CN116581770A - Micro-grid system VSG double-droop control method based on self-adaptive neural network - Google Patents
Micro-grid system VSG double-droop control method based on self-adaptive neural network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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- H—ELECTRICITY
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- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02J2300/26—The renewable source being solar energy of photovoltaic origin involving maximum power point tracking control for photovoltaic sources
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Abstract
The invention discloses a micro-grid system VSG double-droop control method based on a self-adaptive neural network, wherein the micro-grid system framework mainly comprises a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filtering module and a VSG control module, wherein the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to realize maximum power point tracking, the rear stage is a DC-AC grid-connected inverter which is connected with the energy storage unit in parallel to the direct current side, VSG is introduced to provide inertia and damping for the system, a self-adaptive neural network controller is established, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, virtual parameters J and D are obtained by combining with a set virtual parameter selection rule, and the frequency and the voltage of the micro-grid system are regulated through the virtual parameters output by the self-adaptive neural network controller to complete double-droop control. According to the invention, a double-sagging control strategy can be realized, and the stability of the micro-grid during grid connection is improved.
Description
Technical Field
The invention relates to the field of clean energy, in particular to a micro-grid system VSG double-droop control method based on a self-adaptive neural network.
Background
In recent years, new energy power generation such as photovoltaic and wind power is widely applied to large power grids by virtue of the characteristics of cleanliness, high permeability, sustainable utilization and the like. Meanwhile, some serious challenges are brought to the fact that new energy power generation has randomness and volatility, great influence is caused on the stability of a system, the proposal of a micro-grid aims at solving the defects and becomes a current research hot spot, but the micro-grid is easy to cause frequency and voltage collapse when being disturbed due to lack of damping and inertia of the system during grid connection, so that the frequency and the voltage can adapt to the change of the system to ensure the safe operation of the system, a virtual synchronous generator control technology (Virtual Synchronous Generator, VSG) is introduced into the micro-grid, the virtual synchronous generator control technology has inertia and damping characteristics of a synchronous generator, inertia and damping are provided for the micro-grid when the virtual synchronous generator control technology is applied to the micro-grid, and the stability of the output frequency and the voltage of the system is further ensured. The inertia and damping parameters of the VSG are flexible and variable, so in order to improve the stable control of the virtual synchronous machine, the VSG can be improved by an adaptive method. The prior art proposes an adaptive parameter control strategy based on fuzzy control, which adapts inertia and damping parameters according to frequency deviation and rate of change, but does not take into account the influence of droop control characteristics. The relation between the power angle and the active power and the angular frequency oscillation process are fully considered in the prior art, the selection principle of the moment of inertia and the damping coefficient is given, and the stability of the photovoltaic grid connection is effectively improved. But the effect of the voltage is not considered. Aiming at the requirements of the power grid on frequency and rotational inertia under different working conditions, the prior art provides a frequency and virtual inertia multi-parameter self-adaptive control strategy, realizes power grid frequency modulation and parallel/off-grid switching, and does not consider the influence of damping change on the power grid frequency modulation and off-grid switching. In order to solve the problem of voltage and current impact generated during grid connection, the prior art adopts a model prediction control method, utilizes the change of frequency to formulate a weighting coefficient self-adaptive rule of the model prediction control, greatly improves the dynamic response during switching between presynchronization and operation modes, and does not mention the adjustment of the frequency. In the prior art, output speed feedback is adopted to adjust damping, a parameter self-adaptive control strategy is provided by utilizing the characteristic of a work angle, frequency deviation is reduced, and meanwhile, power overshoot is restrained, but the influence of virtual inertia on a system is not considered. The prior art [9] proposes a dual-machine parallel VSG control strategy based on adaptive feedforward control. The power shortage of the feedforward quantity compensation system is adaptively increased through the change of the angular frequency, the response speed and the power distribution precision of the system are improved, the frequency fluctuation and the active power oscillation of the system are restrained, the rapid stability and the safety of the system are ensured, but the Q-U sagging control is not considered. The prior art is based on the fact that the VSG virtual inertia and damping coefficient self-adaptive adjustment is achieved through a fuzzy algorithm, the VSG frequency and power fluctuation in the transient process can be reasonably restrained through the strategy, stable operation of a power grid is maintained, and parameter adjustment is inaccurate due to the fact that a fuzzy control method is adopted.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a micro-grid system VSG dual-droop control method based on a self-adaptive neural network, wherein the micro-grid system framework mainly comprises a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filtering module and a VSG control module, wherein the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to realize maximum power point tracking, the rear stage is a DC-AC grid-connected inverter which is connected with the energy storage unit in parallel to a DC side, VSG is introduced to provide inertia and damping for the system, a self-adaptive neural network controller is established, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, virtual parameters J and D are obtained by combining with a set virtual parameter selection rule, and the frequency and the voltage of the micro-grid system are regulated through virtual parameters output by the self-adaptive neural network controller, so that dual-droop control is completed.
