A fuel cell energy storage device for isolated island microgrids

Abstract

The present invention relates to the field of fuel management technology, specifically a fuel cell energy storage device applied to an isolated island microgrid, the device including an energy input module, a load monitoring module, an energy output regulation module, a fault analysis module, a health management module, and an energy storage coordination module. In the present invention, by synchronizing the real-time adjustment of fuel cell performance with the load demand of the power grid, a load adjustment strategy is implemented, the output power and current are dynamically adjusted, and the energy utilization efficiency and response speed are improved. In terms of fault prediction and health management, by accurately analyzing the operating parameters and performance degradation trends of the fuel cell, not only potential fault points are identified in advance, but also by calculating the lifespan and performance degradation rate, the maintenance plan is optimized, the reliability and continuous operation capability of the device are enhanced, the stability and energy autonomy of the isolated island microgrid are improved, and the dependence on external fuel supply is reduced. At the same time, the environmental impact is reduced to ensure the continuity and safety of the energy supply.

Description

Technical Field

[0001] The present invention relates to the field of fuel management technology, and in particular to a fuel cell energy storage device applied to an isolated island microgrid. Background Art

[0002] The field of fuel management technology encompasses the design and optimization of fuel acquisition, storage, processing, and delivery systems for equipment and devices that rely on a continuous and stable fuel supply, such as fuel cells. Fuel management focuses on efficient fuel use, pollutant emission control, and the reliability and sustainability of the fuel supply chain. This is crucial for improving the overall performance and environmental sustainability of energy devices, particularly in the face of increasingly stringent global energy demands and environmental standards.

[0003] Among them, the fuel cell energy storage device of the isolated island microgrid is used to provide a continuous and self-sufficient power supply on geographically remote islands. The device converts chemical energy into electrical energy through fuel cell technology, and uses energy storage devices to balance supply and demand, ensuring that excess energy is stored when electricity demand is low, and the stored electricity is released during peak periods. This is crucial for isolated islands that cannot rely on conventional power grids for power supply. It not only improves energy autonomy but also enhances environmental sustainability.

[0004] Existing fuel management technologies primarily rely on traditional fuel supply and processing equipment for fuel cell management, lacking the ability to dynamically respond to real-time data. This is particularly true on isolated islands in remote locations, where unstable fuel supplies lead to frequent energy fluctuations and power outages in the power grid. Existing fuel management technologies neglect the prediction and health management of fuel cell performance degradation, limiting optimization and preventative maintenance capabilities, increasing long-term operating costs and the risk of device failure. Regarding environmental protection and energy efficiency, traditional technologies fail to fully utilize modern intelligent monitoring and data analysis methods, and exhibit significant shortcomings in controlling pollutant emissions and improving fuel efficiency. For example, failure to timely optimize fuel use and adjust power output results in energy waste and increases the environmental burden, especially when energy demand fluctuates drastically. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a fuel cell energy storage device for use in an isolated island microgrid.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a fuel cell energy storage device applied to an isolated island microgrid comprises:

[0007] The energy input module records external fuel flow and quality data based on the fuel cell performance parameters of the isolated island microgrid, evaluates the impact of fuel changes on battery performance, and generates stability analysis results;

[0008] The load monitoring module monitors and records the load changes of the isolated island microgrid in real time based on the stability analysis results, analyzes the data change trend, and obtains the load adjustment strategy;

[0009] The energy output regulation module implements the load adjustment strategy, adjusts the fuel cell output power of the isolated island microgrid, monitors real-time power and current data, makes dynamic adjustments based on set thresholds, and generates power regulation parameters;

[0010] The fault analysis module analyzes and predicts potential faults of the fuel cell energy storage through the power adjustment parameters, matches the preset fault mode, identifies performance degradation and fault points, and obtains potential fault prediction results;

[0011] The health management module performs a health assessment of the fuel cell components based on the potential fault prediction results, calculates the lifespan and performance degradation rate of the components, and generates a health assessment result;

[0012] The energy storage coordination module synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, formulates an energy management and maintenance plan, and obtains an energy scheduling strategy according to the operating status of the components.

