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

Abstract

The application 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 following steps: researching the topological structure and working principle of the photovoltaic hybrid energy storage direct-current microgrid and the energy management of the microgrid to obtain the power balance relationship of the direct-current microgrid; establishing a comprehensive operation control strategy of the microgrid, determining that the photovoltaic power generation unit adopts fuzzy control maximum power point tracking control, the grid-connected inverter adopts double closed-loop control of a voltage outer loop and a current inner loop, and the hybrid energy storage unit adopts fuzzy second-order high-pass filtering algorithm control; researching frequency distribution and stability of a traditional second-order high-pass filter, so as to further research a fuzzy logic-based second-order high-pass filter, determine the SOC of the super capacitor as a control index, and adaptively adjust the fuzzy rule of the filtering time constant; comparing the power of the hybrid energy storage system, the direct-current bus voltage and the super capacitor SOC under the fuzzy logic variable time constant second-order high-pass filtering control and the constant second-order high-pass filtering control under variable illumination and load conditions, comparing the super capacitor SOC change curves of the two strategies under the condition of changing the initial SOC value of the super capacitor, and comparing the three-phase current and the current total harmonic distortion of the two strategies, so as to verify that the efficiency of the photovoltaic power generation system is improved under the proposed strategy, the SOC of the super capacitor changes in a reasonable range, the microgrid can be stably operated in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced.

Description

TECHNICAL FIELD

[0001] The application provides a power distribution control method and device for a photovoltaic hybrid energy storage direct-current microgrid based on fuzzy second-order high-pass filtering. BACKGROUND

[0002] New energy has gradually become a common power source for China's power system, but new energy generation is affected by primary energy supply and is difficult to meet the demand for grid peak regulation and frequency regulation. At present, the phenomenon of wind and light abandonment is serious in China's new energy power generation. In order to improve the grid utilization efficiency of wind power generation and photovoltaic power generation, using energy storage technology to smooth the output fluctuation of new energy is the main solution. At present, energy storage devices are mainly divided into two categories: energy storage devices and power storage devices. Common energy storage devices include lithium ion batteries, lead-acid batteries, super capacitors, etc. The energy density of the battery is high, but its power density is small; the power density of the super capacitor is high, but its energy density is small. Combining the advantages of the two kinds of energy storage, the power response speed of the energy storage system can be improved. The hybrid energy storage system can store the excess power of the direct-current microgrid and provide power support for the power load when the renewable energy output is insufficient, and it responds quickly and is complementary to new energy, and is widely used in the problem of smoothing the power fluctuation of the microgrid.

[0003] The power distribution control strategy of the hybrid energy storage system is the key to realize the complementary advantages of the two types of energy storage media. The existing hybrid energy storage power distribution method includes filter-based distribution and intelligent algorithm-based distribution. The filter-based distribution method only decomposes the redundant power of the microgrid into fixed high and low frequency components, and then the super capacitor and the battery in the hybrid energy storage system respond respectively, without considering the capacity characteristics of each energy storage device, so the power distribution accuracy is not high. The intelligent algorithm distribution is a method for improving the power distribution accuracy of the hybrid energy storage system by using intelligent control algorithms, which can make the battery and super capacitor better respond to power according to their own element characteristics, and to a certain extent, improve the high and low frequency distribution accuracy of the redundant power of the microgrid, but it does not consider the overcharge and overdischarge of the super capacitor under long-time system operation, increases the capacity of the super capacitor, and reduces the economy of the microgrid. SUMMARY

[0004] Based on the above background, in view of the problem of poor power distribution effect of the hybrid energy storage system caused by the fixed time constant in the traditional filter distribution algorithm, and the problem of overcharge and overdischarge of the super capacitor caused by long-time system operation, the application provides a power distribution control method and device for a photovoltaic hybrid energy storage direct-current microgrid based on fuzzy second-order high-pass filtering.

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

[0006] To achieve the above purpose, the technical scheme of the application is:

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

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

[0009] S2: Based on step S1, establish a comprehensive operation control strategy for the microgrid, determine that the photovoltaic power generation unit adopts fuzzy control Max Power Point Track (MPPT) control, the grid-connected converter (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 filtering algorithm control;

[0010] S3: Based on step S2, study the frequency distribution and stability of the traditional second-order high-pass filter, and further study to obtain a second-order high-pass filter based on fuzzy logic, determine the SOC of the super capacitor as the control index, and adaptively adjust the fuzzy rules of the filtering time constant;

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

[0012] Further, in step S1, studying the topology structure and working principle of the photovoltaic hybrid energy storage DC microgrid and the energy management of the microgrid to obtain the power balance relationship of the DC microgrid comprises 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 one-way DC / DC converter and is the main power supply unit of the DC microgrid; the hybrid energy storage is composed of a storage battery and a super capacitor, and the storage battery and the super capacitor are connected to the DC bus through a bidirectional DC / DC converter and are mainly used to absorb or release redundant power to ensure stable operation of the microgrid; the resistive load is directly connected to the DC bus to consume power. In addition, the DC microgrid transmits stable power to the power distribution network through a DC / AC inverter.

[0014] The photovoltaic array is composed of multiple groups of photovoltaic cells connected in series and in parallel, and the output current of the photovoltaic cell 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 photogenerated current; I s is the current flowing through the parallel diode; and I sh is the current flowing through the parallel shunt resistance.

[0017] The current I s flowing through the parallel diode and the current I sh flowing through the parallel shunt resistance can be expressed as:

[0018]

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

[0020] Substituting formula (2) and formula (3) into formula (1) gives:

[0021]

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

[0023]

[0024] The general equivalent model of the storage battery is mainly composed of a controlled voltage source and a resistor connected 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 sag in the exponential region; and B is the reciprocal of the capacity in the exponential region.

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

[0028]

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

[0030] S1-2: Photovoltaic power generation units supply electrical energy to the DC microgrid via DC / DC converters. However, the output power of photovoltaics is affected by changes in light intensity and temperature, resulting in a certain degree of randomness and fluctuation in photovoltaic output power. Therefore, energy storage devices of a certain capacity need to be configured in the DC microgrid to provide energy support. These devices discharge when the DC microgrid's redundant power is insufficient and charge when the DC microgrid's redundant power is excessive. Simultaneously, to ensure that the energy storage devices can quickly respond to transient changes and stabilize steady-state processes, 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 via 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 units exceeds the power of the resistive load, and the hybrid energy storage unit reaches its capacity limit and cannot absorb redundant power, the excess energy is fed into the distribution network through a 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] In the formula, P Load P represents the load power. pv P represents the output power of the photovoltaic power generation unit. HESS P represents the power response of the hybrid energy storage unit; it is negative when absorbing electrical energy and positive when releasing electrical energy.Bat Pbat is the output power of the battery SC Psc is the output power of the super capacitor.

[0035] Further, in the step S2, based on the step S1, the comprehensive operation control strategy of the micro-grid is established, the photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control of the fuzzy control, the 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 filtering algorithm control, and the steps include the following steps.

