Secondary Optimization Control Method and System for Photovoltaic Power Supply System Based on Fixed-Time Consistency

Abstract

A secondary optimization control method for a photovoltaic power supply system based on fixed-time consensus includes the following steps: Step 1, set the update rules for the tracking synchronization problem and the coordination synchronization problem and the convergence rate of the state variable xi(t); Step 2, design a voltage restoration controller based on the fixed-time consensus algorithm; Step 3, design a current balancing controller based on the fixed-time consensus algorithm, and combine the voltage restoration controller and the current balancing controller with the optimized droop translation control to achieve secondary optimization control. The present invention can continuously monitor data such as the output current of the power module, timely discover and solve the possible problem of uneven power distribution. Once a current deviation occurs, the system can quickly respond, and with the help of the optimized droop translation control, adjust the working states of each module in combination with the monitored data to ensure the balance of the output current of each power module and reduce the risks of equipment loss and system failure caused by current imbalance.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy and power, 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, rich and sustainable, and 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, increasing from 30.2 GW in 2013 to 382.5 GW in 2023, and will still maintain a compound growth rate of approximately 14% in the next few years. In an actual photovoltaic power supply system, there are often multiple power modules operating in parallel, and achieving coordinated control among the modules is a 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 drop to varying degrees, and the output currents of each module will also deviate to varying degrees. Therefore, it is necessary to design corresponding control algorithms to maintain the voltage stability of the system bus and coordinate the power balance among power sources. The following main problems need to be solved:

[0003] (1) Usually, droop control is adopted to solve the problem of uneven power distribution among modules, but 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 usually applied to solve the problem of uneven power distribution among modules. However, it is worth noting that when faced with load uncertainty, this method exposes serious defects. Specifically, it will cause the bus voltage to drop below the pre-set reference value. This phenomenon does not exist in isolation, and its impact degree is closely related to the change of load power, and shows a significant positive correlation trend, that is, it becomes more serious as the load power increases. The situation 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 highly depends on a stable voltage supply. For example, for precision instruments and sensitive electronic devices with strict requirements for voltage stability, a voltage drop is very likely to cause a significant decline in their performance, abnormal working conditions, and even permanent damage to the equipment. Especially when the load power continues to rise, the stability and reliability of the system are severely tested. Taking the industrial production scenario as an example, when a production line operating at full load encounters a situation of too low voltage, the equipment is very likely to suddenly stop, resulting in the interruption of the production process. This will not only cause direct economic losses, but also delay the established production plan, having an inestimable negative impact on the entire production chain.

[0005] (2) In the photovoltaic power supply system, the impedance differences of the cables between different power modules and the DC bus are relatively large, and the power distribution effect of the traditional DC droop control will also be affected;

[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 inconsistency of the cable impedance causes the resistance encountered by the current during transmission to vary unevenly. In this case, even if the same control strategy is adopted, it is difficult to achieve the expected uniform distribution of the power delivered by different power modules to the DC bus. For example, the output power of the power module connected to the cable with a larger impedance is often restricted to a greater extent, and it is difficult to fully display its theoretical efficiency, and may even fail to reach the due output level. On the contrary, the power module connected to the cable with a smaller impedance may bear too much power output responsibility, causing its working load 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 phenomenon of uneven power distribution caused by the cable impedance difference needs to be taken seriously 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 the photovoltaic power supply system based on the fixed-time consensus algorithm to improve the defects of the above traditional DC droop control. Summary of the Invention

[0008] The present invention is directed to the problems of the prior art 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 includes the following steps:

[0010] Step 1: Set the update rules for the tracking synchronization problem and the coordination synchronization problem and the convergence rate of the state variable x i (t);

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

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

[0013] Further, in Step 1, a distributed consistency control method in fixed-time consistency is adopted to share the computing load, which is divided into a tracking synchronization problem and a coordination synchronization problem.

[0014] Further, in Step 1, for the consistency algorithm of the tracking synchronization problem, set its update rule as:

[0015]

[0016] where 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;

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

[0018]

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

[0020]

[0021] where 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; 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).

