Distributed photovoltaic voltage control method, device, equipment and storage medium

Abstract

The present invention discloses a voltage control method, device, equipment and storage medium for distributed photovoltaics. The method includes: receiving first node data and the collection time of the first node data collected by a measuring device at a grid-connected node of distributed photovoltaics, and recording the receiving time, wherein the first node data includes the output power and node voltage of the grid-connected node; calculating the average communication time lag according to the receiving time and the collection time; using a preset time lag compensation model, predicting the second node data of the grid-connected node in the next cycle according to the first node data; performing timing correction on the second node data according to the average communication time lag to obtain the third node data; generating a reactive control instruction based on the third node data, wherein the reactive control instruction is used to control a photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node. The present invention considers the communication time lag during voltage control, thereby improving the accuracy of voltage control and the stability of the grid voltage.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid voltage control, and in particular to a distributed photovoltaic voltage control method, device, equipment and storage medium. Background Art

[0002] The grid-connected photovoltaic power generation will cause local voltage over-limit problems in multi-level distribution networks. Photovoltaic inverters can adjust reactive power to control voltage, effectively utilize photovoltaic inverter capacity, and exert the ability of fast reactive power compensation, which can alleviate the voltage over-limit phenomenon in the distribution network to a certain extent.

[0003] However, unlike the access form of traditional centralized photovoltaics, distributed photovoltaics are widely dispersed, cover a large area, and have low regional reliability. Control instructions need to rely on communication networks to achieve wide area network exchange at a certain level, which inevitably brings a variety of communication problems such as time delay, data packet loss and disorder, which is not conducive to the rapid response of the control system. Therefore, a distributed photovoltaic voltage control method that copes with random time delay is urgently needed to improve the voltage stability of the regional power grid. Summary of the invention

[0004] The present invention provides a distributed photovoltaic voltage control method, device, equipment and storage medium to solve the technical problem of unstable grid voltage caused by communication time lag in the current voltage control method.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides a voltage control method for distributed photovoltaics, comprising:

[0006] Receiving first node data and the time of collecting the first node data collected by the measuring device at the grid-connected node of the distributed photovoltaic system, and recording the receiving time, wherein the first node data includes the output power and the node voltage of the grid-connected node;

[0007] Calculate the average communication delay based on the receiving time and the collecting time;

[0008] Using a preset time delay compensation model, based on the first node data, predict the second node data of the grid-connected node in the next cycle;

[0009] According to the average communication delay, the second node data is time-series corrected to obtain the third node data;

[0010] Based on the third node data, a reactive power control instruction is generated, and the reactive power control instruction is used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node.

[0011] Preferably, the average communication time delay is calculated according to the receiving time and the collecting time, including:

[0012] Subtract the receiving time from the acquisition time to obtain the communication delay at the receiving time;

[0013] The average communication time delay is calculated by using the preset average communication time delay calculation formula according to the communication time delay. The average communication time delay calculation formula is:

[0014]

[0015] Among them, δ′ t is the average communication delay, q is the average delay correction order, δ t+1-j The communication delay at the reception time.

[0016] Preferably, the expression of the time-delay compensation model is:

[0017]

[0018] Among them, y t+1 is the second node data, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model, y t It is the first node data.

[0019] Preferably, using a preset time delay compensation model, according to the node data, predicting that the grid-connected node is before the second node data of the next cycle, further comprising:

[0020] Obtain multiple historical node data of grid-connected nodes;

[0021] Based on the vector autoregression model, the time series relationship between multiple historical node data is described, and the model parameters of the vector autoregression model are estimated based on the least squares method to obtain the time lag compensation model. The vector autoregression model is:

[0022]

[0023] Among them, y t is the historical node data at the current moment, c is a constant term, ε t is the random error, c+δ t =w,y t-i It is the historical node data of the previous moment.

