Substation battery life online monitoring system and method based on surface charge dynamic characteristics

Abstract

The application belongs to the field of power system equipment state monitoring, and relates to a transformer substation battery life online monitoring system and method based on surface charge dynamics; the method directly installs a parameter collection unit on the battery body, stably collects nV-level surface charge dynamic characteristics in a strong electromagnetic environment, fuses temperature compensation and internal resistance, OCV auxiliary parameters, constructs a weighted fusion model to evaluate the battery health state and diagnose the aging dominant reason, solves the problems of strong invasiveness, poor anti-interference adaptability, early warning lag and surface charge signal collection difficulty in the prior art, reduces SOH evaluation error, and realizes graded alarm.

Description

Technical Field

[0001] This invention belongs to the field of power system equipment condition monitoring, specifically relating to an online monitoring system and method for substation battery life based on the dynamic characteristics of surface charge. Background Technology

[0002] The design life of VRLA batteries in substations under float charging conditions is 8-12 years, but due to the influence of temperature, voltage deviation, and 10kHz-1GHz broadband electromagnetic interference, the actual life is often less than 5 years. The aging is essentially the irreversible degradation of the electrode-electrolyte interface, manifested as positive electrode grid corrosion and electrolyte drying, both of which directly change the charge adsorption and transfer capacity of the interfacial double layer.

[0003] The existing battery life assessment methods have the following defects: (1) They are highly invasive. Traditional capacity testing requires deep charging and discharging outside the DC system, which seriously threatens power supply safety; (2) They have poor anti-interference adaptability. Electromagnetic noise causes internal resistance and terminal voltage measurement errors to be as high as 15% or more; (3) Early aging identification is delayed. Conventional parameters have low sensitivity to initial grid corrosion and electrolyte drying, and cannot achieve early and effective warning; (4) From the perspective of electrochemical principles, although the dynamic characteristics of surface charge can directly characterize the stability of the double layer, nV level signals are difficult to collect stably in a strong electromagnetic environment, and there is a lack of deployment scheme of "direct battery + centralized display in the main control room".

[0004] In view of this, it is very necessary to provide an online monitoring system and method for substation battery life based on the dynamic characteristics of surface charge, so as to solve the above-mentioned defects in the prior art. Summary of the Invention

[0005] The purpose of this invention is to address the problems of existing battery life monitoring systems, such as strong invasiveness, poor anti-interference adaptability, and inability to provide early warnings, as well as the shortcomings of unstable acquisition of surface charge dynamic characteristics under strong electromagnetic environments and the lack of a "direct-installed battery + centralized display in the main control room" deployment. This invention provides a substation battery life online monitoring system and method based on surface charge dynamic characteristics to solve the aforementioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An online monitoring system for substation battery life based on dynamic surface charge characteristics includes: Parameter acquisition unit, fusion processing host unit, and data communication and display unit; The parameter acquisition unit includes: a surface charge dynamic voltage sensing module, a temperature measurement module, and an internal resistance measurement module. The parameter acquisition unit is directly installed on the battery body. The surface charge dynamic voltage sensing module is used to acquire surface charge signals. The temperature measurement module is set close to the battery casing and is used to acquire temperature data in real time. The internal resistance measurement module is used to acquire the internal resistance data of the battery. The fusion processing host unit is communicatively connected to the parameter acquisition unit; the fusion processing host unit is used to acquire the surface charge signal, temperature data, internal resistance change rate and open circuit voltage decay rate of the battery, calculate the battery health status SOH value after feature extraction and temperature compensation, and diagnose the main causes of aging. The data communication and display unit uses a DC power supply display screen to display the SOH value, trend curve and alarm information of the battery in real time, and supports maintenance personnel to query detailed historical data and processing suggestions on the display screen.

[0007] Preferably, the substation battery life online monitoring system based on surface charge dynamic characteristics and multi-feature fusion includes: The surface charge dynamic voltage sensing module includes a voltage follower and a silver-plated metal probe. The silver-plated metal probe is suspended relative to the positive and negative electrode posts of the battery. The voltage follower is installed on a circuit board inside the parameter acquisition unit. The surface charge dynamic voltage sensing module is surrounded by a multi-layer electromagnetic shield and a protective ring structure that is at the same potential as the in-phase input terminal of the voltage follower, so as to stably acquire nV-level surface charge signals in a strong electromagnetic environment. The fusion processing host unit includes: a feature extraction module, a temperature compensation module, an auxiliary parameter processing module, and a SOH assessment and diagnosis module. The feature extraction module fits the acquired surface charge signal and extracts the dominant dissipation time constant τ and the total polarization amplitude A. The temperature compensation module normalizes and compensates the core feature parameters based on temperature data. The auxiliary parameter processing module calculates auxiliary parameters. The SOH assessment and diagnosis module incorporates a weighted fusion model and a multi-manufacturer battery database, calculates the battery SOH value based on the compensated core feature parameters and auxiliary parameters, and diagnoses the dominant causes of aging. The core feature parameters are obtained through the feature extraction module. The auxiliary parameters include the internal resistance change rate. and open-circuit voltage attenuation rate ; The parameter acquisition unit is installed inside the DC power cabinet or on the side of the battery using fasteners. When installed inside the DC power cabinet, the parameter acquisition unit is fixed to the guide rail inside the DC power cabinet using battery rack clips. The height of the silver-plated metal probe is adjusted by adjusting the two-dimensional adjustment knob built into the DC power cabinet, so that the silver-plated metal probe is suspended and opposite to the battery terminal. When installed in the battery compartment, the parameter acquisition unit is fixed to the side of the battery using a wall-mounted bracket or battery rack clips. The multi-layer electromagnetic shielding cover is connected to the grounding busbar at a single point through a grounding wire. Install the battery status display screen inside the DC power supply cabinet.

