Secondary optimization control method and system of photovoltaic power supply system based on fixed time consistency

Abstract

The invention discloses a quadratic optimization control method of a photovoltaic power supply system based on fixed time consistency. The quadratic optimization control method comprises the following steps of 1, setting updating rules of a tracking synchronization problem and a coordination synchronization problem and a convergence speed of a state variable xi (t); 2, designing a voltage recovery controller based on a fixed time consistency algorithm; and 3, designing a current balance controller based on a fixed time consistency algorithm, and combining the voltage recovery controller and the current balance controller with optimized droop translation control to realize secondary optimization control. According to the invention, the possible problem of uneven power distribution can be found and solved in time by continuously monitoring data such as the output current of the power module. Once the current deviates, the system can respond quickly, the working state of each module is adjusted by means of optimized droop translation control in combination with monitoring data, the balance of the output current of each power module is ensured, and the equipment loss and system fault risk caused by current imbalance are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of electric energy and power technology, and specifically relates to a secondary optimization control method and system for a photovoltaic power supply system based on fixed time consistency. Background Art

[0002] Low-carbon renewable energy represented by solar energy has the advantages of being clean, pollution-free, abundant and sustainable. It has great potential in addressing climate change and promoting global energy transformation. With technological progress and cost reduction, the proportion of renewable energy in the global energy structure is gradually increasing. The newly installed capacity of global photovoltaic power generation has shown a continuous growth trend in the past decade, from 30.2GW in 2013 to 382.5GW in 2023, and will remain at a compound growth rate of about 14% in the next few years. In actual photovoltaic power supply systems, there are often multiple power modules running in parallel. The coordinated control between the modules is the prerequisite for the stable operation of the entire system. Under the influence of the uncertainty on the "load" side, the bus voltage of the system will show different degrees of decline, and the output current of each module will also show different degrees of deviation. Therefore, it is necessary to design corresponding control algorithms to maintain the voltage stability of the system bus and coordinate the power balance between power sources. There are mainly the following problems to be solved:

[0003] (1) Droop control is usually used to solve the problem of uneven power distribution between modules. However, this method has serious defects when facing load uncertainty, that is, it will cause the bus voltage to be lower than the set reference value, and this phenomenon will become more serious as the load power increases.

[0004] In the field of power systems, droop control is often used to solve the problem of uneven power distribution between modules. However, it is worth noting that this method exposes serious defects when faced with load uncertainty. Specifically, it will cause the bus voltage to be lower than the preset reference value. This phenomenon does not exist in isolation. Its impact is closely related to the change of load power and shows a significant positive correlation trend, that is, it becomes more serious with the increase of load power. The condition that the bus voltage is lower than the set reference value will trigger a series of chain adverse effects. In actual application scenarios, the normal operation of various electrical equipment connected to the bus is highly dependent on a stable voltage supply. For example, for those precision instruments and sensitive electronic equipment that have strict requirements on voltage stability, the reduction of voltage is very likely to cause a significant decline in their performance, abnormal working state, and even permanent damage to the equipment. Especially when the load power continues to rise, the stability and reliability of the system are subjected to unprecedented severe tests. Taking the industrial production scenario as an example, when a production line in full load operation encounters a low voltage, the equipment is very likely to shut down suddenly, leading to the interruption of the production process. This will not only cause direct economic losses, but also cause established production plans to be delayed, and have immeasurable negative impacts on the entire production chain.

[0005] (2) The impedance of the cables between different power modules and the DC bus in the photovoltaic power supply system varies greatly, which will also affect the power distribution effect of traditional DC droop control;

[0006] In the photovoltaic power supply system, there are significant differences in the impedance of the cables between different power modules and the DC bus. This difference has a substantial impact on the power distribution effect of the traditional DC droop control. The inconsistent cable impedance causes the resistance encountered by the current during transmission to be uneven. In this case, even if the same control strategy is adopted, it is difficult to achieve the expected uniform distribution of the power transmitted to the DC bus by different power modules. For example, the output power of the power module connected to the cable with larger impedance is often restricted to a large extent, and it is difficult to fully demonstrate its theoretical performance, and may even fail to reach the expected output level. On the contrary, the power module connected to the cable with smaller impedance may bear too much power output responsibility, causing its workload to exceed the reasonable range, thereby accelerating the aging and loss of the equipment and reducing the overall reliability and stability of the system. This uneven power distribution phenomenon caused by the difference in cable impedance needs to be given enough attention in the actual operation of the photovoltaic power supply system, and more advanced droop control methods should be adopted to optimize and improve it to ensure the efficient and stable operation of the system.

