Power distribution control method and device for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering

Abstract

The invention discloses a power distribution control method and device for a photovoltaic hybrid energy storage direct current microgrid based on fuzzy second-order high-pass filtering, and the method comprises the steps: researching the topological structure and working principle of the photovoltaic hybrid energy storage direct current microgrid and the energy management of the microgrid, and obtaining a power balance relation of the direct current microgrid; a comprehensive operation control strategy of the micro-grid is established, and it is determined that a photovoltaic power generation unit adopts maximum power point tracking control of fuzzy control, a grid-connected inverter adopts double closed-loop control of a voltage outer loop and a current inner loop, and a hybrid energy storage unit adopts fuzzy second-order high-pass filtering algorithm control; frequency division distribution and stability of a traditional second-order high-pass filter are studied, so that the second-order high-pass filter based on fuzzy logic is further studied, the SOC of the super capacitor is determined as a control index, and a fuzzy rule of a filtering time constant is adaptively adjusted; the established fuzzy logic variable time constant second-order high-pass filtering control simulates and compares the hybrid energy storage system power, the DC bus voltage and the super capacitor SOC under the variable illumination and load conditions with the fixed constant second-order high-pass filtering control, and compares the change curves of the super capacitor SOC of two strategies under the condition of changing the initial SOC value of the super capacitor. Three-phase current and current total harmonic distortion of the two strategies are compared, and it is verified that the efficiency of a photovoltaic power generation system is improved under the strategies, the SOC of a super capacitor changes within a reasonable range, a micro-grid can stably operate in a grid-connected mode, and grid-connected current harmonic distortion is effectively reduced.

Description

Technical Field

[0001] The present invention proposes a power distribution control method and device for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering. Background Art

[0002] Driven by the dual carbon goals, new energy has gradually become a common power source for my country's power system, but new energy power generation is affected by the supply of primary energy and is difficult to meet the peak and frequency regulation needs of the power grid. At present, the phenomenon of wind and solar abandonment in my country's new energy power generation is serious. In order to improve the grid-connected utilization efficiency of wind power generation and photovoltaic power generation, the use of energy storage technology to smooth out the fluctuations in new energy output is the main solution. At present, energy storage devices are mainly divided into two categories: energy-type energy storage devices and power-type energy storage devices. Commonly used energy storage devices include lithium-ion batteries, lead-acid batteries, supercapacitors, etc. The energy density of batteries is high, but their power density is low; the power density of supercapacitors is high, but their energy density is low. Combining the advantages of these two types of energy storage, the power response speed of the energy storage system can be improved. The hybrid energy storage system can store excess electricity in the DC microgrid and provide power support for the power load when the output of renewable energy is insufficient. It has a rapid response and forms a complementary state with new energy. It is widely used to smooth the power fluctuation problem of microgrids.

[0003] The power distribution control strategy of the hybrid energy storage system is the key to achieving the complementary advantages of the two types of energy storage media. The existing hybrid energy storage power distribution methods include filter-based distribution and intelligent algorithm-based distribution. The filter-based distribution method simply decomposes the redundant power of the microgrid into fixed high and low frequency components, which are then responded to by the supercapacitors and batteries in the hybrid energy storage system respectively. It fails to take into account the capacity characteristics of each energy storage device, so the power distribution accuracy is not high. Intelligent algorithm distribution is a method that uses intelligent control algorithms to improve the power distribution accuracy of hybrid energy storage systems. It enables batteries and supercapacitors to better respond to power based on their own component characteristics, and to a certain extent improves the high and low frequency distribution accuracy of the redundant power of the microgrid. However, it does not take into account the overcharging and over-discharging of supercapacitors under long-term operation of the system, which increases the capacity of supercapacitors and reduces the economy of the microgrid. Summary of the invention

[0004] Based on the above background, in order to solve the problem of poor power distribution effect of hybrid energy storage system caused by fixed time constant in traditional filtering allocation algorithm, and the problem of overcharging and over-discharging of supercapacitors caused by long-term operation of the system, the present invention proposes a power distribution control method and device for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering.

[0005] The present invention proposes a second-order high-pass filtering algorithm based on fuzzy logic and applies it to the operation control of the hybrid energy storage system to solve the above problems. By establishing a fuzzy logic relationship between the supercapacitor SOC and the filter constant, a fuzzy controller is designed to adaptively adjust the filter time constant of the high-pass filter, so that the SOC value of the supercapacitor is maintained within a reasonable range, thereby achieving safe and stable operation of the DC microgrid.

[0006] In order to achieve the above object, the technical solution of the present invention is:

[0007] A power distribution control method for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering comprises the following steps:

[0008] S1: Study the topological structure and working principle of photovoltaic hybrid energy storage DC microgrid and the energy management of microgrid, and obtain the power balance relationship of DC microgrid;

[0009] S2: Based on step S1, a comprehensive operation control strategy of the microgrid is established to determine that the photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control of fuzzy control, the grid-connected converter (GCC) adopts the double closed-loop control of voltage outer loop and current inner loop, and the hybrid energy storage unit adopts the fuzzy second-order high-pass filter algorithm control;

[0010] S3: Based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine the fuzzy rule of adaptively adjusting the filter time constant by taking the SOC of the supercapacitor as the control index;

[0011] S4: The fuzzy logic variable time constant second-order high-pass filter control established in step S3 is used to simulate and compare the hybrid energy storage system power, DC bus voltage, and supercapacitor SOC under variable light and load conditions with the fixed constant second-order high-pass filter control. The supercapacitor SOC change curves of the two strategies are compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and total harmonic distortion of the two strategies are also compared to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor changes within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced.

[0012] Furthermore, in step S1, studying the topological structure and working principle of the photovoltaic hybrid energy storage DC microgrid and the energy management of the microgrid, and obtaining the power balance relationship of the DC microgrid includes the following steps:

[0013] S1-1: The working principle of the photovoltaic hybrid energy storage DC microgrid is as follows: the photovoltaic array is connected to the DC bus through a unidirectional DC / DC converter and is the main energy supply unit of the DC microgrid; the hybrid energy storage consists of batteries and supercapacitors, both of which are connected to the DC bus through a bidirectional DC / DC converter, mainly used to absorb or release redundant power to ensure the stable operation of the microgrid; the resistive load is directly connected to the DC bus to consume electrical energy. In addition, the DC microgrid transmits stable electrical energy to the distribution network through a DC / AC inverter.

[0014] The photovoltaic array consists of multiple groups of photovoltaic cells connected in series and parallel. The output current of the photovoltaic cells is:

[0015] I pv =I ph -I s -I sh (1)

[0016] In the formula, I pv is the output current of the photovoltaic cell; I ph is the photocurrent; I s is the current flowing through the parallel diode; I sh is the current flowing through the parallel bypass resistor.

[0017] The current flowing through the parallel diode I s and the current I flowing through the parallel bypass resistor sh It can be expressed as:

[0018]

[0019] In the formula, I o is the reverse saturation current of the diode; q is the electron charge constant (1.6×10 -19 C); A is the diode factor, which ranges from 1 to 5; k is the Boltzmann constant (1.38×10 -23 j / kWh); T abs is the absolute temperature.

[0020] Substituting equation (2) and equation (3) into equation (1), we can obtain:

[0021]

[0022] The output voltage of the photovoltaic cell is:

[0023]

[0024] The general equivalent model of a battery is mainly composed of a controlled voltage source and a resistor in series, and its mathematical expression is:

[0025]

[0026] In the formula, E is the output voltage of the battery; E0 is the internal potential of the battery; K is the polarization voltage of the battery; Q is the rated capacity of the battery; A is the voltage drop in the exponential zone; B is the reciprocal of the capacity in the exponential zone.

[0027] Supercapacitors are double-layer capacitors, and common electrical characteristic equivalent models include first-order RC model, three-branch model, and second-order nonlinear RC model. At present, the classic RC series equivalent model is mainly used in actual engineering and simulation experiments, and its mathematical expression is:

[0028]

[0029] Where V SC is the output voltage of the supercapacitor; R sceq is the equivalent resistance; C SC is the equivalent capacitance; I SC is the output current of the supercapacitor.