[0024] Preferably, the second node data is time-sequence corrected according to the average communication delay to obtain the third node data, including:

[0025] Using the preset correction function, the second node data is time-corrected according to the average communication delay to obtain the third node data. The expression of the preset correction function is:

[0026]

[0027] Among them, y t+1 ' is the third node data, y t+1 is the second node data, y t is the first node data, δ′ t is the average communication delay, and T is the preset data transmission interval.

[0028] Preferably, generating a reactive power control instruction based on the third node data includes:

[0029] According to the third node data, determine the droop coefficient of the photovoltaic inverter;

[0030] Using the preset reactive current calculation formula, according to the droop coefficient, the reactive current control value of the photovoltaic inverter is calculated, and a reactive control instruction including the reactive current control value is generated. The reactive current calculation formula is:

[0031]

[0032] Among them, i qref is the reactive current control value, k P and k I is the control parameter of the linear controller, S is the capacity of the photovoltaic inverter, Q is the reactive power of the photovoltaic inverter, K U is the droop coefficient, and ΔU is the voltage change of the grid-connected node.

[0033] Preferably, determining the droop coefficient of the photovoltaic inverter according to the third node data includes:

[0034] The droop coefficient of the photovoltaic inverter is calculated using the preset droop coefficient calculation formula according to the third node data. The droop coefficient calculation formula is:

[0035]

[0036] Among them, P' t+1 is the output power in the third node data, U' t+1 max is the maximum node voltage in the third node data.

[0037] In a second aspect, the present invention further provides a voltage control device for distributed photovoltaics, comprising:

[0038] A receiving module, used to receive first node data and a collection time of the first node data collected by a measuring device at a distributed photovoltaic grid-connected node, and record the receiving time, wherein the first node data includes an output power and a node voltage of the grid-connected node;

[0039] A calculation module, used for calculating an average communication delay according to a receiving time and a collection time;

[0040] A prediction module, used to predict the second node data of the grid-connected node in the next cycle according to the node data by using a preset time delay compensation model;

[0041] A correction module, used for performing timing correction on the second node data according to the average communication time delay to obtain the third node data;

[0042] The generating module is used to generate reactive power control instructions based on the third node data, and the reactive power control instructions are used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node.

[0043] In a third aspect, the present invention further provides a computer device, comprising a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the voltage control method of distributed photovoltaics as in the first aspect is implemented.

[0044] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the voltage control method of distributed photovoltaics as in the first aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The first node data and the collection time of the first node data collected by the receiving measuring device at the grid-connected node of the distributed photovoltaic are recorded, and the average communication delay is calculated according to the receiving time and the collection time, so that the communication delay is considered during voltage control to avoid various communication problems caused by the communication delay; then, a preset delay compensation model is used to predict the second node data of the grid-connected node in the next cycle according to the node data, so as to adaptively offset the delay effect by using the delay compensation model; and according to the average communication delay, the second node data is time-corrected to obtain the third node data, so as to use the real-time communication delay to further reduce the delay effect and improve the compensation delay accuracy; finally, based on the third node data, a reactive power control instruction is generated, so as to improve the voltage control accuracy and the stability of the grid voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic flow chart of a distributed photovoltaic voltage control method according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of a distributed photovoltaic voltage control device according to an embodiment of the present invention;

[0049] Figure 3 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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 creative work are within the scope of protection of the present invention.

[0051] Please refer to Figure 1 , Figure 1 The following is a flow chart of a distributed photovoltaic voltage control method provided by an embodiment of the present invention. The distributed photovoltaic voltage control method of the embodiment of the present invention can be applied to computer devices, including but not limited to distributed photovoltaic controllers, smart phones, laptops, tablet computers, desktop computers, physical servers, cloud servers and other devices. Figure 1 As shown, the voltage control method of distributed photovoltaic in this embodiment includes steps S101 to S105, which are described in detail as follows:

[0052] Step S101, receiving first node data collected by a measuring device at a distributed photovoltaic grid-connected node and a collection time of the first node data, and recording the receiving time, wherein the first node data includes an output power and a node voltage of the grid-connected node.

