Distributed power grid overvoltage treatment method and device, terminal and storage medium

Abstract

The present application relates to the technical field of distributed power grid regulation, and particularly relates to a distributed power grid overvoltage treatment method, device, terminal and storage medium, the method of the present application first acquires a plurality of load feeder datasets representing a plurality of target load feeder currents; then determines a plurality of target distributed power sources according to a plurality of power feeder datasets and the plurality of load feeder datasets; then determines a plurality of load characteristics corresponding to a plurality of target load feeders according to bus voltage waveform datasets and the plurality of load feeder datasets; and finally adjusts output currents of the plurality of target distributed power sources according to the plurality of load characteristics. The embodiments of the present application do not need to analyze network topological structures, load sizes and distributed power source outputs, so the adjustment mode is relatively simple, the calculation amount is small, the active output of the distributed power source is not adjusted, and the energy waste of the distributed power source is small.

Description

Technical Field

[0001] This invention relates to the field of distributed power grid control technology, and in particular to a method, device, terminal and storage medium for overvoltage control in distributed power grids. Background Technology

[0002] Distributed power generation is a new type of power supply system that is completely different from traditional power supply modes. To meet the needs of specific users or support the economic operation of existing distribution networks, it consists of small, modular, and environmentally compatible independent power sources that are distributed in a decentralized manner near users and have a power generation capacity of several kilowatts to fifty megawatts. It is usually located near users and includes bioenergy power generation, gas turbines, solar power generation and photovoltaic cells, fuel cells, wind power generation, microprocessor gas turbines, internal combustion engines, and storage control technologies.

[0003] In existing distributed grid technologies, the main method of power access relies on distributed generation sources connecting to the main power source. Distributed generation sources alter the power flow distribution and direction of traditional distribution networks. These changes in power flow can affect the steady-state voltage distribution of the grid and may even cause overvoltages at certain nodes in the distribution network, thus preventing distributed photovoltaic power sources from being properly connected to the distribution network.

[0004] Therefore, it is necessary to develop and design a method for managing overvoltage in distributed power grids. Summary of the Invention

[0005] The present invention provides a method, device, terminal and storage medium for managing overvoltage in distributed power grids, which is used to solve the problem of overvoltage in some nodes of the power grid caused by the access of distributed power sources in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a method for managing overvoltage in a distributed power grid, comprising:

[0007] Obtain multiple load feeder datasets characterizing the current of multiple target load feeders. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold.

[0008] Based on multiple power feeder datasets and multiple load feeder datasets, multiple target distributed power sources are identified. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity by connecting to the grid through the power feeders. The target distributed power sources affect the voltage of the target load feeders.

[0009] Based on the bus voltage waveform dataset and the multiple load feeder datasets, multiple load characteristics corresponding to multiple target load feeders are determined, wherein the load characteristics characterize the relationship between load current and bus voltage;

[0010] The output current of the multiple target distributed power sources is adjusted according to the multiple load characteristics.

[0011] In one possible implementation, determining multiple target distributed power sources based on multiple power feeder datasets and the multiple load feeder datasets includes:

[0012] Based on the first formula, the multiple power supply feeder datasets, and the multiple load feeder datasets, multiple first current feature sets corresponding to the multiple power supply feeders and multiple second current feature sets corresponding to the multiple load feeders are extracted, wherein the first formula is:

[0013]

[0014] In the formula, Ifeature(2) is the second element of the first current feature set or the second current feature set, Ifeeder(m) is, M is the total number of elements in the power supply feeder dataset or the load feeder dataset, sin() is the sine function, cos() is the cosine function, ω0 is the angular frequency of the voltage waveform, and Δt is the time difference between two adjacent elements in the power supply feeder dataset or the load feeder dataset.

[0015] Based on the plurality of first current feature sets and the plurality of second current feature sets, the influence coefficients of the plurality of distributed power sources on the plurality of target load feeders are determined;

[0016] The multiple target distributed power sources are determined based on the influence threshold and the influence coefficients of multiple distributed power sources on multiple target load feeders.