[0036] S2-1: the photovoltaic power generation unit adopts the maximum power point tracking (MPPT) control, mainly by measuring the output voltage and output current of the photovoltaic array, through the control of the connected DC / DC converter, the photovoltaic array reaches and maintains the maximum power output under the current environment. There are many existing photovoltaic MPPT control methods, mainly including: perturbation and observation method, conductance increment method, fuzzy control method and the like. As an intelligent control method, the fuzzy control has strong anti-interference and robustness, is suitable for the photovoltaic power generation system which is uncertain and is difficult to describe by an accurate mathematical model. Therefore, the fuzzy control method is adopted to realize the rapid tracking of the photovoltaic maximum power point by dynamically adjusting the step. The output voltage of the photovoltaic power generation unit is determined by the duty cycle of the unidirectional DC / DC converter connected thereto. The corresponding fuzzy MPPT algorithm is designed according to the voltage and power relationship of the photovoltaic array.

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

[0038] The designed fuzzy controller is a double-input single-output, the input variables are the voltage variation dV and the power variation dP, and the output variable is the adjustment amount dD of the duty cycle.

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

[0040] dP=P(t)-P(t-1) (11)

[0041] The fuzzy language variables of the input variables and the output variables are defined as follows:

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

[0043] dP={NB,NS,ZE,PS,PB}

[0044] dD = {NB, NM, NS, ZE, PS, PM, PB}

[0045] Where NB, NM, NS, ZE, PS, PM, PB represent negative big, negative middle, negative small, zero, positive small, positive middle, positive big respectively. Meanwhile, the fuzzy membership functions of input and output variables adopt the uniformly distributed triangular membership functions, and the corresponding domain ranges are dV ∈ [-1, +1], dP ∈ [-1, +1], dD ∈ [-0.5, +0.5] respectively.

[0046] According to the relationship between voltage and power of photovoltaic array, the corresponding fuzzy priori knowledge can be obtained: when dV < 0, dP < 0, the working point is located on the left side of MPP point, the voltage should be increased, thus dD > 0; when dV < 0, dP > 0, the working point is located on the right side of MPP point, the voltage should be decreased, thus dD < 0; when dV > 0, dP < 0, the working point is located on the right side of MPP point, the voltage should be decreased, thus dD < 0; when dV > 0, dP > 0, the working point is located on the left side of MPP point, the voltage should be increased, thus dD > 0. In addition, the value of duty ratio adjustment dD follows the following rules: the step is small when close to MPP point, and the step is large when far from MPP point.

[0047] Through the defined fuzzy language set quantity of input and output variables, 25 rules are formed in total.

[0048] S2-2: The function of grid-connected converter (GCC) is to convert DC into AC so as to be sent into AC distribution network. The GCC adopts d-axis grid voltage oriented vector control form, and the main control targets are as follows: 1) the stability of DC bus voltage is ensured through the control of output current active component; 2) the decoupling control of output active and reactive power is realized, so that the active power of microgrid is smoothly transmitted to distribution network, and certain reactive power support can be provided when necessary.

[0049] The control of GCC adopts double closed-loop control mode of voltage outer ring and current inner ring. Through the control of DC side voltage, the constant of DC bus voltage is ensured, and the stable transmission of electric energy of GCC to distribution network is realized. Specifically, the d-axis current reference i dref is obtained through PI regulation after the difference between DC side voltage reference and actual value is taken. Meanwhile, the q-axis current reference i qref is set to 0 so as to realize grid connection with unit power factor. Then the obtained reference i dref , i qref are compared with actual AC side currents i d , i qThe difference between the DC bus voltage reference value U and the DC bus voltage actual value U is regulated by PI to obtain the current reference value i d q The final dq / abc transformation obtains the modulation number driving SPWM to generate trigger pulses, thereby controlling the GCC on-off.

[0050] S2-3: The DC bus voltage reference value U dcref is subtracted from the DC bus voltage actual value U dc to obtain the current reference value i dcref after PI regulation. i dcref is further passed through a high-pass filter to obtain the high-frequency component i HF , and the difference between i dcref and i HF is the low-frequency component i LF , i HF , i LF are the reference current values of the super capacitor and the battery, and the difference between the two and the corresponding actual current is regulated by PI to obtain the respective duty cycles. Finally, the modulation pulse is input into the PWM generator to control the bidirectional DC / DC converter of the battery and the super capacitor unit.

[0051] Although the hybrid energy storage system control method based on the high-pass filter can achieve frequency distribution and allocation, it fails to consider the SOC of the energy storage element, which is likely to cause overcharging and overdischarging 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 the change of its SOC in a short time is not obvious, while the capacity of the super capacitor is small, and its SOC changes significantly in a short time. Therefore, the control of the SOC of the super capacitor is particularly important. The present application uses a fuzzy second-order filter algorithm to control the hybrid energy storage system, which can ensure that the SOC of the super capacitor is within a reasonable range while achieving the frequency distribution and allocation effect of the hybrid energy storage system.

[0052] The method adds a fuzzy controller based on the second-order high-pass filter algorithm, and adaptively adjusts the filter time constant according to the change of the SOC of the super capacitor, so as to automatically bear the proportion of unbalanced power according to the remaining capacity of the super capacitor, that is, when the SOC is high, the power is large, and when the SOC is low, the power is small.

[0053] In the step S3, based on the step S2, the frequency distribution and stability of the traditional second-order high-pass filter are studied, so as to further research the second-order high-pass filter based on fuzzy logic, and determine that the SOC of the super capacitor is used as a control index, and the fuzzy rules for adaptively adjusting the filter time constant include the following steps:

[0054] ​S3-1: The traditional first-order filtering algorithm is realized by an inertial link and a differential link, and the power of the high-frequency component in the fluctuating power is distributed to the super capacitor for rapid compensation; the power of the remaining frequency is distributed to the battery for compensation, and the transfer function of the first-order high-pass filter is:

[0055]

[0056] In the formula, s is a differential operator; T is a filter time constant.

[0057] With the increase of the order of the filtering algorithm, the filtering effect is more and more obvious. The second-order filtering algorithm is an improvement on the first-order filtering algorithm, mainly obtained by connecting two first-order filtering links in series, and has two differential and inertial links.

[0058] The second-order high-pass filtering algorithm is as follows:

[0059]

[0060] According to the characteristics of the super capacitor having high power density and the battery having high energy density, the high-frequency part of the response power of the hybrid energy storage system is distributed to the super capacitor for compensation, and the low-frequency part is distributed to the battery for compensation, and the following mathematical relationship can be obtained:

[0061]

[0062] In the formula, is the power compensated by the super capacitor; is the power compensated by the battery; P HESS is the power responded by the hybrid energy storage system.

[0063] It is assumed that the power P HESS (t) and its derivative can be Laplace transformed, and P HESS (t) has a final value, and after integrating on the basis of the second-order high-pass filtering, the following formula can be obtained:

[0064]

[0065] As can be seen from formula (16), the integral action of the super capacitor in responding to the high-frequency power tends to 0, that is, the super capacitor will not cause the accumulation of the basic state of charge with the increase of the charging time, thereby increasing the response speed and the life of the super capacitor, eliminating the integral action generated in the traditional first-order filtering, reducing the capacity accumulation of the super capacitor, and ensuring that the super capacitor always works in the best state.