[0053] In this step, the grid-connected node is the grid node where the distributed photovoltaic is connected to the grid system. A measurement device is installed at the grid-connected node of the distributed photovoltaic to collect the first node data of the distributed photovoltaic in real time, transmit the first node data and the collection time corresponding to the first node data to the computer device, and record the reception time when the computer device receives the first node data.

[0054] Step S102: Calculate an average communication delay according to the receiving time and the collecting time.

[0055] In this step, the average communication delay is the average communication time delay between the computer device and the grid-connected node. Optionally, the communication delay of this transmission process is determined according to the reception time and collection time of the first node data in this transmission process, and then the average communication delay is calculated according to the communication delays of multiple transmission processes.

[0056] In one embodiment, the step S102 includes: performing a subtraction operation on the receiving time and the collection time to obtain a communication delay at the receiving time; using a preset average communication delay calculation formula, according to the communication delay, calculating the average communication delay, the average communication delay calculation formula is:

[0057]

[0058] Among them, δ′ t is the average communication delay, q is the average delay correction order, δ t+1-j is the communication time lag at the receiving moment.

[0059] In this embodiment, the average delay correction order can be the total number of historical transmission processes and the current transmission process, then δ t+1-j It includes the communication time delay of the current transmission process and the communication time delay of the historical transmission process, such as the communication time delay of several transmission processes before the current transmission process.

[0060] Step S103: using a preset time lag compensation model, and based on the first node data, predicting the second node data of the grid-connected node in the next cycle.

[0061] In this step, the time lag compensation model is an uncertain time lag compensation model, which is used to solve the problem of lag in voltage control of distributed photovoltaics. Voltage control includes processes such as data acquisition, command interaction, and inverter control execution. Affected by network carrying capacity and channel status, there is a delay in voltage control. The uncertain time lag compensation model can adaptively offset the delay effect and improve the voltage control accuracy.

[0062] Optionally, the expression of the time lag compensation model is:

[0063]

[0064] Among them, y t+1 is the second node data, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model, y t The first node data.

[0065] In this alternative embodiment, w and Φ i is a model parameter, which is estimated based on multiple historical node data. t Input into the above time-delay compensation model to automatically generate the second node data y t+1 .

[0066] Optionally, the construction process of the time lag compensation model includes: acquiring multiple historical node data of the grid-connected node; describing the time series relationship between the multiple historical node data based on a vector autoregression model, and estimating the model parameters of the vector autoregression model based on the least squares method to obtain the time lag compensation model, and the vector autoregression model is:

[0067]

[0068] Among them, y t is the historical node data at the current moment, c is a constant term, ε t is the random error, c+ε t =w,y t-i It is the historical node data of the previous moment.

[0069] In this optional embodiment, p can be determined by the autocorrelation function graph, y t is a K×1 data vector, Φ i is a K×K coefficient matrix, ε t is a random error of K×1. The parameters c and Φ are estimated based on multiple historical node data using the least squares method. i and ε t , thus obtaining the model parameters w and Φ of the time-delay compensation model i and store the model parameters in a computer device.

[0070] Step S104: performing timing correction on the second node data according to the average communication time delay to obtain third node data.

[0071] In this step, the average communication delay is used to perform timing correction to take into account the delay effect of the actual delay on the data transmission process, thereby further improving the accuracy of the delay compensation. Optionally, a preset correction function is used to perform timing correction on the second node data according to the average communication delay to obtain the third node data, and the expression of the preset correction function is:

[0072]

[0073] Among them, y t+1 ' is the third node data, y t+1 is the second node data, y t is the first node data, δ′ t is the average communication delay, and T is the preset data transmission interval duration.

