Fuzzy VSG inertia adaptive control method considering energy storage charge state

Abstract

The invention discloses a fuzzy VSG inertia adaptive control method considering an energy storage charge state. The method comprises the following steps: designing a photovoltaic energy storage VSG system; and establishing a grid-connected inverter model taking the virtual synchronous generator as a main body. Due to the superiority of fuzzy control in processing an output adjustment coefficient X is nonlinear constraint, an inertia self-adaptive control technology considering energy storage constraint fuzzy control is provided, self-adaptive adjustment of virtual inertia is realized, and the virtual inertia can be dynamically changed. Based on a self-adaptive control technology of virtual inertia, three indexes of the virtual inertia, the charge state of the supercapacitor and the system output frequency are compared and analyzed under two working states of limit charging and limit discharging of the supercapacitor; the result shows that the photovoltaic energy storage VSG system has good stability and dynamic performance under the condition of load disturbance; compared with a traditional VSG control technology, frequency fluctuation and output power overshoot of the method are effectively restrained.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid inverter control, and in particular relates to a fuzzy VSG inertia adaptive control method considering the energy storage charge state. Background Art

[0002] In recent years, wind power, photovoltaic and other renewable energy sources have been rapidly developed in various countries with their advantages of cleanliness and high efficiency; however, the large-scale grid-connected operation of photovoltaic power will have an adverse effect on the dynamic response and stability of the power grid. Due to the disadvantages of low inertia and weak damping, the large-scale access of conventional photovoltaic grid-connected inverters to the power grid will lead to insufficient inertia and damping of the overall power grid, making it difficult to provide sufficient voltage support and frequency regulation capabilities for the power grid, thus adversely affecting the stability of the system. To this end, some scholars at home and abroad have proposed a new inverter control technology - virtual synchronous generator (VSG) technology. It combines the motion equation and damping characteristics of the synchronous generator rotor under droop control, so that the power electronic equipment that originally has no inertia and damping has similar frequency and voltage regulation characteristics as the synchronous generator, and also provides inertia and damping for the system. At present, the research on VSG mainly focuses on adaptive inertia control, power decoupling, parameter setting analysis, frequency optimization and other aspects. Although the existing control technology has made some progress in improving the system frequency stability, its limitation is that it ignores the restrictive effect of the energy storage unit's own charging and discharging characteristics on the system virtual inertia, and therefore lacks practicality in actual engineering applications. Therefore, it is necessary to design a fuzzy VSG inertia adaptive control method that takes into account the energy storage charge state to solve the above problem. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a fuzzy VSG inertia adaptive control method taking into account the energy storage charge state, aiming to solve the problem of the restriction of the energy storage unit's own charging and discharging characteristics on the system virtual inertia; the fuzzy VSG inertia adaptive control technology of the present application can give full play to its advantages and realize the adaptive smooth conversion of inertia under two different working conditions of energy storage unit charging and discharging, thereby improving the adaptability of the system inertia to different working conditions, so that the virtual inertia can change dynamically and improve the inertia level of the system.

[0004] In order to achieve the above technical effects, the technical solution adopted by the present invention is:

[0005] A fuzzy VSG inertia adaptive control method considering the energy storage charge state includes the following steps:

[0006] S1, set up a photovoltaic energy storage VSG system, which includes an energy storage unit, a photovoltaic array, an inverter and an LCL filter; the energy storage unit is a supercapacitor; the photovoltaic array unit is connected to the DC bus through a maximum power tracker, which can provide a stable power output for the system; the VSG algorithm of the photovoltaic energy storage VSG system generates a command voltage by detecting power; the energy storage unit realizes the bidirectional energy flow of the system, provides efficient energy management for the system, and provides inertia and power support for the microgrid system;

[0007] Since lithium batteries have certain disadvantages, they cannot suppress the dynamic oscillation of system frequency and have high production costs, so supercapacitors are used as energy storage units. Their power density is much higher than that of lithium batteries, and they also have the advantages of relatively longer cycle life, faster effect, and the ability to stabilize system frequency.

[0008] S2, establish a grid-connected inverter model based on virtual synchronous machine technology; when the supercapacitor is in the two working states of extreme charging and extreme discharging, the energy storage unit actively provides active power and participates in the grid frequency regulation; based on adaptive inertia control technology, realize the conversion of supercapacitors in different charging and discharging states;

[0009] S3, realizing adaptive control of virtual inertia H according to the real-time working state SOC of the supercapacitor;

[0010] S4, the conversion of supercapacitors in different states is realized through virtual inertia adaptive control based on fuzzy control.

