Transformer substation communication power supply storage battery internal resistance detection method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of substation operation and maintenance, in particular to a substation communication power supply storage battery internal resistance detection method and system, electronic equipment and a storage medium, which can ensure the stable operation of a substation communication power supply system and improve the operation safety of a power network. The method comprises the following steps: collecting storage battery multi-element characteristic parameter information, storage battery multi-element external cause parameter information and a storage battery real-time internal resistance value of the substation communication power supply; performing data processing on the multivariate characteristic parameter information of the storage battery, and inputting the processed multivariate characteristic parameter information into a pre-constructed storage battery characteristic parameter analysis model to obtain a storage battery theoretical internal resistance value corresponding to the multivariate characteristic parameter information of the storage battery; performing external cause influence error compensation on the real-time internal resistance value of the storage battery based on the multi-element external cause parameter information of the storage battery to obtain a corrected internal resistance value of the storage battery; and calculating the internal resistance deviation amplitude between the storage battery correction internal resistance value and the storage battery theoretical internal resistance value.

Description

Technical Field

[0001] The present invention relates to the technical field of substation operation and maintenance, and in particular to a method, system, electronic equipment and storage medium for detecting internal resistance of a communication power supply battery of a substation. Background Art

[0002] In modern power systems, substations are key nodes in the power grid, and their stable operation is crucial to ensuring the safety and efficiency of the entire power network. Among them, the communication power system, as the heart of the operation of the internal equipment of the substation, is responsible for providing uninterrupted power supply for various monitoring equipment, protection devices and automation systems to ensure reliable communication of the power system. As an important part of the communication power system, batteries are responsible for providing emergency energy when the main power fails. Therefore, their health status is directly related to whether the substation can maintain necessary communication and service functions in an emergency.

[0003] However, as the usage time increases and the working environment changes, the performance of the battery will gradually deteriorate, and one of the key indicators is the increase in internal resistance. The increase in internal resistance means a decrease in the battery's energy conversion efficiency and a weakening of its power supply capacity. In severe cases, it will cause the battery to be unable to discharge normally, thus affecting the normal operation and emergency response capabilities of the substation. Therefore, accurate and timely detection and evaluation of changes in battery internal resistance is of great significance for preventing faults, extending battery life, and improving the overall reliability of the power system.

[0004] In the existing battery internal resistance detection method, the internal resistance value is usually calculated by directly measuring the battery voltage and current, and then using Ohm's law. However, this existing detection method ignores the multi-dimensional characteristic parameters of the battery itself and the influence of external environmental factors on the internal resistance, resulting in large errors in the measurement results, which cannot meet the accuracy and comprehensiveness requirements of the power system for battery performance evaluation. Summary of the invention

[0005] To solve the above technical problems, the present invention provides a substation communication power supply battery internal resistance detection method, system, electronic device and storage medium, which can ensure the stable operation of the substation communication power supply system and improve the operation safety of the power network.

[0006] In a first aspect, the present invention provides a method for detecting internal resistance of a communication power supply battery in a substation, the method comprising:

[0007] Collect the battery multi-characteristic parameter information, battery multi-external parameter information and real-time internal resistance value of the substation communication power supply;

[0008] Processing the multi-element characteristic parameter information of the battery, and inputting the information into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery;

[0009] Based on the multi-factor external parameter information of the battery, the external factor-influenced error compensation is performed on the real-time internal resistance value of the battery to obtain a corrected internal resistance value of the battery;

[0010] Calculating the internal resistance deviation amplitude between the corrected internal resistance value of the battery and the theoretical internal resistance value of the battery;

[0011] The internal resistance deviation amplitude is matched with a preset battery internal resistance health state range to determine the actual state of the internal resistance of the substation communication power supply battery.

[0012] Furthermore, the battery multi-characteristic parameter information includes the number of charge and discharge cycles, self-discharge rate, battery temperature gradient, historical fault records, battery load type and battery load intensity.

[0013] Furthermore, the battery multi-factor external parameter information includes ambient temperature, ambient humidity, voltage fluctuation, frequency fluctuation, harmonic content and external mechanical vibration.

