Photovoltaic inverter power adjusting method and device based on distributed photovoltaic power generation

Abstract

The invention discloses a photovoltaic inverter power regulation method and device based on distributed photovoltaic power generation. The method comprises the steps that electric power data of a target power distribution network at the current moment are collected, and the electric power data comprise the voltage amplitude of a target node in the target power distribution network; determining a voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude; if the voltage regulation redundancy value is greater than a preset threshold value, determining a target instantaneous voltage phasor histogram entropy of the target node at the current moment and a target source load correlation sensitivity coefficient; determining the target reactive power of the photovoltaic inverter in the target power distribution network at the current moment based on the target instantaneous voltage phasor direct square entropy and the target source load correlation sensitivity coefficient; and performing power regulation on the photovoltaic inverter according to the target reactive power. According to the invention, the technical problem that the power regulation effect of a photovoltaic inverter based on distributed photovoltaic power generation is not ideal in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of power systems, and more specifically, to a method and apparatus for power regulation of a photovoltaic inverter based on distributed photovoltaic power generation. Background Technology

[0002] In multi-source power systems with high penetration rates of distributed photovoltaic (PV) power generation, energy storage, and flexible loads (such as electric vehicle charging stations and reactive power compensation devices), feeder power flows exhibit strong randomness and bidirectional flow characteristics. Due to the significant spatiotemporal coupling effect between power injection and load changes, voltage limits frequently exceed the limits at distribution network nodes. Related technologies employ a static hierarchical droop control method to regulate the power of PV inverters based on distributed PV power generation. However, this method struggles to respond quickly to rapid fluctuations in distributed power sources and sudden load changes, easily leading to reactive power regulation lag, overcompensation, harmonic amplification, and reactive power resource waste, thus affecting power quality and system stability. Therefore, related technologies suffer from unsatisfactory power regulation effects on PV inverters based on distributed PV power generation.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method and apparatus for power regulation of a photovoltaic inverter based on distributed photovoltaic power generation, so as to at least solve the technical problem of unsatisfactory power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation in related technologies.

[0005] According to one aspect of the embodiments of this application, a photovoltaic inverter power regulation method based on distributed photovoltaic power generation is provided, comprising: collecting power data of a target distribution network at the current moment, wherein the power data includes the voltage amplitude of a target node in the target distribution network, and the target node is a node in the target distribution network whose electrical attributes need to be displayed; determining the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; when the voltage regulation redundancy value is greater than a preset threshold, determining the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment, wherein the target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network; determining the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient; and regulating the power of the photovoltaic inverter according to the target reactive power.

[0006] According to another aspect of the embodiments of this application, a photovoltaic inverter power regulation device based on distributed photovoltaic power generation is provided, comprising: a data acquisition module, used to acquire power data of a target distribution network at the current moment, wherein the power data includes the voltage amplitude of a target node in the target distribution network, and the target node is a node in the target distribution network whose electrical attributes need to be displayed; a first determination module, used to determine the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; a second determination module, used to determine the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment when the voltage regulation redundancy value is greater than a preset threshold, wherein the target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuation in the target distribution network; a third determination module, used to determine the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient; and a power regulation module, used to perform power regulation on the photovoltaic inverter according to the target reactive power.

[0007] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores multiple instructions, any one of which is adapted to be loaded by a processor for a photovoltaic inverter power regulation method based on distributed photovoltaic power generation.

[0008] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the photovoltaic inverter power regulation methods based on distributed photovoltaic power generation.

[0009] According to another aspect of the embodiments of this application, a computer program product is provided, which, when executed on a data processing device, is adapted to perform the steps of a photovoltaic inverter power regulation method based on distributed photovoltaic power generation.

[0010] In this embodiment, power data of the target distribution network at the current moment is collected. This power data includes the voltage amplitude of target nodes in the target distribution network, where the target nodes are those whose electrical attributes need to be displayed. Based on the voltage amplitude, the voltage regulation redundancy value of the target nodes at the current moment is determined. This voltage regulation redundancy value represents the basic reactive power demand gap of the target distribution network. If the voltage regulation redundancy value is greater than a preset threshold, the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target nodes at the current moment are determined. The target instantaneous voltage phasor histogram entropy represents the dispersion of the voltage phase angle of the target nodes, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network. Based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient, the target reactive power of the photovoltaic inverters in the target distribution network at the current moment is determined. Power regulation of the photovoltaic inverters is then performed according to the target reactive power. By collecting power data from the distribution network, the system determines the voltage regulation redundancy characteristics, instantaneous voltage phasor histogram entropy, and source-load correlation sensitivity coefficient. This allows for the determination of the target reactive power of the photovoltaic inverter, and the adjustment of the inverter's power based on the target reactive power. This improves the power regulation effect of the photovoltaic inverter based on distributed photovoltaic power generation, thus solving the technical problem of unsatisfactory power regulation performance in related technologies. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0012] Figure 1 This is a flowchart of a photovoltaic inverter power regulation method based on distributed photovoltaic power generation, according to an embodiment of this application.

[0013] Figure 2 This is a schematic diagram of an optional photovoltaic inverter power regulation device based on distributed photovoltaic power generation, according to an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0017] Modbus-TCP is a variation of the Modbus protocol that uses Ethernet for data transmission and encodes Modbus data in TCP / IP (Transmission Control Protocol / Internet Protocol) format for remote device management and communication in industrial automation and control systems.

[0018] According to an embodiment of this application, a method embodiment for power regulation of a photovoltaic inverter based on distributed photovoltaic power generation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] Figure 1 This is a flowchart of a photovoltaic inverter power regulation method based on distributed photovoltaic power generation, according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0020] Step S102: Collect the power data of the target distribution network at the current time. The power data includes the voltage amplitude of the target node in the target distribution network. The target node is the node in the target distribution network whose electrical attributes need to be displayed.

[0021] It is understood that data acquisition equipment is deployed at target nodes in the target distribution network to collect the current power data of the target distribution network. These target nodes are those nodes in the target distribution network whose electrical attributes need to be displayed, including distribution buses and capacitor compensation points. The power data includes, but is not limited to, the voltage amplitude of the target nodes in the target distribution network. Through accurate and comprehensive data acquisition, precise data support can be provided for optimizing the power regulation of photovoltaic inverters, thereby improving the power quality, stability, and rapid response capability of the distribution network.

[0022] Optionally, the aforementioned data acquisition equipment includes, but is not limited to, acquisition micro-synchronous phasor measurement units (μPMU), photovoltaic inverter controllers, energy storage battery management systems (BMS), and load-side intelligent detection terminals.

[0023] Optionally, to achieve online, real-time power regulation of the photovoltaic inverters in the target distribution network, the following four types of data acquisition devices can be deployed at key distribution network nodes (i.e., target nodes, such as buses and capacitor compensation points) to collect power data from the target distribution network. These include: a miniature synchronous phasor measurement unit (MPU): sampling at 50fps (Frames Per Second), meaning one frame is collected every 20ms (milliseconds), used for real-time measurement of the voltage amplitude, voltage phase angle, current amplitude, and current phase angle of the target node; and a photovoltaic inverter controller: used to report the instantaneous active power P within 20ms via the Modbus-TCP protocol. inv With reactive power Q inv Value; Energy Storage Battery Management System: Uploads information such as State of Charge (SOC), State of Health (SOH), battery voltage, and current of the energy storage unit via CAN-FD (Controller Area Network with Flexible Data-rate) bus at 100ms intervals; Load-side Intelligent Detection Terminal: Contains a built-in step current trigger detection chip, reporting load change event identifiers F at 1s (second) granularity. flag .

