A new energy county area power grid frequency voltage cooperative enhancement system and method

Abstract

The application discloses a new energy county area power grid frequency voltage cooperative enhancement system and method, relates to the technical field of new energy power grid. The method comprises the following steps: collecting real-time operation parameters of the county area power grid for mode recognition to obtain a current operation scenario label; calling frequency regulation rule fragments and voltage regulation rule fragments matched with the current operation scenario label to construct an initial regulation rule sequence; performing multi-domain constraint calculation on the sequence to establish a frequency-voltage linkage coupling relationship and generate an optimized regulation rule sequence; calculating an influence echo index between regulation actions, and selecting an inhibition type replacement rule fragment to correct the optimized regulation rule sequence when the influence echo index exceeds a preset echo threshold; and performing consistency review on the corrected regulation rule sequence, and triggering a rollback and a replacement branch when the consistency review fails, wherein if the replacement branch is unavailable, a minimum offset solution is performed to generate a final regulation rule sequence. The application significantly improves the stability and reliability of new energy county area power grid operation.

Description

A frequency and voltage synergistic enhancement system and method for new energy county power grids Technical Field

[0001] This application relates to the field of new energy power grid technology, and in particular to a frequency and voltage synergistic enhancement system and method for new energy county power grids. Background Technology

[0002] With the widespread integration of new energy power generation (such as wind power and photovoltaic power) into county-level power grids, the operating characteristics of the power grid have changed significantly. However, new energy power generation is characterized by volatility and randomness; instantaneous power surges or drops can lead to system frequency deviations, voltage drops, or flicker. At the same time, county-level power grid loads are characterized by dispersion and suddenness; load disturbances can easily trigger power flow reversals, line congestion, or sudden load changes. Independent frequency and voltage regulation often leads to cross-regional interference, inconsistent regulation, and response lag, affecting the safe and stable operation of the power grid.

[0003] In related technologies, most new energy county power grid control methods have a series of problems such as single control strategy, weak cross-domain coordination capability, and difficulty in suppressing echo effects. Specifically, single-domain control methods ignore the frequency and voltage coupling relationship, control strategies based on thresholds or empirical rules lack pertinence, regional coordinated control has action conflicts and delays, and the adaptability to rapid fluctuations of new energy is poor, so there is room for improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a frequency and voltage synergistic enhancement system and method for new energy county power grids to solve the problems mentioned in the background art.

[0005] Firstly, this application provides a method for frequency and voltage synergistic enhancement of a new energy county power grid, which adopts the following technical solution:

[0006] Real-time operating parameters of the county power grid are collected, and pattern recognition is performed on the real-time operating parameters according to a preset scenario feature set to obtain the current operating scenario label;

[0007] Based on the operating scenario label, frequency control rule segments and voltage control rule segments that match the operating scenario label are retrieved from the preset scenario-rule segment matrix to construct an initial control rule sequence for frequency-voltage coordination.

[0008] Multi-domain constraint calculations are performed on the initial control rule sequence to obtain multi-domain constraint parameters. Based on the multi-domain constraint parameters, a linkage coupling relationship between frequency control action and voltage control action is established to generate an optimized control rule sequence containing coupling markers.

[0009] Based on the aforementioned linkage and coupling relationship, the echo index of the influence of frequency regulation action on voltage regulation action and the echo index of the influence of voltage regulation action on frequency regulation are calculated.

[0010] When any of the aforementioned influence echo indices exceeds a preset echo threshold, a suppression-type substitution rule fragment is selected to replace the corresponding control rule fragment, thereby correcting the optimized control rule sequence and obtaining a corrected control rule sequence.

[0011] The modified control rule sequence is subjected to consistency verification. If the verification fails, the control rule rollback mechanism is triggered and the corresponding alternative control rule branch is switched. If all alternative control rules fail the verification, the minimum offset solution rule is executed to generate the final control rule sequence.

[0012] Preferably, the step of collecting real-time operating parameters of the county power grid and performing pattern recognition on the real-time operating parameters according to a preset scenario feature set to obtain the current operating scenario label is as follows:

[0013] Collect real-time operating parameters of the county power grid, including new energy power output, grid voltage curve, system frequency deviation, line power flow parameters and load change rate;

[0014] Based on the real-time operating parameters, the new energy ramp-up rate and drop rate, voltage deviation amplitude and voltage fluctuation frequency, frequency offset trend and change rate, power flow reversal number and power flow congestion index, load disturbance intensity and load sudden change probability are calculated respectively to form multi-dimensional operating characteristic quantities.

[0015] The multidimensional operational features are input into a preset scenario feature set, which contains multiple operational scenario types. Each operational scenario type contains a scenario feature template composed of threshold conditions and rule conditions. The multidimensional operational features are compared item by item according to the threshold conditions and rule conditions to match the corresponding operational scenario features.

[0016] The operating scenario type of the county power grid is determined based on the matched combination of operating scenario features, and the operating scenario type is output as an operating scenario label.

[0017] Preferably, the step of retrieving frequency regulation rule segments and voltage regulation rule segments that match the operating scenario tags from a preset scenario-rule segment matrix based on the operating scenario tags, and constructing an initial frequency-voltage coordinated regulation rule sequence, specifically includes:

[0018] Based on the operating scenario tags, a preset scenario-rule segment matrix is ​​loaded. The scenario-rule segment matrix is ​​indexed according to the operating scenario tags, and each operating scenario tag corresponds to at least one frequency regulation rule segment and at least one voltage regulation rule segment.

[0019] The main class and subclass of the running scenario are extracted from the running scenario label as index parameters, which are used to locate the set of rule fragments corresponding to the running scenario in the scenario-rule fragment matrix.

[0020] Based on the index parameters, frequency regulation rule segments and voltage regulation rule segments corresponding to the running scenario tags are retrieved from the scenario-rule segment matrix, respectively forming a set of candidate frequency regulation rule segments and a set of candidate voltage regulation rule segments;

[0021] The frequency regulation candidate rule fragment set and the voltage regulation candidate rule fragment set are encapsulated and combined according to a unified rule structure to generate an initial regulation rule sequence for frequency-voltage coordination.

[0022] Preferably, the step of performing multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters, establishing a linkage coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters, and generating an optimized control rule sequence containing coupling markers, specifically includes:

[0023] Collect the action parameters of the frequency control rule segment and voltage control rule segment involved in the initial control rule sequence. The action parameters include the action trigger threshold, action amplitude coefficient, action duration, and action priority.

[0024] Based on the action parameters, a set of multi-domain constraint parameters related to the control behavior is calculated. The set of multi-domain constraint parameters includes constraint parameters for the new energy fluctuation domain, constraint parameters for the voltage stability domain, constraint parameters for the frequency security domain, constraint parameters for the power flow security domain, and constraint parameters for the load disturbance domain.

[0025] Based on the multi-domain constraint parameter set, constraint consistency analysis is performed on each frequency control action and voltage control action in the initial control rule sequence to obtain the consistency analysis results.

[0026] Based on the consistency analysis results, a linkage and coupling relationship between frequency regulation and voltage regulation is established, and an optimized regulation rule sequence containing coupling markers is generated.

[0027] Preferably, the step of establishing the linkage coupling relationship between frequency regulation actions and voltage regulation actions based on the consistency analysis results, and generating an optimized regulation rule sequence containing coupling markers, specifically includes:

[0028] Select all pairs of control actions that constitute a linkage relationship from the initial control rule sequence. The pairs of control actions include frequency control action-voltage control action pairs and mutual influence pairs between similar actions.