[0014] Furthermore, the method for processing the multi-element characteristic parameter information of the battery comprises:

[0015] Based on the set data collection frequency, continuously collect a plurality of pieces of the battery multi-element characteristic parameter information;

[0016] Based on the set grouping window, the plurality of pieces of battery multi-characteristic parameter information are divided into a plurality of groups of battery multi-characteristic parameter information;

[0017] For each group of multi-element characteristic parameter information of the battery, characteristic values ​​of various characteristic parameters are calculated to obtain parameter characteristic values ​​capable of representing corresponding characteristic parameters;

[0018] According to the grouping position of each characteristic parameter in the battery multi-characteristic parameter information, fill in the characteristic value of each parameter accordingly to obtain a battery multi-characteristic parameter characteristic set;

[0019] Arrange several groups of battery multi-element characteristic parameter feature sets in time sequence to obtain a battery multi-element characteristic parameter feature matrix capable of characterizing the battery usage status;

[0020] The battery multivariate characteristic parameter feature matrix is ​​used as the input of the battery characteristic parameter analysis model.

[0021] Furthermore, the calculation formula for compensating the external influence error of the real-time internal resistance value of the battery is:

[0022]

[0023] Among them, R c Indicates the corrected internal resistance value, R m Indicates the real-time internal resistance value, T indicates the ambient temperature, T0 indicates the reference temperature, β T The exponential parameter representing the temperature effect, α T represents the temperature influence coefficient, H represents the ambient humidity, H0 represents the reference humidity, α H represents the humidity influence coefficient; ΔV represents the voltage fluctuation change, α V Represents the voltage fluctuation influence coefficient, γ V The exponential parameter representing the influence of voltage fluctuation, ΔF represents the change of frequency fluctuation, α F represents the frequency fluctuation influence coefficient, γ F The exponential parameter representing the effect of frequency fluctuation, Z represents the mechanical vibration frequency, α Z Represents the mechanical vibration frequency influence coefficient, γ Z An exponential parameter representing the frequency of mechanical vibration.

[0024] Furthermore, the calculation formula of the internal resistance deviation amplitude is:

[0025] (R c -R t ) / R t *100%

[0026] Where R c Indicates the corrected internal resistance value, R t Indicates the theoretical internal resistance value.

[0027] Furthermore, an internal resistance deviation amplitude range is set, and each internal resistance deviation amplitude range corresponds to a different battery internal resistance health state; the battery internal resistance health state includes a good state, a slightly degraded state, a moderately degraded state, and a severely degraded state;

[0028] The calculated internal resistance deviation amplitude is compared with the set internal resistance deviation amplitude range by using a matching algorithm to determine the actual state of the battery internal resistance.

[0029] On the other hand, the present application also provides a substation communication power supply battery internal resistance detection system, the system comprising:

[0030] The parameter collection module is used to collect the battery multi-characteristic parameter information of the substation communication power supply, the battery multi-external parameter information and the real-time internal resistance value of the battery;

[0031] A data processing and analysis module, used to process the multi-element characteristic parameter information of the battery, and then input the data into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery;

[0032] An error compensation module, used to perform external factor error compensation on the real-time internal resistance value of the battery according to the multi-factor external parameter information of the battery, so as to obtain a corrected internal resistance value of the battery;

[0033] An internal resistance deviation calculation module, used to calculate the internal resistance deviation amplitude between the battery corrected internal resistance value and the battery theoretical internal resistance value;

[0034] The health status assessment module is used to match the internal resistance deviation amplitude with a preset battery internal resistance health status range to determine the actual status of the internal resistance of the substation communication power supply battery.

