High / low voltage fault aggregation equivalent modeling and evaluation method for improved single machine containing different types of distributed photovoltaics

Abstract

The invention discloses an improved single machine high / low voltage fault aggregation equivalent modeling and evaluation method containing different types of distributed photovoltaics, and belongs to the technical field of power system simulation modeling. In order to solve the problem that there is no equivalence method for hybrid distributed photovoltaic units with and without ride-through capability at present, the method comprises the following steps: obtaining an active current control coefficient KP and a reactive current control coefficient KQ for A-type photovoltaic units; active current and reactive current of a distributed photovoltaic single machine after equivalence during low-voltage or high-voltage fault ride-through of the A-type distributed photovoltaic system are determined; and obtaining an equivalent active current and an equivalent reactive current of the hybrid to-be-equalized area by combining the active current and the reactive current when the B-type distributed photovoltaic system operates in the linear stable interval and enters low-voltage or high-voltage fault ride-through. The invention further provides an equivalent model effectiveness evaluation method giving consideration to simulation curve numerical values and forms for evaluation so as to reflect the difference of working point changes of the distributed photovoltaic model.

Description

Technical Field

[0001] This invention belongs to the field of power system simulation modeling technology, specifically relating to a single-unit high / low voltage fault aggregation equivalent modeling method for photovoltaic systems and an evaluation method for the equivalent effect. Background Technology

[0002] With the advancement of global energy transition, distributed photovoltaic (DPV) power generation is increasingly being used in power systems, becoming an important component of clean energy and sustainable development. DPV refers to photovoltaic power generation systems installed on the user side or near load centers. They are generally characterized by small-scale distribution and low power output, directly connected to the local power grid, and capable of providing some or all of the user's electricity needs. DPV systems are typically connected to 10kV and below distribution networks via inverters, and then transmitted to higher voltage levels via distribution transformers, feeders, and main transformers. Generally, the total installed capacity of a single DPV system does not exceed 6 MW. Compared to traditional centralized power generation systems, DPV offers advantages such as high flexibility, rapid deployment, and reduced grid losses. However, with the large-scale integration of DPV, control strategies for DPV systems in the same area differ. How to effectively equip photovoltaic power generation units within the power system has become an important topic in power system research and application.

[0003] Currently, there are several solutions for aggregated equivalent modeling of distributed photovoltaic power, such as:

[0004] 1. Ye Lin et al. published "Multi-step Clustering and Equivalent Modeling of Distributed Photovoltaic Clusters for Transient Analysis" in Automation of Electric Power Systems, 2023, 47(14): 72-81. This article considers the electrical coupling degree of nodes in the region where the distributed photovoltaic cluster is located, divides the region according to the node voltage sensitivity matrix and defines the region cluster identification number, selects the irradiance of the distributed photovoltaic power station and the inverter control parameters as the clustering index, and completes the division of the distributed photovoltaic cluster. Then, based on the idea of ​​homology equivalent modeling, a multi-machine equivalent model of distributed photovoltaic is established, and the effectiveness of the method is verified in the PSCAD simulation platform.

[0005] 2. Fan Haiwen et al. published "Static Equivalence of Distribution Network with Distributed Photovoltaic Power Based on Convolutional Neural Network" Journal of Shandong University (Engineering Science), 2023, 53(4): 140-148, 156. This article proposes a static equivalence method for distribution networks with distributed photovoltaic power based on convolutional neural networks. Considering the uncertainty and correlation of source and load, it generates photovoltaic and load power scenarios and calculates the power flow of the distribution network based on kernel density estimation and Copula function. For the equivalence problem under each single operation mode, this method completes the model equivalence of the distributed photovoltaic distribution network by identifying the parameters of the distributed photovoltaic distribution network equivalence model.

[0006] 3. Zhang Yongxin et al. published "Equivalent Modeling of Distributed Photovoltaic Power Stations Based on Line Impedance Clustering" in the Journal of Solar Energy, 2022, 43(5): 312-318. This article proposes a clustering equivalent modeling method for distributed photovoltaic power stations, focusing on the modeling and operation characteristics of large-scale distributed photovoltaic power stations. This method includes the equivalent line impedance from each photovoltaic power generation unit to the common connection point in the clustering index of the clustering algorithm, clusters each photovoltaic power generation unit, and then aggregates the photovoltaic power generation units in the same class, thereby completing the equivalent modeling of distributed photovoltaic power stations.

[0007] (1) Traditional equivalence methods typically assume that all distributed photovoltaic (PV) systems in the grid area to be equivalenced are of the same type, either all being Class A PV systems with fault voltage ride-through and reactive power support capabilities, or all being Class B PV systems without fault voltage ride-through capabilities. However, when the area to be equivalenced contains both Class A and Class B PV systems, the traditional single-unit equivalence method can only equivalence the entire PV group to one Class A PV system or one Class B PV system. This cannot fully characterize the dynamic characteristics differences between the two types of PV systems in the grid that have and do not have fault voltage ride-through capabilities. Even if some traditional equivalence methods equivalence Class A and Class B PV systems to two single units respectively, thereby better reflecting the dynamic characteristics of the two types of PV systems, this method will lead to an increase in the number of PV systems after equivalence, increasing the computational burden of simulation, especially in large-scale grid simulation modeling, making it difficult to execute efficiently.

