Distributed photovoltaic cluster modeling method and system based on improved FCM algorithm

Abstract

The invention belongs to the technical field of power electronic systems, and discloses a distributed photovoltaic cluster modeling method and system based on an improved FCM algorithm, and the method comprises the steps: measuring the outlet voltage Vt of a photovoltaic unit, the voltage VPCC of a PCC point, and the phase angle difference theta between the two, deducing a calculation formula of an equivalent electrical distance from the outlet of the photovoltaic unit to the PCC point under a radiation type structure, and calculating the equivalent electrical distance between the outlet of the photovoltaic unit and the PCC point; calculating an equivalent electrical distance under a chain structure and a complex network topology structure, and calculating a line impedance equivalent coefficient; measuring an active power P, a reactive power Q and a port voltage Vt output by the photovoltaic unit, and proposing an index value representing a photovoltaic voltage support function; an equivalent electrical distance, a photovoltaic capacity and a voltage support function are comprehensively considered, and a comprehensive clustering index accurately representing the characteristics of the distributed photovoltaic power generation system is established; an optimal solution obtained through a particle swarm clustering algorithm is used as an initial clustering center, an optimal clustering result is calculated through an FCM algorithm, and an equivalent model is calculated through parameter equivalence;

Description

Technical Field

[0001] The present invention belongs to the technical field of power electronic systems, and in particular relates to a distributed photovoltaic cluster modeling method and system based on an improved FCM algorithm. Background Art

[0002] Vigorously developing distributed photovoltaics is a necessary way to help my country's energy structure achieve green and low-carbon transformation and achieve carbon peak and carbon neutrality. However, the increase in photovoltaic installed capacity causes power flow backhaul, which in turn causes problems such as overvoltage, which will greatly restrict the consumption of distributed photovoltaics. In order to deeply study the voltage operation characteristics of the new distribution network and comprehensively optimize the voltage regulation strategy of photovoltaic inverters, it is necessary to establish a simulation model for the distributed photovoltaic system. However, due to the presence of dozens or even hundreds of grid-connected inverters in large-scale distributed photovoltaic power stations, detailed modeling will have problems such as long simulation time and large simulation scale, and even cause "dimensionality disaster". The existing simulation modeling methods cannot effectively take into account the accuracy and speed of distributed photovoltaic simulation modeling.

[0003] The clustering equivalent modeling method clusters photovoltaic power generation units with similar dynamic characteristics into the same cluster, and equates several photovoltaic units in the cluster to one photovoltaic unit, thereby simplifying the model. Because it takes into account both accuracy and speed, it provides a practical reference for distributed photovoltaic system simulation modeling. In the process of clustering equivalent modeling, the selection of clustering indicators and clustering algorithms greatly affects the accuracy and speed of clustering results. However, for distributed photovoltaic power generation systems, due to their different line impedances, mixed voltage support functions and different photovoltaic capacities, traditional clustering indicators cannot fully characterize the output dynamic characteristics of distributed photovoltaic units, resulting in inaccurate clustering results and large errors between equivalent models and detailed models. In addition, traditional clustering algorithms are highly random and prone to local optimal problems. It is difficult to obtain a stable clustering optimal solution, which will lead to large errors in clustering results and further affect the coordinated control effect of distributed photovoltaics.

[0004] In view of the selection of clustering indicators, some literature uses inverter control parameters as clustering indicators, establishes an offline parameter sensitivity database, calculates the characteristic distance between the parameters of the measured information matching degree online, and performs clustering. Since the filter inductance coefficient of the photovoltaic inverter is highly sensitive to different disturbances, some literature further introduces the filter inductance coefficient of the photovoltaic inverter as a clustering indicator based on the inverter control parameters, so that the dynamic response characteristics of the inverter can be more fully reflected. However, the parameters of photovoltaic inverters are usually difficult to obtain. Some literature uses the active power, reactive power, output voltage and current of the photovoltaic array and inverter as clustering indicators. The clustering results can simultaneously reflect the static and dynamic characteristics of photovoltaic components in two-level photovoltaic power stations. For the selection of indicators under faults, some literature extracts the peak characteristic points of the active and reactive power response curves of the photovoltaic system under three-phase short-circuit faults as clustering indicators. However, the above clustering indicators do not take into account the network structure characteristics of the distributed photovoltaic system and the voltage support function of the photovoltaic inverter. Therefore, it is necessary to comprehensively characterize the clustering indicators of the new characteristics of the distributed photovoltaic system to achieve accurate equivalent modeling of distributed photovoltaics.

[0005] In response to the selection of clustering algorithms, some literature uses the K-means clustering algorithm to calculate the Euclidean distance between samples and divides photovoltaic power stations into several clusters according to the distance. This method has low complexity and is easy to implement. However, the K-means clustering algorithm uses hard clustering, that is, each data point belongs to only one cluster center, and is sensitive to noise and outliers. Therefore, some literature proposes to use the FCM algorithm to improve the problem of the above clustering method being sensitive to noise. Unlike the K-means clustering algorithm, the FCM algorithm uses soft clustering, that is, each data point is assigned to multiple clusters with different membership degrees, and the final clustering result is determined by the size of the membership degree. This algorithm is suitable for processing data with uncertainty and ambiguity, and the clustering results are more in line with objective reality. However, the initial cluster center of the FCM algorithm is randomly selected, and different initial cluster centers lead to completely different clustering results, so it is easy to fall into local optimality. Another literature proposes to introduce the Canopy algorithm before the FCM algorithm to find the initial cluster center. However, this method is greatly affected by the algorithm parameter settings. When the parameters are not set properly, it is easy to fall into the local optimal solution and the accuracy is not high. Therefore, an accurate and efficient distributed photovoltaic cluster modeling method is needed to achieve accurate equivalent modeling of distributed photovoltaics in different scenarios.

[0006] The existing technology brings the following technical problems in industrial applications during the clustering and equivalent modeling of distributed photovoltaic power generation systems:

[0007] 1. Inadequate selection of clustering indicators:

[0008] In the existing technology, clustering indicators are mainly concentrated on the control parameters of photovoltaic inverters, filter inductance coefficients, and active power, reactive power, output voltage and current of the system. Although these indicators can reflect the static and dynamic characteristics of photovoltaic power stations, they fail to fully consider the network structure characteristics and the voltage support function of photovoltaic inverters in distributed photovoltaic systems. These new power system characteristics have an important impact on the control, stability analysis and fault handling of large-scale distributed photovoltaic systems. Clustering models that fail to incorporate these key features are likely to lead to insufficient reflection of the dynamic behavior of the system in the equivalent modeling process, thereby affecting the overall system operation efficiency and safety.