[0066] S3-2: the dynamic performance of the second-order high-pass filtering algorithm with time constant has limitations, which is easy to overcharge and over-discharge the energy storage element, and has an impact on the service life of the energy storage element. Based on the above analysis, a hybrid energy storage power distribution method based on fuzzy logic of second-order high-pass filtering time constant is proposed. The SOC of super capacitor is taken as the control index, and the filtering time constant is adaptively adjusted.

[0067] The input variable of the fuzzy controller is the SOC of the super capacitor SC , and the value range is [0, 100], and the output variable of the fuzzy controller is the filtering time constant T C , and the value range is [0.2, 0.4]. The fuzzy language variables of the input variable and the output variable are defined as:

[0068] SOC SC ={S1, S2, S3, S4, S5, S6, S7, S8, S9}

[0069] T C ={T1, T2, T3, T4, T5, T6, T7, T8, T9}

[0070] In the formula, S1 to S9 represent the super capacitor SOC from small to large; T1 to T9 represent the filtering time constant T C from small to large.

[0071] There is no fixed rule and mode for the selection of the membership function of the fuzzy subset. Considering the factors such as convenient operation and performance familiarity, triangular, trapezoidal and Gaussian membership functions are usually selected, among which the triangular membership function is simple and intuitive, easy to understand and high in calculation efficiency, so the membership functions of the input and output variables of the fuzzy controller are selected as uniformly distributed triangular membership functions.

[0072] From the above analysis of the output power of the super capacitor, it can be seen that the greater the value of T C , the lower the power frequency that the super capacitor can suppress, and the more power it can suppress; the smaller the value of T C , the higher the power frequency that can be suppressed, and the less power that can be suppressed. When the SOC is high, the power is high, and when the SOC is low, the power is low. From this, the fuzzy rules of fuzzy control can be obtained.

[0073] In the step S4, the fuzzy logic variable time constant second-order high-pass filter control established by the step S3 is compared with the constant second-order high-pass filter control simulation in terms of the hybrid energy storage system power, the DC bus voltage, the super capacitor SOC under variable illumination and load conditions, and the change curves of the super capacitor SOC of the two strategies are compared under the condition of changing the initial SOC value of the super capacitor, and the three-phase current and the current total harmonic distortion of the two strategies are compared, verifying that the efficiency of the photovoltaic power generation system is improved, the SOC of the super capacitor changes within a reasonable range, the micro-grid can stably operate in the grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced, including the following steps:

[0074] S4-1: To verify the effectiveness of the variable time constant second-order high-pass filter control based on fuzzy logic proposed in the application, a DC micro-grid simulation model is built on MATLAB / Simulink.

[0075] S4-2: The operation condition of the DC micro-grid is set as follows: the simulation time is 4s, the initial illumination intensity is 800W / m2, the illumination intensity increases to 1000W / m2 at t=2s, the illumination intensity 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 illumination intensity, and the stronger the illumination, the greater the photovoltaic output power.

[0076] The constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter are simulated and compared, mainly in terms of the hybrid energy storage system power, the DC bus voltage, the super capacitor SOC and the like.

[0077] In order to reflect the influence of the fuzzy second-order control on the super capacitor SOC, the initial SOC value of the super capacitor is changed without changing the operation condition. The initial SOC value is set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed.

[0078] In order to further compare the effect of the fuzzy second-order filter from the alternating current aspect, the three-phase current and the current total harmonic distortion under the two controls are introduced.

[0079] The second aspect of the application relates to a power distribution control device of a photovoltaic hybrid energy storage DC micro-grid based on fuzzy second-order high-pass filtering, comprising a memory and one or more processors, the memory storing executable code, and the one or more processors executing the executable code to implement the power distribution control method of the photovoltaic hybrid energy storage DC micro-grid based on fuzzy second-order high-pass filtering.

[0080] The third aspect of the present application relates to a computer readable storage medium, having stored thereon a program 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 application.

[0081] The present application has the following advantages:

[0082] 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 instruction. In order to improve the energy utilization rate of the photovoltaic power generation unit, fuzzy control is used to realize the 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;

[0083] 2) Under the above control strategy, the efficiency of the photovoltaic power generation system is improved, and the SOC of the super capacitor is changed within a reasonable range through adaptive adjustment of the filter constant, effectively prolonging its service life. The photovoltaic hybrid energy storage microgrid can operate stably in grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 is the microgrid architecture diagram of the method of the present application.

[0085] Figure 2 is the photovoltaic unit control block diagram of the method of the present application.

[0086] Figure 3 is the voltage-power curve of the method of the present application.

[0087] Figure 4 is the fuzzy surface diagram of the method of the present application.

[0088] Figure 5 is the grid-connected converter control block diagram of the method of the present application.

[0089] Figure 6 is the traditional control block diagram of the hybrid energy storage unit of the method of the present application.

[0090] Figure 7 is the fuzzy control block diagram of the hybrid energy storage unit of the method of the present application.

[0091] Figure 8 is the Bode plot of the super capacitor output power transfer function of the method of the present application.

[0092] Figure 9 is the Nyquist curve of the second-order high-pass filter of the method of the present application.

[0093] Figure 10is the step response graph of battery and super capacitor power of the method of the present application.

[0094] Figures 11(a)-11(b) is the membership function of fuzzy controller of the method of the present application, wherein figure 11(a) is the super capacitor SOC membership function, and figure 11(b) is the filter time constant membership function.

[0095] Figures 12(a)-12(b) is the relationship curve of super capacitor SOC and filter time constant of the method of the present application, wherein figure 12(a) is the discharging state, and figure 12(b) is the charging state.

[0096] Figure 13 is the photovoltaic and load power graph of the method of the present application.

[0097] Figures 14(a)-14(d) is the system operation characteristic comparison graph under two strategies of the method of the present application, wherein figure 14(a) is the super capacitor power comparison graph, figure 14(b) is the battery power comparison graph, figure 14(c) is the DC bus voltage comparison graph, and figure 14(d) is the super capacitor SOC operation curve comparison graph.

[0098] Figures 15(a)-15(b) is the super capacitor SOC operation curve comparison graph of the method of the present application, wherein figure 15(a) is the comparison graph when the SOC is 20, and figure 15(b) is the comparison graph when the SOC is 90.

[0099] Figures 16(a)-16(d) is the three-phase current comparison graph of two algorithms of the method of the present application, wherein figure 16(a) is the constant second-order filter three-phase current, figure 16(b) is the constant second-order filter current total harmonic distortion, figure 16(c) is the fuzzy second-order filter three-phase current, and figure 16(d) is the fuzzy second-order filter current total harmonic distortion.

[0100] Figure 17 is the flow chart of the method of the present application. Specific implementation method

[0101] The present application is further described below in combination with the drawings.

