Virtual synchronous generator parameter adaptive prediction control method based on fuzzy controller

Abstract

The invention provides a virtual synchronous generator parameter adaptive prediction control method based on a fuzzy controller in order to optimize the frequency dynamic response performance of a virtual synchronous generator in a micro-grid. According to the method, firstly, a fuzzy controller is designed by collecting physical quantities such as voltage and current of a system, and the inertia coefficient and the damping coefficient of the virtual synchronous generator are adjusted in a self-adaptive mode; the adjusted inertia coefficient and damping are used for modifying a prediction model, and the optimal reference power of the virtual synchronous generator is solved through a frequency constraint equation of the system. By adopting the control method provided by the invention, the frequency dynamic response performance of the system can be enhanced, and the stability of the micro-grid system is improved.

Description

Technical Field

[0001] The invention belongs to the field of microgrids, and in particular relates to a virtual synchronous generator parameter adaptive prediction control method based on a fuzzy controller. Background Art

[0002] Energy plays an important role in promoting social development. Electricity, as a clean and efficient form of energy, is related to the national economy and people's livelihood. In response to the energy crisis and environmental pressure, distributed energy such as wind power and solar energy has received more and more attention.

[0003] As an effective form of distributed energy utilization, microgrid technology has received widespread attention since it was proposed. Microgrids have two operating modes: grid-connected operation and island operation. In island mode, since there is no support from the large power grid, the microgrid needs its own units to maintain the stability of the system voltage and frequency, maintain the rated value in steady state, and stabilize its fluctuations within a safe range in transient state. However, since the output of renewable energy is affected by factors such as weather and is random, output fluctuations can easily cause an imbalance in power supply and demand. At the same time, the capacity of the system in island mode is small, and the power fluctuations caused by load switching cannot be ignored.

[0004] In order to compensate for the fluctuations in system power and frequency, energy storage devices are widely used in renewable energy microgrid systems. As the penetration rate of renewable energy in microgrids increases, the inertia required by the system is also increasing. As the main method of providing inertia, virtual synchronous generator control strategy has also been increasingly used.

[0005] When the power generation device in the microgrid is working under rated conditions, the frequency fluctuation is usually caused by the fluctuation on the load side. However, the load fluctuation is unpredictable, so the controller in the microgrid needs to have a certain adaptive adjustment capability. The inertia coefficient and damping coefficient of the traditional virtual synchronous motor control strategy are constant. When the load power fluctuates greatly, it may cause the frequency change of the system to exceed the standard requirements, thereby affecting the stability of the microgrid system. Therefore, it is necessary to improve the traditional virtual synchronous generator control method. When the power fluctuates, the energy storage device absorbs or emits more energy to support the system, thereby reducing the frequency fluctuation and improving the stability of the system. Summary of the invention

[0006] The purpose of the present invention is to provide a virtual synchronous generator parameter adaptive predictive control method based on a fuzzy controller, which collects physical quantities such as system frequency, voltage and current, and uses fuzzy control and model predictive control to optimize and adjust the traditional virtual synchronous motor control method, and adjust the output of the energy storage device in real time. This method can overcome the existing technical defects, improve the power compensation effect of the energy storage device, reduce the frequency fluctuation of the microgrid system and the fluctuation of the frequency change rate, and thus improve the stability of the microgrid system.

[0007] In order to achieve the above object, the present invention adopts the following technical solution.

[0008] First, the fuzzy control method is used to detect the frequency ω and the frequency change rate w in real time, output the adjusted damping coefficient and inertia coefficient values, and send them to the model predictive control (MPC) module and the virtual synchronous generator (VSG) module in real time. By establishing the state space model of the fuzzy MPC-VSG, a prediction model for the output frequency is established. By constraining the change of the output frequency, the rated power of the virtual synchronous generator is predicted and modified, the effect of power compensation is improved, and the suppression effect of frequency fluctuations is enhanced. The block diagram of the parameter adaptive virtual synchronous generator prediction method is shown in the figure. Figure 1 shown.

