Reactive voltage operation evaluation method and system of cluster photovoltaic power station

Abstract

The invention discloses a reactive voltage operation evaluation method and system for a cluster photovoltaic power station, and relates to the technical field of photovoltaic power generation, and the method comprises the steps: calculating a voltage quantitative index based on real-time operation data, and providing a data basis for evaluation; the Kirchhoff's law is used for defining reactive circulation and unbalanced distribution characteristics, and accurate definition and recognition of abnormities are achieved; constructing an equivalent circuit model and revealing topological influence through a sensitivity matrix of load flow calculation; an automatic voltage control mechanism is analyzed, and the root of a regulation problem is revealed; factors such as the electrical distance, the regulation rate and the chain structure power station voltage regulation coupling strength are comprehensively considered, the reactive power output distribution coefficient of each photovoltaic power station is calculated in a weighted mode, and resource coordination is improved; and constructing a quantitative index system based on an abnormal identification and regulation operation mechanism, and carrying out comprehensive state evaluation. The method overcomes the fuzzy limitation of previous description, provides a mathematical physical basis, and supports the scientific decision of operators.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation technology, and in particular to a method and system for evaluating the reactive power and voltage operation of a cluster photovoltaic power station. Background Technology

[0002] Photovoltaic power generation, as an important new energy power generation technology, is undergoing large-scale and clustered development. China's centralized photovoltaic power plants generally adopt a "centralized development, long-distance transmission" model, forming a cluster structure with radial transmission as the main type and chain transmission as a supplement, connecting to the power grid. For example... Figure 1 As shown, photovoltaic power station A is connected to the new energy collection station in a radial pattern, while photovoltaic power stations C and B are connected to the new energy collection station in a chain-like manner. After being stepped up by the transformer of the new energy collection station, they are bundled and connected to the power grid.

[0003] While achieving intensive resource utilization, this clustered development model also brings significant operational characteristics. For example, based on the resource characteristics of grid-connected new energy power generation (large output uncertainty → difficult to accurately control) and equipment characteristics (large number and types of power generation units → low anti-disturbance performance and weak active support capability), and considering the complex network topology, differences in equipment technical characteristics, and lack of collaborative control mechanisms after the aggregation and access of multi-energy complementary power generation, the grid connection of large-scale clustered photovoltaic power plants will have a significant impact on the power flow distribution and grid voltage of the near-area power transmission system, bringing a series of problems to the safe and stable operation of the power system's reactive power and voltage, and seriously restricting the efficient operation level of photovoltaic power plants.

[0004] Currently, the Automatic Voltage Control (AVC) system in photovoltaic (PV) clusters still uses the traditional two-level voltage control framework. The AVC master station of the power grid dispatch center aims to stabilize the voltage of the regional hub bus and issues voltage commands to the AVC substations of each PV power station. Each substation independently tracks its grid connection point voltage.

[0005] Existing research on reactive power and voltage in clustered photovoltaic (PV) power plants largely focuses on optimizing control methods. However, existing methods have significant shortcomings in analyzing and evaluating operational status. This leads to a lack of rigorous, circuit-law-based physical definitions and logical judgment criteria for key anomalies such as "reactive power circulation" and "uneven reactive power output distribution" in clustered PV power plants. Consequently, they cannot accurately identify and quantify reactive power cancellation issues within the cluster, resulting in qualitative problem analysis. Furthermore, there is currently no comprehensive reactive power output distribution calculation method that considers relevant influencing factors to address reactive power circulation and uneven reactive power output distribution. A comprehensive evaluation index system capable of simultaneously quantifying the reactive power operation of clustered PV power plants has also not yet been established. Operators are unable to objectively and comprehensively assess the reactive power and voltage operation of the cluster, and the calculation of reasonable reactive power output distribution lacks mechanistic analysis, calculation step design, and index evaluation.

[0006] like Figure 2 As shown, taking the abnormal reactive power operation of a photovoltaic power plant cluster when automatic voltage control is implemented as an example, this paper illustrates the application problems of the current automatic voltage control mode in photovoltaic power plants clusters. It can be seen that, overall, the reactive power flow of the three lines exhibits significant differences in timing and direction. The reactive power fluctuation of line LB is significant (-50~-70Mvar); the reactive power of line LA is consistently negative (-5~-45Mvar). In contrast, the reactive power of line LC is consistently a small positive value (0.5~2Mvar).

[0007] In summary, under the current AVC control mode, the reactive power-voltage regulation operation of the cluster photovoltaic power station is generally stable. However, in terms of reactive power operation, there are significant differences in reactive power output among the photovoltaic power stations and abnormal situations such as large reactive power flow in the transmission lines of the photovoltaic power stations. Summary of the Invention

[0008] The main purpose of this application is to provide a reactive power and voltage operation assessment method and system for cluster photovoltaic power plants, so as to solve the problems of significant differences in reactive power output among photovoltaic power plants and abnormal situations caused by large reactive power flow in the transmission lines of photovoltaic power plants in the existing technology.

[0009] To achieve the above objectives, this application provides the following technical solution:

[0010] A method for evaluating the reactive power and voltage operation of a cluster photovoltaic power station, the method comprising:

[0011] Step S1: Based on the real-time operation data of the cluster photovoltaic power station, calculate the quantitative index dataset of voltage operation status of each photovoltaic power station according to the preset scheduling voltage curve and the average rated voltage of the system.

[0012] Step S2: Based on the quantitative index dataset, define the reactive power circulation characteristics and the reactive power output imbalance characteristics through Kirchhoff's laws to obtain the anomaly identification results;

[0013] Step S3: Construct an equivalent circuit model based on the topology of the cluster photovoltaic power station, and obtain the reactive voltage sensitivity matrix through power flow calculation, and further obtain the corresponding sensitivity coefficients.

[0014] Step S4: Based on the sensitivity matrix and the reactive voltage automatic control theory, analyze the automatic voltage control mechanism of each photovoltaic power station to obtain a regulation mechanism analysis report;

[0015] Step S5: Calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and the real-time operating data; and calculate the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report.

[0016] Step S6: Based on the anomaly identification results and the control operation mechanism analysis, construct a quantitative index system, and use the quantitative index system to evaluate the operation status of each photovoltaic power station to obtain the reactive power and voltage operation evaluation results.

[0017] Beneficial effects:

[0018] Steps S1 to S6, through systematic data acquisition, anomaly identification, mechanism analysis, allocation calculation, and comprehensive evaluation, achieve a comprehensive, accurate, and dynamic assessment of the reactive power and voltage operation status of the cluster photovoltaic power station. Specifically, Step S1 calculates voltage quantification indicators based on real-time operating data, providing a reliable data foundation for the entire evaluation process and ensuring the timeliness and accuracy of subsequent analyses. Step S2 utilizes this data to define the characteristics of reactive power circulation and unbalanced reactive power output distribution using Kirchhoff's laws, achieving precise definition and quantification of abnormal problems. This overcomes the limitations of previous studies, which suffered from vague descriptions and an inability to quantify judgments, providing a rigorous mathematical and physical foundation for problem analysis. Step S3 obtains a sensitivity matrix through constructing an equivalent circuit model and power flow calculations, revealing the impact of the cluster topology on reactive power and voltage characteristics and forming a framework for analyzing the operating mechanism from an electrical characteristic perspective. Step S4 analyzes the control mechanism of the AVC system based on the sensitivity matrix, deeply revealing the master station's command strategy biased towards voltage control. This approach avoids neglecting issues such as the balanced distribution of reactive power output, differences in regulation rates caused by independent substation responses, and voltage regulation coupling problems between stations in a chain structure. It transitions from phenomenological description to root cause analysis, providing a clear direction for targeted improvements. Step S5 comprehensively considers multiple factors, including electrical distance, regulation rate, and the strength of voltage regulation coupling in a chain structure, to dynamically calculate the reactive power output distribution coefficient. This achieves coordinated allocation of reactive power resources within the cluster, improving the balance and rationality of reactive power output and reducing circulating currents and uneven distribution caused by improper allocation. Step S6 constructs a quantitative indicator system based on anomaly identification results and the analysis of the control operation mechanism. This system covers multiple dimensions, including the balance of reactive power output distribution and the determination and intensity of circulating currents, enabling an objective and comprehensive assessment of the operating status and providing intuitive and scientific decision support for operators.

[0019] As a further improvement to this application, step S1, based on the real-time operation data of the cluster photovoltaic power station, calculates a quantitative index dataset of the voltage operation status of each photovoltaic power station according to the preset scheduling voltage curve and the average rated voltage of the system, including:

[0020] Step S11: Real-time voltage time series data of the 220kV grid-connected bus of each photovoltaic power station are collected through the monitoring system of the cluster photovoltaic power station;

[0021] Step S12: Obtain the preset voltage curve upper and lower limits and the system operating average rated voltage from the superior power dispatching master station as preset benchmarks;

[0022] Step S13: Based on the voltage time series data, calculate the arithmetic mean voltage, voltage deviation rate, and voltage fluctuation rate of the 220kV grid-connected bus of each photovoltaic power station, and integrate them into the quantitative index dataset.

[0023] Step S14: Compare the quantitative indicator dataset with the preset benchmark, and assign an over-limit classification label to values ​​that exceed the preset benchmark;

[0024] Step S15: Merge the over-limit classification labels into the quantitative index dataset.

[0025] Beneficial effects:

[0026] Steps S11 to S15 serve as the data foundation layer for the reactive power and voltage operation assessment method of clustered photovoltaic power plants. Through systematic data acquisition, processing, benchmark comparison, and tag integration, they achieve efficient, accurate, and standardized preprocessing of voltage operation status, thereby providing reliable, real-time, and structured input data for subsequent anomaly identification, mechanism analysis, and comprehensive assessment. Specifically, step S11 collects real-time voltage time-series data of the 220kV grid-connected bus of each photovoltaic power plant through a monitoring system, ensuring the comprehensiveness and timeliness of the data source and avoiding assessment bias due to missing or delayed data. Step S12 introduces the preset upper and lower limits of the voltage curve from the external power dispatch terminal and the system's average rated voltage as benchmarks, providing an objective reference standard for the data processing process and improving the comparability and engineering applicability of the assessment results. Step S13 calculates quantitative indicators such as the voltage arithmetic mean, deviation rate, and volatility based on the collected data, transforming the raw voltage values ​​into analyzable mathematical characteristics. This not only simplifies the complexity of subsequent processing but also enhances the accuracy of data expression; for example, the average value reflects the voltage... The overall level, deviation rate quantifies the degree of deviation from the nominal value, and volatility characterizes the dynamic change characteristics, thus providing multi-dimensional quantitative basis for status assessment; step S14 compares the calculated quantitative indicators with the preset benchmark and assigns graded labels to the out-of-limit values ​​to achieve preliminary automatic identification of voltage anomalies. This not only reduces the risk of errors from manual intervention but also improves the efficiency and consistency of anomaly detection; step S15 merges the out-of-limit graded labels into the quantitative indicator dataset to form a structured comprehensive dataset containing anomaly markers. This ensures that the data retains the original quantitative information while incorporating preliminary diagnostic results, providing directly usable input for anomaly identification in step S2 and avoiding redundant calculations and data inconsistencies.

[0027] As a further improvement to this application, step S2, based on the quantitative index dataset, defines the reactive power circulation characteristics and the reactive power output imbalance characteristics through Kirchhoff's laws to obtain anomaly identification results, including:

[0028] Step S21: Extract the reactive power time series data of each photovoltaic power station from the quantitative index dataset;

[0029] Step S22: Based on the reactive power time series data, calculate the reactive power circulating and canceling each other within the cluster photovoltaic power station using Kirchhoff's laws to obtain the reactive power circulation characteristics;

[0030] Step S23: Calculate the ratio of reactive power output ratio to electrical distance for each photovoltaic power station based on the principle of electrical distance, and obtain the characteristics of the unbalanced distribution of reactive power output.

[0031] Step S24: Integrate the reactive power circulation characteristics and the reactive power output imbalance characteristics to generate a structured anomaly identification result.

