CN111711199A - Self-adaptive unloading resistance switching method for double-fed wind turbine generator - Google Patents

Self-adaptive unloading resistance switching method for double-fed wind turbine generator Download PDF

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CN111711199A
CN111711199A CN202010693050.8A CN202010693050A CN111711199A CN 111711199 A CN111711199 A CN 111711199A CN 202010693050 A CN202010693050 A CN 202010693050A CN 111711199 A CN111711199 A CN 111711199A
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unloading resistance
neural network
wind turbine
unloading
typical
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陈宝刚
刘一星
王世均
刘增里
孙军
周琪
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CSIC Haizhuang Windpower Co Ltd
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CSIC Haizhuang Windpower Co 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
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • H02J3/38—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
    • H02J2203/20—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
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention provides a self-adaptive unloading resistance switching method for a double-fed wind turbine generator, which comprises the following steps of: inputting the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power into an artificial neural network model by using an artificial neural network controller to obtain an unloading resistance value; converting the unloading resistance value into a control signal by using an artificial neural network controller, and sending the control signal to a potentiometer; the control signal controls the potentiometer to carry out unloading resistance self-adaptive switching. The invention can solve the technical problems that in the prior art, the Chopper circuit causes direct-current voltage and direct-current fluctuation due to repeated switching of the unloading circuit, and damages power electronic components such as a converter, a thyristor and the like, thereby influencing the control function and power output of the wind turbine generator.