[0035] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0037] Compared with the prior art, the present invention has the following beneficial effects: the method can predict a more accurate theoretical internal resistance value by combining the multivariate characteristic parameter information of the battery and the pre-built analysis model, which is more precise than the result calculated by relying solely on Ohm's law, and reduces the error caused by ignoring battery characteristics and environmental factors; in addition to considering the characteristics of the battery itself, the influence of external environmental parameters is also taken into account, and the real-time internal resistance value is adjusted through the error compensation mechanism, ensuring that the measurement result can more truly reflect the state of the battery under the current working conditions, thereby improving the comprehensiveness of the evaluation; by calculating the internal resistance deviation amplitude and matching it with the preset health status range, the health status of the battery can be dynamically monitored and evaluated, which can not only timely discover signs of performance degradation, but also provide a scientific basis for preventive maintenance, effectively extending the battery life; the combination of real-time monitoring and precise evaluation enables the operation and maintenance team to take action before the battery performance is significantly reduced, avoid sudden failures, reduce the risk of power outages caused by battery failures, and thus improve the stability and reliability of the power system; accurately grasping the health status of the battery ensures that when the main power fails, the communication power system can rely on the battery to effectively provide emergency energy, maintain the communication and service functions of the substation, and enhance the emergency response capability of the power system;

[0038] The implementation of this method is conducive to promoting the intelligent development of substation operation and maintenance, reducing manual intervention, and improving operation and maintenance efficiency. It also provides possibilities for big data analysis and predictive maintenance strategies, further improving the management level of the power system.

[0039] In summary, this method achieves high-precision monitoring and evaluation of the battery internal resistance through comprehensive analysis and precise compensation, which can ensure the stable operation of the substation communication power supply system and improve the operational safety of the power network. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the present invention;

[0041] Figure 2 It is a flow chart of a method for constructing a battery characteristic parameter analysis model;

[0042] Figure 3 It is the structural diagram of the substation communication power supply battery internal resistance detection system. DETAILED DESCRIPTION

[0043] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0044] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0045] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0046] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0047] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0048] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0049] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0050] The present application is described below in conjunction with the drawings in the present application.

[0051] Embodiment 1: Figure 1 to Figure 2 As shown, the method for detecting the internal resistance of the substation communication power supply battery of the present invention specifically comprises the following steps:

[0052] S1. Collecting the battery multi-characteristic parameter information, battery multi-external parameter information and real-time internal resistance value of the substation communication power supply;

[0053] The specific implementation of step S1 is as follows:

[0054] Battery multi-characteristic parameter information collection:

[0055] Temperature sensors, pressure sensors, and conductivity sensors are installed at key locations of each battery cell or battery pack to monitor the physical state and chemical reaction parameters of the battery. At the same time, data loggers are configured to continuously record long-term operating data such as the battery's charge and discharge history, number of cycles, and battery capacity attenuation.

[0056] By integrating intelligent monitoring software, real-time collection and remote monitoring of sensor data can be achieved; the system automatically records and analyzes key characteristic parameters such as battery charging efficiency, discharge curve, self-discharge rate, etc., providing detailed basic information for subsequent data processing;

[0057] Battery multi-factor external parameter information collection:

[0058] Set up an environmental monitoring station in the substation, equipped with thermometers, hygrometers, barometers and other equipment to monitor external environmental factors such as ambient temperature and humidity in the substation, as these factors directly affect the performance and life of the battery;

[0059] Combined with the monitoring and data acquisition system, it monitors the operation status of the power grid, including voltage fluctuations, frequency changes, etc.;

[0060] Synchronize all the internal and external parameters collected above to the central data processing platform through the communication network to ensure the timeliness and consistency of the data;

[0061] Obtaining real-time internal resistance value:

[0062] Using advanced online internal resistance testers, such as spectrum analysis or AC injection methods, the real-time internal resistance of the battery can be measured safely and quickly without interrupting the battery operation. The internal resistance is calculated by applying a small AC signal to both ends of the battery and analyzing the phase difference and amplitude change of the signal;

[0063] The output data of the internal resistance test equipment is integrated into the above-mentioned intelligent monitoring system, stored and analyzed together with other parameters, in preparation for subsequent processing and evaluation.