[0017] In one possible implementation, determining the influence coefficients of multiple distributed power sources on multiple target load feeders based on the plurality of first current characteristic sets and the plurality of second current characteristic sets includes:

[0018] For each feature set in the plurality of second current feature sets, the following steps are performed:

[0019] Based on the second formula, the second current characteristic set, and the plurality of first current characteristic sets, the influence coefficients of the target load feeder and the plurality of target distributed power sources are determined respectively, wherein the second formula is:

[0020]

[0021] In the formula, Ifactor(k) is the influence coefficient of the k-th distributed generation on the load feeder, and Ifeature pk (n) is the nth element in the kth first current feature set, Ifeature l(n) is the nth element in the second current feature set, and N is the total number of elements in the second current feature set.

[0022] In one possible implementation, the bus voltage waveform dataset and the plurality of load feeder datasets are acquired based on the same time period. The determination of multiple load characteristics corresponding to multiple target load feeders based on the bus voltage waveform dataset and the plurality of load feeder datasets, wherein the load characteristics characterize the relationship between load current and bus voltage, including:

[0023] Zero-phase data is determined from multiple data points in the bus voltage waveform dataset, wherein the zero-phase data is obtained based on the zero phase of the bus voltage waveform;

[0024] The data from the first data position to the zero-phase data position in the bus voltage waveform dataset and the multiple load feeder datasets are cropped, retaining the data after the zero-phase data position;

[0025] The bus voltage waveform dataset and the multiple load feeder datasets are respectively normalized.

[0026] For each of the multiple load feeder datasets, perform the following steps:

[0027] Based on the third formula, the load feeder dataset, and the bus voltage waveform dataset, the phase coefficient is determined, wherein the third formula is:

[0028]

[0029] In the formula, Sp is the phase coefficient, Ubus(q) is the qth data in the bus voltage waveform dataset, Ifeederl(q) is the qth data in the load feeder dataset, and Q is determined according to the bus voltage waveform dataset and the load feeder dataset. The absolute value of the difference between the Qth data in the bus voltage waveform dataset and the Qth data in the load feeder dataset is less than the threshold.

[0030] The power factor of the load is determined based on the load feeder dataset and the bus voltage waveform dataset.

[0031] In one possible implementation, determining the power factor of the load based on the load feeder dataset and the bus voltage waveform dataset includes:

[0032] The power factor of the load is determined according to the fourth formula, the load feeder dataset, and the bus voltage waveform dataset, wherein the fourth formula is:

[0033]

[0034] In the formula, PFC is the power factor, Ubus(m) is the m-th element in the bus voltage waveform dataset, Ifeederl(m) is the m-th element in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0035] In one possible implementation, the load characteristics include a phase coefficient and a power factor, and the adjustment of the output current of the plurality of target distributed power sources based on the plurality of load characteristics includes:

[0036] For each of the multiple load feeder datasets, perform the following steps:

[0037] Based on the fifth formula, power factor, and load feeder data set, the maximum reactive power compensation current is determined, wherein the fifth formula is:

[0038]

[0039] In the formula, IR is the maximum reactive power compensation current, PFC is, Ifeederl(m) is the m-th element of the load feeder dataset, Δt is the time interval between two adjacent elements in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0040] Based on the bus voltage waveform dataset, phase coefficient, maximum reactive power compensation current, influence coefficient, and step adjustment ratio, adjust the output current of the target distributed power source that affects the load feeder current.

[0041] In one possible implementation, adjusting the output current of the target distributed power source affecting the load feeder current based on the bus voltage waveform dataset, phase coefficient, maximum reactive power compensation current, influence coefficient, and step adjustment amount includes:

[0042] Based on the sixth formula, the bus voltage waveform dataset, the phase coefficient, the maximum reactive power compensation current, the influence coefficient, and the step adjustment ratio, adjust the output current of the target distributed power source that affects the load feeder current. The sixth formula is:

[0043]

[0044] In the formula, Io(m) is the output current at the m-th time node in the bus voltage fluctuation cycle, Ifactor is the influence coefficient of the distributed power source on the load feeder, Ps is the step regulation ratio, is the effective value of the active power circuit, is the phase coefficient, Ubus(m) is the m-th element in the bus voltage waveform dataset, and Ubus(max) is the element with the largest value in the bus voltage waveform dataset.