[0009] 2. Inverter parameters are difficult to obtain:

[0010] The parameters of photovoltaic inverters (such as filter inductance) are highly sensitive to the dynamic characteristics of the system, but these parameters are difficult to obtain directly in actual industrial applications. In the absence of these parameters, relying on existing technologies for equivalent modeling will lead to reduced accuracy and affect the accuracy of photovoltaic power station cluster modeling. This problem is particularly prominent in scenarios where precise control of inverter response is required (such as voltage support and dynamic response optimization).

[0011] 3. Limitations of existing clustering algorithms:

[0012] The K-means clustering algorithm commonly used in the prior art has low computational complexity and is easy to implement, but its hard clustering properties make it sensitive to noise and outliers, making it difficult to process complex data with uncertainty and ambiguity in actual scenarios. In addition, although the FCM algorithm uses a soft clustering method and can better handle ambiguity problems in data, its initial clustering center is randomly selected and easily falls into a local optimal solution, resulting in unstable clustering results. The limitations of this clustering algorithm can easily lead to large errors in the equivalent modeling process, especially in large-scale distributed photovoltaic systems, where these errors will be further amplified, affecting the reliability and stability of system control.

[0013] 4. Insufficient scene adaptability:

[0014] Existing clustering algorithms lack sufficient adaptability in the face of different operating scenarios and fault conditions. For example, in special scenarios such as three-phase short-circuit faults, the existing indicator selection and clustering methods cannot fully reflect the characteristics of the system. For large-scale distributed photovoltaic power generation systems, when the system encounters grid disturbances or faults, the existing equivalent model is difficult to accurately reflect the dynamic behavior of the entire system. This makes it difficult to ensure the dynamic stability and safety of the system in the context of the ever-increasing scale of photovoltaic power generation systems.

[0015] 5. Impact of local optimal problems:

[0016] Although some literature attempts to optimize the initial clustering center of FCM by introducing the Canopy algorithm, this method relies heavily on the algorithm parameter settings. If the parameters are not set properly, it will still fall into the local optimum, affecting the accuracy of the clustering results and the reliability of the model. This local optimum problem leads to deviations between system modeling and actual operation in scenarios with complex dynamic characteristics such as distributed photovoltaic power generation systems, ultimately affecting the scheduling, control and operation efficiency of the system.

[0017] In summary, the technical problem of the existing technology in the equivalent modeling of distributed photovoltaic power generation systems is that it fails to comprehensively consider the key characteristics of the system, and the clustering algorithm is prone to local optimality and excessive errors when processing complex data, thus affecting the modeling accuracy, stability and adaptability of the entire system. These problems limit the application potential of distributed photovoltaic power generation systems in intelligent control, optimized operation and fault handling. Summary of the invention

[0018] In view of the problems existing in the prior art, the present invention provides a distributed photovoltaic cluster modeling method based on an improved FCM algorithm, aiming to achieve efficient and accurate cluster modeling of distributed photovoltaics with different electrical distances, mixed voltage support functions and different photovoltaic capacities.

[0019] The present invention is implemented as follows: a distributed photovoltaic cluster modeling method based on an improved FCM algorithm, comprising:

[0020] 1) Measure the photovoltaic unit outlet voltage V t and PCC point voltage V PCC and the phase angle difference θ between the two, derive the calculation formula of the equivalent electrical distance from the photovoltaic unit outlet to the PCC point under the radial structure, and then calculate the equivalent electrical distance under the chain structure and complex network topology, and then calculate the line impedance equivalent coefficient;

[0021] 2) Measure the active power P, reactive power Q and port voltage V of the photovoltaic unit output t , an index value is proposed to characterize the photovoltaic voltage support function;

[0022] 3) Comprehensively consider the equivalent electrical distance, photovoltaic capacity and voltage support function to establish a comprehensive clustering index that accurately characterizes the characteristics of distributed photovoltaic power generation systems;

[0023] 4) The optimal solution obtained by the particle swarm clustering algorithm is used as the initial clustering center, the FCM algorithm is used to calculate the optimal clustering result, and the equivalent model is calculated using parameter equivalents.

[0024] Furthermore, the calculation formula for the equivalent electrical distance from the distributed photovoltaic outlet to the PCC point under the radial structure is:

[0025]

[0026] Among them, A eq represents the equivalent line impedance from the PV unit outlet to the grid, B represents the line impedance from the PCC point to the grid, A represents the line impedance from the PV outlet to the PCC point, I d-eq ,I q-eq represents the current from the PCC point to the grid, the subscripts “d” and “q” represent the d and q axis components respectively, I d ,I q represents the current from the PV unit outlet to the PCC point, and T represents the transformation matrix of the PV units from their respective coordinate systems to the common coordinate system.

[0027] Furthermore, the calculation formula of the line impedance equivalent coefficient is:

[0028]

[0029] Among them LEC i represents the line impedance equivalent coefficient of the i-th node, A ieq A is the equivalent line impedance from the photovoltaic unit connection point of the i-th node to the grid. i represents the line impedance parameter from the ith node PV unit to the PCC, and B represents the line impedance from the PCC to the grid.

[0030] Furthermore, the index value characterizing the voltage support function of the photovoltaic unit inverter is:

[0031]

[0032] Among them, V represents the output voltage of the photovoltaic unit, P represents the active power output of the photovoltaic unit, Q represents the reactive power output of the photovoltaic unit, and C VP Indicates the correlation between the PV unit outlet voltage and active power, C VQ Indicates the correlation between the PV unit outlet voltage and reactive power;

[0033] Furthermore, the distributed photovoltaic clustering index that comprehensively considers the equivalent electrical distance, photovoltaic capacity and voltage support function is:

[0034] CCI=a×C VP +a×C VQ +(1-2a)×A eq

[0035] Among them, CCI represents the comprehensive clustering index of distributed photovoltaics, a represents the normalized photovoltaic capacity, C VP and C VQ They represent the correlation between the PV unit outlet voltage and active power and reactive power, respectively. eq Indicates the equivalent electrical distance;

[0036] Further, the improvement of FCM algorithm includes:

[0037] The particle swarm algorithm searches for the optimal solution. The particles determine the flight direction based on their own best position and the global best position. The global optimal solution is obtained through convergence iteration and serves as the initial clustering center of the FCM algorithm.

