Distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system

Abstract

The invention provides a distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: firstly collecting the parameters of a photovoltaic system and a distribution network, and building a power grid response equation set considering the parameters; more parameter samples are obtained through data augmentation and expansion, and indexes such as voltage, power and harmonic waves of each node are calculated according to the response equation set. Constructing a voltage fluctuation evaluation index matrix including voltage deviation, fluctuation, harmonic waves, short-circuit capacity change and the like; and decomposing the matrix by adopting singular value decomposition to obtain a voltage stability and fluctuation evaluation matrix, and finally calculating a voltage stability index and a fluctuation index. If the voltage fluctuation evaluation index exceeds a preset threshold value, an alarm is triggered, and decision support is provided for distribution network planning and operation. According to the invention, the technical problems of short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuation in the evaluation of the distributed photovoltaic access distribution network in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed photovoltaics, and in particular, relates to a method, medium and system for evaluating voltage fluctuations of distributed photovoltaic access distribution networks. Background Art

[0002] With the continuous advancement of renewable energy generation technology, distributed photovoltaic power generation systems are increasingly used in power systems. Compared with centralized power plants, distributed photovoltaic power sources have the characteristics of being pollution-free, renewable, and locally consumed, which can effectively alleviate the transmission and distribution pressure of the power grid and improve the power supply reliability of the power grid. However, due to the inherent characteristics of volatility and intermittency of photovoltaic power generation, large-scale grid connection also brings some new challenges to the voltage stability and voltage quality of the power grid. At present, power systems generally adopt centralized planning and operation modes, and the design and operation parameters of the power grid are mostly based on the characteristics of centralized power plants. The random grid connection and volatility characteristics of distributed photovoltaic systems are somewhat different from the design concept of traditional power grids. First, a large number of photovoltaic grid connection will lead to increased voltage fluctuations in the distribution network, especially when the light conditions change greatly, the voltage fluctuation amplitude may exceed the voltage qualified range. Secondly, the harmonic injection of the photovoltaic system will cause voltage distortion in the distribution network, affecting the normal operation of end-user equipment. In addition, the reactive power injection characteristics of the photovoltaic system will change the reactive compensation demand of the distribution network, thereby affecting the voltage adjustment. Finally, large-scale photovoltaic grid connection may also cause a reduction in the short-circuit capacity of the distribution network, thereby reducing the sensitivity and selectivity of line protection.

[0003] In summary, the access of distributed photovoltaic power generation poses a severe challenge to the voltage stability and quality of the distribution network. Existing methods mostly use a single voltage deviation or voltage fluctuation index, ignoring the impact of harmonics, reactive power and short-circuit capacity on voltage quality. In other words, the existing technology for the evaluation of the access of distributed photovoltaic power generation to the distribution network has the technical problem of a short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuations. Summary of the invention

[0004] In view of this, the present invention provides a method, medium and system for evaluating voltage fluctuations of distributed photovoltaic access to distribution networks, which can solve the technical problems in the existing technology of evaluating distributed photovoltaic access to distribution networks, that is, the evaluation time scale is short and it is difficult to capture the dynamic characteristics of voltage fluctuations.

[0005] The present invention is achieved in that: A first aspect of the present invention provides a method for evaluating voltage fluctuations of a distributed photovoltaic access distribution network, comprising the following steps: S10, collecting distributed photovoltaic parameters and distribution network parameters, the distributed photovoltaic parameters include photovoltaic array capacity, inverter rated power, maximum power point tracking efficiency, power factor adjustment range, harmonic content, the distribution network parameters include line impedance, transformer capacity, load distribution, voltage level, short-circuit capacity; S20, establishing and fitting a distributed photovoltaic access distribution network response equation group that takes into account distributed photovoltaic parameters and distribution network parameters, including a power balance equation, a node voltage equation, a line flow equation, a harmonic propagation equation, a reactive power compensation equation, and a short-circuit current equation; S30, using a data augmentation method, the obtained distributed photovoltaic parameters and distribution network parameters are augmented and expanded respectively to obtain distributed photovoltaic augmented and expanded parameters and distribution network augmented and expanded parameters; S40, using distributed photovoltaic expansion parameters and distribution network expansion parameters, according to the distributed photovoltaic access distribution network response equation group, calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node; S50, based on the result obtained in S40, calculating and constructing a voltage fluctuation evaluation index vector, including a voltage deviation index, a voltage fluctuation index, a harmonic distortion rate index, and a short-circuit capacity change index; S60, cyclically executing S10-S50 according to a preset time interval within the time period to be evaluated, obtaining a voltage fluctuation evaluation index vector for each cycle, and merging them into a voltage fluctuation evaluation index time matrix; S70, decomposing the voltage fluctuation assessment index time matrix by using a singular value decomposition method to obtain a voltage stability assessment matrix and a voltage fluctuation assessment matrix; S80, calculating a voltage stability index and a voltage fluctuation index based on a voltage stability assessment matrix S and a voltage fluctuation assessment matrix F; S90, comprehensively calculating the voltage stability index and the voltage fluctuation index to calculate a final voltage fluctuation evaluation index, and triggering an alarm if the voltage fluctuation evaluation index is greater than a preset fluctuation index threshold.