[0024] Optionally, due to significant differences in communication links and sampling periods among various data acquisition devices—for example, the μPMU requires synchronization with ±1μs-level GPS (Global Positioning System) to ensure the consistency of timestamps in the acquired data, while the data delay between the photovoltaic inverter and the BMS can reach 50–200ms, and the reporting of load surge events may result in 1–2 cycles of missing frames due to network jitter—the raw power data acquired may exhibit characteristics such as inconsistent sampling intervals, timestamp jitter, missing frames, and fragmented transmission, affecting the subsequent power regulation effect on the photovoltaic inverter. Therefore, time calibration can be performed on all raw power data streams at the edge-side gateway node. A combination of linear interpolation and missing frame compensation can be used to uniformly align all data to the μPMU's 20ms reference period. For missing data caused by communication interruptions or manual debugging, a joint compensation strategy of "zero-orderhold" and "local quadratic interpolation" is used to fill in the data, preventing long-term data breaks from affecting subsequent analysis.

[0025] Optionally, after the above data alignment process, the target node can obtain a complete time-series dataset every 20ms period, including: voltage amplitude V meas With voltage phase angle θ meas Photovoltaic inverter active power P inv With reactive power Q inv Energy storage SOC / SOH, load change event identifier F flag .

[0026] Step S104: Based on the voltage amplitude, determine the voltage regulation redundancy value of the target node at the current moment, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network.

[0027] It is understandable that, based on the voltage amplitude of the target node, the voltage regulation redundancy value of the target node, representing the basic reactive power demand gap of the target distribution network, is determined at the current moment. Determining the voltage regulation redundancy value provides precise demand guidance for the power regulation of the photovoltaic inverter, thereby improving the power regulation effect of the photovoltaic inverter and the operational safety of the distribution network.

[0028] In one optional embodiment, determining the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude includes: determining multiple intrinsic mode function values ​​of the voltage amplitude, the residual value of the voltage amplitude, and the target voltage amplitude of the target node, wherein the intrinsic mode function values ​​are used to describe the fluctuation characteristics of the voltage amplitude at different frequency components, and the residual value is used to describe the low-frequency variation characteristics of the voltage amplitude; and determining the voltage regulation redundancy value based on the multiple intrinsic mode function values, the residual value, the voltage amplitude, and the target voltage amplitude.

[0029] It can be understood that decomposing the voltage amplitude yields multiple intrinsic mode function (IMF) values ​​and residual values. The IMF values ​​describe the fluctuation characteristics of the voltage amplitude at different frequency components, while the residual values ​​describe the low-frequency variation characteristics. The target voltage amplitude for the target node is then determined. Based on the aforementioned IMF values, residual values, target voltage amplitude, and voltage amplitude, the voltage regulation redundancy value of the target node at the current moment is calculated. This voltage regulation redundancy calculation based on the voltage amplitude not only accurately identifies the reactive power demand gap of the target node but also enables power regulation according to the dynamic changes in the distribution network. This improves the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation, ensuring the power quality and stability of the distribution network.

[0030] Optionally, to quantify the compensation requirement between the current voltage state and the target value, a Voltage Regulation Residual (VRR) feature can be defined to comprehensively reflect the voltage deviation between the current voltage amplitude and the target voltage amplitude, as well as its dynamic compensation redundancy across multiple scales. First, the voltage amplitude sequence V... meas (t) An improved trend-removed empirical mode decomposition (IT-EMD) is adopted to decompose the mode into several intrinsic mode functions (IMFs). i The intrinsic mode function (IMF) is represented by (t) and the residual trend (i.e., the residual residual) function Residual(t). IT-EMD introduces a local linear regression detrending operation on the basis of traditional EMD (Empirical Mode Decomposition), which can extract the intrinsic mode function value (IMF) in each iteration. k Previous residual value k-1 (t) is used for detrending processing, which further separates low-frequency trend components more effectively.

[0031] Optionally, IT-EMD is a signal processing method primarily used for the decomposition and analysis of nonlinear and non-stationary signals. This method aims to better separate trend components and transient disturbances in signals, enhancing signal resolution, especially in rapidly changing environments or scenarios with obvious trend signals. Therefore, IT-EMD can be used to decompose the acquired voltage amplitude. By performing detrending decomposition on the voltage amplitude, transient voltage disturbances caused by factors such as load changes and weather conditions, as well as voltage trend changes caused by long-term power supply and demand, system configuration, etc., can be separated. This helps to more accurately identify and address transient problems in voltage control, thereby optimizing voltage regulation strategies and improving the stability and efficiency of the power system.

[0032] Optionally, the decomposition order K can be set to 4, and the final residual value Residual4(t) represents the low-frequency trend. The first few intrinsic mode function (IMF) values ​​are recombined into signal-to-noise components, thereby determining the voltage regulation redundancy value VRR(t) of the voltage regulation redundancy feature at time t. VRR(t) can be determined in the following way:

[0033] VRR(t)=w1·IMF2(t)+w2·IMF3(t)+w3·Residual4(t)-(V * -V meas (t))

[0034] Among them, V * The target voltage (i.e., the target voltage amplitude, typically 1 p.u.) is defined as follows: IMF2(t) and IMF3(t) represent the multiple intrinsic mode function values ​​of the voltage amplitude at time t; Residual4(t) represents the residual value of the voltage amplitude at time t; w1 = 0.2, w2 = 0.3, and w3 = 0.5 are empirical weights, highlighting the dominant role of low-frequency trends in voltage regulation. In this formula, (V * -V meas (t) represents the base voltage deviation, while the remaining terms reflect the redundancy required to compensate for multi-scale disturbances.

[0035] Step S106: When the voltage regulation redundancy value is greater than the preset threshold, determine the target instantaneous voltage phasor histogram entropy of the target node at the current moment, and the target source-load correlation sensitivity coefficient. The target instantaneous voltage phasor histogram entropy is used to represent the degree of dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuation in the target distribution network.

[0036] It is understandable that if the voltage regulation redundancy value exceeds the preset threshold, it indicates a significant reactive power demand gap in the target distribution network, necessitating compensation from the photovoltaic inverters included in the target distribution network. The target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient are determined at the current moment. The target instantaneous voltage phasor histogram entropy represents the dispersion of the voltage phase angle at the target node, while the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network. The calculation of the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient provides a refined control basis for the photovoltaic inverters, significantly improving the accuracy and real-time performance of power regulation, and enhancing the stability and collaborative control capabilities of the distributed photovoltaic power generation system in complex dynamic environments.