Further, the establishing the adaptive neural network controller, acquiring the angular frequency offset and the change slope of the angular frequency in real time through the neural network controller, and combining the set virtual parameter selection rule to obtain virtual parameters J and D, including:
firstly, acquiring an angular frequency offset and a change slope of an angular frequency in real time through a neural network controller, namely:
secondly, the neural network controller obtains virtual parameters J and D according to a given virtual parameter selection rule, namely:
wherein: j (J) 0 And D 0 Representing inertia and damping parameters of the regulated output via the neural network; k (K) j And K d Adjusting coefficients of inertia and damping respectively; t (T) j And T d Is the upper and lower limit of the parameter variation.
Further, the voltage regulating the micro-grid system through the virtual parameter output by the adaptive neural network controller comprises:
virtual parameter regulating voltage output through neural network:
V＝(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ]
wherein: v odi Is the d-axis voltage component of VSG output, L f 、C f 、r f Representing inductance, capacitance and resistance, i, respectively, of the LC filter odi Is the d-axis current component of the VSG output, i d 、i q The d, q-axis current components, v, input for VSG id Is the d-axis current component, ω, of the VSG input i Angular frequency for VSG output;
the tracking error is:
wherein:
another error variable e 2i Expressed as:
e 2i ＝d i v odi -α ij
wherein: alpha ij Is a virtual control quantity.
Further, the adjusting the angular frequency of the micro-grid system through the virtual parameter output by the adaptive neural network controller comprises:
is available from droop control:
ω i ＝ω ni -k p p i
wherein: omega i Angular frequency, ω of the VSG output ni Is the per unit value of angular frequency, P i Is the active power output after filtering.
The error of adjustment is:
the angular frequency output is:
the beneficial effects of the invention are as follows: the technical scheme provided by the invention can adaptively control the frequency, the voltage, the active power and the reactive power, realizes a double-sagging control strategy and improves the stability of the micro-grid during grid connection.
Detailed Description
The technical scheme of the present invention is described in detail below, but the scope of the present invention is not limited to the following.
The technical proposal and the advantages of the invention are more clearly understood, and the invention is further described in detail. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention. It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
The micro-grid system VSG dual-droop control method based on the self-adaptive neural network is characterized in that the micro-grid system framework mainly comprises a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filtering module and a VSG control module, wherein the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to realize maximum power point tracking, the rear stage is a DC-AC grid-connected inverter which is connected with the energy storage unit in parallel to the DC side, the VSG is introduced to provide inertia and damping for the system, a self-adaptive neural network controller is established, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, virtual parameters J and D are obtained by combining a set virtual parameter selection rule, and the frequency and the voltage of the micro-grid system are regulated through the virtual parameters output by the self-adaptive neural network controller, so that dual-droop control is completed.
Further, the establishing the adaptive neural network controller, acquiring the angular frequency offset and the change slope of the angular frequency in real time through the neural network controller, and combining the set virtual parameter selection rule to obtain virtual parameters J and D, including:
firstly, acquiring an angular frequency offset and a change slope of an angular frequency in real time through a neural network controller, namely:
secondly, the neural network controller obtains virtual parameters J and D according to a given virtual parameter selection rule, namely:
wherein: j (J) 0 And D 0 Representing inertia and damping parameters of the regulated output via the neural network; k (K) j And K d Adjusting coefficients of inertia and damping respectively; t (T) j And T d Is the upper and lower limit of the parameter variation.