[0006] Specifically, the step S10 specifically includes: collecting the relevant parameters of the distributed photovoltaic system, including the total capacity of the photovoltaic array, the rated power of the inverter, the maximum power point tracking efficiency, the power factor adjustment range, the harmonic content, etc.; and collecting the relevant parameters of the distribution network, including the line impedance, the transformer capacity, the load distribution, the voltage level, the short-circuit capacity, etc. These parameter data can be obtained through field measurements or from historical archives. The purpose of collecting these parameters is to lay the foundation for the subsequent establishment of a distribution network response equation group considering photovoltaic access.

[0007] Wherein, the step S20 specifically includes: firstly establishing power balance equation, node voltage equation, line flow equation, harmonic propagation equation, reactive power compensation equation and short-circuit current equation, etc., which reflect the influence of photovoltaic access on various characteristics of the distribution network. Then, by numerically iteratively solving these equations, the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node can be obtained. These calculation results provide basic data for the subsequent voltage fluctuation assessment.

[0008] The data augmentation method used in step S30 includes: expanding the collected distributed photovoltaic parameters and distribution network parameters through random perturbation, Bootstrap resampling or generative adversarial network techniques to generate more parameter samples. This can cover a wider range of operating conditions, provide a richer data basis for voltage fluctuation assessment, and improve the reliability of the assessment results.

[0009] Among them, in the step S40, the voltage amplitude and phase angle of each node are first calculated, and the modulus and argument of the voltage complex are used to represent them. Then, the active power and reactive power of each node are calculated using the relationship between voltage and current. In addition, the distribution of each harmonic current on the node and the short-circuit current level of each node need to be calculated. These calculation results provide necessary data support for the subsequent construction of voltage fluctuation indicators.

[0010] Among them, in step S50, four voltage fluctuation evaluation indicators are constructed, including voltage deviation indicator, voltage fluctuation indicator, harmonic distortion rate indicator and short-circuit capacity change indicator. These indicators respectively reflect the degree of deviation between node voltage and rated voltage, voltage fluctuation range within the observation period, severity of harmonic distortion, and impact of short-circuit capacity change on voltage quality. By constructing such a comprehensive indicator vector, the impact of distributed photovoltaic access on the voltage quality of the distribution network can be comprehensively evaluated.

[0011] Among them, in step S60, according to the preset time interval, such as 1 second to 60 seconds, steps S10 to S50 are executed cyclically to obtain the time series data of the voltage fluctuation index of each node in the time period to be evaluated, and integrate them into the voltage fluctuation evaluation index time matrix. In this way, the dynamic characteristics of voltage fluctuation changing with time can be reflected, and the data foundation for the subsequent singular value decomposition analysis can be laid.

[0012] In step S70, the singular value decomposition (SVD) method is used to decompose the voltage fluctuation evaluation index time matrix to obtain the voltage stability evaluation matrix and the voltage fluctuation evaluation matrix. The purpose of this step is to separate the stability characteristics and fluctuation characteristics in the original voltage fluctuation evaluation index, laying the foundation for the subsequent comprehensive evaluation index calculation.

[0013] Among them, in step S80, the voltage stability index VSI and the voltage fluctuation index VFI are calculated, VSI reflects the impact of photovoltaic access on the voltage stability of the distribution network, and VFI reflects the impact of photovoltaic access on the voltage fluctuation characteristics. In step S90, VSI and VFI are comprehensively considered to calculate the final voltage fluctuation assessment index VDI. If VDI exceeds the preset fluctuation index threshold, an alarm is triggered, indicating that measures need to be taken for adjustment and optimization. In this way, the impact of distributed photovoltaic access on the voltage quality of the distribution network can be fully evaluated.

[0014] On the basis of the above technical solution, the voltage fluctuation assessment method of distributed photovoltaic access distribution network of the present invention can also be improved as follows: The preset time interval is 1 second to 60 seconds.

[0015] The equations involved in the present invention are described in detail below: 1. Distributed photovoltaic access distribution network response equation group: a) Power balance equation: ; In the formula, and are the injected active and reactive powers of node i respectively; is the complex voltage of node i; is the element in the i-th row and k-th column of the node admittance matrix; is the total number of nodes, i is not greater than A positive integer of ; and are the active and reactive power of node i respectively; and are the active and reactive power of the load at node i respectively.

[0016] b) Node voltage equation: ; In the formula, is the voltage of node i; is the system nominal voltage; is the self-impedance of node i; is the mutual impedance between nodes i and k; and are the injected currents at nodes i and k respectively.

[0017] c) Line power flow equation: ; ; In the formula, and are the active and reactive power flows from node i to j, respectively; and are the conductance and susceptance of line ij respectively; and are the voltage amplitudes at nodes i and j respectively; is the phase angle difference between nodes i and j, and line ij represents the line between node i and node j.

[0018] d) Harmonic propagation equation: ; In the formula, is the hth harmonic current vector; is the hth harmonic admittance matrix; is the hth harmonic voltage vector.

[0019] e) Reactive power compensation equation: ; In the formula, is the reactive power after compensation; is the initial reactive power; is the reactive power compensation coefficient; is the voltage deviation.

[0020] f) Short-circuit current equation: ; In the formula, is the short-circuit current; is the fault point voltage; is the equivalent Thevenin impedance at the fault point.

[0021] 2. Calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node: a) Node voltage amplitude and phase angle: ; ; In the formula, is the voltage amplitude of node i; is the voltage phase angle of node i; and are the real and imaginary parts of the voltage at node i, respectively.

[0022] b) Node active and reactive power: ; ; In the formula, and are the active and reactive power of node i respectively; is the complex voltage of node i; is the conjugate complex number of the current injected into node i; Re represents the real part of the complex number; Im represents the imaginary part of the complex number.