[0064] In this step, by installing a variety of sensors and data recorders, the multi-characteristic parameter information of the battery can be fully monitored, including physical states and chemical reaction parameters such as temperature, pressure, conductivity, and long-term operation data such as charge and discharge history and number of cycles, to ensure that the acquired data is comprehensive and accurate; the use of intelligent monitoring software and online internal resistance tester can realize real-time data collection and monitoring to ensure the timeliness of the data, and at the same time, the internal resistance value of the battery is obtained in real time, which helps to timely discover the trend of internal resistance changes; combined with intelligent monitoring software and data acquisition system, the collected data can be intelligently analyzed and processed, and key characteristic parameters such as battery charging efficiency, discharge curve, self-discharge rate, etc. can be automatically recorded and analyzed to realize intelligent management; setting up an environmental monitoring station to monitor environmental parameters inside and outside the substation helps to consider the impact of external environmental factors on battery performance and provide a reference basis for subsequent data analysis and internal resistance calibration; synchronizing data to the central data processing platform through the communication network can realize remote monitoring of the substation communication power supply battery and improve operation and maintenance efficiency and timeliness.

[0065] S2, performing data processing on the multi-element characteristic parameter information of the battery, and then inputting the data into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery;

[0066] The main purpose of step S2 is to deduce the theoretical internal resistance value from the collected multi-element characteristic parameter information of the battery through data processing and model analysis, so as to provide an accurate reference standard for the subsequent internal resistance deviation analysis; the specific implementation details are as follows:

[0067] The method for constructing the battery characteristic parameter analysis model includes:

[0068] Collect a large amount of battery sample data, including charge and discharge cycle data, temperature data, and internal resistance data;

[0069] Clean, process and pre-process the collected data to ensure data quality and usability;

[0070] Selecting machine learning models as the model infrastructure based on the nature of the problem and data characteristics, including support vector machines (SVM), random forests, and neural networks;

[0071] Divide the data into training set and test set; use the training set data to adjust the model parameters and optimize the loss function; use cross-validation techniques, such as k-fold cross-validation, to evaluate the generalization ability of the model; find the best model configuration through grid search, random search and other methods;

[0072] Use the test set to evaluate the prediction performance of the model. Common indicators include mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2). Based on the evaluation results, it is necessary to adjust the model structure, features, or parameters for iterative improvement.

[0073] Apply the final optimized model to actual data to verify its accuracy in predicting battery characteristic parameters;

[0074] The method for processing the multi-element characteristic parameter information of the battery comprises:

[0075] Based on the set data collection frequency, continuously collect a plurality of pieces of the battery multi-element characteristic parameter information;

[0076] Based on the set grouping window, the plurality of pieces of battery multi-characteristic parameter information are divided into a plurality of groups of battery multi-characteristic parameter information;

[0077] For each group of multi-element characteristic parameter information of the battery, characteristic values ​​of various characteristic parameters are calculated to obtain parameter characteristic values ​​capable of representing corresponding characteristic parameters;

[0078] According to the grouping position of each characteristic parameter in the battery multi-characteristic parameter information, fill in the characteristic value of each parameter accordingly to obtain a battery multi-characteristic parameter characteristic set;

[0079] Arrange several groups of battery multi-element characteristic parameter feature sets in time sequence to obtain a battery multi-element characteristic parameter feature matrix capable of characterizing the battery usage status;

[0080] The battery multivariate characteristic parameter feature matrix is ​​used as the input of the battery characteristic parameter analysis model.

[0081] In this step, through data processing and model analysis, the theoretical internal resistance value can be inferred from the collected battery multivariate characteristic parameter information, which can improve the prediction accuracy of the internal resistance value and provide a more accurate reference standard for the subsequent internal resistance deviation analysis; the collected battery sample data is cleaned, processed and preprocessed to ensure the quality and availability of the data; by eliminating outliers and missing data, and standardizing the data, the accuracy and reliability of the analysis can be effectively improved; the machine learning model is selected as the basic architecture, and the model is optimized and parameters are adjusted through the data of the training set and the test set, and the generalization ability of the model is evaluated by cross-validation technology, which can continuously improve the performance and prediction ability of the model; through evaluation indicators such as mean square error, root mean square error and determination coefficient, problems in the model can be discovered in time and adjusted, thereby improving the accuracy and stability of the model; the final optimized model is applied to actual data, and its accuracy in predicting battery characteristic parameters is verified, which can ensure the availability and effectiveness of the model in practice.