[0032] Beneficial effects:

[0033] Steps S21 to S24, as the core diagnostic steps of the reactive power and voltage operation assessment method for clustered photovoltaic power plants, achieve precise definition, quantitative identification, and structured characterization of two key anomalies—reactive power circulation and unbalanced reactive power output distribution—through systematic data extraction, application of physical laws, feature quantification, and result integration. This provides a reliable and quantifiable basis for anomaly diagnosis, enabling subsequent topological mechanism analysis and comprehensive evaluation. Specifically, step S21 extracts reactive power time-series data from each photovoltaic power plant from the quantitative index dataset, ensuring that the anomaly identification process is based on actual operating data, avoiding subjective assumptions, and providing real and continuous data input for subsequent analysis. Step S22, based on this data, applies Kirchhoff's laws to calculate the reactive power circulating and canceling within the cluster, clearly defining the physical quantity of reactive power circulation intensity. This transforms the traditionally vaguely described "circulation" phenomenon into a precisely calculable index, overcoming the limitations of previous qualitative judgments and providing a rigorous mathematical basis for determining the existence and severity of circulation. Step S23 calculates the ratio of reactive power output to electrical distance for each photovoltaic power station based on the principle of electrical distance, defining the reactive power output distribution balance. This indicator not only quantifies the relative rationality of power output distribution but also reveals the impact of structural factors (such as reactance differences) on the distribution results, thus elevating the distribution imbalance problem from a superficial description to a mechanistic correlation level. Step S24 integrates circulating current and distribution imbalance characteristics to generate structured anomaly identification results, ensuring that the output includes both binary judgment indicators (such as the presence or absence of circulating current) and continuous quantitative values ​​(such as the magnitude of circulating current and the balance deviation), forming a multi-dimensional diagnostic report. This process, through the combination of physical laws and mathematical models, achieves a shift from "empirical judgment" to "data-driven" approaches, not only improving the objectivity and repeatability of anomaly identification but also providing clear input guidance for the topology analysis in Step S3. For example, areas with severe circulating current require in-depth analysis of electrical coupling relationships, and sites with distribution imbalances require in-depth investigation of sensitivity differences.

[0034] As a further improvement to this application, step S3 involves constructing an equivalent circuit model based on the topology of a clustered photovoltaic power station, obtaining the reactive power-voltage sensitivity matrix through power flow calculation, and further obtaining the corresponding sensitivity coefficients, including:

[0035] Step S31: Extract the photovoltaic power station nodes, transformer nodes, and line parameters of the cluster photovoltaic power station, and form the topology based on the access method of the cluster photovoltaic power station;

[0036] Step S32: Each photovoltaic power station node is equivalent to either a PV node or a PQ node according to actual control needs, and each transformer node is equivalent to a series impedance. The line parameters are equivalent in π type to form an equivalent circuit.

[0037] Step S33: Solve the equivalent circuit using the power flow calculation equation to obtain the Jacobian matrix;

[0038] Step S34: Extract the reactive voltage partial derivative matrix from the Jacobian matrix, and obtain the sensitivity matrix through inverse matrix operation.

[0039] Beneficial effects:

[0040] Steps S31 to S34, as key modeling steps in the reactive power and voltage operation assessment method for clustered photovoltaic power plants, systematically extract topology, construct equivalent circuits, solve power flow equations, and calculate sensitivity matrices. This transforms the actual electrical network into a precise mathematical model, providing a reliable and dynamic electrical foundation for the quantitative analysis of reactive power and voltage characteristics, thus supporting the accuracy, computability, and engineering applicability of the entire assessment process. Specifically, step S31 extracts the parameters of photovoltaic power plant nodes, transformer nodes, and lines, and forms a topology based on the cluster access method, ensuring consistency between the model and the real power grid and avoiding deviations caused by structural simplification, laying a physical foundation for subsequent analysis. Step S32, based on this, equivalences the complex network into a computable model. For example, photovoltaic power plant nodes are classified as PV or PQ nodes according to actual control needs to reflect their operating characteristics; transformers are equivalent to series impedances; and lines are equivalent to π-type lines. This standardization not only simplifies computational complexity but also preserves key electrical relationships, enabling the model to capture both global power flow distribution and refine local interaction effects. Step S33 solves the power flow calculation equations, etc. The process of obtaining the Jacobian matrix transforms the nonlinear electrical problem into a linearly solvable mathematical form. For example, the power balance equation is used to iteratively calculate the node voltage and power distribution, thereby revealing the dynamic characteristics of the system at a specific operating point and providing intermediate data support for sensitivity analysis. Step S34 extracts the reactive voltage partial derivative matrix from the Jacobian matrix and obtains the sensitivity matrix through inverse matrix operations. This quantifies the direct impact of node reactive power changes on voltage amplitude. This matrix-based output makes the electrical coupling relationship measurable and comparable, which not only improves the accuracy of the analysis but also provides direct input for the AVC mechanism analysis in step S4. For example, the sensitivity coefficient can be used to identify high coupling regions for focused evaluation of control interactions.

[0041] As a further improvement to this application, step S4 involves analyzing the automatic voltage control mechanism of each photovoltaic power station based on the aforementioned sensitivity matrix and the reactive voltage automatic control theory, resulting in a regulation mechanism analysis report, including:

[0042] Step S41: Based on the sensitivity matrix and the automatic voltage control theory, analyze the instruction issuance mechanism of the automatic voltage control master station to obtain the analysis results of the master station instruction mechanism.

[0043] Step S42: Based on the analysis results of the master station command mechanism, combined with the sensitivity matrix and the regulation characteristics of each photovoltaic power station, evaluate the independent response behavior of the AVC substation of each photovoltaic power station to obtain a substation response evaluation report;

[0044] Step S43: Based on the substation response evaluation report, analyze the cumulative effect of deviations of power stations with different regulation rates by comparing the response characteristics of each photovoltaic power station, and obtain interactive influence analysis data;

[0045] Step S44: Integrate the analysis results of the main station command mechanism, the substation response evaluation report, and the interaction impact analysis data to obtain the regulation mechanism analysis report.

[0046] Beneficial effects:

[0047] Steps S41 to S44, as the core mechanism analysis in the reactive power and voltage operation assessment method of cluster photovoltaic power plants, achieve in-depth analysis of the operating mechanism of the Automatic Voltage Control (AVC) system through systematic command mechanism analysis, substation response assessment, interaction impact calculation, and report integration. This reveals the inherent defects and interactive contradictions in the system's reactive power and voltage regulation, providing accurate and reliable diagnostic basis for subsequent reactive power output allocation optimization and comprehensive assessment, thus improving the scientific rigor and practicality of the entire assessment method. Specifically, step S41 analyzes the command issuance mechanism of the AVC master station based on sensitivity matrix analysis. By quantifying the master station's optimization strategy aimed at minimizing the grid-connected voltage deviation of each photovoltaic power station, it identifies the system's structural tendency of "emphasizing voltage regulation while neglecting reactive power," meaning the master station overemphasizes the stability of the hub bus voltage while neglecting the rational allocation of reactive power output for each photovoltaic power station. The resulting master station command mechanism analysis not only clarifies the deviation in command generation logic but also provides input guidance for subsequent substation response analysis, such as revealing the lack of inter-station coordination caused by command uniformity. Step S42, based on the master station command mechanism analysis results obtained in Step S41, combines the sensitivity matrix and the regulation characteristics of each photovoltaic power station to evaluate the independent response behavior of each photovoltaic power station's AVC substation. By analyzing the differences in regulation rates (such as the difference between fast and slow response cycles) and electrical coupling effects (such as the influence of mutual sensitivity coefficients), it discovers the asynchronous response phenomenon caused by the substations independently tracking commands. For example, fast-response power stations complete adjustments first while slow-response power stations lag behind, resulting in uneven distribution of reactive power output in the time dimension. The resulting substation response evaluation report quantifies the severity of this incoordination and provides a basis for understanding the interaction effects. The analysis lays the foundation; step S43, based on the substation response assessment report obtained in step S42, calculates the cumulative effect of deviations between fast and slow response substations through matrix operations. For example, it uses the relationship matrix between voltage changes and reactive power regulation to simulate the dynamic superposition in the actual regulation process, quantifying the circulating current and distribution deviations caused by response time differences. For instance, the reactive power regulation of fast-response substations will be superimposed on the lag effect of slow-response substations, forming temporary unevenness before steady state. The resulting interactive impact analysis data not only reveals the time dynamic defects of the AVC system, but also transforms the abstract mechanism into quantifiable indicators (such as cumulative deviation), providing data support for report integration. Step S44 integrates all the results obtained from S41 to S43 (including the analysis results of the master station command mechanism, the substation response evaluation report, and the interaction impact analysis data) to generate a structured control mechanism analysis report. This report systematically summarizes the shortcomings of the AVC mode, such as the one-sidedness of the master station strategy, the lack of coordination among substations, and the exacerbation of electrical coupling due to response differences. It also points out that these mechanism problems are the root causes of reactive power circulation and uneven distribution, providing clear directions for improvement in the reactive power output distribution calculation in step S5, such as incorporating the adjustment rate and coupling strength into the distribution coefficient to compensate for response differences.

[0048] As a further improvement to this application, step S5 calculates the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and the real-time operating data, and calculates the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report, including:

[0049] Step S51: Calculate the electrical distance of each photovoltaic power station based on the real-time operating data and the topology;

[0050] Step S52: Calculate the voltage regulation coupling strength of the chain-structure photovoltaic power station in real time based on the sensitivity matrix;

[0051] Step S53: Obtain the adjustment cycle information from the real-time operation data, and calculate the adjustment rate of each photovoltaic power station based on the adjustment cycle information, and obtain a rate standardization value based on a photovoltaic power station.

[0052] Step S54: Combining the electrical distance of each photovoltaic power station, the quantified value of the coupling strength of the chain-structure photovoltaic power station, the standardized value of the rate of each photovoltaic power station, and the control mechanism analysis report, the reactive power output allocation coefficient of each photovoltaic power station is calculated by weighting.

[0053] Beneficial effects:

[0054] Steps S51 to S54, as the core steps in the reactive power output allocation calculation of the reactive power and voltage operation evaluation method for clustered photovoltaic power plants, achieve accurate, coordinated, and adaptive calculation of reactive power output allocation for each photovoltaic power plant within the cluster through systematic parameter extraction, multi-factor quantification, dynamic weighting, and coefficient generation. This provides a scientific and reasonable allocation basis for subsequent comprehensive evaluation, significantly improving the accuracy and engineering applicability of reactive power and voltage regulation. Step S51 calculates the electrical distance of each photovoltaic power plant based on topology parameters and real-time operating data. By quantifying the relative reactance ratio of each photovoltaic power plant to the busbar, the complex network structure differences are transformed into comparable standardized parameters, revealing the structural influence of topology location on reactive power output allocation and providing basic weighting factors for allocation calculation. Step S52 calculates the voltage regulation coupling strength based on the sensitivity matrix. By dynamically capturing the degree of electrical interaction between nodes, especially for the precise quantification of coupling effects in chain structures, the allocation calculation can reflect the mutual influence relationship under real-time operating conditions, avoiding the limitations of static parameters. Step S53... By calculating the standardized adjustment rate using the adjustment cycle information, the response time differences of each photovoltaic power station are transformed into a unified and comparable speed index, solving the problem of asynchronous adjustment caused by equipment heterogeneity and introducing time-dimensional coordination into the allocation coefficient. Step S54 integrates all the aforementioned parameters (electrical distance, coupling strength, adjustment rate) and the control mechanism analysis report, and calculates the reactive power output allocation coefficient through weighted dynamic calculation. This process not only integrates multiple factors such as structural, electrical and temporal factors, but also adapts to the needs of different engineering scenarios through weight adjustment. The final allocation coefficient has both physical rationality and dynamic adaptability, directly supporting the index evaluation in step S6.

[0055] As a further improvement to this application, step S6 involves constructing a quantitative index system based on the anomaly identification results and the control operation mechanism analysis, and using the quantitative index system to evaluate the operating status of each photovoltaic power station to obtain reactive power and voltage operation evaluation results, including:

[0056] Step S61: Define the calculation formula and judgment logic of each indicator in the quantitative indicator system based on the anomaly identification results to obtain the indicator definition specification;

[0057] Step S62: Based on the indicator definition specifications, combined with the real-time operation data and the control operation mechanism, the specific indicator values ​​of each photovoltaic power station are obtained, and the indicator value dataset is integrated.

[0058] Step S63: Based on the index numerical dataset, the operating status of each photovoltaic power station is quantitatively analyzed through threshold comparison and assessment points to obtain the reactive power and voltage operation evaluation results.