[0013] As a further solution of the present invention, the stability analysis results include fuel flow stability assessment, fuel quality impact assessment and analysis of the impact on battery performance; the load adjustment strategy includes load demand prediction, load response optimization and load management adjustment measures; the power regulation parameters include power adjustment amplitude, real-time current adjustment value and threshold setting update; the potential fault prediction results include fault point identification, performance degradation index and matching failure mode; the health assessment results include component life prediction, performance degradation rate calculation and key component status assessment; the energy scheduling strategy includes operating parameter synchronization, maintenance plan formulation and component status adjustment strategy.

[0014] As a further solution of the present invention, the energy input module includes:

[0015] The data recording submodule collects fuel cell performance parameters of the isolated island microgrid, records fuel flow and quality data, updates the data synchronously, verifies the consistency of data records, and generates fuel quality records;

[0016] The quality assessment submodule analyzes the fuel quality records, compares the real-time data with the usage data, identifies the fluctuation range of the fuel quality, and obtains the quality stability test results;

[0017] The performance impact submodule evaluates the fuel performance based on the quality stability test results, compares and analyzes the relationship between fuel quality and battery output performance, evaluates the impact of performance changes on fuel cell performance, and generates stability analysis results.

[0018] As a further solution of the present invention, the load monitoring module includes:

[0019] The real-time monitoring submodule uses an anomaly detection method based on the stability analysis results to monitor the current and voltage fluctuations of the isolated island microgrid in real time, synchronizes the real-time load data to the monitoring center, and generates real-time grid status;

[0020] The pattern recognition submodule analyzes the periodic fluctuations and changes of the grid load based on the real-time grid status, identifies the load pattern, including the daily use pattern and the abnormal pattern, and obtains the load pattern change record;

[0021] The response mechanism adjustment submodule adjusts the power distribution and load management strategy of the isolated island microgrid according to the load pattern change record, optimizes the power output to match the differentiated load requirements, and generates a load adjustment strategy.

[0022] As a further solution of the present invention, the formula of the anomaly detection method is as follows:

[0023]

[0024] Among them, z ′ is the deviation index of the power grid state, x is the current and voltage values, μ is the average value of historical data, σ is the standard deviation, β is the temperature influence coefficient, T is the real-time temperature, T ref is the reference temperature, γ is the frequency deviation coefficient, and Δf is the frequency deviation.

[0025] As a further solution of the present invention, the energy output regulation module includes:

[0026] The power adjustment submodule adjusts the output power setting of the fuel cell according to the load adjustment strategy, monitors the real-time power and current data of the isolated island microgrid, verifies that the fuel cell output matches the real-time microgrid load demand, and generates adjusted output parameters;

[0027] The threshold adjustment submodule sets the safety and efficiency thresholds of the fuel cell energy storage power output based on the adjusted output parameters, and periodically evaluates and adjusts the thresholds to obtain updated threshold settings;

[0028] The load response change submodule adjusts the power output of the fuel cell energy storage in real time according to the updated threshold setting, responds to the load changes of the isolated island microgrid, optimizes energy distribution and power utilization, verifies the operating efficiency and stability of the fuel cell energy storage, and generates power regulation parameters.

[0029] As a further solution of the present invention, the fault analysis module includes:

[0030] The mode comparison submodule uses the power adjustment parameters to compare the preset fault mode, analyzes abnormal parameters in the fuel cell energy storage operation, matches the associated fault mode, and generates a matching fault mode;

[0031] The performance change identification submodule uses a support vector machine algorithm based on the matched fault mode to iteratively analyze the performance data of the fuel cell, perform trend and rate analysis of performance changes, and obtain performance degradation analysis results;

[0032] The fault point identification submodule predicts potential fault points in the fuel cell energy storage according to the performance degradation analysis results, locates and identifies the cause of the fault in the fuel cell energy storage operation, and generates a potential fault prediction result.