[0009] The present invention adopts a fuzzy controller to dynamically adjust the inertia coefficient and the damping coefficient. The fuzzy controller is designed by using the Mamdani fuzzy system, and the input parameters are normalized by using a quantization factor, and then the normalized input parameters are fuzzified. The present invention sets 5 fuzzy subsets on the fuzzy domain, and the module set {negative large, negative small, zero, positive small, positive large} is used as the input and output fuzzy sets, which are marked as {NL, NS, ZO, PS, PL} respectively.

[0010] The fuzzy controller used in the present invention is as follows Figure 2 As shown in the figure, the input is the voltage frequency deviation and the rate of change of the frequency, and the output is the change of the inertia coefficient and the damping coefficient. The membership function of the input parameter w. is as follows: Figure 3 As shown in (a), the membership function of Δw is Figure 3 (b) As shown in Figure 2, the parameter w reflects the trend of frequency change, so the corresponding membership function is also set to be more sensitive to optimize the output of the fuzzy controller. In these two membership functions, the value ranges of the fuzzy set representation are different, but the meanings of the two are the same, so the same symbols are used to represent them.

[0011] The output inertia coefficient uses the same membership function as the input parameter Δw, namely Figure 3(b) As the damping coefficient has a greater influence on the system frequency, the value range of the damping coefficient in the fuzzy controller is larger than that of the inertia coefficient. Therefore, in the present invention, the membership function of the output parameter UD is the same as that of UJ, but the value range is expanded by five times.

[0012] Since the initial value of the damping coefficient in the model is relatively small, the negative part of the fuzzy rule of the damping coefficient is changed to ZO to ensure that no negative damping coefficient occurs. According to the frequency change trend, the fuzzy rules of the output parameters are summarized as follows: Figure 4 ,in Figure 4 (a) is the fuzzy rule of inertia coefficient, Figure 4 (b) is the fuzzy rule of damping coefficient.

[0013] according to Figure 4 The fuzzy rules in can obtain fuzzy outputs under different input conditions, but the fuzzy output cannot directly affect the system and needs to be defuzzified to make it a certain value. This paper adopts the centroid method as the defuzzification method because compared with other defuzzification methods, the centroid method has a smoother output reasoning control effect, and the output will change even if it corresponds to a small change in the input signal. Using the centroid method, the changes in the corresponding inertia coefficient and damping coefficient UJ and UD can be calculated. The calculation formulas for UJ and UD are:

[0014]

[0015] Among them, AJ(U) and AD(U) are the membership functions of the inertia coefficient J and the damping coefficient D, respectively, and UJ and UD are the domains of the inertia coefficient and the damping coefficient, respectively.

[0016] The inertia coefficient and damping coefficient adjusted by fuzzy control are output to MPC, so that the rotation equation can be rewritten as the following state space equation:

[0017]

[0018] Among them, w = wm-wo. For the virtual synchronous generator, the fluctuation of the grid frequency is very small, and it can be approximately considered that w m ≈w o , taking pm as the control variable, the discrete model of the system can be derived. Since the frequency fluctuation should be limited to a certain range, the constrained MPC optimization problem can be described as:

[0019]

[0020] in,

[0021] Among them, H and G(k) are intermediate matrices obtained in the derivation process, and cm and b(k) are time domain constraint matrices.

[0022] By solving the quadratic programming problem Equation 4, we can obtain an optimal control matrix, which is expressed as After obtaining the optimal solution, take the first element Δpm * As a control signal, it is input into the system. The initial reference active power of the virtual synchronous generator is pref, which is obtained after active droop control:

[0023] pm=pref+Kp(wref-wg) Formula 6

[0024] Therefore, the input active reference power of the virtual synchronous generator can be expressed as follows:

[0025] pVSG(K)=pm+Δpm*(K) Formula 7

[0026] The input reference power adjusted by the two controllers can be obtained from Equation 7. Sending it into the rotation equation of the virtual synchronous motor can improve the power compensation capability, enhance the frequency fluctuation suppression effect, and thus improve the stability of the system.

[0027] Due to the application of the above technical solution, the present invention has the following characteristics:

[0028] 1. The present invention adopts a fuzzy control method, which can adaptively adjust the damping coefficient and inertia coefficient of the virtual synchronous generator according to the frequency fluctuation of the system, thereby improving the inertial support capacity of the energy storage system for the microgrid.