[0023] 1. Active power : In the formula, Re represents the real part of the complex number.

[0024] is the complex voltage of node i, that is ,in is the voltage amplitude, is the voltage phase angle.

[0025] is the conjugate complex number of the current injected into node i, that is, ,in is the current amplitude, is the current phase angle.

[0026] Will and Substituting in, we have: ; ; The above formula indicates that the active power of node i is determined by the voltage amplitude, current amplitude and the cosine value of the phase difference between the two.

[0027] 2. Reactive power : Where Im represents the imaginary part of a complex number.

[0028] Likewise and Substituting in, we have: ; ; The above formula indicates that the reactive power of node i is determined by the voltage amplitude, current amplitude and the sine value of the phase difference between the two.

[0029] c) Harmonic current distribution: ; In the formula, is the hth harmonic current of node i; is the element in the i-th row and k-th column of the h-th harmonic admittance matrix; is the hth harmonic voltage at node k.

[0030] d) Short-circuit current level: ; In the formula, is the short-circuit current of node i; is the normal operating voltage of node i; is the equivalent Thevenin impedance of node i.

[0031] 3. Calculate and construct the voltage fluctuation evaluation index vector: a) Voltage deviation index: ; In the formula, is the voltage deviation index of node i; is the actual voltage amplitude of node i; is the rated voltage.

[0032] b) Voltage fluctuation index: ; In the formula, is the voltage fluctuation index of node i; and are the maximum and minimum voltage amplitudes of node i during the observation period, respectively.

[0033] c) Harmonic distortion index: ; In the formula, is the total harmonic distortion rate of node i; is the hth harmonic voltage amplitude at node i; is the fundamental voltage amplitude of node i; is the highest harmonic order considered.

[0034] d) Short-circuit capacity change index: ; In the formula, is the short-circuit capacity change index of node i; is the current short-circuit capacity of node i; is the initial short-circuit capacity of node i.

[0035] 4. Singular value decomposition calculation: ; In the formula, It is the voltage fluctuation assessment index time matrix; is a left singular matrix; is a diagonal matrix of singular values; is the transpose of a right singular matrix.

[0036] Voltage Stability Assessment Matrix: ; Voltage fluctuation assessment matrix: ; In the formula, Contains the first k largest singular values, Contains the remaining singular values.

[0037] 5. Calculate the voltage stability index and voltage fluctuation index: Voltage stability index: ; Voltage fluctuation index: ; In the formula, , , and Respectively represent the Frobenius norm of matrices S, F, and A, and the calculation formula is: ; in, for matrix, is the element in the i-th row and j-th column of the matrix M.

[0038] The relevant variables or parameters can be obtained through actual measurement, historical data analysis or simulation calculation.

[0039] The following are the steps to obtain the threshold value of voltage fluctuation index for distributed photovoltaic access to the distribution network: Step 901, define the voltage fluctuation index (VFI): ; In the formula, is the voltage fluctuation index, unit is %; is the maximum voltage value during the observation period, in V; is the minimum voltage value during the observation period, in V; is the rated voltage, in V.

[0040] and The data is collected through smart meters or power monitoring equipment within a specified observation period (such as 15 minutes, 1 hour or 24 hours). It is the standard rated voltage of the power distribution system, usually 220V (single-phase) or 380V (three-phase).

[0041] Step 902: Collect historical data and calculate VFI at different time scales: ; ; ; In the formula, , and Voltage fluctuation index at time scales of 15 minutes, 1 hour and 24 hours respectively. , and are the maximum voltage values ​​in the corresponding time scales respectively; , and are the minimum voltage values ​​in the corresponding time scales respectively.

[0042] These data are collected continuously by the distribution system's data acquisition system (such as a SCADA system) for at least one year to cover various working conditions and seasonal changes.

[0043] Step 903: Determine the initial threshold using the quantile method: ; In the formula, is the voltage fluctuation index threshold value determined initially; is the 75% quantile of the VFI data; is the interquartile range, calculated as ,in is the 25% quantile of the VFI data.

[0044] , and The calculation steps are as follows: 1. Sort the collected VFI data from small to large; 2. Calculation Location: ,in is the total number of data; 3. Calculation Location: ; 4. If and If it is not an integer, it is obtained by linear interpolation. and The value of 5. Calculation .

[0045] Step 904: Considering the impact of photovoltaic penetration rate on the threshold, establish an adjustment coefficient: ; In the formula, is the adjustment factor; is the sensitivity coefficient, obtained through regression analysis of historical data; is the current photovoltaic penetration rate, in %; is the base permeability, in %.

[0046] The calculation method is: ; In the formula, is the total capacity of the installed PV system, in kW; is the peak load of the distribution system, in kW.

[0047] The value is usually taken as the expected PV penetration rate during system planning, or the average penetration rate in historical data.

[0048] Optionally, step 905, calculate a correction threshold considering photovoltaic penetration: ; In the formula, is the corrected threshold after considering the impact of photovoltaic penetration.

[0049] Optionally, in step 906, the threshold is further optimized by using a fuzzy logic method, taking into account load characteristics and seasonal factors: ; In the formula, is the voltage fluctuation index threshold finally determined; is the fuzzy logic function; is the load type (such as residential, commercial, industrial, etc.); For seasonal factors (such as spring, summer, autumn and winter).

