CN114726009B - Wind power plant group reactive power hierarchical optimization control method and system considering power prediction - Google Patents

Wind power plant group reactive power hierarchical optimization control method and system considering power prediction Download PDF

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CN114726009B
CN114726009B CN202210643332.6A CN202210643332A CN114726009B CN 114726009 B CN114726009 B CN 114726009B CN 202210643332 A CN202210643332 A CN 202210643332A CN 114726009 B CN114726009 B CN 114726009B
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voltage
wind
power
reactive power
wind power
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CN114726009A (en
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戴剑丰
谢嫦嫦
汤奕
钱俊良
周吉
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Southeast University
Liyang Research Institute of Southeast University
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Liyang Research Institute of Southeast University
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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

Abstract

The invention discloses a wind power plant group reactive power hierarchical optimization control method and a system thereof considering power prediction, wherein the method comprises the following steps: judging whether the wind power access area is a weak voltage area or not; when the voltage drops seriously, the wind power plant group can act in time according to the pre-decision; training a BP neural network to fit a mapping relation between the system operation state and the transient voltage stability index; evaluating the wind power reactive power increment in a voltage weak area in real time; solving a voltage droop gain reference value of each wind turbine generator; ultra-short-term prediction is carried out on the future input wind speed of the fan by adopting a wavelet neural network; calculating the probability density and the expected value of the droop gain of the fan voltage under the predicted wind speed; and taking the voltage droop gain expected value as a pre-instruction to guide the fan to reasonably output reactive power in the future. According to the invention, the wind turbine voltage control pre-command is prepared at the power grid layer, the inter-site layer and the intra-site layer, so that the reactive voltage regulation capability of the wind power plant group is fully exerted, and the transient voltage stability of the power system is timely improved when a voltage weak area fails.

Description

Wind power plant group reactive power hierarchical optimization control method and system considering power prediction
Technical Field
The invention belongs to the technical field of reactive voltage control of a wind power plant group, and particularly relates to a wind power plant group reactive hierarchical optimization control method and system considering power prediction.
Background
With the rapid development of economy and the increasing global demand for energy, the fan is gradually becoming an important power energy resource as a low-carbon and clean distributed power supply. With the proposal of carbon neutralization and carbon peak reaching, the construction of a novel power system mainly based on new energy sources such as wind power, photovoltaic and the like becomes the future trend of energy development in China. However, with the continuous improvement of wind power permeability, the characteristics of randomness, volatility, intermittence and the like of wind power bring a series of burdens to the operation control of a power grid, frequently cause the problem of unbalanced branch tide distribution, and threaten the safe and stable operation of a power system.
In the initial development stage of wind power, the wind power plant has small operation scale and insufficient reactive capacity, so that the regulation effect on the node voltage of an access area is weaker; and large-scale wind power plant groups have large reactive capacity in a non-power full-generation stage, and when the transient voltage is unstable due to serious faults in a wind power access area of the system, the large-scale wind power plant groups can play a large supporting role on the voltage of a regional node. In order to improve the accepting capacity of the power system to a large-scale wind power station group, the wind power station group is considered to exert an active voltage regulation effect and autonomously participate in maintaining the safe and stable operation of the power system so as to improve the voltage stability of a wind power grid-connected region. Therefore, research on reactive voltage control strategies of the wind farm group is urgently needed.