[0030] S1-2: The photovoltaic power generation unit transmits electric energy to the DC microgrid through the DC / DC converter. However, the photovoltaic output power is affected by the light intensity and temperature changes, resulting in a certain randomness and volatility of the photovoltaic output power. Therefore, it is necessary to configure a certain capacity of energy storage equipment in the DC microgrid to provide energy support, discharge when the DC microgrid redundant power is insufficient, and charge when the DC microgrid redundant power is excessive. At the same time, in order to ensure that the energy storage equipment can quickly respond to transient changes and stabilize the steady-state process, a hybrid energy storage system consisting of batteries and supercapacitors is used as the energy storage unit of the DC microgrid. The DC microgrid is connected to the distribution network through a DC / AC inverter. The distribution network provides support for the voltage and frequency on the AC side. When the output power of the photovoltaic power generation unit is greater than the power of the resistive load, and the hybrid energy storage unit reaches the capacity limit and cannot absorb the redundant power, the excess energy is connected to the distribution network through the grid-connected converter.

[0031] The power balance relationship of the DC microgrid can be obtained as follows:

[0032] P Load =P pv +P HESS (8)

[0033] P HESS =P Bat +P SC (9)

[0034] Where P Loa d is the load power; P pv is the output power of the photovoltaic power generation unit; P HESS is the power of the hybrid energy storage unit response, which is negative when absorbing electric energy and positive when releasing electric energy; PBat is the output power of the battery; P SC is the output power of the supercapacitor.

[0035] Furthermore, in step S2, based on step S1, a comprehensive operation control strategy of the microgrid is established, and it is determined that the photovoltaic power generation unit adopts the maximum power point tracking (Max Power Point Track, MPPT) control of fuzzy control, the grid-connected converter (Grid-connected converter, GCC) adopts the double closed-loop control of the voltage outer loop and the current inner loop, and the hybrid energy storage unit adopts the fuzzy second-order high-pass filter algorithm control, including the following steps:

[0036] S2-1: The photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control, which mainly measures the output voltage and output current of the photovoltaic array, and controls the connected DC / DC converter to enable the photovoltaic array to reach and maintain the maximum power output under the current environment. There are many existing photovoltaic MPPT control methods, mainly including: perturbation observation method, conductance increment method, fuzzy control method, etc. Fuzzy control, as an intelligent control method, has strong anti-interference and robustness, and is suitable for photovoltaic power generation, which contains uncertain quantities and is difficult to describe with an accurate mathematical model. Therefore, the present invention adopts the fuzzy control method to dynamically adjust the step size to achieve rapid tracking of the photovoltaic maximum power point. The output voltage of the photovoltaic power generation unit is determined by the duty cycle of the unidirectional DC / DC converter connected to it. The corresponding fuzzy MPPT algorithm is designed according to the voltage and power relationship of the photovoltaic array.

[0037] The voltage change dV and power change dP are input into the fuzzy controller, and the duty cycle adjustment dD is obtained according to the fuzzy rule and sent to the PWM signal generator, which finally outputs the corresponding pulse to control the unidirectional DC / DC converter.

[0038] The designed fuzzy controller has dual input and single output. The input variables are voltage change dV and power change dP, and the output variable is duty cycle adjustment dD.

[0039] dV=V(t)-V(t-1) (10)

[0040] dP=P(t)-P(t-1) (11) The fuzzy linguistic variables that define input variables and output variables are:

[0041] dV={NB,NS,ZE,PS,PB}

[0042] dP={NB,NS,ZE,PS,PB}

[0043] dD={NB,NM,NS,ZE,PS,PM,PB}

[0044] Among them, NB, NM, NS, ZE, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively. At the same time, the fuzzy membership functions of the input variables and output variables use uniformly distributed triangular membership functions, and the corresponding domain ranges are: dV∈[-1,+1], dP∈[-1,+1], and dD∈[-0.5,+0.5].

[0045] According to the relationship between the voltage and power of the photovoltaic array, the corresponding fuzzy prior knowledge can be obtained: when dV<0, dP<0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0; when dV<0, dP>0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP<0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP>0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0. In addition, the value of the duty cycle adjustment dD follows the following rules: the step size is smaller when it is close to the MPP point, and the step size is larger when it is far away from the MPP point.

[0046] By defining the number of fuzzy language sets of input and output variables, a total of 25 rules are formed.

[0047] S2-2: The function of the grid-connected converter (GCC) is to convert DC power into AC power so that it can be fed into the AC distribution network. GCC adopts the form of d-axis grid voltage oriented vector control, with the following control objectives: 1) to ensure the stability of the DC bus voltage by controlling the active component of the output current; 2) to achieve decoupling control of the output active and reactive power, so that the active power of the microgrid can be smoothly transmitted to the distribution network, and can provide certain reactive power support when necessary.

[0048] The control of GCC adopts a dual closed-loop control method with voltage outer loop and current inner loop. By controlling the DC side voltage, the DC bus voltage can be kept constant, and the GCC can stably transmit power to the distribution network. Specifically, the DC side voltage reference value is subtracted from the actual value and then adjusted by PI to obtain the d-axis current reference value i dref At the same time, according to the reactive power demand, the q-axis current reference value i qref Set to 0 to achieve unity power factor grid connection. Then get the reference value i dref 、i qref The actual current i d 、i qAfter the difference is made, it is adjusted by PI and feedforward compensation is introduced to obtain the reference value U of the d and q axis voltages on the AC side of GCC. d , U q Finally, the modulation signal is obtained through dP / abc conversion to drive SPWM to generate trigger pulses, thereby controlling the on and off of GCC.

[0049] S2-3: Set the DC bus voltage reference value U dcref The actual value of the DC bus voltage U dc After the difference is made, the current reference value current i is obtained through PI adjustment. dcref . Then i dcref The high-frequency component i is obtained by high-pass filtering HF ,i dcref with i HF The difference is the low-frequency component i LF ,i HF 、i LF That is, the reference current value of the supercapacitor and the battery. The difference between the two and the corresponding actual current is adjusted by PI to obtain their respective duty cycles. Finally, the PWM generator is input to generate modulation pulses, which in turn control the bidirectional DC / DC converter of the battery and supercapacitor unit.

[0050] Although the hybrid energy storage system control method based on the high-pass filter can realize the frequency division distribution of unbalanced power, it fails to take into account the SOC of the energy storage element, which can easily cause overcharging and over-discharging of the energy storage element, thereby endangering the safe operation of the DC microgrid. Since the battery belongs to energy storage, it can be charged and discharged for a long time, and its SOC does not change significantly in a short time. The capacity of the supercapacitor is small, and its SOC changes more significantly in a short time. Therefore, the control of the SOC of the supercapacitor is particularly important. The present invention adopts a fuzzy second-order filtering algorithm to control the hybrid energy storage system. While achieving the frequency division distribution effect of the hybrid energy storage system, it can ensure that the SOC of the supercapacitor is within a reasonable range of variation.

[0051] This method adds a fuzzy controller on the basis of the second-order high-pass filtering algorithm, and adaptively adjusts the filtering time constant according to the SOC change of the supercapacitor, so as to achieve the purpose of automatically assuming the unbalanced power proportion according to the remaining capacity of the supercapacitor, that is, it bears more power when the SOC is higher and less power when the SOC is lower.

[0052] In step S3, based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine that the SOC of the supercapacitor is used as a control indicator, and the fuzzy rule for adaptively adjusting the filter time constant includes the following steps:

[0053] S3-1: The traditional first-order filtering algorithm is implemented through an inertia link and a differential link, which allocates the power of the high-frequency component of the fluctuating power to the supercapacitor for rapid compensation; the power of the remaining frequencies is allocated to the battery for compensation. The transfer function of the first-order high-pass filter is:

[0054]

[0055] Where s is the differential operator and T is the filtering time constant.

[0056] As the order of the filtering algorithm increases, the filtering effect becomes more obvious. The second-order filtering algorithm is an improvement on the first-order filtering algorithm. It is mainly obtained by connecting two first-order filtering links in series and has two differential and inertial links.

[0057] The second-order high-pass filtering algorithm is as follows:

[0058]

[0059] According to the characteristics of high power density of supercapacitors and high energy density of batteries, the high-frequency part of the response power of the hybrid energy storage system is allocated to the supercapacitor for compensation, and the low-frequency part is allocated to the battery for compensation. The following mathematical relationship can be obtained:

[0060]

[0061] In the formula, The power compensated by the supercapacitor; P is the power compensated by the battery; HESS is the power response of the hybrid energy storage system.