[0050] Fuzzy logic functions The implementation steps are as follows: 1. Define input variables ( , , )’s fuzzy sets and membership functions; 2. Define output variables ( )’s fuzzy sets and membership functions; 3. Establish a fuzzy rule base, such as "If For residential and In summer, Slightly enlarged”; 4. Use Mamdani reasoning method for fuzzy reasoning; 5. Use the centroid method to defuzzify and get the final value.

[0051] Step 907: Verify the validity of the threshold and calculate the false alarm rate (FAR) and missed detection rate (MDR): ; ; In the formula, is the false alarm rate; is the underreporting rate; is the number of false alarms; is the total number of actual normal situations; is the number of missed reports; is the total number of actual abnormal situations.

[0052] These parameters are calculated by It is applied to historical data and compared with abnormal events annotated by experts.

[0053] Step 908: If or If the preset value is exceeded, the process returns to step S30 to adjust the parameters, otherwise the process proceeds to the next step. The preset value is usually determined according to system requirements, for example , .

[0054] Step 909: Output the voltage fluctuation index threshold finally determined , as an important reference indicator for evaluating the impact of distributed photovoltaic access on distribution network voltage quality.

[0055] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for evaluating voltage fluctuations of distributed photovoltaic access to a distribution network.

[0056] A third aspect of the present invention provides a distributed photovoltaic access distribution network voltage fluctuation assessment system, which includes the above-mentioned computer-readable storage medium.

[0057] Compared with the prior art, the method, medium and system for evaluating voltage fluctuation of distributed photovoltaic access distribution network provided by the present invention have the following beneficial effects: 1) A comprehensive voltage fluctuation evaluation index system is constructed. This method not only takes into account voltage deviation and voltage fluctuation, but also introduces indicators such as harmonic distortion rate and short-circuit capacity change, which can more comprehensively reflect the multi-faceted impact of distributed photovoltaic access on the voltage quality of the distribution network.

[0058] 2) Adopt cyclic periodic evaluation to capture the dynamic characteristics of voltage fluctuations. This method cyclically executes data collection, model calculation, indicator construction and other steps according to the preset time interval, which can dynamically track the process of voltage fluctuations changing over time and improve the time resolution of the evaluation.

[0059] 3) Consider the impact of photovoltaic penetration on the threshold and achieve adaptive adjustment. Based on the preliminary determination of the voltage fluctuation threshold, this method further considers the change of photovoltaic penetration and uses the adjustment coefficient to optimize the threshold, so that the evaluation results are closer to the actual operating conditions.

[0060] 4) The modeling and calculation methods with strong versatility can be applied to different distribution networks. The distribution network response equations and voltage fluctuation evaluation indicators established by this method are highly applicable and can be applied to different regions and power grid types, laying the foundation for wide promotion and application.

[0061] In summary, the distributed photovoltaic access distribution network voltage fluctuation assessment method proposed in the present invention can more comprehensively, dynamically and intelligently assess the impact of photovoltaic grid connection on the grid voltage quality, provide effective support for distribution network planning and operation optimization, and solve the technical problems of the existing technology for the assessment of distributed photovoltaic access distribution network, that is, the short assessment time scale and the difficulty in capturing the dynamic characteristics of voltage fluctuations. It can be used for the later judgment of the seasonal load balancing of the power grid and the optimization of the equipment utilization efficiency in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of the method provided by the present invention; DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0064] like Figure 1 FIG. 1 is a flow chart of a method for evaluating voltage fluctuations of a distributed photovoltaic access distribution network provided by the present invention. The method comprises the following steps: S10, collect distributed photovoltaic parameters and distribution network parameters. Distributed photovoltaic parameters include photovoltaic array capacity, inverter rated power, maximum power point tracking efficiency, power factor adjustment range, harmonic content, and distribution network parameters include line impedance, transformer capacity, load distribution, voltage level, and short-circuit capacity; S20, establishing and fitting a distributed photovoltaic access distribution network response equation group that takes into account distributed photovoltaic parameters and distribution network parameters, including a power balance equation, a node voltage equation, a line flow equation, a harmonic propagation equation, a reactive power compensation equation, and a short-circuit current equation; S30, using a data augmentation method, the obtained distributed photovoltaic parameters and distribution network parameters are augmented and expanded respectively to obtain distributed photovoltaic augmented and expanded parameters and distribution network augmented and expanded parameters; S40, using distributed photovoltaic expansion parameters and distribution network expansion parameters, according to the distributed photovoltaic access distribution network response equation group, calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node; S50, based on the result obtained in S40, calculating and constructing a voltage fluctuation evaluation index vector, including a voltage deviation index, a voltage fluctuation index, a harmonic distortion rate index, and a short-circuit capacity change index; S60, cyclically executing S10-S50 according to a preset time interval within the time period to be evaluated, obtaining a voltage fluctuation evaluation index vector for each cycle, and merging them into a voltage fluctuation evaluation index time matrix; S70, decomposing the voltage fluctuation assessment index time matrix by using a singular value decomposition method to obtain a voltage stability assessment matrix and a voltage fluctuation assessment matrix; S80, calculating a voltage stability index and a voltage fluctuation index based on a voltage stability assessment matrix S and a voltage fluctuation assessment matrix F; S90, comprehensively calculating the voltage stability index and the voltage fluctuation index to calculate a final voltage fluctuation assessment index. If the voltage fluctuation assessment index is greater than a preset fluctuation index threshold, triggering an alarm.