[0074] It can be understood that T is the data transmission interval of the grid-connected node, when δ′ t ≠T, it means that the second node data predicted by the time lag compensation model has a prediction error, so based on Calculate the third node data to correct the second node data; when δ′ t =T, it means that the second node data predicted by the time-delay compensation model does not have a prediction error, so no correction is required. The second node data is the third node data, that is, y t+1 '=y t+1 .

[0075] Step S105: generating a reactive power control instruction based on the third node data, wherein the reactive power control instruction is used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node.

[0076] In this step, the interactive influence of photovoltaic output and node voltage is taken into account, and the execution accuracy of control instructions is improved by actively sensing and predicting the delay.

[0077] In one embodiment, the droop coefficient of the photovoltaic inverter is determined according to the third node data; the reactive current control value of the photovoltaic inverter is calculated according to the droop coefficient using a preset reactive current calculation formula, and the reactive control instruction including the reactive current control value is generated. Optionally, the droop coefficient of the photovoltaic inverter is calculated according to the third node data using a preset droop coefficient calculation formula.

[0078] In this embodiment, considering the communication delay, the photovoltaic output power and node voltage are:

[0079]

[0080] Adjust the droop coefficient of the distributed photovoltaic inverter. The droop coefficient calculation formula is:

[0081]

[0082] Among them, P' t+1 is the output power in the third node data, U' t+1 max is the maximum node voltage in the third node data.

[0083] According to the power decoupling control principle of photovoltaic inverter, the active power output by photovoltaic inverter can be expressed as:

[0084]

[0085] Where: u d 、u q 、i d and i q They are respectively the d-axis voltage value, q-axis voltage value, d-axis current value and d-axis voltage value of the photovoltaic inverter in the synchronous dq coordinate system.

[0086] The reactive power output by the photovoltaic inverter is controlled by setting the reactive current set value, and the reference value of the reactive current controlled by the current inner loop is modified, wherein the reactive current calculation formula is:

[0087]

[0088] Among them, i qrefis the reactive current control value, k P and k I is the control parameter of the linear controller, S is the capacity of the photovoltaic inverter, Q is the reactive power of the photovoltaic inverter, K U is the droop coefficient, and ΔU is the voltage change of the grid-connected node.

[0089] Optionally, the linear controller may be a PI controller.

[0090] In order to implement the distributed photovoltaic voltage control method corresponding to the above method embodiment, the corresponding functions and technical effects are achieved. Figure 2 , Figure 2 The structure block diagram of a distributed photovoltaic voltage control device provided by an embodiment of the present invention is shown. For the convenience of explanation, only the parts related to this embodiment are shown. The distributed photovoltaic voltage control device provided by an embodiment of the present invention includes:

[0091] The receiving module 201 is used to receive the first node data collected by the measuring device at the grid-connected node of the distributed photovoltaic system and the collection time of the first node data, and record the receiving time, wherein the first node data includes the output power and node voltage of the grid-connected node;

[0092] A calculation module 202, configured to calculate an average communication time delay according to the receiving time and the collecting time;

[0093] A prediction module 203, configured to predict second node data of the grid-connected node in the next cycle according to the first node data by using a preset time delay compensation model;

[0094] A correction module 204, configured to perform timing correction on the second node data according to the average communication time delay to obtain third node data;

[0095] The generating module 205 is used to generate a reactive power control instruction based on the third node data, wherein the reactive power control instruction is used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node.

[0096] In one embodiment, the calculation module 202 is specifically used for:

[0097] performing a subtraction operation on the receiving time and the collecting time to obtain a communication time lag at the receiving time;

[0098] The average communication time delay is calculated according to the communication time delay using a preset average communication time delay calculation formula. The average communication time delay calculation formula is:

[0099]

[0100] Among them, δ′ t is the average communication delay, q is the average delay correction order, δ t+1-j is the communication time lag at the receiving moment.

[0101] In one embodiment, the expression of the time delay compensation model is:

[0102]

[0103] Among them, y t+1 is the second node data, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model, y t The first node data.

