Intelligent phase checking method and system for substation

Abstract

The present invention belongs to the technical field of smart substations, and specifically relates to a method and system for smart phase checking in substations. The method constructs a dynamic priority evaluation model, integrates multi-source data of four basic factors: voltage phase difference, ambient temperature and humidity, equipment vibration, and historical error, and uses a fuzzy comprehensive evaluation method to calculate weights and dynamically sort them. A multi-port phase scattering model is constructed based on the substation topology, and impedance mismatch points are analyzed with the help of a Smith chart to implement two-stage optimization. A three-layer decision input set is constructed based on the optimization model, and a fuzzy logic-driven adaptive rule engine is designed to dynamically output the operating condition level and match the phase checking strategy. Multi-dimensional quality assessment is performed through real-time monitoring of indicators such as reflection coefficient, dynamically updating factor weights, optimizing fuzzy model parameters, and integrating LSTM and graph neural networks to realize abnormal pattern recognition and compensation strategy solidification, forming a self-evolving closed-loop system, and ultimately achieving a transition from precise matching to autonomous evolution in the phase checking process.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent phase checking of substations, and in particular relates to an intelligent phase checking method and system for substations. Background Art

[0002] Phase verification in smart substations is crucial for the safe and stable operation of power systems. The accuracy of phase verification is directly related to grid connection, equipment commissioning, and reliable system operation. With the rapid development of smart grids, substations are becoming increasingly intelligent, and their operating environments and equipment characteristics are becoming increasingly complex, placing higher demands on phase verification technology.

[0003] Traditional intelligent phase checking methods for substations have numerous shortcomings when faced with the complexities of modern substation environments. For one thing, they often fail to fully account for the impact of various factors on phase checking results. For example, the voltage phase difference factor, a core parameter in phase checking, can be affected by numerous factors, yet traditional technologies may not fully dynamically assess and compensate for these factors.

[0004] Existing technologies have significant flaws in phase-check model construction. Existing phase checks are mostly based on simple electrical connection relationships, failing to fully consider the complexity of the substation topology and the phase response characteristics of each electrical connection port. At the same time, existing models also have shortcomings when dealing with impedance matching issues. Due to the lack of an effective method to identify impedance mismatch points, the phase-check model cannot be optimized in a targeted manner, making it easy for problems such as phase reflection and offset to occur during the actual phase-check process, seriously affecting the accuracy of the phase check. Furthermore, existing technologies lack flexibility and adaptability in terms of phase-check strategy execution and resource scheduling. Existing phase-check strategies often adopt a fixed execution mode and cannot dynamically adjust the phase-check method according to actual operating conditions. Finally, for errors generated during the phase-check process, the compensation mechanism of traditional technologies is not perfect. Existing error compensation methods mostly use fixed compensation coefficients and cannot be dynamically adjusted according to the priority and changing characteristics of the basic factors. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent phase matching method and system for substations. This method integrates dynamic priority evaluation, multi-port phase scattering model and closed-loop feedback to achieve adaptive optimization of phase matching strategy and system self-evolution, thereby improving the phase matching accuracy and stability of intelligent substations.

[0006] The technical solutions of the present invention are as follows:

[0007] One of the technical solutions of the present invention is to provide an intelligent phase checking method for a substation, comprising:

[0008] The voltage phase difference factor, environmental temperature and humidity factor, equipment vibration factor, and historical error factor are used as basic factors, and the dynamic priority of the basic factors is determined by constructing a dynamic priority evaluation model.

[0009] Based on the substation topology, the signal transmission and reflection characteristics of each electrical connection port are abstracted into a multi-port phase scattering model. The Smith chart is used to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model.

[0010] Execute the phase-correction strategy and build a decision input set based on the optimized phase scattering model. Based on fuzzy logic and interface selection, design an adaptive rule engine to dynamically output the phase-correction condition level and automatically select the phase-correction strategy accordingly.

[0011] Multi-dimensional quality assessment is performed by real-time monitoring of the port phase matching and reflection coefficient. The factor weights are dynamically updated and the fuzzy model parameters are optimized based on the evaluation results to form a self-evolving system.

[0012] As a further option of this method, the dynamic priority evaluation model uses a fuzzy comprehensive evaluation method to calculate the weight of each basic factor, including:

[0013] Establish a triangular membership function, a Gaussian membership function, or an S-type membership function to map the eigenvalues ​​of each basic factor to the interval [0,1];

[0014] Define the fuzzy weight vector by the formula Calculate the priority index, where For the The weight of the factors, For the The membership degree of the factor, For the The eigenvalues ​​of the factors;

[0015] Based on the priority index, the voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor and historical error factor are dynamically sorted to drive the phase correction strategy adjustment.

[0016] As a further option of this method, the substation electrical equipment is abstracted into a multi-port network and the scattering matrix is ​​defined for × A complex matrix with elements Indicates port peer port The scattering parameters of

[0017] By formula Calculate the scattering parameters, where is the scattering parameter, For port The reflected wave, For port The reflected wave is the incident wave;

[0018] The multi-port phase scattering model is weightedly optimized by combining the factor priorities output by the dynamic priority evaluation model.

[0019] As a further option of the method, the two-stage optimization includes:

[0020] First-stage optimization: For high-priority ports, impedance parameters are dynamically adjusted using parallel or series reactance elements. For low-priority ports, a fixed compensation strategy is used, with compensation values ​​calculated based on historical error factor feedback.

[0021] Second stage optimization: Optimize the objective function through full-site collaboration Achieve global optimal matching, where is the impedance vector of all ports, Frequency The phase shift, is the reflection coefficient weight coefficient, is the operating frequency band of the substation, For the Port frequency The modulus of the reflection coefficient.