[0022] Furthermore, in step one, for the consensus algorithm of 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 containing N power modules, its control objective can be expressed as:

[0027]

[0028] Among them, T represents the convergence time; Denotes the average value of the system output voltage; U ref Denotes 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 , 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.

[0035] Furthermore, in step three, design a current equalization controller based on the fixed-time consensus algorithm. For a system containing 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 represents the maximum value of I i ;

[0038] Set the current error term as:

[0039]

[0040] Set the current correction term as:

[0041]

[0042] Among them, α I and β I represent control gains and are both positive numbers.

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

[0044]

[0045] Among them, the offset δU of the droop curve i is jointly 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 equalization controller;

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

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

[0048] Among them, ΔU dc represents the specified maximum fluctuation range of the DC bus voltage U dc , 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 for a photovoltaic power supply system based on fixed - time consistency, including a computer module that 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 online monitor data such as the output parameters and power of the photovoltaic power supply system, providing real - time feedback for the system operation. This enables the secondary optimization control method based on fixed - time consistency to adjust according to accurate data, further optimizing the operation of the voltage recovery controller and the current balance controller, and ensuring that the system is always in an efficient and stable operation state. For example, when the load changes, the control strategy can be adjusted in a timely manner according to the monitored data, quickly stabilizing the DC bus voltage near the set value, achieving precise voltage control, and effectively reducing the impact of voltage fluctuations on the system.

[0055] (2) Enhancing system reliability and stability

[0056] The present invention can continuously monitor data such as the output current of the power module, timely discover and solve possible problems of uneven power distribution. Once a current deviation occurs, the system can respond quickly, and through optimized droop translation control, combined with the monitored data, adjust the working state of each module, ensuring the balance of the output current of each power module, reducing the equipment loss and system failure risk caused by current imbalance, thus significantly enhancing the reliability and stability of the entire photovoltaic power supply system, extending the service life of the equipment, and reducing the maintenance cost. Description of the drawings

[0057] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following will briefly introduce the drawings required for use in the description of the specific embodiments or related technologies. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

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

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

[0060] Figure 2bSchematic diagram of the improved secondary control based on the droop translation method in the present invention.

[0061] Figure 3a Schematic diagram of the traditional control process before optimization in the present invention.

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

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

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

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

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

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

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

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

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

[0071] Figure 9b Schematic diagram of the output current I of each parallel power module under two control strategies in the present invention. oi Schematic diagram. Specific implementation mode

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0073] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0074] The following further details the implementation manner 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 includes the following steps:

[0076] Step 1: Set the update rules for the tracking synchronization problem and the coordination synchronization problem and the convergence rate of the state variable x i (t).

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

[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 given reference value of the bus voltage after droop control adjustment of the i-th power module, U dcref and I oi are respectively the reference voltage and output current of the power module, r di is the droop coefficient and can usually be expressed as:

[0084] r di =ΔU dc ×(U dc -ΔU dc ) / P ri

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

[0086] The value of the bus voltage drop ΔU i can be expressed as:

[0087]

[0088] Under traditional DC droop control, the droop curves of the two are shown as curves A and B in Fig. 2(a). When the droop coefficient is changed to a smaller value r d2 , the droop curves of the two then become curves A1 and B1 in Fig. 2(a). Compared with before the change of the droop coefficient, the drop of the DC bus voltage is smaller, but the difference between their output currents increases from |I o1 - I o2 | to |I' o1 - I' o2 |.

[0089] Curves A and B in Fig. 2 are respectively the initial droop curves of the two power units. Points a and a' are the operating points of the two when using the traditional droop control method, and the operating voltage is U dc0 , and the operating currents are I o1 and I o2 respectively. To restore the operating voltage to the reference voltage U dcref , and to achieve current sharing between the two, the two droop curves are respectively translated upward by a distance of δU i . At this time, the droop curves of the two become A2 and B2, and they have the same operating point b. The operating voltage corresponding to this operating point is U dcref , and the operating current is I' o . The above-mentioned droop translation control can be expressed as:

[0090]

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

[0092] The structure of the secondary control is shown in Fig. 3 and mainly includes the following two parts:

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

[0094] (2) Current equalization controller: Its goal is to achieve current sharing of the output currents of each parallel power module affected by the cable impedance.