[0033] As a further solution of the present invention, the formula of the support vector machine algorithm is as follows:

[0034]

[0035] Where f(x) is used to calculate and predict the performance degradation of the fuel cell, x is the new input data point, and x i is the data point in the training dataset, y i is the class label of the data point, K(x i , x, σ, d) is the kernel function, α i is the coefficient of the support vector, b is the bias term, σ and d are the Gaussian kernel parameters, Temp(x) represents the temperature change of the data point, Rate(x) represents the rate of performance change, w1 and w2 are weight coefficients, and n is the number of data points in the training dataset.

[0036] As a further solution of the present invention, the health management module includes:

[0037] The life analysis submodule analyzes the usage records and operating data of the fuel cell components based on the potential fault prediction results, estimates the service life of multiple components, and generates component life assessment results;

[0038] The degradation rate calculation submodule uses the component life assessment results to calculate the performance degradation rate of the key components of the fuel cell, classifies and analyzes the differentiated components, and obtains the performance degradation rate analysis results;

[0039] The health analysis submodule evaluates the health of the entire fuel cell based on the performance degradation rate analysis results, analyzes factors affecting operating efficiency and stability, formulates preventive maintenance measures, and generates health assessment results.

[0040] As a further solution of the present invention, the energy storage coordination module includes:

[0041] The parameter update submodule adjusts and synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, verifies that the parameter settings match the real-time health status and efficiency requirements, and generates synchronized operating parameters;

[0042] The planning submodule uses the synchronous operation parameters to analyze the maintenance requirements of the fuel cell energy storage, formulates regular and temporary maintenance strategies based on the life and performance degradation of the components, and obtains a fuel cell maintenance plan;

[0043] The energy adjustment submodule adopts the fuel cell maintenance plan, optimizes the energy output and load management of the fuel cell according to the operating parameters of the fuel cell energy storage, adjusts the energy distribution to match the expected operating status and load demand, and generates an energy scheduling strategy.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by real-time monitoring of fuel flow and quality data, combined with continuous monitoring of load changes, the real-time adjustment of fuel cell performance is effectively synchronized with the grid load demand. A load adjustment strategy is implemented to dynamically adjust the output power and current, improving energy utilization efficiency and response speed. In terms of fault prediction and health management, by accurately analyzing the operating parameters and performance degradation trends of the fuel cell, not only can potential fault points be identified in advance, but by calculating the lifespan and performance degradation rate, maintenance plans can be optimized, the reliability and continuous operation capability of the device can be enhanced, the stability and energy autonomy of the isolated island microgrid can be improved, and the dependence on external fuel supply can be reduced. At the same time, the environmental impact can be reduced to ensure the continuity and security of the energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the device of the present invention;

[0047] Figure 2 This is a schematic diagram of the device framework of the present invention;

[0048] Figure 3 This is a flow chart of the energy input module of the present invention;

[0049] Figure 4 This is a flow chart of the load monitoring module of the present invention;

[0050] Figure 5This is a flow chart of the energy output regulation module of the present invention;

[0051] Figure 6 This is a flow chart of the fault analysis module of the present invention;

[0052] Figure 7 This is a flow chart of the health management module of the present invention;

[0053] Figure 8 This is a flow chart of the energy storage coordination module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] See also Figures 1 to 2 A fuel cell energy storage device for an isolated island microgrid includes:

[0057] The energy input module records external fuel flow and quality data based on the fuel cell performance parameters of the isolated island microgrid. By comparing and analyzing the relationship between fuel quality and battery output performance, it evaluates the impact of fuel changes on battery performance and generates stability analysis results.

[0058] The load monitoring module uses stability analysis results to monitor and record load changes in isolated island microgrids in real time, identify load patterns, including daily usage patterns and abnormal patterns, analyze data change trends, and obtain load adjustment strategies;

[0059] The energy output regulation module implements load adjustment strategies to adjust the fuel cell output power of the isolated island microgrid, monitors real-time power and current data, makes dynamic adjustments based on set thresholds, and generates power regulation parameters;

[0060] The fault analysis module analyzes and predicts potential faults of fuel cell energy storage through power adjustment parameters, matches preset fault modes, identifies performance degradation and fault points, and obtains potential fault prediction results;

[0061] The health management module uses potential fault prediction results to conduct health assessments on fuel cell components, calculate component lifespan and performance degradation rates, analyze factors affecting operational efficiency and stability, and generate health assessment results.