[0029] 2. On the basis of fuzzy control, the present invention adopts model predictive control to adjust the rated power of the virtual synchronous generator, and modifies the prediction model of the virtual synchronous generator in real time according to the parameters output by the fuzzy controller. It can accurately predict the virtual synchronous generator model after the modified parameters, further suppress the frequency fluctuation and improve the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 : Block diagram of the parameter adaptive virtual synchronous generator prediction method of the present invention;

[0031] Figure 2 : Schematic diagram of the fuzzy controller in the present invention;

[0032] Figure 3 : Membership function diagram of the fuzzy controller input parameters in the present invention;

[0033] Figure 4 : The fuzzy rule diagram used by the fuzzy controller in the present invention;

[0034] Figure 5 : Flow chart of the control scheme implemented in the present invention;

[0035] Figure 6 : The island microgrid system diagram used in the simulation of the present invention;

[0036] Figure 7 : Comparison diagram of synchronous generator output power in the present invention;

[0037] Figure 8 : Comparison diagram of output power of energy storage device in the present invention;

[0038] Fig. 9 : Frequency change comparison diagram in the present invention;

[0039] Fig.10 : Comparison diagram of frequency change rate in the present invention. DETAILED DESCRIPTION

[0040] The technical solution will be clearly and completely described below in conjunction with the preferred examples and drawings of the present invention. It should be understood that the preferred examples are only for illustrating the present invention, not for limiting the scope of protection of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0041] The present invention provides a virtual synchronous generator parameter adaptive predictive control method based on a fuzzy controller. Based on the real-time detection of the system frequency, the fuzzy controller is used to output the change in inertia and damping coefficient, which is simultaneously sent to the virtual synchronous generator and the MPC controller. The MPC controller modifies the reference power of the virtual synchronous generator in real time, so the energy storage system can output or absorb more power to support the load. The frequency fluctuation of the microgrid system is suppressed, further improving the stability of the system.

[0042] The control scheme mainly includes: fuzzy controller unit, model prediction control unit, virtual synchronous generator control unit. Figure 5 As shown in the flowchart, the operation and implementation process of the control scheme is as follows:

[0043] Step S10: by real-time monitoring of voltage and current signals, inputting the voltage and current into a power calculation unit, obtaining the output power of the virtual synchronous generator, and calculating the frequency of the output voltage;

[0044] Step S20: Send the frequency and the frequency change rate to the fuzzy controller unit to obtain the inertia coefficient change UJ and the damping coefficient change UD, and send them to the model prediction control unit and the virtual synchronous generator control unit at the same time;

[0045] Step S30: inputting UJ, UD, output power and voltage frequency into the model prediction control unit, obtaining the optimal power variation by solving, and thus calculating the optimal virtual synchronous generator reference power;

[0046] Step S40: A voltage reference signal is obtained through a traditional virtual synchronous generator control unit, and the voltage reference signal is controlled through a voltage and current double closed loop, and finally a PWM signal is generated to drive the inverter to work.

[0047] With a Figure 6 Taking the isolated island microgrid system shown in the figure as an example, a synchronous generator and an energy storage device are connected in parallel to supply power to the load. The present invention builds a simulation system in MATLAB / SIMULINK, and finally proves the effectiveness of the control scheme proposed by the present invention by comparing different control schemes. Table 1 and Table 2 list the simulation parameters of the synchronous generator and the virtual synchronous generator respectively.

[0048] Table 1 is the synchronous generator parameters, as shown below:

[0049]

[0050] Table 2 shows the parameters of the virtual synchronous generator, as follows:

[0051]

[0052] according to Figure 5 When building the simulation module, different control methods are used for comparison. "VSG" represents the situation of control using the traditional virtual synchronous generator method. "Fuzzy-VSG" represents the situation of control using the fuzzy virtual synchronous generator method. "MPC-VSG" represents the control of the model-predicted virtual synchronous generator method, and the parameters of the virtual synchronous generator are constant. "Fuzzy-MPC-VSG" refers to the parameter-adaptive virtual synchronous generator predictive control method proposed in the present invention. The control parameters are set according to Tables 1 and 2. In order to verify the impact of frequency fluctuations, the inertia coefficient of the synchronous generator is set to be smaller than the inertia coefficient of the virtual synchronous generator. According to the frequency and voltage deviation standards, two droop coefficients can be calculated. At the same time, in order to shorten the frequency recovery time, the initial inertia coefficient in the virtual synchronous generator is set to 1.5kg.m 2 , the damping coefficient is set to 3.2.