[0029] Based on the consistency analysis results, each pair of control actions is subjected to constraint conflict relationship determination, constraint dependency relationship determination, priority triggering relationship determination, and security domain superposition relationship determination to obtain coupling relationship determination results.

[0030] Based on the coupling relationship determination result, a coupling flag is configured for the corresponding control action pair, and a linkage coupling relationship mapping table is generated according to the coupling type.

[0031] Based on the linkage and coupling relationship mapping table, the control actions with coupling relationships in the initial control rule sequence are reordered, associated and bound, or conditionally split to obtain the reordering result, the association and binding result, and the condition splitting result.

[0032] Based on the reordering results, association binding results, and condition splitting results, all control actions are rearranged into an optimized control rule sequence containing coupling tags.

[0033] Preferably, the steps of calculating the echo index of the influence of the frequency regulation action on the voltage regulation action, and the echo index of the influence of the voltage regulation action on the frequency regulation action, based on the aforementioned linkage coupling relationship, are as follows:

[0034] Collect the cross-domain influence parameters contained in the linkage coupling relationship. The cross-domain influence parameters include frequency offset coefficient, voltage offset coefficient, power-frequency sensitivity coefficient, reactive power-voltage sensitivity coefficient, action duration, and action amplitude.

[0035] Based on the aforementioned cross-domain influence parameters, the cross-domain influence of frequency regulation action on the voltage side and the cross-domain influence of voltage regulation action on the frequency side are calculated respectively.

[0036] An echo model containing a time decay factor, a response delay factor, and a feedback adjustment factor is constructed. The cross-domain influence quantity is input into the echo model to obtain the echo quantity of the influence of frequency control action on voltage control action and the echo quantity of the influence of voltage control action on frequency control action, respectively.

[0037] The influence echo quantity is normalized to form the echo index of the influence of frequency regulation action on voltage regulation action, and the echo index of the influence of voltage regulation action on frequency regulation action.

[0038] Preferably, when any of the aforementioned influence echo indices exceeds a preset echo threshold, the step of selecting a suppression-type substitution rule fragment to replace the corresponding control rule fragment, and correcting the optimized control rule sequence to obtain the corrected control rule sequence, specifically includes:

[0039] Extract the influence echo index and the preset echo threshold, obtain the comparison result between the influence echo index and the preset echo threshold, identify the control action pair where the influence echo index exceeds the preset echo threshold, and determine the corresponding target control rule segment;

[0040] The rule type, action direction, and action intensity of the target regulation rule fragment are obtained. Based on the rule type, action direction, and action intensity, a matching suppression-type alternative rule fragment is retrieved from a preset alternative rule fragment library. The suppression-type alternative rule fragment is used to reduce cross-domain feedback effects and weaken the echo amplification trend.

[0041] Extract the action trigger threshold and action priority from the action parameters, determine the excess amplitude affecting the echo index, and determine the replacement strategy based on the action trigger threshold, action priority and excess amplitude affecting the echo index. The replacement strategy includes complete replacement, conditional replacement or amplitude reduction replacement.

[0042] According to the replacement strategy, the target regulation rule fragment is replaced with the corresponding suppression-type substitution rule fragment, and all the replaced regulation rule fragments are integrated to form a modified regulation rule sequence after echo suppression processing.

[0043] Preferably, the process of performing a consistency check on the modified control rule sequence, triggering a control rule rollback mechanism and switching to the corresponding alternative control rule branch when the check fails, and executing the minimum offset solution rule to generate the final control rule sequence, specifically includes:

[0044] The action parameters, coupling markers, and cross-domain influence relationships of all control actions in the modified control rule sequence are obtained. According to the preset consistency review rules, the consistency of action triggering conditions, cross-domain coordination consistency, and security domain coverage consistency are reviewed item by item to obtain the consistency review results.

[0045] When any verification item fails in the consistency verification result, the control action that triggered the failure and its corresponding control rule fragment are identified. Based on the position, type and coupling relationship of the control rule fragment in the corrected control rule sequence, the corresponding alternative control rule branch is selected from the preset alternative control rule tree.

[0046] The alternative control rule branch is loaded and used to replace the original failed control rule fragment. The consistency of the replaced control rule sequence is then re-checked.

[0047] If all the alternative control rule branches fail the consistency check, then the minimum offset solution rule is executed to make minimum range numerical adjustments to the action trigger threshold, action amplitude coefficient, action duration and linkage coupling relationship in the modified control rule sequence so that the adjusted control rule sequence satisfies the safety domain constraint and cross-domain coordination condition.

[0048] The control rules, after being solved by minimum offset, are re-integrated and output as the final control rule sequence.

[0049] Secondly, the frequency and voltage coordinated enhancement system for a new energy county power grid provided in this application adopts the following technical solution:

[0050] A frequency and voltage coordinated enhancement system for a new energy county power grid includes:

[0051] The scenario recognition module collects real-time operating parameters of the county power grid, performs pattern recognition on the real-time operating parameters according to a preset scenario feature set, and obtains the current operating scenario label.

[0052] The initial control module, based on the operating scenario label, retrieves frequency control rule segments and voltage control rule segments that match the operating scenario label from a preset scenario-rule segment matrix, and constructs a frequency-voltage coordinated initial control rule sequence;

[0053] The optimization control module performs multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters, establishes a linkage coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters, and generates an optimized control rule sequence containing coupling markers.

[0054] The echo calculation module, based on the aforementioned linkage and coupling relationship, calculates the echo index of the impact of frequency regulation action on voltage regulation action, and the echo index of the impact of voltage regulation action on frequency regulation.

[0055] The correction and control module, when any of the aforementioned influence echo indices exceeds a preset echo threshold, selects a suppression-type substitution rule fragment to replace the corresponding control rule fragment, and corrects the optimized control rule sequence to obtain a corrected control rule sequence;

[0056] The verification and control module performs consistency verification on the modified control rule sequence. If the verification fails, the control rule rollback mechanism is triggered and the corresponding alternative control rule branch is switched. If all alternative control rules fail the verification, the minimum offset solution rule is executed to generate the final control rule sequence.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. Real-time operating parameters are collected to classify the county power grid into operational scenarios, identifying potential frequency risks, voltage risks, or renewable energy fluctuation risks in the early stages of operation, thus improving the adaptability of the control strategy. Based on the identified operational scenarios, frequency and voltage control rule fragments that best match the current power grid state are extracted from a pre-set knowledge base and combined into an initial coordinated control rule sequence. This ensures that the control strategy is highly targeted and avoids conflicts caused by individual frequency and voltage controls. Multi-dimensional constraint evaluation is performed on the initial control rule sequence to identify potential interactions between different control actions, constructing coupling paths between frequency and voltage control actions to ensure the system can identify hidden dependencies and risk links between control actions. The impact echo index between frequency and voltage control actions is calculated to form a bidirectional echo index used to judge the degree of risk in the control link, accurately assessing the anomalous amplification risk of control coupling and achieving dynamic adaptive optimization of the control rules. When a certain echo index exceeds a safety threshold, a suppression-type alternative rule fragment is automatically selected to replace the risk fragment, achieving dynamic autonomous correction of the control strategy and improving the robustness and safety of the entire control strategy. The revised control rule sequence undergoes consistency verification. If verification fails, rollback and alternative branch switching are used to ensure multi-path protection of the control strategy. If all alternative paths fail, an executable strategy is generated based on the minimum offset principle to ensure that the control action still has a stable effect with minimal intervention. This constructs a fault-tolerant mechanism and a safety net mechanism for the control strategy, making the power grid control system recoverable and ultimately executable, and ensuring that the revised rule sequence maintains consistency in terms of multi-domain constraints, execution logic, and global stability.