[0045] In a second aspect, embodiments of the present invention provide a distributed power grid overvoltage mitigation device for implementing the distributed power grid overvoltage mitigation method as described in the first aspect or any possible implementation thereof, the distributed power grid overvoltage mitigation device comprising:

[0046] The load feeder current acquisition module is used to acquire multiple load feeder datasets that characterize multiple target load feeder currents. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold.

[0047] The target distributed power source determination module is used to determine multiple target distributed power sources based on multiple power feeder datasets and the multiple load feeder datasets. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity through the power feeders and the target distributed power sources affect the voltage of the target load feeders.

[0048] The load feature extraction module is used to determine multiple load features corresponding to multiple target load feeders based on the bus voltage waveform dataset and the multiple load feeder datasets, wherein the load features characterize the relationship between load current and bus voltage.

[0049] as well as,

[0050] A distributed power source output current adjustment module is used to adjust the output current of the multiple target distributed power sources according to the characteristics of the multiple loads.

[0051] Thirdly, embodiments of the present invention provide a terminal, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0052] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0053] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0054] This invention discloses a method for managing overvoltage in a distributed power grid. First, it acquires multiple load feeder datasets characterizing the currents of multiple target load feeders. These datasets include current data for multiple time points corresponding to the target load feeders, where the voltage at the connection point between the target load feeder and the bus exceeds a threshold. Next, based on the multiple power feeder datasets and the multiple load feeder datasets, multiple target distributed power sources are identified. These power feeder datasets include multiple current data corresponding to the power feeders, and the distributed power sources generate electricity through grid connection via the power feeders. The target distributed power sources influence the voltage of the target load feeders. Then, based on the bus voltage waveform dataset and the multiple load feeder datasets, multiple load characteristics corresponding to the multiple target load feeders are determined. These load characteristics characterize the relationship between load current and bus voltage. Finally, the output current of the multiple target distributed power sources is adjusted according to the multiple load characteristics. In this embodiment of the invention, based on the amplitude-frequency characteristics of the load feeder with overvoltage and the amplitude-frequency characteristics of the output current of multiple distributed power sources, the distributed power sources affecting the overvoltage load feeder are identified. Then, based on the extraction of the reactive current of the load, the reactive output current of the distributed power sources is increased according to the reactive current of the load, thereby achieving the purpose of adjusting the load feeder voltage. This embodiment of the invention does not require analysis based on network topology, load size, and distributed power source output. Therefore, the adjustment method is relatively simple, the amount of calculation is small, and the active power output of the distributed power sources is not adjusted, resulting in less energy waste from the distributed power sources. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart of the distributed power grid overvoltage mitigation method provided in the embodiments of the present invention;

[0057] Figure 2 This is a schematic diagram of the distributed power grid structure provided by an embodiment of the present invention;

[0058] Figure 3 This is a functional block diagram of the distributed power grid overvoltage control device provided in the embodiments of the present invention;

[0059] Figure 4 This is a terminal function block diagram provided by an embodiment of the present invention. Detailed Implementation

[0060] In the following description, specific details such as particular system structures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0062] The embodiments of the present invention will be described in detail below. This example is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific operation processes. However, the protection scope of the present invention is not limited to the following embodiments.

[0063] Figure 1 A flowchart of a distributed power grid overvoltage mitigation method provided for an embodiment of the present invention.

[0064] like Figure 1 As shown, a flowchart illustrating the implementation of the distributed power grid overvoltage mitigation method provided by an embodiment of the present invention is presented, and is described in detail below:

[0065] In step 101, multiple load feeder datasets characterizing the current of multiple target load feeders are obtained. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold.