[0082] S3, based on the multi-factor external parameter information of the battery, performing external factor influence error compensation on the real-time internal resistance value of the battery to obtain a corrected internal resistance value of the battery;

[0083] The detailed implementation of step S3, i.e., error compensation of the real-time internal resistance value based on the multi-factor external parameter information of the battery to obtain the corrected internal resistance value, is a technology-intensive operation aimed at improving the detection accuracy and ensuring that the result is closer to the actual state of the battery; the specific operation includes the following:

[0084] The battery multivariate external parameter information includes:

[0085] Ambient temperature, which is monitored in real time by a temperature sensor, because temperature directly affects the conductivity of the electrolyte and the rate of battery chemical reactions;

[0086] Ambient humidity, although it has little direct impact on internal resistance, high humidity may cause corrosion problems and is monitored by humidity sensors;

[0087] Voltage fluctuations: record the voltage fluctuations of the power grid. High voltage may cause overcharging, while low voltage may affect discharge. The voltage monitoring equipment records the fluctuations.

[0088] Frequency fluctuations and unstable frequency will affect charging efficiency, which can be captured by frequency monitoring equipment;

[0089] External mechanical vibration, which may damage the battery structure and is monitored by vibration sensors;

[0090] Based on historical data and electrochemical theory, a compensation model for the effect of external parameters on internal resistance is constructed; the model needs to take into account the quantitative relationship of temperature effect, which is usually in exponential form; the linear or nonlinear effect of other external parameters is determined through statistical analysis; possible interaction effects, that is, the non-simple superposition effect of multiple external factors;

[0091] The real-time monitored external parameter data is input into the preset compensation model; the model calculates the theoretical impact value of the input external parameters on the internal resistance; the calculated theoretical impact value is subtracted from the original real-time internal resistance value to obtain the corrected internal resistance value; this process needs to take into account the impact of positive and negative directions to ensure the accuracy of compensation.

[0092] Specifically, the calculation formula of the compensation model is:

[0093]

[0094] Among them, R c Indicates the corrected internal resistance value, R m Indicates the real-time internal resistance value, T indicates the ambient temperature, T0 indicates the reference temperature, β T The exponential parameter representing the temperature effect, α T represents the temperature influence coefficient, H represents the ambient humidity, H0 represents the reference humidity, α H represents the humidity influence coefficient; ΔV represents the voltage fluctuation change, α V Represents the voltage fluctuation influence coefficient, γ V The exponential parameter representing the influence of voltage fluctuation, ΔF represents the change of frequency fluctuation, α F represents the frequency fluctuation influence coefficient, γ F The exponential parameter representing the effect of frequency fluctuation, Z represents the mechanical vibration frequency, α Z Represents the mechanical vibration frequency influence coefficient, γ Z An exponential parameter representing the frequency of mechanical vibration.

[0095] In this step, by considering the influence of external environmental factors on the internal resistance measurement, the actual internal resistance of the battery can be estimated more accurately, thereby improving the precision and accuracy of the detection; the internal resistance of the battery is affected by a variety of external factors, which may cause deviations in the internal resistance value measured in real time; through error compensation, the influence of these factors on the internal resistance measurement can be eliminated, ensuring that the corrected internal resistance value obtained is closer to the actual state of the battery; as an important part of the communication power supply system, the internal resistance state of the battery directly affects the stability and reliability of the system; by accurately evaluating the internal resistance state of the battery, potential faults can be prevented, the battery life can be extended, and the reliability of the entire power system can be improved; based on the real-time monitoring of external parameter information, the environmental impact on the battery can be discovered in time, and targeted maintenance and care can be carried out.

[0096] S4, calculating the internal resistance deviation amplitude between the corrected internal resistance value of the battery and the theoretical internal resistance value of the battery;

[0097] In step S4, the internal resistance deviation amplitude is calculated by comparing the internal resistance value after correction with the theoretical internal resistance value, so as to evaluate the health status of the battery; the specific calculation method is as follows:

[0098] According to step S3, the internal resistance value measured in real time is adjusted by taking into account the influence of external factors to obtain a corrected internal resistance value;

[0099] In step S2, by analyzing the multivariate characteristic parameters of the battery, a pre-built characteristic parameter analysis model is used to calculate a theoretical internal resistance value;