[0008] (2) The differences in voltage fault ride-through logic and reactive power support capabilities between Class A and Class B distributed photovoltaic systems lead to different voltage response sensitivities during transient processes. These differences manifest as significant changes in the operating point of the photovoltaic system, such as differences in the time of fault entry and exit, which in turn affect the waveform characteristics after equivalence. Traditional distributed photovoltaic equivalence methods often verify the model's effectiveness through steady-state power flow errors and transient relative errors. However, these methods mainly focus on the absolute numerical errors of physical quantities such as power and voltage, neglecting the waveform morphology changes of the photovoltaic system during fault occurrence and recovery. Evaluating the model's accuracy solely through numerical errors cannot fully reflect the complex actions of different types of distributed photovoltaic systems during grid faults, such as the fluctuations caused by frequent voltage ride-throughs or generator tripping.

[0009] In summary, most existing aggregated equivalent modeling methods for distributed photovoltaic (PV) systems employ traditional single-machine equivalent methods or perform multi-machine equivalent modeling based on clustering strategies. These methods largely lack consideration for the differences in dynamic characteristics between PV systems with and without fault voltage ride-through capabilities. Their equivalent modeling accuracy is low or lacks versatility in fault scenarios involving PV systems with different control strategies, making them difficult to apply to specific engineering scenarios. Furthermore, traditional verification of waveform measurement results mostly focuses on numerical error evaluation, failing to adequately reflect the differences in curve morphology due to varying operating points of different PV systems in scenarios involving both types of PV. Therefore, there is an urgent need for a single-machine aggregated equivalent modeling method capable of characterizing the dynamic characteristics of PV systems with different types and for an equivalent modeling performance evaluation index applicable to this scenario. Summary of the Invention

[0010] The present invention addresses the current lack of an equivalent method for hybrid distributed photovoltaic units with and without traversal capabilities, thus failing to achieve equivalent representation of hybrid distributed photovoltaic regions.

[0011] An improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic (PV) systems of different types is proposed. This method performs equivalent modeling on mixed regions of Class A PV units with low voltage ride-through and reactive power support capabilities and Class B PV units without ride-through capabilities, including:

[0012] For Class A photovoltaic units, the active current control coefficient K is obtained by weighted averaging using the ratio of the distributed photovoltaic unit capacity to the total distributed photovoltaic capacity of the area to be equivalent as a weighting coefficient. P and reactive current control coefficient K Q Then based on K P and K Q The equivalent active and reactive currents of a single distributed photovoltaic unit during low-voltage or high-voltage fault ride-through of Class A distributed photovoltaic are determined. Combined with the active and reactive currents of Class B distributed photovoltaic units during operation within the linear stability range and during low-voltage or high-voltage fault ride-through, the equivalent active and reactive currents of the mixed region to be equivalentd are obtained.

[0013] Furthermore, the equivalent active and reactive currents of a single distributed photovoltaic unit during low-voltage or high-voltage fault ride-through for Class A distributed photovoltaic systems are as follows:

[0014] Low voltage fault ride-through:

[0015] (7)

[0016] High-voltage fault ride-through:

[0017] (8)

[0018] In the formula, , These are the equivalent active and reactive currents of a single Class A distributed photovoltaic unit. I represents the number of Class A distributed photovoltaic units. max and I n These are the maximum allowable output current amplitude limit and rated current for Class A distributed photovoltaic units, respectively. Q_max_i It is the maximum limit of reactive current; P s_i U is the active power of the i-th Class A distributed photovoltaic unit. i (t) is the grid connection voltage of the i-th Class A distributed photovoltaic unit.

[0019] Furthermore, the active and reactive currents of Class B distributed photovoltaic systems operating within the linear stability range are as follows:

[0020] (9)

[0021] In the formula, P s_j and Q s_j These are the active and reactive power of the j-th type B distributed photovoltaic unit. u represents the number of Class B distributed photovoltaic units. j (t) is the grid connection point voltage of the j-th type B distributed photovoltaic unit, u1 is the low voltage fault ride-through threshold, and u2 is the high voltage fault ride-through threshold.

[0022] Furthermore, the active and reactive currents of Class B distributed photovoltaic systems during low-voltage or high-voltage fault ride-through are as follows:

[0023] (10)

[0024] In the formula, u1 is the low-voltage fault pass-through threshold and u2 is the high-voltage fault pass-through threshold.

[0025] Furthermore, the equivalent active current and equivalent reactive current of the mixed region to be equivalently valued are as follows:

[0026] The active current and reactive current are equal during low-voltage fault crossing:

[0027] (11)

[0028] The active current and reactive current are equal during high-voltage fault crossing:

[0029] (12)

[0030] In the formula, , These are the equivalent active and reactive currents of a single Class A distributed photovoltaic unit. I represents the number of Class A distributed photovoltaic units. max and I n These are the maximum allowable output current amplitude limit and rated current for Class A distributed photovoltaic units, respectively. Q_max_i It is the maximum limit of reactive current; P s_i U is the active power of the i-th Class A distributed photovoltaic unit. i (t) is the grid connection voltage of the i-th type A distributed photovoltaic unit; P s_j and Q s_j These are the active and reactive power of the j-th type B distributed photovoltaic unit. u represents the number of Class B distributed photovoltaic units. j (t) is the grid connection point voltage of the j-th type B distributed photovoltaic unit, u1 is the low voltage fault ride-through threshold, and u2 is the high voltage fault ride-through threshold.