[0062] The energy storage coordination module synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, formulates the energy management and maintenance plan, and obtains the energy scheduling strategy according to the operating status of the components.

[0063] The stability analysis results include fuel flow stability assessment, fuel quality impact assessment and analysis of the impact on battery performance. The load adjustment strategy includes load demand prediction, load response optimization and load management adjustment measures. The power adjustment parameters include power adjustment amplitude, real-time current adjustment value and threshold setting update. The potential fault prediction results include fault point identification, performance degradation indicators and matching failure modes. The health assessment results include component life prediction, performance degradation rate calculation and key component status assessment. The energy scheduling strategy includes operating parameter synchronization, maintenance plan formulation and component status adjustment strategy.

[0064] See also Figure 2 、 3 , the energy input module includes:

[0065] The data recording submodule collects fuel cell performance parameters of the isolated island microgrid, records fuel flow and quality data, and synchronously updates the data, checks the consistency of the data records, and generates the fuel quality records. The execution process is as follows;

[0066] Fuel cell performance parameters, including fuel flow and quality, are collected for the isolated island microgrid. Flow meters and quality sensors installed on the fuel supply line monitor and record fuel flow and quality indicators in real time. Data is transmitted to central monitoring equipment via a wireless network in real time, ensuring real-time and accuracy. The collected data is initially screened and cleaned to eliminate obvious errors and outliers. A synchronous update mechanism ensures that all collected data maintains consistency in timestamps, avoiding analytical errors caused by data delays or misalignments. Fuel quality records are generated using the following formula:

[0067]

[0068] Among them, Q sync Represents the total quality of the data after synchronization, F i is the fuel flow rate of the ith data point, M iis the corresponding fuel mass, and n is the number of recorded data points.

[0069] The quality assessment submodule analyzes fuel quality records, compares real-time data with usage data, identifies the fluctuation range of fuel quality, and obtains the quality stability test results. The execution process is as follows:

[0070] Fuel stability and reliability are assessed through in-depth analysis of fuel quality records. During the analysis, real-time measured data is compared with historical usage data to identify the range of fuel quality fluctuations. Statistical analysis methods such as standard deviation and variance analysis are used to quantify the stability of fuel quality. Rapidly identifying quality degradation or abnormal fluctuations in the fuel supply is crucial for maintaining battery stability and extending service life. The formula used to obtain quality stability test results is:

[0071]

[0072] Among them, S quality represents the standard deviation of fuel quality, Q i is the mass of the ith measurement, is the average mass and m is the number of measurements.

[0073] The performance impact submodule evaluates fuel performance based on the quality stability test results. By comparing and analyzing the relationship between fuel quality and battery output performance, it evaluates the impact of performance changes on fuel cell performance. The execution process for generating stability analysis results is as follows:

[0074] Based on the quality stability test results, the actual impact of fuel quality fluctuations on battery performance is evaluated. By comparing and analyzing fuel quality data with battery output performance data, the specific impact of different quality fuels on battery performance is evaluated. This helps determine the direct impact of fuel quality on battery performance, as well as potential long-term impacts. By calculating the correlation coefficient between fuel quality fluctuations and battery performance output, the relationship between the two is quantitatively described. A regression model is also used to predict the output performance of the battery under different fuel quality conditions to generate stability analysis results. The formula used is:

[0075] P impact =β0+β1·Q avg

[0076] Among them, P impact is the battery output performance, β0 and β1 are regression model parameters, Q avg is the average value of the fuel mass.