[0102] Example 1

[0103] Reference Figures 1-17 A power distribution control of a photovoltaic hybrid energy storage direct current microgrid based on fuzzy second-order high-pass filtering, comprising the following steps:

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

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

[0106] S3: Based on step S2, the frequency distribution and stability of the traditional second-order high-pass filter are studied, and a second-order high-pass filter based on fuzzy logic is further researched, and the SOC of the super capacitor is determined as the control index, and the fuzzy rule of adaptively adjusting the filter time constant is determined;

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

[0108] Further, in the step S1, the topology structure and working principle of the photovoltaic hybrid energy storage DC micro-grid and the energy management of the micro-grid are researched, and the power balance relationship of the DC micro-grid is obtained, including the following steps:

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

[0110] The working principle of the photovoltaic hybrid energy storage DC micro-grid is as follows: the photovoltaic array is connected to the DC bus through a one-way DC / DC converter and is the main power supply unit of the DC micro-grid; the hybrid energy storage is composed of a storage battery and a super capacitor, and the storage battery and the super capacitor are connected to the DC bus through a bidirectional DC / DC converter and are mainly used to absorb or release redundant power to ensure the stable operation of the micro-grid; the resistive load is directly connected to the DC bus to consume electric energy. In addition, the DC micro-grid transmits stable electric energy to the power distribution network through a DC / AC inverter.

[0111] The photovoltaic array is composed of multiple groups of photovoltaic cells connected in series and in parallel, and the output current of the photovoltaic cell 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 photogenerated current; I s is the current flowing through the parallel diode; and I sh is the current flowing through the parallel bypass resistance.

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

[0115]

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

[0117] The formula (2) and the formula (3) are brought into the formula (1) to obtain:

[0118]

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

[0120]

[0121] The general equivalent model of the storage battery is mainly composed of a controlled voltage source and a resistor connected in series, and the 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 sag in the exponential region; and B is the reciprocal of the capacity in the exponential region.

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

[0125]

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

[0127] S1-2: Photovoltaic power generation units supply electrical energy to the DC microgrid via DC / DC converters. However, the output power of photovoltaics is affected by changes in light intensity and temperature, resulting in a certain degree of randomness and fluctuation in photovoltaic output power. Therefore, energy storage devices of a certain capacity need to be configured in the DC microgrid to provide energy support. These devices discharge when the DC microgrid's redundant power is insufficient and charge when the DC microgrid's redundant power is excessive. Simultaneously, to ensure that the energy storage devices can quickly respond to transient changes and stabilize steady-state processes, 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 via 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 units exceeds the power of the resistive load, and the hybrid energy storage unit reaches its capacity limit and cannot absorb redundant power, the excess energy is fed into the distribution network through a 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] In the formula, P Load P represents the load power. pv P represents the output power of the photovoltaic power generation unit. HESS P represents the power response of the hybrid energy storage unit; it is negative when absorbing electrical energy and positive when releasing electrical energy. BatP is the output power of the battery SC P is the output power of the super capacitor.

[0132] Further, in the step S2, based on the step S1, the comprehensive operation control strategy of the micro-grid is established, the maximum power point tracking (MPPT) control of the photovoltaic power generation unit using fuzzy control is determined, the grid-connected converter (GCC) uses the double closed-loop control of the voltage outer loop and the current inner loop, and the hybrid energy storage unit uses the fuzzy second-order high-pass filter algorithm control, including the following steps:

[0133] S2-1: The photovoltaic power generation unit uses the maximum power point tracking (MPPT) control, mainly by measuring the output voltage and output current of the photovoltaic array, through the control of the connected DC / DC converter, the photovoltaic array reaches and maintains the maximum power output under the current environment. There are many existing photovoltaic MPPT control methods, mainly including: perturbation and observation method, incremental conductance 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 is a system with uncertain quantity and is difficult to describe with an accurate mathematical model. Therefore, the fuzzy control method is adopted to dynamically adjust the step length to realize the rapid tracking of the maximum power point of the photovoltaic. The output voltage of the photovoltaic power generation unit is determined by the duty cycle of the unidirectional DC / DC converter connected thereto. According to the voltage-power curve of the photovoltaic array, the corresponding fuzzy MPPT algorithm is designed. Figure 2 P is the control strategy of the photovoltaic power generation unit. Figure 3 P is the voltage-power curve.

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

[0135] The designed fuzzy controller is a double-input single-output, the input variables are the voltage change dV and the power change dP, and the output variable is the adjustment amount dD of the duty cycle.

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

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

[0138] The fuzzy language variables of the input variables and the output variables are defined as:

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

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

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

[0142] where NB, NM, NS, ZE, PS, PM, PB represent negative big, negative medium, negative small, zero, positive small, positive medium, positive big, respectively. Meanwhile, the fuzzy membership functions of input and output variables are uniform distribution triangular membership functions, and the corresponding domain ranges are dV ∈ [-1, +1], dP ∈ [-1, +1], dD ∈ [-0.5, +0.5], respectively.

[0143] According to the voltage-power curve of photovoltaic array, the corresponding fuzzy priori knowledge can be obtained: when dV < 0, dP < 0, the working point is located on the left side of MPP point, the voltage should be increased, thus dD > 0; when dV < 0, dP > 0, the working point is located on the right side of MPP point, the voltage should be decreased, thus dD < 0; when dV > 0, dP < 0, the working point is located on the right side of MPP point, the voltage should be decreased, thus dD < 0; when dV > 0, dP > 0, the working point is located on the left side of MPP point, the voltage should be increased, thus dD > 0. In addition, the value of duty ratio adjustment dD follows the following rules: the step is small when close to MPP point, and the step is large when far away from MPP point.

[0144] Through the defined fuzzy language set number of input and output variables, a total of 25 rules are formed. The fuzzy rule table of photovoltaic fuzzy MPPT control determined thereby is shown in Table 1. Figure 4 The corresponding fuzzy surface graph is shown in Fig. 1.

[0145] Table 1 Fuzzy control photovoltaic MPPT rule

[0146] Tab.1 Fuzzy control photovoltaic MPPT rule

[0147]

[0148] S2-2: The role of grid-connected converter (GCC) is to convert direct current into alternating current so as to be sent into the alternating current distribution network. The GCC adopts d-axis grid voltage oriented vector control form, and the main control objectives are as follows: 1) the stability of direct current bus voltage is ensured through the control of output current active component; 2) the decoupling control of output active and reactive is realized, so that the active power of microgrid is smoothly transmitted to the distribution network, and a certain reactive support can be provided when necessary. Figure 5 The control block diagram of GCC is shown in Fig. 2.

[0149] InFigure 5 i dref , i d are the reference and actual values of the d-axis component of the grid-side current; i qre f, i q are the reference and actual values of the q-axis component of the grid-side current; u d , u q are the d, 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 are the d, q-axis control voltage components of the grid-connected converter; U dcref , U dc are the reference and actual values of the DC bus voltage.