[0031] Furthermore, the active current control coefficient K P and reactive current control coefficient K Q as follows:

[0032] (5)

[0033] In the formula, K P K represents the active current control coefficient in the traditional single-machine equivalent model. P_i S is the active current control coefficient for the i-th photovoltaic unit. i K represents the rated capacity of the i-th photovoltaic unit. Q K represents the reactive current control coefficient in the traditional single-machine equivalent model. Q_i S is the reactive current control coefficient for the i-th photovoltaic unit. n This represents the total distributed photovoltaic capacity of the area to be equivalent.

[0034] An evaluation method for an improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types is characterized by evaluating the method, including:

[0035] Assuming the total time to be measured is T, then the time length of each equal segment is t=T / n. The numerical similarity of each equal segment is calculated as follows:

[0036] (13)

[0037] In the formula, N represents the time series within each time length t. Quantity, Feq It is the equivalent value of the physical quantity at the grid connection point that needs to be verified, F real These are the measured values ​​of physical quantities at the grid connection point;

[0038] Within a time period t, define R = (R1, R2, ..., R) before two time series are equal. N ) and the equivalent M=(M1,M2,…M N Assume R i M is the value of the physical quantity measured at the grid connection point at time α in the model before equivalence. i These are the values ​​of physical quantities measured at the grid connection point at time point β in the equivalent model. Both time points α and β are within the stated time interval t. The R values ​​in the two sequences are determined. i and M j Distance metric between This leads to the cumulative distance matrix, which is the matrix formed by the minimum cumulative distances from the first element to the αth element of sequence R and from the first element to the βth element of sequence M. The value of the lower right corner of the cumulative distance matrix is ​​then... Represents the minimum cumulative distance between two time series. This leads to the shape similarity over time length t. ;

[0039] Calculate the total similarity within each time length t. g is a tradeoff coefficient; it will be greater than the allowable error value u. As the effective similarity, the number e of effective similarities within a given time interval is counted, and then the total similarity index is obtained. ,according to The effectiveness of the equivalent modeling is evaluated.

[0040] Furthermore, the minimum cumulative distances from the first element to the αth element of sequence R and from the first element to the βth element of sequence M are as follows:

[0041] (15)

[0042] In the formula, min represents taking the minimum value, which is the optimal path for aligning time series R and M.

[0043] Furthermore, in the process of calculating the matrix formed by the minimum cumulative distance from the first element to the αth element of sequence R and from the first element to the βth element of sequence M, the first row and first column of the matrix are initialized according to the boundary conditions:

[0044] (16).

[0045] Furthermore, the physical quantities at the grid connection point that need to be verified include one or more of active power, reactive power, voltage, and current.

[0046] The beneficial effects of this invention are as follows:

[0047] For the equivalent modeling process of distributed photovoltaic (PV) systems, this invention simultaneously considers the dynamic characteristics of two types of PV systems: those with fault voltage ride-through capability and those without. It proposes an improved single-unit high / low voltage fault aggregation equivalent modeling method that includes different types of PV systems. Based on the traditional single-unit aggregation equivalent modeling method for distributed PV systems, this method derives the high / low voltage dynamic response characteristics of the two types of PV power generation units, verifies the fault ride-through dominant parameters of the equivalent unit, and achieves accurate simulation of the fault response characteristics of different PV systems before equivalence. This method is easily implemented in engineering practice.

[0048] To address the verification process of the equivalent model of distributed photovoltaic (PV) power, this invention also proposes an evaluation method for the effectiveness of the equivalent model that considers both the numerical and morphological aspects of the simulation curves. This method effectively reflects the differences in the operating point variations of the distributed PV model. The equivalent and effectiveness evaluation methods described in this invention can improve the simulation modeling accuracy of distributed PV and provide a practically meaningful evaluation standard for the calculation and application of distributed PV in power systems, offering valuable reference for practical engineering projects. Attached Figure Description

[0049] Figure 1 A flowchart illustrating the process for improving aggregated equivalent modeling of high / low voltage faults in single machines;

[0050] Figure 2 Requirements for distributed photovoltaic voltage ride-through technology with voltage ride-through and reactive power support capabilities;

[0051] Figure 3 The topology of the regional power grid example containing two types of distributed photovoltaic power is to be equivalent.

[0052] Figure 4 The topology of the grid to be equivalent in a region containing two types of distributed photovoltaic power;

[0053] Figure 5 The simulation waveforms of the improved equivalent method and the traditional equivalent method of type A only are compared under low voltage ride-through fault conditions.

[0054] Figure 6 The simulation waveforms of the improved equivalent method and the traditional equivalent method (Type B only) under low voltage ride-through faults are compared.

[0055] Figure 7 The simulation waveforms of the improved equivalent method and the traditional equivalent method of type A only are compared under high voltage ride-through fault conditions.