[0008] This system can achieve the following beneficial effects: By using guide rail clips and wall-mounted brackets for installation and fixing, the parameter acquisition unit can be flexibly deployed in the enclosed environment of the DC power cabinet and in the humid and dusty environment of the battery room. With the help of a two-dimensional adjustment knob, the silver-plated metal probe and the electrode are suspended and opposite each other. With the help of a shielded cover for single-point grounding, the problems of strong intrusion and poor anti-interference adaptability of the existing technology are solved.

[0009] Furthermore, this invention also provides an online monitoring method for the lifespan of substation batteries based on the dynamic characteristics of surface charge, comprising the following steps: Step S1: Obtain the initial characteristic parameters of the battery under test; under normal conditions, periodically collect the float charge voltage data, temperature data, and internal resistance data of the battery; when a high current discharge event is detected, trigger the high-frequency sampling mode and execute steps S2-S4. Step S2: Acquire the dynamic decay signal of the charge on the electrode surface; fit the voltage decay curve based on the second-order RC equivalent circuit model to obtain the core characteristic parameters; perform temperature compensation on the core characteristic parameters based on the acquired temperature data to obtain the compensated core characteristic parameters; calculate auxiliary parameters based on the initial characteristic parameters, including the rate of change of internal resistance. and open-circuit voltage attenuation rate ; Step S3: Calculate the current state of health (SOH) of the battery using a weighted fusion model, and diagnose the main causes of battery aging based on core characteristic parameters and auxiliary parameters. Step S4: Transmit the SOH value, battery aging diagnosis results, and core characteristic parameters to the DC power supply display screen, and update the data and trend curves in real time; trigger graded alarms based on the SOH threshold, and display the faulty battery number, SOH value, and cause of aging.

[0010] Preferably, the initial feature parameters in step S1 include: Initial dominant dissipation time constant Initial total polarization amplitude Initial float charge offset Initial internal resistance Initial open-circuit voltage .

[0011] This step achieves the following technical effects: By loading initial feature parameters and model weight coefficients, a basis is provided for subsequent accurate SOH evaluation based on ratio relationships, ensuring the traceability and accuracy of the evaluation model.

[0012] Preferably, step S1 specifically includes: Under normal conditions, the system periodically and synchronously collects the float charge voltage, temperature, and internal resistance data of all battery cells and silently uploads them to the processing host. The discharge current is monitored in real time by a current sensor. When a large current discharge event of ≥0.1C is detected, the system automatically switches to high-frequency sampling mode. The high-frequency sampling mode is as follows: synchronously acquire the dynamic charge decay signal, temperature and internal resistance of the battery electrode surface at a specific sampling frequency.

[0013] This step can achieve the following beneficial effects: Under normal conditions, it periodically collects float charge data to maintain low power consumption. When a high current discharge event is detected, it automatically triggers a high-frequency sampling mode, ensuring rapid acquisition of surface charge dynamic signals under critical operating conditions. This solves the problem that conventional parameters have low sensitivity to early aging and cannot provide effective early warning.

[0014] Preferably, step S2 specifically includes: Step S21: In high-frequency sampling mode, the dynamic decay signal of the electrode surface charge is continuously acquired at a sampling rate of 0.1Hz, and the voltage decay curve is fitted based on the second-order RC equivalent circuit model to obtain the core characteristic parameters characterizing the double-layer state of the electrode interface. The core characteristic parameters include the dominant dissipation time constant. Total polarization amplitude Float charge offset ; Step S22, perform temperature compensation on the core feature parameters: The Arrhenius equation is used to quantify the effect of temperature on charge properties. Parameter data collected at different ambient temperatures are normalized to parameter data under standard temperature conditions to obtain compensated standard core characteristic parameters. , : , , in, This indicates the collected temperature data. The reference temperature, representing standard conditions, is usually set to 25°C. right Perform linear temperature compensation to obtain the compensated core characteristic parameters. : , in, This is the temperature compensation coefficient; The temperature compensation process described above can eliminate evaluation errors caused by temperature fluctuations.

[0015] Step S23, calculate auxiliary parameters: Based on the initial internal resistance and the current measured internal resistance Calculate the rate of change of internal resistance : ; Calculate the open-circuit voltage attenuation rate : , in, This is the initial open-circuit voltage. This is the current measured open-circuit voltage.

[0016] This step can achieve the following beneficial effects: By fitting the voltage decay curve based on the second-order RC equivalent circuit model, the core characteristic parameters characterizing the double-layer state are extracted. The Arrhenius equation and linear compensation are used to eliminate the evaluation error caused by temperature fluctuations, thus solving the problems of unstable acquisition of nV-level surface charge signals and the influence of ambient temperature on evaluation accuracy.