[0150] The control of the GCC adopts a double closed-loop control mode of voltage outer loop and current inner loop. Through the control of the DC side voltage, the constant of the DC bus voltage can be ensured, and at the same time the GCC stably transmits electric energy to the power distribution network. Specifically, the difference between the DC side voltage reference value and the actual value is obtained after PI regulation to obtain the d-axis current reference value i dref , and the q-axis current reference value i qref is set to 0 in order to realize unity power factor grid connection. The reference value i dref , i qref obtained is subtracted from the actual current i d , i q of the AC side after PI regulation, and a feedforward compensation is introduced to obtain the reference value U d , U q of the d, q-axis voltage of the GCC AC side. Finally, the modulation number is obtained through dq / abc transformation to drive SPWM to generate trigger pulses, thereby controlling the on-off of the GCC.

[0151] S2-3: The difference between the DC bus voltage reference value U dcref and the actual value U dc of the DC bus voltage is obtained after PI regulation to obtain the current reference value i dcref . i dcref is then passed through a high-pass filter to obtain the high-frequency component i HF , and the difference between i dcref and i HF is the low-frequency component i LF , i HF , i LF is the reference current value of the super capacitor and the battery, and the difference between the two and the corresponding actual current is obtained after PI regulation to obtain the respective duty cycle. Finally, the modulation pulse is input into the PWM generator to control the bidirectional DC / DC converter of the battery and the super capacitor unit. Figure 6 is the control block diagram of the hybrid energy storage system based on the high-pass filter.

[0152] The control method of the hybrid energy storage system based on the high-pass filter can realize frequency distribution of unbalanced power, but fails to consider the SOC of the energy storage element, which is prone to overcharge and overdischarge of the energy storage element, and further endangers 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 the SOC of the battery changes unobviously in a short time, while the super capacitor has a small capacity, and the SOC of the super capacitor changes obviously in a short time. Therefore, the SOC of the super capacitor is particularly important to control. The present application adopts a fuzzy second-order filter algorithm to control the hybrid energy storage system, which can ensure that the SOC of the super capacitor is within a reasonable range while realizing the frequency distribution effect of the hybrid energy storage system. The corresponding control block diagram is shown in Figure 7 .

[0153] The method adds a fuzzy controller on the basis of the second-order high-pass filter algorithm, and adaptively adjusts the filter time constant according to the SOC change of the super capacitor, so as to automatically bear the proportion of unbalanced power according to the remaining capacity of the super capacitor, that is, when the SOC is high, the power is high, and when the SOC is low, the power is low.

[0154] In the step S3, based on the step S2, the frequency distribution and stability of the traditional second-order high-pass filter are studied, so that the second-order high-pass filter based on fuzzy logic is further researched, the SOC of the super capacitor is determined as a control index, and the fuzzy rules for adaptively adjusting the filter time constant include the following steps:

[0155] S3-1: The traditional first-order filter algorithm is realized by an inertial link and a differential link, and the power of the high-frequency component in the fluctuating power is distributed to the super capacitor for rapid compensation; the power of the remaining frequency is distributed to the battery for compensation, and the transfer function of the first-order high-pass filter is:

[0156]

[0157] In the formula, s is a differential operator; T is a filter time constant.

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

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

[0160]

[0161] According to the characteristics that the super capacitor has high power density and the battery has high energy density, the high frequency part of the power response of the hybrid energy storage system is allocated to the super capacitor for compensation, and the low frequency part is allocated to the battery for compensation, so that the following mathematical relationship can be obtained:

[0162]

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

[0164] It is assumed that the power P HESS (t) and its derivative can be subjected to Laplace transform, and P HESS (t) has a final value, and after integration of based on the second-order high-pass filter, the following can be obtained:

[0165]

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

[0167] In Figure 8 , the angular frequency ω1 corresponds to the time constant T1, and the angular frequency ω2 corresponds to the time constant T2, and T1>T2. When the time constant is T1, the super capacitor can compensate for high frequency power greater than ω1, and when the time constant is T2, the super capacitor can compensate for high frequency power greater than ω2. Therefore, it can be concluded that the greater the time constant T value, the lower the power frequency that can be suppressed by the super capacitor, and the more power that can be suppressed; on the contrary, the higher the power frequency that can be suppressed, and the less power that can be suppressed. By adjusting the time constant, the power distribution of the hybrid energy storage can be controlled, the power value suppressed by the super capacitor can be changed, that is, the SOC of the super capacitor can be changed, so as to avoid the frequent charging and discharging of the super capacitor.

[0168] The stability of the second-order high-pass filter is usually related to the time constant T. In the present application, the time constant in the second-order high-pass filter is designed to be in the range of [0.2, 0.4]. According to formula (14), taking the values of the time constant T as 0.2, 0.3 and 0.4, the Nyquist curves of the second-order high-pass filter can be made as shown in Figure 9 , from Figure 9It can be seen that when T changes in the range of [0.2, 0.4], the three Nyquist curves are basically coincident, and none of them encloses the point (-1, j0), which indicates that the filter is stable in the range of the time constant T. The step response of the battery and the super capacitor power under the second-order high-pass filter is shown in Fig. 8. Figure 10 As shown in Fig. 9, Figure 10 It can be observed that when the time constant T is 0.2, 0.3, and 0.4 respectively, the battery and the super capacitor respond to the low-frequency power and the high-frequency power respectively. With the decrease of T value, the response time becomes faster and faster, and the system power is still frequency-division allocated.

[0169] S3-2: The second-order high-pass filtering algorithm with a constant time constant has limitations in dynamic performance, which is easy to overcharge and over-discharge the energy storage elements, and has an impact on the service life of the energy storage elements. Based on the above analysis, a hybrid energy storage power distribution method based on fuzzy logic of the time constant of the second-order high-pass filter is proposed. The SOC of the super capacitor is taken as the control index to adaptively adjust the filtering time constant.

[0170] The input variable of the fuzzy controller is the SOC of the super capacitor SC , and the value range is [0, 100], and the output variable of the fuzzy controller is the filtering time constant T C , and the value range is [0.2, 0.4]. The fuzzy language variables of the input variable and the output variable are defined as:

[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] In the formula, S1 to S9 represent that the SOC of the super capacitor is from small to large; T1 to T9 represent that the filtering time constant T C is from small to large.

[0174] There is no fixed rule and mode for the selection of the membership function of the fuzzy subset. Considering the factors such as convenient operation and performance familiarity, the triangular, trapezoidal, and Gaussian membership functions are usually selected, among which the triangular membership function is simple and intuitive, easy to understand, and high in calculation efficiency, so the membership functions of the input and output variables of the fuzzy controller are selected as the uniformly distributed triangular membership functions. Fig. 11 is the corresponding membership function distribution.

[0175] From the above analysis of the output power of the super capacitor, it can be seen that: the greater the value of T C , the lower the power frequency that can be smoothed by the super capacitor, and the more power that can be smoothed; the greater the value of T CThe smaller the value, the higher the power frequency that can be suppressed, and the less power that is suppressed. When the SOC is high, more power is assumed, and when the SOC is low, less power is assumed. Thus, the fuzzy rules of the fuzzy control are shown in Table 2. FIG. 12 is a curve showing the relationship between the super capacitor SOC and the filter time constant. From FIG. 12, it can be seen that when discharging, the filter time constant T C increases with the increase of the super capacitor SOC SC , and is generally proportional. When charging, T C decreases with the decrease of the super capacitor SOC SC , and is generally inversely proportional.