[0062] Assume that the hybrid energy storage system responds to power P HESS (t) and its derivatives can be Laplace transformed, and P HESS (t) has a final value, based on the second-order high-pass filter After integration, we can get:

[0063]

[0064] It can be seen from formula (16) that the integral effect of the supercapacitor in response to high-frequency power tends to 0, that is, the supercapacitor will not accumulate its basic state of charge as the charging time increases, thereby increasing the response speed and life of the supercapacitor, eliminating the integral effect generated in the traditional first-order filter, reducing the capacity accumulation of the supercapacitor, and ensuring that the supercapacitor always works in the best state.

[0065] S3-2: The second-order high-pass filter algorithm with a fixed time constant has limitations in dynamic performance, which can easily cause the energy storage element to be overcharged or over-discharged, affecting the life of the energy storage element. Based on the above analysis, a hybrid energy storage power allocation method with a second-order high-pass filter time constant based on fuzzy logic is proposed. The SOC of the supercapacitor is used as a control indicator to adaptively adjust the filter time constant.

[0066] The input variable of the designed fuzzy controller is the SOC of the supercapacitor. SC , whose value range is [0,100], and the output variable of the fuzzy controller is the filter time constant T C , whose value range is [0.2,0.4]. The fuzzy linguistic variables that define the input variables and output variables are:

[0067] SOC SC ={S1,S2,S3,S4,S5,S6,S7,S8,S9}

[0068] T C ={T1,T2,T3,T4,T5,T6,T7,T8,T9}

[0069] Where, S1 to S9 represent the supercapacitor SOC from small to large; T1 to T9 represent the filter time constant T C From small to big.

[0070] There are no fixed rules and patterns for the selection of fuzzy subset membership functions. Considering factors such as convenient calculation and familiarity with performance, triangular, trapezoidal, and Gaussian membership functions are usually selected. Among them, the triangular membership function is simple and intuitive, easy to understand, and has high computational efficiency. Therefore, the membership functions of the input and output variables of the fuzzy controller use uniformly distributed triangular membership functions.

[0071] From the above analysis of the supercapacitor output power, we can know that: T C The larger the value, the lower the power frequency that the supercapacitor can smooth out, and the more power it can smooth out; T C The smaller the value, the higher the power frequency that can be smoothed, and the less power is smoothed. When the SOC is high, the power is more, and when the SOC is low, the power is less. From this, the fuzzy rules of fuzzy control can be obtained.

[0072] In step S4, the fuzzy logic variable time constant second-order high-pass filter control established in step S3 is used to simulate and compare the power, DC bus voltage, and supercapacitor SOC of the hybrid energy storage system under variable illumination and load conditions with the fixed constant second-order high-pass filter control, and the change curves of the supercapacitor SOC of the two strategies are compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and the total harmonic distortion of the current of the two strategies are also compared to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor changes within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced. The following steps are included:

[0073] S4-1: In order to verify the effectiveness of the variable time constant second-order high-pass filter control based on fuzzy logic proposed in the present invention, a DC microgrid simulation model was built on MATLAB / Simulink.

[0074] S4-2: The operating conditions of the DC microgrid are set as follows: the simulation duration is 4s, the initial light intensity is 800W / m2, the light intensity increases to 1000W / m2 at t=2s, and decreases to 600W / m2 at t=3s. The initial load is 5000W, and the load decreases to 2000W at t=1s, and then remains unchanged at 2000W. The photovoltaic output power changes with the light intensity. The stronger the light, the greater the photovoltaic output power.

[0075] The simulation comparison between the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter is carried out, mainly from the aspects of hybrid energy storage system power, DC bus voltage, supercapacitor SOC, etc.

[0076] In order to reflect the impact of fuzzy second-order control on the supercapacitor SOC, the operating conditions remain unchanged and only the initial SOC value of the supercapacitor is changed. The initial SOC values ​​are set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed.

[0077] In order to further compare the effect of fuzzy second-order filtering from the AC aspect, the three-phase current and current total harmonic distortion under two control methods are introduced.

[0078] The second aspect of the present invention relates to a power distribution control device for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the power distribution control method for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering of the present invention.

[0079] The third aspect of the present invention relates to a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the power distribution control method of the photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering of the present invention.

[0080] The beneficial effects of the present invention are:

[0081] 1) In order to ensure the stable and efficient operation of the photovoltaic hybrid energy storage DC microgrid, the power distribution strategy of the microgrid is designed according to the energy balance principle, and the corresponding control strategy is designed according to the characteristics of each unit to respond to the power command. In order to improve the energy utilization rate of the photovoltaic power generation unit, fuzzy control is used to achieve rapid tracking of the maximum power point. In order to ensure the effect of frequency distribution of the hybrid energy storage system, a variable time constant second-order high-pass filter control strategy based on fuzzy logic is proposed;

[0082] 2) Under the above control strategy, the efficiency of the photovoltaic power generation system is improved, and the SOC of the supercapacitor is changed within a reasonable range by adaptively adjusting the filter constant, effectively extending its service life. The photovoltaic hybrid energy storage microgrid can operate stably in the grid-connected mode, and the harmonic distortion of the grid-connected current is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is a microgrid architecture diagram of the method of the present invention.

[0084] Figure 2 It is a control block diagram of a photovoltaic unit according to the method of the present invention.

[0085] Figure 3 is the voltage-power curve of the method of the present invention.

[0086] Figure 4 It is the fuzzy surface diagram of the method of the present invention.

[0087] Figure 5 It is a control block diagram of a grid-connected converter according to the method of the present invention.

[0088] Figure 6 It is a conventional control block diagram of a hybrid energy storage unit of the method of the present invention.

[0089] Figure 7 It is a fuzzy control block diagram of a hybrid energy storage unit of the method of the present invention.

[0090] Figure 8 It is the Bode diagram of the supercapacitor output power transfer function of the method of the present invention.

[0091] Fig. 9 It is the Nyquist curve of the second-order high-pass filter of the method of the present invention.

[0092] Fig.101 is a step response diagram of the battery and supercapacitor power of the method of the present invention.

[0093] Figure 11(a) to Figure 11(b) It is the fuzzy controller membership function of the method of the present invention, wherein FIG11( a ) is the supercapacitor SOC membership function, and FIG11( b ) is the filter time constant membership function.

[0094] Figure 12(a) to Figure 12(b) 12( a ) is a curve showing the relationship between the supercapacitor SOC and the filtering time constant of the method of the present invention, wherein FIG. 12( a ) is a discharge state, and FIG. 12( b ) is a charge state.

[0095] Fig.13 It is a photovoltaic and load power diagram of the method of the present invention.

[0096] Figure 14(a) to Figure 14(d) 14(a) is a comparison diagram of system operating characteristics under two strategies of the method of the present invention, wherein FIG. 14(b) is a comparison diagram of supercapacitor power, FIG. 14(c) is a comparison diagram of battery power, and FIG. 14(d) is a comparison diagram of supercapacitor SOC operating curves.

[0097] Figure 15(a) to Figure 15(b) 15( a ) is a comparison diagram of the supercapacitor SOC operating curves of the method of the present invention, wherein FIG. 15( a ) is a comparison diagram when the SOC is 20, and FIG. 15( b ) is a comparison diagram when the SOC is 90.

[0098] Figure 16(a) to Figure 16(d) It is a comparison diagram of the three-phase current of the two algorithms of the method of the present invention, wherein Figure 16 (a) is the constant constant second-order filtering three-phase current, Figure 16 (b) is the constant constant second-order filtering current total harmonic distortion, Figure 16 (c) is the fuzzy second-order filtering three-phase current, and Figure 16 (d) is the fuzzy second-order filtering current total harmonic distortion.

[0099] Fig.17 is a flow chart of the method of the present invention. Specific implementation methods

[0101] The present invention will be further described below in conjunction with the accompanying drawings.