[0017] Preferably, step S21, which involves fitting a voltage decay curve based on a second-order RC equivalent circuit model to obtain core characteristic parameters characterizing the double-layer state at the electrode interface, specifically includes: Based on the second-order RC equivalent circuit model of VRLA, the voltage decay curve is fitted using the recursive least squares method. The expression for the voltage decay curve is as follows: , in, Let be the voltage value at time t. Open circuit voltage, , The polarization amplitude, , It is a time constant; The core feature parameters are extracted by fitting the contributions of the electric double-layer capacitance branch C1-R1 and the electrode polarization branch C2-R2.

[0018] This step can achieve the following beneficial effects: By fitting the voltage decay curve using a second-order RC equivalent circuit model, and accurately separating the contributions of the double-layer capacitance branch and the electrode polarization branch using the recursive least squares method, core characteristic parameters such as the dominant dissipation time constant and the total polarization amplitude are extracted. This solves the problem that traditional methods can only measure macroscopic voltage and internal resistance but cannot distinguish between different aging mechanisms such as grid corrosion and electrolyte drying at the electrochemical interface level. It provides characteristic parameters with clear physical meaning and high correlation for subsequent diagnosis of the dominant causes of aging.

[0019] Preferably, step S3 specifically includes: Step S31, the initial feature parameters are... , and Compensated core feature parameters Compensated standard core feature parameters and and auxiliary parameters and Input the weighted fusion model to calculate the current state of health (SOH) value of the battery:

[0020] in, The weights of each parameter are determined through an accelerated aging test at 45°C. , and These are the core characteristic parameters after temperature compensation. The rate of change of internal resistance, This represents the open-circuit voltage decay rate.

[0021] Step S32: Based on the change patterns of core characteristic parameters and auxiliary parameters, and using battery aging diagnosis rules, diagnose the dominant cause of the current aging of the battery. The battery aging diagnosis rules include: When the floating charge offset Increase and dominate the dissipation time constant When the value decreases, it indicates that the difference between the steady-state OCV and the nominal float charge voltage increases, reflecting a reduction in the active area of ​​the electrode. It is determined that the main cause of battery aging is "corrosion of the positive plate grid". When the total polarization amplitude Decrease and rate of change of internal resistance When the value increases, it indicates that the electrolyte drying out has blocked the ion conduction pathway and weakened the polarization effect. Therefore, the main cause of battery aging is determined to be "electrolyte drying out".

[0022] This step can achieve the following beneficial effects: By combining the compensated core characteristic parameters with auxiliary parameters such as internal resistance change rate and OCV decay rate through a weighted fusion model, the SOH value is calculated. Based on the parameter change pattern, the main cause of aging is diagnosed, which solves the problems of low evaluation accuracy and inability to distinguish between different aging mechanisms such as grid corrosion and electrolyte drying in traditional methods.

[0023] Preferably, in step S4, a graded alarm is triggered based on the SOH threshold, and the faulty battery number, SOH value, and cause of aging are displayed, specifically including: When SOH is less than or equal to the first SOH threshold, a yellow maintenance warning will pop up with an audio-visual prompt; when SOH is less than or equal to the second SOH threshold, a red emergency alarm pop-up will pop up, indicating the faulty battery number, SOH value and cause of aging; the first SOH threshold is greater than the second SOH threshold.

[0024] This step can achieve the following beneficial effects: The hierarchical alarm mechanism issues different maintenance warnings and emergency alarms when the SOH drops to different thresholds, and directly displays the faulty battery number and the cause of aging. This solves the problem that maintenance personnel cannot quickly locate faulty batteries and determine the type of aging, and is compatible with the "distributed data collection-centralized operation and maintenance" management model of substations.

[0025] The beneficial effects of this invention are as follows: It possesses multi-scenario adaptability: the parameter acquisition unit is directly mounted on the battery body, and through the design of guide rail clips and wall-mounted brackets, it adapts to the enclosed environment of DC power cabinets and the humid and dusty environment of battery rooms, without requiring modification of existing layouts, greatly improving versatility and solving the problem of strong invasiveness in existing technologies; it has excellent anti-interference performance: adopting a multi-layer electromagnetic shielding cover and protective ring structure, combined with a flight-level operational amplifier, it overcomes the problem of stable acquisition of nV-level surface charge signals under 10kHz-1GHz wideband electromagnetic interference, reducing measurement errors; it has high evaluation accuracy and timely early warning: based on the dynamic characteristics of surface charge, it integrates temperature compensation and internal resistance, OCV... The system assists in verification by constructing a hierarchical weighted evaluation model, which can reduce SOH evaluation errors and significantly improve accuracy compared to the traditional internal resistance method. It can identify initial grid corrosion and electrolyte drying 6-12 months in advance, enabling early warning. It is highly compatible with various operation and maintenance modes: through a "distributed acquisition-centralized monitoring" architecture, the monitoring data of each battery is displayed on the DC power supply screen, supporting real-time monitoring, trend analysis, and hierarchical alarms. It is adapted to the "distributed installation + centralized operation and maintenance" management mode of substations, reducing operation and maintenance costs. Furthermore, the system is compatible with substation standard protocols such as IEC61850 and can seamlessly connect with existing DC systems and monitoring networks. It is simple to install, has low maintenance costs, and is valuable for large-scale application.

[0026] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects.

[0027] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0029] Figure 1 This is the overall architecture diagram of the substation battery life online monitoring system based on surface charge dynamic characteristics provided by the present invention.

[0030] Figure 2 This is a flowchart of the online monitoring method for substation battery life based on the dynamic characteristics of surface charge provided by the present invention.