[0022] As a further option of this method, the decision input set includes three layers of data:

[0023] Basic layer: including phase offset, reflection coefficient modulus, and impedance matching status;

[0024] State layer: includes dynamic priority indicators of basic factors output by the dynamic priority evaluation model;

[0025] Strategy layer: including compensation coefficients and priority weights driven by historical errors;

[0026] The data are normalized and mapped to a uniform dimension interval.

[0027] As a further option of this method, the adaptive rule engine includes:

[0028] Construct a fuzzy logic rule base, define the input variables as phase offset, reflection coefficient, and basic factor dynamic priority index, and divide the fuzzy intervals into "high / medium / low";

[0029] Dynamically match the core phase interface type based on fuzzy reasoning results. High-speed synchronous interface is enabled for optimal working conditions, low-bandwidth interface is switched to for intermediate working conditions, and redundant interface is activated for poor working conditions.

[0030] Output core phase execution strategy, including full-node parallel calibration of superior strategy, historical error sorting polling of intermediate strategy, and dual-channel redundant verification of poor strategy.

[0031] As a further option of this method, the multi-dimensional quality assessment includes:

[0032] A three-level quality indicator system is defined: base-layer indicators include port reflection coefficient modulus, phase offset, and impedance normalization deviation; status-layer indicators include dynamic priority weight volatility, historical error recurrence probability, and equipment vibration anomaly; and performance-layer indicators include phase strategy resource utilization, interface switching response delay, and site-wide collaborative optimization convergence speed.

[0033] Perform weighted analysis on real-time data through a sliding window algorithm to generate a multi-dimensional quality assessment report.

[0034] As a further option of this method, the factor weight dynamic update mechanism includes:

[0035] Normalize the voltage phase difference, temperature and humidity, vibration amplitude and historical error data collected in real time;

[0036] Adjust the fuzzy interval parameters of the triangle membership function 、 and ,in 、 and are fuzzy interval parameters, representing the lower limit, optimal value and upper limit of the factor eigenvalue respectively;

[0037] Introducing a time decay factor , where 0< <1, by the formula Update the weights, where For the Moment The fuzzy weight vector of the basic factors, is a quality assessment indicator, For the The eigenvalues ​​of the basic factors.

[0038] As a further option of this method, the fuzzy model parameter optimization includes:

[0039] The kernel density estimation method is used to dynamically divide the fuzzy interval;

[0040] Assign dynamic confidence coefficients to fuzzy rules, which are calculated jointly by the historical rule triggering accuracy and the current quality assessment deviation rate;

[0041] Genetic algorithm is introduced to optimize the shape parameters of fuzzy membership function;

[0042] When multiple fuzzy rules conflict, the DS evidence theory is used to fuse multi-source evidence and output the working condition level judgment with the maximum comprehensive confidence.

[0043] As a further option of this method, the abnormal pattern recognition and compensation strategy generation steps in the self-evolution system include:

[0044] Long short-term memory networks are used to capture the long-term dependencies of voltage phase difference and equipment vibration;

[0045] Modeling electrical coupling relationships in substation topology using graph neural networks;

[0046] The identified abnormal patterns are labeled and stored, and the compensation solutions that recur frequently are solidified into new fuzzy rules.

[0047] As a further option of this method, the voltage phase difference factor is obtained by Calculate; where, is the voltage phase difference factor, is the actual measured voltage phase difference, is the maximum voltage phase difference allowed by the system;

[0048] The environmental temperature and humidity factors are Calculate; where, is the environmental temperature and humidity factor, and are the weights of temperature and humidity, and are functions of the effects of temperature and humidity on device performance;

[0049] The equipment vibration factor is Calculate; where, is the equipment vibration factor, and are the weights of vibration frequency and amplitude respectively, and are functions of the effects of vibration frequency and amplitude on equipment performance;

[0050] The historical error factor is based on Calculate; where, is the historical error factor, For the The weight of the historical error, For the The severity of the historical error.

[0051] As a further option of this method, the core phase execution strategy includes:

[0052] Optimal strategy: Relying on high-precision timestamps to achieve full-port parallel calibration, and using a load balancing algorithm to prioritize the matching speed of the main transformer and busbar;

[0053] Intermediate strategy: Allocate limited bandwidth resources based on a time slice rotation strategy, monitor node progress in real time, and dynamically release computing resources;

[0054] Differential strategy: The primary and backup channels are independently compared. Deviations in the comparison results trigger a secondary optimization process. In the event of an abnormality, the system automatically switches to the backup channel and notifies the operation and maintenance personnel.

[0055] The second technical solution of the present invention is to provide a substation intelligent phase checking method system, comprising:

[0056] The dynamic priority evaluation module is used to determine the dynamic priority of the basic factors by building a dynamic priority evaluation model based on the voltage phase difference factor, the ambient temperature and humidity factor, the equipment vibration factor, and the historical error factor.

[0057] The multi-port modeling and optimization module is used to abstract the signal transmission and reflection characteristics of each electrical connection port into a multi-port phase scattering model based on the substation topology. It uses Smith charts to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model.

[0058] The core phase strategy execution module is used to execute the core phase strategy and build a decision input set based on the optimized phase scattering model. Based on fuzzy logic and interface selection ideas, an adaptive rule engine is designed to dynamically output the core phase working condition level and automatically select the core phase strategy based on it.

[0059] The quality assessment and optimization module is used to perform multi-dimensional quality assessment by real-time monitoring of port phase matching and reflection coefficient. It dynamically updates factor weights and optimizes fuzzy model parameters based on the assessment results to form a self-evolving system.