[0056] Figure 8 A comparison of simulation waveforms under high-voltage ride-through faults using the improved equivalent method and the traditional equivalent method using only type B. Detailed Implementation

[0057] This invention proposes an improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic (PV) systems with different types of distributed PV. It is applicable to scenarios where the regional PV grid to be equated contains both types of distributed PV systems with and without fault ride-through capabilities during grid aggregation equivalent modeling. Compared to traditional single-unit distributed PV aggregation modeling methods, the method of this invention derives the high / low voltage dynamic response characteristics of distributed PV power generation units with and without fault ride-through capabilities under different line locations and output conditions, and verifies the fault ride-through dominant parameters of the equivalent unit. The method of this invention can accurately simulate the fault response characteristics of different distributed PV systems before equation, thereby improving the equation modeling accuracy of the power grid model in the power system. A detailed description with specific implementation details follows.

[0058] Combination Figure 1 This embodiment describes an improved single-unit high / low voltage fault aggregation equivalent modeling method and corresponding evaluation method for distributed photovoltaic systems of different types, comprising the following steps:

[0059] First, improved single-unit high / low voltage fault aggregation equivalent modeling is performed, including different types of distributed photovoltaic systems, such as:

[0060] (1) For two application scenarios of distributed photovoltaics with and without fault ride-through capability, the basic parameters of the two distributed photovoltaics with and without fault ride-through capability in the photovoltaic power generation system to be equivalent are obtained respectively.

[0061] In the current power distribution network, distributed photovoltaic (PV) units vary greatly in terms of fault ride-through capability. Based on low voltage ride-through capability, PV units can be divided into two categories.

[0062] Class A photovoltaic units: They have low voltage ride-through (LVRT) and reactive power support capabilities, and can maintain grid-connected operation during voltage transient drops, and actively inject reactive current to support grid voltage according to voltage deviation.

[0063] Class B photovoltaic units: These units lack grid-connectivity and will quickly disconnect from the grid or operate in derating mode when the bus voltage drops below a set threshold to protect the inverter and grid interface. Within the same distribution area, these two types of photovoltaic units may coexist and respond to fault disturbances with different control logics.

[0064] Within the same power distribution area, these two types of photovoltaic systems may coexist and respond to fault disturbances with different control logics.

[0065] The parameters required for single-unit aggregation equivalent of distributed photovoltaic systems mainly include:

[0066] The basic parameters of a photovoltaic power generation system include the rated capacity and rated voltage of each distributed photovoltaic unit;

[0067] Inverter control parameters, including current limiting factor and reactive power regulation factor K. Q Thresholds u1 and u2 for entering low voltage and high voltage ride-through, and voltage protection thresholds for inverter grid-connected operation;

[0068] System operating parameters include grid connection point voltage, current sampling value, line impedance, power factor, and steady-state operating point.

[0069] Meanwhile, considering the differences between Class A and Class B distributed photovoltaic systems, it is necessary to count the number of distributed photovoltaic units in both categories and classify them according to the type of distributed photovoltaic system when obtaining parameters.

[0070] The difference between the two types of distributed photovoltaic dynamic characteristics is as follows:

[0071] For Class A distributed photovoltaic systems with voltage ride-through and reactive power support capabilities, their voltage ride-through capability can be referenced from the national standard requirements for photovoltaic power plants. For example... Figure 2 The requirements for distributed photovoltaic voltage ride-through technology are shown in the figure. Figure 2 The upper and middle diagrams correspond to the low voltage ride-through technology requirements. Figure 2 The lower diagram corresponds to the high-voltage ride-through requirements. When distributed photovoltaic (PV) systems operate in the shaded area, they must maintain connection to the grid; conversely, if operating in other areas, protective measures should be implemented to allow disconnection from the grid. The control strategy for Class A distributed PV systems typically employs a control system centered on a PLL (phase-locked loop) and a dq dual-closed-loop PI regulator. This system monitors bus voltage amplitude changes in real time and ensures stable inverter output and rapid response through limiting and adjusting the slope of active and reactive current components. During transient faults, Class A units proactively provide reactive current support based on target current commands, thereby improving system voltage stability.

[0072] For Class B distributed photovoltaic systems that lack voltage support capabilities, the active power generated during steady-state operation is... and reactive power as follows:

[0073] (4)

[0074] In the formula, P0 is the reference value of active power in steady state; u(t) is the grid connection point voltage; I max φ is the current limiting value; φ is the power factor.

[0075] In existing technologies, when the voltage drops but is not below the tripping threshold, the inverter typically operates in constant power factor or active power priority current limiting mode, where the unit's output power decreases linearly with the voltage. Once the voltage deviation exceeds the set threshold, the system controller triggers the grid disconnection protection logic, reducing both active and reactive power injected at the grid connection point to zero. After the fault is cleared and the voltage returns to the rated range, the Class B unit resumes operation via the reconnection criteria and delay logic.

[0076] (2) Based on the difference in dynamic characteristics between the two types of distributed photovoltaics, the traditional single-unit aggregated equivalent model of distributed photovoltaics is improved to obtain an improved single-unit high / low voltage fault aggregated equivalent model containing different types of distributed photovoltaics.