Description

Self-adaptive unloading resistance switching method for double-fed wind turbine generator
Technical Field
The invention relates to the technical field of wind power generation, in particular to a self-adaptive unloading resistance switching method for a double-fed wind turbine generator.
Background
With the wide use of wind power generation, the influence of a wind generating set on a power grid is larger and larger when the wind generating set is connected to the power grid. In order to guarantee the stability of the operation of the power grid, the related standards stipulate that the wind generating set is allowed to be disconnected only after the voltage of the power grid drops to a specified value, so that the low-voltage ride-through capability becomes a hard requirement for the grid connection of the existing wind generating set. The Chopper circuit can play an important role in the low-voltage ride-through control period of the double-fed wind turbine generator and can effectively inhibit potential safety hazards caused by sudden rise of direct-current side voltage.
For a double-fed wind turbine generator, when a voltage drop is caused by a short-circuit fault of a power grid, the input and output power on a direct-current bus of a converter is unbalanced, and the voltage of the direct-current bus is increased. During the low voltage ride through, to protect the switching device, the switching device is normally turned off, and a Crowbar circuit (dc side Crowbar protection circuit) is put into operation. However, if the Crowbar circuit has too large a resistance, the capacitance of the dc bus will be charged, causing the dc bus voltage to rise further. The introduction of the traditional Chopper circuit aims to consume redundant charges on a capacitor of a discharged direct current bus by repeatedly switching an unloading resistor through a threshold, so as to play a role in reducing voltage and reduce the voltage of the direct current bus.
However, when the conventional Chopper circuit switches the unloading resistor, the influence caused by the fluctuation of direct-current voltage and direct-current due to repeated switching of the unloading circuit is not considered. The rapid fluctuation of the direct current voltage and the direct current may damage power electronic components such as a current transformer and a thyristor, and influence the control function and the power output of the wind turbine generator.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a self-adaptive unloading resistance switching method for a double-fed wind turbine generator, which aims to solve the technical problem that in the prior art, a Chopper circuit causes direct-current voltage and direct-current fluctuation due to repeated switching of an unloading circuit, and damages power electronic components such as a converter and a thyristor, and further influences the control function and power output of the wind turbine generator.
The technical scheme adopted by the invention is that the invention relates to a self-adaptive unloading resistance switching method for a double-fed wind turbine generator.
In a first implementation, the method comprises the following steps:
inputting the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power into an artificial neural network model by using an artificial neural network controller to obtain an unloading resistance value;
converting the unloading resistance value into a control signal by using an artificial neural network controller, and sending the control signal to a potentiometer;
the control signal controls the potentiometer to carry out unloading resistance self-adaptive switching.
In combination with the first implementable manner, in a second implementable manner, the artificial neural network model is a back propagation neural network model.
With reference to the second implementable manner, in a third implementable manner, the modeling of the back propagation neural network model includes the steps of:
s1, selecting a typical working condition, obtaining a typical unloading resistance value according to a grid-connected model of the double-fed wind generating set, and constructing a typical unloading resistance database;
and S2, inputting a plurality of groups of data in the typical unloading resistance database into a back propagation neural network, and training the back propagation neural network to obtain a back propagation neural network model.
With reference to the third implementable manner, in a fourth implementable manner, the step S1 specifically includes the following:
selecting a typical working condition, and calculating a theoretical value range of a typical unloading resistor;
establishing a grid-connected model of the doubly-fed wind generating set through a simulation platform, wherein the grid-connected model comprises a converter control sub-model, a power grid-converter-generator sub-model, a master control-variable pitch sub-model and a transmission chain-wind turbine sub-model;
and (3) obtaining a plurality of typical unloading resistance values under various typical working conditions by using a grid-connected model and combining the theoretical value range of the typical unloading resistance.
With reference to the fourth implementable manner, in a fifth implementable manner, a theoretical value range of the typical unloading resistance satisfies the following formula:
Figure BDA0002589957630000031
in the above formula, RcIs a typical unloading resistance, UdcIs the voltage on the DC bus capacitor, PRCmaxFor maximum allowable power of the discharge resistance, UdcmaxMaximum voltage allowed by DC bus capacitor, PeIs the rated power of the wind turbine generator, ufFor fault voltage, β is overload multiple of converter, igIs the net side current.
With reference to the third implementable manner, in a sixth implementable manner, in step S2, the grid voltage, the dc-side capacitor voltage, the capacitor-side power, and the capacitor-side power in the multiple sets of data are used as inputs of the back propagation neural network, and the typical unloading resistance value is used as an output of the back propagation neural network, so as to perform training.
With reference to the first implementable manner, in a seventh implementable manner, the potentiometer is a digital potentiometer.
According to the technical scheme, the beneficial technical effects of the invention are as follows:
1. by adopting the technical scheme in the embodiment, the unloading resistance can be adaptively adjusted during low-voltage ride through, repeated switching of a switching tube of the chooper circuit is reduced, and voltage fluctuation of a direct-current side is effectively reduced. The damage to power electronic components such as a converter and a thyristor is avoided, and the control function and power output of the wind turbine generator are further influenced.
2. Because the value of the unloading resistance is influenced by a plurality of parameters and the correlation between the values is complex, a BP neural network (back propagation neural network) is selected, and the method is very suitable for solving the problem of complex internal mechanism.
Drawings
In order to more clearly illustrate the embodiments of the present invention, reference will now be made to the appended drawings, which are briefly described as required in the detailed description (or prior art description). In all the drawings, the elements or parts are not necessarily drawn to actual scale.
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2(a) is a schematic diagram of a converter control submodel;
FIG. 2(b) is a schematic diagram of a grid-converter-generator submodel;
FIG. 2(c) is a schematic diagram of a Master control-Pitch submodel;
FIG. 2(d) is a schematic view of a chain-wind turbine sub-model;
FIG. 3 is a BP neural network model modeling flowchart;
FIG. 4 is a schematic diagram of a BP neural network model;
FIG. 5 is a diagram of a digital potentiometer circuit configuration;
fig. 6 is an unloading resistance adaptive switching control schematic diagram.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Examples
As shown in fig. 1, the embodiment provides a self-adaptive unloading resistance switching method for a doubly-fed wind turbine generator, which includes the following steps:
inputting the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power into an artificial neural network model by using an artificial neural network controller to obtain an unloading resistance value;
converting the unloading resistance value into a control signal by using an artificial neural network controller, and sending the control signal to a potentiometer;
the control signal controls the potentiometer to carry out unloading resistance self-adaptive switching.
The operation of the present embodiment will be described in detail below.
1. Constructing a typical unloading resistance database
For double-fed wind generating sets with different models and specifications, the values of the unloading resistance are different. And selecting a typical working condition and constructing a typical unloading resistance database aiming at a double-fed wind generating set with a certain model and specification. The typical unloading resistance database is constructed by the following steps:
1.1 theoretical value range for calculating typical unloading resistance
Because the power change on the direct current bus capacitor is the difference between the input power and the output power at two ends of the capacitor, the power at two sides of the direct current bus capacitor meets the following formula (1):
Figure BDA0002589957630000051
in the above formula (1), UdcIs the voltage on the dc bus capacitor. The left side of the equal sign of the equation represents the power change on the direct current bus capacitor, and the right side of the equal sign of the equation represents the difference between the input power and the output power at two ends of the direct current bus capacitor.
Neglecting losses, using RCRepresenting the value of the unloading resistance, R can be obtainedCThe calculation formula of (a) is as follows:
Figure BDA0002589957630000052
in the above formula (2), UdcmaxThe maximum voltage allowed by the direct current bus capacitor.
By combining the formula (1) and the formula (2), the theoretical value range of the typical unloading resistance can be obtained, and the following formula is satisfied:
Figure BDA0002589957630000053
in the above formula (3), RcIs a typical unloading resistance, UdcIs the voltage on the DC bus capacitor, PRCmaxFor maximum allowable power of the discharge resistance, UdcmaxMaximum voltage allowed by DC bus capacitor, PeIs the rated power of the wind turbine generator, ufFor fault voltage, β is overload multiple of converter, igIs the net side current.
1.2 building a grid-connected model of the doubly-fed wind generating set through the simulation platform, selecting a plurality of typical unloading resistance values under various typical working conditions by using the grid-connected model, and forming a typical unloading resistance database
In this embodiment, Matlab/Simulink may be selected for simulation, and a constructed grid-connected model is shown in fig. 2. Fig. 2(a) is a schematic diagram of a converter control submodel, fig. 2(b) is a schematic diagram of a power grid-converter-generator submodel, fig. 2(c) is a schematic diagram of a master control-variable pitch submodel, and fig. 2(d) is a schematic diagram of a transmission chain-wind turbine submodel.
When the grid voltage is connected to the power grid, a plurality of different voltage drop conditions can appear, some voltage drop conditions can appear frequently, and each voltage drop condition appearing frequently corresponds to a typical working condition. In the embodiment, 10 to 20 typical working conditions, preferably 20, are selected to obtain data samples with good typical unloading resistance. According to the simulation values of the grid voltage, the direct-current side capacitor voltage, the capacitor grid side power and the capacitor machine side power in the grid-connected model under the typical working conditions, a plurality of typical unloading resistance values under various typical working conditions can be obtained by combining the theoretical value range of the typical unloading resistance in the step S11, and the series of typical unloading resistance values together form a typical unloading resistance database. The typical unloading resistance database comprises a plurality of groups of data, wherein each group of data specifically comprises power grid voltage, direct current side capacitor voltage, capacitor grid side power, capacitor machine side power and a typical unloading resistance value.
2. Building artificial neural network model
In actual engineering, besides typical working conditions, voltage drop conditions which do not belong to the typical working conditions exist, and for the voltage drop conditions, the unloading resistance value under the voltage drop condition is obtained according to a data model, so that the effect of self-adaptive switching of the unloading resistance under various voltage drop conditions can be realized.
In this embodiment, an artificial neural network is selected to construct the data model. Specifically, because the value of the unloading resistor is affected by a plurality of parameters and the correlation between the values is complex, a BP neural network (back propagation neural network) is selected in this embodiment and is suitable for solving the problem of complex internal mechanisms. The modeling flow of the BP neural network model is shown in FIG. 3, and the model of the BP neural network is shown in FIG. 4. In the BP neural network, the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power of each group of data in a typical unloading resistance database are used as input, and a typical unloading resistance value is used as output for training. So the number of nodes of the input layer is 4 and the number of nodes of the output layer is 1.
The hidden layer is 1 layer and meets the following empirical formula:
Figure BDA0002589957630000061
in the above formula (4), l is the number of hidden layer nodes, n is the number of input layer nodes, m is the number of output layer nodes, and a is a constant between [1,10 ]. In this embodiment, the number of nodes of the selected hidden layer is 4.
The model eliminates errors through repeated learning for a certain number of times, and training is completed when the errors reach expected values. In this embodiment, the expected value of the error is set to 1.00 e-5.
The trained BP neural network model can obtain the unloading resistance value by inputting the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power.
3. Adaptive switching for controlling unloading resistance by using BP neural network controller
In the embodiment, the unloading resistor is a potentiometer which is controlled by an electric signal, so that the unloading resistor is convenient to adjust and long in service life. In this embodiment, a digital potentiometer is preferably used, the adjustment accuracy of the digital potentiometer is high, and the circuit configuration thereof is as shown in fig. 5.
During the low voltage ride through period, the double-fed wind generating set main control system sends a signal to the unloading circuit to control the unloading circuit to start working. When the unloading resistor is adaptively switched, as shown in fig. 6, the BP neural network controller obtains the unloading resistance value according to the power grid voltage, the dc side capacitor voltage, the capacitor grid side power, and the capacitor machine side power input by the BP neural network. And then the BP neural network controller converts the unloading resistance value into a control signal and sends the control signal to the digital potentiometer, and the digital potentiometer completes the self-adaptive switching of the unloading resistance. Switching can be completed at one time under normal conditions, and repeated switching is basically not needed.
By adopting the technical scheme in the embodiment, the unloading resistance can be adaptively adjusted during low-voltage ride through, repeated switching of a switching tube of the chooper circuit is reduced, and voltage fluctuation of a direct-current side is effectively reduced. The damage to power electronic components such as a converter and a thyristor is avoided, and the control function and power output of the wind turbine generator are further influenced.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (7)