[0102] Example 1

[0103] Reference Figure 1 to Figure 17 , a power distribution control of photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, including the following steps:

[0104] S1: Study the topological structure and working principle of photovoltaic hybrid energy storage DC microgrid and the energy management of microgrid, and obtain the power balance relationship of DC microgrid;

[0105] S2: Based on step S1, a comprehensive operation control strategy of the microgrid is established to determine that the photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control of fuzzy control, the grid-connected converter (GCC) adopts the double closed-loop control of voltage outer loop and current inner loop, and the hybrid energy storage unit adopts the fuzzy second-order high-pass filter algorithm control;

[0106] S3: Based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine the fuzzy rule of adaptively adjusting the filter time constant by taking the SOC of the supercapacitor as the control index;

[0107] S4: The fuzzy logic variable time constant second-order high-pass filter control established in step S3 is used to simulate and compare the hybrid energy storage system power, DC bus voltage, and supercapacitor SOC under variable light and load conditions with the fixed constant second-order high-pass filter control. The supercapacitor SOC change curves of the two strategies are compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and total harmonic distortion of the two strategies are also compared to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor changes within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced.

[0108] Furthermore, in step S1, studying the topological structure and working principle of the photovoltaic hybrid energy storage DC microgrid and the energy management of the microgrid, and obtaining the power balance relationship of the DC microgrid includes the following steps:

[0109] S1-1: Figure 1 This is the topology of a photovoltaic hybrid energy storage DC microgrid. Figure 1 In the filter inductor L pv , switch tube S5, diode D5 constitute a unidirectional DC / DC converter of the photovoltaic power generation unit; filter inductor L b 、Filter capacitor C b , switch tubes S3 and S4, diodes D3 and D4 form a bidirectional DC / DC converter of the battery unit; filter inductor L SC 、Filter capacitor C SC , switch tubes S1 and S2, diodes D1 and D2 form a bidirectional DC / DC converter of the supercapacitor unit; C dc The DC bus side voltage stabilizing capacitor, T1~T6 form a grid-connected inverter, L1, L2 and C f Form an LCL filter circuit.

[0110] The working principle of the photovoltaic hybrid energy storage DC microgrid is as follows: the photovoltaic array is connected to the DC bus through a unidirectional DC / DC converter and is the main energy supply unit of the DC microgrid; the hybrid energy storage consists of batteries and supercapacitors, both of which are connected to the DC bus through a bidirectional DC / DC converter, mainly used to absorb or release redundant power to ensure the stable operation of the microgrid; the resistive load is directly connected to the DC bus to consume electrical energy. In addition, the DC microgrid transmits stable electrical energy to the distribution network through a DC / AC inverter.

[0111] The photovoltaic array consists of multiple groups of photovoltaic cells connected in series and parallel. The output current of the photovoltaic cells is:

[0112] I pv =I ph -I s -I sh (1)

[0113] In the formula, I pv is the output current of the photovoltaic cell; I ph is the photocurrent; I s is the current flowing through the parallel diode; I sh is the current flowing through the parallel bypass resistor.

[0114] The current flowing through the parallel diode I s and the current I flowing through the parallel bypass resistor sh It can be expressed as:

[0115]

[0116] In the formula, I o is the reverse saturation current of the diode; q is the electron charge constant (1.6×10 -19 C); A is the diode factor, which ranges from 1 to 5; k is the Boltzmann constant (1.38×10 -23 j / kWh); T abs is the absolute temperature.

[0117] Substituting equation (2) and equation (3) into equation (1), we can obtain:

[0118]

[0119] The output voltage of the photovoltaic cell is:

[0120]

[0121] The general equivalent model of a battery is mainly composed of a controlled voltage source and a resistor in series, and its mathematical expression is:

[0122]

[0123] In the formula, E is the output voltage of the battery; E0 is the internal potential of the battery; K is the polarization voltage of the battery; Q is the rated capacity of the battery; A is the voltage drop in the exponential zone; B is the reciprocal of the capacity in the exponential zone.

[0124] Supercapacitors are double-layer capacitors, and common electrical characteristic equivalent models include first-order RC model, three-branch model, and second-order nonlinear RC model. At present, the classic RC series equivalent model is mainly used in actual engineering and simulation experiments, and its mathematical expression is:

[0125]

[0126] Where V SC is the output voltage of the supercapacitor; R sceq is the equivalent resistance; C SC is the equivalent capacitance; I SC is the output current of the supercapacitor.

[0127] S1-2: The photovoltaic power generation unit transmits electric energy to the DC microgrid through the DC / DC converter. However, the photovoltaic output power is affected by the light intensity and temperature changes, resulting in a certain randomness and volatility of the photovoltaic output power. Therefore, it is necessary to configure a certain capacity of energy storage equipment in the DC microgrid to provide energy support, discharge when the DC microgrid redundant power is insufficient, and charge when the DC microgrid redundant power is excessive. At the same time, in order to ensure that the energy storage equipment can quickly respond to transient changes and stabilize the steady-state process, a hybrid energy storage system consisting of batteries and supercapacitors is used as the energy storage unit of the DC microgrid. The DC microgrid is connected to the distribution network through a DC / AC inverter. The distribution network provides support for the voltage and frequency on the AC side. When the output power of the photovoltaic power generation unit is greater than the power of the resistive load, and the hybrid energy storage unit reaches the capacity limit and cannot absorb the redundant power, the excess energy is connected to the distribution network through the grid-connected converter.

[0128] The power balance relationship of the DC microgrid can be obtained as follows:

[0129] P Load =P pv +P HESS (8)

[0130] P HESS =P Bat +P SC (9)

[0131] Where P Load is the load power; P pv is the output power of the photovoltaic power generation unit; P HESS is the power of the hybrid energy storage unit response, which is negative when absorbing electric energy and positive when releasing electric energy; P Batis the output power of the battery; P SC is the output power of the supercapacitor.

[0132] Furthermore, in step S2, based on step S1, a comprehensive operation control strategy of the microgrid is established, and it is determined that the photovoltaic power generation unit adopts the maximum power point tracking (Max Power Point Track, MPPT) control of fuzzy control, the grid-connected converter (Grid-connected converter, GCC) adopts the double closed-loop control of the voltage outer loop and the current inner loop, and the hybrid energy storage unit adopts the fuzzy second-order high-pass filter algorithm control, including the following steps:

[0133] S2-1: The photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control, which mainly measures the output voltage and output current of the photovoltaic array, and controls the connected DC / DC converter to enable the photovoltaic array to reach and maintain the maximum power output under the current environment. There are many existing photovoltaic MPPT control methods, mainly including: perturbation observation method, conductance increment method, fuzzy control method, etc. Fuzzy control, as an intelligent control method, has strong anti-interference and robustness, and is suitable for photovoltaic power generation, which contains uncertain quantities and is difficult to describe with an accurate mathematical model. Therefore, the present invention adopts the fuzzy control method to dynamically adjust the step size to achieve rapid tracking of the photovoltaic maximum power point. The output voltage of the photovoltaic power generation unit is determined by the duty cycle of the unidirectional DC / DC converter connected to it. The corresponding fuzzy MPPT algorithm is designed according to the voltage-power curve of the photovoltaic array. Figure 2 Control strategy for photovoltaic power generation units. Figure 3 is the voltage-power curve.

[0134] The voltage change dV and power change dP are input into the fuzzy controller, and the duty cycle adjustment dD is obtained according to the fuzzy rule and sent to the PWM signal generator, which finally outputs the corresponding pulse to control the unidirectional DC / DC converter.

[0135] The designed fuzzy controller has dual input and single output. The input variables are voltage change dV and power change dP, and the output variable is duty cycle adjustment dD.

[0136] dV=V(t)-V(t-1) (10)

[0137] dP=P(t)-P(t-1) (11)

[0138] The fuzzy linguistic variables that define input variables and output variables are:

[0139] dV={NB,NS,ZE,PS,PB}

[0140] dP={NB,NS,ZE,PS,PB}

[0141] dD={NB,NM,NS,ZE,PS,PM,PB}

[0142] Among them, NB, NM, NS, ZE, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively. At the same time, the fuzzy membership functions of the input variables and output variables use uniformly distributed triangular membership functions, and the corresponding domain ranges are: dV∈[-1,+1], dP∈[-1,+1], and dD∈[-0.5,+0.5].

[0143] According to the voltage-power curve of the photovoltaic array, the corresponding fuzzy prior knowledge can be obtained: when dV<0, dP<0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0; when dV<0, dP>0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP<0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP>0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0. In addition, the value of the duty cycle adjustment dD follows the following rules: the step size is smaller when it is close to the MPP point, and the step size is larger when it is far away from the MPP point.