[0077] See also Figure 2 、 4 , the load monitoring module includes:

[0078] Based on the stability analysis results, the real-time monitoring submodule uses anomaly detection methods to monitor the current and voltage fluctuations of the isolated island microgrid in real time, synchronizes the real-time load data to the monitoring center, and generates the real-time grid status. The execution process is as follows;

[0079] Based on the stability analysis results, anomaly detection methods are used to monitor the current and voltage of the isolated island microgrid in real time. This data is synchronized to the monitoring center to capture the grid status in real time. Current and voltage data are then transmitted to the central monitoring equipment via a wireless network. This real-time data anomaly detection enables rapid identification and response to potential grid instability factors, improving the safety and reliability of the installation. Real-time monitoring provides the operations team with a timely understanding of the grid status, which is crucial for maintenance and troubleshooting, and generates real-time grid status.

[0080] The formula for the anomaly detection method is as follows:

[0081]

[0082] Where z′ is the deviation index of the power grid state, x is the current and voltage values, μ is the average value of historical data, σ is the standard deviation, β is the temperature influence coefficient, T is the real-time temperature, and T ref is the reference temperature, γ is the frequency deviation coefficient, and Δf is the frequency deviation.

[0083] The execution process is as follows:

[0084] Through real-time monitoring data, the current current or voltage value x is obtained, the difference from the historical average value μ is calculated and divided by the standard deviation σ for standardization, and the deviation caused by temperature is added. The deviation is calculated by multiplying the temperature difference TT by the temperature influence coefficient β. ref The deviation caused by the change of grid frequency needs to be added to the calculated value. The deviation is obtained by multiplying the frequency deviation coefficient γ by the frequency deviation Δf. The calculated z′ value is the comprehensive deviation index of the grid state, which is used to evaluate the real-time state of the grid. The specific coefficients β and γ can be obtained by fitting historical data to ensure the accuracy of the calculation.

[0085] The pattern recognition submodule analyzes the periodic fluctuations and changes of the grid load based on the real-time grid status, identifies the load pattern, including daily usage pattern and abnormal pattern, and obtains the execution process of the load pattern change record as follows;

[0086] Based on the real-time grid status, we identify the periodic fluctuations and change patterns of the grid load. We use data analysis technology to analyze current and voltage data to distinguish between normal operating modes and abnormal modes. Identifying patterns is crucial for optimizing grid operation and preventing faults. By comparing historical data, we can effectively predict and identify potential operational problems. We optimize the grid's response strategy to adapt to changing operating conditions and obtain a record of load pattern changes. The formula used is:

[0087]

[0088] Among them, P m represents the result of pattern recognition, α is the amplitude adjustment coefficient, ω n and φ n are the frequency and phase of the nth cycle, t is the time, and N is the total number of cycles considered.

[0089] The response mechanism adjustment submodule adjusts the power distribution and load management strategies of the isolated island microgrid based on the load pattern change records, optimizes the power output to match the differentiated load demands, and generates the execution process of the load adjustment strategy as follows;

[0090] Based on the load pattern change records, the load management and power distribution strategies of the power grid are adjusted, including real-time adjustment of the power output of the power grid to respond to actual load demand changes and ensure the efficient and stable operation of the power grid. By optimizing power output, energy waste can be reduced and the overall efficiency of the device can be improved. It can also improve the grid's ability to respond to emergencies and improve the reliability and safety of the device. The load adjustment strategy is generated using the formula:

[0091]

[0092] Among them, R s Represents the effectiveness index of the response strategy, L i is the demand of the i-th load point, P i is the corresponding power output, β is the adjustment coefficient, and M is the total number of load points.

[0093] See also Figure 2 、 5 , the energy output regulation module includes:

[0094] The power adjustment submodule adjusts the fuel cell output power settings based on the load adjustment strategy, monitors the real-time power and current data of the isolated island microgrid, verifies that the fuel cell output matches the real-time microgrid load demand, and generates the adjusted output parameters. The execution process is as follows;

[0095] According to the load adjustment strategy, the output settings of the fuel cell are adjusted in a timely manner. This process involves inputting precise commands to the fuel cell control device to ensure that the output power can meet the high load demand during peak periods while also reducing energy waste during low load periods, improving the overall energy efficiency and response speed of the device. The adjusted output parameters are generated using the formula:

[0096] P adj =γ·(P req -P cur )

[0097] Among them, P adj represents the adjusted output power, γ is the adjustment coefficient, P req is the real-time power demand of the microgrid, P cur is the current output power of the fuel cell.