Further, the voltage regulating the micro-grid system through the virtual parameter output by the adaptive neural network controller comprises:
virtual parameter regulating voltage output through neural network:
V＝(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ]
wherein: v odi Is the d-axis voltage component of VSG output, L f 、C f 、r f Representing inductance, capacitance and resistance, i, respectively, of the LC filter odi Is the d-axis current component of the VSG output, i d 、i q The d, q-axis current components, v, input for VSG id Is the d-axis current component, ω, of the VSG input i Angular frequency for VSG output;
the tracking error is:
wherein:
another error variable e 2i Expressed as:
e 2i ＝d i v odi -α ij
wherein: alpha ij Is a virtual control quantity.
Further, the adjusting the angular frequency of the micro-grid system through the virtual parameter output by the adaptive neural network controller comprises:
is available from droop control:
ω i ＝ω ni -k p p i
wherein: omega i Angular frequency, ω of the VSG output ni Is the per unit value of angular frequency, P i Is the active power output after filtering.
The error of adjustment is:
the angular frequency output is:
specifically, the micro-grid system architecture mainly comprises a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filter module and a VSG control module, wherein the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to realize maximum power point tracking, the rear stage is a DC-AC grid-connected inverter which is connected with the energy storage unit in parallel on a direct current side, VSG is introduced to provide inertia and damping for the system, double droop stable control is realized, and the stability of the system is improved.
The outer loop control of the VSG is a power control loop, the inner loop is a double-droop control, the control of the VSG on frequency and voltage is influenced by adjusting virtual inertia and damping, and the deviation of the VSG is reduced to be within an allowable range, so that the double-droop control is influenced, and the grid connection target is realized.
The VSG realizes frequency control through a rotor motion equation, and in order to conveniently control the variable-current inverter, a model is processed in a reduced-order mode, and a virtual synchronous machine is modeled to obtain the rotor motion equation:
wherein: j represents the moment of inertia of the virtual synchronous machine, omega is the instantaneous angular frequency of the rotor, omega 0 Is the rated angular frequency of the rotor, D is the damping parameter of VSG, delta is the work angle of VSG, P m Representing mechanical power. The virtual moment of inertia J can reduce the frequency deviation, and the damping coefficient D can inhibit voltage fluctuation and reduce the deviation. According to the Q-U droop control characteristic of the VSG, the voltage control equation of the VSG can be obtained as follows:
U q ＝K q (Q ref -Q)+U n (2)
wherein: u (U) q Target voltage, K, desired after system optimization q Represents the reactive sag coefficient, Q ref Representing the reactive power reference value of the system, wherein Q is the real-time reactive power of VSG, U n Is the rated voltage.
The frequency control equation is derived from the f-p droop control characteristics of the VSG:
wherein: f is the target frequency to be adjusted, f n Is rated frequency, P and P ref Respectively a reference value and an actual value of active power, K p Is the droop control coefficient of the active power.
In order to ensure the stability of the output voltage, excitation adjustment is adopted, and an excitation adjustment control equation is [17-18]:
E＝k v ∫[K q (Q ref -Q)+(U 0 -U q )] (4)
combining (1), (2), (3) and (4) to obtain a double-sagging closed-loop function
The traditional virtual synchronous machine utilizes the rotor motion equation of the formula (1) to adjust output parameters, and the frequency and the voltage are regulated and controlled through the selected virtual parameters in advance, so that the method has great limitation, the optimal regulation and control can not be carried out in real time corresponding to the change of the system, and the frequency and the voltage of the system can cause larger errors to influence the grid connection stability, and therefore, the method for self-adapting the virtual parameters is researched to solve the problem.
The classical control theory can know that the angular frequency and damping ratio of VSG are respectively:
wherein:
the influence of the virtual moment of inertia J and the damping coefficient D on the system can be known by the formulas (5) and (6), when the J takes a fixed value, a pole can be changed along with the change of the damping coefficient D, when the D is gradually increased, the pole can gradually approach to the negative real axis from the negative virtual axis, at the moment, the stability of the system is gradually improved, when the damping coefficient D (assuming that D=30) takes a fixed value, the change of the virtual moment of inertia J also brings great influence to the pole, and if the J value is smaller, the under damping condition is born, and the stability of the system is reduced. However, if a larger J is used, although it plays a role in supporting the frequency, this choice will result in a large frequency deviation, which can easily lead to system breakdown.