[0059] 2. A systematic screening process is used to identify potentially coupled control action pairs from the initial rule sequence, establishing a pairwise relationship structure between rules. By including cross-domain and same-domain actions in a unified candidate pool, the coverage and controllability of the rule system are significantly improved. Based on multi-domain consistency analysis results, control action pairs are comprehensively evaluated from four dimensions: constraint conflict, constraint dependency, priority triggering, and security domain superposition, clarifying the specific coupling mode between each pair of actions. By determining the conflict, dependency, and synergy relationships between actions, risk points caused by the lack of cross-domain analysis in traditional control systems are effectively identified, enabling in-depth mining of cross-domain control logic and improving the predictability and security boundary clarity of power system control. By constructing a mapping table, complex cross-domain coupling logic can be directly invoked by the execution module, achieving model-based management at the engineering level. The rule sequence is structurally optimized based on the mapping table, and actions with coupling relationships are rearranged to achieve risk avoidance, logical sequence reconstruction, and linkage mode construction in the control chain. By employing three methods—reordering, binding, and splitting—the rule sequence is structured into a control logic that conforms to dynamic operational constraints, significantly enhancing its adaptability to new energy fluctuations, load disturbances, and cross-domain coupling impacts. Integrating all optimization results generates a final optimized control rule sequence containing coupling markers, transforming the control rules from a separate, static rule set to a multi-domain coupled dynamic rule set, achieving unified coordination between frequency and voltage control.

[0060] 3. Identify out-of-limit actions, locate potential control actions that could cause cross-domain echo amplification, clearly define the target objects requiring correction, and intervene before or during action execution to prevent grid instability caused by cross-domain linkages, thereby improving the safety and reliability of the control rule sequence. Introduce matching suppression rule fragments, mapping actions requiring correction to suppression substitution strategies. Select the most appropriate suppression measures for different action types to achieve refined suppression of cross-domain feedback, avoiding direct disabling or abrupt weakening of actions, and ensuring the continuity and safety of control effects. Through refined substitution strategies, flexibly select correction methods based on the actual degree of echo exceeding limits and action priority, improving the system's adaptability and controllability to echo suppression, and ensuring the stability and coordination of the grid control sequence under complex operating scenarios. Integrate various substitution fragments into a complete control sequence to achieve dynamic correction of optimized control rules. While maintaining frequency-voltage coordination, actively suppress echo amplification effects, significantly enhancing the safety, stability, and robustness of county power grids under high-proportion renewable energy access. Attached Figure Description

[0061] Figure 1 is a schematic diagram of the specific steps of an embodiment of the frequency and voltage synergistic enhancement method for a new energy county power grid according to the present invention.

[0062] Figure 2 is a schematic diagram of the module connection of an embodiment of a new energy county power grid frequency and voltage coordinated enhancement system according to the present invention. Detailed Implementation

[0063] The present invention will be further described in detail below with reference to the embodiments and Figures 1-2, but the embodiments of the present invention are not limited thereto.

[0064] This invention discloses a method for frequency and voltage synergistic enhancement of a new energy county power grid, specifically including the following steps:

[0065] Step S1: Collect real-time operating parameters of the county power grid, and perform pattern recognition on the real-time operating parameters according to the preset scenario feature set to obtain the current operating scenario label;

[0066] Step S2: Based on the operating scenario label, retrieve the frequency control rule segment and voltage control rule segment that match the operating scenario label from the preset scenario-rule segment matrix to construct an initial control rule sequence for frequency-voltage coordination;

[0067] Step S3: Perform multi-domain constraint calculation on the initial control rule sequence to obtain multi-domain constraint parameters, establish the linkage coupling relationship between frequency control action and voltage control action based on the multi-domain constraint parameters, and generate an optimized control rule sequence containing coupling markers;

[0068] Step S4: Based on the aforementioned linkage and coupling relationship, calculate the echo index of the influence of frequency regulation action on voltage regulation action, and the echo index of the influence of voltage regulation action on frequency regulation.

[0069] Step S5: When any of the influence echo indices exceeds the preset echo threshold, a suppression-type substitution rule fragment is selected to replace the corresponding control rule fragment, and the optimized control rule sequence is corrected to obtain the corrected control rule sequence.

[0070] Step S6: Perform consistency verification on the modified control rule sequence. If the verification fails, trigger the control rule rollback mechanism and switch to the corresponding alternative control rule branch. If all alternative control rules fail the verification, execute the minimum offset solution rule to generate the final control rule sequence.

[0071] In practical applications, real-time operating parameters are collected to classify the county power grid into operational scenarios, identifying potential frequency risks, voltage risks, or renewable energy fluctuation risks in the early stages of operation, thus improving the adaptability of the control strategy. Based on the identified operational scenarios, frequency and voltage control rule fragments that best match the current power grid state are extracted from a pre-set knowledge base and combined into an initial coordinated control rule sequence. This ensures that the control strategy is highly targeted and avoids conflicts caused by individual frequency and voltage controls. The initial control rule sequence undergoes multi-dimensional constraint evaluation to identify potential interactions between different control actions, constructing coupling paths between frequency and voltage control actions. This ensures the system can identify hidden dependencies and risk links between control actions. The impact echo index between frequency and voltage control actions is calculated to form a bidirectional echo index used to judge the degree of risk in the control link, accurately assessing the aberration amplification risk of control coupling and achieving dynamic adaptive optimization of the control rules. When a certain echo index exceeds a safety threshold, a suppression-type alternative rule fragment is automatically selected to replace the risk fragment, achieving dynamic autonomous correction of the control strategy and improving the robustness and safety of the entire control strategy. The revised control rule sequence undergoes consistency verification. If verification fails, rollback and alternative branch switching are used to ensure multi-path protection of the control strategy. If all alternative paths fail, an executable strategy is generated based on the minimum offset principle to ensure that the control action still has a stable effect with minimal intervention. This constructs a fault-tolerant mechanism and a safety net mechanism for the control strategy, making the power grid control system recoverable and ultimately executable, and ensuring that the revised rule sequence maintains consistency in terms of multi-domain constraints, execution logic, and global stability.

[0072] The steps of collecting real-time operating parameters of the county power grid and performing pattern recognition on the real-time operating parameters according to a preset scenario feature set to obtain the current operating scenario label are as follows:

[0073] Step S11: Collect real-time operating parameters of the county power grid, including new energy power output, grid voltage curve, system frequency deviation, line power flow parameters and load change rate;

[0074] Step S12: Based on the real-time operating parameters, calculate the new energy ramp-up rate and drop rate, voltage deviation amplitude and voltage fluctuation frequency, frequency offset trend and change rate, power flow reversal number and power flow congestion index, load disturbance intensity and load change probability, and form multi-dimensional operating characteristic quantities.

[0075] Based on the power output value of new energy sources, the ratio of the power difference between adjacent sampling points to the sampling period is taken as the instantaneous power change rate. The average and maximum values ​​of the positive change rate are taken as the energy rise rate, and the average and maximum absolute values ​​of the negative change rate are taken as the new energy fall rate. These are used to characterize the degree of disturbance to the frequency stability of the county power grid caused by the rapid rise and fall of new energy sources.

[0076] Based on the grid voltage curve, the deviation of each monitoring point from the rated voltage is calculated. At the same time, within the set observation window, the root mean square value, peak deviation and duration of the voltage deviation are counted. The number of times the voltage exceeds a given small fluctuation threshold is also counted to form the voltage fluctuation frequency, which characterizes the degree of voltage stability and fluctuation activity.