[0060] The beneficial effects brought about by the technical solutions provided in the embodiments of the present application include at least the following beneficial effects:

[0061] This technical solution achieves breakthrough innovation in smart substation phase analysis technology by constructing a dynamic priority assessment model, a multi-port phase scattering model, and a closed-loop feedback self-evolution system. Its core advantages lie in: The multi-factor dynamic priority assessment model deeply integrates multi-dimensional data such as voltage phase difference, ambient temperature and humidity, equipment vibration, and historical errors. Combining fuzzy comprehensive evaluation with triangular membership functions, it dynamically adjusts factor weights, overcoming the limitations of traditional static models and significantly improving adaptability under complex operating conditions. The multi-port phase scattering model and two-stage optimization accurately identify impedance mismatch points using Smith charts. Combining single-port basic matching with full-station collaborative optimization, they minimize phase offset and optimize return loss across a wide bandwidth. An adaptive phase analysis strategy and fuzzy logic rule engine dynamically output excellent / medium / poor operating condition grades based on a three-tiered decision input set, differentially matching high-speed synchronous interfaces, low-bandwidth time-sharing scheduling, or redundant channel verification strategies, balancing efficiency and fault tolerance. The closed-loop feedback system drives factor weight updates and fuzzy model parameter optimization through multi-dimensional quality assessment. Deep learning technology is introduced to solidify high-frequency compensation solutions as new rules, enabling system autonomous learning and fault prediction.

[0062] This technical solution has been piloted in multiple smart substations, verifying its significant application effects and practical value. In terms of improving phase accuracy and efficiency, through multi-port collaborative optimization, the modulus of the port reflection coefficient is reduced to below the threshold, the maximum phase offset of the entire station is controlled within the allowable range, the speed of parallel calibration of all nodes under excellent conditions is increased by more than 30%, the limited bandwidth utilization rate under intermediate conditions is increased by 25%, and the fault switching response time under poor conditions is shortened to milliseconds. In terms of enhancing environmental adaptability and fault tolerance, the dynamic priority evaluation model can respond in real time to environmental coupling effects such as temperature and humidity fluctuations and vibration anomalies, and maintain phase stability even in extreme weather conditions. The dual-channel redundant verification mechanism of the poor strategy effectively isolates hardware faults and ensures continuous operation of the power grid. In terms of intelligent and standardized integration, the output of MMS service messages that comply with the IEC61850 standard is seamlessly connected to the SCADA system. The full life cycle health management module generates digital portraits and efficiency heat maps, providing data-driven decision support for digital operation and maintenance.

[0063] This technical solution builds a complete technical system for smart substation core phase technology through the four-dimensional collaboration of "multi-factor dynamic priority modeling - multi-port impedance optimization - adaptive strategy execution - closed-loop self-evolution", solving the bottleneck problems of traditional methods in environmental adaptability, resource allocation efficiency and long-term stability. Its innovation is reflected in:

[0064] The dynamic priority model breaks through the limitations of static weights and realizes multi-factor nonlinear coupling analysis;

[0065] The dual-stage optimization algorithm takes into account both single-port matching and full-station coordination to improve broadband performance;

[0066] The hierarchical strategy of the fuzzy logic rule engine matches resource constraints to ensure the priority of key nodes;

[0067] The self-evolving system achieves equipment aging compensation and fault mode self-learning through a data-driven closed loop.

[0068] This technology has been piloted in multiple smart substations, verifying its significant advantages in improving phase analysis accuracy, reducing operation and maintenance costs, and enhancing system robustness, providing a replicable technical path for the intelligent upgrade of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a schematic diagram of the overall process of the intelligent phase verification method for substations;

[0070] Figure 2 This is a detailed flow chart of the steps of the intelligent phase checking method S100 for a substation;

[0071] Figure 3 Detailed flow chart of step S200 of intelligent phase checking method for substation;

[0072] Figure 4 Detailed flow chart of step S300 of intelligent phase checking method for substation;

[0073] Figure 5 This is a detailed flow chart of the steps S400 of the intelligent phase checking method for a substation. DETAILED DESCRIPTION

[0074] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0075] Traditional substation intelligent phase checking technology faces many technical bottlenecks when facing the complex operating environment and high-precision phase checking requirements of modern smart substations. In order to improve the accuracy, reliability and adaptability of phase checking, a new phase checking method is urgently needed that can comprehensively consider multi-source data, dynamically evaluate the priority of basic factors, optimize phase checking models, intelligently schedule resources, and achieve precise error compensation. Figure 1 , which shows a substation intelligent phase checking method provided by an embodiment of the present invention, the method comprising:

[0076] S100: The voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor, and historical error factor are used as basic factors. A dynamic priority evaluation model is constructed to determine the dynamic priority of the basic factors.

[0077] S200: Based on the substation topology, the signal transmission and reflection characteristics of each electrical connection port are abstracted into a multi-port phase scattering model. The Smith chart is used to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model.

[0078] S300: Executes the phase-correction strategy and constructs a decision input set based on the optimized phase scattering model. Based on fuzzy logic and interface selection, an adaptive rule engine is designed to dynamically output the phase-correction condition level and automatically select the phase-correction strategy accordingly.

[0079] S400: Performs multi-dimensional quality assessment by real-time monitoring of port phase matching and reflection coefficient. Dynamically updates factor weights and optimizes fuzzy model parameters based on the assessment results, forming a self-evolving system.

[0080] The specific plan is as follows:

[0081] In the intelligent phase analysis method for substations, S100 involves taking multiple influencing factors as basic factors and judging the dynamic priority of these basic factors through a dynamic evaluation model that combines multi-source data fusion with environmental coupling.

[0082] Please refer to Figure 2 , which shows a flowchart of an exemplary substation intelligent phase checking method S100 of the present application, including:

[0083] S110: Acquire multi-source heterogeneous data and calculate multi-dimensional basic factor data;

[0084] The multi-dimensional basic factors include: voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor, and historical error factor. The data corresponding to the multi-dimensional basic factors includes: voltage phase difference data, ambient temperature and humidity data, equipment vibration data, and historical error data.