[0059] Beneficial effects:

[0060] Steps S61 to S63, as the final evaluation stage of the reactive power and voltage operation evaluation method for cluster photovoltaic power plants, achieve an objective, quantitative, and operable comprehensive evaluation of the reactive power and voltage operation status through systematic index definition, numerical calculation, and comprehensive judgment. This transforms the results of all the aforementioned analysis steps into intuitive decision support information, thus completing a full closed loop from data acquisition to status diagnosis. Specifically, step S61, based on the anomaly identification results obtained in step S2 (including reactive power circulation characteristics and reactive power output imbalance characteristics), defines the calculation formulas and judgment logic for each index in the quantitative index system. For example, it clarifies the judgment threshold for reactive power circulation intensity based on Kirchhoff's laws and the balance of reactive power output distribution (e.g., using 1 as the ideal value). The resulting index definition specifications not only unify the evaluation standards but also ensure a strict correspondence between the indicators and physical phenomena, overcoming the ambiguity of relying on qualitative descriptions in traditional evaluations. Step S62, based on this specification and combining real-time operating data and the reactive power output distribution coefficient obtained in step S5, calculates the specific index values ​​for each photovoltaic power plant, such as... The process of calculating the reactive power output ratio and circulating current is integrated into a structured numerical dataset. This process transforms abstract features into comparable values, making the operating status measurable and traceable, and providing direct input for subsequent threshold analysis. Step S63 uses the numerical dataset as a basis to quantitatively analyze the operating status of each photovoltaic power station through preset threshold comparison and weighted evaluation methods. For example, when the circulating current intensity exceeds the threshold μ, it is determined that there is abnormal circulating current. When the distribution balance deviates from 1 and exceeds the tolerance, it is marked as unbalanced. The final reactive power and voltage operation evaluation results are output in the form of a structured report, covering the status level classification, the severity of the anomaly, and the improvement priority.

[0061] To achieve the above objectives, this application also provides the following technical solutions:

[0062] A reactive power and voltage operation assessment system for a cluster photovoltaic power station, wherein the reactive power and voltage operation assessment system is applied to the reactive power and voltage operation assessment method described above, and the reactive power and voltage operation assessment system includes:

[0063] The quantitative index calculation module is used to calculate the quantitative index dataset of the voltage operation status of each photovoltaic power station based on the real-time operation data of the cluster photovoltaic power station, according to the preset scheduling voltage curve and the average rated voltage of the system operation.

[0064] An anomaly identification definition module is used to define reactive power circulation characteristics and reactive power output imbalance characteristics based on the quantitative index dataset and Kirchhoff's laws to obtain anomaly identification results.

[0065] The sensitivity matrix calculation module is used to construct an equivalent circuit model based on the topology of a cluster photovoltaic power station, and obtain the reactive voltage sensitivity coefficient through power flow calculation to obtain the sensitivity matrix.

[0066] The regulation mechanism analysis module is used to analyze the automatic voltage control mechanism of each photovoltaic power station based on the sensitivity matrix and obtain a regulation mechanism analysis report;

[0067] The reactive power output allocation coefficient calculation module is used to calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and the real-time operation data, and to calculate the reactive power output allocation coefficient by weighting it with the sensitivity matrix and the regulation mechanism analysis report.

[0068] The reactive power and voltage operation assessment module is used to construct a quantitative index system based on the anomaly identification results and the control operation mechanism, and to conduct an operation status assessment of each photovoltaic power station through the quantitative index system to obtain the reactive power and voltage operation assessment results.

[0069] To achieve the above objectives, this application also provides the following technical solutions:

[0070] An electronic device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the reactive voltage operation evaluation method described above.

[0071] To achieve the above objectives, this application also provides the following technical solutions:

[0072] A computer-readable storage medium storing program instructions that, when executed by a processor, can implement the reactive power voltage operation evaluation method described above. Attached Figure Description

[0073] Figure 1 Background Art: A schematic diagram of a typical topology of a clustered photovoltaic power station, which is an embodiment of the reactive power and voltage operation evaluation method of this application.

[0074] Figure 2 Background Art for an embodiment of the reactive power and voltage operation assessment method of a cluster photovoltaic power station of this application: An example diagram of abnormal operation of a cluster photovoltaic power station (reactive power flow).

[0075] Figure 3 This is a flowchart illustrating the steps of an embodiment of a reactive power and voltage operation assessment method for a cluster photovoltaic power station according to this application.

[0076] Figure 4 This is an equivalent circuit diagram of a cluster photovoltaic power station according to an embodiment of the reactive power and voltage operation evaluation method of the present application.

[0077] Figure 5This is a current AVC mode diagram of a cluster photovoltaic power station, which is an embodiment of the reactive power and voltage operation evaluation method of the present application.

[0078] Figure 6 This is an equivalent circuit diagram (chain-connected part) of a cluster photovoltaic power station, which is an embodiment of the reactive power and voltage operation evaluation method of the cluster photovoltaic power station according to this application.

[0079] Figure 7 This application presents an embodiment of a method for evaluating the reactive power and voltage operation of a clustered photovoltaic power station, which measures the grid connection and bus voltage operation of the photovoltaic power station.

[0080] Figure 8 Figure 1 shows a typical time period of reactive power circulation problem in an embodiment of the reactive power and voltage operation evaluation method for a cluster photovoltaic power station according to this application;

[0081] Figure 9 Figure 4 shows a typical time period of reactive power circulation problem in an embodiment of the reactive power and voltage operation evaluation method of a cluster photovoltaic power station according to this application;

[0082] Figure 10 Figure 2 shows a typical time period of the reactive power output imbalance problem in one embodiment of the reactive power and voltage operation evaluation method of a cluster photovoltaic power station according to this application;

[0083] Figure 11 Figure 3 shows a typical time period of the reactive power output imbalance problem in one embodiment of the reactive power and voltage operation evaluation method of a cluster photovoltaic power station according to this application;

[0084] Figure 12 This is a sensitivity coefficient result diagram of the reactive power output of the 220kV busbar of the collection station and each photovoltaic power station in one embodiment of the reactive power and voltage operation evaluation method of the cluster photovoltaic power station of this application.

[0085] Figure 13 This is a comparison diagram of the effects of the original allocation calculation method and the improved calculation method in an embodiment of the reactive power and voltage operation evaluation method of a cluster photovoltaic power station according to this application. The four parts in the figure are time period 1, time period 2, time period 3 and time period 4 in sequence.

[0086] Figure 14 This is a functional module diagram of an embodiment of a reactive power and voltage operation evaluation system for a cluster photovoltaic power station according to this application;

[0087] Figure 15 This is a schematic diagram of the structure of an embodiment of the electronic device of this application;

[0088] Figure 16 This is a schematic diagram of the structure of one embodiment of the storage medium of this application. Detailed Implementation

[0089] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0090] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0091] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0092] like Figure 1 As shown, a reactive power and voltage operation assessment method for a cluster photovoltaic power station is provided. The reactive power and voltage operation assessment method includes:

[0093] Step S1: Based on the real-time operation data of the cluster photovoltaic power station, calculate the quantitative index dataset of the voltage operation status of each photovoltaic power station according to the preset scheduling voltage curve and the average rated voltage of the system.

[0094] Further, step S1, based on the real-time operation data of the cluster photovoltaic power station, calculates a quantitative index dataset of the voltage operation status of each photovoltaic power station according to the preset scheduling voltage curve and the average rated voltage of the system, specifically including the following steps:

[0095] Step S11: Real-time voltage time series data of the 220kV grid-connected bus of each photovoltaic power station is collected through the monitoring system of the cluster photovoltaic power station.

[0096] Preferably, the voltage time series data of the 220kV grid-connected bus of each photovoltaic power station is collected in real time through the monitoring system of the cluster photovoltaic power station (such as SCADA system), with the sampling period set to 1 minute to ensure the timeliness and integrity of the data.

[0097] Step S12: Obtain the preset upper and lower limits of the voltage curve and the average rated voltage of the system from the upper-level power dispatch terminal as preset benchmarks.

[0098] Preferably, the upper and lower limits of the preset voltage curve and the average rated voltage of the system are obtained from the power dispatching department. These data are stored in tabular form, including timestamps and corresponding voltage limits.

[0099] Step S13: Calculate the arithmetic mean voltage, voltage deviation rate, and voltage fluctuation rate of the 220kV grid-connected bus for each photovoltaic power station based on the voltage time series data, and integrate them into a quantitative index dataset.

[0100] Preferably, the maximum voltage value, minimum voltage value, and arithmetic mean can be used. Deviation rate A comprehensive quantitative analysis of the overall operation is conducted from the perspectives of volatility (VFR), etc.

[0101] (1).

[0102] (2).

[0103] (3).

[0104] In the formula, This represents the voltage value (kV) of photovoltaic power station i at time t. This represents the nominal voltage value (kV) of photovoltaic power station i, taken as 230kV.

[0105] Step S14: Compare the quantitative indicator dataset with the preset benchmark, and assign an over-limit classification label to values ​​that exceed the preset benchmark.

[0106] Preferably, the comparison between the quantitative indicator dataset and the preset benchmark can be achieved through the following code block:

[0107] def check_voltage_limit(U_actual, U_upper, U_lower):

[0108] if U_actual > U_upper:

[0109] return "exceeded the upper limit"

[0110] elif U_actual < U_lower:

[0111] return "beyond the lower limit"

[0112] else:

[0113] return "normal"

[0114] Step S15: Merge the out-of-limit classification labels into the quantitative index dataset.

[0115] Beneficial effects:

[0116] Steps S11 to S15 serve as the data foundation layer for the reactive power and voltage operation assessment method of clustered photovoltaic power plants. Through systematic data acquisition, processing, benchmark comparison, and tag integration, they achieve efficient, accurate, and standardized preprocessing of voltage operation status, thereby providing reliable, real-time, and structured input data for subsequent anomaly identification, mechanism analysis, and comprehensive assessment. Specifically, step S11 collects real-time voltage time-series data of the 220kV busbar of each photovoltaic power plant through a monitoring system, ensuring the comprehensiveness and timeliness of the data source and avoiding assessment bias due to missing or delayed data. Step S12 introduces the preset upper and lower limits of the voltage curve from the external power dispatch terminal as a benchmark, providing an objective reference standard for the data processing process and improving the comparability and engineering applicability of the assessment results. Step S13 calculates quantitative indicators such as the voltage arithmetic mean, deviation rate, and volatility rate based on the collected data, transforming the raw voltage values ​​into analyzable mathematical characteristics. This not only simplifies the complexity of subsequent processing but also enhances the accuracy of data expression. For example, the average value reflects the overall voltage level, and the deviation rate reflects the voltage level. The rate quantifies the deviation from the nominal value, and the volatility characterizes the dynamic change characteristics, thus providing a multi-dimensional quantitative basis for state assessment; step S14 compares the calculated quantitative indicators with the preset benchmark and assigns graded labels to the out-of-limit values ​​to achieve preliminary automatic identification of voltage anomalies. This not only reduces the risk of errors from manual intervention but also improves the efficiency and consistency of anomaly detection; step S15 merges the out-of-limit graded labels into the quantitative indicator dataset to form a structured comprehensive dataset containing anomaly markers. This ensures that the data retains the original quantitative information and incorporates preliminary diagnostic results, providing directly usable input for anomaly identification in step S2 and avoiding redundant calculations and data inconsistencies.

[0117] Step S2: Based on the quantitative index dataset, define the reactive power circulation characteristics and the unbalanced reactive power output distribution characteristics using Kirchhoff's laws to obtain the anomaly identification results.

[0118] Further, in step S2, based on the quantitative index dataset, the reactive power circulation characteristics and the reactive power output imbalance characteristics are defined using Kirchhoff's laws to obtain the anomaly identification results. This specifically includes the following steps:

[0119] Step S21: Extract the reactive power time series data of each photovoltaic power station from the quantitative index dataset.

[0120] Step S22: Based on the reactive power time series data, calculate the reactive power circulating and canceling each other within the cluster photovoltaic power station using Kirchhoff's laws to obtain the reactive power circulation characteristics.

[0121] Preferably, to better analyze the abnormal reactive power operation of a cluster photovoltaic power station, the relevant phenomena are first further analyzed and defined. Reactive power circulation problem in a cluster photovoltaic power station:

[0122] When reactive power flows within a photovoltaic power plant cluster, the reactive power circulating within the cluster between the transmission lines of each photovoltaic power plant, with the 220kV busbar as the transmission port, cancels each other out and does not provide effective support to the power grid. This reactive power circulation phenomenon depends on its intensity.