[0007] Based on the above research background analysis, there is an urgent need for a secondary optimization controller for photovoltaic power supply systems based on a fixed-time consistency algorithm to improve the defects of the above traditional DC droop control. Summary of the invention

[0008] The present invention is aimed at the existing technical problems and proposes a secondary optimization control method and system for a photovoltaic power supply system based on fixed time consistency. The present invention is implemented through the following technical solutions:

[0009] A secondary optimization control method for a photovoltaic power supply system based on fixed time consistency comprises the following steps:

[0010] Step 1: Set the update rules and state variables x for tracking synchronization problems and coordinating synchronization problems i (t) convergence speed;

[0011] Step 2: Design a voltage recovery controller based on a fixed time consistency algorithm;

[0012] Step 3: Design a current balancing controller based on a fixed-time consistency algorithm, combine the voltage recovery controller and the current balancing controller with the optimized droop translation control to achieve secondary optimization control.

[0013] Furthermore, in step one, a distributed consistency control method in fixed time consistency is used to share the computing load, which is divided into tracking synchronization problems and coordination synchronization problems.

[0014] Furthermore, in step 1, for the consistency algorithm for tracking synchronization problems, its update rule is set as:

[0015]

[0016] Among them, x ref Indicates the reference state; b i Represents the tracking coefficient, i.e., the state node x i With reference state x ref The weight of the communication edge between them, when the state node x i With reference state x ref When connected i ≠0, otherwise b i =0;

[0017] The asymptotic convergence consistency of the tracking synchronization problem can be expressed as:

[0018]

[0019] State variable x i The convergence rate of (t) is:

[0020]

[0021] Among them, e xi Represents the state variable x iError between the reference value; function sig(x) k = sign(x)|x| k Denotes the acceleration operator introduced by the algorithm; α, β, and γ denote control gains and are all positive numbers; p and q are odd integers and satisfy 0 < p < q; the function tanh(x) is a monotonically increasing odd function with a range of (-1, 1).

[0022] Furthermore, in step one, for the consensus algorithm for the coordination and synchronization problem, set its update rule as:

[0023]

[0024] State variable x i (t) has a convergence rate of:

[0025]

[0026] Furthermore, in step two, design a voltage recovery controller based on the fixed-time consensus algorithm. For a system with N power modules, its control objective can be expressed as:

[0027]

[0028] Among them, T represents the convergence time; U(t) represents the average value of the system output voltage; U ref Represents the DC bus reference voltage value;

[0029] Set the voltage error term as:

[0030]

[0031] Among them, 0 < a ij < 1 represents the control weight, b i Is the tracking coefficient. When the node can receive the system reference voltage U ref When, b i = 1, otherwise b i = 0;

[0032] Set the voltage correction term as:

[0033]

[0034] Among them, α U 、β U And γ U Denote control gains and are all positive numbers.

[0035] Furthermore, in step three, design a current equalization controller based on the fixed-time consensus algorithm. For a system with N power modules, the control objective of the i-th power module can be expressed as:

[0036]

[0037] Among them, I i Represents the output current of the i-th power module, I i max I i The maximum value of

[0038] Set the current error term to:

[0039]

[0040] Set the current correction term to:

[0041]

[0042] Among them, α I and β I Represents control gain and is a positive number.

[0043] Furthermore, in step 3, the optimized droop translation control expression is:

[0044]

[0045] Among them, the offset of the droop curve δU i It is composed of the output voltage information and output current information of each parallel power module, and is generated by the voltage recovery controller and the current balancing controller;

[0046] U dci * is the reference value of the bus voltage after droop control of the i-th power module, U dcref and I oi are the reference voltage and output current of the power module, r di is the droop coefficient, which is regarded as a virtual resistor. Its value depends on the voltage source capacity and the DC bus voltage level, and is expressed as:

[0047] r di =ΔU dc ×(U dc -ΔU dc ) / P ri

[0048] Among them, ΔU dc Indicates the specified DC bus voltage U dc Maximum fluctuation range, P ri is the rated power of the i-th power module;

[0049] Reference voltage The specific expression is:

[0050]

[0051] The present invention also relates to a secondary optimization control system of a photovoltaic power supply system based on fixed time consistency, comprising a computer module, which applies the above-mentioned face super-resolution method based on visual language prior.