[0104] In one embodiment, the device further comprises:

[0105] An acquisition module, used for acquiring a plurality of historical node data of the grid-connected node;

[0106] A model is established to describe the time series relationship between the plurality of historical node data based on a vector autoregression model, and the model parameters of the vector autoregression model are estimated based on the least squares method to obtain the time lag compensation model. The vector autoregression model is:

[0107]

[0108] Among them, y t is the historical node data at the current moment, c is a constant term, ε t is the random error, c+ε t =w,y t-i It is the historical node data of the previous moment.

[0109] In one embodiment, the modified module 204 is specifically configured to:

[0110] Using a preset correction function, the second node data is time-series corrected according to the average communication delay to obtain the third node data. The expression of the preset correction function is:

[0111]

[0112] Among them, y t+1 ' is the third node data, y t+1 is the second node data, y t is the first node data, δ′ t is the average communication delay, and T is the preset data transmission interval duration.

[0113] In one embodiment, the generating module 205 includes:

[0114] A determination unit, configured to determine a droop coefficient of the photovoltaic inverter according to the third node data;

[0115] A calculation unit is used to calculate the reactive current control value of the photovoltaic inverter according to the droop coefficient using a preset reactive current calculation formula, and generate the reactive control instruction including the reactive current control value, wherein the reactive current calculation formula is:

[0116]

[0117] Among them, i qref is the reactive current control value, k P and k I is the control parameter of the linear controller, S is the capacity of the photovoltaic inverter, Q is the reactive power of the photovoltaic inverter, K U is the droop coefficient, and ΔU is the voltage change of the grid-connected node.

[0118] In one embodiment, the determining unit is specifically configured to:

[0119] The droop coefficient of the photovoltaic inverter is calculated according to the third node data using a preset droop coefficient calculation formula, and the droop coefficient calculation formula is:

[0120]

[0121] Among them, P' t+1 is the output power in the third node data, U' t+1 max is the maximum node voltage in the third node data.

[0122] The above-mentioned distributed photovoltaic voltage control device can implement the distributed photovoltaic voltage control method of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiment of the present invention can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0123] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Figure 3 As shown, the computer device 3 of this embodiment includes: at least one processor 30 ( Figure 3 Only one is shown in the figure) a processor, a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, and when the processor 30 executes the computer program 32, the steps in any of the above method embodiments are implemented.

[0124] The computer device 3 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a cloud server, etc. The computer device may include but is not limited to a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0125] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0126] In some embodiments, the memory 31 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 31 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 31 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0127] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0128] An embodiment of the present invention provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.

[0129] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0130] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0131] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A voltage control method for distributed photovoltaics, It is characterized in that include: Receiving first node data collected by a measuring device at a distributed photovoltaic grid-connected node and a collection time of the first node data, and recording the receiving time, wherein the first node data includes an output power and a node voltage of the grid-connected node; Calculating an average communication time delay according to the receiving time and the collecting time; Acquire multiple historical node data of the grid-connected node, describe the time series relationship between the multiple historical node data based on a vector autoregression model, and estimate the model parameters of the vector autoregression model based on the least squares method to obtain a time lag compensation model; wherein the vector autoregression model is: t =c+ In the formula, y t is the historical node data at the current moment, c is a constant term, ε t is the random error, c+ε t =w,y t-i is the historical node data of the previous moment, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-lag compensation model; Using a preset time delay compensation model, according to the first node data, predict the second node data of the grid-connected node in the next cycle; wherein the expression of the time delay compensation model is: t+1 =w+ In the formula, y t+1 is the second node data, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model, y t The first node data; Using a preset correction function, the second node data is time-corrected according to the average communication delay to obtain the third node data; wherein the expression of the preset correction function is: In the formula, y t+1 , is the third node data, y t+1 is the second node data, y t is the first node data, δ t ′ is the average communication delay, and T is the preset data transmission interval duration; The droop coefficient of the photovoltaic inverter is calculated according to the third node data using a preset droop coefficient calculation formula; wherein the droop coefficient calculation formula is: Where P t,+1 is the output power in the third node data, U , t+1 max is the maximum node voltage in the third node data; The reactive current control value of the photovoltaic inverter is calculated according to the droop coefficient using a preset reactive current calculation formula, and a reactive control instruction including the reactive current control value is generated. The reactive control instruction is used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node. The reactive current calculation formula is: In the formula, i qref is the reactive current control value, k P and k I is the control parameter of the linear controller, S is the capacity of the photovoltaic inverter, Q is the reactive power of the photovoltaic inverter, K U is the droop coefficient, and ΔU is the voltage change of the grid-connected node.