[0037] Optionally, if the voltage regulation redundancy value is less than or equal to a preset threshold, it indicates that the basic reactive power demand gap of the target distribution network is small. The reactive power gap of the target distribution network can be compensated in the following ways: If the voltage regulation redundancy value is lower than the preset threshold and the voltage of the target distribution network is stable within the target range, the current reactive power compensation strategy can be maintained, i.e., the reactive power output of the photovoltaic inverter or energy storage unit remains unchanged. This avoids unnecessary adjustments and reduces energy loss and equipment losses. If the voltage regulation redundancy value is close to but does not exceed the preset threshold, the reactive power output of the photovoltaic inverter or energy storage unit can be fine-tuned to gradually approach the target voltage amplitude. The fine-tuning amount should be determined based on the specific operating conditions of the target distribution network and the capacity of the equipment. Typically, it involves a small increase or decrease based on the current reactive power output of the photovoltaic inverter or energy storage unit. For example, the reactive power output of the photovoltaic inverter can be increased or decreased by a certain percentage based on the current set value.

[0038] In one optional embodiment, determining the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment includes: determining the initial instantaneous voltage phasor histogram entropy of the target node at the current moment based on the voltage phase angle of the target node included in the power data; determining the initial source-load correlation sensitivity coefficient at the current moment based on the reactive power of the photovoltaic inverter included in the power data; and normalizing the initial instantaneous voltage phasor histogram entropy and the initial source-load correlation sensitivity coefficient to obtain the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient.

[0039] It is understandable that the initial instantaneous voltage phasor histogram entropy at the target node is determined based on the voltage phase angle included in the power data of the target distribution network at the current moment. The initial source-load correlation sensitivity coefficient is determined based on the reactive power of the photovoltaic inverter included in the power data of the target distribution network at the current moment. To ensure that the instantaneous voltage phasor histogram entropy and the source-load correlation sensitivity coefficient have the same comparison scale in dynamic scenarios, the initial instantaneous voltage phasor histogram entropy and the initial source-load correlation sensitivity coefficient are normalized respectively to obtain the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient. By calculating the initial instantaneous voltage phasor histogram entropy and the initial source-load correlation sensitivity coefficient and performing normalization, not only can the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation be improved, but also the response speed and accuracy of photovoltaic inverters to voltage fluctuations can be enhanced, thereby strengthening the stability and coordination of the power system in complex dynamic environments.

[0040] In one optional embodiment, determining the initial instantaneous voltage phasor histogram entropy of the target node at the current moment based on the voltage phase angle of the target node included in the power data includes: determining multiple voltage phase angles of the target node, wherein the multiple voltage phase angles include the voltage phase angle at the current moment and the voltage phase angles corresponding to multiple historical moments before the current moment; determining the voltage phase angle increments corresponding to multiple moments based on the multiple voltage phase angles, wherein the multiple moments include the current moment and multiple historical moments; determining multiple voltage phase angle intervals based on the number of moments, the number of missing frames at multiple moments, and the voltage phase angle increments corresponding to the multiple moments, wherein the number of missing frames refers to the number of moments within the multiple moments where the voltage phase angle could not be successfully acquired; determining the probability distribution corresponding to the multiple voltage phase angle intervals based on the voltage phase angle increments corresponding to the multiple moments and the multiple voltage phase angle intervals; and determining the initial instantaneous voltage phasor histogram entropy based on the probability distribution corresponding to the multiple voltage phase angle intervals and the missing frame compensation coefficient, wherein the missing frame compensation coefficient is used to quantify the degree of missing frames at multiple moments.

[0041] The initial instantaneous voltage phasor histogram entropy of the target node at the current moment is determined as follows: First, multiple voltage phase angles of the target node are determined, including the voltage phase angle at the current moment and the voltage phase angles corresponding to multiple historical moments prior to the current moment. Second, based on the aforementioned multiple voltage phase angles, the voltage phase angle increments corresponding to multiple moments, including the current moment and multiple historical moments, are determined. Next, multiple voltage phase angle intervals are determined based on the number of moments, the number of missing frames, and the voltage phase angle increments corresponding to the multiple moments. The number of missing frames refers to the number of moments in the multiple moments where the voltage phase angle could not be successfully acquired. Then, based on the voltage phase angle increments corresponding to the multiple moments and the multiple voltage phase angle intervals, the probability distributions corresponding to the multiple voltage phase angle intervals are calculated. These probability distributions are used to quantify the number of voltage phase angles falling into the voltage phase angle intervals. Finally, based on the probability distributions corresponding to the multiple voltage phase angle intervals and the missing frame compensation coefficient, the initial instantaneous voltage phasor histogram entropy is determined, where the missing frame compensation coefficient is used to quantify the degree of missing frames at multiple moments. The above methods can enhance the performance of photovoltaic inverters based on distributed photovoltaic power generation in dynamic power control, improve the power system's response speed and regulation accuracy to power fluctuations in the distribution network, reduce control errors caused by missing frames, and promote the robust operation and efficient power management of the power system.

[0042] Optionally, when line loss diffusion or current redistribution causes drastic dispersion in the voltage phase angle, fixed droop control is prone to insufficient or excessive compensation. To address this, instantaneous voltage phase histogram entropy (IVPH) can be introduced to quantify the dispersion and uncertainty of phase angle increments within a short time window.

[0043] Optionally, the voltage phase angle increment sequence Δθ can be recorded every 200ms time window (including 10 μPMU sampling points). i (n)=θ i (n)-θ i (n-1), n=1,2,…,N,N=10. Let the actual effective number of samples be N. eff = N-λ, where λ represents the number of missing frames, N represents the number of time points across multiple time points, n represents the nth time point, and θ i (n) represents the voltage phase angle at the nth time, Δθ i (n) represents the voltage phase angle increment at the nth time.

[0044] Optionally, an adaptive binning strategy can be adopted to divide N eff Voltage phase angle increment Δθ i Values ​​are divided into There are equidistant intervals (i.e., voltage phase angle intervals), and the number of samples h in each interval is counted. b This yields the probability distributions corresponding to multiple voltage phase angle intervals. The probability distribution p for the b-th voltage phase angle interval is... b The following methods can be used to determine this:

[0045]

[0046] Where B represents the number of voltage phase angle intervals; h b This represents the number of samples within the b-th voltage phase angle interval, i.e., the number of voltage phase angles falling within the b-th voltage phase angle interval.

[0047] Optionally, to suppress the overestimation of the target instantaneous voltage phasor histogram entropy caused by missing frames, a missing frame compensation coefficient γ can be introduced. The missing frame compensation coefficient γ can be determined as follows:

[0048]

[0049] Finally, the initial instantaneous voltage phasor histogram entropy IVPH(t) at time t can be determined as follows:

[0050]

[0051] Where, the coefficient ε = 10 -9This is used to prevent logarithmic divergence. γ decreases as the number of missing numbers increases, which is used to suppress the artificially high initial instantaneous voltage phasor histogram entropy caused by the sparse histogram, ensuring that IVPH(t) can stably reflect the discrete level of the true voltage phase angle.