[0052] Beneficial Effects

[0053] (1) Real-time monitoring and precise control

[0054] The computer module of the present invention can monitor the output parameters and power of the photovoltaic power supply system online, and provide real-time feedback for the system operation. This enables the secondary optimization control method based on fixed time consistency to be adjusted according to accurate data, further optimize the work of the voltage recovery controller and the current balancing controller, and ensure that the system is always in an efficient and stable operating state. For example, when the load changes, the control strategy can be adjusted in time according to the monitored data, so that the DC bus voltage is quickly stabilized near the set value, achieving accurate voltage control, and effectively reducing the impact of voltage fluctuations on the system.

[0055] (2) Enhance system reliability and stability

[0056] The present invention can timely discover and solve the possible problem of uneven power distribution by continuously monitoring the output current and other data of the power module. Once the current deviates, the system can respond quickly, with the help of optimized droop translation control, combined with monitoring data to adjust the working state of each module, to ensure the balance of the output current of each power module, reduce equipment loss and system failure risks caused by current imbalance, thereby significantly enhancing the reliability and stability of the entire photovoltaic power supply system, extending the service life of the equipment, and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a conventional control block diagram of the present invention and an equivalent circuit diagram when two power modules are operated in parallel.

[0059] Figure 2a It is a schematic diagram of the traditional droop control in the present invention.

[0060] Figure 2b It is a schematic diagram of the improved secondary control based on the droop translation method in the present invention.

[0061] Figure 3a This is a schematic diagram of the traditional control flow before optimization in the present invention.

[0062] Figure 3b It is a schematic diagram of secondary optimization control of the photovoltaic power supply system block diagram in the present invention.

[0063] Figure 4 It is a schematic diagram of the composition structure of the graph in the basic mathematical theory (graph theory) used in the present invention.

[0064] Figure 5 is the DC bus voltage U under the two control strategies in the present invention dc Schematic diagram.

[0065] Figure 6a is the output current I of each parallel power module of the conventional droop control in the present invention oi Schematic diagram.

[0066] Figure 6b The output current I of each parallel power module with improved secondary control in the present invention oi Schematic diagram.

[0067] Figure 7 Schematic diagram of the switching process of the two control strategies in the present invention.

[0068] Figure 8a is the DC bus voltage U of the conventional droop control in the present invention dc Schematic diagram.

[0069] Figure 8b The DC bus voltage U of the improved secondary control in the present invention is dc Schematic diagram.

[0070] Figure 9a is the output current I of each parallel power module under the two control strategies of the present invention oi Schematic diagram.

[0071] Figure 9b is the output current I of each parallel power module under the two control strategies of the present invention oi Schematic diagram. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0073] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0074] The implementation manner is further described in detail below with reference to the accompanying drawings.

[0075] A secondary optimization control method for a photovoltaic power supply system based on fixed time consistency of the present invention comprises the following steps:

[0076] Step 1: Set the update rules and state variables x for tracking synchronization problems and coordinating synchronization problems i (t) convergence speed;

[0077] exist Figure 1 In, R l1 , R l2 are the cable impedances from the two power modules to the common connection point; R o1 , R o2 are the internal impedances of the two power modules respectively; U o1 and U o2 are the output voltages of the two power modules respectively; I load is the current of the common load on the DC bus; I o1 ,I o2 and are the output currents of the two power modules respectively, and can be expressed as follows:

[0078]

[0079] The ratio of the output currents of the two power modules is:

[0080]

[0081] The voltage-current characteristic equation of this method can be expressed as:

[0082]

[0083] Among them, U dci * is the reference value of the bus voltage after droop control of the i-th power module, U dcref and I oi are the reference voltage and output current of the power module, r di is the droop coefficient, which can usually be expressed as:

[0084] rdi=ΔUdc×(Udc-ΔUdc) / Pri

[0085] Among them, ΔU dc Indicates the specified DC bus voltage U dc Maximum fluctuation range, P ri is the rated power of the i-th power module.

[0086] Bus voltage drop value ΔU i It can be expressed as:

[0087]

[0088] Under the traditional DC droop control, the droop curves of the two are shown as curves A and B in Figure 2(a). d2 At this time, the droop curves of the two become curves A1 and B1 in Figure 2(a). Compared with before the droop coefficient changes, the drop of the DC bus voltage is smaller, but the output current difference between the two changes from |I o1 -I o2 |Increased to |I o ′1-I o ′2|.