The reactive voltage control of a wind farm group is generally divided into three layers, namely a grid layer, an inter-farm layer and an intra-farm layer. The power grid layer monitors a grid-connected point voltage reference value or a total reactive power demand instruction of the wind power plant in the running state of the system; the inter-site layer issues a voltage or reactive power output instruction of a grid-connected point of the wind power plant to the inner layer of the wind power plant according to the upper layer instruction; and each wind turbine generator in the inner layer of the farm directs an internal control link to output reactive power for voltage regulation according to the upper layer instruction. In the existing research of reactive voltage control strategies of wind power plant groups, the problems that different control layers are difficult to coordinate, the space distribution of each wind power plant is wide, the calculated amount is large, and the online application is difficult exist. The problem of voltage fluctuation of a grid-connected point of a field group under the condition of wind speed fluctuation is mainly solved, and a reactive voltage coordination control strategy in the field group when a system of a wind power plant group breaks down is less involved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: when a weak voltage area accessed by wind power fails to cause serious node voltage drop, the wind generation set reasonably outputs reactive power to adjust the node voltage through dynamic reactive power demand evaluation, calculation of a voltage droop gain reference value of the wind generation set considering reactive loss and probability density and expected value of the voltage droop gain of the wind generation set, and the transient voltage stability of the system is ensured.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention relates to a wind power plant group reactive power hierarchical optimization control method considering power prediction, which comprises the following steps:
step 1: the method comprises the following steps that a single-ring theorem in a random matrix theory is adopted by an electric network layer, short-circuit fault simulation is carried out on each node in a system steady-state operation mode, the voltage weak degree of each node is calculated, a voltage weak area is defined, and whether a wind power access area is a voltage weak area or not is judged;
step 2: if the wind power access area is judged to be a voltage weak area, an emergency voltage control pre-decision is carried out on the wind power plant group, so that when the voltage in the voltage weak area drops seriously in the future set time, the wind power plant group acts in time according to the emergency voltage control pre-decision;
and step 3: setting a short-circuit fault for the node of the voltage weak area, calculating a transient voltage stability index of the voltage weak area, obtaining off-line sample data, and training a mapping relation between a running steady state and the transient voltage stability index of a BP neural network fitting system;
and 4, step 4: calculating the sensitivity of wind power output reactive power to the transient voltage stability index through a BP neural network algorithm, and evaluating the reactive power increase of the wind power plant group in the voltage weak area in real time according to the current system operation state;
and 5: the inter-field layer solves the voltage droop gain reference value of each wind turbine generator through a load flow calculation model according to the reactive power demand evaluation result of the wind power field group;
step 6: adopting a wavelet neural network to predict the wind speed of the wind turbine in the future;
and 7: the field inner layer calculates fan reactive capacity prediction data according to the fan active output of a wind speed section where the predicted wind speed is located and on the basis of the fan active and reactive coupling characteristics; calculating the probability density of the voltage droop gain of the fan and the expected value of the voltage droop gain of the fan under the predicted wind speed according to the reference value of the voltage droop coefficient by adopting a Gaussian mixture probability density estimation method;
and 8: and taking the fan voltage droop gain expected value as a pre-instruction to guide the fan to output reactive power in the future.
In step 5, the solving method of the voltage droop gain reference value is as follows:
step 5.1: under the steady state and transient state conditions of the system, estimating results according to the known system running state and the reactive power demand of the wind power plant group;
step 5.2: and determining a solving model of the voltage and the output reactive power of the grid-connected point of the wind power plant on the basis of the steady state and the transient state of the system and considering the reactive loss, and calculating the voltage droop gain reference value.
In step 5.2, the solving model method of the wind power plant grid-connected point voltage and the output reactive power is as follows:
when the system is in a stable operation state, the output reactive power of the wind power plant cluster sink point is approximately equal to the output reactive power of the wind power plant cluster sampled in a sample under a stable state, and an objective function is expressed as follows:
Figure 163332DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 843712DEST_PATH_IMAGE002
outputting reactive power of a wind power field cluster gathering point in the flow iteration process under a steady state;
Figure 489324DEST_PATH_IMAGE003
outputting the actual value of reactive power for the wind power field group in the sample under the steady state,
Figure 786313DEST_PATH_IMAGE004
is the absolute value of the difference between the reactive powers in the steady-state process;