[0066] In step 102, multiple target distributed power sources are determined based on multiple power feeder datasets and multiple load feeder datasets. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity through the power feeders and the target distributed power sources affect the voltage of the target load feeders.

[0067] In some embodiments, step 102 includes:

[0068] Based on the first formula, the multiple power supply feeder datasets, and the multiple load feeder datasets, multiple first current feature sets corresponding to the multiple power supply feeders and multiple second current feature sets corresponding to the multiple load feeders are extracted, wherein the first formula is:

[0069]

[0070] In the formula, Ifeature(2) is the second element of the first current feature set or the second current feature set, Ifeeder(m) is, M is the total number of elements in the power supply feeder dataset or the load feeder dataset, sin() is the sine function, cos() is the cosine function, ω0 is the angular frequency of the voltage waveform, and Δt is the time difference between two adjacent elements in the power supply feeder dataset or the load feeder dataset.

[0071] Based on the plurality of first current feature sets and the plurality of second current feature sets, the influence coefficients of the plurality of distributed power sources on the plurality of target load feeders are determined;

[0072] The multiple target distributed power sources are determined based on the influence threshold and the influence coefficients of multiple distributed power sources on multiple target load feeders.

[0073] In some implementations, determining the influence coefficients of multiple distributed power sources on multiple target load feeders based on the plurality of first current characteristic sets and the plurality of second current characteristic sets includes:

[0074] For each feature set in the plurality of second current feature sets, the following steps are performed:

[0075] Based on the second formula, the second current characteristic set, and the plurality of first current characteristic sets, the influence coefficients of the target load feeder and the plurality of target distributed power sources are determined respectively, wherein the second formula is:

[0076]

[0077] In the formula, Ifactor(k) is the influence coefficient of the k-th distributed generation on the load feeder, and Ifeature pk (n) is the nth element in the kth first current feature set, Ifeature l (n) is the nth element in the second current feature set, and N is the total number of elements in the second current feature set.

[0078] For example, such as Figure 2 As shown in the figure, this diagram illustrates a distributed power grid. In the diagram, the main power source 201 supplies power to multiple loads 204 through the bus 202 and the load feeder 203. Multiple distributed power sources 206 are connected to the bus 201 through the power feeder 205 to achieve grid-connected power generation.

[0079] The power flow of a power grid connected to distributed generation sources depends not only on the network topology and the connection point of the distributed generation sources, but also on the output of the distributed generation sources and the load size of the loads.

[0080] In this embodiment of the invention, the distributed power sources affecting the load feeder current are analyzed using current data from the load feeder and the distributed power source. By adjusting the output current of the distributed power sources affecting the load current, especially by adjusting the reactive current of the distributed power sources, the purpose of overvoltage control is achieved.

[0081] In some implementations, the amplitude-frequency characteristics of the load feeder current and the power supply feeder current are first extracted using a first formula, and these characteristics are arranged in a predetermined order to obtain the current characteristic set of the corresponding feeder. The first formula is:

[0082]

[0083] In the formula, Ifeature(2) is the second element of the first current feature set or the second current feature set, Ifeeder(m) is, M is the total number of elements in the power supply feeder dataset or the load feeder dataset, sin() is the sine function, cos() is the cosine function, ω0 is the angular frequency of the voltage waveform, and Δt is the time difference between two adjacent elements in the power supply feeder dataset or the load feeder dataset.

[0084] Based on the current characteristic set, we can analyze the distributed power sources that affect the entire load feeder current for the load feeder. Specifically, we analyze the load feeder current characteristics and the power supply feeder current characteristics. If there is a significant similarity between the two, for example, if the proportion of a certain amplitude-frequency characteristic is roughly the same in the load feeder and the power supply feeder, then we can determine that this power supply feeder is the feeder that mainly supplies the load feeder current.

[0085] One analytical method is through the second formula:

[0086]

[0087] In the formula, Ifactor(k) is the influence coefficient of the kth distributed power source on the load feeder, Ifeaturepk(n) is the nth element of the kth first current feature set, Ifeaturel(n) is the nth element of the second current feature set, and N is the total number of elements in the second current feature set.