By analysis, the virtual inertia J and the damping coefficient D can be obtained, and the stability of the system can be influenced. Therefore, in order to solve the problems of frequency fluctuation and the like caused by various emergency situations, J and D should be adjusted simultaneously to improve the stability of the system.
The active power shows the change rule of oscillation attenuation, the critical stable point of the active power is also continuously changed, and the change of the angular frequency can be divided into four phases, namely phase 1: [ t ] 1 -t 2 ]The change slope of the angular frequency gradually decreases from a positive value to zero at the moment, so that the angular frequency gradually increases, the error with the rated angular frequency is also continuously increased, the corresponding power angle change is changed to move from the point a to the point b, the active power is also continuously increased, the grid connection stability of the system is affected, the values of inertia J and damping D are required to be increased, adverse factors caused by the angular frequency deviation and the change rate are restrained, and the stage 2: [ t ] 2 -t 3 ]The slope of the angular velocity change gradually decreases from zero to a negative value, the value of the angular velocity gradually approaches to the rated value, but there is still an error, the process of the change of the power angle is that the power angle moves from the point b to the point c, the change rate of the active power decreases from a positive value to zero, and the power reaches the maximum value, so that in order to enable the angular frequency to rapidly decrease to the rated angular velocity, the value of inertia should be reduced, if the angular frequency offset is too large, the damping D can be increased to further inhibit the deviation, and the change of the remaining two stages is the same as the change rule of the former two stages. The selection rules for the inertia J and damping D obtained from this analysis are shown in table 1.
TABLE 1 selection rules for inertia J and damping D
The artificial neural network can be understood as being composed of a large number of neurons through connecting weights, and can perform operations such as information storage, parallel processing, spontaneous learning and the like. The algorithm of the artificial neural network is mainly BP (Back Propagation) algorithm, which is also called back propagation algorithm. The BP algorithm can be seen as a function, which is composed of units whose nonlinearity can be changed, has some nonlinearity capabilities, such as mapping capability, and can flexibly deal with problems of learning coefficients of a network, and the like, and meanwhile, in other fields, such as: the method has wide application prospect in signal processing, fault diagnosis, intelligent control and the like.
Through the selection rule of the virtual parameters, in order to restrain the offset of frequency and voltage, stable double-droop control is realized, so that stable grid connection is realized, and therefore, the virtual parameters are regulated in real time by adopting the idea of neural network control, and the control method is as follows:
firstly, acquiring an angular frequency offset and a change slope of an angular frequency in real time through a neural network controller, namely:
secondly, the neural network controller obtains virtual parameters J and D according to a given virtual parameter selection rule, namely:
wherein: j (J) 0 And D 0 Representing inertia and damping parameters of the regulated output via the neural network; k (K) j And K d Adjusting coefficients of inertia and damping respectively; t (T) j And T d Is the upper and lower limit of the parameter variation.
The virtual parameters output by the neural network can be used for adjusting the voltage in real time:
V＝(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ] (11)
wherein: v odi Is the d-axis voltage component output by the virtual synchronous machine, L f 、C f 、r f Respectively represent LCInductance, capacitance and resistance of filter, i odi Is the d-axis current component of the VSG output, i d 、i q The d, q-axis current components, v, input for VSG id Is the d-axis current component of the VSG input. Omega i The angular frequency of the VSG output. The tracking error is furthermore:
from the above, it can be known that when the virtual synchronous machine distributes reactive power by using the droop coefficient, accurate voltage adjustment cannot be achieved, so that the error function e is set in advance li A compromise between voltage and reactive power can be achieved, but the defined error function requires a secondary control of the voltage, i.e. e 1i Reducing v odi -v ref The deviation between them is reduced by the neural network controller, so equation (12) can be transformed into:
wherein:another error variable e 2i Can be expressed as:
e 2i ＝d i v odi -α ij (14)
wherein: alpha ij Is a virtual control quantity.
Checking the stability of Lyapunov theory
The lyapu function is set as:
differentiating it and taking into formula (13) can be obtained:
substitution of formula (14) into formula (16) can result in:
alpha in the formula i Expressed by the following formula:
substitution of formula (18) into formula (17) yields:
another lyapunov function is derived from the above formula:
the controller of the model can be obtained by combining the formulas (11), (19), (20):
v id ＝-e 1i -k 2i e 2i +F i (x i ) (21)
above-mentionedCan be expressed as:
using neural networks to connectCan be converted into:
the neural network controller adjusts in real time, first by droop control:
ω i ＝ω ni -k p p i (24)
wherein: omega i The angular frequency omega of the virtual synchronous machine output ni Is the per unit value of angular frequency, P i Is the active power output after filtering.