[0123] From the perspective of energy conservation, applying Kirchhoff's law: the sum of reactive power output of each photovoltaic power station is equal to the sum of the net reactive power transmitted to the grid through the 220kV busbar and the reactive power circulating within the photovoltaic power station cluster and canceling each other out.

[0124] (4).

[0125] In the formula: This refers to the net reactive power (Mvar) transmitted to the power grid through the 220kV busbar. This refers to the reactive power (Mvar) that circulates and cancels each other out on the transmission lines within a photovoltaic power plant cluster.

[0126] Further definition will Defined as reactive power circulation intensity, it refers to the reactive power circulating and canceling each other out within a photovoltaic power plant cluster. Ideally, all photovoltaic power plants should coordinate to provide reactive power support to the grid. In this case:

[0127] (5).

[0128] However, in actual operation, when the reactive power output of a photovoltaic power cluster is not coordinated (the directions are inconsistent), a situation arises where reactive power flows internally within the photovoltaic power cluster and cancels each other out, thus generating reactive power circulation within the photovoltaic power cluster, i.e.:

[0129] (6).

[0130] Step S23: Calculate the ratio of reactive power output of each photovoltaic power station to electrical distance based on the principle of electrical distance, and obtain the characteristics of unbalanced reactive power output distribution.

[0131] Preferably, in order to achieve an objective and comprehensive assessment of the operating status, this embodiment constructs a quantitative assessment index system that includes multiple dimensions. This assessment index evaluates the reactive power output allocation and its balance.

[0132] Indicator 1: Reactive power output ratio. Define q. i Let i be the ratio of the absolute value of the reactive power output of a certain power station i in the photovoltaic power station cluster to the sum of the absolute values ​​of the reactive power output of all power stations in the cluster.

[0133] (7).

[0134] In the formula, Let Mvar be the reactive power of photovoltaic power station i.

[0135] Indicator 2: Uneven distribution of reactive power output. Generally, when a cluster of photovoltaic power plants jointly supports the voltage of the busbar, the reactive power output of each photovoltaic power plant needs to be rationally distributed according to their electrical distance. Defined as the ratio of the reactance value of a certain power station i to the 220kV busbar of the collecting substation to the sum of the reactance values ​​of all power stations in the cluster to the 220kV busbar of the collecting substation, that is:

[0136] (8).

[0137] In the formula, This represents the reactance value from photovoltaic power station i to the 220kV busbar of the collection substation.

[0138] Indicator 3: Reactive power output balance of power station i. The reactive power output distribution balance is calculated and evaluated by combining the reactive power output ratio of each power station with the electrical distance. This determines the reactive power output distribution balance of power station i within the photovoltaic power cluster. It can be represented as:

[0139] (9).

[0140] From the above formula, we can see that Furthermore, the closer its value is to 1, the more balanced the distribution of reactive power output; the further its value is from 1, the more unbalanced the distribution of reactive power output.

[0141] Step S24: Integrate reactive power circulation characteristics and reactive power output imbalance characteristics to generate structured anomaly identification results.

[0142] Beneficial effects:

[0143] Steps S21 to S24, as the core diagnostic steps of the reactive power and voltage operation assessment method for clustered photovoltaic power plants, achieve precise definition, quantitative identification, and structured characterization of two key anomalies—reactive power circulation and unbalanced reactive power output distribution—through systematic data extraction, application of physical laws, feature quantification, and result integration. This provides a reliable and quantifiable basis for anomaly diagnosis, enabling subsequent topological mechanism analysis and comprehensive evaluation. Specifically, step S21 extracts reactive power time-series data from each photovoltaic power plant from the quantitative index dataset, ensuring that the anomaly identification process is based on actual operating data, avoiding subjective assumptions, and providing real and continuous data input for subsequent analysis. Step S22, based on this data, applies Kirchhoff's laws to calculate the reactive power circulating and canceling within the cluster, clearly defining the physical quantity of reactive power circulation intensity. This transforms the traditionally vaguely described "circulation" phenomenon into a precisely calculable index, overcoming the limitations of previous qualitative judgments and providing a rigorous mathematical basis for determining the existence and severity of circulation. Step S23 calculates the ratio of reactive power output to electrical distance for each photovoltaic power station based on the principle of electrical distance, defining the reactive power output distribution balance. This indicator not only quantifies the relative rationality of power output distribution but also reveals the impact of structural factors (such as reactance differences) on the distribution results, thus elevating the distribution imbalance problem from a superficial description to a mechanistic correlation level. Step S24 integrates circulating current and distribution imbalance characteristics to generate structured anomaly identification results, ensuring that the output includes both binary judgment indicators (such as the presence or absence of circulating current) and continuous quantitative values ​​(such as the magnitude of circulating current and the balance deviation), forming a multi-dimensional diagnostic report. This process, through the combination of physical laws and mathematical models, achieves a shift from "empirical judgment" to "data-driven" approaches, not only improving the objectivity and repeatability of anomaly identification but also providing clear input guidance for the topology analysis in Step S3. For example, areas with severe circulating current require in-depth analysis of electrical coupling relationships, and sites with distribution imbalances require in-depth investigation of sensitivity differences.

[0144] Step S3: Construct an equivalent circuit model based on the topology of the clustered photovoltaic power station, and obtain the reactive voltage sensitivity matrix and sensitivity coefficients through power flow calculation.

[0145] Further, step S3 involves constructing an equivalent circuit model based on the topology of the clustered photovoltaic power station, and obtaining the reactive power voltage sensitivity coefficient through power flow calculation to obtain the sensitivity matrix. This specifically includes the following steps:

[0146] Step S31: Extract the photovoltaic power station nodes, transformer nodes, and line parameters of the cluster photovoltaic power station, and form a topology based on the access method of the cluster photovoltaic power station.

[0147] Step S32: Each photovoltaic power station node is equivalent to either a PV node or a PQ node according to actual control needs, and each transformer node is equivalent to a series impedance. The line parameters are equivalent in π type to form an equivalent circuit.

[0148] Step S33: Solve the equivalent circuit using the power flow calculation equation to obtain the Jacobian matrix.

[0149] Step S34: Extract the reactive voltage partial derivative matrix from the Jacobian matrix and obtain the sensitivity matrix through inverse matrix operation.

[0150] Preferably, ① the mechanism analysis based on topology and power flow calculation:

[0151] like Figure 4 As shown, Figure 4 For the equivalent circuit of a cluster photovoltaic power station, the electrical characteristics of the sending-end network topology of the cluster photovoltaic power station are the basis for determining its reactive power and voltage operation characteristics. Based on Figure 1 After equivalent processing of the three-station topology, the following is obtained: Figure 4 The 6-node equivalent circuit diagram shown has power stations A through C connected to nodes 1 through 3 respectively. Node 1 is connected to node 4 (the 220kV busbar of the collecting station) radially via line LA. Node 3 is connected to node 2 via line LC, and then to node 4 via line LB, forming a chain-like series structure. After being stepped up to 500kV at the collecting station (node ​​5), the voltage is fed into the main grid (node ​​6) via four lines Ls.

[0152] In the power flow equivalent model, a photovoltaic power station can be equivalently represented as a PV or PQ node according to actual control needs. The lines adopt a π-type equivalent, and the transformers are considered to have leakage reactance equivalent to series impedance. From the system power flow equations, we can see that:

[0153] (10).

[0154] In the formula: , These represent the injected active power (MW) and reactive power (Mvar) at node i, respectively. , The voltages (kV) at nodes i and j are respectively. The element (S) is the admittance matrix element; the superscript * indicates taking the conjugate.

[0155] For high-voltage transmission lines, it can be approximated as much smaller And during normal operation:

[0156] , Substitute into the power flow equation:

[0157] (11).

[0158] For equation (11) at the running point Linearization of the vicinity yields the power flow equations in Jacobian matrix form:

[0159] (12).

[0160] In the formula: , These are the increments of active power and reactive power, respectively. , These are the increments of the voltage phase angle and amplitude, respectively; , , , These are the four submatrices of the Jacobian matrix.

[0161] In the analysis of reactive voltage, the main focus is on the submatrix. Its elements are Therefore, the submatrix The diagonal elements (self-sensitivity coefficient) and off-diagonal elements (mutual sensitivity coefficient) are as follows:

[0162] (13).

[0163] because much smaller , The sensitivity matrix S is defined as the Jacobian matrix. The inverse matrix, i.e.: (14).

[0164] In the formula: This indicates the degree to which the reactive power change of photovoltaic power station j affects the voltage amplitude of photovoltaic power station i.

[0165] Among them, B ij Y is the admittance matrix element ij The imaginary part of G is the susceptance; ij Y is the admittance matrix element ij The real part is the electrical conductance.

[0166] For the chain structure (nodes 2 and 3), its voltage regulation coupling can be analyzed using sensitivity coefficients. The reactive power regulation of node 3 not only affects its own voltage but also influences the voltage of node 2 through the line LC. The chain structure results in a significantly larger mutual sensitivity coefficient between nodes 2 and 3 compared to a radial structure. When the AVC requires both nodes to maintain the target voltage simultaneously, node 2 must compensate for both its own deviation and the coupling effect caused by the regulation of node 3, exacerbating the uneven distribution of reactive power output. Furthermore, the equivalent reactance of node 3 is greater than that of node 1, requiring it to undertake more reactive power regulation to maintain the same voltage level, constituting a structural cause of the uneven distribution.

[0167] Beneficial effects:

[0168] Steps S31 to S34, as key modeling steps in the reactive power and voltage operation assessment method for clustered photovoltaic power plants, systematically extract topology, construct equivalent circuits, solve power flow equations, and calculate sensitivity matrices. This transforms the actual electrical network into a precise mathematical model, providing a reliable and dynamic electrical foundation for the quantitative analysis of reactive power and voltage characteristics, thus supporting the accuracy, computability, and engineering applicability of the entire assessment process. Specifically, step S31 extracts the parameters of photovoltaic power plant nodes, transformer nodes, and lines, and forms a topology based on the cluster access method, ensuring consistency between the model and the real power grid and avoiding deviations caused by structural simplification, laying a physical foundation for subsequent analysis. Step S32, based on this, equivalences the complex network into a computable model, such as classifying photovoltaic power plant nodes as PV or PQ nodes to reflect their operating characteristics, equivalences transformers as series impedances, and adopts π-type equivalents for lines. This standardization not only simplifies computational complexity but also preserves key electrical relationships, enabling the model to capture both global power flow distribution and refine local interaction effects. Step S33 solves the equivalent circuits through power flow calculation equations. Obtaining the Jacobian matrix transforms the nonlinear electrical problem into a linearly solvable mathematical form. For example, the power balance equation is used to iteratively calculate node voltage and power distribution, thereby revealing the dynamic characteristics of the system at a specific operating point and providing intermediate data support for sensitivity analysis. Step S34 extracts the reactive voltage sensitivity coefficients from the Jacobian matrix and obtains the sensitivity matrix through inverse matrix operations, quantifying the direct impact of node reactive power changes on voltage amplitude. This matrix-based output makes the electrical coupling relationship measurable and comparable, not only improving the accuracy of the analysis but also providing direct input for the AVC mechanism analysis in step S4. For example, the sensitivity coefficients can be used to identify high coupling regions for focused evaluation of control interactions.

[0169] Step S4: Based on the sensitivity matrix and automatic voltage control theory, analyze the automatic voltage control mechanism of each photovoltaic power station to obtain a regulation mechanism analysis report.

[0170] Further, in step S4, the automatic voltage control mechanism of each photovoltaic power station is analyzed based on the sensitivity matrix to obtain a regulation mechanism analysis report, which specifically includes the following steps:

[0171] Step S41: Based on the sensitivity matrix and automatic voltage control theory, analyze the command issuance mechanism of the automatic voltage control master station to obtain the analysis results of the master station command mechanism.

[0172] Step S42: Based on the analysis results of the master station command mechanism, combined with the sensitivity matrix and the regulation characteristics of each photovoltaic power station, evaluate the independent response behavior of each photovoltaic power station's AVC substation and obtain the substation response evaluation report.