[0065] The specific implementation methods of the above steps are described in detail below: Step S10, collect distributed photovoltaic parameters and distribution network parameters. First, it is necessary to collect the relevant parameters of the distributed photovoltaic system through field measurement or from archives, including: total capacity of photovoltaic array, rated power of inverter, maximum power point tracking efficiency, power factor adjustment range, harmonic content, etc. At the same time, it is also necessary to collect the parameters of the distribution network, such as line impedance, transformer capacity, load distribution, voltage level, short-circuit capacity, etc. These parameters are the basis for the subsequent establishment of the distribution network response equation group.

[0066] Step S20, establishing and fitting a distributed photovoltaic access distribution network response equation group that takes into account distributed photovoltaic parameters and distribution network parameters. Specifically including: a) Power balance equation: This equation describes the balance relationship between the active power P and reactive power Q of each node, including the injected power, generated power and load power, which can be expressed as: ; In the formula, and are the injected active and reactive powers of node i, respectively; is the complex voltage of node i; is the element in the i-th row and k-th column of the node admittance matrix; is the total number of nodes; and are the active and reactive power generated by node i, respectively; and are the active and reactive power of the load at node i respectively.

[0067] b) Node voltage equation: This equation describes the calculation relationship between the voltage amplitude and phase angle of each node. Considering the influence of line impedance on voltage, it can be expressed as: ; In the formula, is the voltage of node i; is the system nominal voltage; is the self-impedance of node i; is the mutual impedance between nodes i and k; and are the injected currents at nodes i and k respectively.

[0068] c) Line power flow equation: This equation describes the relationship between the active power P and reactive power Q of each line, the line conductance g and susceptance b, and the voltage amplitude V and phase angle difference θ at both ends of the node, which can be expressed as: ; ; In the formula, and are the active and reactive power flows from node i to j, respectively; and are the conductance and susceptance of line ij respectively; and are the voltage amplitudes at nodes i and j respectively; is the phase angle difference between nodes i and j.

[0069] d) Harmonic propagation equation: This equation describes the harmonic currents and the corresponding harmonic voltage The relationship between them can be expressed as: ; In the formula, is the hth harmonic current vector; is the hth harmonic admittance matrix; is the hth harmonic voltage vector.

[0070] e) Reactive power compensation equation: This equation describes the process of reactive power compensation device compensating according to voltage deviation, which can be expressed as: ; In the formula, is the reactive power after compensation; is the initial reactive power; is the reactive power compensation coefficient; is the voltage deviation.

[0071] f) Short-circuit current equation: This equation describes the short-circuit current at each node , and the fault point voltage and the equivalent Thevenin impedance The relationship between them can be expressed as: ; In the formula, is the short-circuit current; is the fault point voltage; is the equivalent Thevenin impedance at the fault point.

[0072] The above six equations constitute the distribution network response equation group considering distributed photovoltaic access, covering key factors such as power balance, voltage characteristics, power flow transmission, harmonic propagation, reactive power compensation and short-circuit current. By numerically iteratively solving the equation group, the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node can be obtained.

[0073] Step S30, using data augmentation, the obtained distributed photovoltaic parameters and distribution network parameters are augmented respectively to obtain distributed photovoltaic augmented parameters and distribution network augmented parameters. The purpose of this step is to generate more parameter samples through simulation to cover various possible working conditions and provide a broader data basis for subsequent voltage fluctuation assessment. Commonly used data augmentation methods include random perturbation, Bootstrap resampling, etc.

[0074] Step S40, using the distributed photovoltaic augmentation parameters and the distribution network augmentation parameters, according to the distributed photovoltaic access distribution network response equation group, calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node. The specific calculation process is as follows: a) The calculation formula of node voltage amplitude and phase angle is: ; ; In the formula, is the voltage amplitude of node i; is the voltage phase angle of node i; and are the real and imaginary parts of the voltage at node i, respectively.

[0075] b) The calculation formula of node active power P and reactive power Q is: ; ; In the formula, and are the active and reactive power of node i respectively; is the complex voltage of node i; is the conjugate complex number of the current injected into node i; Re represents the real part of the complex number; Im represents the imaginary part of the complex number.

[0076] c) The calculation formula for harmonic current distribution is: ; In the formula, is the hth harmonic current of node i; is the element in the i-th row and k-th column of the h-th harmonic admittance matrix; is the hth harmonic voltage at node k.

[0077] d) The formula for calculating the short-circuit current level is: ; In the formula, is the short-circuit current of node i; is the normal operating voltage of node i; is the equivalent Thevenin impedance of node i.

[0078] Through the above formula, the voltage, power, harmonics and short-circuit characteristics of each node can be calculated, providing basic data for subsequent voltage fluctuation assessment.

[0079] Step S50, based on the result obtained in S40, calculate and construct a voltage fluctuation evaluation index vector. Specifically, it includes the following four indicators: a) Voltage deviation index : ; In the formula, is the voltage deviation index of node i; is the actual voltage amplitude of node i; is the rated voltage. This indicator reflects the degree of deviation between the node voltage and the rated voltage.

[0080] b) Voltage fluctuation index : ; In the formula, is the voltage fluctuation index of node i; and are the maximum and minimum voltage amplitudes of node i during the observation period. This indicator reflects the fluctuation range of node voltage during the observation period.

[0081] c) Harmonic distortion index : ; In the formula, is the total harmonic distortion rate of node i; is the hth harmonic voltage amplitude at node i; is the fundamental voltage amplitude of node i; is the highest harmonic order considered. This indicator reflects the severity of the harmonic components in the node voltage.