The error in adjustment can be expressed as:
the lyapunov function selected is:
differentiating the obtained product to obtain:
v fi ＝e ωi d i (27)
substituting formula (25) into formula (27) yields:
substitution of formula (24) into (28) yields:
then neural network control is used, namely:
the angular velocity output is therefore:
wherein:is the weight of the neural network, lambda i (x i ) Is the approximation error of the neural network control.
The error is controlled in a reasonable range through the neural network controller, and the system stability can be greatly improved after the control of the neural network by utilizing the Lyapunov theory.
The frequency is regulated by a neural network controller, so that active power can be distributed as required, and stable control of p-f is realized. In order to make omega i And omega ref The error of (2) is within a reasonable range, and is regulated in real time by utilizing a neural network controller, and is firstly obtained by droop control:
ω i ＝ω ni -k p p i (24)
wherein: omega i The angular frequency omega of the virtual synchronous machine output ni Is the per unit value of angular frequency, P i Is the active power output after filtering.
The error in adjustment can be expressed as:
the lyapunov function selected is:
differentiating the obtained product to obtain:
v fi ＝e ωi d i (27)
substituting formula (25) into formula (27) yields:
substitution of formula (24) into (28) yields:
then neural network control is used, namely:
the angular velocity output is therefore:
the foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (4)
1. The micro-grid system VSG dual-droop control method based on the self-adaptive neural network is characterized in that the micro-grid system framework mainly comprises a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filtering module and a VSG control module, wherein the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to realize maximum power point tracking, the rear stage is a DC-AC grid-connected inverter which is connected with the energy storage unit in parallel to the DC side, the VSG is introduced to provide inertia and damping for the system, a self-adaptive neural network controller is established, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, virtual parameters J and D are obtained by combining a set virtual parameter selection rule, and the frequency and the voltage of the micro-grid system are regulated through the virtual parameters output by the self-adaptive neural network controller, so that dual-droop control is completed.
2. The method for controlling VSG dual droop of a micro-grid system based on an adaptive neural network according to claim 1, wherein the establishing the adaptive neural network controller, by using the neural network controller, obtains the angular frequency offset and the slope of the change of the angular frequency in real time, and combines the set virtual parameter selection rules to obtain the virtual parameters J and D, includes:
firstly, acquiring an angular frequency offset and a change slope of an angular frequency in real time through a neural network controller, namely:
secondly, the neural network controller obtains virtual parameters J and D according to a given virtual parameter selection rule, namely:
wherein: j (J) 0 And D 0 Representing inertia and damping parameters of the regulated output via the neural network; k (K) j And K d Adjusting coefficients of inertia and damping respectively; t (T) j And T d Is the upper and lower limit of the parameter variation.
3. The adaptive neural network-based micro-grid system VSG dual droop control method according to claim 2, wherein the adjusting the voltage of the micro-grid system by the virtual parameter outputted from the adaptive neural network controller comprises:
virtual parameter regulating voltage output through neural network:
V＝(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ]
wherein: v odi Is the d-axis voltage component of VSG output, L f 、C f 、r f Representing inductance, capacitance and resistance, i, respectively, of the LC filter odi Is the d-axis current component of the VSG output, i d 、i q The d, q-axis current components, v, input for VSG id Is the d-axis current component, ω, of the VSG input i Angular frequency for VSG output;
the tracking error is:
wherein:
another error variable e 2i Expressed as:
e 2i ＝d i v 0di -α ij
wherein: alpha ij Is a virtual control quantity.
4. The adaptive neural network-based micro-grid system VSG dual droop control method according to claim 3, wherein the adjusting the angular frequency of the micro-grid system by the virtual parameter outputted from the adaptive neural network controller comprises:
is available from droop control:
ω i ＝ω ni -k p p i
wherein: omega i Angular frequency, ω of the VSG output ni Is the per unit value of angular frequency, P i Is the active power output after filtering.
The error of adjustment is:
the angular frequency output is:
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