[0144] By defining the number of fuzzy language sets of input and output variables, a total of 25 rules are formed. The fuzzy rule table of photovoltaic fuzzy MPPT control determined in this way is shown in Table 1. Figure 4 is the corresponding fuzzy surface diagram.

[0145] Table 1 Fuzzy control photovoltaic MPPT rules table

[0146] Tab.1 Fuzzy control photovoltaic MPPT rule

[0147]

[0148] S2-2: The function of the grid-connected converter (GCC) is to convert DC power into AC power so that it can be fed into the AC distribution network. GCC adopts the form of d-axis grid voltage oriented vector control, with the following control objectives: 1) to ensure the stability of the DC bus voltage by controlling the active component of the output current; 2) to achieve decoupling control of the output active and reactive power, so that the active power of the microgrid can be smoothly transmitted to the distribution network, and can provide certain reactive power support when necessary. Figure 5 This is the control block diagram of GCC.

[0149] exist Figure 5 in,i dref 、i d is the reference value and actual value of the d-axis component of the grid-side current; i qref 、i q is the reference value and actual value of the q-axis component of the grid-side current; u d 、u q are the d and q axis components of the grid voltage; W is the grid angular frequency; L1+L2 is the grid-side filter inductance; U d , U q is the d and q axis control voltage component of the grid-connected converter; U dcref , U dc are the reference value and actual value of the DC bus voltage.

[0150] The control of GCC adopts a dual closed-loop control method with voltage outer loop and current inner loop. By controlling the DC side voltage, the DC bus voltage can be kept constant, and the GCC can stably transmit power to the distribution network. Specifically, the DC side voltage reference value is subtracted from the actual value and then adjusted by PI to obtain the d-axis current reference value i dref At the same time, according to the reactive power demand, the q-axis current reference value i qref Set to 0 to achieve unity power factor grid connection. Then get the reference value i dref 、i qref The actual current i d 、i q After the difference is made, it is adjusted by PI and feedforward compensation is introduced to obtain the reference value U of the d and q axis voltages on the AC side of GCC. d , U q Finally, the modulation signal is obtained through dq / abc conversion to drive SPWM to generate trigger pulses, thereby controlling the on and off of GCC.

[0151] S2-3: Set the DC bus voltage reference value U dcref The actual value of the DC bus voltage U dc After the difference is made, the current reference value current i is obtained through PI adjustment. dcref . Then i dcref The high-frequency component i is obtained by high-pass filtering HF ,i dcref with i HF The difference is the low-frequency component i LF ,i HF 、i LF That is, the reference current value of the supercapacitor and the battery. The difference between the two and the corresponding actual current is adjusted by PI to obtain their respective duty cycles. Finally, the PWM generator is input to generate modulation pulses, which in turn control the bidirectional DC / DC converter of the battery and supercapacitor unit. Figure 6 This is the control block diagram of the hybrid energy storage system based on high-pass filter.

[0152] Although the hybrid energy storage system control method based on the high-pass filter can realize the frequency division distribution of unbalanced power, it fails to take into account the SOC of the energy storage element, which can easily cause overcharging and over-discharging of the energy storage element, thereby endangering the safe operation of the DC microgrid. Since the battery is an energy storage type and can be charged and discharged for a long time, its SOC does not change significantly in a short time, while the capacity of the supercapacitor is small, and its SOC changes more significantly in a short time. Therefore, the control of the SOC of the supercapacitor is particularly important. The present invention uses a fuzzy second-order filtering algorithm to control the hybrid energy storage system. While achieving the frequency division distribution effect of the hybrid energy storage system, it can ensure that the SOC of the supercapacitor is within a reasonable range of variation. The corresponding control block diagram is shown as follows. Figure 7 shown.

[0153] This method adds a fuzzy controller on the basis of the second-order high-pass filtering algorithm, and adaptively adjusts the filtering time constant according to the SOC change of the supercapacitor, so as to achieve the purpose of automatically assuming the unbalanced power proportion according to the remaining capacity of the supercapacitor, that is, it bears more power when the SOC is higher and less power when the SOC is lower.

[0154] In step S3, based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine that the SOC of the supercapacitor is used as a control indicator, and the fuzzy rule for adaptively adjusting the filter time constant includes the following steps:

[0155] S3-1: The traditional first-order filtering algorithm is implemented through an inertia link and a differential link, which allocates the power of the high-frequency component of the fluctuating power to the supercapacitor for rapid compensation; the power of the remaining frequencies is allocated to the battery for compensation. The transfer function of the first-order high-pass filter is:

[0156]

[0157] Where s is the differential operator and T is the filtering time constant.

[0158] As the order of the filtering algorithm increases, the filtering effect becomes more obvious. The second-order filtering algorithm is an improvement on the first-order filtering algorithm. It is mainly obtained by connecting two first-order filtering links in series and has two differential and inertial links.

[0159] The second-order high-pass filtering algorithm is as follows:

[0160]

[0161] According to the characteristics of high power density of supercapacitors and high energy density of batteries, the high-frequency part of the response power of the hybrid energy storage system is allocated to the supercapacitor for compensation, and the low-frequency part is allocated to the battery for compensation. The following mathematical relationship can be obtained:

[0162]

[0163] In the formula, The power compensated by the supercapacitor; P is the power compensated by the battery; HESS is the power response of the hybrid energy storage system.

[0164] Assume that the hybrid energy storage system responds to power P HESS (t) and its derivatives can be Laplace transformed, and P HESS (t) has a final value, based on the second-order high-pass filter After integration, we can get:

[0165]

[0166] It can be seen from formula (16) that the integral effect of the supercapacitor when responding to high-frequency power tends to 0, that is, the supercapacitor will not cause its basic state of charge to accumulate with the increase of charging time, thereby increasing the response speed and life of the supercapacitor, eliminating the integral effect generated in the traditional first-order filter, reducing the capacity accumulation of the supercapacitor, and ensuring that the supercapacitor always works in the best state. According to formula (14), the Bode diagram of the supercapacitor output power transfer function can be obtained as follows: Figure 8 shown.

[0167] exist Figure 8 In the figure, angular frequency ω1 corresponds to time constant T1, angular frequency ω2 corresponds to time constant T2, and T1>T2. When the time constant is T1, the supercapacitor can compensate for high-frequency power greater than ω1, and when the time constant is T2, the supercapacitor can compensate for high-frequency power greater than ω2. From this, we can conclude that the larger the value of the time constant T, the lower the power frequency that the supercapacitor can smooth out, and the more power it can smooth out; conversely, the higher the power frequency that can be smoothed out, the less power it can smooth out. By adjusting the time constant, the power distribution of the hybrid energy storage can be controlled, and the power value smoothed by the supercapacitor can be changed, that is, the SOC of the supercapacitor can be changed to avoid frequent charging and discharging of the supercapacitor.

[0168] The stability of the second-order high-pass filter is usually related to the time constant T. The time constant range of the second-order high-pass filter designed by the present invention is [0.2, 0.4]. According to formula (14), the Nyquist curve of the second-order high-pass filter can be drawn by taking the values ​​of the time constant T as 0.2, 0.3, and 0.4 respectively. Fig. 9 ,from Fig. 9It can be seen that when T changes in the range of [0.2, 0.4], the three Nyquist curves basically overlap, and none of them surround the point (-1, j0), indicating that the filter is stable within the range of the time constant T. The step response of the battery and supercapacitor power under the second-order high-pass filter is as follows: Fig.10 As shown, from Fig.10 It can be observed that when the time constant T is 0.2, 0.3, and 0.4, the battery and supercapacitor respond to low-frequency power and high-frequency power respectively. As the T value decreases, the response time becomes faster and faster, and the system power is still divided and distributed.

[0169] S3-2: The second-order high-pass filter algorithm with a fixed time constant has limitations in dynamic performance, which can easily cause the energy storage element to be overcharged or over-discharged, affecting the life of the energy storage element. Based on the above analysis, a hybrid energy storage power allocation method with a second-order high-pass filter time constant based on fuzzy logic is proposed. The SOC of the supercapacitor is used as a control indicator to adaptively adjust the filter time constant.