[0077] Considering the aforementioned differences in the dynamic characteristics of distributed photovoltaic (PV) systems, the limitations of traditional distributed PV single-unit aggregation methods are analyzed:

[0078] Traditional methods for equivalence of distributed photovoltaic (PV) units assume that PV units within the same area are generally of the same type. Therefore, in traditional unit equivalence, no modification to control parameters is needed; the original control parameters can be used directly. However, when equivalence is involved for different types of PV units, traditional methods use a capacity-weighted approach to calculate both the PV unit's intrinsic parameters and control parameters. During a fault, the control parameters for active and reactive current are calculated using a weighted average with the ratio of the PV unit's capacity to the total capacity of the PV units in the area to be equivalenced. The specific calculation method is as follows:

[0079] (5)

[0080] In the formula, K P K represents the active current control coefficient in the traditional single-machine equivalent model. P_i S is the active current control coefficient for the i-th photovoltaic unit. i K represents the rated capacity of the i-th photovoltaic unit. Q K represents the reactive current control coefficient in the traditional single-machine equivalent model. Q_i S is the reactive current control coefficient for the i-th photovoltaic unit. n This represents the total distributed photovoltaic capacity of the area to be equivalent.

[0081] After obtaining the active current control coefficient K P and reactive current control coefficient K Q Subsequently, traditional equivalent methods are often based on the same fixed type of distributed photovoltaic model to obtain the active and reactive currents within the steady-state control range, and then derive the active and reactive currents during low-voltage ride-through and high-voltage ride-through, thus completing the single-unit aggregate equivalent of distributed photovoltaic.

[0082] In the scenario described in this invention, traditional distributed photovoltaic (PV) aggregation equivalence methods do not consider the type differences between Class A and Class B distributed PV systems, leading to significant equivalence errors. These errors are primarily caused by two factors. First, traditional single-unit aggregation equivalence methods for distributed PV lack consideration for differences in fault ride-through characteristics, failing to accurately reflect the operating status of distributed PV in the grid to be equivalenced. Second, traditional equivalence methods struggle to characterize the differences in the operating points of these PV units, such as the speed of power recovery during fault recovery and the different times for entering and exiting fault ride-through.

[0083] Therefore, this invention proposes an improved equivalent modeling method for traditional single-unit high / low voltage faults in distributed photovoltaic systems, the specific process of which is as follows:

[0084] Based on the above-mentioned traditional distributed photovoltaic single-unit aggregation equivalent method, the active current control coefficient K is obtained. P and reactive current control coefficient K Q Subsequently, the methods for calculating active and reactive currents were improved. Within the linear control interval, for Class A distributed photovoltaic systems, the active and reactive currents of a single photovoltaic unit are calculated according to the following formula:

[0085] (6)

[0086] In the formula, i P_nomal_i and i Q_nomal_i Let u be the active current and reactive current of the i-th type A distributed photovoltaic unit in steady state. i (t) is the grid connection point voltage of the photovoltaic unit, and u0 is the initial voltage.

[0087] Since the equivalent value of a single unit in a Class A distributed photovoltaic system needs to characterize the active and reactive current outputs of all Class A distributed photovoltaic systems at the same time before the equivalent value, based on this, and combined with Figure 1 The voltage ride-through requirements for distributed photovoltaic (PV) systems are as follows: when a Class A distributed PV system rides through a low-voltage or high-voltage fault, the equivalent active and reactive currents of a single distributed PV unit are shown in equations (7) and (8), respectively:

[0088] (7)

[0089] (8)

[0090] In the formula, , These are the equivalent active and reactive currents of a single Class A distributed photovoltaic unit. I represents the number of Class A distributed photovoltaic units. max and I n These are the maximum allowable output current amplitude limit and rated current for Class A distributed photovoltaic units, respectively.Q_max_i It is the maximum limit of reactive current; P s_i U is the active power of the i-th Class A distributed photovoltaic unit. i (t) is the grid connection point voltage of the i-th Class A distributed photovoltaic unit. Formulas (7) and (8) correspond to the low-voltage or high-voltage fault ride-through conditions of Class A distributed photovoltaic units, respectively.

[0091] For Class B distributed photovoltaic systems, the active and reactive currents are as follows when operating within the linear stability range:

[0092] (9)

[0093] In the formula, P s_j and Q s_j These are the active and reactive power of the j-th type B distributed photovoltaic unit. u represents the number of Class B distributed photovoltaic units. j (t) is the grid connection point voltage of the j-th type B distributed photovoltaic unit, u1 is the low voltage fault ride-through threshold, and u2 is the high voltage fault ride-through threshold.

[0094] When the grid connection voltage of the photovoltaic unit is u j When the boundary is exceeded, the system controller will trigger the grid disconnection protection logic, and the active and reactive power injected into the grid connection point will both drop to zero, which is reflected in the active and reactive currents, as shown in the following formula:

[0095] (10)

[0096] Based on the current expressions (6) to (8) for type A distributed photovoltaic, and combined with the current expressions (9) and (10) for type B distributed photovoltaic, a single-unit high / low voltage fault aggregation equivalent model containing both types of distributed photovoltaic can be derived. The equivalent results of active and reactive current in the low voltage fault crossing interval are shown in Equation (11); the equivalent results of active and reactive current in the high voltage fault crossing interval are shown in Equation (12).

[0097] (11)

[0098] (12)

[0099] In the formula, the superscript EQ indicates the relevant parameter values ​​after equivalence using the improved single-machine equivalent method, and n A and n B This indicates that there are n in the model to be equivalent. A There are n Class A distributed photovoltaic units. B One Class B distributed photovoltaic unit.