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

[0177] Tab.2 Fuzzy control second-order filtering rule

[0178]

[0179]

[0180] In the step S4, the fuzzy logic variable time constant second-order high-pass filter control established by the step S3 is compared with the constant second-order high-pass filter control in terms of the power of the hybrid energy storage system, the DC bus voltage, the super capacitor SOC under variable illumination and load conditions, and the change curves of the super capacitor SOC of the two strategies are compared under the condition of changing the initial SOC value of the super capacitor. The three-phase current and the total harmonic distortion of the current of the two strategies are also compared to verify the efficiency improvement of the photovoltaic power generation system under the proposed strategy, the SOC of the super capacitor changes within a reasonable range, the microgrid can stably operate in the grid-connected mode, and the harmonic distortion of the grid-connected current is effectively reduced, including the following steps:

[0181] S4-1: To verify the effectiveness of the variable time constant second-order high-pass filter control based on fuzzy logic proposed in the present application, a DC microgrid simulation model as shown in FIG. 12 is built on MATLAB / Simulink. The system parameters are shown in Table 3. Figure 1

[0182] Table 3 Simulation parameters Tab.3Simulation parameters

[0183]

[0184] ​S4-2: The operating condition of the DC micro-grid is set as follows: the simulation time is 4s, the initial light intensity is 800W / m2, the light intensity increases to 1000W / m2 at t=2s, and the light intensity 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, and the stronger the light intensity, the greater the photovoltaic output power. Figure 13 The power of the photovoltaic and the load.

[0185] The simulation comparison is made between the constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter, mainly in terms of the power of the hybrid energy storage system, the DC bus voltage, the super capacitor SOC, etc. FIG. 14 is a simulation comparison result diagram of the constant second-order high-pass filter and the fuzzy variable constant second-order high-pass filter. The subscript 1 represents a constant time constant, and the subscript 2 represents a fuzzy variable time constant. FIG. 14(a) is a comparison of the super capacitor power, FIG. 14(b) is a comparison of the battery power, FIG. 14(c) is a comparison of the DC bus voltage, and FIG. 14(d) is a comparison of the super capacitor SOC operation curve.

[0186] In order to reflect the influence of the fuzzy second-order control on the super capacitor SOC, the operating condition is unchanged, and only the initial SOC value of the super capacitor is changed. The initial SOC value is set to 20 and 90 respectively, and the simulation results under the two strategies are compared and analyzed. FIG. 15 is a variation curve of the super capacitor SOC. FIG. 15(a) is when the SOC is 20, and FIG. 15(b) is when the SOC is 90.

[0187] In order to further compare the effect of the fuzzy second-order filter from the AC aspect, the three-phase current and the total harmonic distortion of the current under the control of the two kinds are introduced. FIG. 16 is a comparison of the three-phase current under the control of the two kinds. FIG. 16(a) and FIG. 16(c) are respectively the three-phase current under the control of the constant second-order filter and the three-phase current under the control of the fuzzy second-order filter, and FIG. 16(b) and FIG. 16(d) are respectively the THD under the control of the constant second-order filter and the THD under the control of the fuzzy second-order filter.

[0188] In order for those skilled in the art to better understand the present application, the example analysis includes the following:

[0189] I. Example description and simulation result analysis

[0190] The simulation comparison of constant second-order high-pass filter and fuzzy variable second-order high-pass filter is carried out, and comparison and analysis are mainly carried out from the aspects of hybrid energy storage system power, DC bus voltage, super capacitor SOC and the like. Fig. 14 is a simulation comparison result diagram of constant second-order high-pass filter and fuzzy variable second-order high-pass filter. The subscript 1 represents a constant time constant, and the subscript 2 represents a fuzzy variable time constant. Fig. 14(a) is a comparison of super capacitor power, Fig. 14(b) is a comparison of battery power, Fig. 14(c) is a comparison of DC bus voltage, and Fig. 14(d) is a comparison of super capacitor SOC operation curve.

[0191] As shown in Fig. 14(a) and Fig. 14(b), when t is 1s and 2s, the hybrid energy storage discharges the release power, and when t is 3s, the hybrid energy storage charges the absorption power. The super capacitor responds to high-frequency power, and the battery responds to low-frequency power. Under the two control strategies, the system unbalanced power can be frequency-allocated and distributed when the light intensity and the load change. Compared with the constant second-order high-pass filter algorithm, the strategy proposed in the application can adaptively adjust the filter constant in the case of light intensity and load mutation, so that the frequency-allocated and distributed effect of the second-order high-pass filter on the power is more significant.

[0192] As shown in Fig. 14(c), it can be observed that the DC bus voltage under the two strategies is basically stable at 700V when the light intensity changes. Under the constant second-order high-pass filter control, the DC bus voltage has an overshoot of 18.41% at 0.028s, is stable at 700V at 0.124s, has an overshoot of 0.36% when the load mutates at t=1s, is stable at 1.10s, has an overshoot of 0.71% when the light intensity changes at t=2s, is restored to 700V at 2.12s, has an overshoot of 1.42% when the photovoltaic fluctuates at 3s, and is stable at 700V at 3.15s. When the fuzzy second-order high-pass filter control is adopted, the DC bus voltage has an overshoot of 17.23% at 0.025s, is stable at 700V at 0.118s, has an overshoot of 0.22% at t=1s, is stable at 1.05s, has an overshoot of 0.43% at t=2s, is restored to 700V at 2.07s, has an overshoot of-1.07% at 3s, and is stable at 700V at 3.09s. The results show that the overshoot of the strategy proposed in the application is smaller than that of the constant second-order filter control, the anti-interference performance is strong, and the response speed is fast.

[0193] As shown in Fig. 14(d), the comparison of super capacitor SOC curves under constant and fuzzy variable constant control can further show that under the fuzzy second-order filter control, the super capacitor has a better effect of responding to high-frequency power, and the change of super capacitor SOC is smaller.

[0194] In order to reflect the influence of fuzzy second-order control on the super capacitor SOC, the operating condition is unchanged, and only the initial SOC value of the super capacitor 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. FIG. 15 is a curve of the change of the super capacitor SOC. FIG. 15(a) is when the SOC is 20, and FIG. 15(b) is when the SOC is 90.

[0195] When the SOC of the super capacitor is 20, the super capacitor is in an over-discharged state. As can be seen from FIG. 15(a), when the constant second-order high-pass filtering method is used, the effect of the super capacitor in responding to high-frequency power is poor, and the super capacitor works in the over-discharged state for a long time. When the strategy proposed in the present application is used, the fuzzy control can adaptively adjust the filtering time constant to control the power smoothed by the super capacitor, so that the SOC has a trend of recovering to a reasonable range, and the super capacitor can work in a reasonable range, while the ability of the super capacitor in responding to high-frequency power is improved. When the SOC of the super capacitor is 90, the super capacitor is in an over-charged state. As can be seen from FIG. 15(b), compared with the constant second-order high-pass filtering, the strategy proposed in the present application can ensure the ability of responding to high-frequency power, while making the SOC of the super capacitor gradually work in a reasonable range, preventing the super capacitor from working in the over-charged state for a long time, and effectively prolonging the service life of the super capacitor.