[0170] The input variable of the designed fuzzy controller is the SOC of the supercapacitor. SC , whose value range is [0,100], and the output variable of the fuzzy controller is the filter time constant T C , whose value range is [0.2,0.4]. The fuzzy linguistic variables that define the input variables and output variables are:

[0171] SOC SC ={S1,S2,S3,S4,S5,S6,S7,S8,S9}

[0172] T C ={T1,T2,T3,T4,T5,T6,T7,T8,T9}

[0173] Where, S1 to S9 represent the supercapacitor SOC from small to large; T1 to T9 represent the filter time constant T C From small to big.

[0174] There is no fixed rule or pattern for the selection of fuzzy subset membership functions. Considering the convenience of operation and familiarity of performance, triangular, trapezoidal, and Gaussian membership functions are usually selected. Among them, the triangular membership function is simple and intuitive, easy to understand, and has high computational efficiency. Therefore, the membership functions of the input and output variables of the fuzzy controller use uniformly distributed triangular membership functions. Figure 11 shows the corresponding membership function distribution.

[0175] From the above analysis of the supercapacitor output power, we can know that: T C The larger the value, the lower the power frequency that the supercapacitor can smooth out, and the more power it can smooth out; T CThe smaller the value, the higher the power frequency that can be smoothed, and the smoothed power is also reduced. When the SOC is higher, the power is more, and when the SOC is lower, the power is less. The fuzzy rules of fuzzy control can be obtained as shown in Table 2. Figure 12 is the relationship curve between the corresponding supercapacitor SOC and the filter time constant. From Figure 12, the filter time constant T during discharge can be obtained. C With the supercapacitor SOC SC When charging, T C With SOC SC decreases with the decrease of , and the overall relationship is inversely proportional.

[0176] Table 2 Fuzzy control second-order filtering rule

[0177]

[0178]

[0179] In step S4, the fuzzy logic variable time constant second-order high-pass filter control established in step S3 is used to simulate and compare the power, DC bus voltage, and supercapacitor SOC of the hybrid energy storage system under variable illumination and load conditions with the fixed constant second-order high-pass filter control, and the change curves of the supercapacitor SOC of the two strategies are compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and the total harmonic distortion of the current of the two strategies are also compared to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor changes within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced. The following steps are included:

[0180] S4-1: In order to verify the effectiveness of the variable time constant second-order high-pass filter control based on fuzzy logic proposed in this invention, a control system is built on MATLAB / Simulink as follows: Figure 1 The DC microgrid simulation model is shown in Figure 3. The system parameters are shown in Table 3.

[0181] Tab.3Simulation parameters

[0182]

[0183] S4-2: The operating conditions of the DC microgrid are set as follows: the simulation duration is 4s, the initial light intensity is 800W / m2, the light intensity increases to 1000W / m2 at t=2s, and decreases to 600W / m2 at t=3s. The initial load is 5000W, and the load decreases to 2000W at t=1s, and then remains unchanged at 2000W. The photovoltaic output power changes with the light intensity. The stronger the light, the greater the photovoltaic output power. Fig.13 is the photovoltaic and load power.

[0184] The simulation comparison of the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter is carried out, mainly from the aspects of hybrid energy storage system power, DC bus voltage, supercapacitor SOC, etc. Figure 14 is a simulation comparison result diagram of the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter. The subscript 1 represents the fixed time constant, and the subscript 2 represents the fuzzy variable time constant. Figure 14 (a) is the comparison of supercapacitor power, Figure 14 (b) is the comparison of battery power, Figure 14 (c) is the comparison of DC bus voltage, and Figure 14 (d) is the comparison of supercapacitor SOC operation curves.

[0185] In order to reflect the impact of fuzzy second-order control on the supercapacitor SOC, the operating conditions remain unchanged and only the initial SOC value of the supercapacitor is changed. The initial SOC values ​​are set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed. Figure 15 shows the change curve of the supercapacitor SOC. Figure 15(a) is when the SOC is 20, and Figure 15(b) is when the SOC is 90.

[0186] In order to further compare the effect of fuzzy second-order filtering from the AC aspect, the three-phase current and total harmonic distortion of current under two control methods are introduced. Figure 16 is a comparison of the three-phase current of the two algorithms. Figure 16 (a) and Figure 16 (c) are the three-phase current under constant second-order filtering control and the three-phase current under fuzzy second-order filtering control, respectively. Figure 16 (b) and Figure 16 (d) are the THD under constant second-order filtering control and the THD under fuzzy second-order filtering control, respectively.

[0187] In order to enable those skilled in the art to better understand the present invention, the example analysis includes the following components:

[0188] 1. Case description and simulation results analysis

[0189] The simulation comparison of the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter is carried out, mainly from the aspects of hybrid energy storage system power, DC bus voltage, supercapacitor SOC, etc. Figure 14 is a simulation comparison result diagram of the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter. The subscript 1 represents the fixed time constant, and the subscript 2 represents the fuzzy variable time constant. Figure 14 (a) is the comparison of supercapacitor power, Figure 14 (b) is the comparison of battery power, Figure 14 (c) is the comparison of DC bus voltage, and Figure 14 (d) is the comparison of supercapacitor SOC operation curves.

[0190] As shown in Figure 14(a) and Figure 14(b), when t is 1s and 2s, the hybrid energy storage discharges and releases power, and when t is 3s, the charging absorbs power, in which the supercapacitor responds to high-frequency power and the battery responds to low-frequency power. Under both control strategies, the unbalanced power of the system can be divided and distributed when the light intensity and load change. Compared with the fixed constant second-order high-pass filter algorithm, the strategy proposed in the present invention can adaptively adjust the filter constant when the light intensity and load change suddenly, so that the second-order high-pass filter has a more significant effect on the frequency distribution of power.

[0191] It can be observed from Figure 14(c) that when the light intensity changes, the DC bus voltage under the two strategies is basically stable at 700 V. Under the constant second-order high-pass filter control, the DC bus voltage has an overshoot of 18.41% at 0.028s, stabilizes at 700V at 0.124s, overshoots of 0.36% at t=1s when the load changes suddenly, and recovers to stability at 1.10s, overshoots of 0.71% at t=2s when the light intensity changes, recovers to 700V at 2.12s, overshoots of 1.42% at 3s when the photovoltaic fluctuation occurs, and then stabilizes at 700V at 3.15s. When fuzzy second-order high-pass filter control is adopted, the DC bus voltage overshoot is 17.23% at 0.025s, stabilizes to 700V at 0.118s, overshoot is 0.22% at t=1s, recovers to stability at 1.05s, overshoot is 0.43% at t=2s, recovers to 700V at 2.07s, overshoot is -1.07% at 3s, and then stabilizes at 700V at 3.09s. The results show that the strategy proposed in the present invention has smaller overshoot than the fixed constant second-order filter control, stronger anti-interference ability, and faster response speed.

[0192] From the comparison of the supercapacitor SOC curves under fixed constant and fuzzy variable constant control in Figure 14(d), it can be further seen that under fuzzy second-order filtering control, the supercapacitor responds better to high-frequency power and the change in supercapacitor SOC is smaller.

[0193] In order to reflect the impact of fuzzy second-order control on the supercapacitor SOC, the operating conditions remain unchanged and only the initial SOC value of the supercapacitor is changed. The initial SOC values ​​are set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed. Figure 15 shows the change curve of the supercapacitor SOC. Figure 15(a) is when the SOC is 20, and Figure 15(b) is when the SOC is 90.

[0194] When the SOC of the supercapacitor is 20, the supercapacitor is in an over-discharge state. As shown in Figure 15 (a), when the constant second-order high-pass filtering method is used, the supercapacitor responds to high-frequency power poorly and works in an over-discharge state for a long time. When the strategy proposed in the present invention is adopted, fuzzy control can adaptively adjust the filter time constant to control the power smoothed by the supercapacitor, so that its SOC has a tendency to recover to a reasonable range, ensuring that the supercapacitor can work within a reasonable range, and at the same time improving the ability of the supercapacitor to respond to high-frequency power. When the SOC of the supercapacitor is 90, the supercapacitor is in an overcharged state. As shown in Figure 15 (b), compared with the constant second-order high-pass filtering, the strategy proposed in the present invention ensures the ability to respond to high-frequency power while gradually making the supercapacitor SOC work within a reasonable range, preventing it from working in an overcharged state for a long time, and effectively extending the service life of the supercapacitor.