[0088] In step 103, based on the bus voltage waveform dataset and the multiple load feeder datasets, multiple load characteristics corresponding to multiple target load feeders are determined, wherein the load characteristics characterize the relationship between load current and bus voltage.

[0089] In some implementations, the bus voltage waveform dataset and the plurality of load feeder datasets are obtained based on the same time period, and step 103 includes:

[0090] Zero-phase data is determined from multiple data points in the bus voltage waveform dataset, wherein the zero-phase data is obtained based on the zero phase of the bus voltage waveform;

[0091] The data from the first data position to the zero-phase data position in the bus voltage waveform dataset and the multiple load feeder datasets are cropped, retaining the data after the zero-phase data position;

[0092] The bus voltage waveform dataset and the multiple load feeder datasets are respectively normalized.

[0093] For each of the multiple load feeder datasets, perform the following steps:

[0094] Based on the third formula, the load feeder dataset, and the bus voltage waveform dataset, the phase coefficient is determined, wherein the third formula is:

[0095]

[0096] In the formula, Sp is the phase coefficient, Ubus(q) is the qth data in the bus voltage waveform dataset, Ifeederl(q) is the qth data in the load feeder dataset, and Q is determined according to the bus voltage waveform dataset and the load feeder dataset. The absolute value of the difference between the Qth data in the bus voltage waveform dataset and the Qth data in the load feeder dataset is less than the threshold.

[0097] The power factor of the load is determined based on the load feeder dataset and the bus voltage waveform dataset.

[0098] In some implementations, determining the power factor of the load based on the load feeder dataset and the bus voltage waveform dataset includes:

[0099] The power factor of the load is determined according to the fourth formula, the load feeder dataset, and the bus voltage waveform dataset, wherein the fourth formula is:

[0100]

[0101] In the formula, PFC is the power factor, Ubus(m) is the m-th element in the bus voltage waveform dataset, Ifeederl(m) is the m-th element in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0102] For example, one method to manage overvoltage is to address reactive current by using distributed power sources to absorb the current in the load feeders as much as possible, thereby mitigating the overvoltage problem.

[0103] Reactive current includes both lagging and leading cases. One method for analyzing reactive current is to align the bus voltage waveform with zero phase and then analyze the difference between the bus voltage waveform and the current in the load feeder. This method applies the third formula:

[0104]

[0105] In the formula, Sp is the phase coefficient, Ubus(q) is the qth data in the bus voltage waveform dataset, Ifeederl(q) is the qth data in the load feeder dataset, and Q is determined based on the bus voltage waveform dataset and the load feeder dataset. The absolute value of the difference between the Qth data in the bus voltage waveform dataset and the Qth data in the load feeder dataset is less than a threshold.

[0106] According to this formula, if the obtained phase coefficient is positive, it means that the current phase lags behind the voltage phase, indicating an inductive load. Otherwise, if it is negative, it means that the current phase leads the voltage phase, indicating a capacitive load.

[0107] Then, the power factor is determined according to the fourth formula, which is:

[0108]

[0109] In the formula, PFC is the power factor, Ubus(m) is the m-th element in the bus voltage waveform dataset, Ifeederl(m) is the m-th element in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0110] In step 104, the output current of the multiple target distributed power sources is adjusted according to the multiple load characteristics.

[0111] In some embodiments, the load characteristics include a phase coefficient and a power factor, and step 104 includes:

[0112] For each of the multiple load feeder datasets, perform the following steps:

[0113] Based on the fifth formula, power factor, and load feeder data set, the maximum reactive power compensation current is determined, wherein the fifth formula is:

[0114]

[0115] In the formula, IR is the maximum reactive power compensation current, PFC is, Ifeederl(m) is the m-th element of the load feeder dataset, Δt is the time interval between two adjacent elements in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0116] Based on the bus voltage waveform dataset, phase coefficient, maximum reactive power compensation current, influence coefficient, and step adjustment ratio, adjust the output current of the target distributed power source that affects the load feeder current.