[0089] Curves A and B in FIG2 are the initial droop curves of the two power units, respectively. Points a and a' are the operating points of the two units when the traditional droop control method is adopted. The operating voltage is U dc0 , the working current is I o1 and I o2 To restore the working voltage to the reference voltage U dcref , and realize the equal flow between the two, and shift the two droop curves upward by δU i The distance between them is 200m, at which time the droop curves of the two become A2 and B2, and they have the same working point b, and the working voltage corresponding to this working point is U dcref , the working current is I' o The above droop translation control can be expressed as:

[0090]

[0091] Among them, the offset of the droop curve δU i It is composed of the output voltage information and output current information of each parallel power module and is generated by secondary control.

[0092] The structure of the secondary control is shown in Figure 3, which mainly includes the following two parts:

[0093] (1) Voltage recovery controller: Its goal is to restore the bus voltage affected by droop control to the set value;

[0094] (2) Current balancing controller: Its goal is to balance the output currents of each parallel-connected power module affected by the cable impedance.

[0095] The schematic diagram of the mathematical theory of graph theory is as follows Figure 4As shown, for a photovoltaic power supply system, the communication topology between each power module belongs to an undirected graph. In an undirected graph, the weight of an edge is symmetric, that is, the edge weight from node i to node j is equal to the edge weight from node j to node i, that is, a ij = a ji . The communication topology between nodes is generally described by the Laplacian matrix L = [l ij ∈ R N×N , and its specific form can be expressed as:

[0096]

[0097] Adopt the distributed consensus control method in fixed-time consensus to allocate the computing load, which is divided into the tracking synchronization problem and the coordination synchronization problem.

[0098] For the consensus algorithm of the tracking synchronization problem, set its update rule as:

[0099]

[0100] Among them, x ref represents the reference state; b i represents the tracking coefficient, that is, the weight of the communication edge between the state node x i and the reference state x ref . When the state node x i is connected to the reference state x ref , b i ≠0, otherwise b i = 0;

[0101] The asymptotic convergence consensus of the tracking synchronization problem can be expressed as:

[0102]

[0103] The convergence speed of the state variable x i (t) is:

[0104]

[0105] Among them, e xi represents the error between the state variable x i and the reference value; the function sig(x) k = sign(x)|x| k represents the acceleration operator introduced by the algorithm; α, β, and γ represent control gains and are all positive numbers; p and q are odd integers and satisfy 0 < p < q; the function tanh(x) is a monotonically increasing odd function, and its value range is (-1, 1).

[0106] For the consensus algorithm of the coordination synchronization problem, set its update rule as:

[0107]

[0108] State variable x i The convergence rate of (t) is:

[0109]

[0110] Step 2: Design a voltage recovery controller based on a fixed time consistency algorithm;

[0111] The voltage recovery controller based on the fixed time consistency algorithm contains a system with N power modules, and its control target can be expressed as:

[0112]

[0113] Where T represents the convergence time; U(t) represents the average value of the system output voltage; U ref Indicates the DC bus reference voltage value;

[0114] Set the voltage error term to:

[0115]

[0116] Among them, 0 ij <1 indicates control weight, b i is the tracking coefficient. When the node can receive the system reference voltage U ref When b i =1, otherwise b i =0;

[0117] Set the voltage correction term to:

[0118]

[0119] Among them, α U , β U and γ U Represents control gain and is a positive number.

[0120] Step 3: Design a current balancing controller based on a fixed-time consistency algorithm, combine the voltage recovery controller and the current balancing controller with the optimized droop translation control to achieve secondary optimization control.

[0121] For a system consisting of N power modules, the control target of the i-th power module can be expressed as:

[0122]

[0123] Among them, I i Represents the output current of the i-th power module, I i ​max I i The maximum value of

[0124] Set the current error term to:

[0125]

[0126] Set the current correction term to:

[0127]

[0128] Among them, α I and β I Represents control gain and is a positive number.