when the system is in a transient state process, the output reactive power of the wind power plant group grid-connected point in the load flow calculation model process is enabled to be approximately equal to the output reactive power evaluation result of the wind power plant group in the voltage weak area, and the objective function is expressed as:
Figure 770712DEST_PATH_IMAGE005
wherein, the first and the second end of the pipe are connected with each other,
Figure 633494DEST_PATH_IMAGE006
outputting reactive power for a wind power field cluster collection point in the flow iteration process under the transient state;
Figure 432430DEST_PATH_IMAGE007
outputting a reactive power evaluation result for the wind power station group under the transient state,
Figure 482294DEST_PATH_IMAGE008
is the absolute value of the difference between the reactive powers in the transient process;
wherein the content of the first and second substances,
Figure 332701DEST_PATH_IMAGE009
the expression of (a) is:
Figure 784411DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 838735DEST_PATH_IMAGE011
the reactive power is the reactive power required to be increased by the wind power station group under the transient condition;
the active and reactive power balance constraints are expressed as:
Figure 110317DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 561152DEST_PATH_IMAGE013
which represents the number of nodes that are to be connected,
Figure 8314DEST_PATH_IMAGE014
、
Figure 525883DEST_PATH_IMAGE015
respectively generating active power and reactive power for the generator;
Figure 222443DEST_PATH_IMAGE016
、
Figure 942881DEST_PATH_IMAGE017
load active power and reactive power respectively;
Figure 775708DEST_PATH_IMAGE018
is the node voltage;
Figure 559118DEST_PATH_IMAGE019
、
Figure 8554DEST_PATH_IMAGE020
are respectively a node
Figure 643935DEST_PATH_IMAGE021
And node
Figure 128006DEST_PATH_IMAGE022
Conductance and susceptance of the branch between;
Figure 480053DEST_PATH_IMAGE023
is a node
Figure 479102DEST_PATH_IMAGE024
、
Figure 714911DEST_PATH_IMAGE025
Voltage phase angle difference therebetween;
the node voltage
Figure 492637DEST_PATH_IMAGE026
The magnitude inequality constraint of (a) is expressed as:
Figure 788489DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 946938DEST_PATH_IMAGE028
、
Figure 547290DEST_PATH_IMAGE029
respectively as the minimum value and the maximum value of the node voltage amplitude;
the wind farm reactive power inequality constraint may be expressed as:
Figure 740374DEST_PATH_IMAGE030
in the formula (I), the compound is shown in the specification,
Figure 941548DEST_PATH_IMAGE031
、
Figure 151075DEST_PATH_IMAGE032
respectively as the minimum value and the maximum value of the reactive power output of the wind power plant.
Under two states of a steady state and a transient state, calculating according to the voltage of a grid-connected point of the wind power plant and the output reactive power to obtain a voltage droop coefficient reference value of each wind power plant, wherein the voltage droop coefficient reference value is specifically represented as follows:
Figure 728687DEST_PATH_IMAGE033
wherein the content of the first and second substances,
Figure 510698DEST_PATH_IMAGE034
is as follows
Figure 882774DEST_PATH_IMAGE035
Voltage droop coefficient reference values of the wind power plants;
Figure 723471DEST_PATH_IMAGE036
、
Figure 839195DEST_PATH_IMAGE037
are respectively the first
Figure 741292DEST_PATH_IMAGE038
Transient voltage and steady voltage of grid-connected points of the wind power plant;
Figure 284269DEST_PATH_IMAGE039
、
Figure 671650DEST_PATH_IMAGE040
respectively in transient state and in steady state
Figure 325485DEST_PATH_IMAGE041
The output reactive power of each wind farm;
according to the first
Figure 82089DEST_PATH_IMAGE041
Calculating a voltage droop gain reference value of an internal wind turbine generator set by using a voltage droop coefficient reference value of each wind power plant, wherein the specific expression is as follows:
Figure 61546DEST_PATH_IMAGE042
wherein the content of the first and second substances,
Figure 730031DEST_PATH_IMAGE043
is a first
Figure 187557DEST_PATH_IMAGE044
Voltage droop gain reference values of wind turbine generators in the wind power plants;Dthe number of the fans in the wind power plant.
A wind farm group reactive power hierarchical optimization control system considering power prediction comprises the following components: a network interface, a memory, and a processor; wherein the content of the first and second substances,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory for storing a computer program operable on the processor;
the processor is used for executing the steps of the wind farm group reactive power hierarchical optimization control method considering power prediction when the computer program is run.
A computer storage medium storing a program for wind farm group reactive power stratification optimization control considering power prediction, which when executed by at least one processor implements the steps of the above wind farm group reactive power stratification optimization control method considering power prediction.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the grid layer, the inter-site layer and the inner-site layer control strategy of the wind power plant group are coordinated and controlled, and before node voltage drops caused by serious faults of a voltage weak area of wind power access, an emergency voltage control pre-decision is carried out in advance through power prediction, so that the wind power reactive compensation capability is fully exerted, and the transient voltage stability of a large-scale wind power access area is improved;
2. according to the method, the reactive loss problem of the inter-field transmission line is considered in the inter-field voltage droop gain reference value calculation, the reactive power output by each wind power plant is distributed through the particle swarm algorithm, and the condition that the reactive power flowing into the power grid by the wind power plant group cannot reach the reactive power demand evaluation result is avoided.