[0100] An equivalent model is obtained based on the improved equivalent modeling method, and equivalent calculations are performed. Then, the equivalent modeling effect of the traditional single-unit aggregated equivalent modeling method and the improved single-unit high / low voltage fault aggregated equivalent model containing different types of distributed photovoltaics is compared and verified in the high / low voltage ride-through scenario.

[0101] This implementation method takes into account both numerical similarity and shape similarity, and proposes an evaluation method for the equivalence validity of two types of distributed photovoltaic systems.

[0102] When verifying the effectiveness of equivalent distributed photovoltaic systems including Class A and Class B, traditional methods for evaluating the effectiveness of equivalent systems have the following shortcomings:

[0103] The differences in voltage fault ride-through logic and reactive power support capabilities between Type A and Type B distributed photovoltaic (PV) systems lead to varying voltage response sensitivities during transient processes. These differences manifest as significant variations in the PV system's operating point, such as differences in fault entry and exit times, thus affecting the equivalent waveform characteristics. Traditional distributed PV equivalent methods often verify model effectiveness through steady-state power flow errors and transient relative errors. However, these methods primarily focus on the absolute numerical errors of physical quantities such as power and voltage, neglecting the waveform morphology changes of the PV system during fault occurrence and recovery. Evaluating model accuracy solely through numerical errors cannot fully reflect the dynamic response differences between different types of PV systems during grid faults. Especially during the fault ride-through phase, the fluctuations caused by frequent voltage ride-throughs, differences in power recovery speed, and generator disconnection have a more significant impact on grid stability and its recovery process. Therefore, simple numerical error evaluation methods cannot comprehensively reflect the accuracy and dynamic characteristics of the improved equivalent model.

[0104] To overcome the shortcomings of traditional evaluation methods, this invention also proposes a comprehensive evaluation method based on shape similarity and numerical similarity, with the specific evaluation steps as follows:

[0105] The aforementioned method for evaluating the equivalent effects of two types of distributed photovoltaic power grids includes numerical similarity M, shape similarity S, total similarity X, and a tradeoff coefficient g. The method involves dividing the time length of the measured waveform to be compared into n equal parts for a curve comparison graph of the simulation results, and calculating the numerical similarity within each part. and shape similarity The calculation method and formula are as follows.

[0106] Assuming the total time duration is T, then the length of each equal segment is t = T / n. The numerical similarity for each segment is then... Calculate as follows.

[0107] (13)

[0108] In the formula, N represents the time series within each time length t. The quantity, N, can be selected based on the actual length. If high accuracy of the evaluation index is required, the sampling can be increased. eq It is the equivalent value of the physical quantity at the grid connection point that needs to be verified, F real These are the measured values ​​of physical quantities at the grid connection point (values ​​of the detailed model). The physical quantities at the grid connection point that need to be verified include active power, reactive power, voltage, and current. All of these can be selected as the physical quantities to be verified, or one or more of them can be selected as the physical quantities to be verified.

[0109] Considering that distributed photovoltaic (PV) systems are highly sensitive to voltage fluctuations—that is, the different rates of voltage rise or fall before and after voltage equalization—the timing of entering and exiting voltage crossover will differ, especially for Class B distributed PV systems of different specifications and geographical locations, where the timing of power cut-off will vary. Therefore, this invention introduces a Dynamic Time Warping (DTW) algorithm, which can effectively measure the overall morphological differences between two time series during voltage dips and recovery, and accurately reflect the subtle differences between them during the time alignment process.

[0110] Within the same time period t as described in equation (13), we define R = (R1, R2, ... R) before the two time series are equal. N ) and the equivalent M=(M1,M2,…M N Assume R i M represents the values ​​of the physical quantities (active power, reactive power, voltage, and current) measured at the grid connection point at time α in the model before equivalence. i The value of the physical quantity measured at the grid connection point at time point β in the equivalent model is given. Since both time points α and β are within the time interval t, the distance between the two points is:

[0111] (14)

[0112] First, initialize the cumulative distance matrix D by setting D(0,0)=0. To find the optimal matching path, calculate the cumulative distance matrix, which is the minimum cumulative distance from the first element to the αth element of sequence R, and from the first element to the βth element of sequence M. The recursive formula is:

[0113] (15)

[0114] In the formula, min represents taking the minimum value, which is the optimal path for aligning time series R and M.

[0115] In DTW calculations, the initial point must be compared with other points. Therefore, the first row and first column of the matrix are initialized according to boundary conditions. These boundary conditions are applied to the elements of the first row and first column, and since recursion can only proceed in one direction—horizontally to the right or vertically downwards—for example, the element D(1, β) in the first row can only be obtained by accumulating the distance d(R) between the previous element D(1, β-1) and the current time point. α M β Therefore, the first row and first column are usually initialized according to boundary conditions as follows:

[0116] (16)

[0117] After calculating the entire cumulative distance matrix, the DTW distance is the value in the bottom right corner of the matrix (that is...). ), representing the minimum cumulative distance between two time series:

[0118] (17)

[0119] Finally, based on the results calculated by the dynamic time warping algorithm described above, the shape similarity within time length t is... According to the following formula.