[0173] Step S43: Based on the substation response assessment report, the cumulative effect of deviation between fast and slow response substations is calculated by comparing the response characteristics of each photovoltaic power station and performing matrix operations on different adjustment rates to obtain interactive impact analysis data.

[0174] Step S44: Integrate the analysis results of the main station command mechanism, the substation response evaluation report, and the interaction impact analysis data to obtain the control mechanism analysis report.

[0175] Preferably, see Figure 5 , Figure 5 The current AVC (Automatic Valve Capacity) mode for cluster photovoltaic power plants is analyzed based on the following mechanism:

[0176] The AVC system control mode directly determines the reactive power distribution characteristics of the cluster. Existing systems (such as...) Figure 5 As shown, the lack of mechanisms at both the master station command calculation and the substation response execution levels has led to an uneven distribution of reactive power output.

[0177] Under the provincial voltage control architecture, the AVC master station issues voltage commands to each substation. Each substation measures the voltage at the grid connection point. Deviation from command value and self-sensitivity Calculate reactive power compensation :

[0178] (15).

[0179] At the main station level, current AVC generally adopts an optimization strategy aimed at minimizing voltage deviation, requiring the voltage at the grid connection point of each photovoltaic power station to be close to the median of the operating range. However, it does not fully consider the reasonable allocation of reactive power output, and there is a tendency to "emphasize voltage regulation and neglect reactive power".

[0180] At the substation execution level, each photovoltaic power station's AVC independently tracks voltage commands, lacking inter-station coordination. Due to differences in the regulation rates between the energy management platform and the voltage regulation equipment, the reactive power regulation cycle T of each photovoltaic power station... i The difference lies in the timing. When some power stations have short adjustment cycles (e.g., 10s) while others have longer cycles (e.g., 30s), the fast-response power station will prioritize completing the adjustment to meet voltage requirements. The actual reactive power adjustment process for each photovoltaic power station is as follows:

[0181] (16).

[0182] In the formula: Let Mvar be the reactive power regulation of power station i at time t. Mvar represents the reactive power sharing of power station i by other power stations that have completed adjustments at the same time.

[0183] Before the slow-response power station completes its regulation, the fast-response power station has already undertaken most of the tasks, ultimately resulting in an imbalance where the fast-response power station outputs too much power and the slow-response power station outputs too little power.

[0184] In summary, current AVC master station level focuses primarily on voltage control and assessment, neglecting the reactive power support of different power stations and the influence relationships between adjacent power stations. Meanwhile, at the AVC substation level, there are differences in AVC response (reactive power-voltage regulation) rates and electrical coupling relationships between power stations. These influencing factors combine to ultimately manifest as uneven reactive power output distribution, severe reactive power cancellation between stations, and even significant circulating current phenomena, among other anomalies.

[0185] Beneficial effects:

[0186] Steps S41 to S44, as the core mechanism analysis in the reactive power and voltage operation assessment method of cluster photovoltaic power plants, achieve in-depth analysis of the operating mechanism of the Automatic Voltage Control (AVC) system through systematic command mechanism analysis, substation response assessment, interaction impact calculation, and report integration. This reveals the inherent defects and interactive contradictions in the system's reactive power and voltage regulation, providing accurate and reliable diagnostic basis for subsequent reactive power output allocation optimization and comprehensive assessment, thus improving the scientific rigor and practicality of the entire assessment method. Specifically, step S41 analyzes the command issuance mechanism of the AVC master station based on sensitivity matrix analysis. By quantifying the master station's optimization strategy aimed at minimizing voltage deviation, it identifies the system's structural tendency of "emphasizing voltage regulation while neglecting reactive power," meaning the master station overemphasizes the stability of the hub bus voltage while neglecting the rational allocation of reactive power output for each photovoltaic power station. The resulting master station command mechanism analysis not only clarifies the deviation in command generation logic but also provides input guidance for subsequent substation response analysis, such as revealing the lack of inter-station coordination caused by command uniformity. Step S42, based on the master station command mechanism analysis results obtained in Step S41, evaluates the independent response behavior of each photovoltaic power station AVC substation by combining the sensitivity matrix. By analyzing the differences in adjustment rates (such as the difference in fast and slow response cycles) and electrical coupling effects (such as the influence of mutual sensitivity coefficients), it discovers the asynchronous response phenomenon caused by the substations independently tracking commands. For example, fast-response power stations complete adjustments first while slow-response power stations lag behind, resulting in uneven distribution of reactive power output in the time dimension. The obtained substation response evaluation report quantifies the severity of this incoordination, laying the foundation for interaction impact analysis. Step S43, based on the substation response evaluation report obtained in Step S42, calculates the cumulative effect of deviation between fast and slow-response power stations through matrix operations. For example, it uses the relationship matrix between voltage changes and reactive power regulation to simulate the dynamic superposition in the actual adjustment process, quantifying the circulating current and distribution deviation caused by response time differences. For example, the reactive power regulation of fast-response stations will be superimposed on the lag effect of slow-response stations, forming a temporary unevenness before steady state. The obtained interaction impact analysis data not only reveals the time dynamic defects of the AVC system, but also transforms the abstract mechanism into quantifiable indicators (such as cumulative deviation), providing data support for report integration. Step S44 integrates all the results obtained from S41 to S43 (including the analysis results of the master station command mechanism, the substation response evaluation report, and the interaction impact analysis data) to generate a structured control mechanism analysis report. This report systematically summarizes the shortcomings of the AVC mode, such as the one-sidedness of the master station strategy, the lack of coordination among substations, and the exacerbation of electrical coupling due to response differences. It also points out that these mechanism problems are the root causes of reactive power circulation and uneven distribution, providing clear directions for improvement in the reactive power output distribution calculation in step S5, such as incorporating the adjustment rate and coupling strength into the distribution coefficient to compensate for response differences.

[0187] Step S5: Calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and real-time operation data, and calculate the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report.

[0188] Further, step S5 involves calculating the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and real-time operating data. This is then weighted with the sensitivity matrix and the regulation mechanism analysis report to calculate the reactive power output allocation coefficient for each photovoltaic power station. Specifically, this includes the following steps:

[0189] Step S51: Calculate the electrical distance of each photovoltaic power station based on real-time operating data and topology.

[0190] Step S52: Calculate the voltage regulation coupling strength of the chain-structure photovoltaic power station in real time based on the sensitivity matrix.

[0191] Step S53: Obtain the regulation cycle information from the real-time operation data, and calculate the regulation rate of each photovoltaic power station based on the regulation cycle information, and obtain a rate standardization value based on a photovoltaic power station.

[0192] In step S54, the reactive power output allocation coefficient of each photovoltaic power station is calculated by combining the electrical distance of each photovoltaic power station, the quantified value of the coupling strength of the chain structure photovoltaic power station, the standardized value of the rate of each photovoltaic power station, and the regulation mechanism analysis report.

[0193] Preferably, see Figure 6 , Figure 6 This refers to the chain-connected part of the equivalent circuit of a cluster photovoltaic power station. To alleviate problems such as uneven reactive power distribution, excessive circulating current and conduction in the AVC response, this paper proposes a reactive power distribution method that comprehensively considers sensitivity, electrical distance, chain-structure voltage regulation coupling strength and regulation rate, and establishes evaluation indicators. Figure 6 For cluster photovoltaic power stations Figure 4 Based on this, consider the equivalent circuit after the chain-connected part.

[0194] The improved reactive power output balance distribution calculation mainly consists of the following four steps:

[0195] Step 1: Determine the original network topology ( Figure 4 Based on real-time operational data and parameters, continuous power flow calculations are performed within each scheduling cycle to obtain the real-time sensitivity coefficient. .

[0196] Step 2: Quantify the main influencing factors, which mainly include:

[0197] Quantization of voltage regulation coupling strength of the chain structure at time t :

[0198] (17).

[0199] In the formula: the numerator represents the direct impact of power station i on power station j, and the denominator represents the self-regulation capability of each photovoltaic power station.

[0200] The electrical distance for each power station within the region is calculated using equations (18) and (19). and adjustment rate :

[0201] (18).

[0202] (19).

[0203] In the formula: Let Ω be the reactance value from power station i to the busbar. Let be the reactive power regulation cycle of power station i, s; Let be the maximum regulation period of the cluster, s. In high-voltage power grids, reactance is the most critical parameter determining reactive voltage characteristics. The electrical distance defined in this paper can reasonably simplify the quantification of structural differences caused by network topology and reduce computational complexity.

[0204] Step 3: Distribute reactive power output from a cluster collaboration perspective. The allocation coefficient for photovoltaic power station i is:

[0205] (20).

[0206] In the formula: to The weights can be set according to the actual project requirements; n represents the number of photovoltaic power stations. For the topology after equivalent processing ( Figure 6 In the equation, the mutual sensitivity coefficient between each photovoltaic power station and the 220kV busbar (node ​​4) of the collection station at time t is given.

[0207] Step 4: Obtain the reactive power adjustment of each photovoltaic power station. :

[0208] (twenty one).

[0209] In the formula: Mvar is the total reactive power support requirement of the cluster at time t, which is calculated by equation (15) for the aggregation bus (node ​​4).

[0210] Beneficial effects:

[0211] Steps S51 to S54, as the core steps in the reactive power output allocation calculation of the reactive power and voltage operation evaluation method for clustered photovoltaic power plants, achieve accurate, coordinated, and adaptive calculation of reactive power output allocation for each photovoltaic power plant within the cluster through systematic parameter extraction, multi-factor quantification, dynamic weighting, and coefficient generation. This provides a scientific and reasonable allocation basis for subsequent comprehensive evaluation, significantly improving the accuracy and engineering applicability of reactive power and voltage regulation. Step S51 calculates the electrical distance of each photovoltaic power plant based on topology parameters and real-time operating data. By quantifying the relative reactance ratio of each photovoltaic power plant to the busbar, the complex network structure differences are transformed into comparable standardized parameters, revealing the structural influence of topology location on reactive power output allocation and providing basic weighting factors for allocation calculation. Step S52 calculates the voltage regulation coupling strength based on the sensitivity matrix. By dynamically capturing the degree of electrical interaction between nodes, especially for the precise quantification of coupling effects in chain structures, the allocation calculation can reflect the mutual influence relationship under real-time operating conditions, avoiding the limitations of static parameters. Step S53... By calculating the standardized adjustment rate using the adjustment cycle information, the response time differences of each photovoltaic power station are transformed into a unified and comparable speed index, solving the problem of asynchronous adjustment caused by equipment heterogeneity and introducing time-dimensional coordination into the allocation coefficient. Step S54 integrates all the aforementioned parameters (electrical distance, coupling strength, adjustment rate) and the control mechanism analysis report, and calculates the reactive power output allocation coefficient through weighted dynamic calculation. This process not only integrates multiple factors such as structural, electrical and temporal factors, but also adapts to the needs of different engineering scenarios through weight adjustment. The final allocation coefficient has both physical rationality and dynamic adaptability, directly supporting the index evaluation in step S6.

[0212] Step S6 involves constructing a quantitative index system based on the anomaly identification results and the aforementioned control operation mechanism analysis, and then using this quantitative index system to evaluate the operational status of each photovoltaic power station, thereby obtaining reactive power and voltage operation evaluation results. Further, Step S6, which involves constructing a quantitative index system based on the anomaly identification results and reactive power output allocation coefficient, and then using this quantitative index system to evaluate the operational status of each photovoltaic power station, thereby obtaining reactive power and voltage operation evaluation results, specifically includes the following steps:

[0213] Step S61: Based on the anomaly identification results, define the calculation formula and judgment logic of each indicator in the quantitative indicator system to obtain the indicator definition specification.

[0214] Step S62: Based on the indicator definition specifications, combined with real-time operating data and the aforementioned control and operation mechanism, calculate the specific indicator values ​​for each photovoltaic power station, and integrate them to obtain an indicator value dataset.

[0215] Step S63: Based on the numerical dataset of indicators, the operating status of each photovoltaic power station is quantitatively analyzed through threshold comparison and assessment points to obtain the reactive power and voltage operation evaluation results.

[0216] Preferably, the above text indicates the distribution of reactive power output and its degree of imbalance. Therefore:

[0217] ② Reactive current circulation assessment and its severity:

[0218] Indicator 4: Existence and determination of reactive power circulation. The magnitude and direction of reactive power flowing on the lines from each power station within the cluster to the 220kV busbar of the merging substation, and their mutual cancellation. At time t, line L... ij The direction of reactive power flow:

[0219] (twenty two).