2. The voltage control method of distributed photovoltaic according to claim 1, It is characterized in that The calculating the average communication time delay according to the receiving time and the collecting time comprises: performing a subtraction operation on the receiving time and the collecting time to obtain a communication time lag at the receiving time; The average communication time delay is calculated according to the communication time delay using a preset average communication time delay calculation formula. The average communication time delay calculation formula is: Among them, δ t ′ is the average communication delay, q is the average delay correction order, δ t+1-j is the communication time lag at the receiving moment.

3. A voltage control device for distributed photovoltaics, It is characterized in that include: A receiving module, used to receive first node data collected by a measuring device at a distributed photovoltaic grid-connected node and a collection time of the first node data, and record the receiving time, wherein the first node data includes an output power and a node voltage of the grid-connected node; A calculation module, used for calculating an average communication time delay according to the receiving time and the collecting time; A prediction module is used to obtain multiple historical node data of the grid-connected node, describe the time series relationship between the multiple historical node data based on a vector autoregression model, and estimate the model parameters of the vector autoregression model based on the least squares method to obtain a time lag compensation model, and use a preset time lag compensation model to predict the second node data of the grid-connected node in the next cycle according to the first node data; wherein the vector autoregression model is: In the formula, y t is the historical node data at the current moment, c is a constant term, ε t is the random error, c+ε t =w,y t-i is the historical node data of the previous moment, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model; the expression of the time-delay compensation model is: In the formula, y t+1 is the second node data, w is the model parameter, p is the lag order of the time-delay compensation model, Φ i is the coefficient matrix of the time-delay compensation model, y t The first node data; A correction module is used to use a preset correction function to perform timing correction on the second node data according to the average communication delay to obtain third node data; wherein the expression of the preset correction function is: In the formula, y t+1 , is the third node data, y t+1 is the second node data, y t is the first node data, δ t ′ is the average communication delay, and T is the preset data transmission interval duration; A generating module is used to calculate the droop coefficient of the photovoltaic inverter according to the third node data using a preset droop coefficient calculation formula, and to calculate the reactive current control value of the photovoltaic inverter according to the droop coefficient using a preset reactive current calculation formula, and to generate a reactive control instruction including the reactive current control value, wherein the reactive control instruction is used to control the photovoltaic inverter to adjust the reactive power of the grid-connected node to adjust the voltage of the grid-connected node; wherein the droop coefficient calculation formula is: Where P t,+1 is the output power in the third node data, U , t+1 max is the maximum node voltage in the third node data; the reactive current calculation formula is: In the formula, i qref is the reactive current control value, k P and k I is the control parameter of the linear controller, S is the capacity of the photovoltaic inverter, Q is the reactive power of the photovoltaic inverter, K U is the droop coefficient, and ΔU is the voltage change of the grid-connected node.

4. A computer device, It is characterized in that It comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the voltage control method of distributed photovoltaic system as claimed in any one of claims 1 to 2 is implemented.

5. A computer-readable storage medium, It is characterized in that It stores a computer program, and when the computer program is executed by a processor, the voltage control method of distributed photovoltaics as described in any one of claims 1 to 2 is implemented.

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