[0053] Under load fluctuation conditions, in order to compare the suppression effects of different control methods on frequency fluctuations, during the simulation, the synchronous generator and the energy storage device jointly supplied power to the load 8 seconds before the start of the simulation, and then a 5-kilowatt load was suddenly added at 8 seconds to simulate the power fluctuation on the load side.

[0054] Figure 7 and Figure 8 The output power fluctuation diagrams of the synchronous generator and the energy storage device under four different control strategies are displayed. The results show that compared with other virtual synchronous generator control methods, the parameter-adaptive virtual synchronous generator predictive control method proposed in the present invention can provide more power to the load when the load changes suddenly, thereby making the output power of the synchronous generator rise more smoothly.

[0055] After the sudden load is added, the frequency change and frequency change rate under different control methods are as follows: Fig. 9 , 10 As shown. Under the control methods of "VSG", "Fuzzy-VSG", "MPC-VSG" and "Fuzzy-MPC-VSG", the frequency changes are 0.24, 0.20, 0.18 and 0.16 Hz respectively. Compared with the model prediction virtual synchronous generator method, the frequency change of the method proposed in the present invention is reduced by 11%, which improves the quality of the output power. At the same time, Fig.10 It can also be seen that the frequency change rate of the system is also reduced accordingly, further improving the stability of the system.

[0056] It can be seen from the above simulation results that the use of the proposed parameter adaptive virtual synchronous generator predictive control method in the present invention can enable the energy storage device to provide more power when the load fluctuates, while reducing the frequency fluctuation and the frequency change rate fluctuation, further improving the stability of the system, which further confirms the effectiveness of the proposed control method.

[0057] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. The general principles defined in this article can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown in this article, but should conform to the widest scope consistent with the principles and novel features disclosed in this article. All equivalent changes or modifications made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. A virtual synchronous generator parameter adaptive predictive control method based on fuzzy controller, characterized in that: The method comprises a parameter adaptive control unit and a model prediction control unit: A virtual synchronous generator parameter adaptive predictive control method based on fuzzy controller is used to optimize the virtual synchronous generator control; In microgrids, due to factors such as load switching, large load power changes in the system will cause large fluctuations in system frequency; Since the inertia coefficient and damping coefficient in traditional virtual synchronous motor control are fixed and cannot adapt to unknown load fluctuations, a parameter adaptive method using fuzzy control is proposed; The parameter adaptive control unit adopts fuzzy control. By setting the corresponding fuzzy rules, a fuzzy controller is obtained; the controller can adaptively adjust the inertia coefficient and damping coefficient according to the fluctuation of the system frequency; The model prediction control unit adopts model prediction control, and modifies the prediction model in real time according to the inertia coefficient and damping coefficient obtained by the parameter adaptive control unit. By solving the constraint equation, the optimal input reference power of the virtual synchronous generator is output in real time, thereby enhancing the suppression effect of frequency fluctuations.

2. The method according to claim 1, characterized in that: A parameter adaptive control method includes: A dual-input dual-output fuzzy controller is used to implement parameter adaptive control. The system voltage is collected and the system frequency and frequency change rate are calculated, which are sent to the fuzzy controller as input. The membership functions of frequency and frequency change rate are designed, and the fuzzy rules of inertia coefficient and damping coefficient are designed at the same time to obtain the corresponding fuzzy output; then the centroid method is used for defuzzification, and finally the corresponding changes in inertia coefficient and damping coefficient are output.

3. The method according to claim 1, characterized in that Model predictive control method process: A prediction model was built using adaptive inertia coefficient and damping coefficient. Compared with traditional model predictive control, this method can better cope with changes in different loads. This method outputs the optimal reference power of the virtual synchronous generator and can achieve better frequency fluctuation suppression effect.

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