[0117] In some implementations, adjusting the output current of the target distributed power source affecting the load feeder current based on the bus voltage waveform dataset, phase coefficient, maximum reactive power compensation current, influence coefficient, and step adjustment amount includes:

[0118] Based on the sixth formula, the bus voltage waveform dataset, the phase coefficient, the maximum reactive power compensation current, the influence coefficient, and the step adjustment ratio, adjust the output current of the target distributed power source that affects the load feeder current. The sixth formula is:

[0119]

[0120] In the formula, Io(m) is the output current at the m-th time node in the bus voltage fluctuation cycle, Ifactor is the influence coefficient of the distributed power source on the load feeder, Ps is the step regulation ratio, is the effective value of the active power circuit, is the phase coefficient, Ubus(m) is the m-th element in the bus voltage waveform dataset, and Ubus(max) is the element with the largest value in the bus voltage waveform dataset.

[0121] For example, for distributed power sources, the reactive current output should be adjusted according to its correlation with the load feeder, and also according to the reactive current of the load, wherein the reactive current of the load is determined according to the fifth formula:

[0122]

[0123] In the formula, IR is the maximum reactive power compensation current, PFC is, Ifeederl(m) is the m-th element of the load feeder dataset, Δt is the time interval between two adjacent elements in the load feeder dataset, and M is the total number of elements in the load feeder dataset.

[0124] The expression for adjusting the reactive current of the load to the output of the distributed power source is as follows:

[0125]

[0126] In the formula, Io(m) is the output current at the m-th time node in the bus voltage fluctuation cycle, Ifactor is the influence coefficient of the distributed power source on the load feeder, Ps is the step regulation ratio, is the effective value of the active power circuit, is the phase coefficient, Ubus(m) is the m-th element in the bus voltage waveform dataset, and Ubus(max) is the element with the largest value in the bus voltage waveform dataset.

[0127] An embodiment of the distributed power grid overvoltage mitigation method of the present invention first acquires multiple load feeder datasets characterizing the feeder currents of multiple target loads. These load feeder datasets include multiple current data points corresponding to multiple time nodes for the target load feeders, where the voltage at the connection point between the target load feeder and the bus exceeds a threshold. Then, based on the multiple power feeder datasets and the multiple load feeder datasets, multiple target distributed power sources are identified. These power feeder datasets include multiple current data points corresponding to the power feeders, and the distributed power sources generate electricity through grid connection via the power feeders. The target distributed power sources influence the voltage of the target load feeders. Next, based on the bus voltage waveform dataset and the multiple load feeder datasets, multiple load characteristics corresponding to the multiple target load feeders are determined. These load characteristics characterize the relationship between load current and bus voltage. Finally, the output current of the multiple target distributed power sources is adjusted according to the multiple load characteristics. In this embodiment of the invention, based on the amplitude-frequency characteristics of the load feeder with overvoltage and the amplitude-frequency characteristics of the output current of multiple distributed power sources, the distributed power sources affecting the overvoltage load feeder are identified. Then, based on the extraction of the reactive current of the load, the reactive output current of the distributed power sources is increased according to the reactive current of the load, thereby achieving the purpose of adjusting the load feeder voltage. This embodiment of the invention does not require analysis based on network topology, load size, and distributed power source output. Therefore, the adjustment method is relatively simple, the amount of calculation is small, and the active power output of the distributed power sources is not adjusted, resulting in less energy waste from the distributed power sources.

[0128] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] The following are embodiments of the apparatus of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0130] Figure 3 This is a functional block diagram of the distributed power grid overvoltage control device provided in the embodiments of the present invention, with reference to... Figure 3 The distributed power grid overvoltage mitigation device 3 includes: a load feeder current acquisition module 301, a target distributed power source determination module 302, a load feature extraction module 303, and a distributed power source output current adjustment module 304, wherein:

[0131] The load feeder current acquisition module 301 is used to acquire multiple load feeder datasets that characterize multiple target load feeder currents. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold.