[0129] Furthermore, in step 3, the optimized droop translation control expression is:

[0130]

[0131] Among them, the offset of the droop curve δU i It is composed of the output voltage information and output current information of each parallel power module, and is generated by the voltage recovery controller and the current balancing controller;

[0132] U dci * is the reference value of the bus voltage after droop control of the i-th power module, U dcref and I oi are the reference voltage and output current of the power module, r di is the droop coefficient, which is regarded as a virtual resistor. Its value depends on the voltage source capacity and the DC bus voltage level, and is expressed as:

[0133] r di =ΔU dc ×(U dc -ΔU dc ) / P ri

[0134] Among them, ΔU dc Indicates the specified DC bus voltage U dc Maximum fluctuation range, P ri is the rated power of the i-th power module;

[0135] Reference voltage The specific expression is:

[0136]

[0137] The present invention adopts the Lyapunov stability theorem to prove the stability of the designed optimization controller. First, construct the Lyapunov function V:

[0138]

[0139] Where, the voltage error vector e U =[e U1 ,e U2 ,...,e Un ] T , current error vector e I =[e I1 ,e I2 ,...,e In ] T .

[0140] Taking the derivative of the Lyapunov function we can get:

[0141]

[0142] For the analysis of the voltage recovery controller, the first part can be further written as:

[0143]

[0144] Among them, the matrix H = L + B is a non-singular matrix, and the matrix B = diag{b1, b2, ..., b N} represents the diagonal matrix of tracking coefficients, the matrix L represents the Laplace matrix, and η1(H) represents the minimum eigenvalue of H.

[0145] Let variable H U1 , H U2 and H U3 for:

[0146]

[0147] Let 0<2p / (2q-p)<1, then we can get 0<p / q<2 / 3, and further get:

[0148]

[0149] For the analysis of the current balancing regulator, the second part can be further written as:

[0150]

[0151] Similarly, the following relationship can be obtained:

[0152]

[0153] The Lyapunov function satisfies the following relationship:

[0154]

[0155] Let θ = min{(1 / 2λ U )η1(H)N (3p-4q)p ,(1 / λ I )η2(L)N (3p-4q)p}, the upper bound of the system setting time is:

[0156]

[0157] Based on the above analysis, it is proved that the control strategy can ensure that the system max The voltage is restored to the set value and the current is evenly distributed within a limited time.

[0158] Effect verification

[0159] 1. Simulation analysis and experimental verification

[0160] Both simulation and experiment are based on three power modules (PM i Taking the parallel operation of the power modules as an example, in order to verify the improvement effect of the improved secondary control algorithm on the defects of the traditional droop control, two methods are used to control the parallel power modules. The circuit parameters of each module are shown in Table 1.

[0161] Table 1 System circuit parameters

[0162]

[0163] The simulation system model refers to the structure shown in Figure 3, which consists of modules such as DC source, interface converter, transmission line, common load, primary control and secondary control. In order to verify the voltage recovery and current balancing functions of the control strategy proposed in the present invention, the common load R on the DC bus is set to dc is 50Ω, 20Ω and 10Ω, record the DC bus voltage U dc The output current I of each power module oi The waveform of Figure 5 Figure 6 and Table 2 are the simulation results of the DC bus voltage, and Figure 6 and Table 3 are the simulation results of the output current.

[0164] Depend on Figure 5 As shown in Table 2, when the load changes, the DC bus voltage under traditional droop control has obvious peaks and drops. After the droop control is corrected by the fixed time consistency control method of this subject, the DC bus voltage can be restored to within ±5% of its initial set value within a certain period of time, and has a smaller peak, ensuring that the voltage deviation does not exceed the maximum fluctuation range allowed by the system.

[0165] Table 2 DC bus voltage steady-state error under different loads

[0166]

[0167] By comparing the steady-state values ​​of the output currents of the three power modules under different loads in FIG6 (a), FIG6 (b) and Table 3, it can be found that compared with the traditional DC droop control, the fixed time consistency method proposed in the present invention can significantly reduce the output current deviation caused by inconsistent line impedance, and effectively achieve the balanced distribution of the output power of each power module. In addition, when encountering a sudden load change, this method can also quickly control the output current back to a stable state.

[0168] Table 3 Simulation results of power module output current under different loads

[0169]

[0170] The experiment of the present invention will be carried out by connecting RT Box 1 back to back, and the waveform of the experiment will be recorded by an oscilloscope. The circuit parameters of the experiment are the same as those in Table 1. In order to verify the voltage recovery and current balancing functions of the control strategy proposed in the present invention, the common load R dc The resistances are 200Ω, 100Ω and 50Ω, and the bus voltage and the voltage and current waveforms of each power unit are recorded respectively.

[0171] Figure 7 The output voltage waveforms of each power unit before and after the secondary control is adopted. It can be seen that under the traditional droop control, there is a large deviation between the voltages of each power unit, which will cause the bus voltage to deviate from the given value. After the secondary control, the average value of the bus voltage of each power module is adjusted to the same value. Although the fluctuation increases, it is more helpful to maintain the bus voltage.