[0038] The FCM algorithm updates the membership matrix and selects the cluster centers based on the initial cluster centers obtained by the particle swarm algorithm, and iterates to obtain the optimal clustering result.

[0039] Another object of the present invention is to provide a distributed photovoltaic cluster simplified model system based on the improved FCM algorithm, comprising:

[0040] Voltage measurement module, used to measure the photovoltaic unit outlet voltage V t and PCC point voltage V PCC ;

[0041] Phase angle measurement module, used to measure the phase angle difference θ between the photovoltaic unit outlet voltage and the PCC point voltage;

[0042] Power measurement module, used to measure the active power P and reactive power Q output by the photovoltaic unit;

[0043] Line impedance measurement module, used to measure line impedance Z s =R s +jX s ;

[0044] The module for deriving the equivalent electrical distance relationship is used to derive the expression of the equivalent electrical distance from the photovoltaic outlet to the PCC point under the radial structure, chain structure and complex topology structure;

[0045] A line impedance equivalent coefficient calculation module is used to calculate the variation coefficient of equivalent line impedance;

[0046] The voltage support function index value calculation module is used to calculate the active power P, reactive power Q and port voltage V output by the photovoltaic unit. t , calculate the index value that characterizes the photovoltaic voltage support function;

[0047] A comprehensive clustering index calculation module is used to calculate the clustering index used for clustering according to the equivalent electrical distance, voltage support function and photovoltaic capacity;

[0048] Particle swarm algorithm module, used to search for the global optimal solution and calculate the optimal cluster center using comprehensive clustering indicators and particle swarm algorithm;

[0049] The fuzzy C-means clustering algorithm module is used to cluster several photovoltaic units using the optimal clustering center obtained by the particle swarm algorithm to obtain the optimal clustering result.

[0050] Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distributed photovoltaic cluster modeling method based on the improved FCM algorithm.

[0051] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the distributed photovoltaic cluster modeling method based on the improved FCM algorithm.

[0052] Another object of the present invention is to provide an information data processing terminal, which is used to implement the distributed photovoltaic equivalent model system based on the improved FCM algorithm.

[0053] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0054] First, in view of the technical problems existing in the above-mentioned prior art, some creative technical effects are brought about after solving the problems. The specific description is as follows:

[0055] (1) The equivalent electrical distance calculation method proposed in the present invention derives the equivalent line impedance from the outlet of the photovoltaic unit to the power grid under the radial structure, and then calculates the equivalent line impedance under the chain structure and complex network topology, taking into account the equivalent electrical distance calculation of various network topologies, rather than simply superimposing the line impedance.

[0056] (2) The line impedance equivalent coefficient calculation module proposed in the present invention quantifies the evolution characteristics of the equivalent line impedance by calculating the variation coefficient of the equivalent line impedance;

[0057] (3) The comprehensive clustering index proposed in the present invention comprehensively considers the network topology, voltage support function and photovoltaic capacity of the distributed photovoltaic system, and further accurately reflects the dynamic response characteristics of the photovoltaic unit.

[0058] (4) The cluster modeling method proposed in the present invention can globally search for the optimal solution, avoiding the disadvantage of traditional algorithms that randomly select initial cluster centers and thus fall into local optimality, and can stably achieve accurate clustering equivalence.

[0059] (5) The cluster modeling method proposed in the present invention has strong applicability and can achieve accurate and rapid clustering for equivalent modeling of distributed photovoltaic systems under different complex network topologies and mixed voltage support functions.

[0060] Second, the present invention provides a distributed photovoltaic cluster modeling method and system based on an improved FCM algorithm. In view of the characteristics of distributed photovoltaic systems with different line impedances, mixed voltage support functions, and different photovoltaic capacities, the equivalent electrical distance under the radial structure, chain structure, and complex network topology is derived and calculated by measuring the line impedance at the photovoltaic unit outlet, PCC point, and grid voltage, as well as the line impedance on each line, and then the line impedance equivalent coefficient is calculated. Then, the active power, reactive power, and voltage output by the photovoltaic unit are measured, and the index value characterizing the photovoltaic voltage support function is calculated. Then, the photovoltaic capacity is normalized and used as the weight of the equivalent electrical distance and voltage support function to establish a comprehensive clustering index that accurately characterizes the characteristics of the distributed photovoltaic system. Considering that the traditional clustering algorithm randomly selects the initial clustering center and is prone to fall into the local optimum, the present invention introduces a particle swarm algorithm to perform a global search on the data sample, and uses the calculated optimal solution as the initial clustering center, calculates the optimal clustering result using the FCM algorithm, and calculates the equivalent model using the parameter equivalent value.

[0061] Third, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0062] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0063] The cluster modeling method disclosed in the present invention can realize the cluster modeling of distributed photovoltaic systems with mixed voltage support functions: measure the photovoltaic units, PCC points and grid voltages and each line impedance, deduce and calculate the equivalent electrical distance under the radial structure, chain structure and complex network topology, and then calculate the line impedance equivalent coefficient, measure the active power, reactive power and voltage output by the photovoltaic unit, calculate the index value characterizing the photovoltaic voltage support function, and then establish a comprehensive clustering index that accurately characterizes the characteristics of the distributed photovoltaic system. The optimal solution obtained by the particle swarm clustering algorithm is used as the initial clustering center, the FCM algorithm is used to calculate the optimal clustering result, and the equivalent model is calculated using parameter equivalence. The cluster modeling method can be widely used in the cluster modeling equivalent circuits of distributed photovoltaic systems with complex network topologies and mixed voltage support functions. Due to the comprehensive consideration of the equivalent electrical distance and photovoltaic voltage support characteristics, it can accurately reflect the dynamic characteristics of the photovoltaic unit. At the same time, the cluster modeling method introduces the particle swarm algorithm to improve and overcome the shortcomings of the traditional FCM algorithm, greatly improving the accuracy of the clustering results.

[0064] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0065] At present, the cluster modeling methods at home and abroad use the output characteristics of photovoltaic units as clustering indicators, but this indicator is only applicable to centralized photovoltaic cluster modeling, and does not take into account the characteristics of different line impedances, mixed voltage support functions, and different photovoltaic capacities in distributed photovoltaic systems. When there are complex network topologies and different control methods such as Volt-Var, constant power factor, and constant reactive power in photovoltaic systems, existing studies only collect the output characteristics of photovoltaic units such as voltage, active power, and reactive power as clustering indicators. These indicators cannot fully characterize the electrical characteristics of distributed photovoltaic systems, which affects the accuracy of clustering results. In addition, the existing clustering algorithm uses a random selection of initial clustering centers for subsequent clustering calculations. When the initial clustering centers are not selected properly, it is easy to fall into local optimality, which not only affects the rapidity of the clustering process, but also leads to inaccurate distributed photovoltaic clustering results. There is a large error between the equivalent model and the detailed model, which greatly limits its application in engineering. The cluster modeling method provided by the present invention fills this gap and provides a model basis for the clustering equivalence problem of distributed photovoltaic power generation systems.