[0052] Optionally, to ensure comparability of IVPHs, the normalized initial instantaneous voltage phasor histogram entropy IVPH(t) is updated in real time. An exponential moving average can be used to determine the reference instantaneous voltage phasor histogram entropy IVPH at time t. max (t). The reference instantaneous voltage phasor histogram entropy IVPH at time t. max (t) can be determined in the following way:

[0053] IVPH max (t)=(1-β)·IVPH max (t-1)+β·IVPH(t)

[0054] The coefficient β = 0.01 is used to slowly track environmental changes.

[0055] Alternatively, IVPH can be used. max (t) Normalize the initial instantaneous voltage phasor histogram entropy IVPH(t) to obtain the target instantaneous voltage phasor histogram entropy. The target instantaneous voltage phasor histogram entropy k at time t. V (t) can be determined in the following way:

[0056]

[0057] Wherein, the target instantaneous voltage phasor histogram entropy k V (t) can reflect the proportion of the voltage phase angle dispersion at time t to the histogram entropy of the reference voltage phasor, and is used to determine the target reactive power of the photovoltaic inverter.

[0058] In one optional embodiment, determining the initial source-load correlation sensitivity coefficient at the current moment based on the reactive power of the photovoltaic inverter included in the power data includes: determining multiple reactive powers of the photovoltaic inverter, wherein the multiple reactive powers include the reactive power at the current moment and the reactive power corresponding to multiple historical moments before the current moment; determining the reactive power increment corresponding to multiple moments based on the multiple reactive powers, wherein the multiple moments include the current moment and multiple historical moments; determining the cross-domain cross-entropy based on the reactive power increment corresponding to multiple moments and the step current signal corresponding to multiple moments, wherein the step current signal is used to indicate whether the load has experienced a current step change, and the cross-domain cross-entropy is used to quantify the degree of coupling between the reactive power change of the photovoltaic inverter and the current step change of the load; and determining the initial source-load correlation sensitivity coefficient based on the cross-domain cross-entropy.

[0059] The initial source-load correlation sensitivity coefficient of the target node at the current moment can be determined as follows: First, determine multiple reactive power values ​​of the photovoltaic inverter, including the reactive power at the current moment and the reactive power values ​​corresponding to multiple historical moments prior to the current moment. Second, based on the above multiple reactive power values, determine the reactive power increments corresponding to multiple moments, including the current moment and multiple historical moments. These reactive power increments reflect the instantaneous changes in the reactive power output of the photovoltaic inverter. Next, based on the reactive power increments and step current signals corresponding to multiple moments, determine the cross-domain cross-entropy used to quantify the coupling degree between the reactive power changes of the photovoltaic inverter and the current step changes of the load. The step current signal is used to indicate whether the load has experienced a current step change. Finally, determine the initial source-load correlation sensitivity coefficient based on the cross-domain cross-entropy. The process of determining the initial source-load correlation sensitivity coefficient based on power data not only helps the photovoltaic inverter accurately determine the cause of reactive power fluctuations but also enables it to respond quickly to load changes, avoid overcompensation, enhance the adaptability and coordinated control capabilities of the power system, and improve the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation.

[0060] Optionally, to distinguish whether reactive power fluctuations are caused by load abrupt changes or power output adjustments, a Source-Load Correlation Sensitivity Coefficient (SHSC) is proposed. A load abrupt change event identifier F is defined. flag When the load-side intelligent detection terminal detects a step current and reports F flag When δ(t) = 1, it indicates that a current step has occurred. In this case, let the step current signal δ(t) at time t = 1; otherwise, let δ(t) = 0.

[0061] Optionally, the reactive power increment ΔQ of the photovoltaic inverter at time t inv (t) can be determined in the following way:

[0062] ΔQ inv (t)=Q inv (t)-Q inv (t-Δt),Δt=20ms

[0063] Among them, Q inv (t) represents the reactive power of the photovoltaic inverter at time t; Q inv (t-Δt) represents the reactive power of the photovoltaic inverter at the previous time t; Δt represents the time interval between the two sampling times.

[0064] Optionally, within a 2-second sliding window (a total of 100 time points t) iApproximately 100 reactive power samples were collected. Based on the reactive power increments and step current signals at multiple times, the cross-domain cross-entropy CE(t) at time t was determined. CE(t) can be determined as follows:

[0065]

[0066] in, The sigmoid function; μ is taken within the window |ΔQ inv | median; M represents the number of sampling times; δ(t) i ) represents time t i The step current signal; ΔQ inv (t i ) represents time t i The increase in reactive power.

[0067] The smaller CE(t) is, the higher the synchronicity between reactive power fluctuations and load events; conversely, the larger CE(t) is, the more reactive power fluctuations are mainly caused by the power supply side.

[0068] Optionally, to further enhance the sensitivity to sudden load events, the time derivative of CE(t) can be used as the original sensitivity. The original sensitivity at time t is SHSC. raw (t) can be determined in the following way:

[0069]

[0070] Optionally, at the instant of the load change, CE(t) decreases, and SHSC... raw (t) shows a negative spike. To suppress noise, a parameterless exponential integral filter can be used to process the original sensitivity to obtain t. k The parameterless exponential integral result of the original sensitivity at time t: I(t) k ). I(t) k It can be determined in the following way:

[0071] I(t k )=(1-αΔt)·I(t k-1 )+αΔt·SHSC raw (t k )

[0072] Wherein, the sampling time interval Δt = 0.02s (i.e., the time interval between two adjacent moments), and the coefficient α = 50s. -1 .

[0073] Optionally, to avoid symbol confusion and enhance the distinguishability under low perturbation conditions, the initial source-load correlation sensitivity coefficient at time t is... The following methods can be used to determine this:

[0074]

[0075] like This indicates that the load dominates the reactive power fluctuation; This indicates that the power supply dominates reactive power fluctuations.

[0076] Optionally, the initial source load correlation sensitivity coefficient can be normalized using EMA to obtain the target source load correlation sensitivity coefficient. The target source load correlation sensitivity coefficient k at time t... S (t) can be determined in the following way:

[0077]

[0078] in, This represents the reference source-load correlation sensitivity coefficient at time t.

[0079] Step S108: Based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient, determine the target reactive power of the photovoltaic inverter in the target distribution network at the current moment.

[0080] It is understandable that the target reactive power of the photovoltaic inverter in the target distribution network at the current moment is determined based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient. This target reactive power is the power value that the photovoltaic inverter should output at the next moment, predicted based on the power data of the target distribution network at the current moment. Dynamically adjusting the reactive power of the photovoltaic inverter based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient can not only optimize the dynamic response of the photovoltaic inverter and improve the power control accuracy, but also enhance the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation and strengthen the adaptive capability of the power system in complex distribution network environments.

[0081] In one optional embodiment, the target reactive power of the photovoltaic inverter in the target distribution network at the current moment is determined based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient. This includes: updating the initial droop impedance based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient to obtain the predicted droop impedance of the photovoltaic inverter, wherein the predicted droop impedance is used to adjust the reactive power of the photovoltaic inverter; and determining the target reactive power based on the predicted droop impedance.