[0095] The schematic diagram of the graph theory of mathematical theory is as Figure 4 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 rate 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 for coordination and synchronization problems, set its update rule as follows:

[0107]

[0108] The state variable x i (t) has a convergence rate of:

[0109]

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

[0111] For a system with N power modules, the control objective of the voltage recovery controller based on the fixed-time consensus algorithm can be expressed as:

[0112]

[0113] where T represents the convergence time; represents the average value of the system output voltage; U ref represents the DC bus reference voltage value;

[0114] Set the voltage error term as:

[0115]

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

[0117] Set the voltage correction term as:

[0118]

[0119] where α U , β U and γ U represent control gains and are all positive numbers.

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

[0121] For a system with N power modules, the control objective 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, and I i max represents the maximum value of I i ;

[0124] Set the current error term as:

[0125]

[0126] Set the current correction term as:

[0127]

[0128] Among them, α I and β I represent control gains and are both positive numbers.

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

[0130]

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

[0132] U dci * is the given reference value of the bus voltage after droop control adjustment for the i-th power module, U dcref and I oi are the reference voltage and output current of the power module respectively, and r di is the droop coefficient, regarded as a virtual resistance, and its value selection 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 represents the specified maximum fluctuation range of the DC bus voltage U dc , and P ri is the rated power of the i-th power module;

[0135] Reference voltage The specific expression is:

[0136]

[0137] For the optimized controller designed in the present invention, the Lyapunov stability theorem is used to prove its stability. First, construct the Lyapunov function V:

[0138]

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

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

[0141]

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

[0143]

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

[0145] Let the variables H U1 , H U2 and H U3 be:

[0146]

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

[0148]

[0149] For the analysis of the current balance 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 settling time is:

[0156]

[0157] Based on the above analysis, it is proved that the control strategy can ensure that the system can restore the voltage to the set value and achieve current sharing within a finite time of up to T max .

[0158] Effect verification

[0159] 1. Simulation analysis and experimental verification

[0160] Both the simulation and the experiment take the parallel operation of three power modules (denoted by PM i ) 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 respectively 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 and is composed of modules such as a DC source, an interface converter, a transmission line, a common load, a primary control, and a secondary control. To verify the voltage recovery and current equalization functions of the control strategy proposed in the present invention, the common load R dc on the DC bus is respectively set to 50 Ω, 20 Ω, and 10 Ω at the simulation times t = 0 - 3 s, t = 3 - 6 s, and t = 6 - 9 s, and the waveforms of the DC bus voltage U dc and the output current I oi of each power module are recorded, where Figure 5 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] From Figure 5As can be seen from Table 2, when the load changes, the DC bus voltage under traditional droop control has obvious spikes and drops. After correcting the droop control using the fixed-time consensus control method of this research topic, the DC bus voltage can recover within ±5% of its initial set value within a certain time, and has smaller spikes, ensuring that the voltage deviation does not exceed the maximum allowable fluctuation range of the system.

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

[0166]

[0167]

[0168] By comparing the steady-state values of the output currents of the three power modules under different loads in Fig. 6(a), Fig. 6(b) and Table 3, it can be found that, compared with traditional DC droop control, the fixed-time consensus method proposed in this invention can significantly reduce the output current deviation caused by inconsistent line impedances, and effectively achieve the balanced distribution of the output powers of each power module. In addition, when encountering sudden load changes, this method can also quickly re-control the output current to a stable state.

[0169] Table 3 Simulation results of output currents of power modules under different loads

[0170]

[0171] The experiment of this invention will be carried out in the back-to-back connection mode of RT Box 1, and an oscillograph will be used to record the waveforms of the experiment. The circuit parameters of the experiment are the same as those in Table 1. To verify the voltage recovery and current balancing functions of the control strategy proposed in this invention, the common load R on the DC bus is respectively set to 200 Ω, 100 Ω and 50 Ω, and the waveforms of the bus voltage and the voltage and current of each power unit are recorded respectively. dc be 200 Ω, 100 Ω and 50 Ω, and record the waveforms of the bus voltage and the voltage and current of each power unit respectively.