[0031] Figure 3 This is a three-dimensional schematic diagram of the installation structure of the parameter acquisition unit provided by the present invention.

[0032] Figure 4 This is a two-dimensional front view schematic diagram of the installation structure of the parameter acquisition unit provided by the present invention.

[0033] Figure 5 This is a schematic diagram of the surface charge parameter acquisition unit provided by the present invention.

[0034] Figure 6 This is a schematic diagram of the voltage decay curve and characteristic parameter extraction provided by the present invention.

[0035] Among them, 1-battery body, 2-battery positive and negative electrode posts, 3-silver-plated metal probe, 4-transparent insulating dust cover, 5-polytetrafluoroethylene insulating sleeve, 6-two-dimensional adjustment knob, 7-multi-layer electromagnetic shielding cover, 8-drying box, 9-parameter acquisition unit, 10-PT100 temperature sensor, 11-internal resistance measurement module, 12-mounting fastener, 13-protection ring structure amplifier circuit, 14-fusion processing host unit, 15-data communication and display unit. Detailed Implementation

[0036] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0037] Example 1: like Figure 1As shown in the figure, this embodiment provides an online monitoring system for the lifespan of substation batteries based on the dynamic characteristics of surface charge, comprising: Parameter acquisition unit 9, fusion processing host unit 14, and data communication and display unit 15; The parameter acquisition unit 9 includes: a surface charge dynamic voltage sensing module, a temperature measurement module, and an internal resistance measurement module; the surface charge dynamic voltage sensing module is used to acquire surface charge signals; the temperature measurement module is installed close to the battery casing and is used to acquire temperature data in real time; the internal resistance measurement module is used to acquire the internal resistance data of the battery. The fusion processing host unit 14 is communicatively connected to the parameter acquisition unit 9; the fusion processing host unit 14 is used to acquire the surface charge signal, temperature data, internal resistance change rate and open circuit voltage decay rate of the battery, calculate the SOH value of the battery after feature extraction and temperature compensation, and diagnose the main causes of aging. The data communication and display unit 15 uses a DC screen to display the SOH value, trend curve and alarm information of the battery in real time, and supports maintenance personnel to query detailed historical data and processing suggestions on the screen. The substation battery life online monitoring system based on surface charge dynamic characteristics provided in this embodiment includes: The surface charge dynamic voltage sensing module includes a voltage follower and a silver-plated metal probe 3. The voltage follower is constructed using a femtoampere-level operational amplifier. The silver-plated metal probe 3 is fixed by a polytetrafluoroethylene insulating sleeve 5 and is suspended relative to the positive and negative electrode posts 2 of the battery. The voltage follower is mounted on a circuit board inside the parameter acquisition unit 9. Figure 5 As shown, the surface charge dynamic voltage sensing module is externally covered with a transparent insulating dustproof cover 4, and its periphery is equipped with a multi-layer electromagnetic shield 7 and a protective ring structure 13 that is at the same potential as the in-phase input terminal of the amplifier, for stable acquisition of nV-level surface charge signals in a strong electromagnetic environment; the parameter acquisition unit 9 is also equipped with a drying box 8, which contains molecular sieve desiccant to keep the cavity dry and prevent the humid environment from affecting the measurement accuracy; the temperature measurement module is set close to the battery shell and uses a PT100 temperature sensor 10 to acquire temperature data in real time; the internal resistance measurement module uses an internal resistance measurement module 11 to acquire the internal resistance data of the battery; The fusion processing host unit 14 includes: a feature extraction module, a temperature compensation module, an auxiliary parameter processing module, and a SOH assessment and diagnosis module. The feature extraction module is used to fit the acquired surface charge signal and extract the dominant dissipation time constant τ and the total polarization amplitude A. The temperature compensation module is used to normalize and compensate the core feature parameters based on temperature data. The auxiliary parameter processing module is used to calculate auxiliary parameters. The SOH assessment and diagnosis module has a built-in weighted fusion model and a multi-manufacturer battery database, used to calculate the battery SOH value based on the compensated core feature parameters and auxiliary parameters, and diagnose the dominant causes of aging. The core feature parameters are obtained through the feature extraction module. The auxiliary parameters include the internal resistance change rate. and open-circuit voltage attenuation rate ; The parameter acquisition unit 9 is installed inside the DC power supply cabinet or the battery room via a fixing component; wherein, when the parameter acquisition unit 9 is installed inside the DC power supply cabinet, as... Figure 3 and Figure 4 As shown, the parameter acquisition unit is fixed to the guide rail inside the DC power cabinet using mounting fasteners such as battery rack clips 12. The height of the silver-plated metal probe 3 is adjusted by the two-dimensional adjustment knob 6 built into the DC power cabinet, so that the silver-plated metal probe 3 is suspended opposite the positive and negative terminals 2 of the battery, with a distance between them less than or equal to 5mm. When the parameter acquisition unit is installed in the battery compartment, as... Figure 3 and Figure 4 As shown, the parameter acquisition unit 9 is fixed to the side of the battery by mounting brackets or battery rack clips 12. The multi-layer electromagnetic shield 7 is connected to the grounding busbar at a single point through a 1.5mm² grounding wire, and the grounding resistance is less than or equal to 1Ω. Install the battery status display screen inside the DC power supply cabinet.