[0077] Based on the system frequency deviation, the positive and negative trends of the frequency deviation are calculated. The frequency offset trend is obtained through linear fitting or first-order difference. The average rate of change, maximum rate of change and direction of change of frequency per unit time are further calculated to reflect the risk of continuous frequency offset and dynamic oscillation characteristics.

[0078] Based on the power flow parameters of the line, the real-time sequence of power flow direction and magnitude is obtained. The number of times the power flow direction changes from forward to reverse within the window is counted to obtain the number of power flow reversals. The power flow congestion index is formed based on the ratio of the line rated capacity to the real-time power flow. Its peak value and mean value in each time segment are calculated to characterize the power flow stability and power flow congestion risk of the county power grid.

[0079] Based on the load change rate, the absolute value of the change rate is statistically analyzed to form the load disturbance intensity. Using a method based on probability statistics or interval change threshold, the probability of the load undergoing a sudden change (exceeding the change threshold) within a set window is calculated as the load change probability, which characterizes the potential disturbance capability of load behavior to voltage and frequency.

[0080] Step S13: Input the multidimensional operation feature quantity into a preset scenario feature set. The scenario feature set contains multiple operation scenario types, and each operation scenario type contains a scenario feature template composed of threshold conditions and rule conditions. The multidimensional operation feature quantity is compared item by item according to the threshold conditions and rule conditions to match the corresponding operation scenario features.

[0081] The system includes a pre-set scenario feature set containing multiple operational scenario types. This scenario feature set includes at least scenarios such as rapid rise of new energy sources, rapid fall of new energy sources, active voltage disturbance, continuous frequency deviation, frequent power flow reversal, aggravated power flow congestion, and risk of sudden load changes. Each operational scenario contains a scenario feature template composed of threshold conditions and rule conditions. The threshold conditions include interval thresholds, upper limit thresholds, or combined thresholds. The rule conditions include logical relationships, condition triggers, or rules that are jointly satisfied by multiple indicators.

[0082] Step S14: Determine the operation scenario type of the county power grid based on the matched operation scenario feature combination, and output the operation scenario type as an operation scenario label.

[0083] In practical applications, by simultaneously collecting key operating parameters such as renewable energy output, voltage, frequency, power flow, and load, the operating status of the power grid is accurately characterized, ensuring that scenario identification is based on real, dynamic, and multi-dimensional power grid operating information. The raw operating parameters are transformed into multi-dimensional characteristic indicators that describe the dynamic behavior of the power grid, revealing potential trends in renewable energy fluctuations, voltage quality, frequency stability, and power flow load distribution. Through structured, multi-dimensional feature extraction, key operating characteristics such as sudden disturbances and rapid changes are captured, improving the accuracy of scenario classification. Based on the established scenario feature template, the input multi-dimensional features are accurately compared to identify operating scenario features that meet preset conditions. The scenario feature template includes threshold conditions and logical rules, enabling the identification of various complex scenarios, such as high photovoltaic fluctuation scenarios, continuous frequency shift scenarios, and high-risk power flow reversal scenarios. This achieves rapid classification of complex power grid behavior patterns and contextualized and personalized control strategies. The matched scenario features are combined and transformed into operational scenario labels that can be used for logical control. These labels serve as input identifiers for subsequent control rule retrieval, constraint judgment, and coordinated control processes, thereby enabling the structuring and automation of the control decision-making process. Differentiated frequency-voltage coordinated control schemes are automatically generated based on different operational scenarios, improving control response speed and strategy adaptability.

[0084] Based on the operational scenario label, the step of retrieving frequency regulation rule segments and voltage regulation rule segments that match the operational scenario label from a preset scenario-rule segment matrix to construct an initial frequency-voltage coordinated regulation rule sequence is as follows:

[0085] Step S21: Based on the running scenario tags, load a preset scenario-rule segment matrix. The scenario-rule segment matrix is ​​indexed according to the running scenario tags. Each running scenario tag corresponds to at least one frequency regulation rule segment and at least one voltage regulation rule segment.

[0086] Step S22: Extract the main class and subclass of the running scenario from the running scenario label as index parameters, which are used to locate the set of rule fragments corresponding to the running scenario in the scenario-rule fragment matrix;

[0087] Based on the running scenario label, the scenario type and scenario level corresponding to the label are parsed, and the index parameters corresponding to the label are extracted. The index parameters include the scenario main class (such as the new energy rapid climb class, voltage disturbance active class, frequency offset continuous class, etc.) and the scenario subclass (such as rapid climb high risk, power flow reversal active medium level, etc.), which are used to locate the corresponding rule fragment set in the scenario-rule fragment matrix.

[0088] Step S23: Based on the index parameters, retrieve the frequency control rule fragment and voltage control rule fragment corresponding to the running scenario label from the scenario-rule fragment matrix respectively, and form a set of candidate frequency control rule fragments and a set of candidate voltage control rule fragments respectively;

[0089] Step S24: The frequency regulation candidate rule fragment set and the voltage regulation candidate rule fragment set are encapsulated and combined according to a unified rule structure to generate an initial regulation rule sequence for frequency-voltage coordination.

[0090] In practical applications, the scenario-rule fragment matrix serves as a knowledge structure carrier, ensuring the contextualization and rapid retrieval of control strategies. The pre-constructed scenario-rule fragment matrix establishes a mapping relationship between complex control experience and operational scenario characteristics. After operational scenario identification, a set of control rule fragments related to the power grid operational scenario is automatically loaded from a pre-set knowledge base. This enables control strategies to automatically respond based on scenario indexes, transforming the selection of control strategies from manual experience to a systematic, structured, and automated process, significantly improving the adaptability and response speed of control strategies. By parsing operational scenario tags, the main and subclasses of operational scenarios are extracted to form index parameters for accurately finding rule fragments. Specific control rule regions are quickly located within the scenario-rule fragment matrix, ensuring consistency of the retrieval target. Decomposing operational scenario tags into main and subclasses significantly improves the granularity of rule fragment retrieval, avoiding the problem of overgeneralization of control strategies due to reliance solely on coarse-grained scenario categories. Simultaneously, it enhances the targeting of control strategies, making them more aligned with specific scenario characteristics, thereby improving the effectiveness and engineering practicality of control. By retrieving frequency control rule fragment sets and voltage control rule fragment sets from the scenario-rule fragment matrix using index parameters, it is ensured that both types of control logic match the physical characteristics of the current operating scenario. The establishment of candidate rule fragment sets avoids a one-size-fits-all application of control strategies, instead selecting the most suitable control fragment based on the actual operating scenario, improving the accuracy and response efficiency of frequency-voltage coordinated control. By encapsulating the two types of rule fragments through a unified rule structure, rules from different sources and templates have a consistent field format. These rules are then combined according to the requirements of coordinated control to generate a structured initial control rule sequence. From an engineering perspective, this ensures the standardization of control logic, avoids rule conflicts and logical inconsistencies, and improves the overall reliability of the system.

[0091] The steps of performing multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters, establishing the linkage coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters, and generating an optimized control rule sequence containing coupling markers are as follows:

[0092] Step S31: Collect the action parameters of the frequency control rule segment and voltage control rule segment involved in the initial control rule sequence. The action parameters include the action trigger threshold, the action amplitude coefficient, the action duration, and the action priority.