[0085] In one possible implementation, voltage phase difference data is obtained via a voltage transformer and a current transformer. Ambient temperature and humidity data are obtained via a temperature and humidity sensor. Equipment vibration data is obtained via an accelerometer or a vibration sensor. Historical error data is extracted from a historical record database.

[0086] The voltage phase difference factor, the ambient temperature and humidity factor, the equipment vibration factor, and the historical error factor are calculated based on the voltage phase difference data, the ambient temperature and humidity data, the equipment vibration data, and the historical error data.

[0087] In one possible implementation, the voltage phase difference factor is calculated using the following formula:

[0088] ;

[0089] in, is the voltage phase difference factor, is the actual measured voltage phase difference, is the maximum voltage phase difference allowed by the system.

[0090] In one possible implementation, the ambient temperature and humidity factor is calculated by comprehensively considering the effects of temperature and humidity:

[0091] ;

[0092] in, is the environmental temperature and humidity factor, and are the weights of temperature and humidity, and are functions of the effects of temperature and humidity on device performance, respectively.

[0093] In one possible implementation, the equipment vibration factor is calculated using the following formula:

[0094] ;

[0095] in, is the equipment vibration factor, and are the weights of vibration frequency and amplitude respectively, and are functions of the effects of vibration frequency and amplitude on device performance, respectively.

[0096] In one possible implementation, the historical error factor is calculated based on the error frequency and severity in the historical data:

[0097] ;

[0098] in, is the historical error factor, For the The weight of the historical error, For the The severity of the historical error.

[0099] S120: Build a dynamic priority evaluation model.

[0100] By building a dynamic priority assessment model, we can determine the priority of factors such as voltage phase difference, ambient temperature and humidity, equipment vibration, and historical errors during the phase confirmation process. The core of the dynamic priority assessment model is to integrate multi-source data and dynamically adjust the weight of each factor based on environmental coupling effects, thereby improving the adaptability and accuracy of the phase confirmation strategy.

[0101] The dynamic priority assessment model uses a multi-factor weighted comprehensive evaluation method, in which the weight of each factor is calculated using a fuzzy comprehensive evaluation method. The basic idea of ​​this method is to use fuzzy mathematics theory to combine qualitative analysis with quantitative calculation to more reasonably reflect the impact of each factor under different environmental conditions. The input of the model is the characteristic index of each factor, and the output is the priority weight of each factor.

[0102] Specifically, Represents four basic factors, namely voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor, and historical error factor. The corresponding eigenvectors are ,in Indicates the In order to calculate the weight of each factor, we first need to establish the fuzzy membership function , to measure the influence of eigenvalues ​​on the nuclear phase process;

[0103] The form of the fuzzy membership function is selected according to actual needs, including triangular membership function, Gaussian membership function or S-type membership function.

[0104] For example, using the triangular membership function, the The membership degree of each basic factor is expressed as:

[0105] ;

[0106] in, 、 and are fuzzy interval parameters, representing the lower limit, optimal value and upper limit of the factor eigenvalue respectively, Indicates the The quantified eigenvalues ​​of the basic factors. Through the membership function, the eigenvalues ​​of each factor can be mapped to the interval [0,1] to measure its influence on the nuclear phase process.

[0107] After determining the membership of each factor, the fuzzy comprehensive evaluation method is used to calculate the weight of each basic factor. Let the fuzzy weight vector be , the calculation formula of fuzzy comprehensive evaluation is:

[0108] ;

[0109] in, For the The weight of the factors satisfies ; For the The membership degree of the factor, For the The eigenvalues ​​of the factors;

[0110] The priority index reflects the overall impact of various factors on the phase checking process in the current environment and can be used to dynamically adjust the phase checking strategy. The voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor, and historical error factor are dynamically sorted by the priority index.

[0111] In the substation intelligent phase analysis method, S200 abstracts the signal transmission and reflection characteristics of each electrical connection port into a multi-port phase scattering model based on the substation topology. It uses the Smith chart to analyze the port phase scattering characteristics, identify impedance mismatch points, and implements a two-stage optimization of the multi-port phase scattering model.

[0112] Please refer to Figure 3 , which shows a flowchart of an exemplary substation intelligent phase checking method S200 of the present application, and its contents include:

[0113] S210: Multi-port phase scattering model construction.

[0114] In the intelligent phase matching process of a substation, the construction of a multi-port phase scattering model is the foundation for achieving high-precision phase matching. Based on the priority order of the fundamental factors and the substation topology, the phase response characteristics of each electrical connection port are abstracted into a multi-port phase scattering model, and the phase response characteristics of each port are described using scattering parameters (S parameters).

[0115] In one possible implementation, the electrical equipment of the substation is considered as a multi-port network, where each port corresponds to an electrical connection point. ports, then its scattering matrix for × A complex matrix with elements Indicates port peer port The scattering parameters describe the transmission and reflection characteristics of the signal between ports and are the key to analyzing phase mismatch.

[0116] The multi-port phase scattering model is defined as follows:

[0117] ;

[0118] in, is the scattering parameter, For port The reflected wave, For port The reflected wave is the incident wave.

[0119] The construction of the multi-port phase scattering model provides a mathematical basis for subsequent Smith chart analysis and optimization, enabling the phase kernel process to accurately identify impedance mismatch points and implement dynamic adjustments.

[0120] S220: Use the Smith chart to analyze the port phase scattering characteristics and identify impedance mismatch points.