[0098] The threshold adjustment submodule sets the safety and efficiency thresholds of the fuel cell energy storage power output based on the adjusted output parameters, and periodically evaluates and adjusts the thresholds to obtain the updated threshold setting execution process as follows;

[0099] Based on the adjusted output parameters, the device is ensured to operate safely and efficiently. The device's operating data, such as power output and energy efficiency, is regularly evaluated, and the thresholds are adjusted based on the evaluation results. Adjustments are made to adapt to factors such as environmental changes and equipment aging, ensuring device reliability and performance. Threshold adjustments not only respond to actual grid demand but also consider the device's safe operating standards, resulting in updated threshold settings. The formula used is:

[0100] Θ new =Θ old +δ·(η opt -η obs )

[0101] Among them, Θ new is the updated threshold, Θ old is the original threshold, δ is the adjustment factor, η obs and η opt are the observed value of root efficiency and the expected optimal efficiency, respectively.

[0102] The load response change submodule adjusts the power output of the fuel cell energy storage in real time according to the updated threshold settings, responds to the load changes of the isolated island microgrid, optimizes energy distribution and power utilization, verifies the operating efficiency and stability of the fuel cell energy storage, and generates the power regulation parameters. The execution process is as follows;

[0103] Based on the updated threshold settings, the power output of the fuel cell is dynamically adjusted, the grid load data is monitored in real time, and the output power is quickly adjusted according to the preset threshold to optimize energy distribution and power utilization. This ensures the stability of the grid in the face of demand fluctuations and the efficient operation of the fuel cell, reduces energy loss and improves response efficiency, and generates power regulation parameters using the formula:

[0104]

[0105] Among them, P out is the regulated output power, λ is the response adjustment coefficient, L i is the demand of the ith load, T i is the corresponding response time, and v is the total amount of load.

[0106] See also Figure 2 、 6 , the fault analysis module includes:

[0107] The mode comparison submodule uses power regulation parameters to compare preset fault modes, analyze abnormal parameters in the fuel cell energy storage operation, match associated fault modes, and generate the matching fault mode. The execution process is as follows;

[0108] Using power regulation parameters, fault pattern recognition is performed, and parameters collected during the operation of the fuel cell energy storage device are analyzed in real time. By comparing with preset fault pattern data, patterns that match known faults can be quickly identified. This involves data processing and pattern recognition technology, aiming to promptly detect and prevent greater damage caused by potential faults, and generate matching fault patterns. The formula used is:

[0109]

[0110] Among them, M p Indicates the matching degree, w i is the weight of the i-th parameter, P reg is the preset failure mode parameter, P obs are the observed operating parameters and o is the total number of parameters.

[0111] The performance change identification submodule uses the support vector machine algorithm based on the matched fault mode to iteratively analyze the fuel cell performance data, perform performance change trend and rate analysis, and obtain the performance degradation analysis results. The execution process is as follows;

[0112] Based on the matching failure modes, the support vector machine algorithm is used to iteratively analyze the performance data of the fuel cell. Through in-depth analysis of the data, the trend and rate of change of performance changes can be identified, and the degradation of fuel cell performance can be accurately predicted. The trend analysis of performance degradation helps to timely adjust maintenance strategies and optimize device operations, and obtain performance degradation analysis results.

[0113] The formula of the support vector machine algorithm is as follows:

[0114]

[0115] Where f(x) is used to calculate and predict the performance degradation of the fuel cell, x is the new input data point, and x i is the data point in the training dataset, y i is the class label of the data point, K(x i , x, σ, d) is the kernel function, α i is the coefficient of the support vector, b is the bias term, σ and d are the Gaussian kernel parameters, Temp(x) represents the temperature change of the data point, Rate(x) represents the rate of performance change, w1 and w2 are weight coefficients, and n is the number of data points in the training dataset.