[0100] The calculation formula of internal resistance deviation amplitude is:

[0101] (R c -R t ) / R t *100%

[0102] Where R c Indicates the corrected internal resistance value, R t Indicates the theoretical internal resistance value;

[0103] If the calculated result is a positive value, it means that the actual internal resistance is higher than the theoretical expectation, which may indicate that the battery is aging, worn out, or has other performance degradation problems;

[0104] If the result is negative, the actual internal resistance is lower than the theoretical value, which may indicate that the battery performance is better than expected to a certain extent, but further analysis is usually required to confirm whether there is data error or measurement deviation;

[0105] Larger deviations may require further diagnosis or intervention, which may include battery maintenance, calibration, or replacement.

[0106] In this step, the calculation of the internal resistance deviation amplitude is used to determine the health status of the battery; by comparing the difference between the actual internal resistance and the theoretical internal resistance, the operation and maintenance personnel can quickly understand whether the battery has aging, loss or other performance degradation problems; a larger internal resistance deviation amplitude may indicate that the battery performance is abnormal, which may indicate that the battery is about to fail or performance degradation; the operation and maintenance personnel can promptly discover potential problems based on the changing trend of the internal resistance deviation amplitude and take corresponding preventive measures to prevent power system interruption or damage caused by battery failure; based on the analysis results of the internal resistance deviation amplitude, the operation and maintenance personnel can carry out targeted maintenance and optimization measures; for situations with large internal resistance deviations, further diagnosis or maintenance work may be required.

[0107] S5, matching the internal resistance deviation amplitude with a preset battery internal resistance health state range to determine the actual state of the internal resistance of the substation communication power supply battery;

[0108] In step S5, the previously calculated internal resistance deviation amplitude is matched with the preset health status range to determine the actual health status of the battery in the substation communication power supply; the specific operation is as follows:

[0109] Define the health status range:

[0110] The battery health range needs to be defined based on industry standards, manufacturer guidelines and historical data; the battery health range includes:

[0111] Good condition, the internal resistance deviation is within a very small range, indicating that the battery performance is close to that of a new battery or in the best condition;

[0112] Slight degradation, where the internal resistance deviation increases within a certain range, means that the battery begins to experience a slight performance decline, but can still meet most needs;

[0113] Moderate degradation, with greater internal resistance deviation, means that battery performance has significantly decreased and needs to be closely monitored or replaced;

[0114] Severe degradation, extremely large internal resistance deviation, the battery is close to or can no longer meet emergency power supply needs and needs to be replaced immediately;

[0115] Automatic matching and evaluation:

[0116] In the substation monitoring system, the calculated internal resistance deviation amplitude is automatically compared with the above health status range using a software algorithm. The system outputs the battery health level and direct suggestions based on the matching results. This information is a direct operational guide for the operation and maintenance team, facilitating quick decision-making.

[0117] Maintenance and Response:

[0118] The matching results trigger corresponding early warning mechanisms, such as color coding, email notifications or SMS alerts, to ensure that maintenance personnel are informed immediately; maintenance or replacement plans are formulated based on health status, giving priority to "severely degraded" batteries to prevent potential power outages; long-term tracking, recording and analysis of the change trend of internal resistance deviation provide data support for the continuous optimization of battery maintenance strategies;

[0119] Security Lifecycle Management:

[0120] Combine the internal resistance deviation amplitude with historical data to predict the remaining battery life and optimize resource allocation and budget planning; analyze the impact of different environments and usage modes on internal resistance, and adjust the operating strategy to optimize battery performance.

[0121] In this step, by matching the internal resistance deviation amplitude with the preset health status range, the actual health status of the battery can be accurately determined; it helps to timely discover the degradation trend of battery performance, take maintenance or replacement measures in advance, and avoid the risk of unnecessary power outages caused by battery failure; based on the intelligent diagnostic logic of the battery management system, it can automatically match and evaluate the health status of the battery, reduce the need for manual intervention, and improve operation and maintenance efficiency; enable the operation and maintenance team to focus more on important decision-making and operational tasks, and improve overall work efficiency; the health level and suggestions output by the matching results provide support for the operation and maintenance team to make quick decisions; through the early warning mechanism, relevant personnel are notified in time so that they can quickly take necessary maintenance or replacement measures, reducing the risk of power outages caused by battery failure and ensuring the stable operation of the power system; long-term tracking, recording and analysis of the change trend of the internal resistance deviation provide data support for the continuous optimization of the battery maintenance strategy; combined with historical data, it can predict the remaining life of the battery, optimize resource allocation and budget planning, thereby realizing safe life cycle management of the battery.