Drawings
FIG. 1 is a block diagram of a wind farm group reactive power hierarchical optimization control strategy considering power prediction;
FIG. 2 is a 10-machine 39 node system architecture diagram including a large-scale wind farm group;
FIG. 3 is a typical topology of a wind farm cluster;
FIG. 4 is a typical daily system load and wind power fluctuation curve;
FIG. 5 is a graph of total reactive power output by a wind farm group;
FIG. 6 is a graph of a simulation of node voltage.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, a wind farm group reactive power hierarchical optimization control method considering power prediction includes the following steps:
(1) step 1, a power grid layer adopts a single-ring theorem in a random matrix theory, and each node short-circuit fault simulation is carried out in a steady-state operation mode of a 10-machine 39-node system to calculate the voltage weakness degree of each node (the specific calculation method is that the fault is removed after a period of time is maintained until the system recovers to stably operate, voltage data of each node in the simulation process is obtained, voltage weak node evaluation indexes in the fault process of the node are calculated based on the data, the characteristic value statistical indexes of each node are sequenced, the larger the index value is, the weaker the larger the index value is, the voltage of the node is indicated), when the characteristic value statistical indexes are smaller than a preset threshold value, the voltage weak node is determined, and meanwhile, whether a wind power access area is a voltage weak area or not is judged, and the voltage weak area is defined.
(2) And 2, if the wind power access area is identified as a weak voltage area, enabling the wind power plant group to carry out emergency voltage control pre-decision, so that the wind power plant group can act in time according to the pre-decision when the voltage in the weak area drops seriously in a period of time in the future.
(3) And 3, setting short-circuit faults on the voltage weak nodes in a system steady-state neighborhood (the steady-state neighborhood is a stable region before the faults), calculating transient voltage stability indexes of the voltage weak regions, further obtaining off-line sample data, and training a BP neural network to fit a mapping relation between the steady state and the transient voltage stability indexes of the system operation state.
(4) And 4, firstly, based on a certain typical daily system load and wind power curve shown in the figure 4, obtaining a predicted value of the transient voltage stability index at each moment through a BP neural network algorithm, comparing the predicted value with an actual value, and displaying that the prediction error of the BP neural network is smaller as a result. And then calculating the sensitivity of the wind power output reactive power to the transient voltage stability index through a BP neural network, and evaluating the wind power reactive power increment in a voltage weak area in real time according to the system running state, wherein the reactive power demand of the wind power station group at the 12 th hour is 128.8 Mvar.
(5) And 5, solving the voltage droop gain reference value of each wind turbine and the voltage droop coefficient reference value of the wind power plant through a load flow calculation model on the basis of considering reactive loss according to the reactive power demand evaluation result of the wind power plant group by the inter-plant layer.
Wherein, the step 5 comprises the following steps: firstly, under the conditions of a steady state and a transient state of a system, according to the running state of the system and the reactive power demand evaluation result of a wind power plant group, on the basis of considering reactive power loss, respectively calculating the voltage A and the output reactive power of the corresponding wind power plant grid-connected point by adopting a particle swarm algorithm. The wind farm group topology is shown in FIG. 3. The solving model of the voltage A and the output reactive power of the wind power plant grid-connected point is concretely represented as follows:
when the system is in a stable operation state, the output reactive power of the wind power plant cluster convergent point B is enabled to be approximately equal to the output reactive power of the wind power plant cluster sampled in a sample under a stable state, and an objective function can be expressed as follows:
Figure 64247DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 716070DEST_PATH_IMAGE002
outputting reactive power of a wind power field cluster gathering point in the flow iteration process under a steady state;
Figure 514262DEST_PATH_IMAGE003
outputting the actual value of reactive power for the wind power field group in the sample under the steady state,
Figure 775479DEST_PATH_IMAGE045
is the difference in reactive power in steady state operation.
When the system is in a transient state process, the output reactive power of the wind power plant group grid-connected point A in the load flow calculation model process is enabled to be approximately equal to the output reactive power evaluation result of the wind power plant group in the weak area, and the objective function can be expressed as follows:
Figure 819525DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 875206DEST_PATH_IMAGE006
outputting reactive power for a wind power field cluster collection point in the flow iteration process under the transient state;
Figure 691852DEST_PATH_IMAGE047
outputting a reactive power evaluation result for the wind power station group under the transient state,
Figure 491181DEST_PATH_IMAGE008
is the difference in reactive power under transient operation.