[0011] Preferably, in the grid-connected inverter model with virtual synchronous machine technology as the main body, the active frequency control of the grid-connected inverter based on the virtual synchronous generator technology is:

[0012]

[0013] Where H is the virtual inertia; ω and ω 0 are the angular frequencies of the VSG unit output and the power grid respectively; D is the damping coefficient; Pref is the reference active power value; and P is the active power actually output by the VSG.

[0014] Furthermore, the supercapacitor SOC is used to represent the working state of the energy storage system, which is divided into:

[0015]

[0016] Where a is the discharge limit value of the supercapacitor and b is the charging limit value.

[0017] Preferably, the active frequency control formula is simplified:

[0018]

[0019] Wherein, is the change of active power. Since the frequency change remains constant in a short time, the change of virtual inertia H will also affect the change. When the supercapacitor is close to the critical state of charge and discharge, in order to reduce the damage caused by excessive charge and discharge of the supercapacitor, the virtual inertia H needs to be adjusted to the smallest possible value. In this case, the change of active power will also decrease accordingly. Therefore, the adaptive control of virtual inertia H can be realized according to the real-time working state SOC of the supercapacitor.

[0020] Preferably, in step S3, since the value range of the inverse tangent function is limited to a certain range, it can play a role in limiting the amplitude in engineering practice. The present invention adopts the inverse tangent function fitting and combines the actual working conditions to obtain the following formula to represent the virtual inertia H under the action of SOC;

[0021] The adaptive control method for realizing the virtual inertia H according to the real-time working state SOC of the supercapacitor is:

[0022] The inverse tangent function is used to fit and combined with the actual working conditions to obtain the following formula to express the virtual inertia H under the action of SOC:

[0023]

[0024] Where a is the discharge limit of the supercapacitor, b is the charge limit; parameters k1 and k2 are virtual inertia control parameters, H 0 is the nominal inertia of the system when it is running stably, and X is the adjustment coefficient. Due to the application of the inverse tangent function, this technology reduces the virtual inertia H, thereby reducing the change in active power, thereby reducing the output of the supercapacitor. The more serious the over-charging and discharging of the supercapacitor, the more H is reduced, and the better the control effect. Compared with the control technology that maintains a constant inertia VSG, the adaptive control of the virtual inertia H is realized, which can optimize the operating state of the supercapacitor.

[0025] The beneficial effects of the present invention are as follows:

[0026] Due to the superiority of fuzzy control in processing the output regulation coefficient X as a nonlinear constraint, the present invention proposes an inertia adaptive control technology taking into account the energy storage constraint fuzzy control, realizes the adaptive adjustment of the virtual inertia, and allows the virtual inertia to change dynamically; based on the adaptive control technology of virtual inertia, a comparative analysis is carried out on three indicators, namely, virtual inertia, supercapacitor charge state and system output frequency, under two working states of supercapacitor extreme charging and extreme discharging. The results show that the photovoltaic energy storage VSG system has good stability and dynamic performance under load disturbance; compared with the traditional VSG control technology, the frequency fluctuation and output power overshoot are suppressed. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the topological structure of the photovoltaic energy storage VSG system of the present invention;

[0028] Figure 2 This is the active power-frequency control block diagram of the present invention;

[0029] Figure 3 This is a diagram of the energy storage SOC regulation structure based on fuzzy control of the present invention;

[0030] Figure 4 It is a schematic diagram of the membership function of the present invention;

[0031] Figure 5 Schematic diagram of SOC membership function of the present invention;

[0032] Figure 6 This is a schematic diagram of the membership function of the adjustment coefficient X of the present invention;

[0033] Figure 7 It is a schematic diagram of the result of fuzzy reasoning of the present invention;

[0034] Figure 8 It is a schematic diagram of the change of the virtual inertia H of the supercapacitor in the charging state of the present invention;

[0035] Fig. 9 It is a schematic diagram of the energy storage SOC output of the supercapacitor charging state of the present invention;

[0036] Fig.10 A schematic diagram of the system output frequency f of the supercapacitor in the charging state of the present invention;

[0037] Fig.11 It is a schematic diagram of the change of the virtual inertia H of the supercapacitor in the discharge state of the present invention;