[0195] In order to further compare the effect of fuzzy second-order filtering from the AC aspect, the three-phase current and current total harmonic distortion under two control methods are introduced. Figure 16(a) to Figure 16(d) The three-phase current of the two algorithms is compared. Figure 16 (a) and Figure 16 (c) are the three-phase current under the constant second-order filter control and the three-phase current under the fuzzy second-order filter control, respectively. Figure 16 (b) and Figure 16 (d) are the THD under the constant second-order filter control and the THD under the fuzzy second-order filter control, respectively.

[0196] It can be seen from Figure 16(a) and Figure 16(c) that after the load mutation at t=1s, the systems under the two control strategies can operate stably in the grid-connected state. The THD value of the grid-connected current controlled by the fixed constant second-order filter is 4.10%, and the THD value of the grid-connected current controlled by the fuzzy second-order filter is 3.62%, both of which meet the grid-connected requirements. However, compared with the fixed constant second-order filter, the fuzzy variable constant second-order filter can adaptively adjust the filter time constant, better suppress the current harmonics, make the AC current THD value lower, and the grid-connected effect is better.

[0197] In summary, the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor varies within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the harmonic distortion of the grid-connected current is effectively reduced.

[0198] Example 2

[0199] The present embodiment relates to a power distribution control device for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the power distribution control method for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering of Example 1.

[0200] Example 3

[0201] This embodiment relates to a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the power distribution control method of the photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering of Example 1 is implemented.

[0202] In this specification, the schematic description of the present invention does not necessarily refer to the same embodiment or example, and those skilled in the art may combine and combine different embodiments or examples described in this specification. In addition, the contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept, and the protection scope of the present invention should not be considered as limited to the specific forms described in the implementation cases. The protection scope of the present invention also includes equivalent technical means that those skilled in the art can think of based on the inventive concept.

Claims

1. A power distribution control method for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, characterized in that: The following steps are involved: S1: Study the topological structure and working principle of photovoltaic hybrid energy storage DC microgrid and the energy management of microgrid, and obtain the power balance relationship of DC microgrid; S2: Based on step S1, a comprehensive operation control strategy of the microgrid is established to determine that the photovoltaic power generation unit adopts fuzzy control of maximum power point tracking MPPT control, the grid-connected inverter GCC adopts double closed-loop control of voltage outer loop and current inner loop, and the hybrid energy storage unit adopts fuzzy second-order high-pass filter algorithm control; S3: Based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine the fuzzy rule of adaptively adjusting the filter time constant by taking the SOC of the supercapacitor as the control index; S4: The fuzzy logic variable time constant second-order high-pass filter control established in step S3 is used to simulate and compare the hybrid energy storage system power, DC bus voltage, and supercapacitor SOC under variable light and load conditions with the fixed constant second-order high-pass filter control. The supercapacitor SOC change curves of the two strategies are compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and total harmonic distortion of the two strategies are also compared to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the supercapacitor changes within a reasonable range, the microgrid can operate stably in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced.

2. The power distribution control method of photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering according to claim 1 is characterized in that: The step S1 comprises the following steps: S1-1: The working principle of the photovoltaic hybrid energy storage DC microgrid is as follows: the photovoltaic array is connected to the DC bus through a unidirectional DC / DC converter and is the main energy supply unit of the DC microgrid; the hybrid energy storage consists of batteries and supercapacitors, both of which are connected to the DC bus through a bidirectional DC / DC converter, mainly used to absorb or release redundant power to ensure the stable operation of the microgrid; the resistive load is directly connected to the DC bus to consume electrical energy; in addition, the DC microgrid transmits stable electrical energy to the distribution network through a DC / AC inverter; The photovoltaic array consists of multiple groups of photovoltaic cells connected in series and parallel. The output current of the photovoltaic cells is: I pv =I ph -I s -I sh (1) In the formula, I ph is the output current of the photovoltaic cell; I ph is the photocurrent; I s is the current flowing through the parallel diode; I sh is the current flowing through the parallel bypass resistor; The current flowing through the parallel diode I s and the current I flowing through the parallel bypass resistor sh It can be expressed as: In the formula, I o is the reverse saturation current of the diode; q is the electron charge constant (1.6×10 -19 C); A is the diode factor, which ranges from 1 to 5; k is the Boltzmann constant (1.38×10 -23 j / kWh); T abs is the absolute temperature; Substituting equation (2) and equation (3) into equation (1), we can obtain: The output voltage of the photovoltaic cell is: The general equivalent model of a battery is mainly composed of a controlled voltage source and a resistor in series, and its mathematical expression is: In the formula, E is the output voltage of the battery; E0 is the internal potential of the battery; K is the polarization voltage of the battery; Q is the rated capacity of the battery; A is the voltage drop in the exponential region; B is the reciprocal of the capacity in the exponential region; Supercapacitors are double-layer capacitors. Common electrical characteristic equivalent models include first-order RC model, three-branch model, and second-order nonlinear RC model. Currently, the classic RC series equivalent model is mainly used in actual engineering and simulation experiments. Its mathematical expression is: Where V SC is the output voltage of the supercapacitor; R sceq is the equivalent resistance; C SC is the equivalent capacitance; I SC is the output current of the supercapacitor; S1-2: A certain capacity of energy storage equipment is configured in the DC microgrid to provide energy support, and the energy storage equipment is discharged when the redundant power of the DC microgrid is insufficient, and charged when the redundant power of the DC microgrid is excessive. At the same time, in order to ensure that the energy storage equipment can quickly respond to transient changes and stabilize the steady-state process, a hybrid energy storage system consisting of batteries and supercapacitors is used as the energy storage unit of the DC microgrid. The DC microgrid is connected to the distribution network through a DC / AC inverter, and the distribution network provides support for the voltage and frequency on the AC side. When the output power of the photovoltaic power generation unit is greater than the power of the resistive load, and the hybrid energy storage unit reaches the capacity limit and cannot absorb the redundant power, the excess energy is connected to the distribution network through the grid-connected converter. The power balance relationship of the DC microgrid can be obtained as follows: P Load =P pv +P HESS (8) P HESS =P Bat +P SC (9) Where P Load is the load power; P pv is the output power of the photovoltaic power generation unit; P HESS is the power of the hybrid energy storage unit response, which is negative when absorbing electric energy and positive when releasing electric energy; P Bat is the output power of the battery; P SC is the output power of the supercapacitor.