Wherein the content of the first and second substances,
Figure 578348DEST_PATH_IMAGE048
the expression of (a) is:
Figure 336089DEST_PATH_IMAGE010
wherein
Figure 967927DEST_PATH_IMAGE011
The wind power station group needs increased reactive power under the transient condition.
The active and reactive power balance constraints can be expressed as:
Figure 803903DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 509690DEST_PATH_IMAGE013
is the number of the nodes that are present,
Figure 907174DEST_PATH_IMAGE049
、
Figure 698412DEST_PATH_IMAGE015
respectively generating active power and reactive power for the generator;
Figure 341008DEST_PATH_IMAGE016
、
Figure 901303DEST_PATH_IMAGE017
load active power and reactive power respectively;
Figure 735267DEST_PATH_IMAGE050
is the node voltage;
Figure 748222DEST_PATH_IMAGE051
、
Figure 197439DEST_PATH_IMAGE020
are respectively nodes
Figure 612240DEST_PATH_IMAGE021
And node
Figure 289209DEST_PATH_IMAGE052
Conductance and susceptance of the branch between;
Figure 789460DEST_PATH_IMAGE023
is a node
Figure 272394DEST_PATH_IMAGE021
、
Figure 308745DEST_PATH_IMAGE052
Voltage phase angle difference therebetween.
Node voltage
Figure 484512DEST_PATH_IMAGE053
The magnitude inequality constraint may be expressed as:
Figure 472059DEST_PATH_IMAGE054
wherein the content of the first and second substances,
Figure 758684DEST_PATH_IMAGE055
、
Figure 381033DEST_PATH_IMAGE056
the minimum value and the maximum value of the node voltage amplitude are respectively.
The wind farm reactive power inequality constraint may be expressed as:
Figure 727701DEST_PATH_IMAGE057
in the formula (I), the compound is shown in the specification,
Figure 202545DEST_PATH_IMAGE058
、
Figure 292860DEST_PATH_IMAGE032
respectively as the minimum value and the maximum value of the reactive power output of the wind power plant.
And secondly, calculating a voltage droop coefficient reference value and a voltage droop gain reference value of the wind turbine generator based on the voltage A and the output reactive power of the wind power plant grid-connected point obtained by solving under the steady state and the transient state. The specific calculation steps are as follows:
firstly, under two states of a steady state and a transient state, calculating according to the voltage of a grid-connected point of the wind power plant and the output reactive power to obtain a reference value of the voltage droop coefficient of each wind power plant. It can be specifically expressed as:
Figure 38225DEST_PATH_IMAGE033
wherein the content of the first and second substances,
Figure 290214DEST_PATH_IMAGE034
is as follows
Figure 252354DEST_PATH_IMAGE035
Voltage droop coefficient reference values of the wind power plants;
Figure 880782DEST_PATH_IMAGE036
and
Figure 737443DEST_PATH_IMAGE037
are respectively the first
Figure 160334DEST_PATH_IMAGE038
Transient voltage and steady voltage of grid-connected points of the wind power plant;
Figure 609770DEST_PATH_IMAGE039
、
Figure 41888DEST_PATH_IMAGE040
respectively in transient state and in steady state
Figure 230686DEST_PATH_IMAGE041
The output reactive power of each wind power plant;
according to the first
Figure 90058DEST_PATH_IMAGE041
Calculating a voltage droop gain reference value of an internal wind turbine generator set by using a voltage droop coefficient reference value of each wind power plant, wherein the specific expression is as follows:
Figure 761211DEST_PATH_IMAGE042
wherein the content of the first and second substances,
Figure 262599DEST_PATH_IMAGE043
is a first
Figure 834132DEST_PATH_IMAGE044
Voltage droop gain reference values of wind turbine generators in the wind power plants;Dthe number of the fans in the wind power plant.
Under the condition of considering reactive loss, the reference value of the voltage droop coefficient of each wind power plant and the reference value of the voltage droop gain of the wind power generation unit in the plant are obtained through calculation and are shown in table 1.