[0038] Fig.12 It is a schematic diagram of the energy storage SOC output of the supercapacitor in the discharge state of the present invention;

[0039] Fig.13 Schematic diagram of the system output frequency f of the supercapacitor in the discharge state of the present invention. DETAILED DESCRIPTION

[0040] Embodiment 1:

[0041] like Figure 1 As shown, a fuzzy VSG inertia adaptive control method considering the energy storage charge state includes the following steps:

[0042] S1, set up a photovoltaic energy storage VSG system, which includes an energy storage unit, a photovoltaic array, an inverter and an LCL filter; the energy storage unit is a supercapacitor; the photovoltaic array unit is connected to the DC bus through a maximum power tracker, which can provide a stable power output for the system; the VSG algorithm of the photovoltaic energy storage VSG system generates a command voltage by detecting power; the energy storage unit realizes the bidirectional energy flow of the system, provides efficient energy management for the system, and provides inertia and power support for the microgrid system;

[0043] Since lithium batteries have certain disadvantages, they cannot suppress the dynamic oscillation of system frequency and have high production costs, so supercapacitors are used as energy storage units. Their power density is much higher than that of lithium batteries, and they also have the advantages of relatively longer cycle life, faster effect, and the ability to stabilize system frequency.

[0044] S2, establish a grid-connected inverter model based on virtual synchronous machine technology; when the supercapacitor is in the two working states of extreme charging and extreme discharging, the energy storage unit actively provides active power and participates in the grid frequency regulation; based on adaptive inertia control technology, realize the conversion of supercapacitors in different charging and discharging states;

[0045] S3, realizing adaptive control of virtual inertia H according to the real-time working state SOC of the supercapacitor;

[0046] S4, the conversion of supercapacitors in different states is realized through virtual inertia adaptive control based on fuzzy control.

[0047] like Figure 2 As shown, preferably, in the grid-connected inverter model with virtual synchronous machine technology as the main body, the active frequency control of the grid-connected inverter based on the virtual synchronous generator technology is:

[0048]

[0049] Where H is the virtual inertia; ω and ω 0 are the angular frequencies of the VSG unit output and the power grid respectively; D is the damping coefficient; Pref is the reference active power value; and P is the active power actually output by the VSG.

[0050] Furthermore, the supercapacitor SOC is used to represent the working state of the energy storage system, which is divided into:

[0051]

[0052] Where a is the discharge limit value of the supercapacitor and b is the charging limit value.

[0053] Preferably, the active frequency control formula is simplified:

[0054]

[0055] Wherein, is the change of active power. Since the frequency change remains constant in a short time, the change of virtual inertia H will also affect the change. When the supercapacitor is close to the critical state of charge and discharge, in order to reduce the damage caused by excessive charge and discharge of the supercapacitor, the virtual inertia H needs to be adjusted to the smallest possible value. In this case, the change of active power will also decrease accordingly. Therefore, the adaptive control of virtual inertia H can be realized according to the real-time working state SOC of the supercapacitor.

[0056] Preferably, in step S3, since the value range of the inverse tangent function is limited to a certain range, it can play a role in limiting the amplitude in engineering practice. The present invention adopts the inverse tangent function fitting and combines the actual working conditions to obtain the following formula to represent the virtual inertia H under the action of SOC;

[0057] The adaptive control method for realizing the virtual inertia H according to the real-time working state SOC of the supercapacitor is:

[0058] The inverse tangent function is used to fit and combined with the actual working conditions to obtain the following formula to express the virtual inertia H under the action of SOC:

[0059]

[0060] Where a is the discharge limit of the supercapacitor, b is the charge limit; parameters k1 and k2 are virtual inertia control parameters, H 0 is the nominal inertia of the system when it is running stably, and X is the adjustment coefficient. Due to the application of the inverse tangent function, this technology reduces the virtual inertia H, thereby reducing the change in active power, thereby reducing the output of the supercapacitor. The more serious the over-charging and discharging of the supercapacitor, the more H is reduced, and the better the control effect. Compared with the control technology that maintains a constant inertia VSG, the adaptive control of the virtual inertia H is realized, which can optimize the operating state of the supercapacitor.

[0061] Embodiment 2:

[0062] This embodiment provides a fuzzy VSG inertia adaptive control technology that takes into account the energy storage charge state. Since the adjustment coefficient X of the energy storage output is a variable value with nonlinear characteristics, fuzzy control has significant advantages in dealing with such problems. In addition, because the fuzzy controller is not as complicated as other control technologies, it is faster, eliminates additional sensing elements, does not require additional deep discharge and overcharge protection, and does not require mathematical calculations and is easy to implement, this embodiment selects fuzzy control to control the charging and discharging of the supercapacitor.