[0066] Fourth, the distributed photovoltaic cluster modeling method based on the improved FCM algorithm of the present invention solves multiple key technical problems in the prior art in industrial applications and has achieved significant technical progress. The following is a detailed analysis of the problems solved by the technical solution in industrial applications and its technical progress:

[0067] 1. Problems of existing technologies: low modeling accuracy and difficulty in handling complex photovoltaic cluster network structures

[0068] Existing photovoltaic cluster modeling methods often cannot accurately handle complex network topologies. In particular, in large-scale distributed photovoltaic systems, the calculation of equivalent electrical distances is often inaccurate, resulting in large deviations in modeling results. In addition, existing technologies do not fully consider important parameters such as the voltage support function of photovoltaic units and photovoltaic capacity, resulting in errors in the model when reflecting actual operating conditions.

[0069] The present invention measures the outlet voltage, PCC point voltage and phase angle difference θ of the photovoltaic unit, combines the equivalent electrical distance calculation formula under the radial, chain and complex network topology structures, and then calculates the line impedance equivalent coefficient, which significantly improves the accuracy of modeling. In addition, the three core parameters of equivalent electrical distance, photovoltaic capacity and voltage support function are comprehensively considered, and a comprehensive clustering index is established, which further improves the accuracy of modeling and can accurately reflect the actual operating characteristics of the photovoltaic cluster.

[0070] 2. Problems of existing technologies: Clustering algorithms are sensitive to initial conditions and have slow convergence speed

[0071] When dealing with distributed photovoltaic clusters, the traditional FCM (Fuzzy C-means) clustering algorithm is often limited by the selection of the initial cluster center, easily falls into the local optimum, and has a slow convergence speed. This leads to low computational efficiency in practical applications, especially when dealing with large-scale photovoltaic clusters, and it is difficult to meet the requirements of real-time modeling and rapid response.

[0072] The present invention introduces a particle swarm optimization algorithm, and obtains the global optimal solution as the initial clustering center of the FCM algorithm through the particle swarm optimization algorithm. This not only speeds up the convergence speed of the FCM algorithm, but also avoids the risk of falling into the local optimum, thereby achieving rapid calculation of the optimal clustering result. This improvement greatly improves the efficiency of distributed photovoltaic cluster modeling, especially in large-scale application scenarios.

[0073] 3. Existing technical problems: Insufficient consideration of the voltage support capacity of photovoltaic units, affecting the stability of the power grid

[0074] Existing technologies often ignore the voltage support capability provided by photovoltaic units in the power grid, resulting in the failure to fully consider the impact of photovoltaic units on the voltage stability of the power grid during the modeling process. This deficiency will result in the inability of distributed photovoltaic systems to fully utilize the potential of photovoltaic unit voltage support during the voltage regulation process, thereby causing risks such as voltage fluctuations and power grid instability.

[0075] The present invention proposes an index value to characterize the voltage support function of photovoltaic units by measuring the active power, reactive power and port voltage of photovoltaic units. This innovative design can accurately evaluate the support capacity of photovoltaic units for grid voltage, so that the actual impact of each photovoltaic unit can be fully considered in the modeling process, thereby optimizing the voltage stability in grid operation and improving the reliability and safety of the overall system.

[0076] 4. Existing technical issues: Failure to effectively consider the complexity of large-scale distributed photovoltaic systems

[0077] As the scale of distributed photovoltaic systems continues to expand, existing modeling technologies find it difficult to effectively handle the complex electrical relationships between large-scale photovoltaic unit clusters, especially when multiple photovoltaic units are connected in different topologies. Existing simplified models often cannot accurately express their electrical characteristics.

[0078] The present invention establishes a set of comprehensive clustering indicators by comprehensively considering the equivalent electrical distance, capacity difference and voltage support function between photovoltaic units, which can effectively deal with complex photovoltaic system topology. Combined with the improved FCM algorithm, it can not only deal with the complexity of large-scale photovoltaic clusters, but also generate accurate equivalent models, greatly improving the applicability and scalability of the model, and is particularly suitable for modeling and optimization of large-scale photovoltaic power generation systems in the industry.

[0079] 5. Significant economic benefits and application value

[0080] The technical solution of the present invention significantly improves the accuracy and efficiency of photovoltaic cluster modeling, thereby reducing the operation and maintenance costs of photovoltaic systems. At the same time, the accuracy of the model makes grid scheduling more reasonable and accurate, reduces the risks brought by voltage fluctuations, and improves the reliability of power supply. This improvement can bring significant economic and social benefits to grid operators and photovoltaic power generation companies in large-scale application scenarios, and help promote and popularize distributed photovoltaic power generation.

[0081] The present invention has demonstrated significant technological progress in solving the problems of modeling accuracy, computational efficiency, voltage support capability assessment, and complex system processing in the prior art. In industrial applications, it not only improves the management and dispatching efficiency of distributed photovoltaic clusters, but also enhances the safety and stability of power grid operation, providing strong support for the widespread application of photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0083] Figure 1 is a flow chart of a distributed photovoltaic cluster modeling method based on an improved FCM algorithm provided by an embodiment of the present invention;

[0084] Figure 2 Schematic diagram of distributed photovoltaic equivalent line impedance under a radial structure provided by an embodiment of the present invention;

[0085] Figure 3 is a phasor schematic diagram of photovoltaic units converted from their respective coordinate systems to a common coordinate system provided by an embodiment of the present invention;

[0086] Figure 4 Schematic diagram of equivalent line impedance of distributed photovoltaic under chain structure provided by an embodiment of the present invention;

[0087] Figure 5 Schematic diagram of distributed photovoltaic equivalent line impedance under a hybrid structure provided by an embodiment of the present invention;

[0088] Figure 6 It is an overall framework diagram of the improved FCM algorithm provided by an embodiment of the present invention;

[0089] Figure 7 It is a structural diagram of a distributed photovoltaic cluster modeling method based on an improved FCM algorithm provided in an embodiment of the present invention;