[0121] Based on the multi-port phase scattering model, Smith chart analysis is used to identify impedance mismatch points at each port. A Smith chart is a polar plot used to visualize complex impedance, providing a direct view of the normalized impedance characteristics of a port and its deviation from the ideal match.

[0122] In one possible implementation, the steps of analyzing the port phase scattering characteristics using a Smith chart and identifying the impedance mismatch point include:

[0123] S221: Calculate the normalized impedance and reflection coefficient.

[0124] For any port , its normalized impedance Defined as: ;in, is the actual impedance of the port, is the system characteristic impedance, typically 50Ω or 75Ω. After normalization, the impedance value is mapped to the unit circle of the Smith chart for intuitive analysis.

[0125] Reflection coefficient It is a key parameter to measure the degree of port impedance matching, and its calculation formula is: The modulus of the reflection coefficient The closer the reflection coefficient point is to the center of the Smith chart, the better the impedance matching.

[0126] S222: Analyze the reflection coefficients of all ports.

[0127] Assume that the substation contains ports, then the reflection coefficient vector It can be used to quantify the matching status of each port. The criteria for determining the mismatch point are usually based on the threshold of the reflection coefficient modulus:

[0128] ;

[0129] in, is the preset reflection coefficient threshold. If the reflection coefficient of a port exceeds this threshold, it is considered that there is a significant impedance mismatch at that port and the location is marked as a mismatch point.

[0130] Through Smith chart analysis, the impedance mismatch points of each port can be efficiently identified, providing precise adjustment directions for subsequent two-stage optimization.

[0131] S230: Based on the identified mismatch points, perform a two-stage optimization on the multi-port phase scattering model.

[0132] After identifying the impedance mismatch point, the goal of the first stage optimization is to minimize the phase reflection coefficient of a single port by adjusting the impedance parameters of each port, thereby achieving basic impedance matching.

[0133] In one possible implementation, for high-priority ports, parallel or series reactive elements are typically used for dynamic adjustment to reduce the reflection coefficient. Exemplarily, by adding adjustable reactive elements to change the reactive portion of the port, the optimal matching state is gradually approached.

[0134] In one possible implementation, for low-priority ports, due to resource limitations, a fixed compensation strategy is typically adopted. For example, a fixed capacitor or inductor is connected in parallel to the port. In this case, the compensation value is calculated based on feedback from historical error factors.

[0135] Finally, after the first stage of optimization is completed, the reflection coefficient modulus of each port should be lower than the preset threshold to ensure the realization of basic impedance matching.

[0136] After completing the single-port basic impedance matching, the goal of the second stage optimization is to further minimize the maximum phase offset within the operating frequency band and reduce the overall reflection coefficient modulus through full-station collaborative optimization.

[0137] In a possible implementation, the second stage achieves global optimal matching by comprehensively considering the coupling effect between ports and dynamic priority weights.

[0138] Specifically, the objective function of the whole-station collaborative optimization is defined as:

[0139] ;

[0140] in, is the impedance vector of all ports, Frequency The phase shift, is the reflection coefficient weight coefficient, is the operating frequency band of the substation, For the Port frequency The modulus of the reflection coefficient.

[0141] Minimizing the objective function of full-station collaborative optimization means achieving the minimum phase offset and lowest return loss across the entire frequency band. To solve this optimization problem, an iterative optimization algorithm is typically employed. Exemplary algorithms include the Newton-Raphson method or a genetic algorithm.

[0142] Finally, after the second stage of optimization is completed, the maximum phase offset within the entire station operating frequency band should be controlled within the allowable range, and the reflection coefficient modulus of each port should be further reduced to ensure high-precision matching of the phase core process.

[0143] In the intelligent phase checking method for substations, S300 executes the phase checking strategy and constructs a decision input set based on the optimized phase scattering model. Based on fuzzy logic and interface selection ideas, an adaptive rule engine is designed to dynamically output the phase checking condition level and automatically select the phase checking strategy accordingly.

[0144] Please refer to Figure 4 , which shows a flowchart of an exemplary substation intelligent phase checking method S300 of the present application, and its contents include:

[0145] S310: Constructing a decision input set based on the optimized phase scattering model.

[0146] The role of the decision input set is to convert complex phase matching information into executable decision parameters, providing a data basis for subsequent operating condition level evaluation and strategy selection.

[0147] Extract parameters from the site-wide collaborative optimization model, including phase offset, reflection coefficient modulus, and impedance matching status for each port. Normalize the data to eliminate the influence of different dimensions. For example, convert the phase offset to a relative error percentage, and map the reflection coefficient modulus to the [0, 1] interval. Simultaneously, extract the dynamic priority, the fundamental factor output by the dynamic priority evaluation model in S100, and simultaneously construct a decision input set containing three layers of data.

[0148] In one possible implementation, the constructed decision input set containing three layers of data includes:

[0149] Base layer: physical parameters directly derived from the whole-station collaborative optimization model, such as phase offset and reflection coefficient.

[0150] State layer: Dynamic priority indicators of the basic factors output by the dynamic priority evaluation model.

[0151] Strategy layer: compensation coefficients and priority weights driven by historical errors to guide resource allocation.

[0152] S320: Design an adaptive rule engine.

[0153] The goal of the adaptive rule engine is to dynamically generate the core phase operating condition level (excellent / medium / poor) based on the decision input set, and realize strategy matching through fuzzy logic and interface selection ideas.

[0154] In one possible implementation, the adaptive rule engine design steps include:

[0155] S321: Build a fuzzy logic rule base.

[0156] Define input variables, including phase offset, reflection coefficient, and basic factor dynamic priority index.

[0157] Divide the fuzzy sets and define three fuzzy intervals of "high", "medium" and "low" for each variable.

[0158] Formulate fuzzy rules. For example, “If the phase matching is high and the environment is stable, the operating condition is excellent”; “If the equipment vibration is abnormal and the historical errors are frequent, the operating condition is poor”.