Table 1 shows the solving results of the optimal power flow model
Figure 598826DEST_PATH_IMAGE059
If the traditional strategy is adopted, namely the loss of the output reactive power of the wind power plant on the power transmission line is not considered, the reactive power requirements of the wind power plant group are uniformly distributed to each wind power plant, and the output reactive power of the wind power plant, the voltage values of the grid-connected points and the voltage droop gain reference value of the wind turbine generator set under the steady state and the transient state are shown in the table 2.
TABLE 2 traditional tactical flow resolution results
Figure 22854DEST_PATH_IMAGE060
The data in the table 1 and the data in the table 2 are compared, if the reactive loss of the output reactive power of the wind power plant on the inter-plant power transmission line is not considered, the obtained voltage droop gain reference value is smaller, the output reactive power of the wind power plant group cannot meet the reactive power requirement of the voltage weak area, the reactive loss problem of the inter-plant power transmission line is considered by the provided strategy, and the reactive power requirement of the system voltage weak area can be met.
(6) And 6, performing ultra-short term prediction on the future input wind speed of the fan by using a wavelet neural network, and performing ultra-short term prediction on the future input wind speed of the fan by using the wavelet neural network in a reference document [ Master and flood, Yanhusuo, Dingtongsheng, and the like ].
(7) And 7, calculating the fan reactive capacity prediction data by the inner layer of the field according to the fan active output of the wind speed section where the predicted wind speed is located and based on the fan active and reactive coupling characteristics. And calculating the probability density of the voltage droop gain of the wind turbine and the expected value of the voltage droop gain of the wind turbine under the predicted wind speed according to the voltage droop coefficient reference value by adopting a Gaussian mixture probability density estimation method (specifically, the method comprises the steps of clustering original data samples of the voltage droop coefficient based on a probability density function of a Gaussian mixture model to obtain multiple groups of data and form corresponding Gaussian sub-distributions, carrying out weighted combination on the multiple obtained Gaussian sub-distributions to obtain the probability density distribution of the original data samples of the voltage droop coefficient, and further adopting an expected value model to obtain the expected value of the reactive voltage droop gain of each wind speed section).
(8) And 8, taking the voltage droop gain expected value as a pre-instruction to guide the fan to output reasonable reactive power in the future. In order to verify the regulation effect of the proposed strategy on the node voltage after the system fault, a 10-machine 39-node system shown in fig. 2 is built in PSCAD simulation software, and wind power plants are respectively connected to nodes 5, 14 and 29. And taking the system simulation data of the 12 th hour of the typical day in the graph 4, carrying out parameter setting on the established model in PSCAD software, and respectively carrying out four three-phase short-circuit faults on the No. 38 node, wherein the 3 rd second fault starts and the fault duration is 0.25 second. The first simulation ensures that the wind power plant group of the node No. 29 does not adopt voltage droop control when the system has short-circuit fault, namely, does not output reactive power; the second simulation sets the voltage droop gain of each wind turbine in the wind power plant group according to the voltage droop gain reference value in the table 1, and after the fault starts, the wind power plant group outputs a certain node voltage of a reactive power regulation weak area, and the simulation curve of the total reactive power output by the wind power plant group is shown in fig. 5; third simulation enables the voltage droop gain of each wind turbine generator in the wind power plant group to be a self-adaptive droop gain expected value; and the fourth simulation leads each wind turbine generator in the wind power plant group to adopt voltage droop control. The simulation results in a node voltage curve of No. 29 as shown in FIG. 6.
Firstly, the total reactive power output by the wind power plant group is obtained according to the simulation curve of the total reactive power output by the wind power plant group in the graph of fig. 5, and after the voltage of a node drops in the simulation, the total reactive power output by the wind power plant group is maintained at about 130Mvar, which is more consistent with the required reactive power demand evaluation result of a voltage weak area, and the accuracy of the strategy is verified. Then, a voltage simulation curve which adopts voltage droop control and a voltage simulation curve which does not adopt voltage droop control are compared to obtain the voltage droop control method, and the voltage droop control link of the wind power plant group can guide the wind generation sets to output reactive power to effectively adjust the node voltage of the area where the wind generation sets are located. And finally, compared with voltage simulation curves adopting different control strategies, the voltage droop control method can more reasonably utilize the reactive capacity of the wind turbine generator system and improve the stability level of the node voltage of the system compared with the fixed voltage droop control method.