[0090] FIG8 (a) is the active power and reactive power curves of the model before decoupling and the equivalent model after decoupling at the PCC point under the radial structure provided by an embodiment of the present invention, (b) is the active power and reactive power curves of the model before decoupling and the equivalent model after decoupling at the PCC point under the chain structure provided by an embodiment of the present invention, (c) is the active power and reactive power curves of the model before decoupling and the equivalent model after decoupling at the PCC point under the hybrid structure provided by an embodiment of the present invention;

[0091] Fig. 9 is a decoupled radial power distribution network diagram of a distributed photovoltaic system including 36 photovoltaic units provided in an embodiment of the present invention;

[0092] Fig.10 It is a reactive power curve at the PCC point of an equivalent model obtained by using the FCM algorithm and the improved FCM algorithm disclosed by the present invention and a detailed model;

[0093] Fig.11 It is an active power curve at the PCC point of an equivalent model obtained by using the FCM algorithm provided by the embodiment of the present invention and using the improved FCM algorithm disclosed by the present invention, as well as a detailed model. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0095] The following are two industrial application examples of the distributed photovoltaic cluster modeling method based on the improved FCM algorithm:

[0096] Example 1: Distributed Photovoltaic Power Generation Management System in Smart Grid

[0097] In the operation of a large-scale smart grid, the grid contains multiple distributed photovoltaic power stations, which are distributed in multiple areas of cities and rural areas. Due to different geographical locations and access points, these photovoltaic power stations have great differences in power supply stability and grid voltage support functions. By introducing a photovoltaic cluster modeling method based on an improved FCM algorithm, power operators can accurately analyze the equivalent electrical distance between each photovoltaic unit and the PCC point, the active power and reactive power of each unit, and further measure its voltage support capability.

[0098] In actual application, the system first measures the voltage and power output of the photovoltaic unit, then optimizes the initial cluster center through the particle swarm algorithm, and uses the FCM algorithm to optimize the cluster analysis of these photovoltaic units. Through this process, operators can divide the photovoltaic units in the power grid into different categories and form an equivalent model for real-time monitoring and adjustment. The implementation of this system not only improves the power dispatch efficiency of the power grid, but also enhances the stability of distributed photovoltaic power generation and reduces the risk of failure caused by voltage fluctuations.

[0099] Example 2: Distributed photovoltaic cluster optimization control in a large-scale industrial park

[0100] A large industrial park installed multiple photovoltaic power stations for independent power supply and connected them to the power grid. Due to the wide distribution range and uneven capacity of photovoltaic units, the voltage fluctuations within the park are relatively frequent, affecting the overall power quality. In order to optimize the photovoltaic power generation system within the park, the park management adopted a cluster modeling method based on the improved FCM algorithm.

[0101] By measuring the voltage, output power, and electrical distance of the photovoltaic unit to the PCC point, the management system can construct comprehensive clustering indicators for the photovoltaic clusters in the park. Under the dual optimization of the particle swarm algorithm and the FCM algorithm, the photovoltaic power stations in the park are effectively grouped, and an equivalent model for each group is established. The management uses these models to adjust and control the voltage fluctuations within the park in real time, ensuring the stability and efficiency of photovoltaic power generation. At the same time, through optimized control, the shutdown and loss of industrial equipment caused by voltage instability are reduced, and the continuity and economic benefits of industrial production are improved.

[0102] In view of the problems existing in the prior art, the present invention provides a distributed photovoltaic cluster modeling method and an equivalent model system based on an improved FCM algorithm. The present invention is described in detail below with reference to the accompanying drawings.

[0103] like Figure 1 As shown, a distributed photovoltaic cluster modeling method based on an improved FCM algorithm includes the following steps:

[0104] 1) Measure the photovoltaic unit outlet voltage V t and PCC point voltage V PCC and the phase angle difference θ between the two, derive the calculation formula of the equivalent electrical distance from the photovoltaic unit outlet to the PCC point under the radial structure, and then calculate the equivalent electrical distance under the chain structure and complex network topology, and then calculate the line impedance equivalent coefficient;

[0105] 2) Measure the active power P, reactive power Q and port voltage V of the photovoltaic unit output t , an index value is proposed to characterize the photovoltaic voltage support function;

[0106] 3) Comprehensively consider the equivalent electrical distance, photovoltaic capacity and voltage support function to establish a comprehensive clustering index that accurately characterizes the characteristics of distributed photovoltaic power generation systems;

[0107] 4) The optimal solution obtained by the particle swarm clustering algorithm is used as the initial clustering center, the FCM algorithm is used to calculate the optimal clustering result, and the equivalent model is calculated using parameter equivalents.

[0108] This distributed photovoltaic cluster modeling method based on the improved FCM algorithm achieves accurate photovoltaic power generation system modeling and analysis through multiple steps. First, in step 1, the equivalent electrical distance from the photovoltaic unit to the PCC point is derived by measuring the photovoltaic unit outlet voltage and the PCC (common coupling point) point voltage and the phase angle difference θ between the two. This calculation formula was originally derived for a simple radial structure and further extended to more complex chain structures and complex network topologies. The purpose of this step is to accurately calculate the electrical connection distance between photovoltaic units, thereby laying the foundation for subsequent modeling.

[0109] In step 2, the system further measures the output active power P, reactive power Q and port voltage of the photovoltaic unit. Through these parameters, an index value is proposed to characterize the photovoltaic voltage support function. This index value is introduced to measure the differences in the voltage support capabilities of photovoltaic units, ensuring that the impact and support capabilities of each photovoltaic unit on the grid voltage are fully considered during the modeling process. Accurate characterization of the voltage support function is crucial for the modeling of photovoltaic clusters, and can reflect the voltage stability and overall performance within the cluster.

[0110] Next, step 3 comprehensively considers the equivalent electrical distance, photovoltaic capacity and voltage support function to establish a comprehensive clustering index. This index can more comprehensively characterize the characteristics of distributed photovoltaic power generation systems, especially in large-scale photovoltaic clusters. This comprehensive index can well describe the characteristics of each photovoltaic unit and provide a more scientific and reasonable basis for subsequent cluster analysis. Through this clustering index, the system can group photovoltaic units, thereby simplifying the modeling process of the entire photovoltaic cluster.