[0159] S322: Dynamically matching the core phase interface type based on the fuzzy reasoning result.

[0160] Specifically, for excellent operating conditions, a high-speed synchronous interface is enabled to support parallel phase checking across all nodes. For medium operating conditions, a low-bandwidth interface is switched to, and polling is performed in a time-sharing manner based on historical error sorting. For poor operating conditions, a redundant interface is activated, and independent phase checking is started on the primary and backup channels.

[0161] S330: Based on the decision input set and the adaptive rule engine, dynamically output the core phase execution strategy.

[0162] In one possible implementation, the output core phase execution strategy includes:

[0163] S331: Optimal strategy. This strategy utilizes a high-speed synchronization interface to simultaneously initiate phase alignment on all electrical connection ports, relying on high-precision timestamps to ensure operational consistency. A load balancing algorithm dynamically allocates computing resources, prioritizing matching speeds for high-priority nodes such as main transformers and busbars.

[0164] S332: Intermediate Strategy. Nodes are sorted by historical error severity, prioritizing calibration of nodes with high-frequency errors. For example, circuit breakers with a history of impedance mismatches are prioritized. Based on a time-slice round-robin strategy, limited bandwidth resources are allocated to the highest-ranked nodes, ensuring that critical nodes complete matching first. The matching progress of each node is monitored in real time. If a node completes early, its occupied computing resources are immediately released to make them available to subsequent nodes.

[0165] S333: Differential Strategy. This strategy uses dual-channel redundant phase checking and verification. The primary channel performs conventional phase checking, while the backup channel performs independent verification using a different algorithm. The phase matching results of the primary and backup channels are compared. If the deviation exceeds a threshold, a secondary optimization process is triggered and an exception log is recorded. If a channel exhibits persistent anomalies, the system automatically switches to the backup channel and notifies maintenance personnel to troubleshoot the hardware failure.

[0166] In the substation's intelligent phase-checking approach, the S400 implements differentiated compensation control for fundamental factors based on real-time dynamic priority weights. Low-priority factors use slowly varying compensation to prevent system oscillations, while high-priority factors use sudden change suppression to ensure phase-checking stability. Ultimately, the compensation values ​​are dynamically injected into the phase-checking strategy execution chain.

[0167] Please refer to Figure 5 , which shows a flowchart of an exemplary substation intelligent phase checking method S400 of the present application, and its contents include:

[0168] S410: Real-time monitoring and multi-dimensional quality assessment.

[0169] By deploying a distributed sensor network and edge computing nodes, dynamic parameters such as phase matching, reflection coefficient, equipment vibration amplitude, temperature and humidity, and historical error trigger frequency are collected in real time from each electrical connection port within the substation. Multimodal data fusion technology is used to align physical layer signals with environmental layer data in time and space, constructing a multidimensional data stream that includes timestamps, spatial locations, and parameter correlations.

[0170] In one possible implementation, based on a preset nuclear phase quality assessment matrix, a three-level quality indicator system is defined, including:

[0171] Basic layer indicators include: port reflection coefficient modulus, phase offset, and impedance normalization deviation;

[0172] Status layer indicators include: dynamic priority weight volatility, historical error recurrence probability, and equipment vibration anomaly;

[0173] Performance layer indicators include: core strategy resource utilization, interface switching response delay, and site-wide collaborative optimization convergence speed.

[0174] The real-time data is weightedly analyzed through a sliding window algorithm. Combined with the typical operating condition characteristics in the offline historical database, the quality deviation trend of the current nuclear phase process is dynamically identified, and a multi-dimensional quality assessment report is generated to provide a decision-making basis for subsequent parameter optimization.

[0175] S420: Dynamic update mechanism of factor weights.

[0176] Based on the quality evaluation results output by S410, the incremental fuzzy comprehensive evaluation method is used to perform online correction on the factor weights of the dynamic priority evaluation model in S100.

[0177] In one possible implementation, the online correction step includes:

[0178] S421: Normalize the voltage phase difference, temperature and humidity, vibration amplitude, and historical error data collected in real time to eliminate dimensional differences.

[0179] S422: According to the deviation distribution characteristics in the latest quality assessment report, adjust the fuzzy interval parameters of the triangle membership function in S120 to make the membership curve closer to the nonlinear response characteristics of the current working conditions.

[0180] S423: Introducing time decay factor (0< <1), perform weighted fusion of the historical weight vector and the current quality deviation gradient, and use the gradient descent algorithm to update the fuzzy weight of each factor. The formula is:

[0181] ;

[0182] in, For the Moment The fuzzy weight vector of the basic factors, is a quality assessment indicator, For the The eigenvalues ​​of the basic factors.

[0183] S424: Recalculate the priority index based on the updated weight vector , perform real-time priority sorting on the voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor and historical error factor, and drive the strategy switching of the adaptive rule engine in S300.

[0184] S430: Optimizing fuzzy model parameters.

[0185] According to the static characteristics of the fuzzy logic rule base in S320, a parameter self-tuning mechanism is designed.

[0186] In one possible implementation, the steps for designing the parameter self-tuning mechanism include:

[0187] S431: Based on the probability density distribution of input variables such as phase offset and reflection coefficient in S410, a kernel density estimation method is used to dynamically divide the fuzzy intervals into "high / medium / low", replacing the traditional manually set fixed thresholds;

[0188] S432: assigning a dynamic confidence coefficient to the fuzzy rule preset in S321, the coefficient being calculated jointly by the historical rule triggering accuracy and the current quality assessment deviation rate;

[0189] S433: Introducing genetic algorithms to optimize the shape parameters of fuzzy membership functions, so that the fuzzy reasoning results are more consistent with the nonlinear coupling effects under complex working conditions;

[0190] S434: When multiple fuzzy rules are triggered simultaneously and the conclusions are contradictory, the DS evidence theory is used to fuse multi-source evidence, calculate the confidence distribution of each conclusion, and output the working condition level judgment with the maximum comprehensive confidence.