[0122] Embodiment 2: Figure 3 As shown, the substation communication power supply battery internal resistance detection system of the present invention specifically includes the following modules:

[0123] The parameter collection module is used to collect the battery multi-characteristic parameter information of the substation communication power supply, the battery multi-external parameter information and the real-time internal resistance value of the battery;

[0124] A data processing and analysis module, used to process the multi-element characteristic parameter information of the battery, and then input the data into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery;

[0125] An error compensation module, used to perform external factor error compensation on the real-time internal resistance value of the battery according to the multi-factor external parameter information of the battery, so as to obtain a corrected internal resistance value of the battery;

[0126] An internal resistance deviation calculation module, used to calculate the internal resistance deviation amplitude between the battery corrected internal resistance value and the battery theoretical internal resistance value;

[0127] The health status assessment module is used to match the internal resistance deviation amplitude with a preset battery internal resistance health status range to determine the actual status of the internal resistance of the substation communication power supply battery.

[0128] By introducing the battery characteristic parameter analysis model, the system can comprehensively consider the multivariate characteristic parameters of the battery and then predict the theoretical internal resistance value that is closer to the actual situation; it not only improves the accuracy of internal resistance measurement, but also enhances the comprehensiveness of battery performance evaluation; the error compensation module can correct the measured internal resistance value according to external environmental factors, reduce the interference of environmental changes on the detection results, make the measurement results more reliable, and effectively solve the error problem caused by the traditional method ignoring the influence of external factors; the combination of the internal resistance deviation calculation module and the health status evaluation module enables the system to dynamically monitor the health status of the battery and promptly detect the internal resistance that is too large The module can automatically determine the health level of the battery according to the internal resistance deviation amplitude, and provide intuitive status reports and early warning information for operation and maintenance personnel, so as to facilitate timely measures, optimize operation and maintenance strategies, and improve the overall reliability and emergency response capabilities of the power system. The system integrates automatic data collection, processing, analysis and evaluation functions, which reduces the tediousness and errors of manual operations and improves operation and maintenance efficiency. Model-based predictive analysis and intelligent decision support provide technical support for efficient management of substations.

[0129] In summary, the system significantly improves the accuracy and efficiency of battery internal resistance detection through highly integrated and intelligent design, providing strong support for ensuring the stable operation of substation communication power supply and the safe and efficient operation of the power system.

[0130] The various variations and specific embodiments of the substation communication power battery internal resistance detection method in the aforementioned embodiment 1 are also applicable to the substation communication power battery internal resistance detection system of the present embodiment. Through the aforementioned detailed description of the substation communication power battery internal resistance detection method, those skilled in the art can clearly know the implementation method of the substation communication power battery internal resistance detection system of the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0131] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting internal resistance of a communication power battery in a substation, characterized in that: The method comprises: Collect the battery multi-characteristic parameter information, battery multi-external parameter information and real-time internal resistance value of the substation communication power supply; Processing the multi-element characteristic parameter information of the battery, and inputting the information into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery; Based on the multi-factor external parameter information of the battery, the external factor-influenced error compensation is performed on the real-time internal resistance value of the battery to obtain a corrected internal resistance value of the battery; Calculating the internal resistance deviation amplitude between the corrected internal resistance value of the battery and the theoretical internal resistance value of the battery; The internal resistance deviation amplitude is matched with a preset battery internal resistance health state range to determine the actual state of the internal resistance of the substation communication power supply battery.

2. The method for detecting internal resistance of a substation communication power supply battery according to claim 1, characterized in that: The battery multi-characteristic parameter information includes the number of charge and discharge cycles, self-discharge rate, battery temperature gradient, historical fault records, battery load type and battery load intensity.