[0196] In order to further compare the effect of fuzzy second-order filtering from the alternating current aspect, the three-phase current and the total harmonic distortion of the current under the control of the two strategies are introduced. Figures 16(a)-16(d) The three-phase currents of the two algorithms are compared. FIG. 16(a) and FIG. 16(c) are respectively the three-phase currents under the control of the constant second-order filtering and the fuzzy second-order filtering, and FIG. 16(b) and FIG. 16(d) are respectively the THD under the control of the constant second-order filtering and the fuzzy second-order filtering.

[0197] As can be seen from FIG. 16(a) and FIG. 16(c), after the load mutation at t = 1s, the system under the control of the two strategies can stably operate in the grid-connected state. The THD value of the grid-connected current under the control of the constant second-order filtering is 4.10%, and the THD value of the grid-connected current under the control of the fuzzy second-order filtering is 3.62%, both of which meet the grid-connected requirements. However, compared with the constant second-order filtering, the fuzzy variable constant second-order filtering can adaptively adjust the filtering time constant, better suppresses the current harmonics, makes the THD value of the alternating current lower, and has a better grid-connected effect.

[0198] In summary, under the strategy proposed in the present application, the efficiency of the photovoltaic power generation system is improved, the SOC of the super capacitor changes in a reasonable range, the micro-grid can stably operate in the grid-connected mode, and the harmonic distortion of the grid-connected current is effectively reduced.

[0199] Embodiment 2

[0200] The embodiment relates to a power distribution control device of a photovoltaic hybrid energy storage direct current microgrid based on fuzzy second-order high-pass filtering, comprising a memory and one or more processors, the memory stores executable codes, and the one or more processors execute the executable codes to implement the power distribution control method of the photovoltaic hybrid energy storage direct current microgrid based on fuzzy second-order high-pass filtering in the embodiment 1.

[0201] Embodiment 3

[0202] The embodiment relates to a computer readable storage medium, which stores a program, and the program is executed by a processor to implement the power distribution control method of the photovoltaic hybrid energy storage direct current microgrid based on fuzzy second-order high-pass filtering in the embodiment 1.

[0203] In the specification, the illustrative expressions of the present application are not necessarily directed to the same embodiment or example, and a person skilled in the art can combine and combine different embodiments or examples described in the specification. In addition, the content described in the embodiment of the specification is only a list of implementation forms of the inventive concept, and the protection scope of the present application should not be regarded as limited to the specific forms stated in the embodiment, and the protection scope of the present application also includes the equivalent technical means that can be thought by a person skilled in the art according to the inventive concept.

Claims

1. A power distribution control method for a photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering, characterized in that, Includes the following steps: S1: Study the topology 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, establish a comprehensive operation control strategy for the microgrid, determine that the photovoltaic power generation unit adopts fuzzy control for maximum power point tracking (MPPT), the grid-connected inverter (GCC) adopts dual closed-loop control with 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 allocation and stability of the traditional second-order high-pass filter are studied, and a second-order high-pass filter based on fuzzy logic is obtained. The fuzzy rule of adaptively adjusting the filter time constant by taking the SOC of the supercapacitor as the control index is determined. Design a fuzzy controller with the state of charge (SOC) of the supercapacitor as the input variable. SC Its value range is [0, 100], and the output variable of the fuzzy controller is the filter time constant T. C Its value range is [0.2, 0.4]; the fuzzy linguistic variables for the input and output variables are defined as follows: SOC SC {S1,S2,S3,S4,S5,S6,S7,S8,S9} T C {T1,T2,T3,T4,T5,T6,T7,T8,T9} In the formula, S1 to S9 represent the supercapacitor's SOC from small to large; T1 to T9 represent the filtering time constant T. C From small to large; The membership functions of the input and output variables of the fuzzy controller are uniformly distributed triangular membership functions. The fuzzy rule for fuzzy control is derived from the analysis of the supercapacitor's output power: T C The higher the value, the lower the power frequency that the supercapacitor can suppress, and the more power it can suppress; 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 handled, and when the SOC is lower, less power is handled; S4: The fuzzy logic variable time constant second-order high-pass filter control established in step S3 was compared with the constant second-order high-pass filter control under variable illumination and load conditions. The power, DC bus voltage, and supercapacitor SOC of the hybrid energy storage system were simulated and compared. The change curves of supercapacitor SOC under the two strategies were compared under the condition of changing the initial SOC value of the supercapacitor. The three-phase current and total harmonic distortion of the current were also compared between the two strategies. It was verified that the efficiency of the photovoltaic power generation system under the proposed strategy is improved. The SOC of the supercapacitor changes within a reasonable range. The microgrid can operate stably in grid-connected mode, and the grid-connected current harmonic distortion is effectively reduced.

2. The power distribution control method for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in claim 1, characterized in that, Step S1 includes 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 power 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 bidirectional DC / DC converters, mainly used to absorb or release redundant power to ensure the stable operation of the microgrid; resistive loads are 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; A photovoltaic array consists of multiple 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 pv I is the output current of the photovoltaic cell. ph Photocurrent; I s I is the current flowing through the parallel diode; sh This is the current flowing through the parallel bypass resistor; Current I flowing through the parallel diode s and the current I flowing through the parallel bypass resistor sh It can be represented as: In the formula, I o q is the reverse saturation current of the diode; q is the electron charge constant (1.6 × 10⁻⁶). -19 C); A is the diode factor, ranging from 1 to 5; k is the Boltzmann constant (1.38 × 10⁻⁶). -23 j / kWh); T abs Absolute temperature; Substituting equations (2) and (3) into equation (1), we get: The output voltage of the photovoltaic cell is: The general equivalent model of a battery mainly consists of a controlled voltage source connected in series with a resistor, 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 sag in the exponential region; and B is the reciprocal of the capacity in the exponential region. Supercapacitors are double-layer capacitors, and common equivalent models for their electrical characteristics include the first-order RC model, the three-branch model, and the second-order nonlinear RC model. Currently, the classic RC series equivalent model is mainly used in practical engineering and simulation experiments, and its mathematical expression is: In the formula, V SC R is the output voltage of the supercapacitor; sceq C is the equivalent resistance; SC I is the equivalent capacitance. SC This refers to 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. It discharges when the redundant power of the DC microgrid is insufficient and charges when the redundant power of the DC microgrid is excessive. At the same time, in order to ensure that the energy storage equipment can respond quickly to transient changes and stable steady-state processes, a hybrid energy storage system composed 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 of 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 fed into 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) In the formula, P Load P represents the load power. pv P represents the output power of the photovoltaic power generation unit. HESS P represents the power response of the hybrid energy storage unit; it is negative when absorbing electrical energy and positive when releasing electrical energy. Bat P is the output power of the battery. SC This refers to the output power of the supercapacitor.