[0191] S440: Adaptive adjustment of the core phase strategy.

[0192] Based on the dynamic operating condition level output by S430 and the factor priority updated by S420, the following strategy adjustments are performed:

[0193] For optimal working conditions, the 5G high-speed synchronization interface is enabled to achieve parallel delivery and result aggregation of phase calibration tasks for all stations.

[0194] For intermediate working conditions, the system switches to the LoRa low-power wide area network interface and schedules calibration tasks in different levels according to the severity of historical errors.

[0195] For poor working conditions, the redundant fiber optic channel is activated, and the independent phase verification and result cross-verification of the main and backup dual channels are started;

[0196] S450: Abnormal pattern recognition and compensation strategy generation.

[0197] Build an abnormal pattern recognition engine based on deep learning to perform feature extraction and pattern clustering on multi-source data collected by S410.

[0198] In one possible implementation, a long short-term memory network is used to capture long-term dependencies on parameters such as voltage phase difference and equipment vibration, identifying periodic mismatch patterns. A graph neural network is used to model the electrical coupling between ports in the substation topology, locating cascading mismatches caused by neighboring node faults.

[0199] Identified abnormal patterns are stored in a tagged format, and high-frequency recurrence compensation solutions are solidified as new fuzzy rules within the adaptive rule engine within S300. For example, "If the vibration amplitude of a circuit breaker continuously exceeds the standard and the reflection coefficient is greater than 0.3, then initiate dynamic tuning of the reactive element."

[0200] Through the coordinated operation of the above sub-steps, S400 has achieved a complete closed loop from data collection, quality assessment to strategy evolution, enabling the phase-checking system to compensate for equipment aging, adapt to environmental mutations, and self-identify new fault modes, ultimately achieving a leap from "precise matching" to "autonomous evolution" in substation intelligent phase-checking technology.

[0201] This application also provides a substation intelligent phase verification method system, including:

[0202] The dynamic priority evaluation module is used to determine the dynamic priority of the basic factors by building a dynamic priority evaluation model based on the voltage phase difference factor, the ambient temperature and humidity factor, the equipment vibration factor, and the historical error factor.

[0203] The multi-port modeling and optimization module is used to abstract the signal transmission and reflection characteristics of each electrical connection port into a multi-port phase scattering model based on the substation topology. It uses Smith charts to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model.

[0204] The core phase strategy execution module is used to execute the core phase strategy and build a decision input set based on the optimized phase scattering model. Based on fuzzy logic and interface selection ideas, an adaptive rule engine is designed to dynamically output the core phase working condition level and automatically select the core phase strategy based on it.

[0205] The quality assessment and optimization module is used to perform multi-dimensional quality assessment by real-time monitoring of port phase matching and reflection coefficient. It dynamically updates factor weights and optimizes fuzzy model parameters based on the assessment results to form a self-evolving system.

[0206] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0207] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0208] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0209] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be applied in the widest sense consistent with the principles and novel features of the present invention.

[0210] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. The intelligent phase checking method for substations is characterized by: include: The voltage phase difference factor, environmental temperature and humidity factor, equipment vibration factor, and historical error factor are used as basic factors, and the dynamic priority of the basic factors is determined by constructing a dynamic priority evaluation model. Based on the substation topology, the signal transmission and reflection characteristics of each electrical connection port are abstracted into a multi-port phase scattering model. The Smith chart is used to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model. Execute the kernel phase strategy and construct the decision input set based on the optimized multi-port phase scattering model; Based on fuzzy logic and interface selection ideas, an adaptive rule engine is designed to dynamically output the nuclear phase working condition level and automatically select the nuclear phase strategy based on it; By real-time monitoring of the port phase matching and reflection coefficient, multi-dimensional quality assessment is performed. The factor weights are dynamically updated and the fuzzy model parameters are optimized based on the assessment results, forming a self-evolving system. The two-stage optimization includes: First-stage optimization: For high-priority ports, impedance parameters are dynamically adjusted using parallel or series reactance elements. For low-priority ports, a fixed compensation strategy is used, with compensation values ​​calculated based on historical error factor feedback. Second stage optimization: Optimize the objective function through full-site collaboration Achieve global optimal matching, where is the impedance vector of all ports, Frequency The phase shift, is the reflection coefficient weight coefficient, is the operating frequency band of the substation, For the Port frequency The modulus of the reflection coefficient.

2. The intelligent phase checking method for substation according to claim 1, characterized in that: The dynamic priority evaluation model uses a fuzzy comprehensive evaluation method to calculate the weight of each basic factor, including: Establish a triangular membership function, a Gaussian membership function, or an S-type membership function to map the eigenvalues ​​of each basic factor to the interval [0,1]; Define the fuzzy weight vector by the formula Calculate the priority index, where For the The weight of the factors, For the The membership degree of the factor, For the The eigenvalues ​​of the factors; Based on the priority index, the voltage phase difference factor, ambient temperature and humidity factor, equipment vibration factor and historical error factor are dynamically sorted to drive the phase correction strategy adjustment.

3. The intelligent phase checking method for substation according to claim 1, characterized in that: Abstract the substation electrical equipment into a multi-port network and define the scattering matrix for × A complex matrix with elements Indicates port peer port The scattering parameters of By formula Calculate the scattering parameters, where is the scattering parameter, For port The reflected wave, For port The reflected wave is the incident wave; The multi-port phase scattering model is weightedly optimized by combining the factor priorities output by the dynamic priority evaluation model.