[0038] This step can achieve the following beneficial effects: By using guide rail clips and wall-mounted brackets for installation and fixing, the parameter acquisition unit can be flexibly deployed in the enclosed environment of the DC power cabinet and in the humid and dusty environment of the battery room. With the help of a two-dimensional adjustment knob, the silver-plated metal probe and the electrode are suspended and opposite each other. With the help of a shielded cover for single-point grounding, the problems of strong intrusion and poor anti-interference adaptability of the existing technology are solved.

[0039] Example 2: like Figure 2 As shown in the figure, this embodiment provides an online monitoring method for the lifespan of substation batteries based on the dynamic characteristics of surface charge, comprising the following steps: Step S1: Obtain the initial characteristic parameters of the battery under test; under normal conditions, periodically collect the float charge voltage data, temperature data, and internal resistance data of the battery; when a high current discharge event is detected, trigger the high-frequency sampling mode and execute steps S2-S4. The initial feature parameters in step S1 include: Initial dominant dissipation time constant Initial total polarization amplitude Initial float charge offset Initial internal resistance Initial open-circuit voltage ; Step S1 specifically includes: Under normal conditions, the system periodically and synchronously collects the float charge voltage, temperature, and internal resistance data of all battery cells and silently uploads them to the processing host. The discharge current is monitored in real time by a current sensor. When a large current discharge event of ≥0.1C is detected, such as a circuit breaker tripping event, the system automatically switches to high-frequency sampling mode. The high-frequency sampling mode is as follows: synchronously acquire the dynamic charge decay signal, temperature and internal resistance of the battery electrode surface at a sampling rate of 0.1Hz.

[0040] This step can achieve the following beneficial effects: By loading initial feature parameters and model weight coefficients, a basis is provided for subsequent accurate SOH evaluation based on ratio relationships, ensuring the traceability and accuracy of the evaluation model. Under normal conditions, periodic acquisition of float charge data maintains low power consumption. When a high-current discharge event is detected, a high-frequency sampling mode is automatically triggered, ensuring rapid acquisition of surface charge dynamic signals under critical operating conditions. This solves the problem of low sensitivity of conventional parameters to early aging and the inability to provide effective early warning.

[0041] Step S2, as follows Figure 6 As shown, the dynamic decay signal of the electrode surface charge is collected, and the voltage decay curve is fitted based on a second-order RC equivalent circuit model to obtain the core characteristic parameters. Temperature compensation is performed on the core characteristic parameters based on the collected temperature data to obtain the compensated core characteristic parameters. Auxiliary parameters are calculated based on the initial characteristic parameters, including the rate of change of internal resistance. and open-circuit voltage attenuation rate ; Step S2 specifically includes: Step S21: In high-frequency sampling mode, the dynamic decay signal of the electrode surface charge is continuously recorded for 8 hours at a sampling rate of 0.1Hz, and the voltage decay curve is fitted based on the second-order RC equivalent circuit model to obtain the core characteristic parameters characterizing the double-layer state of the electrode interface. The core characteristic parameters include the dominant dissipation time constant. Total polarization amplitude Float charge offset ; Step S22, perform temperature compensation on the core feature parameters: The influence of temperature on charge properties was quantified using the Arrhenius equation. Parameter data collected at different ambient temperatures (-10℃ to 55℃) were normalized to parameter data under a standard temperature condition (25℃) to obtain compensated parameters. , : , , in, This indicates the collected temperature data. The reference temperature, representing standard conditions, is usually set to 25°C. right Perform linear temperature compensation to obtain the compensated core characteristic parameters. : , in, This is the temperature compensation coefficient; The temperature compensation process described above can eliminate evaluation errors caused by temperature fluctuations.

[0042] Step S23, calculate auxiliary parameters: Based on the initial internal resistance and the current measured internal resistance Calculate the rate of change of internal resistance : ; Calculate the open-circuit voltage attenuation rate : , in, This is the initial open-circuit voltage. This is the current measured open-circuit voltage.

[0043] This step can achieve the following beneficial effects: By fitting the voltage decay curve based on the second-order RC equivalent circuit model, the core characteristic parameters characterizing the double-layer state are extracted. The Arrhenius equation and linear compensation are used to eliminate the evaluation error caused by temperature fluctuations, thus solving the problems of unstable acquisition of nV-level surface charge signals and the influence of ambient temperature on evaluation accuracy.

[0044] In step S21, the voltage decay curve is fitted based on a second-order RC equivalent circuit model to obtain the core characteristic parameters characterizing the double-layer state of the electrode interface, specifically including: Based on the second-order RC equivalent circuit model of VRLA, the voltage decay curve is fitted using the recursive least squares method. The expression for the voltage decay curve is as follows: , in, Let be the voltage value at time t. Open circuit voltage, , The polarization amplitude, , It is a time constant; The core feature parameters are extracted by fitting the contributions of the electric double-layer capacitance branch C1-R1 and the electrode polarization branch C2-R2.

[0045] This step can achieve the following beneficial effects: By fitting the voltage decay curve using a second-order RC equivalent circuit model, and accurately separating the contributions of the double-layer capacitance branch and the electrode polarization branch using the recursive least squares method, core characteristic parameters such as the dominant dissipation time constant and the total polarization amplitude are extracted. This solves the problem that traditional methods can only measure macroscopic voltage and internal resistance but cannot distinguish between different aging mechanisms such as grid corrosion and electrolyte drying at the electrochemical interface level. It provides characteristic parameters with clear physical meaning and high correlation for subsequent diagnosis of the dominant causes of aging.