[0082] d) Short-circuit capacity change index : ; In the formula, is the short-circuit capacity change index of node i; is the current short-circuit capacity of node i; is the initial short-circuit capacity of node i. This indicator reflects the impact of distributed photovoltaic access on the system short-circuit capacity.

[0083] The above four indicators cover the key factors that affect voltage quality, such as voltage deviation, voltage fluctuation, harmonic distortion and short-circuit capacity change. By constructing such an evaluation index vector, the impact of distributed photovoltaic access on the voltage stability and voltage quality of the distribution network can be comprehensively evaluated.

[0084] Step S60: cyclically execute S10-S50 according to the preset time interval during the time period to be evaluated, obtain the voltage fluctuation evaluation index vector of each cycle, and merge them into the voltage fluctuation evaluation index time matrix. The preset time interval is generally 1 second to 60 seconds, which can be adjusted according to actual needs. By cyclically executing S10-S50, the time series data of the voltage fluctuation index of each node during the evaluation time period can be obtained, and these data are integrated into the voltage fluctuation evaluation index time matrix A, laying the foundation for the subsequent singular value decomposition analysis.

[0085] Step S70: Decompose the voltage fluctuation evaluation index time matrix A using a singular value decomposition (SVD) method to obtain a voltage stability evaluation matrix S and a voltage fluctuation evaluation matrix F. The calculation formula of the singular value decomposition is: ; In the formula, It is the time matrix of voltage fluctuation assessment indicators; is a left singular matrix; is a diagonal matrix of singular values; is the transpose of a right singular matrix.

[0086] The calculation formulas of voltage stability assessment matrix S and voltage fluctuation assessment matrix F are: ; ; in, Contains the first k largest singular values, Contains the remaining singular values. The purpose of this step is to decompose the original voltage fluctuation evaluation index time matrix A into the voltage stability part S and the voltage fluctuation part F through SVD decomposition, laying the foundation for the subsequent comprehensive evaluation index calculation.

[0087] Step S80: Calculate the voltage stability index VSI and the voltage fluctuation index VFI based on the voltage stability evaluation matrix S and the voltage fluctuation evaluation matrix F. The specific calculation formula is: Voltage stability index: ; Voltage fluctuation index: ; In the formula, , , and Respectively represent the Frobenius norm of matrices S, F, and A, and the calculation formula is: ; in, for matrix, is the element in the i-th row and j-th column of the matrix M.

[0088] The VSI and VFI indexes respectively reflect the impact of distributed photovoltaic access on the voltage stability and voltage fluctuation characteristics of the distribution network, providing a basis for comprehensive evaluation.

[0089] Step S90, the voltage stability index VSI and the voltage fluctuation index VFI are integrated to calculate the final voltage fluctuation evaluation index. If the voltage fluctuation evaluation index is greater than a preset fluctuation index threshold, an alarm is triggered. The calculation formula of the voltage fluctuation evaluation index can be in the following form: ; In the formula, is the voltage fluctuation assessment index; and They are the weight coefficients of VSI and VFI respectively, which can be adjusted according to actual needs.

[0090] The preset fluctuation index threshold can be determined according to the voltage quality standard of the power company, for example, it can be set to 5% or 10%. If the threshold is exceeded, it means that the access of distributed photovoltaics has had a significant impact on the voltage quality of the distribution network, and timely measures need to be taken to adjust and optimize it.

[0091] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for evaluating voltage fluctuations of distributed photovoltaic access to a distribution network.

[0092] A third aspect of the present invention provides a distributed photovoltaic access distribution network voltage fluctuation assessment system, which includes the above-mentioned computer-readable storage medium.

[0093] Specifically, the principle of the present invention is: 1) Establishment of distribution network response equations. This method first establishes a response equation group that takes into account distributed photovoltaic parameters and distribution network parameters, including power balance equations, node voltage equations, line flow equations, harmonic propagation equations, reactive power compensation equations, and short-circuit current equations. These equations reflect the interaction mechanism between distributed photovoltaic systems and distribution networks, laying the foundation for evaluating the physical model of voltage fluctuations.

[0094] By numerically solving these equations, we can obtain the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node. These calculation results provide necessary data support for the subsequent construction of voltage fluctuation indicators.

[0095] 2) Application of data augmentation technology. Based on the collection of distributed photovoltaic parameters and distribution network parameters, this method uses data augmentation technologies such as random perturbation, resampling or generative adversarial networks to generate more parameter samples. This can cover a wider range of operating conditions, provide a richer data basis for voltage fluctuation assessment, and improve the reliability of the assessment results.

[0096] 3) Utilization of singular value decomposition. This method uses singular value decomposition to decompose the voltage fluctuation evaluation index time matrix to obtain the voltage stability evaluation matrix and the voltage fluctuation evaluation matrix. The purpose of this step is to separate the stability characteristics and fluctuation characteristics in the original voltage fluctuation evaluation index. Singular value decomposition is a commonly used matrix decomposition method that can transform any real matrix into Decompose into in the form of and is an orthogonal matrix, is a singular value diagonal matrix. This decomposition method can effectively extract the matrix To provide support for further data analysis and processing.

[0097] 4) Introduction of fuzzy logic. This method introduces the idea of ​​fuzzy logic when determining the voltage fluctuation index threshold. By defining fuzzy input variables such as load type and seasonal factors, and the voltage fluctuation index threshold as a fuzzy output variable, a corresponding fuzzy rule base is established. Then, the reasoning method is used for fuzzy reasoning, and finally the optimized threshold considering the actual working conditions is obtained.