3. The power distribution control of photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as claimed in claim 2 is characterized in that: The step S2 includes the following steps: S2-1: The photovoltaic power generation unit adopts maximum power point tracking MPPT control, which measures the output voltage and output current of the photovoltaic array and controls the connected DC / DC converter to enable the photovoltaic array to reach and maintain the maximum power output under the current environment; the fuzzy control method is adopted to dynamically adjust the step size to achieve rapid tracking of the photovoltaic maximum power point; the output voltage of the photovoltaic power generation unit is determined by the duty cycle of the unidirectional DC / DC converter connected to it; the corresponding fuzzy MPPT algorithm is designed according to the voltage and power relationship of the photovoltaic array; The voltage change dV and power change dP are input into the fuzzy controller, and the duty cycle adjustment dD is obtained according to the fuzzy rule and sent to the PWM signal generator, which finally outputs the corresponding pulse to control the unidirectional DC / DC converter. The designed fuzzy controller has two inputs and one output. The input variables are the voltage change dV and the power change dP, and the output variable is the duty cycle adjustment dD. dV=V(t)-V(t-1) (10) dP=P(t)-P(t-1) (11) The fuzzy linguistic variables that define input variables and output variables are: dV={NB,NS,ZE,PS,PB} dP={NB,NS,ZE,PS,PB} dD={NB,NM,NS,ZE,PS,PM,PB} Among them, NB, NM, NS, ZE, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively; at the same time, the fuzzy membership functions of the input variables and output variables adopt uniformly distributed triangular membership functions, and the corresponding domain ranges are: dV∈[-1,+1], dP∈[-1,+1], dD∈[-0.5,+0.5]; According to the voltage and power relationship of the photovoltaic array, the corresponding fuzzy prior knowledge can be obtained: when dV<0, dP<0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0; when dV<0, dP>0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP<0, the working point is located on the right side of the MPP point, and the voltage should be reduced, so dD<0; when dV>0, dP>0, the working point is located on the left side of the MPP point, and the voltage should be increased, so dD>0; in addition, the value of the duty cycle adjustment dD follows the following rules: the step size is smaller when it is close to the MPP point, and the step size is larger when it is far away from the MPP point; By defining the number of fuzzy language sets of input and output variables, a total of 25 rules are formed; S2-2: The grid-connected inverter GCC converts DC power into AC power so that it can be fed into the AC distribution network; GCC adopts the d-axis grid voltage-oriented vector control form, and the control objectives include: 1) ensuring the stability of the DC bus voltage by controlling the active component of the output current; 2) realizing the decoupling control of the output active and reactive power, so that the active power of the microgrid can be smoothly transmitted to the distribution network, and can provide certain reactive power support when necessary; The control of GCC adopts a dual closed-loop control method with voltage outer loop and current inner loop. The DC bus voltage can be kept constant by controlling the DC side voltage, and the GCC can stably transmit electric energy to the distribution network. Specifically, the DC side voltage reference value is subtracted from the actual value and then adjusted by PI to obtain the d-axis current reference value i dref At the same time, according to the reactive power demand, the q-axis current reference value i dref Set it to 0 to achieve unity power factor grid connection; then get the reference value i dref 、i qref The actual current i d 、i q After the difference is made, it is adjusted by PI and feedforward compensation is introduced to obtain the reference value U of the d and q axis voltage on the AC side of GCC d , U q ; Finally, the modulation signal is obtained through dq / abc conversion to drive SPWM to generate trigger pulses, thereby controlling the on and off of GCC; S2-3: Set the DC bus voltage reference value U dcref The actual value of the DC bus voltage U dc After the difference is made, the current reference value current i is obtained through PI adjustment. dcref ; then i dcref The high-frequency component i is obtained by high-pass filtering HF ,i dcref with i HF The difference is the low-frequency component i LF ,i HF 、i LF That is, the reference current value of the supercapacitor and the battery. The difference between the two and the corresponding actual current is adjusted by PI to obtain their respective duty cycles; finally, the PWM generator is input to generate modulation pulses, and then the bidirectional DC / DC converter of the battery and supercapacitor unit is controlled; Although the hybrid energy storage system control method based on high-pass filter can realize the frequency division distribution of unbalanced power, it fails to consider the SOC of energy storage components, which can easily cause overcharge and over-discharge of energy storage components, thereby endangering the safe operation of DC microgrid; since the battery belongs to energy storage, it can be charged and discharged for a long time, and its SOC does not change significantly in a short time, while the capacity of supercapacitor is small, and its SOC changes significantly in a short time; therefore, the control of supercapacitor SOC is particularly important; the fuzzy second-order filtering algorithm is used to control the hybrid energy storage system, which can ensure that the SOC of the supercapacitor is within a reasonable range of variation while achieving the frequency division distribution effect of the hybrid energy storage system; A fuzzy controller is added on the basis of the second-order high-pass filtering algorithm. The filtering time constant is adaptively adjusted according to the SOC change of the supercapacitor, so as to achieve the purpose of automatically assuming the unbalanced power proportion according to the remaining capacity of the supercapacitor, that is, it bears more power when the SOC is higher and less power when the SOC is lower.

4. The power distribution control method of photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as claimed in claim 3 is characterized in that: In step S3, based on step S2, the frequency division distribution and stability of the traditional second-order high-pass filter are studied, so as to further study and obtain a second-order high-pass filter based on fuzzy logic, and determine that the SOC of the supercapacitor is used as a control indicator, and the fuzzy rule for adaptively adjusting the filter time constant includes the following steps: S3-1: Study the frequency division and stability of the traditional second-order high-pass filter, and further study the second-order high-pass filter based on fuzzy logic; The traditional first-order filtering algorithm is implemented through an inertia link and a differential link, which allocates the power of the high-frequency component of the fluctuating power to the supercapacitor for rapid compensation; the power of the remaining frequencies is allocated to the battery for compensation. The transfer function of the first-order high-pass filter is: In the formula, s is the differential operator; T is the filtering time constant; As the order of the filtering algorithm increases, the filtering effect becomes more obvious; the second-order filtering algorithm is an improvement on the first-order filtering algorithm, which is obtained by connecting two first-order filtering links in series and has two differential and inertial links; The second-order high-pass filtering algorithm is as follows: According to the characteristics of high power density of supercapacitors and high energy density of batteries, the high-frequency part of the response power of the hybrid energy storage system is allocated to the supercapacitor for compensation, and the low-frequency part is allocated to the battery for compensation. The following mathematical relationship can be obtained: In the formula, The power compensated by the supercapacitor; P is the power compensated by the battery; HESS is the power response of the hybrid energy storage system; Assume that the hybrid energy storage system responds to power P HESS (t) and its derivatives can be Laplace transformed, and P HESS (t) has a final value, based on the second-order high-pass filter After integration, we can get: It can be seen from formula (16) that the integral effect of the supercapacitor when responding to high-frequency power tends to 0, that is, the supercapacitor will not accumulate its basic state of charge as the charging time increases, thereby increasing the response speed and life of the supercapacitor, eliminating the integral effect generated in the traditional first-order filter, reducing the capacity accumulation of the supercapacitor, and ensuring that the supercapacitor always works in the best state; S3-2: The second-order high-pass filter algorithm with a fixed time constant has limitations in dynamic performance, which can easily cause the energy storage element to be overcharged or over-discharged, thus affecting the life of the energy storage element. Based on the above analysis, the SOC of the supercapacitor is used as a control indicator to adaptively adjust the filter time constant. The input variable of the designed fuzzy controller is the SOC of the supercapacitor. SC , whose value range is [0,100], and the output variable of the fuzzy controller is the filter time constant T C , whose value range is [0.2,0.4]; the fuzzy linguistic variables defining the input variables and output variables are: <h2 style=";text-align:left;direction:ltr">SOC<h2 style=";text-align:left;direction:ltr"> SC <h2 style=";text-align:left;direction:ltr"> (S1, S2, S3, S4, S5, S6, S7, S8, S9) <h2 style=";text-align:left;direction:ltr">T<h2 style=";text-align:left;direction:ltr"> C <h2 style=";text-align:left;direction:ltr"> (T1,T2,T3,T4,T5,T6,T7,T8,T9) Where, S1 to S9 represent the supercapacitor SOC from small to large; T1 to T9 represent the filter time constant T C From small to large; The membership functions of the input and output variables of the fuzzy controller use uniformly distributed triangular membership functions; The fuzzy rules of fuzzy control are obtained from the above analysis of supercapacitor output power: T C The larger the value, the lower the power frequency that the supercapacitor can smooth out, and the more power it can smooth out; T C The smaller the value, the higher the power frequency that can be smoothed, and the less power is smoothed; when the SOC is higher, more power is borne, and when the SOC is lower, less power is borne;.

5. The power distribution control method of photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as claimed in claim 4 is characterized in that: ,Step S4 includes the following steps: S4-1: To verify the effectiveness of the variable time constant second-order high-pass filter control method based on fuzzy logic, a DC microgrid simulation model was built on MATLAB / Simulink; S4-2: The operating conditions of the DC microgrid are set as follows: the simulation duration is 4s, the initial light intensity is 800W / m2, the light intensity increases to 1000W / m2 at t=2s, and decreases to 600W / m2 at t=3s; the initial load is 5000W, the load decreases to 2000W at t=1s, and then remains unchanged at 2000W; the photovoltaic output power changes with the light intensity, the stronger the light, the greater the photovoltaic output power; The simulation comparison of the fixed constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter is carried out, and the comparison and analysis are carried out from the aspects of the hybrid energy storage system power, DC bus voltage, supercapacitor SOC, etc. In order to reflect the impact of fuzzy second-order control on the SOC of supercapacitors, the operating conditions remain unchanged and only the initial SOC value of the supercapacitor is changed. The initial SOC values ​​are set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed. In order to further compare the effect of fuzzy second-order filtering from the AC aspect, the three-phase current and current total harmonic distortion under two control methods are introduced.

6. A power distribution control device for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, characterized in that: It comprises a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, it is used to implement the power distribution control method of the photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the power distribution control method of a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in any one of claims 1 to 5 is implemented.

Images