[0046] Step S3: Calculate the current state of health (SOH) of the battery using a weighted fusion model, and diagnose the main causes of battery aging based on core characteristic parameters and auxiliary parameters. Step S3 specifically includes: Step S31, the initial feature parameters are... , and Compensated core feature parameters Compensated standard core feature parameters and and auxiliary parameters and Input the weighted fusion model to calculate the current state of health (SOH) value of the battery:

[0047] The weights of each parameter are set as follows: =0.3, = 0.25, =0.25, =0.15, =0.05, , and These are the core characteristic parameters after temperature compensation. The rate of change of internal resistance, This represents the open-circuit voltage decay rate.

[0048] The parameter weights of the weighted fusion model were calibrated through a 45°C accelerated aging test (200 charge-discharge cycles); the specific experimental procedure is as follows: Multiple VRLA batteries of the same model were selected and accelerated aging was carried out in a high-temperature environment of 45℃, with 200 charge-discharge cycles to simulate the aging process of several years of normal use. After each loop, core feature parameters are collected. , and Collect auxiliary parameters and The actual SOH value was collected through cable capacity testing; 80% of the experimental data was used for training, and 20% of the experimental data was used for validation. Establish a linear weighted model: ; Using the measured SOH value as the target value, and with the constraints that k1+k2+k3+k4+k5= 1 and each weight ≥ 0, the optimal weight coefficients are solved by multiple linear regression or least squares method. Cross-validation is performed on the linear weighted model to ensure its generalization ability; The model can be automatically calibrated every 6 months: the weighting coefficients are optimized based on recent operating data to adapt to the actual operating conditions of the substation.

[0049] Step S32: Based on the change patterns of core characteristic parameters and auxiliary parameters, and using battery aging diagnosis rules, diagnose the dominant cause of the current aging of the battery. The battery aging diagnosis rules include: When the floating charge offset Increase and dominate the dissipation time constant When the value decreases, it indicates that the difference between the steady-state OCV and the nominal float charge voltage increases, reflecting a reduction in the active area of ​​the electrode. It is determined that the main cause of battery aging is "corrosion of the positive plate grid". When the total polarization amplitude Decrease and rate of change of internal resistance When the value increases, it indicates that the electrolyte drying out has blocked the ion conduction pathway and weakened the polarization effect. Therefore, the main cause of battery aging is determined to be "electrolyte drying out".

[0050] This step can achieve the following beneficial effects: By combining the compensated core characteristic parameters with auxiliary parameters such as internal resistance change rate and OCV decay rate through a weighted fusion model, the SOH value is calculated. Based on the parameter change pattern, the main cause of aging is diagnosed, which solves the problems of low evaluation accuracy and inability to distinguish between different aging mechanisms such as grid corrosion and electrolyte drying in traditional methods.

[0051] Step S4: Transmit the SOH value, battery aging diagnosis results, and core characteristic parameters to the DC power supply display screen, and update the data and trend curves in real time; trigger graded alarms based on the SOH threshold, and display the faulty battery number, SOH value, and cause of aging.

[0052] In step S4, a graded alarm is triggered based on the SOH threshold, and the faulty battery number, SOH value, and cause of aging are displayed, specifically including: When SOH is less than or equal to the first SOH threshold, a yellow maintenance warning will pop up with an audio-visual prompt; when SOH is less than or equal to the second SOH threshold, a red emergency alarm pop-up will pop up, indicating the faulty battery number, SOH value and cause of aging; the first SOH threshold is greater than the second SOH threshold.

[0053] This step can achieve the following beneficial effects: The hierarchical alarm mechanism issues different maintenance warnings and emergency alarms when the SOH drops to different thresholds, and directly displays the faulty battery number and the cause of aging. This solves the problem that maintenance personnel cannot quickly locate faulty batteries and determine the type of aging, and is compatible with the "distributed data collection-centralized operation and maintenance" management model of substations.

[0054] Example 3: This embodiment uses a 110kV DC system of a 110kV substation as an example to illustrate the specific deployment method of the system described in this invention within a DC power cabinet. The DC system consists of 54 2V / 150Ah VRLA batteries, mounted on a rack in a separate battery room. Step 1, Parameter Acquisition Unit Deployment: A total of 7 8-channel parameter acquisition units were installed, each fixed to the side of the battery rack via wall-mounted brackets; each parameter acquisition unit was connected to the fusion processing host unit via single-mode fiber optic cable with a transmission distance of 800m; the fusion processing host unit communicated with the DC screen display in the main control room via a forward isolation device; the grounding terminal of the shielding cover was connected to the grounding grid in the battery room, with a measured grounding resistance of 0.6Ω.

[0055] Step 2, System Configuration: Import the device ICD file into the dedicated display screen of the DC power supply in the main control room, complete the IEC61850 model configuration, and map data points (such as BatCell32 / SOH, BatCell32 / AgingCause); load the initial parameters for "2V / 200AhVRLA": τ0=1.58×10 4s, A0=1.03V, ΔV0=0.022V, R0=0.17mΩ, weighting coefficients [k1,k2,k3,k4,k5]=[0.3,0.25,0.25,0.15,0.05]; set alarm thresholds: SOH≤80% triggers a yellow audible and visual alarm, SOH≤60% triggers a red audible and visual emergency alarm; configure remote communication to map key data such as the lowest SOH of the entire group, the number of batteries with serious alarms, and the total alarm signal to the DL / T634.5104 protocol point table, and connect to the dispatch data network through a forward isolation device.