[0220] In the formula: j is the 220kV busbar of the collection substation (end of the line), +1 indicates that reactive power flows to the collection busbar, and -1 indicates that it flows in the opposite direction.

[0221] The necessary and sufficient condition for the existence of reactive power circulation at time t is that the reactive power flows in at least two lines have opposite signs:

[0222] (twenty three).

[0223] Indicator 5: Magnitude of reactive power conduction. Assume all... Let X be the set of routes, and all If the set of lines is Y, then the reactive power transmitted to the next higher voltage level at time t is... for:

[0224] (twenty four).

[0225] In the formula: and Mvar represents the reactive power flow of line m and line n, respectively.

[0226] Indicator 6: Reactive power circulation volume. If reactive power circulation exists at time t, it represents the portion where reactive power injected and absorbed by the cluster cancels each other out.

[0227] (25).

[0228] In the formula: This is the reactive power flow of line j, Mvar. A threshold value can be set in the engineering process. ,when It is determined that reactive circulating current exists at time t.

[0229] Beneficial effects:

[0230] Steps S61 to S63, as the final evaluation stage of the reactive power and voltage operation evaluation method for cluster photovoltaic power plants, achieve an objective, quantitative, and operable comprehensive evaluation of the reactive power and voltage operation status through systematic index definition, numerical calculation, and comprehensive judgment. This transforms the results of all the aforementioned analysis steps into intuitive decision support information, thus completing a full closed loop from data acquisition to status diagnosis. Specifically, step S61, based on the anomaly identification results obtained in step S2 (including reactive power circulation characteristics and reactive power output imbalance characteristics), defines the calculation formulas and judgment logic for each index in the quantitative index system. For example, it clarifies the judgment threshold for reactive power circulation intensity based on Kirchhoff's laws and the balance of reactive power output distribution (e.g., using 1 as the ideal value). The resulting index definition specifications not only unify the evaluation standards but also ensure a strict correspondence between the indicators and physical phenomena, overcoming the ambiguity of relying on qualitative descriptions in traditional evaluations. Step S62, based on this specification and combining real-time operating data and the reactive power output distribution coefficient obtained in step S5, calculates the specific index values ​​for each photovoltaic power plant, such as... The process of calculating the reactive power output ratio and circulating current is integrated into a structured numerical dataset. This process transforms abstract features into comparable values, making the operating status measurable and traceable, and providing direct input for subsequent threshold analysis. Step S63 uses the numerical dataset as a basis to quantitatively analyze the operating status of each photovoltaic power station through preset threshold comparison and weighted evaluation methods. For example, when the circulating current intensity exceeds the threshold μ, it is determined that there is abnormal circulating current. When the distribution balance deviates from 1 and exceeds the tolerance, it is marked as unbalanced. The final reactive power and voltage operation evaluation results are output in the form of a structured report, covering the status level classification, the severity of the anomaly, and the improvement priority.

[0231] Beneficial effects:

[0232] Steps S1 to S6, through systematic data acquisition, anomaly identification, mechanism analysis, allocation calculation, and comprehensive evaluation, achieve a comprehensive, accurate, and dynamic assessment of the reactive power and voltage operation status of the cluster photovoltaic power station. Specifically, Step S1 calculates voltage quantification indicators based on real-time operating data, providing a reliable data foundation for the entire evaluation process and ensuring the timeliness and accuracy of subsequent analyses. Step S2 utilizes this data to define the characteristics of reactive power circulation and unbalanced reactive power output distribution using Kirchhoff's laws, achieving precise definition and quantification of abnormal problems. This overcomes the limitations of previous studies, which suffered from vague descriptions and lacked quantifiable judgment, providing a rigorous mathematical and physical foundation for problem analysis. Step S3 obtains a sensitivity matrix through constructing an equivalent circuit model and power flow calculations, revealing the impact of the cluster topology on reactive power and voltage characteristics and forming a framework for analyzing the operating mechanism from an electrical characteristic perspective. Step S4 analyzes the control mechanism of the AVC system based on the sensitivity matrix, deeply revealing the main station's control mechanism. The strategy prioritizes voltage control while neglecting reactive power distribution, differences in regulation rates caused by independent substation responses, and electrical coupling issues. This facilitates the transition from symptom description to root cause analysis, providing a clear direction for targeted improvements. Step S5 comprehensively considers multiple factors such as electrical distance, voltage regulation coupling strength, and regulation rate to dynamically calculate the reactive power distribution coefficient. This achieves coordinated allocation of reactive power resources within the cluster, improving the balance and rationality of reactive power output and reducing circulating current and unevenness caused by improper allocation. Step S6 constructs a quantitative indicator system based on anomaly identification results and distribution coefficients, covering multiple dimensions such as reactive power distribution balance, circulating current judgment and intensity. This enables an objective and comprehensive assessment of the operating status, providing intuitive and scientific decision support for operators.

[0233] In addition, this embodiment provides complete practical data:

[0234] This embodiment is based on Figure 1 Taking a photovoltaic power station cluster as an example, with its topology, parameters, and actual operating data during a typical period, we will analyze the problems and calculate and evaluate the indicators. The main relevant parameters are shown in Tables 1 to 3.

[0235] Table 1. Information on photovoltaic power plants.

[0236]

[0237] Table 2, Transmission Line Parameters.

[0238]

[0239] Table 3, Transformer Parameters.

[0240]

[0241] Taking the daytime period of July 11, 2025 as an example, this study analyzes and evaluates the operation of the photovoltaic power station cluster. During the actual operation of the AVC closed-loop control system, multiple instances of reactive power circulation and uneven reactive power output distribution were observed. Four relatively prominent actual operation scenarios were selected during the daytime period of July 11, 2025: period 1 (7:36 to 7:45), period 4 (19:31 to 19:40), period 2 (12:16 to 12:25), and period 3 (16:26 to 16:35). Based on the overall operation of the 220kV bus voltage at the grid connection points and collection stations of each photovoltaic power station, operational analyses were conducted on the problems occurring in each scenario, followed by evaluation based on a defined indicator system.

[0242] (1) Identification of reactive voltage operation scenarios of cluster photovoltaic power stations.

[0243] Figure 7 The table shows the operating status of the grid connection point voltage and the bus voltage of each photovoltaic power station after the implementation of automatic voltage control in a photovoltaic power station cluster. The daytime operating data is also presented in Table 4. As can be seen from the figure, the average voltage at the grid connection point of each photovoltaic power station remains between 228.5 and 229.0 kV, close to the rated level of 230 kV, indicating good overall operation.

[0244] Table 4 shows the overall voltage status of each photovoltaic power station's grid connection point and collection bus.

[0245]

[0246] (2) Identification of reactive power circulation and unbalanced distribution of reactive power output.

[0247] like Figure 8 and Figure 9 As shown, during time periods 1 (7:36 to 7:45) and 4 (19:31 to 19:40), the reactive power directions of lines LA and LB are opposite, indicating that there is reactive power interaction and cancellation between power stations A and BC, amounting to approximately 16 Mvar and 35 Mvar respectively. This suggests that the local reactive power balance within the cluster has not been well achieved.

[0248] like Figure 10 and Figure 11 As shown, during periods 2 (12:16-12:25) and 3 (16:26-16:35), power stations A and B bear the main reactive power output, while the output of power station C remains low. This results in significant fluctuations in reactive power on lines LA and LB. The reactive power transmission at the collection station remains at a large negative value, indicating that reactive power flows between the power stations and the collection station, and resources are not fully utilized.

[0249] (3) Analysis and verification experiment of reactive voltage operation mechanism based on topology and AVC mode.

[0250] To further verify and analyze the relevant factors affecting the reactive power-voltage operation of the photovoltaic power plant cluster, experiments were designed and simulation verification analyses were conducted from the following aspects:

[0251] Experiment 1: Reactive power-voltage sensitivity (high voltage bus nodes of various photovoltaic power stations / 220kV bus nodes of collection stations).

[0252] Based on actual daytime operating data (sampling period 1 min) for a certain period, this study calculated the collection bus voltage of the collection station and the reactive power sensitivity of each station at 1440 time sections, such as... Figure 12 As shown in the figure, the average values ​​are shown in Table 5.

[0253] Table 5 shows the average sensitivity coefficients among the key nodes.

[0254]

[0255] The results show that the average reactive power-voltage sensitivity coefficients of the 220kV busbar at the collection station to photovoltaic power stations A, B, and C exhibit significant differences: the absolute value of the sensitivity coefficient of power station B is the largest, while the absolute value of the sensitivity coefficient of power station C is the smallest, which is closely related to their positions in the topology.

[0256] Experiment 2: The impact of electrical distance on the reactive power output of photovoltaic power plants and reactive power flow in power lines.

[0257] Using time-section data at 12:00 on a certain day, with a grid-connected voltage of 230kV for each photovoltaic power station, the calculation results of the control effect after considering the electrical distance are shown in Table 6.

[0258] Table 6. Statistical table of simulation results and evaluation indicators for Experiment 2.

[0259]

[0260] Analysis shows that:

[0261] ① Stability of 220kV bus voltage at the substation: Regardless of whether electrical distance is considered, the 220kV bus voltage at the substation remains within the safe operating range. Without considering electrical distance, the substation bus voltage is 228.08kV, with a deviation of -0.83%; after considering electrical distance, the voltage drops to 227.73kV, with a deviation of -1.14%, both meeting the requirements for safe and stable operation of the power grid.

[0262] ② Reactive power flow and circulation: After considering electrical distance, the reactive power flow pattern of the system changed. The sum of the absolute values ​​of reactive power flow on the lines decreased from 60.84 Mvar when not considered to 51.87 Mvar, and the overall reactive power flow on the lines decreased by 14.7%; the reactive power circulation flow increased from 8.37 Mvar to 17.96 Mvar.

[0263] ③ Reactive power output distribution balance: The balance of reactive power output distribution has been significantly improved, which is the most prominent effect of considering electrical distance control. Without considering electrical distance, the reactive power output ratio of the three stations is 13.75%:85.28%:0.97%, presenting an extremely unbalanced distribution pattern—station B undertakes 85.28% of the reactive power regulation task, while station C participates in almost no regulation (only 0.97%). After considering electrical distance, the distribution ratio is optimized to 65.37%:12.72%:21.9%. Although station A undertakes a higher proportion, the overall distribution pattern is more reasonable. This indicates that the coordination strategy based on electrical distance can effectively improve the balance of reactive power resource allocation.

[0264] ④ Reactive power transmission: Without considering electrical distance, 44.11 Mvar of reactive power is transmitted to the system; after considering electrical distance, the reactive power transmission is reduced to 7.68 Mvar, a decrease of 82.6%. The reduction in transmission can effectively achieve local balance of reactive power, reduce network losses and voltage drops caused by long-distance reactive power transmission, and help improve the operating efficiency and stability of the sending-end system.

[0265] In summary, the results of Experiment 2 fully verify that electrical distance is one of the important factors affecting the reactive power output distribution and reactive power flow of a cluster photovoltaic power station.

[0266] Experiment 3: The impact of different response times on reactive power output and reactive power flow of photovoltaic power plants.

[0267] Based on the topology of the cluster photovoltaic power station described above, each photovoltaic power station has an active power of 60% of its rated capacity and an initial voltage of 228.5kV. After 40 seconds, the voltage needs to be adjusted to 229kV (AVC dead zone: 0.3kV). Seven simulation scenarios with different response time combinations are set up as shown in Table 7.

[0268] Table 7. Response time settings for Experiment 3 (unit: s).

[0269]

[0270] (Note: In scenarios 3-7, the electrical distance from each substation to the 220kV busbar of the collection station is reduced to half of its original value.)

[0271] The calculation results for each scenario obtained through power flow calculation are shown in Table 8.

[0272] Table 8 shows the simulation results and statistical analysis indicators of Experiment 3.

[0273]

[0274] Analysis shows that:

[0275] ① Voltage Stability: From the voltage control performance perspective, all test scenarios successfully adjusted the bus voltage from the initial 228.5kV to the target range, stabilizing it between 228.91kV and 229.493kV, with a voltage deviation range of 0.22% to 0.47%, within the allowable deviation range. The voltage control accuracy in scenarios (3-1 to 3-3) showed a decreasing trend—0.22% deviation at 10 seconds, 0.36% at 20 seconds, and 0.47% at 40 seconds, indicating that rapid response helps improve voltage control accuracy. Voltage deviations in other scenarios were generally concentrated around 0.46%, slightly higher than synchronous rapid response, but still within a completely acceptable range.