4. The intelligent phase checking method for substation according to claim 1, characterized in that: The decision input set contains three layers of data: Basic layer: including phase offset, reflection coefficient modulus, and impedance matching status; State layer: includes dynamic priority indicators of basic factors output by the dynamic priority evaluation model; Strategy layer: including compensation coefficients and priority weights driven by historical errors; The data are normalized and mapped to a uniform dimension interval.

5. The intelligent phase checking method for substation according to claim 1, characterized in that: The adaptive rule engine includes: Construct a fuzzy logic rule base, define the input variables as phase offset, reflection coefficient, and basic factor dynamic priority index, and divide the fuzzy intervals into "high / medium / low"; Dynamically match the core phase interface type based on fuzzy reasoning results. High-speed synchronous interface is enabled for optimal working conditions, low-bandwidth interface is switched to for intermediate working conditions, and redundant interface is activated for poor working conditions. Output core phase execution strategy, including full-node parallel calibration of superior strategy, historical error sorting polling of intermediate strategy, and dual-channel redundant verification of poor strategy.

6. The intelligent phase checking method for substation according to claim 1, characterized in that: The multi-dimensional quality assessment includes: A three-level quality indicator system is defined: base-layer indicators include port reflection coefficient modulus, phase offset, and impedance normalization deviation; status-layer indicators include dynamic priority weight volatility, historical error recurrence probability, and equipment vibration anomaly; and performance-layer indicators include phase strategy resource utilization, interface switching response delay, and site-wide collaborative optimization convergence speed. Perform weighted analysis on real-time data through a sliding window algorithm to generate a multi-dimensional quality assessment report.

7. The intelligent phase checking method for substation according to claim 1, characterized in that: The dynamic update mechanism of the factor weights includes: Normalize the voltage phase difference, temperature and humidity, vibration amplitude and historical error data collected in real time; Adjust the fuzzy interval parameters of the triangle membership function 、 and ,in 、 and are fuzzy interval parameters, representing the lower limit, optimal value and upper limit of the factor eigenvalue respectively; Introducing a time decay factor , where 0< <1, by formula Update the weights, where For the Moment The fuzzy weight vector of the basic factors, is a quality assessment indicator, For the The eigenvalues ​​of the basic factors.

8. The intelligent phase checking method for substation according to claim 1, characterized in that: The fuzzy model parameter optimization includes: The kernel density estimation method is used to dynamically divide the fuzzy interval; Assign dynamic confidence coefficients to fuzzy rules, which are calculated jointly by the historical rule triggering accuracy and the current quality assessment deviation rate; Genetic algorithm is introduced to optimize the shape parameters of fuzzy membership function; When multiple fuzzy rules conflict, the DS evidence theory is used to fuse multi-source evidence and output the working condition level judgment with the maximum comprehensive confidence.

9. The intelligent phase checking method for substation according to claim 1, characterized in that: The steps of abnormal pattern recognition and compensation strategy generation in the self-evolution system include: Long short-term memory networks are used to capture the long-term dependencies of voltage phase difference and equipment vibration; Modeling electrical coupling relationships in substation topology using graph neural networks; The identified abnormal patterns are labeled and stored, and the compensation solutions that recur frequently are solidified into new fuzzy rules.

10. The intelligent phase checking method for a substation according to claim 1 or 2, characterized in that: The voltage phase difference factor is obtained by Calculate; where, is the voltage phase difference factor, is the actual measured voltage phase difference, is the maximum voltage phase difference allowed by the system; The environmental temperature and humidity factors are Calculate; where, is the environmental temperature and humidity factor, and are the weights of temperature and humidity, and are functions of the effects of temperature and humidity on device performance; The equipment vibration factor is Calculate; where, is the equipment vibration factor, and are the weights of vibration frequency and amplitude respectively, and are functions of the effects of vibration frequency and amplitude on equipment performance; The historical error factor is based on Calculate; where, is the historical error factor, For the The weight of the historical error, For the The severity of the historical error.

11. The intelligent phase checking method for substation according to claim 1, characterized in that: The execution of the nuclear phase strategy includes: Optimal strategy: Relying on high-precision timestamps to achieve full-port parallel calibration, and using a load balancing algorithm to prioritize the matching speed of the main transformer and busbar; Intermediate strategy: Allocate limited bandwidth resources based on a time slice rotation strategy, monitor node progress in real time, and dynamically release computing resources; Differential strategy: The primary and backup channels are independently compared. Deviations in the comparison results trigger a secondary optimization process. In the event of an abnormality, the system automatically switches to the backup channel and notifies the operation and maintenance personnel.

12. A system using the intelligent phase checking method for a substation according to any one of claims 1 to 11, characterized in that: include: The dynamic priority evaluation module is used to determine the dynamic priority of the basic factors by building a dynamic priority evaluation model based on the voltage phase difference factor, the ambient temperature and humidity factor, the equipment vibration factor, and the historical error factor. The multi-port modeling and optimization module is used to abstract the signal transmission and reflection characteristics of each electrical connection port into a multi-port phase scattering model based on the substation topology. It uses Smith charts to analyze the port phase scattering characteristics, identify impedance mismatch points, and implement a two-stage optimization of the multi-port phase scattering model. The core phase strategy execution module is used to execute the core phase strategy and build a decision input set based on the optimized multi-port phase scattering model. Based on fuzzy logic and interface selection ideas, an adaptive rule engine is designed to dynamically output the core phase condition level and automatically select the core phase strategy based on it. The quality assessment and optimization module is used to perform multi-dimensional quality assessment by real-time monitoring of port phase matching and reflection coefficient. It dynamically updates factor weights and optimizes fuzzy model parameters based on the assessment results to form a self-evolving system.

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