[0098] Fuzzy logic is good at dealing with problems with uncertainty and ambiguity, and can better simulate the human decision-making thinking process. In this method, the introduction of fuzzy logic helps to incorporate actual factors such as load characteristics and seasonal changes into the threshold determination process, making the evaluation results closer to the actual operating conditions.

[0099] In order to better understand and implement the present invention, a specific embodiment of the present invention is provided below: the power supply area of ​​the distribution network in this embodiment is located in a suburban area of ​​a city, and the main loads include residential electricity, commercial electricity and a small amount of industrial electricity. The basic parameters of the distribution network are as follows: Table 1 Basic parameters of distribution network

[0100] According to historical statistics, when the distribution network was not connected to distributed photovoltaics, the voltage compliance rate was maintained at around 99.8%, meeting the basic requirements for grid operation.

[0101] 2. Access to distributed photovoltaic systems In recent years, the installed capacity of distributed photovoltaic systems in the distribution network area has increased rapidly and has now reached 10MW. The main parameters of the photovoltaic system are as follows: Table 2 Distributed photovoltaic system parameters

[0102] Based on the installed capacity of photovoltaic systems and the peak load of distribution networks, the photovoltaic penetration rate in the region is approximately 31.3%.

[0103] 3. Voltage Fluctuation Assessment Process According to the evaluation method of the present invention, the specific implementation steps are as follows: Step S10: Collect distributed photovoltaic parameters and distribution network parameters First, through on-site measurement and archive query, the relevant parameter data of the above distribution network and distributed photovoltaic system were collected to lay the foundation for subsequent evaluation and analysis. These parameters include: 1) Distributed photovoltaic parameters Total PV array capacity: ; Inverter rated power: ; Maximum Power Point Tracking Efficiency: ; Power factor adjustment range: ; Harmonic content: ; 2) Distribution network parameters Line impedance: ; Transformer capacity: ; Load distribution: ; Voltage level: ; Short circuit capacity: ; Step S20: Establishing the distributed photovoltaic access distribution network response equation group Based on the above collected parameter data, the following distribution network response equations are established: a) Power balance equation ; b) Node voltage equation ; c) Line power flow equation ; ; d) Harmonic propagation equation ; e) Reactive power compensation equation ; f) Short-circuit current equation ; Step S30: Using data augmentation technology In order to better simulate the actual operating conditions, the random perturbation method is used to expand the distributed photovoltaic parameters and distribution network parameters collected above. The details are as follows: 1) Photovoltaic parameter expansion Based on the original parameters, Add a normal distribution within the range The random noise was removed and 100 sets of expanded photovoltaic parameter samples were obtained.

[0104] 2) Network configuration parameter expansion A similar method is used to add random noise to the distribution network parameters to generate 100 sets of expanded distribution network parameter samples.

[0105] Through the above data expansion, a wider parameter sample space is obtained, providing a richer data basis for subsequent voltage fluctuation evaluation.

[0106] Step S40: Calculate voltage characteristics The distribution network response equations established in step S20 are combined with the parameter samples obtained by expanding step S30 to calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node. The specific results are excerpted as follows: 1) Node voltage amplitude and phase angle Node 1 voltage amplitude:

[0107] Node 1 voltage phase angle:

[0108] Node 2 voltage amplitude:

[0109] Node 2 voltage phase angle:

[0110] 2) Node active power and reactive power Active power of node 1:

[0111] Node 1 reactive power:

[0112] Active power of node 2:

[0113] Node 2 reactive power:

[0114] 3) Harmonic current distribution Node 13th harmonic current:

[0115] Node 25th harmonic current:

[0116] 4) Short-circuit current level Node 1 short circuit current:

[0117] Node 2 short circuit current:

[0118] From the above calculation results, it can be seen that the grid connection of the photovoltaic system has a certain degree of impact on the voltage, power, harmonics and short-circuit characteristics of the distribution network.

[0119] Step S50: Constructing voltage fluctuation evaluation index Based on the voltage characteristic calculation results obtained in step S40, the following four voltage fluctuation evaluation indicators are constructed: 1) Voltage deviation index Node 1 voltage deviation:

[0120] Node 2 voltage deviation:

[0121] 2) Voltage fluctuation index Node 1 voltage fluctuation:

[0122] Node 2 voltage fluctuation:

[0123] 3) Harmonic distortion index Total harmonic distortion rate of node 1:

[0124] Node 2 total harmonic distortion:

[0125] 4) Short-circuit capacity change index Short-circuit capacity change of node 1:

[0126] Short-circuit capacity change of node 2:

[0127] From the above indicators, it can be seen that the voltage deviation caused by photovoltaic grid connection is small, the voltage fluctuation is within a reasonable range, and the harmonic distortion rate and short-circuit capacity changes are also at an acceptable level. However, as the penetration rate of photovoltaics continues to increase, these indicators still need to be closely monitored.

[0128] Step S60: Constructing a voltage fluctuation evaluation index time matrix According to the preset 1-minute time interval, steps S10 to S50 are executed repeatedly, and a total of 1 hour of voltage fluctuation evaluation index data is collected. These index data are integrated into the voltage fluctuation evaluation index time matrix , which lays the foundation for the subsequent singular value decomposition analysis.

[0129] Step S70: Singular value decomposition analysis The voltage fluctuation evaluation index time matrix obtained in step S60 is By performing singular value decomposition, we can get the voltage stability assessment matrix and voltage fluctuation assessment matrix .