[0056] Step 3, Monitoring Process: The system collects data at 5-minute intervals for each battery. The voltage was stable between 0.02 and 0.04V, and the SOH value was ≥90%. The data was uploaded to the DC power supply display screen in the main control room in real time. The trend curve was stable and no alarms were generated. A single-phase ground fault occurred on the 110kV line, and the No. 35 circuit breaker tripped. The discharge current became 0.2C. The event was captured by the GOOSE message, and the high-frequency sampling mode was immediately triggered. Taking battery No. 32 in the battery compartment as an example, the dominant dissipation time constant τ = 1.05 × 10⁻⁶ is obtained by fitting the voltage decay curve based on a second-order RC model. 4 s, total polarization amplitude A = 0.72V; using the Arrhenius equation, temperature compensation was applied to the data collected at the current ambient temperature of 28℃ to obtain the compensated dominant dissipation time constant. =1.02×10 4 s. The compensated total polarization amplitude A_comp = 0.70V; the steady-state OCV of the float charge is 2.18V, and the nominal float charge voltage is 2.25V, therefore... =0.07V; Current measured internal resistance R=0.26mΩ, =0.17 / 0.26≈0.654; OCV decay rate ΔOCV=7%, new battery OCV is 2.34V, current is 2.18V; The current state of health (SOH) of the battery is calculated, and it is found that the SOH of battery No. 32 is 58.8%, which reaches the emergency alarm threshold. according to" Increase and The parameters indicating a decrease in SOH were determined to be primarily due to "positive grid corrosion." A red emergency alarm pop-up immediately appeared on the DC power supply screen in the main control room, displaying "Battery No. 32 SOH = 58.8% (Emergency Alarm), Cause of Aging: Positive Grid Corrosion," and simultaneously triggered an audible and visual alert. Maintenance personnel checked the battery's trend data for the past three months on the display screen, confirming a continuous decrease in SOH, with an average monthly decrease of 3.2%, and scheduled maintenance and replacement for the following day.

[0057] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A substation battery life online monitoring system based on surface charge dynamic characteristics, characterized in that, include: Parameter acquisition unit, fusion processing host unit, and data communication and display unit; The parameter acquisition unit includes: a surface charge dynamic voltage sensing module, a temperature measurement module, and an internal resistance measurement module; the surface charge dynamic voltage sensing module is used to acquire surface charge signals; the temperature measurement module is installed close to the battery casing and is used to acquire temperature data in real time; the internal resistance measurement module is used to acquire the internal resistance data of the battery. The fusion processing host unit is communicatively connected to the parameter acquisition unit; the fusion processing host unit is used to acquire the surface charge signal, temperature data, internal resistance change rate and open circuit voltage decay rate of the battery, calculate the SOH value of the battery after feature extraction and temperature compensation, and diagnose the main causes of aging. The data communication and display unit uses a DC power supply display screen to display the SOH value, trend curve and alarm information of the battery in real time, and supports maintenance personnel to query detailed historical data and processing suggestions on the display screen.

2. The online monitoring system for substation battery life based on surface charge dynamic characteristics according to claim 1, characterized in that, The surface charge dynamic voltage sensing module includes a voltage follower and a silver-plated metal probe. The silver-plated metal probe is suspended relative to the positive and negative electrode posts of the battery. The voltage follower is installed on a circuit board inside the parameter acquisition unit. The surface charge dynamic voltage sensing module is surrounded by a multi-layer electromagnetic shield and a protective ring structure that is at the same potential as the in-phase input terminal of the voltage follower, so as to stably acquire nV-level surface charge signals in a strong electromagnetic environment. The fusion processing host unit includes: a feature extraction module, a temperature compensation module, an auxiliary parameter processing module, and a SOH assessment and diagnosis module. The feature extraction module fits the acquired surface charge signal and extracts the dominant dissipation time constant τ and the total polarization amplitude A. The temperature compensation module normalizes and compensates the core feature parameters based on temperature data. The auxiliary parameter processing module calculates auxiliary parameters. The SOH assessment and diagnosis module incorporates a weighted fusion model and a multi-manufacturer battery database, calculates the battery SOH value based on the compensated core feature parameters and auxiliary parameters, and diagnoses the dominant causes of aging. The core feature parameters are obtained through the feature extraction module. The auxiliary parameters include the internal resistance change rate. and open-circuit voltage attenuation rate ; The parameter acquisition unit is installed inside the DC power cabinet or the battery compartment using fasteners. When installed inside the DC power cabinet, the parameter acquisition unit is fixed to the guide rail inside the DC power cabinet using battery rack clips. The height of the silver-plated metal probe is adjusted by adjusting the two-dimensional adjustment knob built into the DC power cabinet, so that the silver-plated metal probe is suspended and opposite to the battery terminal. When installed inside the battery compartment, the parameter acquisition unit is fixed to the side of the battery using a wall-mounted bracket or battery rack clips. The multi-layer electromagnetic shielding cover is connected to the grounding busbar at a single point through a grounding wire. Install the battery status display screen inside the DC power supply cabinet.