[0120] (18)

[0121] The total similarity is defined as:

[0122] (19)

[0123] In the formula, g is a weighting coefficient, typically ranging from 0.5 to 0.7. A larger value indicates a greater emphasis on waveform similarity. X ε The larger the value, the higher the total similarity within the given time interval t, and the better the performance of the equivalence method.

[0124] Furthermore, given a similarity error allowable value u, if If the curve waveform fits well within the time period t, and the overall similarity meets the error requirements, then it is considered effective.

[0125] Finally, the number e of time intervals that satisfy the total similarity criterion is counted, and the final total similarity is calculated using the following formula:

[0126] (20)

[0127] When X is 1, the two curves are considered to be completely overlapping. The number of equal time divisions n determines the accuracy and computational load of the evaluation method, and can be reasonably selected according to a certain multiple of the actual simulation step size.

[0128] The advantages of the equivalent effect evaluation method described in this embodiment are as follows:

[0129] (1) The method is simple and the effectiveness is evaluated through a single parameter index, without the need for complex calculations and judgments;

[0130] (2) Applicable to scenarios including Class A and Class B distributed photovoltaics, and can accurately reflect the differences in dynamic characteristics between the two types of photovoltaics;

[0131] (3) Take the similarity of waveforms into account and introduce dynamic time warping algorithm to express the error quantitatively through numerical values.

[0132] After completing the improved single-unit high / low voltage fault aggregation equivalent for different types of distributed photovoltaic systems, the model building and effectiveness verification of the equivalent method described in this invention are as follows:

[0133] based on Figure 3 The example shown is a power grid regional calculation applicable to the application scenario of this invention. This calculation model is based on an electromagnetic model on the PSCAD simulation platform. External equivalent sources characterize the characteristics of the external power grid. For ease of identification, distributed photovoltaic (PV) systems are highlighted. The equivalent distributed PV single-unit model is constructed as follows: Figure 4 As shown, the relevant equivalent parameters of the improved equivalent method are calculated, with other irrelevant components set to ideal states. A comparison is then made with the traditional single-unit aggregation equivalent method for distributed photovoltaics, taking the dynamic characteristics of only a single type of photovoltaic as an example. Then, low-voltage and high-voltage fault tests are performed. Specifically, the low-voltage condition involves triggering a three-phase short-circuit fault at the external port for 1 second, causing the voltage to drop below 0.6 pu; the high-voltage condition involves triggering a three-phase short-circuit fault at the external port for 1 second, causing the voltage to rise above 1.1 pu. The response characteristics at the grid connection point after equivalent methods are compared under different equivalent methods. The simulation results are as follows: Figures 5-8 As shown in the table below, the evaluation results of the equivalent model evaluation method, based on the simulation measurement comparison results of the example, are as follows: the higher the total similarity, the higher the curve similarity and the smaller the numerical error. The traditional equivalent method considering only type A distributed photovoltaic is denoted as Traditional Method A, and the traditional equivalent method considering only type B distributed photovoltaic is denoted as Traditional Method B (in this case, all generators are switched off according to type B logic after the equivalent method is completed). The weight coefficient g is 0.6, the single-segment similarity threshold u is 0.8, the number of time periods n is divided into 1000 segments, the judgment time period is 3s to 6s for low-voltage faults, and 6s to 9s for high-voltage faults.

[0134] Table 1: Comparison of the effectiveness evaluation results of the improved single-unit high / low voltage fault aggregated equivalent model and the traditional equivalent method

[0135]

[0136] Based on the effectiveness evaluation results of the calculation examples, the improved single-unit high / low voltage fault aggregation equivalent modeling method of the present invention, which includes different types of distributed photovoltaics, has a significantly improved overall similarity X of power and current simulation waveforms compared with the traditional single-unit equivalent method of distributed photovoltaics in grid equivalent scenarios that include two types of distributed photovoltaics with and without fault voltage ride-through capabilities. It has higher equivalent accuracy, can effectively reflect the fault ride-through characteristics and operating point differences of distributed photovoltaics, and has good adaptability.

[0137] The above examples of the present invention are merely illustrative of the computational model and process of the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An improved method for aggregated equivalent modeling of single-unit high / low voltage faults in distributed photovoltaic systems of different types, characterized in that, Equivalence is performed on a mixed area of ​​Class A photovoltaic units with low-voltage ride-through and reactive power support capabilities and Class B photovoltaic units without ride-through capabilities, including: For Class A photovoltaic units, the active current control coefficient K is obtained by weighted averaging using the ratio of the distributed photovoltaic unit capacity to the total distributed photovoltaic capacity of the area to be equivalent as a weighting coefficient. P and reactive current control coefficient K Q Then based on K P and K Q The equivalent active and reactive currents of a single distributed photovoltaic unit during low-voltage or high-voltage fault ride-through of Class A distributed photovoltaic are determined. Combined with the active and reactive currents of Class B distributed photovoltaic units during operation within the linear stability range and during low-voltage or high-voltage fault ride-through, the equivalent active and reactive currents of the mixed region to be equivalentd are obtained.