In summary, the wind farm group reactive power hierarchical optimization control strategy considering the predicted wind speed considers the reactive power supporting capability of the wind farm group according to the characteristics of the wind farm group, and by means of wind farm group reactive power demand evaluation, wind farm voltage droop coefficient reference value solution, ultra-short-term predicted wind speed, and fan adaptive droop gain probability density and expected value solution, when the wind farm group knows that the area is a voltage weak area, an emergency voltage control decision can be made in advance, so that when the voltage weak area fails to cause serious node voltage drop, the reactive power supporting capability can be fully exerted, the total reactive power demand of the voltage weak area is met, and the transient voltage stability of a wind power access area is effectively improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims (5)

1. A wind power plant group reactive power hierarchical optimization control method considering power prediction is characterized by comprising the following steps:
step 1: the method comprises the following steps that a single-ring theorem in a random matrix theory is adopted by an electric network layer, short-circuit fault simulation is carried out on each node in a system steady-state operation mode, the voltage weak degree of each node is calculated, a voltage weak area is defined, and whether a wind power access area is a voltage weak area or not is judged;
step 2: if the wind power access area is judged to be a weak voltage area, the wind power station group carries out emergency voltage control pre-decision, so that when the voltage in the weak voltage area drops seriously in the future set time, the wind power station group acts in time according to the emergency voltage control pre-decision;
and step 3: setting a short-circuit fault for the node of the voltage weak area, calculating a transient voltage stability index of the voltage weak area to obtain off-line sample data, and training a mapping relation between a running steady state and the transient voltage stability index of a BP neural network fitting system;
and 4, step 4: calculating the sensitivity of wind power output reactive power to the transient voltage stability index through a BP neural network algorithm, and evaluating the reactive power increase of the wind power plant group in the voltage weak area in real time according to the current system operation state;
and 5: the inter-site layer solves the voltage droop gain reference value of each wind turbine generator through a load flow calculation model according to the reactive power demand evaluation result of the wind power plant group, and the solving method of the voltage droop gain reference value is as follows:
step 5.1: under the steady state and transient state conditions of the system, estimating results according to the known system running state and the reactive power demand of the wind power plant group;
step 5.2: on the basis of system steady state and transient state, on the basis of considering reactive loss, determining a solving model of the voltage and the output reactive power of a grid connection point of the wind power plant, and calculating a voltage droop gain reference value;
and 6: adopting a wavelet neural network to predict the wind speed of the wind turbine in the future;
and 7: the field inner layer calculates fan reactive capacity prediction data according to the fan active output of a wind speed section where the predicted wind speed is located and on the basis of the fan active and reactive coupling characteristics; calculating the probability density of the voltage droop gain of the fan and the expected value of the voltage droop gain of the fan under the predicted wind speed according to the reference value of the voltage droop coefficient by adopting a Gaussian mixture probability density estimation method;
and 8: and taking the fan voltage droop gain expected value as a pre-instruction to guide the fan to output reactive power in the future.
2. The wind farm group reactive power hierarchical optimization control method considering power prediction as claimed in claim 1, wherein in step 5.2, the solving model method of wind farm grid-connected point voltage and output reactive power is as follows:
when the system is in a stable operation state, the output reactive power of the wind power plant cluster sink point is approximately equal to the output reactive power of the wind power plant cluster sampled in a sample under a stable state, and an objective function is expressed as follows:
Figure 852102DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 612117DEST_PATH_IMAGE002
outputting reactive power of a wind power field cluster gathering point in the flow iteration process under a steady state;
Figure 96188DEST_PATH_IMAGE003
outputting the actual value of reactive power for the wind power field group in the sample under the steady state,
Figure 657357DEST_PATH_IMAGE004
is the absolute value of the difference between the reactive powers in the steady-state process;
when the system is in a transient state process, the output reactive power of the grid-connected point of the wind power plant group in the load flow calculation process is approximately equal to the output reactive power evaluation result of the wind power plant group in the voltage weak area, and the objective function is expressed as follows:
Figure 187564DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 455997DEST_PATH_IMAGE006
outputting reactive power for a wind power field cluster collection point in the flow iteration process under the transient state;
Figure 528995DEST_PATH_IMAGE007
outputs a reactive power evaluation result for the wind power plant group under the transient state,
Figure 559268DEST_PATH_IMAGE008
is the absolute value of the difference between the reactive powers in the transient process;
wherein the content of the first and second substances,
Figure 983296DEST_PATH_IMAGE009
the expression of (a) is:
Figure 476592DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 466414DEST_PATH_IMAGE011
the reactive power is the reactive power required to be increased for the wind power station group under the transient condition;
the active and reactive power balance constraints are expressed as:
Figure 667588DEST_PATH_IMAGE012
wherein, the first and the second end of the pipe are connected with each other,
Figure 47754DEST_PATH_IMAGE013
which represents the number of nodes that are to be connected,
Figure 330093DEST_PATH_IMAGE014
、
Figure 377683DEST_PATH_IMAGE015
respectively generating active power and reactive power for the generator;
Figure 749759DEST_PATH_IMAGE016
、
Figure 617221DEST_PATH_IMAGE017
load active power and reactive power respectively;
Figure 497059DEST_PATH_IMAGE018
is the node voltage;
Figure 399156DEST_PATH_IMAGE019
、
Figure 535608DEST_PATH_IMAGE020
are respectively nodes
Figure 860672DEST_PATH_IMAGE021
And node
Figure 514507DEST_PATH_IMAGE022
Conductance and susceptance of the branch between;
Figure 536690DEST_PATH_IMAGE023
is a node
Figure 516147DEST_PATH_IMAGE024
、
Figure 862596DEST_PATH_IMAGE025
Voltage phase angle difference therebetween;
the node voltage
Figure 320122DEST_PATH_IMAGE026
The magnitude inequality constraint of (a) is expressed as:
Figure 196811DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 81591DEST_PATH_IMAGE028
、
Figure 912406DEST_PATH_IMAGE029
respectively as the minimum value and the maximum value of the node voltage amplitude;
the wind farm reactive power inequality constraint may be expressed as:
Figure 501519DEST_PATH_IMAGE030
in the formula (I), the compound is shown in the specification,
Figure 232714DEST_PATH_IMAGE031
、
Figure 52509DEST_PATH_IMAGE032
respectively as the minimum value and the maximum value of the reactive power output of the wind power plant.
3. The wind farm group reactive power hierarchical optimization control method considering power prediction according to claim 2, characterized in that, in two states of a steady state and a transient state, a voltage droop coefficient reference value of each wind farm is obtained by calculation according to a wind farm grid-connected point voltage and an output reactive power, and is specifically represented as:
Figure 869156DEST_PATH_IMAGE033
wherein the content of the first and second substances,
Figure 668484DEST_PATH_IMAGE034
is as follows
Figure 254187DEST_PATH_IMAGE035
Voltage droop coefficient reference values of the wind power plants;
Figure 982233DEST_PATH_IMAGE036
、
Figure 286176DEST_PATH_IMAGE037
are respectively the first
Figure 889195DEST_PATH_IMAGE038
Transient voltage and steady voltage of grid-connected points of the wind power plant;
Figure 329404DEST_PATH_IMAGE039
、
Figure 485142DEST_PATH_IMAGE040
respectively in transient state and in steady state
Figure 276381DEST_PATH_IMAGE041
The output reactive power of each wind power plant;
according to the first
Figure 683091DEST_PATH_IMAGE041
Calculating a voltage droop gain reference value of an internal wind turbine generator set by using a voltage droop coefficient reference value of each wind power plant, wherein the specific expression is as follows:
Figure 977807DEST_PATH_IMAGE042
wherein the content of the first and second substances,
Figure 516497DEST_PATH_IMAGE043
is a first
Figure 529453DEST_PATH_IMAGE044
Voltage droop gain reference values of wind turbine generators in the wind power plants;Dthe number of the fans in the wind power plant.
4. A wind farm group reactive power hierarchical optimization control system considering power prediction is characterized in that the wind farm group reactive power hierarchical optimization control system considering power prediction comprises: a network interface, a memory, and a processor; wherein the content of the first and second substances,
the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements;
the memory for storing a computer program operable on the processor;
the processor is configured to execute the steps of the wind farm group reactive power hierarchical optimization control method considering power prediction according to any one of claims 1 to 3 when the computer program is run.
5. A computer storage medium, characterized in that it stores a program for wind farm group reactive power stratification optimization control taking into account power prediction, which program, when executed by at least one processor, implements the steps of the wind farm group reactive power stratification optimization control taking into account power prediction method according to any one of claims 1 to 3.
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