[0132] The target distributed power source determination module 302 is used to determine multiple target distributed power sources based on multiple power feeder datasets and the multiple load feeder datasets. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity through the power feeders and the target distributed power sources affect the voltage of the target load feeders.

[0133] The load feature extraction module 303 is used to determine multiple load features corresponding to multiple target load feeders based on the bus voltage waveform dataset and the multiple load feeder datasets, wherein the load features characterize the relationship between load current and bus voltage.

[0134] The distributed power supply output current adjustment module 304 is used to adjust the output current of the multiple target distributed power supplies according to the characteristics of the multiple loads.

[0135] Figure 4 This is a functional block diagram of the terminal provided in an embodiment of the present invention. For example... Figure 4 As shown, the terminal 4 in this embodiment includes a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can run on the processor 400. When the processor 400 executes the computer program 402, it implements the steps of the various distributed power grid overvoltage control methods and embodiments described above, for example... Figure 1 Steps 101 to 104 are shown.

[0136] For example, the computer program 402 may be divided into one or more modules / units, which are stored in the memory 401 and executed by the processor 400 to complete the present invention.

[0137] The terminal 4 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will understand that... Figure 4 This is merely an example of terminal 4 and does not constitute a limitation on terminal 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal 4 may also include input / output devices, network access devices, buses, etc.

[0138] The processor 400 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), 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.

[0139] The memory 401 can be an internal storage unit of the terminal 4, such as a hard disk or memory of the terminal 4. The memory 401 can also be an external storage device of the terminal 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 4. Furthermore, the memory 401 can include both internal storage units and external storage devices of the terminal 4. The memory 401 is used to store the computer program 402 and other programs and data required by the terminal 4. The memory 401 can also be used to temporarily store data that has been output or will be output.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0141] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various methods and apparatus embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0147] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for managing overvoltage in distributed power grids, characterized in that, include: Obtain multiple load feeder datasets characterizing the current of multiple target load feeders. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold. Based on multiple power feeder datasets and multiple load feeder datasets, multiple target distributed power sources are identified. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity by connecting to the grid through the power feeders. The target distributed power sources affect the voltage of the target load feeders. Based on the bus voltage waveform dataset and the multiple load feeder datasets, multiple load characteristics corresponding to multiple target load feeders are determined, wherein the load characteristics characterize the relationship between load current and bus voltage; Adjusting the output current of the multiple target distributed power sources according to the multiple load characteristics includes: The load characteristics include phase coefficient and power factor. For each of the multiple load feeder datasets, the following steps are performed: Based on the fifth formula, power factor, and load feeder data set, the maximum reactive power compensation current is determined, wherein the fifth formula is: In the formula, For the maximum reactive power compensation current, For power factor, For the load feeder dataset, the first One element, The time interval between two adjacent elements in the load feeder dataset. This represents the total number of elements in the load feeder dataset. Based on the sixth formula, the bus voltage waveform dataset, the phase coefficient, the maximum reactive power compensation current, the influence coefficient, and the step adjustment ratio, adjust the output current of the target distributed power source that affects the load feeder current. The sixth formula is: In the formula, The first in the bus voltage fluctuation period Output current at each time point This is the influence coefficient of distributed generation on load feeders. For step adjustment ratio, This represents the effective value of the active power circuit. For phase coefficients, The first in the bus voltage waveform dataset One element, It is the element with the largest central value in the bus voltage waveform data set.

