-level control is adopted under the condition of normal fluctuation, and the minute-level control is adopted in the emergency state.
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