[0276] ② Reactive Power Flow and Circulation: The impact of response time on reactive power circulation exhibits both regularity and site-specific differences. In scenarios 3-1 to 3-3, as the response time increases from 10 seconds to 40 seconds, the reactive power circulation shows a monotonically increasing trend. This indicates that the longer the response time, the more intermediate states each photovoltaic power station experiences during the process of reaching steady state, the greater the accumulated adjustment deviation, and the more severe the inter-station reactive power circulation ultimately becomes. Scenarios 3-4 to 3-6 reveal differences in site characteristics. The response time of power station B has the most significant impact on circulation—scenario 3-5 (power station B 40 seconds, others 10 seconds) generated the largest circulating current of 17.7 Mvar.

[0277] ③ Reactive power output distribution balance: The balance of reactive power output distribution exhibits complex variations under different response time configurations. The imbalance in scenarios 3-1 to 3-3 remains at a moderate level of 0.42-0.54. The imbalance in other scenarios varies considerably, ranging from an optimal 0.21 (3-4) to a worst 0.93 (3-6). Among these, the slow-response power station C causes the most severe imbalance (imbalance 0.93 in 3-6). Although the response delay of power station B generates a large amount of circulating current, the imbalance remains at a relatively good level of 0.30 (3-5). This is because the existence of circulating current actually allows each photovoltaic power station to participate in regulation, avoiding any single station dominating. Power station A, with a response delay (3-4), achieves good balance (0.21), with the three stations sharing the load at 47.45%, 12.76%, and 39.80%, respectively.

[0278] ④ Reactive power transmission: In scenarios 3-1 to 3-3, the transmission volume decreases sharply with the extension of response time—8.5 Mvar at 10 seconds, 3.9 Mvar at 20 seconds, and only 0.4 Mvar at 40 seconds, a decrease of 95.3%. The transmission volume in different response time scenarios (3-4 to 3-6) generally remains at a low level of 1-7 Mvar. It is worth noting that the transmission volume in scenario 3-6 (slow response of power plant C) is 6.9 Mvar, which is relatively high.

[0279] ⑤ After the electrical distance is halved from scenario 3-4 to scenario 3-7, the special status of the series-connected node power station B is amplified, and it undertakes the main task of reactive power balancing. Reducing the electrical distance exacerbates the imbalance in reactive power output distribution and the amount of reactive power transmitted to other areas.

[0280] In summary, the results of Experiment 3 demonstrate the crucial role of response time in reactive power regulation of clustered photovoltaic power plants. Power plant B, due to its series connection location and high sensitivity, has the greatest impact on the circulating current index; power plant C, due to its terminal location, has the greatest impact on the distribution balance; power plant A, as an independent grid station, has relatively independent response characteristics, which actually contributes to system balance.

[0281] (4) Calculation method for reactive power output allocation of cluster photovoltaic power station.

[0282] The results calculated according to the method described in this embodiment are compared with actual operating data. Figure 13 As shown.

[0283] (5) Indicator calculation and evaluation analysis.

[0284] Table 9 shows the voltage values ​​of the 220kV busbars at the grid connection point and collection station of the photovoltaic power station during typical periods. The values ​​in the table indicate that the voltage operation is good, within the upper and lower limits of the voltage curve given by the power dispatching department.

[0285] Table 9 shows the operating status of the 220kV bus voltage at the grid connection point and collection station of the photovoltaic power station during typical periods.

[0286]

[0287] Table 10 shows the calculation of reactive power conduction, reactive power circulation determination, and reactive power circulation volume evaluation indicators for cluster photovoltaic power plants. Table 11 shows the calculation of evaluation indicators for the degree of reactive power output distribution balance of cluster photovoltaic power plants.

[0288] Table 10. Calculation of reactive power conduction and circulation indices for cluster photovoltaic power plants.

[0289]

[0290] Table 11. Calculation of reactive power output distribution balance index of cluster photovoltaic power station.

[0291]

[0292] Analysis reveals that for periods 1 and 4, significant reactive power circulation exists between the photovoltaic power stations. The opposite reactive power directions of LA and LB indicate an inter-station reactive power flow through the 220kV busbar of the collection station, representing a reactive power circulation problem of 13.490 Mvar. Simultaneously, in period 4, reactive power is absorbed from the external grid at the 220kV busbar of the collection station, and a reactive power circulation of approximately 12.930 Mvar exists between LB and LA. This demonstrates that effective local reactive power balance cannot be achieved within the photovoltaic power cluster. For periods 2 and 3, the reactive power output of the photovoltaic power stations exhibits an uneven distribution. Power stations B and A provide substantial reactive power output, with power station A's output fluctuating dramatically and even alternating between positive and negative values. However, power station C's reactive power output remains consistently low, below 1 Mvar. The entire photovoltaic power cluster has not achieved a balanced output distribution. At the same time, this also led to large fluctuations and large values ​​of reactive power on LA and LB. The reactive power transmitted by the collection station was consistently a large negative value, indicating that reactive power flowed between the photovoltaic power station and the collection station, and that it was not able to effectively mobilize all resources to achieve local reactive power balance.

[0293] In summary, the analysis and evaluation method proposed in this embodiment can effectively identify and quantify abnormal reactive power and voltage operation problems in cluster photovoltaic power plants. Through implementation analysis, significant reactive power circulation problems were observed in time periods 1 and 4, with circulation volumes exceeding 10 Mvar. In time periods 2 and 3, the imbalance in reactive power output distribution was more pronounced, with power plants A and B bearing the majority of the reactive power output, while power plant C had insufficient output, resulting in a reactive power output distribution balance degree far exceeding 1 (the standard value is 1). The constructed comprehensive evaluation index system, including reactive power circulation determination and reactive power output distribution balance indicators, provides operators with intuitive and quantitative decision-making basis, verifying the practicality and effectiveness of the invention and realizing a shift from experience-driven to data-driven decision-making.

[0294] Based on the above method embodiments, this application also provides corresponding functional module embodiments, namely, a reactive power and voltage operation assessment system for clustered photovoltaic power plants, see [link to relevant documentation]. Figure 14 In this embodiment, the reactive voltage operation assessment system is applied to the reactive voltage operation assessment method as described in the above embodiments.

[0295] Specifically, the reactive power and voltage operation evaluation system includes a quantitative index calculation module 1, an anomaly identification definition module 2, a sensitivity matrix calculation module 3, a control mechanism analysis module 4, a reactive power output distribution coefficient calculation module 5, and a reactive power and voltage operation evaluation module 6, which are connected by electrical or signal connections in sequence.

[0296] The system comprises the following modules: Quantitative Index Calculation Module 1 calculates a dataset of quantitative indicators for the voltage operation status of each photovoltaic power station based on real-time operating data from the cluster photovoltaic power station, according to a preset scheduling voltage curve and the average rated voltage of the system; Anomaly Identification and Definition Module 2 defines reactive power circulation characteristics and unbalanced reactive power output distribution characteristics based on the quantitative indicator dataset using Kirchhoff's laws, and obtains anomaly identification results; Sensitivity Matrix Calculation Module 3 constructs an equivalent circuit model based on the topology of the cluster photovoltaic power station, and obtains the reactive power voltage sensitivity coefficient through power flow calculation, thus obtaining a sensitivity matrix; Regulation Mechanism Analysis Module 4 analyzes the automatic voltage control mechanism of each photovoltaic power station based on the sensitivity matrix, and obtains a regulation mechanism analysis report; Reactive Power Output Distribution Coefficient Calculation Module 5 calculates the electrical distance, voltage regulation coupling strength, and regulation rate of each photovoltaic power station based on the topology and real-time operating data, and calculates the reactive power output distribution coefficient by weighting it with the sensitivity matrix and the regulation mechanism analysis report; Reactive Power Voltage Operation Evaluation Module 6 constructs a quantitative indicator system based on the anomaly identification results and the reactive power output distribution coefficient, and evaluates the operation status of each photovoltaic power station through the quantitative indicator system, thus obtaining reactive power voltage operation evaluation results.

[0297] Furthermore, the quantification index calculation module 1 specifically includes a first quantification index calculation unit, a second quantification index calculation unit, a third quantification index calculation unit, a fourth quantification index calculation unit, and a fifth quantification index calculation unit that are electrically or signal-connected in sequence; wherein, the fifth quantification index calculation unit is electrically or signal-connected to the anomaly identification definition module 2.

[0298] The system comprises five main components: a first quantitative indicator calculation unit for collecting real-time voltage time-series data of the 220kV grid-connected bus of each photovoltaic power station through the monitoring system of the cluster photovoltaic power station; a second quantitative indicator calculation unit for obtaining the preset upper and lower limits of the voltage curve and the average rated voltage of the system from the external power dispatch terminal as preset benchmarks; a third quantitative indicator calculation unit for calculating the arithmetic mean of the voltage, voltage deviation rate, and voltage fluctuation rate of the 220kV grid-connected bus of each power station based on the voltage time-series data, and integrating them into a quantitative indicator dataset; a fourth quantitative indicator calculation unit for comparing the quantitative indicator dataset with the preset benchmarks and assigning an over-limit classification label to values ​​exceeding the preset benchmarks; and a fifth quantitative indicator calculation unit for merging the over-limit classification labels into the quantitative indicator dataset.

[0299] Furthermore, the anomaly identification definition module 2 specifically includes a first anomaly identification definition unit, a second anomaly identification definition unit, a third anomaly identification definition unit, and a fourth anomaly identification definition unit that are electrically or signal-connected in sequence; wherein, the first anomaly identification definition unit is electrically or signal-connected to the fifth quantification index calculation unit, and the fourth anomaly identification definition unit is electrically or signal-connected to the sensitivity matrix calculation module 3.

[0300] The system comprises four sub-units: the first sub-unit extracts reactive power time series data for each photovoltaic power station from the quantitative index dataset; the second sub-unit calculates the reactive power circulating and canceling within the photovoltaic power station cluster based on Kirchhoff's laws, using the reactive power time series data, to obtain reactive power circulation characteristics; the third sub-unit calculates the ratio of reactive power output to electrical distance for each photovoltaic power station according to the electrical distance principle, to obtain reactive power output imbalance characteristics; and the fourth sub-unit integrates reactive power circulation characteristics and reactive power output imbalance characteristics to generate structured sub-unit identification results.

[0301] Furthermore, the sensitivity matrix calculation module 3 specifically includes a first sensitivity matrix calculation unit, a second sensitivity matrix calculation unit, a third sensitivity matrix calculation unit, and a fourth sensitivity matrix calculation unit that are electrically or signal-connected in sequence; wherein, the first sensitivity matrix calculation unit is electrically or signal-connected to the fourth anomaly identification definition unit, and the fourth sensitivity matrix calculation unit is electrically or signal-connected to the regulation mechanism analysis module 4.

[0302] The first sensitivity matrix calculation unit is used to extract the photovoltaic power station nodes, transformer nodes, and line parameters of the cluster photovoltaic power station and form a topology based on the access method of the cluster photovoltaic power station. The second sensitivity matrix calculation unit is used to convert each photovoltaic power station node into either a PV node or a PQ node according to the actual control requirements, and to convert each transformer node into a series impedance. The line parameters are equivalent in π type to form an equivalent circuit. The third sensitivity matrix calculation unit is used to solve the equivalent circuit through the power flow calculation equation to obtain the Jacobian matrix. The fourth sensitivity matrix calculation unit is used to extract the reactive voltage partial derivative matrix from the Jacobian matrix and obtain the sensitivity matrix through inverse matrix operation.

[0303] Furthermore, the regulation mechanism analysis module 4 specifically includes a first regulation mechanism analysis unit, a second regulation mechanism analysis unit, a third regulation mechanism analysis unit, and a fourth regulation mechanism analysis unit that are electrically or signal-connected in sequence; wherein, the first regulation mechanism analysis unit is electrically or signal-connected to the fourth sensitivity matrix calculation unit, and the fourth regulation mechanism analysis unit is electrically or signal-connected to the reactive power output distribution coefficient calculation module 5.