[0063] like Figure 3As shown, the input and output fuzzy sets of fuzzy control are represented by 7 letters: {NL, NM, NS, ZO, PS, PM, PL}, namely {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. The fuzzy set refers to the collection of membership shapes of "triangle in the middle and trapezoid at both ends". The defuzzification method of this embodiment adopts the centroid method, and the formulated fuzzy rules are shown in Table 1.

[0064]

[0065] Table 1: Fuzzy rules;

[0066] The adjustment coefficient X of the fuzzy rule output is set, and the output domain is set to [0,1]. In order to maintain the stability of the DC bus voltage, the nonlinear approximation characteristics of fuzzy control are introduced, which can significantly reduce the frequency overshoot and improve the system's anti-disturbance ability.

[0067] The design of fuzzy controller mainly includes three key parts, namely fuzzification, fuzzy reasoning and defuzzification. These three parts will be elaborated in detail below:

[0068] Fuzzification: In the fuzzy controller of energy storage regulation, there are two input variables Δω and SOC; the triangular membership function is mainly used for fuzzy processing, and the input variables are divided into five levels: NL (negative large), NS (negative small), ZO (zero), PS (positive small), PL (positive large); the membership functions of these input variables are as follows Figure 4 shown.

[0069] Another input is the energy storage SOC, which ranges from [0, 1]. The triangular, trapezoidal and Z-type membership functions are mainly used. According to different working conditions, it is divided into 7 levels: NL (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PL (positive large). Its membership function is as follows: Figure 5 shown.

[0070] The value range of the energy storage SOC adjustment coefficient X is also between [0, 1]. The triangular and Z-type membership functions are mainly used to divide it into 7 levels: NL (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), PL (positive large). The membership function is as follows: Figure 6 shown.

[0071] like Figure 7As shown, fuzzy reasoning: when SOC is in a higher range, it means that the energy stored in the energy storage unit is relatively sufficient, and it is suitable to discharge the VSG system; if the absolute value of Δω is large and less than 0, it indicates that there is a large power shortage in the VSG system. In order to prevent the VSG system power from dropping sharply and causing system frequency fluctuations, it is necessary to rely on the output power of the energy storage unit to supplement the power of the VSG system; in this case, a larger adjustment coefficient X is used to reduce the value of the virtual inertia H so that the SOC drops to a normal range.

[0072] When the SOC is in a lower range, it means that the energy stored in the energy storage unit is relatively insufficient, and it is appropriate to charge the VSG system. If the absolute value of Δω is large and greater than 0, it indicates that the VSG system power is quite excessive, and the energy storage unit needs to absorb the excess power to avoid a sharp increase in VSG power. In this case, a larger adjustment coefficient X can also be used to increase the value of the virtual inertia H so that the SOC increases to the normal range.

[0073] If the SOC is in a lower range, if the absolute value of Δω is large and less than 0, a smaller adjustment coefficient X should be used to increase the value of the virtual inertia H so that the SOC increases to the normal range.

[0074] If the SOC is in a higher range, if the absolute value of Δω is large and greater than 0, a smaller adjustment coefficient X should be used to reduce the value of the virtual inertia H so that the SOC drops to the normal range;

[0075] Based on this regulation law, combined with the input and output membership functions, the fuzzy rules are shown in Table 1.

[0076] Another outstanding advantage of this embodiment is that when the energy storage charge state is in different states, the virtual inertia can achieve smooth switching of different working conditions, thereby realizing the characteristics of adaptive control.

[0077] In summary, this embodiment has significant advantages in dealing with nonlinear problems by giving full play to the fuzzy control. By inputting the variable angular frequency change Δω and the energy storage unit state of charge SOC, the nonlinear adjustment coefficient X is output to control the virtual inertia H, thereby improving the inertia flexibility of the system and further improving the frequency stability of the system.

[0078] like Figure 8 to Figure 13 As shown, in order to verify the effectiveness of the technology proposed in this embodiment, this embodiment establishes a photovoltaic energy storage VSG system based on the Simulink simulation platform. The virtual inertia control technology is started at 3s. In order to simulate the charging limit condition of the supercapacitor, the initial SOC state is 0.2462, and the 20kW load is suddenly cut off at 5s.