[0093] Step S32: Based on the action parameters, calculate the multi-domain constraint parameter set related to the control behavior. The multi-domain constraint parameter set includes new energy fluctuation domain constraint parameters, voltage stability domain constraint parameters, frequency security domain constraint parameters, power flow security domain constraint parameters, and load disturbance domain constraint parameters.

[0094] The system extracts the action parameters involved in all frequency control rule segments and voltage control rule segments from the initial control rule sequence. Based on these action parameters, it constructs multi-domain constraint parameter sets related to five types of control behaviors, including new energy fluctuation domain constraint parameters (reflecting the power uncertainty caused by the fluctuation of renewable energy access), voltage stability domain constraint parameters (reflecting the bus voltage stability conditions), frequency security domain constraint parameters (reflecting the system frequency offset tolerance and recovery dynamic requirements), power flow security domain constraint parameters (reflecting the line power flow constraints and safety margin), and load disturbance domain constraint parameters (reflecting the sensitivity of rapid load changes to control actions). Based on the above multi-domain constraints, the system forms a constraint vector corresponding to each control action.

[0095] Step S33: Based on the multi-domain constraint parameter set, perform constraint consistency analysis on each frequency control action and voltage control action in the initial control rule sequence to obtain the consistency analysis results;

[0096] Step S34: Based on the consistency analysis results, establish the linkage and coupling relationship between frequency regulation action and voltage regulation action, and generate an optimized regulation rule sequence containing coupling markers.

[0097] Establish the linkage and coupling relationship between frequency regulation actions and voltage regulation actions. For example, determine the linkage logic that the rapid frequency correction action must be accompanied by the voltage support action and the triggering of the voltage compensation action must be synchronized with the frequency offset trend. Add coupling marks to the relevant rule segments in the rule sequence to generate an optimized regulation rule sequence.

[0098] In practical applications, parameters such as trigger thresholds, amplitude coefficients, durations, and priorities are collected. These parameters are then mapped to safety and stability constraints from a multi-domain perspective through standardized formatting, forming a multi-domain constraint parameter set. By introducing multi-dimensional constraints such as renewable energy fluctuations, voltage stability, frequency security, power flow security, and load disturbances, the external conditions, limiting boundaries, and dynamic factors of each control action are quantitatively characterized, significantly improving the scientific rigor and reliability of the control strategy generation. Cross-analysis of the compatibility of frequency and voltage control actions under multi-domain constraints is conducted to identify potential conflicts (e.g., frequency control actions causing voltage exceedances), synergistic relationships (e.g., increasing reactive power support can promote frequency stability), and priority contradictions, forming a complete constraint consistency mapping. This avoids the problem of single-domain optimization versus cross-domain conflict in traditional control methods. By identifying and resolving cross-domain conflicts before rule generation, it ensures that the control strategy does not generate cascading risks during execution. Based on the consistency analysis results, a cross-domain linkage coupling relationship is constructed for frequency regulation actions and voltage regulation actions. Coupling markers are added to the rule sequence, enabling the output rule sequence to have dynamic linkage capability, cross-domain coordination capability, and conflict avoidance capability. The regulation rule evolves from independent domain control to multi-domain coupled control, realizing real-time linkage between voltage stability and frequency security regulation. This fundamentally improves the overall coordination of the regulation strategy and the system response efficiency, and can significantly enhance the operational resilience of the power grid in a high-penetration environment of new energy sources.

[0099] Based on the consistency analysis results, the steps for establishing the linkage and coupling relationship between frequency regulation and voltage regulation actions, and generating an optimized regulation rule sequence containing coupling markers, are as follows:

[0100] Step S341: Select all pairs of control actions that constitute a linkage relationship from the initial control rule sequence. The pairs of control actions include frequency control action-voltage control action pairs and mutual influence pairs between similar actions.

[0101] Step S342: Based on the consistency analysis results, determine the constraint conflict relationship, constraint dependency relationship, priority triggering relationship, and security domain superposition relationship for each pair of control actions to obtain the coupling relationship determination result.

[0102] Based on the consistency analysis results, the following relationship determinations are performed on each pair of control actions: constraint conflict determination: if two actions conflict in multi-domain constraints, they are identified as conflict type; constraint dependency determination: if the triggering condition or effect of one action depends on another action, it is marked as dependency type; priority triggering determination: the impact of the order of action triggering is analyzed and identified as priority triggering type; security domain superposition determination: if the simultaneous triggering of two actions can improve system stability, it is identified as cooperative enhancement type; thus forming the coupling relationship determination results.

[0103] Step S343: Based on the coupling relationship determination result, configure coupling tags for the corresponding control action pairs, and generate a linkage coupling relationship mapping table according to the coupling type;

[0104] Step S344: Based on the linkage coupling relationship mapping table, the control actions with coupling relationships in the initial control rule sequence are reordered, associated and bound, or conditionally split to obtain the reordering result, association and binding result, and condition splitting result.

[0105] Among them, reordering: for example, rearranging priority-triggered action pairs according to priority; association binding: binding collaborative enhancement actions as linked execution units, for example, triggering A must trigger B in conjunction; conditional splitting: conditionally splitting conflicting or partially dependent actions, such as splitting an action into actions triggered when conditions are met and actions prohibited when conditions are not met.

[0106] Step S345: Based on the reordering results, association binding results, and condition splitting results, all control actions are rearranged into an optimized control rule sequence containing coupling tags.

[0107] In practical applications, potentially coupled pairs of control actions are systematically screened from the initial rule sequence, establishing a pairwise relationship structure between rules. By including cross-domain actions (frequency-voltage) and intra-domain actions (frequency-frequency, voltage-voltage) in a unified candidate pool, the problems of missing correlations and incomplete linkage logic in traditional power grid control rules are solved, significantly improving the coverage and controllability of the rule system. Based on the results of multi-domain consistency analysis, control action pairs are comprehensively judged from four dimensions: constraint conflict, constraint dependency, priority triggering, and security domain superposition, clarifying the specific coupling mode between each pair of actions. By judging the conflict, dependency, and coordination relationships between actions, risk points caused by the lack of cross-domain analysis in traditional control systems are effectively identified, improving the predictability and security boundary clarity of power system control. By constructing a mapping table, a standardized representation of the relationships between control actions is achieved, enabling complex cross-domain coupling logic to be directly invoked by the execution module, realizing model-based management at the engineering level. Based on the mapping table, the rule sequence is structurally optimized, and actions with coupling relationships are rearranged to achieve risk avoidance, logical sequence reconstruction, and linkage mode construction in the control chain. By employing three methods—reordering, binding, and splitting—the rule sequence is structured into a control logic that conforms to dynamic operational constraints, significantly enhancing its adaptability to new energy fluctuations, load disturbances, and cross-domain coupling impacts. Integrating all optimization results generates the final optimized control rule sequence containing coupling markers, achieving unified coordination between frequency and voltage control.

[0108] Based on the aforementioned linkage relationship, the steps for calculating the echo index of the influence of frequency regulation on voltage regulation and the echo index of the influence of voltage regulation on frequency regulation are as follows:

[0109] Step S41: Collect the cross-domain influence parameters contained in the linkage coupling relationship. The cross-domain influence parameters include frequency offset coefficient, voltage offset coefficient, power-frequency sensitivity coefficient, reactive power-voltage sensitivity coefficient, action duration, and action amplitude.

[0110] Step S42: Based on the cross-domain influence parameters, calculate the cross-domain influence of the frequency control action on the voltage side and the cross-domain influence of the voltage control action on the frequency side, respectively.