[0304] The system comprises four main components: a first regulation mechanism analysis unit, a second regulation mechanism analysis unit, and a third regulation mechanism analysis unit. The first second unit analyzes the command issuance mechanism of the automatic voltage control master station based on the sensitivity matrix and automatic voltage control theory, obtaining the master station command mechanism analysis results. The second third unit analyzes the independent response behavior of each photovoltaic power station's AVC substation based on the master station command mechanism analysis results, combined with the sensitivity matrix and the regulation characteristics of each photovoltaic power station, obtaining a substation response evaluation report. The third fourth unit analyzes the cumulative effect of deviations of power stations with different regulation rates by comparing the response characteristics of each photovoltaic power station based on the substation response evaluation report, obtaining interaction impact analysis data. The fourth fifth unit integrates the master station command mechanism analysis results, the substation response evaluation report, and the interaction impact analysis data to obtain a regulation mechanism analysis report.

[0305] Furthermore, the reactive power output allocation coefficient calculation module 5 specifically includes a first reactive power output allocation coefficient calculation unit, a second reactive power output allocation coefficient calculation unit, a third reactive power output allocation coefficient calculation unit, and a fourth reactive power output allocation coefficient calculation unit that are electrically or signal-connected in sequence; wherein, the first reactive power output allocation coefficient calculation unit is electrically or signal-connected to the fourth control mechanism analysis unit, and the fourth reactive power output allocation coefficient calculation unit is electrically or signal-connected to the reactive power voltage operation evaluation module 6.

[0306] The system comprises four main components: a first reactive power output allocation coefficient calculation unit, a second reactive power output allocation coefficient calculation unit, and a fourth reactive power output allocation coefficient calculation unit. The first reactive power output allocation coefficient calculation unit is used to calculate the voltage regulation coupling strength of the chain-structure photovoltaic power station based on real-time operating data and topology. The second reactive power output allocation coefficient calculation unit is used to calculate the regulation rate of each photovoltaic power station based on the regulation cycle information, and obtain a rate standardization value for each photovoltaic power station. The third reactive power output allocation coefficient calculation unit is used to combine the electrical distance of each photovoltaic power station, the quantified value of the coupling strength of the chain-structure photovoltaic power station, the rate standardization value of each photovoltaic power station, and the regulation mechanism analysis report to calculate the reactive power output allocation coefficient of each photovoltaic power station using a weighted average.

[0307] Furthermore, the reactive power and voltage operation evaluation module 6 specifically includes a first reactive power and voltage operation evaluation unit, a second reactive power and voltage operation evaluation unit, and a third reactive power and voltage operation evaluation unit that are electrically or signal-connected in sequence; wherein, the first reactive power and voltage operation evaluation unit is electrically or signal-connected to the fourth reactive power output distribution coefficient calculation unit.

[0308] The first reactive power and voltage operation evaluation unit is used to define the calculation formula and judgment logic of each indicator in the quantitative indicator system based on the anomaly identification results, and obtain the indicator definition specification; the second reactive power and voltage operation evaluation unit is used to obtain the specific indicator value of each photovoltaic power station based on the indicator definition specification, combined with real-time operation data and the control operation mechanism, and integrate them to obtain the indicator numerical dataset; the third reactive power and voltage operation evaluation unit is used to perform quantitative analysis on the operation status of each photovoltaic power station based on the indicator numerical dataset through threshold comparison and assessment points, and obtain the reactive power and voltage operation evaluation result.

[0309] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. For additional content such as preferred options, extensions, limitations, examples, principle explanations, and beneficial effects, please refer to the above embodiments. This embodiment will not repeat them here.

[0310] Figure 15 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Figure 15 As shown, the electronic device 7 includes a processor 71 and a memory 72 coupled to the processor 71.

[0311] The memory 72 stores program instructions for implementing the federated learning-based collaborative energy-saving method for government data clusters in any of the above embodiments.

[0312] The processor 71 is used to execute program instructions stored in the memory 72 for collaborative energy saving of government data clusters based on federated learning.

[0313] The processor 71 can also be referred to as a CPU (Central Processing Unit). The processor 71 may be an integrated circuit chip with signal processing capabilities. The processor 71 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0314] Furthermore, Figure 16 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. See also: Figure 16In this embodiment of the application, the storage medium 8 stores program instructions 81 capable of implementing all the above methods. These program instructions 81 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0315] In the several embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, signal, or other forms.

[0316] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for evaluating the reactive power and voltage operation of a cluster photovoltaic power station, characterized in that, The reactive power and voltage operation assessment method includes: Step S1: Based on the real-time operation data of the cluster photovoltaic power station, calculate the quantitative index dataset of voltage operation status of each photovoltaic power station according to the preset scheduling voltage curve and the average rated voltage of the system. Step S2: Based on the quantitative index dataset, define the reactive power circulation characteristics and the reactive power output imbalance characteristics through Kirchhoff's laws to obtain the anomaly identification results; Step S3: Construct an equivalent circuit model based on the topology of the cluster photovoltaic power station, and obtain the reactive voltage sensitivity matrix through power flow calculation, and further obtain the corresponding sensitivity coefficients. Step S4: Based on the sensitivity matrix and the reactive voltage automatic control theory, analyze the automatic voltage control mechanism of each photovoltaic power station to obtain a regulation mechanism analysis report; Step S5: Calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and the real-time operating data; and calculate the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report. Step S6: Based on the anomaly identification results and the control operation mechanism analysis, construct a quantitative index system, and use the quantitative index system to evaluate the operation status of each photovoltaic power station to obtain the reactive power and voltage operation evaluation results.

2. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S1: Based on the real-time operation data of the cluster photovoltaic power station, calculate the quantitative index dataset of the voltage operation status of each photovoltaic power station according to the preset dispatch voltage curve and the average rated voltage of the system, including: Step S11: Real-time voltage time series data of the 220kV grid-connected bus of each photovoltaic power station are collected through the monitoring system of the cluster photovoltaic power station; Step S12: Obtain the preset voltage curve upper and lower limits and the system operating average rated voltage from the superior power dispatching master station as preset benchmarks; Step S13: Based on the voltage time series data, calculate the arithmetic mean voltage, voltage deviation rate, and voltage fluctuation rate of the 220kV grid-connected bus of each photovoltaic power station, and integrate them into the quantitative index dataset. Step S14: Compare the quantitative indicator dataset with the preset benchmark, and assign an over-limit classification label to values ​​that exceed the preset benchmark; Step S15: Merge the over-limit classification labels into the quantitative index dataset.

3. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S2: Based on the quantified index dataset, define the reactive power circulation characteristics and the reactive power output imbalance characteristics using Kirchhoff's laws to obtain anomaly identification results, including: Step S21: Extract the reactive power time series data of each photovoltaic power station from the quantitative index dataset; Step S22: Based on the reactive power time series data, calculate the reactive power circulating and canceling each other within the cluster photovoltaic power station using Kirchhoff's laws to obtain the reactive power circulation characteristics; Step S23: Calculate the ratio of reactive power output ratio to electrical distance for each photovoltaic power station based on the principle of electrical distance, and obtain the characteristics of the unbalanced distribution of reactive power output. Step S24: Integrate the reactive power circulation characteristics and the reactive power output imbalance characteristics to generate a structured anomaly identification result.

4. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S3: Construct an equivalent circuit model based on the topology of the clustered photovoltaic power station, and obtain the reactive power-voltage sensitivity matrix through power flow calculation, further obtaining the corresponding sensitivity coefficients, including: Step S31: Extract the photovoltaic power station nodes, transformer nodes, and line parameters of the cluster photovoltaic power station, and form the topology based on the access method of the cluster photovoltaic power station; Step S32: Each photovoltaic power station node is equivalent to either a PV node or a PQ node according to actual control needs, and each transformer node is equivalent to a series impedance. The line parameters are equivalent in π type to form an equivalent circuit. Step S33: Solve the equivalent circuit using the power flow calculation equation to obtain the Jacobian matrix; Step S34: Extract the reactive voltage partial derivative matrix from the Jacobian matrix, and obtain the sensitivity matrix through inverse matrix operation.

5. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S4: Based on the sensitivity matrix and the reactive power voltage automatic control theory, analyze the automatic voltage control mechanism of each photovoltaic power station to obtain a regulation mechanism analysis report, including: Step S41: Based on the sensitivity matrix and the automatic voltage control theory, analyze the instruction issuance mechanism of the automatic voltage control master station to obtain the analysis results of the master station instruction mechanism. Step S42: Based on the analysis results of the master station command mechanism, combined with the sensitivity matrix and the regulation characteristics of each photovoltaic power station, evaluate the independent response behavior of the AVC substation of each photovoltaic power station to obtain a substation response evaluation report; Step S43: Based on the substation response evaluation report, analyze the cumulative effect of deviations of power stations with different regulation rates by comparing the response characteristics of each photovoltaic power station, and obtain interactive influence analysis data; Step S44: Integrate the analysis results of the main station command mechanism, the substation response evaluation report, and the interaction impact analysis data to obtain the regulation mechanism analysis report.

6. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S5: Based on the topology and real-time operating data, calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station, and calculate the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report, including: Step S51: Calculate the electrical distance of each photovoltaic power station based on the real-time operating data and the topology; Step S52: Calculate the voltage regulation coupling strength of the chain-structure photovoltaic power station in real time based on the sensitivity matrix; Step S53: Obtain the adjustment cycle information from the real-time operation data, and calculate the adjustment rate of each photovoltaic power station based on the adjustment cycle information, and obtain a rate standardization value based on a photovoltaic power station. Step S54: Combining the electrical distance of each photovoltaic power station, the quantified value of the coupling strength of the chain-structure photovoltaic power station, the standardized value of the rate of each photovoltaic power station, and the control mechanism analysis report, the reactive power output allocation coefficient of each photovoltaic power station is calculated by weighting.

7. The reactive power and voltage operation evaluation method according to claim 1, characterized in that, Step S6: Based on the anomaly identification results and the control operation mechanism analysis, a quantitative index system is constructed, and the operation status of each photovoltaic power station is evaluated through the quantitative index system to obtain reactive power and voltage operation evaluation results, including: Step S61: Define the calculation formula and judgment logic of each indicator in the quantitative indicator system based on the anomaly identification results to obtain the indicator definition specification; Step S62: Based on the indicator definition specifications, combined with the real-time operation data and the control operation mechanism, the specific indicator values ​​of each photovoltaic power station are obtained, and the indicator value dataset is integrated. Step S63: Based on the index numerical dataset, the operating status of each photovoltaic power station is quantitatively analyzed through threshold comparison and assessment points to obtain the reactive power and voltage operation evaluation results.

8. A reactive power and voltage operation assessment system for a cluster photovoltaic power station, wherein the reactive power and voltage operation assessment system is applied to the reactive power and voltage operation assessment method as described in any one of claims 1 to 7, characterized in that, The reactive power and voltage operation evaluation system includes: The quantitative index calculation module is used to calculate the quantitative index dataset of the voltage operation status of each photovoltaic power station based on the real-time operation data of the cluster photovoltaic power station, according to the preset scheduling voltage curve and the average rated voltage of the system operation. An anomaly identification definition module is used to define reactive power circulation characteristics and reactive power output imbalance characteristics based on the quantitative index dataset and Kirchhoff's laws to obtain anomaly identification results. The sensitivity matrix calculation module is used to construct an equivalent circuit model based on the topology of a cluster photovoltaic power station, and obtain the reactive voltage sensitivity matrix through power flow calculation, and further obtain the corresponding sensitivity coefficients. The regulation mechanism analysis module is used to analyze the automatic voltage control mechanism of each photovoltaic power station based on the sensitivity matrix and the reactive voltage automatic control theory, and to obtain a regulation mechanism analysis report. The reactive power output allocation coefficient calculation module is used to calculate the electrical distance, regulation rate, and voltage regulation coupling strength of each photovoltaic power station based on the topology and the real-time operation data, and to calculate the reactive power output allocation coefficient of each photovoltaic power station by weighting it with the sensitivity matrix and the regulation mechanism analysis report. The reactive power and voltage operation assessment module is used to construct a quantitative index system based on the anomaly identification results and the control operation mechanism analysis, and to conduct an operation status assessment of each photovoltaic power station through the quantitative index system to obtain the reactive power and voltage operation assessment results.

9. An electronic device, characterized in that, The method includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the reactive voltage operation evaluation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions that, when executed by a processor, enable the reactive voltage operation evaluation method as described in any one of claims 1 to 7.

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