2. The distributed power grid overvoltage control method according to claim 1, characterized in that, The step of determining multiple target distributed power sources based on multiple power feeder datasets and multiple load feeder datasets includes: Based on the first formula, the multiple power supply feeder datasets, and the multiple load feeder datasets, multiple first current feature sets corresponding to the multiple power supply feeders and multiple second current feature sets corresponding to the multiple load feeders are extracted, wherein the first formula is: In the formula, For the first current characteristic set or the second current characteristic set, the first One element, for, This represents the total number of elements in the power feeder dataset or load feeder dataset. It is a sine function. It is a cosine function. The angular frequency of the voltage waveform. The time difference between two adjacent elements in a power feeder dataset or a load feeder dataset; Based on the plurality of first current feature sets and the plurality of second current feature sets, the influence coefficients of the plurality of distributed power sources on the plurality of target load feeders are determined; The multiple target distributed power sources are determined based on the influence threshold and the influence coefficients of multiple distributed power sources on multiple target load feeders; The step of determining the influence coefficients of multiple distributed power sources on multiple target load feeders based on the multiple first current feature sets and the multiple second current feature sets includes: For each feature set in the plurality of second current feature sets, the following steps are performed: Based on the second formula, the second current characteristic set, and the plurality of first current characteristic sets, the influence coefficients of the target load feeder and the plurality of target distributed power sources are determined respectively, wherein the second formula is: In the formula, For the first The impact coefficient of a distributed power source on the load feeder. For the first The first current feature set of the first current feature set One element, For the second current characteristic set, the first One element, This represents the total number of elements in the second current characteristic set.

3. The distributed power grid overvoltage control method according to claim 1, characterized in that, The bus voltage waveform dataset and the multiple load feeder datasets were acquired based on the same time period. The step of determining multiple load characteristics corresponding to multiple target load feeders based on the bus voltage waveform dataset and the multiple load feeder datasets includes: Zero-phase data is determined from multiple data points in the bus voltage waveform dataset, wherein the zero-phase data is obtained based on the zero phase of the bus voltage waveform; The data from the first data position to the zero-phase data position in the bus voltage waveform dataset and the multiple load feeder datasets are cropped, retaining the data after the zero-phase data position; The bus voltage waveform dataset and the multiple load feeder datasets are respectively normalized. For each of the multiple load feeder datasets, perform the following steps: Based on the third formula, the load feeder dataset, and the bus voltage waveform dataset, the phase coefficient is determined, wherein the third formula is: In the formula, For phase coefficients, The first in the bus voltage waveform dataset One data point, For the load feeder dataset, the first One data point, Based on the bus voltage waveform dataset and the load feeder dataset, the first [value] in the bus voltage waveform dataset is determined. The data in the load feeder dataset is the first The absolute value of the data difference is less than the threshold; The power factor of the load is determined based on the load feeder dataset and the bus voltage waveform dataset.

4. The distributed power grid overvoltage control method according to claim 3, characterized in that, Determining the power factor of the load based on the load feeder dataset and the bus voltage waveform dataset includes: The power factor of the load is determined according to the fourth formula, the load feeder dataset, and the bus voltage waveform dataset, wherein the fourth formula is: In the formula, For power factor, The first in the bus voltage waveform dataset One element, For the load feeder dataset, the first One element, This represents the total number of elements in the load feeder dataset.

5. A distributed power grid overvoltage control device, characterized in that, For implementing the distributed power grid overvoltage mitigation method as described in any one of claims 1-4, the distributed power grid overvoltage mitigation device comprises: The load feeder current acquisition module is used to acquire multiple load feeder datasets that characterize multiple target load feeder currents. The load feeder datasets include multiple current data corresponding to multiple time nodes of the target load feeders, and the voltage at the connection point between the target load feeder and the bus exceeds a threshold. The target distributed power source determination module is used to determine multiple target distributed power sources based on multiple power feeder datasets and the multiple load feeder datasets. The power feeder datasets include multiple current data corresponding to the power feeders. The distributed power sources generate electricity through the power feeders and the target distributed power sources affect the voltage of the target load feeders. The load feature extraction module is used to determine multiple load features corresponding to multiple target load feeders based on the bus voltage waveform dataset and the multiple load feeder datasets, wherein the load features characterize the relationship between load current and bus voltage. as well as, A distributed power source output current adjustment module is used to adjust the output current of the multiple target distributed power sources according to the characteristics of the multiple loads.

6. A terminal comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 4 above.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4 above.

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