[0111] Step S43: Construct an echo model that includes a time decay factor, a response delay factor, and a feedback adjustment factor. Input the cross-domain influence quantity into the echo model to obtain the echo quantity of the influence of frequency control action on voltage control action and the echo quantity of the influence of voltage control action on frequency control action, respectively.

[0112] Step S44: Normalize the influence echo quantity to form the influence echo index of frequency regulation action on voltage regulation action and the influence echo index of voltage regulation action on frequency regulation action.

[0113] In practical applications, a systematic collection of all quantitative parameters that may affect cross-domain linkage is used to transform the potential linkage effects of control actions into calculable quantities, providing scientific data for frequency-voltage coupling risk assessment and rule optimization. Abstract parameters of control actions are transformed into quantified cross-domain impact quantities. By calculating these cross-domain impact quantities, quantitative analysis of frequency-voltage linkage effects is achieved, revealing potential negative or enhancing effects. Dynamic characteristics and feedback regulation are introduced, transforming static cross-domain impact quantities into dynamic echo quantities that consider time and system response. The echo model can simulate the chain reactions between actions in actual operation, enabling control rule optimization to consider not only instantaneous effects but also system delays and feedback, improving the robustness and safety of the rule sequence. The echo index, as a quantitative evaluation indicator, provides a clear decision-making basis for cross-domain linkage optimization, realizing automated, quantitative, and traceable management of frequency-voltage coordinated control, thereby significantly improving the stability and reliability of county power grids under high-proportion renewable energy access.

[0114] When any of the aforementioned influence echo indices exceeds a preset echo threshold, a suppression-type substitution rule fragment is selected to replace the corresponding control rule fragment, and the optimized control rule sequence is corrected to obtain the corrected control rule sequence. Specifically, the steps are as follows:

[0115] Step S51: Extract the influence echo index and the preset echo threshold, obtain the comparison result between the influence echo index and the preset echo threshold, identify the control action pair where the influence echo index exceeds the preset echo threshold, and determine the corresponding target control rule segment.

[0116] Step S52: Obtain the rule type, action direction and action intensity of the target regulation rule fragment; based on the rule type, action direction and action intensity, retrieve the matching suppression-type alternative rule fragment from the preset alternative rule fragment library; the suppression-type alternative rule fragment is used to reduce the cross-domain feedback effect and weaken the echo amplification trend.

[0117] Step S53: Extract the action trigger threshold and action priority from the action parameters, determine the over-limit amplitude affecting the echo index, and determine the replacement strategy based on the action trigger threshold, action priority and over-limit amplitude affecting the echo index. The replacement strategy includes complete replacement, conditional replacement or amplitude reduction replacement.

[0118] Replacement strategies include complete replacement, conditional replacement, or amplitude reduction replacement. Complete replacement involves completely replacing the original rule fragment with an inhibition-type rule fragment. Conditional replacement involves replacing the rule fragment when a specific condition is met. Amplitude reduction replacement involves reducing the amplitude of the action or the trigger threshold.

[0119] Step S54: According to the replacement strategy, the target regulation rule fragment is replaced with the corresponding suppression-type substitution rule fragment, and all the replaced regulation rule fragments are integrated to form a modified regulation rule sequence after echo suppression processing.

[0120] In practical applications, identifying excessive actions and locating potential control actions that could cause cross-domain echo amplification is crucial. Clearly defining the target objects requiring correction and intervening before or during action execution prevents grid instability caused by cross-domain linkages, thus enhancing the safety and reliability of the control rule sequence. Matching suppression rule fragments are introduced, mapping actions requiring correction to suppression-type substitution strategies. The most appropriate suppression measures are selected for different action types, achieving refined suppression of cross-domain feedback and avoiding direct disabling or abrupt weakening of actions, ensuring the continuity and safety of control effects. Through refined substitution strategies, correction methods are flexibly selected based on the actual degree of echo exceeding limits and action priority, improving the system's adaptability and controllability to echo suppression and ensuring the stability and coordination of the grid control sequence under complex operating scenarios. Integrating various substitution fragments into a complete control sequence enables dynamic correction of optimized control rules. While maintaining frequency-voltage coordination, it actively suppresses echo amplification effects, significantly enhancing the safety, stability, and robustness of county-level power grids under high-proportion renewable energy integration.

[0121] The modified control rule sequence undergoes a consistency check. If the check fails, a control rule rollback mechanism is triggered, and the system switches to the corresponding alternative control rule branch. If all alternative control rules fail the check, the minimum offset solution rule is executed to generate the final control rule sequence. Specifically:

[0122] Step S61: Obtain the action parameters, coupling markers and cross-domain influence relationships of all control actions in the modified control rule sequence. According to the preset consistency review rules, review the consistency of action triggering conditions, cross-domain coordination consistency and security domain coverage consistency item by item to obtain the consistency review results.

[0123] The control sequence is reviewed item by item. The consistency review of action triggering conditions checks whether there are any conflicts in the triggering thresholds, time windows and priorities of each action; the consistency review of cross-domain coordination checks whether there are any violations of coupling constraints in the linkage and coupling relationship between frequency and voltage control actions; and the consistency review of safety domain coverage checks whether the control actions meet the constraint requirements for safety domains such as frequency, voltage, power flow and load disturbance.

[0124] Step S62: When any verification item fails in the consistency verification result, identify the control action that triggered the failure and its corresponding control rule fragment. Based on the position, type and coupling relationship of the control rule fragment in the corrected control rule sequence, select the corresponding alternative control rule branch from the preset alternative control rule tree.

[0125] Step S63: Load the alternative control rule branch, use it to replace the original failed control rule fragment, and re-perform consistency verification on the replaced control rule sequence;

[0126] Load the selected alternative control rule branch and replace the original failed control rule fragment with it. Then, re-perform a consistency check on the replaced control rule sequence. If the check passes, the sequence becomes the final control rule sequence; otherwise, proceed to the minimum offset solution stage.

[0127] Step S64: If all the alternative control rule branches fail the consistency check, then the minimum offset solution rule is executed to make minimum range numerical adjustments to the action trigger threshold, action amplitude coefficient, action duration and linkage coupling relationship in the modified control rule sequence so that the adjusted control rule sequence satisfies the safety domain constraint and cross-domain coordination condition.

[0128] Step S64: The control rules after minimum offset solution are re-integrated and output as the final control rule sequence.

[0129] In practical applications, consistency verification can identify potential conflicts or inconsistencies before the actual execution of control actions, ensuring the logical and safety-constrained rationality of the control rule sequence. This guarantees the reliability and security of county-level power grid control operations, preventing grid oscillations or local instability caused by cross-domain coupling or action conflicts, and achieving safe control under high-proportion renewable energy access. When the consistency verification result shows that any verification item fails, a rollback and substitution mechanism provides alternative execution plans for the failed action, ensuring that the control rule sequence can quickly switch when conflicts or inconsistencies occur, maintaining the continuity and security of power grid control, reducing operational risks in complex multi-domain scenarios, and improving the reliability and flexibility of the control sequence. Iterative verification verifies and optimizes correction strategies, ensuring the feasibility and cross-domain coordination of alternative actions in the sequence, and avoiding the introduction of new potential conflicts by replacement actions. The action trigger threshold, action amplitude coefficient, action duration, and linkage coupling relationship in the modified control rule sequence are adjusted to the minimum range to optimize conflicting actions that cannot be corrected by alternative strategies. This ensures that the control sequence meets all constraints, achieving refined optimization of control rules and ultimate safety assurance. Under a high proportion of renewable energy, this ensures the reliable execution of the frequency-voltage coordinated enhancement scheme for the county power grid, enhancing the stability and robustness of power grid control. Ultimately, the control rule sequence guarantees the stable operation of the power grid under high-proportion renewable energy access, achieving the comprehensive goals of cross-domain coordination, echo suppression, and safety domain protection, providing a complete solution for intelligent dispatching of county power grids.