2. The improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in claim 1, is characterized in that... The equivalent active and reactive currents of a single distributed photovoltaic unit during low-voltage or high-voltage fault ride-through for Class A distributed photovoltaic systems are as follows: Low voltage fault ride-through: (7) High-voltage fault ride-through: (8) In the formula, , These are the equivalent active and reactive currents of a single Class A distributed photovoltaic unit. I represents the number of Class A distributed photovoltaic units. max and I n These are the maximum allowable output current amplitude limit and rated current for Class A distributed photovoltaic units, respectively. Q_max_i It is the maximum limit of reactive current; P s_i U is the active power of the i-th Class A distributed photovoltaic unit. i (t) is the grid connection voltage of the i-th Class A distributed photovoltaic unit.

3. The improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in claim 2, is characterized in that... The active and reactive currents of Class B distributed photovoltaic systems during operation within the linear stability range are as follows: (9) In the formula, P s_j and Q s_j These are the active and reactive power of the j-th type B distributed photovoltaic unit. u represents the number of Class B distributed photovoltaic units. j (t) is the grid connection point voltage of the j-th type B distributed photovoltaic unit, u1 is the low voltage fault ride-through threshold, and u2 is the high voltage fault ride-through threshold.

4. The improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in claim 3, is characterized in that... The active and reactive currents of Class B distributed photovoltaic systems during low-voltage or high-voltage fault ride-through are as follows: (10) In the formula, u1 is the low-voltage fault pass-through threshold and u2 is the high-voltage fault pass-through threshold.

5. An improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in any one of claims 1 to 4, characterized in that... The equivalent active current and equivalent reactive current of the mixed equivalent region are as follows: The active current and reactive current are equal during low-voltage fault crossing: (11) The active current and reactive current are equal during high-voltage fault crossing: (12) In the formula, , These are the equivalent active and reactive currents of a single Class A distributed photovoltaic unit. I represents the number of Class A distributed photovoltaic units. max and I n These are the maximum allowable output current amplitude limit and rated current for Class A distributed photovoltaic units, respectively. Q_max_i It is the maximum limit of reactive current; P s_i U is the active power of the i-th Class A distributed photovoltaic unit. i (t) is the grid connection voltage of the i-th type A distributed photovoltaic unit; P s_j and Q s_j These are the active and reactive power of the j-th type B distributed photovoltaic unit. u represents the number of Class B distributed photovoltaic units. j (t) is the grid connection point voltage of the j-th type B distributed photovoltaic unit, u1 is the low voltage fault ride-through threshold, and u2 is the high voltage fault ride-through threshold.

6. The improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in claim 5, is characterized in that... Active current control coefficient K P and reactive current control coefficient K Q as follows: (5) In the formula, K P K represents the active current control coefficient in the traditional single-machine equivalent model. P_i S is the active current control coefficient for the i-th photovoltaic unit. i K represents the rated capacity of the i-th photovoltaic unit. Q K represents the reactive current control coefficient in the traditional single-machine equivalent model. Q_i S is the reactive current control coefficient for the i-th photovoltaic unit. n This represents the total distributed photovoltaic capacity of the area to be equivalent.

7. An evaluation method for an improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, characterized in that, An evaluation is conducted on the improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems of different types, as described in any one of claims 1 to 6, including: Assuming the total time to be measured is T, then the time length of each equal segment is t=T / n. The numerical similarity of each equal segment is calculated as follows: (13) In the formula, N represents the time series within each time length t. Quantity, F eq It is the equivalent value of the physical quantity at the grid connection point that needs to be verified, F real These are the measured values ​​of physical quantities at the grid connection point; Within a time period t, define R = (R1, R2, ..., R) before two time series are equal. N ) and the equivalent M=(M1,M2,…M N Assume R i M is the value of the physical quantity measured at the grid connection point at time α in the model before equivalence. i These are the values ​​of physical quantities measured at the grid connection point at time point β in the equivalent model. Both time points α and β are within the stated time interval t. The R values ​​in the two sequences are determined. i and M j Distance metric between This leads to the cumulative distance matrix, which is the matrix formed by the minimum cumulative distances from the first element to the αth element of sequence R and from the first element to the βth element of sequence M. The value of the lower right corner of the cumulative distance matrix is ​​then... Represents the minimum cumulative distance between two time series. This leads to the shape similarity over time length t. ; Calculate the total similarity within each time length t. g is a tradeoff coefficient; it will be greater than the allowable error value u. As the effective similarity, the number e of effective similarities within a given time interval is counted, and then the total similarity index is obtained. ,according to The effectiveness of the equivalent modeling is evaluated.

8. The evaluation method for the improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems with different types of photovoltaics, as described in claim 7, is characterized in that... The minimum cumulative distances from the first element to the αth element of sequence R and from the first element to the βth element of sequence M are as follows: (15) In the formula, min represents taking the minimum value, which is the optimal path for aligning time series R and M.

9. The evaluation method for the improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems with different types, as described in claim 8, is characterized in that... In calculating the matrix formed by the minimum cumulative distances from the first element to the αth element of sequence R and from the first element to the βth element of sequence M, the first row and first column of the matrix are initialized according to the boundary conditions: (16)。 10. An evaluation method for an improved single-unit high / low voltage fault aggregation equivalent modeling method for distributed photovoltaic systems containing different types of photovoltaics, as described in any one of claims 7 to 9, characterized in that... The physical quantities at the grid connection point that need to be verified include one or more of active power, reactive power, voltage, and current.

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