[0172] Figure 7 are the output voltage waveforms of each power unit before and after adopting the secondary control. It can be seen that under traditional droop control, there are large deviations 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 values of the bus voltages of each power module are adjusted to the same value. Although the fluctuations increase, it is more helpful to maintain the bus voltage.

[0173] Fig. 8 shows the bus voltage waveforms under two control methods, and Table 4 shows the detailed data of the waveforms. It can be seen that when the common load changes, the bus voltage under the traditional droop control method has obvious drops, while the bus voltage under the secondary control method can recover to the set value.

[0174] Table 4 Steady-state error of DC bus voltage under two control strategies

[0175]

[0176] Figure 9 shows the output current waveforms of each power module under two control methods, and Table 5 shows the detailed data of the waveforms. It can be seen that there are certain deviations between the currents of each module under the traditional droop control method; while after the secondary control is adopted, without increasing the current fluctuation, the currents of each module are maintained near a certain same value, significantly improving the power distribution effect of the system.

[0177] Table 5 Output current values of each parallel power module under two control strategies

[0178]

Claims

1. A secondary optimization control method for a photovoltaic power supply system based on fixed-time consistency, characterized in that, It includes the following steps: Step 1: Set the update rules and status variable x for tracking synchronization problems and coordinating synchronization problems i (t) convergence rate; Step 2: Design a voltage recovery controller based on the fixed-time consensus algorithm; Step 3: Design a current equalization controller based on the fixed-time consensus algorithm, and combine the voltage recovery controller and the current equalization controller with the optimized droop translation control to achieve secondary optimization control; In Step 1, for the consensus algorithm of the tracking synchronization problem, set its update rule as: where 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; The asymptotic convergence consensus of the tracking synchronization problem can be expressed as: State variable x i (t) has a convergence rate of: 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); In Step 1, for the consensus algorithm of the coordination synchronization problem, set its update rule as: State variable x i (t) has a convergence rate of: In Step 2, 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: Among them, T represents the convergence time; represents the average value of the system output voltage; U ref represents the DC bus reference voltage value; Set the voltage error term as: Among them, 0 < a ij <1 represents the control weight, and b i is the tracking coefficient. When the node can receive the system reference voltage U ref , b i = 1; otherwise, b i = 0; Set the voltage correction term as: where α U , β U and γ U represent control gains and are all positive numbers; In Step 3, 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: Among them, I i represents the output current of the i-th power module, I i max represents the maximum value of I i ; Set the current error term as: Set the current correction term as: where α I and β I represent control gains and are both positive numbers.

2. The secondary optimization control method of a photovoltaic power supply system based on fixed-time consistency according to claim 1, characterized in that, In Step 1, adopt the distributed consensus control method in the fixed-time consensus to allocate the computing load, which is divided into the tracking synchronization problem and the coordination synchronization problem.

3. The secondary optimization control method of a photovoltaic power supply system based on fixed-time consistency according to claim 1, characterized in that, In Step 3, the expression of the optimized droop translation control is: Among them, the offset δU of the sag curve i is jointly 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 equalization controller; U dci * is the given reference value of the bus voltage after droop control adjustment for the i-th power module, U dcref and I oi are the reference voltage and output current of the power module respectively, r di is the droop coefficient, regarded as a virtual resistance, and the selection of its value depends on the voltage source capacity and the DC bus voltage level, expressed as: r di = ΔU dc × (U dc - ΔU dc ) / P ri Among them, ΔU dc represents the specified DC bus voltage U dc within the maximum fluctuation range, and P ri is the rated power of the i-th power module; Reference voltage The specific expression is as follows:

4. A secondary optimization control system for a photovoltaic power supply system based on fixed-time consistency, characterized in that, It includes a computer module that applies the secondary optimization control method of the photovoltaic power supply system based on the fixed-time consensus according to any one of claims 1 to 3.

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