[0116] The execution process is as follows:

[0117] Calculate the kernel function K(x i , x, σ, d), making the similarity metric more adaptable to complex data features. Temperature Temp(x) and performance change rate Rate(x) are added as independent features and multiplied by weight coefficients w1 and w2 respectively. The values ​​of the two coefficients are determined by cross-validation optimization. All values ​​are summed up and the bias term b is added to complete the comprehensive analysis of performance degradation and obtain the predicted value.

[0118] The fault point identification submodule predicts potential fault points in the fuel cell energy storage based on the performance degradation analysis results, locates and identifies the cause of the fuel cell energy storage operation failure, and generates the potential fault prediction results. The execution process is as follows;

[0119] Based on the performance degradation analysis results, not only can potential failure points in the fuel cell be predicted and identified, but also the cause of the failure can be located, which is crucial for ensuring the continued operation and safety of the device. By accurately analyzing the performance data of the fuel cell, future failures can be predicted, preventive measures can be taken in advance, and potential failure prediction results can be generated. The formula used is:

[0120]

[0121] Among them, F p represents the predicted value of the fault point, f k is the predictor weight of the k-th fault point, Xk is the corresponding performance index, and r is the number of failure factors.

[0122] See also Figure 2 、 7 , the health management module includes:

[0123] The life analysis submodule analyzes the usage records and operating data of fuel cell components based on the potential fault prediction results, estimates the service life of multiple components, and generates component life assessment results. The execution process is as follows;

[0124] Based on the potential failure prediction results, a detailed life assessment of fuel cell components is conducted. The usage records and real-time operating data of each component are deeply analyzed, and the expected service life of key components is estimated using statistical methods and life cycle models. This helps to predict the replacement time and maintenance cycle of each component, optimize the operating efficiency of the fuel cell and reduce maintenance costs. The component life assessment results are generated using the formula:

[0125]

[0126] Among them, L c Represents the estimated average life of the component, d i is the usage time of the i-th component, θ is the decay parameter, and f is the total number of components.

[0127] The degradation rate calculation submodule uses the component life assessment results to calculate the performance degradation rate of key fuel cell components, classifies and analyzes differentiated components, and obtains the performance degradation rate analysis results. The execution process is as follows;

[0128] The performance degradation rate of each key fuel cell component is calculated using the component life assessment results. Through detailed classification of different types of components and performance data analysis, the degradation rate of each component can be accurately identified and calculated, providing a scientific basis for maintenance and replacement strategies. The performance degradation rate analysis results are obtained using the formula:

[0129]

[0130] Among them, R d Indicates the performance degradation rate, S f and S i are the initial and final performance states of the component, respectively, and T is the time interval.

[0131] The health analysis submodule evaluates the health of the entire fuel cell based on the performance degradation rate analysis results, analyzes the factors affecting the operating efficiency and stability, formulates preventive maintenance measures, and generates the health assessment results. The execution process is as follows;

[0132] Based on the performance degradation rate analysis results, the overall health status of the fuel cell device is comprehensively evaluated. The performance degradation data of each component is analyzed. Combined with the operating efficiency and stability factors of the device, targeted preventive maintenance measures are formulated. The measures are aimed at identifying and resolving faults in advance, extending the service life of the fuel cell, maintaining efficient operation of the device, and generating health assessment results. The formula used is:

[0133]

[0134] Among them, H s represents the health score of the device, r j is the decay rate of the jth component, w j is the weight factor of the component in the device, and f is the total number of components.

[0135] See also Figure 2 、 8 , the energy storage coordination module includes:

[0136] The parameter update submodule adjusts and synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, verifies that the parameter settings match the real-time health status and efficiency requirements, and generates the execution process of synchronized operating parameters as follows;

[0137] Based on the health assessment results, it is responsible for accurately adjusting and synchronizing the operating parameters of the fuel cell energy storage device to ensure that all operating parameters such as voltage, current and temperature settings can match the current health status and operating efficiency requirements of the battery. Through real-time monitoring and adjustment, the performance stability and output efficiency of the fuel cell are effectively improved, and the synchronized operating parameters are generated. The formula used is:

[0138] P new =P old +k·(ΔH)

[0139] Among them, P new is the adjusted parameter, P old is the original parameter, ΔH is the change caused by the health assessment result, and k is the adjustment coefficient.