[0111] Finally, in step 4, the system first obtains the optimal solution through the particle swarm optimization algorithm (PSO), and uses it as the initial clustering center of the FCM (fuzzy C-means) algorithm. On this basis, the FCM algorithm is used to optimally cluster the photovoltaic units, and then the optimal clustering results are obtained. Based on these clustering results, the parameters are equalized and the equivalent model is generated. This process greatly improves the accuracy and efficiency of photovoltaic cluster modeling, ensuring that accurate equivalent models can be obtained in complex systems for subsequent analysis and regulation.

[0112] according to Figure 2The equivalent schematic diagram of the radiation structure photovoltaic system shown in the figure shows that the grid-side converter grid-connected line matrix equation of the equivalent front circuit is:

[0113]

[0114] in It represents the line impedance parameter matrix from the PV unit outlet to the PCC point, U td ,U tq They represent the d-axis and q-axis voltage components of the photovoltaic unit in its own coordinate system, U pccd ,U pccq Respectively represent the voltage components of the PCC point on the d and q axes, I d ,I q They represent the d-axis and q-axis current components of the photovoltaic unit in its own coordinate system. After rearranging the formula, we can get:

[0115]

[0116] The subscripts 1 and 2 represent the electrical quantities of the first photovoltaic unit and the second photovoltaic unit, respectively. The grid-connected grid-side converter of each photovoltaic unit realizes vector control in its own phase-locked coordinate system, and the grid-connected grid-side converter is gathered through the collector network to form a new energy station. The voltage vector of the common connection point after the photovoltaic units are gathered through the line impedance is used as the reference direction to establish a common coordinate system. The phasor diagram is as follows: Figure 3 As shown, the conversion matrix that converts the grid-side converter variables from their respective coordinate systems to the common coordinate system can be obtained:

[0117]

[0118] where x d-1 ,x q-1 ,x d-2 ,x q-2 They represent the d-axis and q-axis components of the first photovoltaic unit and the second photovoltaic unit under the common coordinate axis, respectively. 1d ,x 1q ,x 2d ,x 2q They represent the d-axis component and the q-axis component of the first photovoltaic unit and the second photovoltaic unit in their own coordinate systems respectively, and T1 and T2 represent the conversion matrices of the first photovoltaic unit and the second photovoltaic unit respectively.

[0119] Arrange the above formulas, and equate the d-axis and q-axis current components in their respective coordinate systems to the common coordinate system, and obtain the current expression in the common coordinate system:

[0120]

[0121] Among them I d-1 ,Id-1 ,I d-2 ,I q-2 They represent the d-axis and q-axis current components of the first photovoltaic unit and the second photovoltaic unit in the common coordinate system. The current at the common connection point is equal to the sum of the output currents of each grid-side converter in the common coordinate system, and its expression is:

[0122]

[0123] Keep the output voltage of the photovoltaic unit unchanged, that is, the difference between the output voltage and the grid voltage remains unchanged before and after the equivalence, and the line matrix equation from the photovoltaic outlet to the PCC point is obtained as follows:

[0124]

[0125] Where B represents the line impedance parameter matrix from the PCC point to the power grid.

[0126] Keeping the consistency of the models before and after the equivalent circuit, the grid-connected line matrix equation of the grid-side converter of the equivalent circuit is obtained as follows:

[0127]

[0128] Arranging the above formulas yields:

[0129]

[0130] Among them A 1eq ,A 2eq Respectively represent the equivalent electrical distance from the first PV unit and the second PV unit outlet to the grid.

[0131] For the chain structure, in order to ensure the consistency of the calculation method, the radial structure is regarded as a special form of the chain structure. The equivalent line impedance parameters of the grid-connected distributed photovoltaic under the chain structure can be calculated by multiple equivalents based on the radial structure. Figure 4 The equivalent schematic diagram of the chain-structure photovoltaic system shown in the figure can be used to calculate the equivalent electrical distance. It is worth mentioning that for complex topological structures, they can be regarded as topological reorganizations of radial structures and chain structures, and the equivalent electrical distance can still be calculated by a similar method. Figure 5 The equivalent schematic diagram of the hybrid photovoltaic system shown can be used to calculate the equivalent electrical distance.

[0132] Furthermore, the calculation formula of the line impedance equivalent coefficient is:

[0133]

[0134] Among them LEC i represents the line impedance equivalent coefficient of the i-th node, A ieqA is the equivalent line impedance from the photovoltaic unit connection point of the i-th node to the grid. i represents the line impedance parameter from the ith node PV unit to the PCC, and B represents the line impedance from the PCC to the grid.

[0135] At present, the common voltage support functions are Volt-Var control, constant power factor control and constant reactive power control. However, for distributed photovoltaic power generation systems, it is difficult to accurately and quickly know the voltage support function used by each photovoltaic inverter. Therefore, it is necessary to find suitable indicators to characterize the voltage support function of the photovoltaic inverter. The three control methods are closely related to the active power and voltage or reactive power and voltage at the photovoltaic outlet. The three control methods can be completely distinguished according to the above correlation. Furthermore, it is proposed to use the correlation between voltage and active power and the correlation between voltage and reactive power as clustering indicators, and calculate the correlation coefficient between samples as an estimate of the overall correlation coefficient to characterize the voltage support function of photovoltaics. The calculation formula of the correlation coefficient is as follows:

[0136]

[0137] where c ij For sample F i With sample F j The correlation coefficient between i ,F j ) is the sample F i With sample F j The covariance between Var(F i ) and Var(F j ) are samples F i and sample F j The variance of .

[0138] Furthermore, the difference in photovoltaic capacity also has a certain impact on the clustering results. For photovoltaic units with larger capacity, their voltage support function has a stronger ability to regulate the voltage of the distribution network. At this time, the voltage support function is used as a more important clustering indicator, that is, the weight of the voltage support function is greater, which is conducive to accurate voltage regulation. For photovoltaic units with smaller capacity, in order to facilitate the allocation of centralized voltage regulation instructions, photovoltaics with closer equivalent electrical distances should be clustered into a group. At this time, the weight of the equivalent electrical distance is greater. In summary, using the normalized capacity value as the weight, the comprehensive clustering index (CCI) is proposed as follows:

[0139] CCI=a×C VP +a×C VQ +(1-2a)×A eq

[0140] Where a is the normalized capacity weight, C vp is the correlation coefficient between voltage and active power, C vq is the correlation coefficient between voltage and reactive power, A eq is the equivalent electrical distance.