[0130]

[0131]

[0132]

[0133] Through singular value decomposition, the stability and fluctuation characteristics of the original voltage fluctuation evaluation index are separated, laying the foundation for the calculation of the comprehensive evaluation index.

[0134] Step S80: Calculate the comprehensive evaluation index Based on the voltage stability evaluation matrix obtained in step S70 and voltage fluctuation assessment matrix , the voltage stability index was calculated and voltage fluctuation index :

[0135]

[0136] The voltage stability index VSI is close to 1, indicating that the voltage stability of the distribution network is less affected by photovoltaic grid connection; the voltage fluctuation index VFI is 0.35, reflecting the certain impact of photovoltaic access on the voltage fluctuation characteristics.

[0137] Step S90: Analysis of evaluation results Taking into account the voltage stability index and voltage fluctuation index, the final voltage fluctuation assessment index is calculated. :

[0138] After investigation, the voltage fluctuation index threshold stipulated by the power company in the region is . Due to the calculated , indicating that under the current photovoltaic access level, the voltage fluctuation of the distribution network is still within the controllable range and no special measures are needed for the time being.

[0139] However, as the scale of photovoltaic grid connection continues to expand, voltage fluctuation problems may gradually emerge. Based on this, the impact of photovoltaic penetration on the threshold is further analyzed: 1) The photovoltaic penetration rate increased by 10%, reaching 41.3%. It rose to 0.73, which is still less than the threshold of 0.75.

[0140] 2) The photovoltaic penetration rate increased by 20%, reaching 51.3%. It rose to 0.78, exceeding the threshold of 0.75, and regulatory measures need to be taken.

[0141] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks, characterized in that: The following steps are involved: S10, collecting distributed photovoltaic parameters and distribution network parameters, the distributed photovoltaic parameters include photovoltaic array capacity, inverter rated power, maximum power point tracking efficiency, power factor adjustment range, harmonic content, the distribution network parameters include line impedance, transformer capacity, load distribution, voltage level, short-circuit capacity; S20, establishing and fitting a distributed photovoltaic access distribution network response equation group that takes into account distributed photovoltaic parameters and distribution network parameters, including a power balance equation, a node voltage equation, a line flow equation, a harmonic propagation equation, a reactive power compensation equation, and a short-circuit current equation; S30, using a data augmentation method, the obtained distributed photovoltaic parameters and distribution network parameters are augmented and expanded respectively to obtain distributed photovoltaic augmented and expanded parameters and distribution network augmented and expanded parameters; S40, using distributed photovoltaic expansion parameters and distribution network expansion parameters, according to the distributed photovoltaic access distribution network response equation group, calculate the voltage amplitude, phase angle, active power, reactive power, harmonic current distribution and short-circuit current level of each node; S50, based on the result obtained in S40, calculating and constructing a voltage fluctuation evaluation index vector, including a voltage deviation index, a voltage fluctuation index, a harmonic distortion rate index, and a short-circuit capacity change index; S60, cyclically executing S10-S50 according to a preset time interval within the time period to be evaluated, obtaining a voltage fluctuation evaluation index vector for each cycle, and merging them into a voltage fluctuation evaluation index time matrix; S70, decomposing the voltage fluctuation assessment index time matrix by using a singular value decomposition method to obtain a voltage stability assessment matrix and a voltage fluctuation assessment matrix; S80, calculating a voltage stability index and a voltage fluctuation index based on a voltage stability assessment matrix S and a voltage fluctuation assessment matrix F; S90, comprehensively calculating the voltage stability index and the voltage fluctuation index to calculate a final voltage fluctuation evaluation index, and triggering an alarm if the voltage fluctuation evaluation index is greater than a preset fluctuation index threshold.

2. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 1, characterized in that: The preset time interval is 1 second to 60 seconds.

3. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 1, characterized in that: The power balance equation is specifically expressed as: ; In the formula, and are the injected active and reactive powers of node i respectively; is the complex voltage of node i; is the element in the i-th row and k-th column of the node admittance matrix; is the total number of nodes; and are the active and reactive power of node i respectively; and are the active and reactive power of the load at node i respectively.

4. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 3, characterized in that: The node voltage equation is specifically expressed as: ; In the formula, is the voltage of node i; is the system nominal voltage; is the self-impedance of node i; is the mutual impedance between nodes i and k; and are the injected currents at nodes i and k respectively.

5. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 4, characterized in that: The line power flow equation is specifically expressed as: ; ; In the formula, and are the active and reactive power flows from node i to j, respectively; and are the conductance and susceptance of line ij respectively; and are the voltage amplitudes at nodes i and j respectively; is the phase angle difference between nodes i and j.

6. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 5, characterized in that: The harmonic propagation equation is specifically expressed as: ; In the formula, is the hth harmonic current vector; is the hth harmonic admittance matrix; is the hth harmonic voltage vector.

7. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 6, characterized in that: The reactive power compensation equation is specifically expressed as: ; In the formula, is the reactive power after compensation; is the initial reactive power; is the reactive power compensation coefficient; is the voltage deviation.

8. A method for evaluating voltage fluctuations of distributed photovoltaic access distribution networks according to claim 7, characterized in that: The short-circuit current equation is specifically expressed as: ; In the formula, is the short-circuit current; is the fault point voltage; is the equivalent Thevenin impedance at the fault point.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, the method for evaluating voltage fluctuations of a distributed photovoltaic access distribution network as described in any one of claims 1 to 8 is used.

10. A distributed photovoltaic access distribution network voltage fluctuation assessment system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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