[0079] When the supercapacitor is working in the charging state, the corresponding fuzzy control SOC-VSG technology will gradually reduce the virtual inertia as the SOC increases, and the change of virtual inertia is significantly more significant than that of traditional VSG control. In addition, when the supercapacitor transitions from the extreme charging state to the normal state, the fuzzy control SOC-VSG technology is used. The supercapacitor enters the safe operation state (SOC=0.25) earlier than the traditional VSG control technology. Compared with the traditional VSG control, the loss of the supercapacitor is reduced, so the service life of the supercapacitor can be extended, and the SOC rises relatively smoothly; in addition, the frequency oscillation of the VSG output using the fuzzy control SOC-VSG technology is significantly smaller than that of the frequency output of the traditional VSG control technology, and the curve is smoother, which improves the frequency stability of the system. Therefore, the fuzzy control SOC-VSG technology introduced in this embodiment helps to improve the operating state of the supercapacitor, thereby improving the stability of the entire system.

[0080] The virtual inertia control technology starts at 3s. The initial value of SOC is set to 0.755, and a load of 20kW is suddenly put into operation at 5s. When the supercapacitor is working in the discharge state, the virtual inertia corresponding to the fuzzy control SOC-VSG technology decreases as the SOC decreases, and the change of virtual inertia is obviously more obvious than that of traditional VSG control. In addition, when the supercapacitor transitions from the extreme discharge state to the normal state, the fuzzy control SOC-VSG technology is used. The supercapacitor enters the safe operation state (SOC=0.75) earlier than the traditional VSG control technology, effectively reducing the loss of the supercapacitor, thereby extending the service life of the supercapacitor, and the SOC decreases relatively slowly; in addition, when the load is disturbed, compared with the traditional VSG control technology, the frequency change of the VSG output under the fuzzy control SOC-VSG technology is smaller, the curve is smoother, and the frequency stability and anti-interference of the system are effectively improved. Therefore, the fuzzy control SOC-VSG technology introduced in this embodiment improves the operating condition of the supercapacitor, thereby improving the stability of the system.

Claims

1. A fuzzy VSG inertia adaptive control method considering the energy storage charge state, characterized in that: The following steps are involved: S1, setting up a photovoltaic energy storage VSG system, which includes an energy storage unit, a photovoltaic array, an inverter and an LCL filter; the energy storage unit is a supercapacitor; the photovoltaic array unit is connected to the DC bus through a maximum power tracker; S2, establish a grid-connected inverter model based on virtual synchronous machine technology; when the supercapacitor is in the two working states of extreme charging and extreme discharging, the energy storage unit actively provides active power and participates in grid frequency regulation; based on adaptive inertia control technology, realize the conversion of supercapacitors in different charging and discharging states; S3, realizing adaptive control of virtual inertia H according to the real-time working state SOC of the supercapacitor; S4, the conversion of supercapacitors in different states is realized through virtual inertia adaptive control based on fuzzy control.

2. A fuzzy VSG inertia adaptive control method considering the energy storage charge state according to claim 1, characterized in that: In step S2, in the grid-connected inverter model with the virtual synchronous machine technology as the main body, the active frequency control of the grid-connected inverter based on the virtual synchronous generator technology is: Where H is the virtual inertia; ω and ω0 are the angular frequencies of the VSG unit output and the power grid respectively; D is the damping coefficient; Pref is the reference active power value; and P is the active power actually output by the VSG.

3. A fuzzy VSG inertia adaptive control method considering the energy storage charge state according to claim 2, characterized in that: Simplify the active frequency control formula: Where ΔP is the change in active power. When the supercapacitor runs close to the critical state of charge and discharge, the virtual inertia H decreases and the change in active power decreases. Thus, the adaptive control of the virtual inertia H is realized according to the real-time working state SOC of the supercapacitor.

4. A fuzzy VSG inertia adaptive control method considering the energy storage charge state according to claim 3, characterized in that: In step S3, the adaptive control method for realizing the virtual inertia H according to the real-time working state SOC of the supercapacitor is: The inverse tangent function is used to fit and combined with the actual working conditions to obtain the following formula to express the virtual inertia H under the action of SOC: Where a is the discharge limit of the supercapacitor, b is the charging limit, parameters k1 and k2 are virtual inertia control parameters, H0 is the nominal inertia of the system when it is running stably, and X is the adjustment coefficient.

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