1. The self-adaptive unloading resistance switching method of the doubly-fed wind turbine generator is characterized by comprising the following steps of:
inputting the power grid voltage, the direct current side capacitor voltage, the capacitor grid side power and the capacitor machine side power into an artificial neural network model by using an artificial neural network controller to obtain an unloading resistance value;
converting the unloading resistance value into a control signal by using the artificial neural network controller, and sending the control signal to a potentiometer;
and the control signal controls the potentiometer to carry out unloading resistance self-adaptive switching.
2. The unloading resistance self-adaptive switching method of the doubly-fed wind turbine generator set according to claim 1, characterized in that: the artificial neural network model is a back propagation neural network model.
3. The unloading resistance adaptive switching method of the doubly-fed wind turbine generator according to claim 2, wherein the modeling of the back propagation neural network model comprises the following steps:
s1, selecting a typical working condition, obtaining a typical unloading resistance value according to a grid-connected model of the double-fed wind generating set, and constructing a typical unloading resistance database;
and S2, inputting a plurality of groups of data in the typical unloading resistance database into a back propagation neural network, and training the back propagation neural network to obtain a back propagation neural network model.
4. The unloading resistance self-adaptive switching method of the doubly-fed wind turbine generator set according to claim 3, wherein the step S1 specifically comprises the following steps:
selecting a typical working condition, and calculating a theoretical value range of a typical unloading resistor;
establishing a grid-connected model of the doubly-fed wind generating set through a simulation platform, wherein the grid-connected model comprises a converter control sub-model, a power grid-converter-generator sub-model, a master control-variable pitch sub-model and a transmission chain-wind turbine sub-model;
and obtaining a plurality of typical unloading resistance values under various typical working conditions by using the grid-connected model and combining the theoretical value range of the typical unloading resistance.
5. The unloading resistance self-adaptive switching method of the doubly-fed wind turbine generator set according to claim 4, wherein the theoretical value range of the typical unloading resistance satisfies the following formula:
Figure FDA0002589957620000021
in the above formula, RcIs a typical unloading resistance, UdcIs the voltage on the DC bus capacitor, PRCmaxFor maximum allowable power of the discharge resistance, UdcmaxMaximum voltage allowed by DC bus capacitor, PeIs the rated power of the wind turbine generator, ufFor fault voltage, β is overload multiple of converter, igIs the net side current.
6. The unloading resistance self-adaptive switching method of the doubly-fed wind turbine generator set according to claim 3, characterized in that: in step S2, the grid voltage, the dc-side capacitor voltage, the capacitor grid-side power, and the capacitor-side power in the multiple sets of data are used as inputs of the back propagation neural network, and the typical unloading resistance value is used as an output of the back propagation neural network, so as to perform training.
7. The unloading resistance self-adaptive switching method of the doubly-fed wind turbine generator set according to claim 1, characterized in that: the potentiometer is a digital potentiometer.
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CN104410098A (en) * 2015-01-07 2015-03-11 上海电机学院 Doubly-fed asynchronous generator set low voltage ride through control system and control method thereof
CN104682433A (en) * 2015-03-10 2015-06-03 国网四川省电力公司 Double-fed induction generator wind turbine Crowbar resistance design method based on fuzzy membership function
CN108649610A (en) * 2018-06-08 2018-10-12 西北农林科技大学 A kind of DFIG low-voltage ride-through methods

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CN113064029A (en) * 2021-03-17 2021-07-02 南京传积兴自动化科技有限公司 High-voltage direct-current insulation monitoring system and monitoring method
CN113315359A (en) * 2021-05-07 2021-08-27 清华大学 Unloading method of direct-current modular multilevel unloading circuit
CN113315359B (en) * 2021-05-07 2022-06-03 清华大学 Unloading method of direct-current modular multi-level unloading circuit

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Application publication date: 20200925