[0141] Furthermore, the overall framework of the improved fuzzy C-means clustering algorithm is shown in Figure 6 As shown. The objective function and constraints of the fuzzy C-means clustering algorithm are as follows:

[0142]

[0143] Among them, u ij is the sample point x i Relative to the cluster center v j The membership degree of the cluster, m is the fuzzy index (m>1), d ij is the sample point x i With cluster center v j The Euclidean distance. The fuzzy classification of the sample set is obtained by iterative optimization of the objective function. In order to minimize the objective function J, the Lagrange multiplier method is used on the objective function while satisfying the constraints to obtain the membership matrix U and the cluster center v j .

[0144]

[0145] The specific description of the algorithm is as follows:

[0146]

[0147]

[0148] Since fuzzy C clustering uses a random method to select the initial cluster center, the clustering result is extremely dependent on the selection of the initial center point. Once the initial center point is selected incorrectly, it will have a great impact on the subsequent clustering process, and it is difficult to obtain the most ideal clustering result. At the same time, the number of clustering iterations will also increase. The randomly selected initial center point has great uncertainty, which also directly affects the clustering effect. It is proposed to first use the particle swarm algorithm to globally optimize and select the optimal cluster center, and then use the fuzzy C-means clustering algorithm to obtain the optimal clustering result.

[0149] The position of particle i in N-dimensional space is represented by vector X i =(x1,x2,…,x N ), the flight speed is represented by vector V i =(v1,v2,…,v N ). The particle updates its velocity and position using the following formula:

[0150] vi =w×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i )

[0151] x i =x i +v i

[0152] Where w is the inertia factor, representing the trade-off between global optimization ability and local optimization ability, v i Refers to the speed of the particle, rand() is a random number between (0,1), x i is the current position of the particle, c1 and c2 are learning factors. The pseudo code of the particle swarm algorithm is as follows:

[0153]

[0154]

[0155] like Figure 7 As shown, the distributed photovoltaic cluster modeling method and equivalent model system based on the improved FCM algorithm provided by the embodiment of the present invention include:

[0156] Voltage measurement module, used to measure the photovoltaic unit outlet voltage V t and PCC point voltage V PCC ;

[0157] Phase angle measurement module, used to measure the phase angle difference θ between the photovoltaic unit outlet voltage and the PCC point voltage;

[0158] Power measurement module, used to measure the active power P and reactive power Q output by the photovoltaic unit;

[0159] Line impedance measurement module, used to measure line impedance Z s =R s +jX s ;

[0160] The module for deriving the equivalent electrical distance relationship is used to derive the expression of the equivalent electrical distance from the photovoltaic outlet to the PCC point under the radial structure, chain structure and complex topology structure;

[0161] A line impedance equivalent coefficient calculation module is used to calculate the variation coefficient of equivalent line impedance;

[0162] The voltage support function index value calculation module is used to calculate the active power P, reactive power Q and port voltage V output by the photovoltaic unit. t, calculate the index value that characterizes the photovoltaic voltage support function;

[0163] A comprehensive clustering index calculation module is used to calculate the clustering index used for clustering according to the equivalent electrical distance, voltage support function and photovoltaic capacity;

[0164] Particle swarm algorithm module, used to search for the global optimal solution and calculate the optimal cluster center using comprehensive clustering indicators and particle swarm algorithm;

[0165] The fuzzy C-means clustering algorithm module is used to cluster several photovoltaic units using the optimal clustering center obtained by the particle swarm algorithm to obtain the optimal clustering result.

[0166] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a distributed photovoltaic cluster modeling method based on an improved FCM algorithm.

[0167] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a distributed photovoltaic cluster modeling method based on an improved FCM algorithm.

[0168] The application embodiment of the present invention provides an information data processing terminal, which is used to implement a distributed photovoltaic cluster modeling method and an equivalent model system based on an improved FCM algorithm.

[0169] The present invention provides two embodiments of the invention: first, in a radial structure, a chain structure and a hybrid structure, the equivalent electrical distance calculation method disclosed in the present invention is used to simplify the network structure into the form of "photovoltaic unit-line impedance-grid", and the simulation results are compared with those before simplification to verify its accuracy; second, in a large-scale distributed photovoltaic system, the clustering results of the improved FCM algorithm disclosed in the present invention are compared with those of the existing FCM algorithm to verify the effectiveness and advantages of the proposed clustering algorithm.

[0170] In a first embodiment, Figure 2 The radiant structure shown, Figure 3 The chain structure shown and Figure 4 The line impedance parameters and line impedance equivalent coefficients before and after decoupling under the hybrid structure are shown in Table 1, Table 2, and Table 3, respectively. The model before decoupling and the equivalent model after decoupling are simulated, and the simulation results are shown in Figure 8. The active power and reactive power curves of the PCC before and after decoupling are basically the same, which further proves the effectiveness of the equivalent electrical distance solution method disclosed in the present invention in decoupling distributed photovoltaics under radial structure, chain structure and hybrid structure.

[0171] In the second embodiment, first Fig. 9 The distributed photovoltaic system with 36 photovoltaic units shown in the figure is clustered and simulated in the decoupled radial distribution network using the improved FCM algorithm proposed in the present invention, wherein photovoltaic units of different colors represent Volt-Var control, constant reactive power control and constant power factor control, respectively, and the depth of the color of the equivalent line impedance represents the numerical value. The larger the equivalent line impedance, the darker the color. The installed capacity of each photovoltaic unit is shown in Table 4, and the total photovoltaic installed capacity is 2640kW. The reactive power curve and active power curve of the equivalent model obtained by the FCM algorithm and the improved FCM algorithm disclosed in the present invention and the detailed model at the PCC point are shown respectively. Fig.10 and Fig.11 As shown, it can be seen that the active and reactive curves of the equivalent model obtained by using the improved FCM algorithm disclosed in the present invention are basically consistent with the detailed model at the PCC point, and the relative error of the reactive curve compared with the traditional FCM algorithm is reduced from 15.8% to 2.11%, which proves the effectiveness of the distributed photovoltaic cluster modeling method based on the improved FCM algorithm disclosed in the present invention.