[0172] Figure 8 shows the bus voltage waveforms under the two control methods, and Table 4 shows the detailed data of the waveforms. It can be seen that when the public load changes, the bus voltage using the traditional droop control method has a significant drop, while the bus voltage using the secondary control method can be restored to the set value.

[0173] Table 4 DC bus voltage steady-state error under two control strategies

[0174]

[0175] Figure 9 shows the output current waveforms of each power module under the two control methods, and Table 5 shows the detailed data of the waveforms. It can be seen that under the traditional droop control method, there is a certain deviation between the currents of each module; after adopting the secondary control, the current of each module is maintained near a certain same value without increasing the current fluctuation, which significantly improves the power distribution effect of the system.

[0176] Table 5 Output current values ​​of each parallel power module under two control strategies

[0177]

Claims

1. A secondary optimization control method for a photovoltaic power supply system based on fixed time consistency, characterized in that: The following steps are involved: Step 1: Set the update rules and state variables x for tracking synchronization problems and coordinating synchronization problems i (t) convergence speed; Step 2: Design a voltage recovery controller based on a fixed time consistency algorithm; Step 3: Design a current balancing controller based on a fixed-time consistency algorithm, combine the voltage recovery controller and the current balancing controller with the optimized droop translation control to achieve secondary optimization control.

2. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step one, the distributed consistency control method in fixed time consistency is used to share the computing load, which is divided into tracking synchronization problems and coordination synchronization problems.

3. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step 1, for the consistency algorithm that tracks synchronization problems, set its update rule as follows: Among them, x ref Indicates the reference state; b i Represents the tracking coefficient, i.e., the state node x i With reference state x ref The weight of the communication edge between them, when the state node x i With reference state x ref When connected i ≠0, otherwise b i =0; The asymptotic convergence consistency of the tracking synchronization problem can be expressed as: State variable x i The convergence rate of (t) is: where, e xi represents the error between the state variable x i and the reference value; the function sig(x) k = sign(x)|x| k represents the acceleration operator introduced by the algorithm; α, β, and γ represent control gains and are all positive numbers; p and q are odd integers and satisfy 0 < p < q; the function tanh(x) is a monotonically increasing odd function with a value range of (-1, 1).

4. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step 1, for the consistency algorithm of the coordination synchronization problem, set its update rule as: State variable x i The convergence rate of (t) is:

5. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step 2, a voltage recovery controller based on a fixed-time consistency algorithm is designed. For a system with N power modules, the control objective can be expressed as: Where T represents the convergence time; Indicates the average value of system output voltage; U ref Indicates the DC bus reference voltage value; Set the voltage error term to: Among them, 0 ij <1 indicates control weight, b i is the tracking coefficient. When the node can receive the system reference voltage U ref When b i =1, otherwise b i =0;​ Set the voltage correction term to: Among them, α U , β U and γ U Represents control gain and is a positive number.

6. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step 3, a current balancing controller based on a fixed-time consistency algorithm is designed. For a system containing N power modules, the control target of the i-th power module can be expressed as: Among them, I i Represents the output current of the i-th power module, I i max Indicates I i The maximum value of Set the current error term to: Set the current correction term to: Among them, α I and β I Represents control gain and is a positive number.

7. According to the secondary optimization control method of the photovoltaic power supply system based on fixed time consistency described in claim 1, it is characterized in that: In step 3, the optimized droop translation control expression is: Among them, the offset of the droop curve δU i It is composed of the output voltage information and output current information of each parallel power module, and is generated by the voltage recovery controller and the current balancing controller; U dci * is the reference value of the bus voltage after droop control of the i-th power module, U dcref and I oi are the reference voltage and output current of the power module, r di is the droop coefficient, which is regarded as a virtual resistor. Its value depends on the voltage source capacity and the DC bus voltage level, and is expressed as: r di =ΔU dc ×(U dc -ΔU dc ) / P ri Among them, ΔU dc Indicates the specified DC bus voltage U dc Maximum fluctuation range, P ri is the rated power of the i-th power module; Reference voltage The specific expression is:

8. A secondary optimization control system for a photovoltaic power supply system based on fixed time consistency, characterized in that: It comprises a computer module, which applies the face super-resolution method based on visual language prior as described in any one of claims 1 to 7.

Images