3. A method for online monitoring of substation battery life using the monitoring system described in claim 1 or 2, characterized in that, Includes the following steps: Step S1: Obtain the initial characteristic parameters of the battery under test; under normal conditions, periodically collect the float charge voltage data, temperature data, and internal resistance data of the battery; when a high current discharge event is detected, trigger the high-frequency sampling mode and execute steps S2-S4. Step S2: Collect the dynamic decay signal of the charge on the electrode surface, fit the voltage decay curve based on the second-order RC equivalent circuit model, and obtain the core feature parameters; Temperature compensation is performed on the core feature parameters based on the collected temperature data to obtain the compensated core feature parameters. Calculate auxiliary parameters based on initial feature parameters; Step S3: Calculate the current state of health (SOH) of the battery using a weighted fusion model, and diagnose the main causes of battery aging based on core characteristic parameters and auxiliary parameters. Step S4: Transmit the SOH value, battery aging diagnosis results, and core characteristic parameters to the DC power supply display screen, and update the data and trend curves in real time; trigger graded alarms based on the SOH threshold, and display the faulty battery number, SOH value, and cause of aging.

4. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 3, characterized in that, The initial characteristic parameters in step S1 include: the initial dominant dissipation time constant. Initial total polarization amplitude Initial float charge offset Initial internal resistance Initial open-circuit voltage .

5. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 3, characterized in that, Step S1 specifically includes: Under normal conditions, the system periodically and synchronously collects the float charge voltage, temperature, and internal resistance data of all battery cells and silently uploads them to the processing host. The discharge current is monitored in real time by a current sensor. When a large current discharge event of ≥0.1C is detected, the system automatically switches to high-frequency sampling mode. The high-frequency sampling mode is as follows: synchronously acquire the dynamic charge decay signal, temperature and internal resistance of the battery electrode surface at a specific sampling frequency.

6. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 3, characterized in that, Step S2 specifically includes: Step S21: In high-frequency sampling mode, the dynamic decay signal of the electrode surface charge is continuously acquired at a sampling rate of 0.1Hz, and the voltage decay curve is fitted based on the second-order RC equivalent circuit model to obtain the core characteristic parameters characterizing the double-layer state of the electrode interface. The core characteristic parameters include the dominant dissipation time constant. Total polarization amplitude Float charge offset ; Step S22, perform temperature compensation on the core feature parameters: The Arrhenius equation is used to quantify the effect of temperature on charge properties. Parameter data collected at different ambient temperatures are normalized to parameter data under standard temperature conditions to obtain compensated standard core characteristic parameters. , : ， ， in, This indicates the collected temperature data. The reference temperature, representing standard conditions, is usually set to 25°C. right Perform linear temperature compensation to obtain the compensated core characteristic parameters. : ， in, This is the temperature compensation coefficient; The temperature compensation process described above can eliminate evaluation errors caused by temperature fluctuations.

7. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 6, characterized in that, Step S2 further includes: Step S23, calculate auxiliary parameters: Based on the initial internal resistance and the current measured internal resistance Calculate the rate of change of internal resistance : ； Calculate the open-circuit voltage attenuation rate : ， in, This is the initial open-circuit voltage. This is the current measured open-circuit voltage.

8. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 3, characterized in that, Step S3 specifically includes: Step S31, the initial feature parameters are... , and Compensated core feature parameters Compensated standard core feature parameters and and auxiliary parameters and Input the weighted fusion model to calculate the current state of health (SOH) value of the battery: ， in, The weights of each parameter are determined through an accelerated aging test at 45°C. , and These are the core characteristic parameters after temperature compensation. The rate of change of internal resistance, This represents the open-circuit voltage attenuation rate. Step S32: Based on the change patterns of core characteristic parameters and auxiliary parameters, and using battery aging diagnosis rules, diagnose the dominant cause of the current aging of the battery. The battery aging diagnosis rules include: When the floating charge offset Increase and dominate the dissipation time constant When the value decreases, it indicates that the difference between the steady-state OCV and the nominal float charge voltage increases, reflecting a reduction in the electrode active area. It is determined that the main cause of battery aging is "corrosion of the positive electrode grid". When the total polarization amplitude Decrease and rate of change of internal resistance When the value increases, it indicates that the electrolyte drying out has blocked the ion conduction pathway and weakened the polarization effect. Therefore, the main cause of battery aging is determined to be "electrolyte drying out".

9. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 3, characterized in that, In step S4, a graded alarm is triggered based on the SOH threshold, and the faulty battery number, SOH value, and cause of aging are displayed, specifically including: When SOH is less than or equal to the first SOH threshold, a yellow maintenance warning will pop up with an audio-visual prompt; when SOH is less than or equal to the second SOH threshold, a red emergency alarm pop-up will pop up, indicating the faulty battery number, SOH value and cause of aging; the first SOH threshold is greater than the second SOH threshold.

10. The method for online monitoring of substation battery life based on surface charge dynamic characteristics according to claim 6, characterized in that, In step S21, the voltage decay curve is fitted based on a second-order RC equivalent circuit model to obtain the core characteristic parameters characterizing the double-layer state of the electrode interface, specifically including: Based on the second-order RC equivalent circuit model of VRLA, the voltage decay curve is fitted by recursive least squares method. The contributions of the double-layer capacitor branch C1-R1 and the electrode polarization branch C2-R2 are separated by fitting, and the core feature parameters are extracted. The expression for the voltage decay curve is: ， in, Let be the voltage value at time t. Open circuit voltage, , The polarization amplitude, , is the time constant.

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