[0082] It is understandable that the initial droop impedance is updated based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient at the current moment, resulting in the predicted droop impedance of the photovoltaic inverter. This predicted droop impedance is used to adjust the reactive power of the photovoltaic inverter. Based on the predicted droop impedance, the target reactive power of the photovoltaic inverter at the current moment is determined. By updating the initial droop impedance to determine the predicted droop impedance, the response speed of the photovoltaic inverter can be improved, the power control speed and accuracy of the photovoltaic inverter can be optimized, and thus the power regulation effect can be improved.

[0083] In one alternative embodiment, determining the target reactive power based on the predicted droop impedance includes: determining the voltage deviation of the target node based on the voltage amplitude and the target voltage amplitude of the target node; and determining the target reactive power based on the predicted droop impedance and the voltage deviation.

[0084] It is understandable that the voltage deviation of the target node at the current moment is determined based on the difference between the voltage amplitude of the target node and the target voltage amplitude. This deviation can be used to assess whether the voltage of the target node deviates from the target level and the degree of deviation. Based on the predicted droop impedance and the voltage deviation, the target reactive power of the photovoltaic inverter at the current moment is determined. By dynamically adjusting the predicted droop impedance and calculating the target reactive power based on the real-time voltage deviation, not only can the response accuracy of the photovoltaic inverter to voltage fluctuations in the distribution network be improved, but the stability and distributed operation capability of the power system can also be enhanced, significantly improving the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation.

[0085] Optionally, to achieve multi-dimensional feature-driven adaptive power regulation, the target instantaneous voltage phasor histogram entropy k can be used. V Sensitivity coefficient k related to the target source load S Constructing a coupling suppression function Coupling suppression function The following methods can be used to determine this:

[0086]

[0087] The coupling suppression function reflects the synergistic effect of the two indices, and only when the voltage phase angle is discrete (i.e., the target instantaneous voltage phasor histogram entropy k) V High) and strong coupling between load and power supply (i.e., the sensitivity coefficient k of the target source-load correlation) S When high) occur simultaneously, The value decreases significantly, thus greatly reducing droop resistance; if only a single indicator is high, then... Approaching 1, the original droop impedance remains unchanged.

[0088] Optionally, the droop impedance at time t is R Q(t) (i.e., initial droop impedance), predicted droop impedance is R. Q (t+1). R Q (t+1) can be determined in the following way:

[0089]

[0090] Initial value R Q (0) can be set based on field experience or historical statistics (e.g., 10Ω). As the system operates, R... Q Make adaptive adjustments.

[0091] Optionally, the target reactive power of the photovoltaic inverter is Q. set (t+1). This can be determined in the following way:

[0092]

[0093] If V meas <V * Then Q set If V > 0, the photovoltaic inverter outputs reactive power to boost the voltage; if V meas >V * The photovoltaic inverter absorbs reactive power to reduce voltage. Here, V meas (t) and V * The difference is implicit in the voltage regulation redundancy value VRR, ensuring that power compensation takes into account both transient and trend conditions.

[0094] Step S110: Adjust the power of the photovoltaic inverter according to the target reactive power.

[0095] It is understandable that the photovoltaic inverter's power is adjusted according to its target reactive power at the current moment. Adjusting the photovoltaic inverter's power based on the target reactive power not only improves the inverter's immediate response to energy fluctuations in the target distribution network, but also enhances the stability and optimal resource allocation of the power system.

[0096] Optionally, to avoid voltage overshoot caused by multiple photovoltaic inverters "competing for compensation," a lightweight neighbor broadcast mechanism can be introduced. Each photovoltaic inverter completes its local Q... set After calculation, the current k value is broadcast to neighboring photovoltaic inverters with a topology radius of 1 via the edge gateway. V k S Q set With V meas .

[0097] Optionally, when calculating the local VRR, the receiving photovoltaic inverter can include the trend term Residual. 4,localCooperative correction is performed on (t) to obtain Residual4(t). Residual4(t) can be determined in the following way:

[0098] Residual4(t)←Residual 4,local (t)-β adj ·(V meas,j (t-1)-V * )

[0099] Where, β adj =0.1 represents the suppression coefficient of the neighboring photovoltaic inverter, j represents the neighboring photovoltaic inverter, and local represents the local photovoltaic inverter. If the voltage amplitude of the neighboring photovoltaic inverter is close to or exceeds the target, the local photovoltaic inverter appropriately reduces its compensation level to achieve weak coupling and coordination, thereby improving the overall stability of the power system.

[0100] Optionally, the regulation effect of the photovoltaic inverter power regulation method based on distributed photovoltaic power generation can be analyzed and verified in typical scenarios. Analysis shows that if only the load changes stepwise but the voltage phase angle remains stable, then k... V ≈0,k S ≈1, R Q Unchanged, Q set VRR is dominant; if grid connection disturbances and load surges occur concurrently, then k V With k S All are high. Significant decrease, R Q Decrease, Q set Rapid increase to achieve a strong response; if only power supply fine-tuning and load stability are achieved, then k S ≈1, R Q Unaffected, avoid oscillation.

[0101] Through the above steps S102 to S110, the voltage regulation redundancy characteristics, instantaneous voltage phasor histogram entropy, and source-load correlation sensitivity coefficient can be determined by collecting power data from the distribution network. This allows for the determination of the target reactive power of the photovoltaic inverter, and the power of the photovoltaic inverter based on distributed photovoltaic power generation can be adjusted according to the target reactive power. This achieves the technical effect of improving the power regulation effect of the photovoltaic inverter based on distributed photovoltaic power generation, thereby solving the technical problem of unsatisfactory power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation in related technologies.

[0102] Based on the above embodiments and optional embodiments, this application proposes an optional implementation method for a photovoltaic inverter power regulation method based on distributed photovoltaic power generation. The steps of this method include:

[0103] Step S1: Data acquisition and time synchronization.

[0104] To achieve online and real-time power regulation of photovoltaic inverters in the target distribution network, the following four types of data acquisition equipment need to be deployed at key distribution network nodes (i.e., target nodes, such as busbars and capacitor compensation points):

[0105] 1. Miniature synchronous phasor measurement unit: The sampling frequency is 50fps, which means that one frame is collected every 20ms. It is used to measure the voltage amplitude, voltage phase angle, current amplitude and current phase angle of the target node in real time.

[0106] 2. Photovoltaic inverter controller: Used to report instantaneous active power P within 20ms via Modbus-TCP protocol. inv With reactive power Q inv value;

[0107] 3. Energy Storage Battery Management System: Uploads information such as the state of charge, health, battery pack voltage, and current of the energy storage unit via CAN-FD bus at 100ms intervals;

[0108] 4. Load-side intelligent detection terminal: Built-in step current trigger detection chip, reporting load change event identifier F with 1 second granularity. flag .

[0109] Due to significant differences in communication links and sampling periods among various data acquisition devices—for example, the μPMU requires synchronization with ±1μs (microsecond) level GPS to ensure consistent timestamps in the acquired data, while the data delay between the photovoltaic inverter and the BMS can reach 50–200ms—and the reporting of load surge events may experience 1–2 cycles of missing frames due to network jitter. In summary, the raw power data acquired may exhibit characteristics such as inconsistent sampling intervals, timestamp jitter, missing frames, and fragmented transmission, affecting the subsequent power regulation effect on the photovoltaic inverter.