[0130] A frequency and voltage coordinated enhancement system for a new energy county power grid, which utilizes the frequency and voltage coordinated enhancement method for a new energy county power grid as described above, includes:

[0131] The scenario recognition module collects real-time operating parameters of the county power grid, performs pattern recognition on the real-time operating parameters according to a preset scenario feature set, and obtains the current operating scenario label.

[0132] The initial control module, based on the operating scenario label, retrieves frequency control rule segments and voltage control rule segments that match the operating scenario label from a preset scenario-rule segment matrix, and constructs a frequency-voltage coordinated initial control rule sequence;

[0133] The optimization control module performs multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters, establishes a linkage coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters, and generates an optimized control rule sequence containing coupling markers.

[0134] The echo calculation module, based on the aforementioned linkage and coupling relationship, calculates the echo index of the impact of frequency regulation action on voltage regulation action, and the echo index of the impact of voltage regulation action on frequency regulation.

[0135] The correction and control module, when any of the aforementioned influence echo indices exceeds a preset echo threshold, selects a suppression-type substitution rule fragment to replace the corresponding control rule fragment, and corrects the optimized control rule sequence to obtain a corrected control rule sequence;

[0136] The verification and control module performs consistency verification on the modified control rule sequence. If the verification fails, the control rule rollback mechanism is triggered and the corresponding alternative control rule branch is switched. If all alternative control rules fail the verification, the minimum offset solution rule is executed to generate the final control rule sequence.

[0137] In practical applications, the scenario recognition module collects real-time operating parameters of the county power grid and performs pattern recognition based on a preset scenario feature set to generate current operating scenario labels. This enables accurate identification of the power grid's operating status and rapid judgment of different operating scenarios, providing a data foundation for subsequent control rule selection and ensuring the relevance and effectiveness of control schemes. Using the initial control module, based on the operating scenario labels, matching frequency and voltage control rule fragments are retrieved from the scenario-rule fragment matrix to construct an initial frequency-voltage coordinated control rule sequence. This maps the operating scenario to a preset control strategy, achieving preliminary cross-domain coordinated control and providing an initial control framework for power grid stability and response speed. The optimized control module performs multi-domain constraint calculations on the initial control rule sequence, establishing a linkage and coupling relationship between frequency and voltage actions, and generating an optimized control rule sequence containing coupling markers. Through cross-domain constraint analysis and coupling relationship construction, the coordination and consistency of control actions are ensured, improving the stability and reliability of the power grid under complex operating conditions. Using an echo calculation module, based on the linkage and coupling relationship, the echo index of the impact of frequency regulation actions on voltage actions and voltage regulation actions on frequency actions is calculated. This assesses the feedback effect between cross-domain regulation actions, promptly identifies potential echo amplification risks, and provides a quantitative basis for optimizing the regulation sequence. Through a correction regulation module, when the impact echo index exceeds a preset threshold, a suppression-type alternative rule segment is selected to replace the corresponding regulation rule segment, resulting in a corrected regulation rule sequence. This suppresses cross-domain echo effects, reduces the risk of frequency-voltage regulation conflicts or over-response, and improves the safety and stability of regulation actions. Using a verification regulation module, the corrected regulation rule sequence undergoes consistency verification. If verification fails, a rollback mechanism is triggered and the system switches to an alternative rule branch. If all alternative rules fail, a minimum offset solution is executed to generate the final regulation rule sequence, ensuring that the final regulation rule sequence meets requirements in terms of logic, cross-domain coordination, and safety constraints, achieving reliable execution of the regulation strategy and high grid operation safety.

[0138] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for coordinated frequency and voltage enhancement in a new energy county power grid, characterized in that, Includes the following steps: Real-time operating parameters of the county power grid are collected, and pattern recognition is performed on the real-time operating parameters according to a preset scenario feature set to obtain the current operating scenario label; Based on the operating scenario label, frequency control rule segments and voltage control rule segments that match the operating scenario label are retrieved from the preset scenario-rule segment matrix to construct an initial control rule sequence for frequency-voltage coordination. Multi-domain constraint calculations are performed on the initial control rule sequence to obtain multi-domain constraint parameters. Based on the multi-domain constraint parameters, a linkage coupling relationship between frequency control action and voltage control action is established to generate an optimized control rule sequence containing coupling markers. Based on the aforementioned linkage and coupling relationship, the echo index of the influence of frequency regulation action on voltage regulation action and the echo index of the influence of voltage regulation action on frequency regulation are calculated. When any of the aforementioned influence echo indices exceeds a preset echo threshold, a suppression-type substitution rule segment is selected to replace the corresponding regulation rule segment, and the optimized regulation rule sequence is corrected to obtain a corrected regulation rule sequence. The modified control rule sequence is subjected to consistency verification. If the verification fails, the control rule rollback mechanism is triggered and the corresponding alternative control rule branch is switched. If all alternative control rules fail the verification, the minimum offset solution rule is executed to generate the final control rule sequence.

2. The method for frequency and voltage synergistic enhancement of a new energy county power grid according to claim 1, characterized in that, The steps of collecting real-time operating parameters of the county power grid and performing pattern recognition on these parameters according to a preset scenario feature set to obtain the current operating scenario label are as follows: First, collect real-time operating parameters of the county power grid, including renewable energy power output, grid voltage curve, system frequency deviation, line power flow parameters, and load change rate. Second, based on these real-time operating parameters, calculate renewable energy ramp-up and sag rates, voltage deviation amplitude and voltage fluctuation frequency, frequency offset trend and change rate, power flow reversal times and power flow congestion index, load disturbance intensity and load mutation probability, forming multi-dimensional operating feature quantities. Third, input these multi-dimensional operating feature quantities into a preset scenario feature set, which contains multiple operating scenario types, and each operating scenario type contains a scenario feature template composed of threshold conditions and rule conditions. Compare each multi-dimensional operating feature quantity according to the threshold conditions and rule conditions to match the corresponding operating scenario features. Fourth, determine the operating scenario type of the county power grid based on the matched operating scenario feature combinations, and output the operating scenario type as an operating scenario label.

3. The method for frequency and voltage coordinated enhancement of a new energy county power grid according to claim 1, characterized in that, The step of retrieving frequency control rule segments and voltage control rule segments that match the operating scenario tags from a preset scenario-rule segment matrix based on the operating scenario tags, and constructing an initial control rule sequence for frequency-voltage coordination, specifically involves: loading a preset scenario-rule segment matrix based on the operating scenario tags, wherein the scenario-rule segment matrix is ​​indexed according to the operating scenario tags, and each operating scenario tag corresponds to at least one frequency control rule segment and at least one voltage control rule segment; The main class and subclass of the running scenario are extracted from the running scenario label as index parameters, which are used to locate the set of rule segments corresponding to the running scenario in the scenario-rule segment matrix; according to the index parameters, the frequency regulation rule segment and voltage regulation rule segment corresponding to the running scenario label are retrieved from the scenario-rule segment matrix respectively, forming the frequency regulation candidate rule segment set and the voltage regulation candidate rule segment set respectively; The frequency regulation candidate rule fragment set and the voltage regulation candidate rule fragment set are encapsulated and combined according to a unified rule structure to generate an initial regulation rule sequence for frequency-voltage coordination.