3. The power distribution control of the photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in claim 2, characterized in that, Step S2 includes the following steps: S2-1: The photovoltaic power generation unit adopts maximum power point tracking (MPPT) control. By measuring the output voltage and current of the photovoltaic array and controlling the connected DC / DC converter, the photovoltaic array can reach and maintain maximum power output under the current environment. Fuzzy control method is used 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 its connected unidirectional DC / DC converter. A corresponding fuzzy MPPT algorithm is designed based on 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 rules. This adjustment is then sent to the PWM signal generator, which finally outputs the corresponding pulse to control the unidirectional DC / DC converter. The designed fuzzy controller is a dual-input single-output controller. The input variables are voltage change dV and 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 for the input and output variables are defined as follows: dV = {NB, NS, ZE, PS, PB} dP = {NB, NS, ZE, PS, PB} dD = {NB, NM, NS, ZE, PS, PM, PB} Wherein, NB, NM, NS, ZE, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively; meanwhile, the fuzzy membership functions of the input and output variables adopt uniformly distributed triangular membership functions, and the corresponding universe of discourse ranges are: dV∈[-1,+1], dP∈[-1,+1], and dD∈[-0.5,+0.5], respectively. Based on the voltage and power relationship of the photovoltaic array, the following fuzzy prior knowledge can be obtained: When dV<0, dP<0, the operating point is to the left of the MPP point, and the voltage should be increased, therefore dD>0; when dV<0, dP>0, the operating point is to the right of the MPP point, and the voltage should be decreased, therefore dD<0; when dV>0, dP<0, the operating point is to the right of the MPP point, and the voltage should be decreased, therefore dD<0; when dV>0, dP>0, the operating point is to the left of the MPP point, and the voltage should be increased, therefore dD>0; in addition, the value of the duty cycle adjustment dD follows the following rule: the step size is smaller when close to the MPP point, and larger when far from the MPP point; A total of 25 rules are formed by defining the number of fuzzy language sets for the input and output variables; S2-2: The grid-connected inverter GCC converts DC power into AC power for distribution to the AC distribution network. The GCC adopts a d-axis grid voltage-oriented vector control, and the control objectives include: 1) ensuring the stability of the DC bus voltage by controlling the active component of the output current; 2) achieving 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 some reactive power support when necessary. The control of the GCC employs a dual closed-loop control method with an outer voltage loop and an inner current loop. By controlling the DC-side voltage, the constant DC bus voltage can be ensured, while simultaneously enabling the GCC to stably transmit electrical energy to the distribution network. Specifically, the d-axis current reference value i is obtained by subtracting the DC-side voltage reference value from the actual value and then adjusting it via a PI controller. dref At the same time, the q-axis current reference value i is adjusted according to the reactive power demand. qref Set it to 0 to achieve unity power factor grid connection; then set the obtained reference value i dref i qref With the actual current i on the AC side d i q After subtraction and PI regulation, and with the introduction of feedforward compensation, the reference values ​​U of the d-axis and q-axis voltages on the GCC AC side are obtained. d U q Finally, the modulation signal is obtained through dq / abc transformation to drive SPWM to generate trigger pulses, thereby controlling the on / off state of GCC; S2-3: Set the DC bus voltage reference value U dcref The actual value of DC bus voltage U dc The current reference value i is obtained by subtraction and PI regulation. dcref Then i dcref The high-frequency component i is obtained through a high-pass filter. HF i dcref with i HF The difference is the low-frequency component i LF i HF i LF This refers to the reference current values ​​for the supercapacitor and the battery. The difference between these two values ​​and the corresponding actual current is used to obtain their respective duty cycles through PI regulation. Finally, the input is sent to the PWM generator to generate modulation pulses, which in turn control the bidirectional DC / DC converters of the battery and supercapacitor units. While the high-pass filter-based control method for hybrid energy storage systems can achieve frequency division and distribution of unbalanced power, it fails to consider the state of charge (SOC) of the energy storage components, which can easily lead to overcharging and over-discharging of the components, thereby jeopardizing the safe operation of the DC microgrid. Since batteries are energy storage devices that can be charged and discharged for extended periods, their SOC changes in the short term are not significant. However, supercapacitors have smaller capacities, and their SOC changes more noticeably in the short term. Therefore, controlling the SOC of supercapacitors is particularly important. Using a fuzzy second-order filtering algorithm to control the hybrid energy storage system can achieve the desired frequency division and distribution while ensuring that the SOC of the supercapacitor remains within a reasonable range. A fuzzy controller was added to the second-order high-pass filtering algorithm to adaptively adjust the filtering time constant according to the SOC change of the supercapacitor. This achieves the purpose of automatically taking on the proportion of unbalanced power based on the remaining capacity of the supercapacitor, that is, taking on more power when the SOC is higher and taking on less power when the SOC is lower.

4. The power distribution control method for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in claim 3, characterized in that, In step S3, based on step S2, the frequency division allocation and stability of the traditional second-order high-pass filter are studied, thereby further studying and obtaining a second-order high-pass filter based on fuzzy logic, including the following steps: Traditional first-order filtering algorithms utilize an inertial element and a differentiating element to allocate the high-frequency components 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 and more obvious; the second-order filtering algorithm is an improvement on the first-order filtering algorithm. It is obtained by connecting two first-order filtering stages in series and has two differential and inertial stages. The second-order high-pass filtering algorithm is as follows: Based on the high power density of supercapacitors and the high energy density of batteries, by allocating the high-frequency component of the hybrid energy storage system's response power to the supercapacitor for compensation and the low-frequency component to the battery for compensation, the following mathematical relationship can be obtained: In the formula, The power compensated by the supercapacitor; Power compensated by the battery; P HESS The power of the hybrid energy storage system response; Assuming the power P of the hybrid energy storage system response HESS Both (t) and its derivative can be subjected to Laplace transform, and P HESS (t) has a final value, and based on the second-order high-pass filtering, ... After integration, we can obtain: As can be seen from equation (16), 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. This increases the response speed and lifespan of the supercapacitor, eliminates the integral effect generated in the traditional first-order filter, reduces the capacity accumulation of the supercapacitor, and ensures that the supercapacitor always works in the best state.

5. The power distribution control method for photovoltaic hybrid energy storage DC microgrid based on fuzzy second-order high-pass filtering as described in claim 4, 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 constant at 2000W; the photovoltaic output power changes with the light intensity, the stronger the light, the greater the photovoltaic output power; Simulations were conducted to compare and contrast constant second-order high-pass filtering and fuzzy variable-constant second-order high-pass filtering, and the comparisons and analyses were performed on the power, DC bus voltage, and supercapacitor SOC of the hybrid energy storage system. To demonstrate the impact of fuzzy second-order control on the SOC of supercapacitors, the operating conditions remained unchanged, but the initial SOC value of the supercapacitors was changed. The initial SOC values ​​were set to 20 and 90, and the simulation results under the two strategies were compared and analyzed. To further compare the effects of fuzzy second-order filtering from an AC perspective, three-phase current and total harmonic distortion of current under two control conditions are introduced.

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

Images