[0110] To this end, time calibration is performed on all raw power data streams at the edge gateway node. A combination of linear interpolation and frame loss compensation is used to align all data to the 20ms reference period of the μPMU. For missing data caused by communication interruptions or manual debugging, a joint compensation strategy of "zero-orderhold" and "local quadratic interpolation" is used to fill in the missing data, preventing long-term data gaps from affecting subsequent analysis.

[0111] After the above data alignment process, the target node can obtain a complete time series dataset every 20ms period, including: voltage amplitude V meas With voltage phase angle θ meas Photovoltaic inverter active power P invWith reactive power Q inv Energy storage SOC / SOH, load change event identifier F flag .

[0112] Step S2: Determine the voltage regulation redundancy value of the voltage regulation redundancy feature.

[0113] To quantify the compensation requirement between the current voltage state and the target value, a voltage regulation redundancy feature (VRR) is defined to comprehensively reflect the voltage deviation between the voltage amplitude and the target voltage amplitude, as well as its dynamic compensation redundancy at multiple scales.

[0114] First, the voltage amplitude sequence V meas (t) Improved Trend-Removed Empirical Mode Decomposition (IT-EMD) is used to decompose the mode into several intrinsic mode functions (IMFs). i The intrinsic mode function (IMF) is related to the residual trend (i.e., the residual residual). IT-EMD introduces a local linear regression detrending operation on the basis of traditional EMD (Empirical Mode Decomposition), extracting the intrinsic mode function value (IMF) in each step. k Previous residual value k-1 (t) is used for detrending processing, which further separates low-frequency trend components more effectively.

[0115] The decomposition order K is set to 4, and the final residual value Residual4(t) represents the low-frequency trend. The first few intrinsic mode function (IMF) values ​​are recombined into signal-to-noise components, and then the voltage regulation redundancy value VRR(t) of the voltage regulation redundancy feature at time t is determined. The method for determining VRR(t) is the same as in the above embodiment, and will not be repeated here.

[0116] Step S3: Determine the target instantaneous voltage phasor histogram entropy.

[0117] When line loss diffusion or current redistribution causes drastic dispersion in the voltage phase angle, fixed droop control is prone to insufficient or excessive compensation. To address this, the Instantaneous Voltage Phase Histogram Entropy (IVPH) is introduced to quantify the dispersion and uncertainty of phase angle increments within a short time window.

[0118] Within each 200ms time window (including 10 μPMU sampling points), the voltage phase angle increment sequence Δθ is recorded. i (n)=θi (n)-θ i (n-1), n=1,2,…,N,N=10. Let the actual effective number of samples be N. eff = N-λ, where λ represents the number of missing frames, N represents the number of time points across multiple time points, n represents the nth time point, and θ i (n) represents the voltage phase angle at the nth time, Δθ i (n) represents the voltage phase angle increment at the nth time.

[0119] An adaptive binning strategy is adopted to divide N eff Voltage phase angle increment Δθ i Values ​​are divided into There are equidistant intervals (i.e., voltage phase angle intervals), and the number of samples h in each interval is counted. b This yields the probability distributions corresponding to multiple voltage phase angle intervals. The probability distribution p for the b-th voltage phase angle interval is... b The method for determining the value is the same as in the above embodiments, and will not be repeated here.

[0120] To suppress the overestimation of the target instantaneous voltage phasor histogram entropy caused by missing frames, a missing frame compensation coefficient γ is introduced. The method for determining the missing frame compensation coefficient γ is the same as in the above embodiment, and will not be repeated here.

[0121] The method for determining the initial instantaneous voltage phasor histogram entropy IVPH(t) at time t is the same as in the above embodiment, and will not be repeated here.

[0122] To ensure comparability of IVPHs, the normalized initial instantaneous voltage phasor histogram entropy IVPH(t) is updated in real time. An exponential moving average is used to determine the reference instantaneous voltage phasor histogram entropy IVPH at time t. max (t). The reference instantaneous voltage phasor histogram entropy IVPH at time t. max The method for determining (t) is the same as in the above embodiments, and will not be repeated here.

[0123] Utilizing IVPH max (t) Normalize the initial instantaneous voltage phasor histogram entropy IVPH(t) to obtain the target instantaneous voltage phasor histogram entropy. The target instantaneous voltage phasor histogram entropy k at time t. V The method for determining (t) is the same as in the above embodiments, and will not be repeated here.

[0124] Step S4: Determine the target source load correlation sensitivity coefficient.

[0125] To distinguish whether reactive power fluctuations are caused by sudden load changes or power output adjustments, a Source-Load Correlation Sensitivity Coefficient (SHSC) is proposed.

[0126] Define the event identifier F for the load mutation event. flag When the load-side intelligent detection terminal detects a step current and reports F flag When δ(t) = 1, it indicates that a current step has occurred. In this case, let the step current signal δ(t) at time t = 1; otherwise, let δ(t) = 0.

[0127] The reactive power increment ΔQ of the photovoltaic inverter at time t inv The method for determining (t) is the same as in the above embodiments, and will not be repeated here.

[0128] Within a 2-second sliding window (a total of 100 time points t) i Approximately 100 reactive power samples were collected. Based on the reactive power increments and step current signals corresponding to multiple time points, the cross-domain cross-entropy CE(t) at time t was determined. The method for determining CE(t) is the same as in the above embodiment and will not be repeated here.

[0129] The smaller CE(t) is, the higher the synchronicity between reactive power fluctuations and load events; conversely, the larger CE(t) is, the more reactive power fluctuations are mainly caused by the power supply side.

[0130] To further enhance the sensitivity to sudden load events, the time derivative of CE(t) is taken as the original sensitivity. The original sensitivity at time t is SHSC. raw The method for determining (t) is the same as in the above embodiments, and will not be repeated here.

[0131] At the instant of the load change, CE(t) decreases, and SHSC... raw (t) shows a negative spike. To suppress noise, a parameterless exponential integral filter is used to process the original sensitivity, resulting in t. k The parameterless exponential integral result of the original sensitivity at time t: I(t) k ). I(t) k The method for determining ) is the same as in the above embodiments, and will not be repeated here.

[0132] To avoid symbol confusion and enhance the discriminative ability under low perturbation conditions, the initial source-load correlation sensitivity coefficient at time t is... The method for determining the value is the same as in the above embodiments, and will not be repeated here.

[0133] like This indicates that the load dominates the reactive power fluctuation; This indicates that the power supply dominates reactive power fluctuations.

[0134] Similarly, the initial source load correlation sensitivity coefficient is normalized using EMA to obtain the target source load correlation sensitivity coefficient. The target source load correlation sensitivity coefficient k at time t is... SThe method for determining (t) is the same as in the above embodiments, and will not be repeated here.

[0135] Step S5: Determine the target reactive power of the photovoltaic inverter.

[0136] To achieve adaptive power regulation driven by multidimensional features, the target instantaneous voltage phasor histogram entropy k is used. V Sensitivity coefficient k related to the target source load S Constructing a coupling suppression function Coupling suppression function The construction method is the same as in the above embodiments, and will not be repeated here.