[0172] Table 1 Line parameters before and after decoupling of the radial structure

[0173]

[0174] Table 2 Line parameters before and after chain structure decoupling

[0175]

[0176] Table 3 Line parameters before and after hybrid structure decoupling

[0177]

[0178] Table 4 Installed capacity of each photovoltaic unit

[0179]

[0180] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0181] 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 modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A distributed photovoltaic cluster modeling method based on an improved FCM algorithm, characterized in that: include: 1) Measure the photovoltaic unit outlet voltage V t and PCC point voltage V PCC and the phase angle difference θ between the two, derive the calculation formula of the equivalent electrical distance from the photovoltaic unit outlet to the PCC point under the radial structure, and then calculate the equivalent electrical distance under the chain structure and complex network topology, and then calculate the line impedance equivalent coefficient; 2) Measure the active power P, reactive power Q and port voltage V of the photovoltaic unit output t , an index value is proposed to characterize the photovoltaic voltage support function; 3) Comprehensively consider the equivalent electrical distance, photovoltaic capacity and voltage support function to establish a comprehensive clustering index that accurately characterizes the characteristics of distributed photovoltaic power generation systems; 4) The optimal solution obtained by the particle swarm clustering algorithm is used as the initial clustering center, the FCM algorithm is used to calculate the optimal clustering result, and the equivalent model is calculated using parameter equivalents.

2. The distributed photovoltaic cluster modeling method based on the improved FCM algorithm according to claim 1, characterized in that: The calculation formula for the equivalent electrical distance from the distributed photovoltaic outlet to the PCC point under the radial structure is: Among them, A eq represents the equivalent line impedance from the PV unit outlet to the grid, B represents the line impedance from the PCC point to the grid, A represents the line impedance from the PV outlet to the PCC point, I d-eq ,I q-eq represents the current from the PCC point to the grid, the subscripts "d" and "q" represent the d and q axis components respectively, I d ,I q represents the current from the PV unit outlet to the PCC point, and T represents the transformation matrix of the PV units from their respective coordinate systems to the common coordinate system. Furthermore, the calculation formula of the line impedance equivalent coefficient is: Among them LEC i represents the line impedance equivalent coefficient of the i-th node, A ieq A is the equivalent line impedance from the photovoltaic unit connection point of the i-th node to the grid. i represents the line impedance parameter from the ith node PV unit to the PCC, and B represents the line impedance from the PCC to the grid.

3. The distributed photovoltaic cluster modeling method based on the improved FCM algorithm according to claim 1, characterized in that: The index value that characterizes the voltage support function of the photovoltaic unit inverter is: In the formula, V represents the output voltage of the photovoltaic unit, P represents the active power output of the photovoltaic unit, Q represents the reactive power output of the photovoltaic unit, and C represents the VP Indicates the correlation between the PV unit outlet voltage and active power, C VQ Indicates the correlation between the PV unit outlet voltage and reactive power.

4. The distributed photovoltaic cluster modeling method based on the improved FCM algorithm according to claim 1, characterized in that: The distributed photovoltaic clustering index that comprehensively considers equivalent electrical distance, photovoltaic capacity and voltage support function is: <h2 style=";text-align:left;direction:ltr">CCI = a×C<h2 style=";text-align:left;direction:ltr"> VP <h2 style=";text-align:left;direction:ltr"> +a×C<h2 style=";text-align:left;direction:ltr"> VQ <h2 style=";text-align:left;direction:ltr"> +(1-2a)×A<h2 style=";text-align:left;direction:ltr"> eq Among them, CCI represents the comprehensive clustering index of distributed photovoltaics, a represents the normalized photovoltaic capacity, C VP and C VQ They represent the correlation between the PV unit outlet voltage and active power and reactive power, respectively. eq Indicates the equivalent electrical distance.

5. The distributed photovoltaic cluster modeling method based on the improved FCM algorithm according to claim 1, characterized in that: Improvements to the FCM algorithm include: The particle swarm algorithm searches for the optimal solution. The particles determine the flight direction based on their own best position and the global best position. The global optimal solution is obtained through convergence iteration and serves as the initial clustering center of the FCM algorithm. The FCM algorithm updates the membership matrix and selects the cluster centers based on the initial cluster centers obtained by the particle swarm algorithm, and iterates to obtain the optimal clustering result.

6. A distributed photovoltaic cluster equivalent model system based on an improved FCM algorithm that implements the distributed photovoltaic cluster modeling method based on the improved FCM algorithm as claimed in any one of claims 1 to 5, characterized in that: include: Photovoltaic measurement module, used to measure the photovoltaic unit outlet voltage, PCC point voltage and the phase angle difference θ between the two, derive and calculate the equivalent electrical distance from the photovoltaic unit outlet to the PCC point under the radial structure, chain structure and complex network topology; A line impedance equivalent coefficient calculation module is used to calculate the variation coefficient of equivalent line impedance; Power measurement module, used to measure the output active power P, reactive power Q and port voltage V of the photovoltaic unit t , and calculate the voltage support function index of the photovoltaic unit; Clustering index calculation module, which is used to comprehensively consider equivalent electrical distance, photovoltaic capacity and voltage support function to establish comprehensive clustering index of distributed photovoltaic system; The clustering calculation module is used to use the optimal solution of the particle swarm algorithm as the initial clustering center, calculate the optimal clustering result using the FCM algorithm, and generate an equivalent model through equivalent parameter calculation.

7. The distributed photovoltaic cluster modeling system based on the improved FCM algorithm according to claim 1, characterized in that: The photovoltaic measurement module further comprises: The calculation unit, based on the measured values ​​of the PV unit outlet voltage and the PCC point voltage and the phase angle difference θ, uses the equivalent electrical distance calculation formula to derive and calculate the equivalent electrical distance under the radial, chain and complex network topology structures, and then calculates the line impedance equivalent coefficient.

8. The distributed photovoltaic cluster modeling system based on the improved FCM algorithm according to claim 1, characterized in that: The power measurement module further comprises: The power calculation unit is used to calculate the index value characterizing the voltage support function of the photovoltaic unit inverter through a formula, and the index value is calculated based on the outlet voltage, output active power and reactive power of the photovoltaic unit and their related parameters.

9. The distributed photovoltaic cluster modeling system based on the improved FCM algorithm according to claim 1, characterized in that: The clustering index calculation module further includes: The index calculation unit is used to calculate the comprehensive clustering index of distributed photovoltaics, which integrates the photovoltaic capacity, equivalent electrical distance, and the correlation between the photovoltaic unit outlet voltage and the active power and reactive power.

10. The distributed photovoltaic cluster modeling system based on the improved FCM algorithm according to claim 1, characterized in that: The clustering calculation module includes: A particle swarm optimization unit, used to take the optimal solution of the particle swarm algorithm as the initial cluster center; The FCM algorithm unit is used to update the membership matrix and select cluster centers according to the initial cluster centers provided by the particle swarm optimization unit, iteratively calculate to obtain the optimal clustering result and generate an equivalent model.

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