3. The method for detecting internal resistance of a substation communication power supply battery according to claim 1, characterized in that: The battery multi-factor external parameter information includes ambient temperature, ambient humidity, voltage fluctuation, frequency fluctuation, harmonic content and external mechanical vibration.

4. The method for detecting internal resistance of a substation communication power supply battery according to claim 1 or 2, characterized in that: The method for processing the multi-element characteristic parameter information of the battery comprises: Based on the set data collection frequency, continuously collect a plurality of pieces of the battery multi-element characteristic parameter information; Based on the set grouping window, the plurality of pieces of battery multi-characteristic parameter information are divided into a plurality of groups of battery multi-characteristic parameter information; For each group of multi-element characteristic parameter information of the battery, characteristic values ​​of various characteristic parameters are calculated to obtain parameter characteristic values ​​capable of representing corresponding characteristic parameters; According to the grouping position of each characteristic parameter in the battery multi-characteristic parameter information, fill in the characteristic value of each parameter accordingly to obtain a battery multi-characteristic parameter characteristic set; Arrange several groups of battery multi-element characteristic parameter feature sets in time sequence to obtain a battery multi-element characteristic parameter feature matrix capable of characterizing the battery usage status; The battery multivariate characteristic parameter feature matrix is ​​used as the input of the battery characteristic parameter analysis model.

5. The method for detecting internal resistance of a substation communication power supply battery according to claim 1, characterized in that: The calculation formula for compensating the external influence error of the real-time internal resistance value of the battery is: Among them, R c Indicates the corrected internal resistance value, R m Indicates the real-time internal resistance value, T indicates the ambient temperature, T0 indicates the reference temperature, β T The exponential parameter representing the temperature effect, α T represents the temperature influence coefficient, H represents the ambient humidity, H0 represents the reference humidity, α H represents the humidity influence coefficient; ΔV represents the voltage fluctuation change, α V Represents the voltage fluctuation influence coefficient, γ V The exponential parameter representing the influence of voltage fluctuation, ΔF represents the change of frequency fluctuation, α F represents the frequency fluctuation influence coefficient, γ F The exponential parameter representing the effect of frequency fluctuation, Z represents the mechanical vibration frequency, α Z Represents the mechanical vibration frequency influence coefficient, γ Z An exponential parameter representing the frequency of mechanical vibration.

6. The method for detecting internal resistance of a substation communication power supply battery according to claim 1, characterized in that: The calculation formula of internal resistance deviation amplitude is: (R c -R t ) / R t *100% Where R c Indicates the corrected internal resistance value, R t Indicates the theoretical internal resistance value.

7. The method for detecting internal resistance of a substation communication power supply battery according to claim 1, characterized in that: Setting an internal resistance deviation amplitude range, each internal resistance deviation amplitude range corresponds to a different battery internal resistance health state; the battery internal resistance health state includes a good state, a slightly degraded state, a moderately degraded state, and a severely degraded state; The calculated internal resistance deviation amplitude is compared with the set internal resistance deviation amplitude range by using a matching algorithm to determine the actual state of the battery internal resistance.

8. A substation communication power supply battery internal resistance detection system, characterized in that: The system comprises: The parameter collection module is used to collect the battery multi-characteristic parameter information of the substation communication power supply, the battery multi-external parameter information and the real-time internal resistance value of the battery; A data processing and analysis module, used to process the multi-element characteristic parameter information of the battery, and then input the data into a pre-built battery characteristic parameter analysis model to obtain a theoretical internal resistance value of the battery corresponding to the multi-element characteristic parameter information of the battery; An error compensation module, used to perform external factor error compensation on the real-time internal resistance value of the battery according to the multi-factor external parameter information of the battery, so as to obtain a corrected internal resistance value of the battery; An internal resistance deviation calculation module, used to calculate the internal resistance deviation amplitude between the battery corrected internal resistance value and the battery theoretical internal resistance value; The health status assessment module is used to match the internal resistance deviation amplitude with a preset battery internal resistance health status range to determine the actual status of the internal resistance of the substation communication power supply battery.

9. An electronic device for detecting internal resistance of a battery for a communication power supply in a substation, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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