[0137] The coupling suppression function reflects the synergistic effect of the two indices, and only when the voltage phase angle is discrete (i.e., the target instantaneous voltage phasor histogram entropy k) V High) and strong coupling between load and power supply (i.e., the sensitivity coefficient k of the target source-load correlation) S When high) occur simultaneously, The value decreases significantly, thus greatly reducing droop resistance; if only a single indicator is high, then... Approaching 1, the original droop impedance remains unchanged.

[0138] The droop impedance at time t is R Q (t) (i.e., initial droop impedance), predicted droop impedance is R. Q (t+1). R Q The method for determining (t+1) is the same as in the above embodiment, and will not be repeated here. The initial value R... Q (0) can be set based on field experience or historical statistics (e.g., 10Ω). As the system operates, R... Q Make adaptive adjustments.

[0139] The target reactive power of the photovoltaic inverter is Q set The method for determining (t+1) is the same as in the above embodiments, and will not be repeated here. If V meas <V * Then Q set If V > 0, the photovoltaic inverter outputs reactive power to boost the voltage; if V meas >V * The photovoltaic inverter absorbs reactive power to reduce voltage. Here, V meas (t) and V * The difference is implicit in the voltage regulation redundancy value VRR, ensuring that power compensation takes into account both transient and trend conditions.

[0140] Step S6: Distributed collaborative mechanism for multiple photovoltaic inverters.

[0141] To avoid voltage overshoot caused by multiple photovoltaic inverters "competing for compensation," a lightweight neighbor broadcast mechanism is introduced. Each photovoltaic inverter completes its local Q...set After calculation, the current k value is broadcast to neighboring photovoltaic inverters with a topology radius of 1 via the edge gateway. V k S Q set With V meas .

[0142] When the receiving photovoltaic inverter calculates the local VRR, it considers the trend term Residual. 4,local The residual4(t) is obtained by performing a collaborative correction. The method for determining the residual4(t) is the same as in the above embodiment, and will not be repeated here.

[0143] If the voltage amplitude of a neighboring photovoltaic inverter is close to or exceeds the target, the local photovoltaic inverter should appropriately reduce its compensation level to achieve weak coupling and coordination, thereby improving the overall stability of the power system.

[0144] Step S7, Control performance and closed-loop response.

[0145] The overall control closed-loop delay mainly includes: data synchronization and compensation (approximately 10ms), IT-EMD and feature calculation (approximately 5ms), and photovoltaic inverter execution delay (5–10ms). The total delay control completes the initial response within 20ms and achieves convergence within 200ms, which is significantly better than 1s-level centralized control.

[0146] Analysis of typical scenarios shows that if only the load increases but the voltage phase angle remains stable, then k V ≈0,k S ≈1, R Q Unchanged, Q set VRR is dominant; if grid connection disturbances and load surges occur concurrently, then k V With k S All are high. Significant decrease, R Q Decrease, Q set Rapid increase to achieve a strong response; if only power supply fine-tuning and load stability are achieved, then k S ≈1, R Q Unaffected, avoid oscillation.

[0147] In summary, by synergistically utilizing the triple features of VRR, target instantaneous voltage phasor histogram entropy, and target source-load correlation sensitivity coefficient, a photovoltaic inverter power regulation method with multi-scale sensing, dynamic sensitivity adjustment, and weak distributed coordination capabilities is constructed. This method can effectively regulate the voltage of the distribution network, address the complex, random, and bidirectional voltage fluctuation problems in multi-source power systems, and improve the renewable energy absorption capacity and distribution network operation stability.

[0148] The above-mentioned optional implementation methods achieve at least the following effects: Calculating voltage regulation redundancy based on voltage amplitude not only accurately identifies the reactive power demand gap at target nodes but also allows for power regulation based on dynamic changes in the distribution network, thereby improving the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation and ensuring the power quality and stability of the distribution network; Calculating the initial instantaneous voltage phasor histogram entropy and initial source-load correlation sensitivity coefficient and performing normalization processing not only improves the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation but also enhances the response speed and accuracy of photovoltaic inverters to energy fluctuations, strengthening the stability and coordination of the power system in complex dynamic environments; Dynamically adjusting the predicted droop impedance and calculating the target reactive power based on real-time voltage deviation not only improves the response accuracy of photovoltaic inverters to energy fluctuations in the distribution network but also enhances the stability and distributed operation capability of the power system, significantly improving the power regulation effect of photovoltaic inverters based on distributed photovoltaic power generation.

[0149] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0150] This embodiment also provides a photovoltaic inverter power regulation device based on distributed photovoltaic power generation. This device is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0151] According to an embodiment of this application, an apparatus embodiment for implementing a photovoltaic inverter power regulation method based on distributed photovoltaic power generation is also provided. Figure 2 This is a schematic diagram of a photovoltaic inverter power regulation device based on distributed photovoltaic power generation, according to an embodiment of this application. Figure 2 As shown, the photovoltaic inverter power regulation device based on distributed photovoltaic power generation includes a data acquisition module 202, a first determination module 204, a second determination module 206, a third determination module 208, and a power regulation module 210. The device will be described below.

[0152] The data acquisition module 202 is used to acquire the power data of the target distribution network at the current time. The power data includes the voltage amplitude of the target node in the target distribution network. The target node is the node in the target distribution network whose electrical attributes need to be displayed.

[0153] The first determining module 204 is connected to the data acquisition module 202 and is used to determine the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude. The voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network.

[0154] The second determining module 206, connected to the first determining module 204, is used to determine the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment when the voltage regulation redundancy value is greater than a preset threshold. The target instantaneous voltage phasor histogram entropy is used to represent the degree of dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuation in the target distribution network.

[0155] The third determining module 208, connected to the second determining module 206, is used to determine the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient.

[0156] The power regulation module 210, connected to the third determination module 208, is used to regulate the power of the photovoltaic inverter according to the target reactive power.

[0157] In a photovoltaic inverter power regulation device based on distributed photovoltaic power generation provided in this application embodiment, a data acquisition module 202 is set up to collect the power data of the target distribution network at the current moment. The power data includes the voltage amplitude of target nodes in the target distribution network, where the target nodes are nodes in the target distribution network whose electrical attributes need to be displayed. A first determination module 204, connected to the data acquisition module 202, is used to determine the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude. The voltage regulation redundancy value represents the basic reactive power demand gap of the target distribution network. A second determination module 206, connected to the first determination module 204, is used to determine the voltage regulation redundancy value when it is large. Under a preset threshold, the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment are determined. The target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network. The third determining module 208, connected to the second determining module 206, is used to determine the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient. The power adjustment module 210, connected to the third determining module 208, is used to adjust the power of the photovoltaic inverter according to the target reactive power. By collecting power data from the distribution network, the system determines the voltage regulation redundancy characteristics, instantaneous voltage phasor histogram entropy, and source-load correlation sensitivity coefficient. This allows for the determination of the target reactive power of the photovoltaic inverter, and the adjustment of the inverter's power based on the target reactive power. This improves the power regulation effect of the photovoltaic inverter based on distributed photovoltaic power generation, thus solving the technical problem of unsatisfactory power regulation performance in related technologies.