4. The method for frequency and voltage coordinated enhancement of a new energy county power grid according to claim 1, characterized in that, The steps of performing multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters, establishing a linkage and coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters, and generating an optimized control rule sequence containing coupling markers are as follows: First, action parameters related to the frequency control rule segments and voltage control rule segments involved in the initial control rule sequence are collected. These action parameters include action trigger thresholds, action amplitude coefficients, action durations, and action priorities. Based on these action parameters, a set of multi-domain constraint parameters related to the control behavior is calculated. This set includes constraint parameters for the new energy fluctuation domain, voltage stability domain, frequency security domain, power flow security domain, and load disturbance domain. Based on the set of multi-domain constraint parameters, a constraint consistency analysis is performed on each frequency control action and voltage control action in the initial control rule sequence to obtain consistency analysis results. Based on the consistency analysis results, a linkage and coupling relationship between frequency regulation and voltage regulation is established, and an optimized regulation rule sequence containing coupling markers is generated.

5. The method for frequency and voltage synergistic enhancement of a new energy county power grid according to claim 4, characterized in that, The step of establishing the linkage and coupling relationship between frequency regulation actions and voltage regulation actions based on the consistency analysis results, and generating an optimized regulation rule sequence containing coupling markers, specifically includes: selecting all regulation action pairs that constitute a linkage relationship from the initial regulation rule sequence, wherein the regulation action pairs include frequency regulation action-voltage regulation action pairs and mutual influence pairs between similar actions; and, based on the consistency analysis results, determining constraint conflict relationship, constraint dependency relationship, priority triggering relationship, and safety domain superposition relationship for each regulation action pair to obtain the coupling relationship determination result. Based on the coupling relationship determination result, a coupling flag is configured for the corresponding control action pair, and a linkage coupling relationship mapping table is generated according to the coupling type. Based on the linkage and coupling relationship mapping table, the control actions with coupling relationships in the initial control rule sequence are reordered, associated and bound, or conditionally split to obtain the reordering result, the association and binding result, and the condition splitting result. Based on the reordering results, association binding results, and condition splitting results, all control actions are rearranged into an optimized control rule sequence containing coupling tags.

6. The method for frequency and voltage synergistic enhancement of a new energy county power grid according to claim 1, characterized in that, The steps of calculating the echo index of the influence of frequency regulation action on voltage regulation action and the echo index of the influence of voltage regulation action on frequency regulation based on the linkage coupling relationship are as follows: Collect the cross-domain influence parameters contained in the linkage coupling relationship, including frequency offset coefficient, voltage offset coefficient, power-frequency sensitivity coefficient, reactive power-voltage sensitivity coefficient, action duration, and action amplitude; based on the cross-domain influence parameters, calculate the cross-domain influence amount generated by the frequency regulation action on the voltage side and the cross-domain influence amount generated by the voltage regulation action on the frequency side; construct an echo model including a time decay factor, a response delay factor, and a feedback adjustment factor; input the cross-domain influence amount into the echo model to obtain the echo amount of the influence of frequency regulation action on voltage regulation action and the echo amount of the influence of voltage regulation action on frequency regulation action; The influence echo quantity is normalized to form the echo index of the influence of frequency regulation action on voltage regulation action, and the echo index of the influence of voltage regulation action on frequency regulation action.

7. The method for frequency and voltage coordinated enhancement of a new energy county power grid according to claim 4, characterized in that, The step of selecting a suppression-type substitution rule fragment to replace the corresponding control rule fragment when any of the influence echo indices exceeds a preset echo threshold, and correcting the optimized control rule sequence to obtain the corrected control rule sequence, specifically involves: extracting the influence echo index and the preset echo threshold, obtaining the comparison result between the influence echo index and the preset echo threshold, identifying the control action pair where the influence echo index exceeds the preset echo threshold, and determining the corresponding target control rule fragment; The target regulation rule fragment is identified by obtaining its rule type, action direction, and action intensity. Based on these parameters, a matching suppression-type alternative rule fragment is retrieved from a pre-defined alternative rule fragment library. This suppression-type alternative rule fragment is used to reduce cross-domain feedback effects and weaken echo amplification trends. The action trigger threshold and action priority are extracted from the action parameters to determine the excess amplitude affecting the echo index. Based on the action trigger threshold, action priority, and excess amplitude affecting the echo index, a replacement strategy is determined. This replacement strategy includes complete replacement, conditional replacement, or amplitude weakening replacement. According to the replacement strategy, the target regulation rule fragment is replaced with the corresponding suppression-type alternative rule fragment. All replaced regulation rule fragments are then integrated to form a modified regulation rule sequence processed by echo suppression.

8. The method for frequency and voltage synergistic enhancement of a new energy county power grid according to claim 5, characterized in that, The steps of performing consistency verification on the modified control rule sequence, triggering a control rule rollback mechanism and switching to the corresponding alternative control rule branch when verification fails, and executing the minimum offset solution rule to generate the final control rule sequence if all alternative control rules fail verification, are as follows: The steps involve obtaining the action parameters, coupling markers, and cross-domain influence relationships of all control actions in the modified control rule sequence; verifying the consistency of action triggering conditions, cross-domain coordination consistency, and security domain coverage consistency item by item according to preset consistency verification rules to obtain consistency verification results; when any verification item in the consistency verification results fails, identifying the failed control action and its corresponding control rule fragment; and selecting the corresponding alternative control rule branch from the preset alternative control rule tree based on the position, type, and coupling relationship of the control rule fragment in the modified control rule sequence. The alternative control rule branches are loaded and used to replace the original failed control rule fragments. The consistency of the replaced control rule sequence is then re-checked. If all the alternative control rule branches fail the consistency check, the minimum offset solution rule is executed to make minimum range numerical adjustments to the action trigger threshold, action amplitude coefficient, action duration, and linkage coupling relationship in the modified control rule sequence so that the adjusted control rule sequence satisfies the safety domain constraint and cross-domain coordination condition. The control rules after minimum offset solution are then re-integrated and output as the final control rule sequence.

9. A frequency and voltage coordinated enhancement system for a new energy county power grid, characterized in that, The method for frequency and voltage coordinated enhancement of a new energy county power grid as described in any one of claims 1-8 includes: a scenario recognition module, which collects real-time operating parameters of the county power grid and performs pattern recognition on the real-time operating parameters according to a preset scenario feature set to obtain the current operating scenario label; an initial control module, which, based on the operating scenario label, retrieves frequency control rule segments and voltage control rule segments that match the operating scenario label from a preset scenario-rule segment matrix to construct an initial control rule sequence for frequency-voltage coordination; and an optimized control module, which performs multi-domain constraint calculations on the initial control rule sequence to obtain multi-domain constraint parameters and establishes a linkage coupling relationship between frequency control actions and voltage control actions based on the multi-domain constraint parameters. The system generates an optimized control rule sequence containing coupling markers; an echo calculation module calculates the echo index of the impact of frequency control actions on voltage control actions, and the echo index of the impact of voltage control actions on frequency control, based on the linkage coupling relationship; a correction control module selects a suppression-type alternative rule segment to replace the corresponding control rule segment when any of the impact echo indices exceeds a preset echo threshold, correcting the optimized control rule sequence to obtain a corrected control rule sequence; and a verification control module performs a consistency verification on the corrected control rule sequence. If the verification fails, a control rule rollback mechanism is triggered and the system switches to the corresponding alternative control rule branch. If all alternative control rules fail the verification, the minimum offset solution rule is executed to generate the final control rule sequence.

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