[0158] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0159] It should be noted that the data acquisition module 202, the first determining module 204, the second determining module 206, the third determining module 208, and the power adjustment module 210 correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0160] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0161] The aforementioned photovoltaic inverter power regulation device based on distributed photovoltaic power generation may also include a processor and a memory. The data acquisition module 202, the first determination module 204, the second determination module 206, the third determination module 208, the power regulation module 210, etc. are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0162] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0163] This application provides a non-volatile storage medium storing a program that, when executed by a processor, implements a photovoltaic inverter power regulation method based on distributed photovoltaic power generation.

[0164] This application provides an electronic device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: collecting power data of a target distribution network at the current moment, wherein the power data includes the voltage amplitude of a target node in the target distribution network, and the target node is a node in the target distribution network whose electrical attributes need to be displayed; determining the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; if the voltage regulation redundancy value is greater than a preset threshold, determining the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment, wherein the target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network; determining the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient; and adjusting the power of the photovoltaic inverter according to the target reactive power. The device in this document can be a server, PC, etc.

[0165] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: collecting power data of a target distribution network at the current moment, wherein the power data includes the voltage amplitude of a target node in the target distribution network, and the target node is a node in the target distribution network whose electrical attributes need to be displayed; determining the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; if the voltage regulation redundancy value is greater than a preset threshold, determining the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment, wherein the target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuations in the target distribution network; determining the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient; and adjusting the power of the photovoltaic inverter according to the target reactive power.

[0166] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0172] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0174] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for regulating the power of a photovoltaic inverter based on distributed photovoltaic power generation, characterized in that, include: Collect power data of the target distribution network at the current time, wherein the power data includes the voltage amplitude of the target node in the target distribution network, and the target node is the node in the target distribution network whose electrical attributes need to be displayed; Based on the voltage amplitude, the voltage regulation redundancy value of the target node at the current moment is determined, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; When the voltage regulation redundancy value is greater than a preset threshold, the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment are determined. The target instantaneous voltage phasor histogram entropy is used to represent the dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuation in the target distribution network. Based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient, the target reactive power of the photovoltaic inverter in the target distribution network at the current moment is determined. The photovoltaic inverter is power regulated according to the target reactive power.

2. The method according to claim 1, characterized in that, Determining the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude includes: Determine multiple intrinsic mode function values ​​of the voltage amplitude, the residual value of the voltage amplitude, and the target voltage amplitude of the target node, wherein the intrinsic mode function values ​​are used to describe the fluctuation characteristics of the voltage amplitude at different frequency components, and the residual value is used to describe the low-frequency variation characteristics of the voltage amplitude; The voltage regulation redundancy value is determined based on the multiple intrinsic mode function values, the residual value, the voltage amplitude, and the target voltage amplitude.

3. The method according to claim 1, characterized in that, The determination of the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment includes: Based on the voltage phase angle of the target node included in the power data, determine the initial instantaneous voltage phasor histogram entropy of the target node at the current moment; Based on the reactive power of the photovoltaic inverter included in the power data, the initial source-load correlation sensitivity coefficient at the current moment is determined; The initial instantaneous voltage phasor histogram entropy and the initial source-load correlation sensitivity coefficient are normalized to obtain the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient.

4. The method according to claim 3, characterized in that, The step of determining the initial instantaneous voltage phasor histogram entropy of the target node at the current moment based on the voltage phase angle of the target node included in the power data includes: Determine multiple voltage phase angles of the target node, wherein the multiple voltage phase angles include the voltage phase angle at the current moment, and the voltage phase angles corresponding to multiple historical moments prior to the current moment; Based on the multiple voltage phase angles, the voltage phase angle increments corresponding to multiple moments are determined, wherein the multiple moments include the current moment and the multiple historical moments; Based on the number of moments at multiple times, the number of missing frames at the multiple times, and the voltage phase angle increments corresponding to the multiple times, multiple voltage phase angle intervals are determined, wherein the number of missing frames refers to the number of moments within the multiple times where the voltage phase angle could not be successfully acquired. Based on the voltage phase angle increments corresponding to the multiple time points and the multiple voltage phase angle intervals, the probability distributions corresponding to the multiple voltage phase angle intervals are determined respectively; Based on the probability distributions corresponding to the multiple voltage phase angle intervals and the frame loss compensation coefficient, the initial instantaneous voltage phasor histogram entropy is determined, wherein the frame loss compensation coefficient is used to quantify the degree of frame loss at the multiple moments.

5. The method according to claim 3, characterized in that, The determination of the initial source-load correlation sensitivity coefficient at the current moment based on the reactive power of the photovoltaic inverter included in the power data includes: Determine multiple reactive powers of the photovoltaic inverter, wherein the multiple reactive powers include the reactive power at the current moment and the reactive power corresponding to multiple historical moments before the current moment; Based on the multiple reactive power values, the reactive power increments corresponding to multiple time points are determined, wherein the multiple time points include the current time point and the multiple historical time points; Based on the reactive power increments corresponding to the multiple time points and the step current signals corresponding to the multiple time points, the cross-domain cross-entropy is determined. The step current signal is used to indicate whether the load has experienced a current step change, and the cross-domain cross-entropy is used to quantify the degree of coupling between the reactive power change of the photovoltaic inverter and the current step change of the load. Based on the cross-domain cross-entropy, the initial source-load correlation sensitivity coefficient is determined.

6. The method according to any one of claims 1 to 5, characterized in that, The determination of the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient includes: Based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient, the initial droop impedance is updated to obtain the predicted droop impedance of the photovoltaic inverter, wherein the predicted droop impedance is used to adjust the reactive power of the photovoltaic inverter. The target reactive power is determined based on the predicted droop impedance.

7. The method according to claim 6, characterized in that, Determining the target reactive power based on the predicted droop impedance includes: Based on the voltage amplitude and the target voltage amplitude of the target node, the voltage deviation of the target node is determined; The target reactive power is determined based on the predicted droop impedance and the voltage deviation.

8. A photovoltaic inverter power regulation device based on distributed photovoltaic power generation, characterized in that, include: The data acquisition module is used to acquire the power data of the target distribution network at the current time. The power data includes the voltage amplitude of the target node in the target distribution network, and the target node is the node in the target distribution network whose electrical attributes need to be displayed. The first determining module is used to determine the voltage regulation redundancy value of the target node at the current moment based on the voltage amplitude, wherein the voltage regulation redundancy value is used to represent the basic reactive power demand gap of the target distribution network; The second determining module is used to determine the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient of the target node at the current moment when the voltage regulation redundancy value is greater than a preset threshold. The target instantaneous voltage phasor histogram entropy is used to represent the degree of dispersion of the voltage phase angle of the target node, and the target source-load correlation sensitivity coefficient is used to determine the cause of reactive power fluctuation in the target distribution network. The third determining module is used to determine the target reactive power of the photovoltaic inverter in the target distribution network at the current moment based on the target instantaneous voltage phasor histogram entropy and the target source-load correlation sensitivity coefficient. The power regulation module is used to regulate the power of the photovoltaic inverter according to the target reactive power.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the photovoltaic inverter power regulation method based on distributed photovoltaic power generation as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the photovoltaic inverter power regulation method based on any one of claims 1 to 7.

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