[0140] The planning submodule uses the synchronous operation parameters to analyze the maintenance requirements of the fuel cell energy storage and formulates regular and temporary maintenance strategies based on the life and performance degradation of the components. The execution process of the fuel cell maintenance plan is as follows;

[0141] Using synchronized operating parameters, we analyze and determine the maintenance requirements of the fuel cell energy storage device. Taking into account the expected lifespan and current performance degradation of each component, we develop a series of regular and temporary maintenance strategies to maximize the effective operating time of the fuel cell and maintain optimal performance. The fuel cell maintenance plan is obtained using the formula:

[0142]

[0143] Among them, M plan Indicates the detailed steps of the maintenance plan, t i represents the running time of the component, β is a parameter adjusted based on the performance degradation rate, and h is the number of components that need to be maintained.

[0144] The energy adjustment submodule uses the fuel cell maintenance plan to optimize the fuel cell's energy output and load management based on the fuel cell energy storage operating parameters, adjusts the energy distribution to match the expected operating status and load demand, and generates the execution process of the energy scheduling strategy as follows;

[0145] Adopt fuel cell maintenance plan, adjust and optimize the energy output and load management strategy of fuel cell energy storage device, ensure that energy distribution can accurately match the expected operating status and load demand, optimize the overall energy efficiency and operating cost of the device. By dynamically adjusting energy output parameters, improve the device's ability to respond to load changes, generate energy scheduling strategy, and use the formula:

[0146] E adj =E init (1+α·ΔL)

[0147] Among them, E adj is the adjusted energy output, E init is the initial energy setting, ΔL is the change in load demand, and α is the adjustment factor.

[0148] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A fuel cell energy storage device applied to an isolated island microgrid, characterized in that: The device comprises: The energy input module records external fuel flow and quality data based on the fuel cell performance parameters of the isolated island microgrid, evaluates the impact of fuel changes on battery performance, and generates stability analysis results; The load monitoring module monitors and records the load changes of the isolated island microgrid in real time based on the stability analysis results, analyzes the data change trend, and obtains the load adjustment strategy; The energy output regulation module implements the load adjustment strategy, adjusts the fuel cell output power of the isolated island microgrid, monitors real-time power and current data, makes dynamic adjustments based on set thresholds, and generates power regulation parameters; The fault analysis module analyzes and predicts potential faults of the fuel cell energy storage through the power adjustment parameters, matches the preset fault mode, identifies performance degradation and fault points, and obtains potential fault prediction results; The health management module performs a health assessment of the fuel cell components based on the potential fault prediction results, calculates the lifespan and performance degradation rate of the components, and generates a health assessment result; The energy storage coordination module synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, formulates an energy management and maintenance plan, and obtains an energy scheduling strategy based on the operating status of the components; The fault analysis module includes: The mode comparison submodule uses the power adjustment parameters to compare the preset fault mode, analyzes abnormal parameters in the fuel cell energy storage operation, matches the associated fault mode, and generates a matching fault mode; The performance change identification submodule uses a support vector machine algorithm based on the matched fault mode to iteratively analyze the performance data of the fuel cell, perform trend and rate analysis of performance changes, and obtain performance degradation analysis results; The fault point identification submodule predicts potential fault points in the fuel cell energy storage according to the performance degradation analysis results, locates and identifies the cause of the failure of the fuel cell energy storage operation, and generates a potential fault prediction result; The formula of the support vector machine algorithm is as follows: in, To calculate and predict the performance degradation of fuel cells, is a new input data point, is the data point in the training dataset, is the class label of the data point, is the kernel function, is the coefficient of the support vector, is the bias term, and is the Gaussian kernel parameter, represents the temperature change of the data point, Indicates the rate of change of performance, and is the weight coefficient, is the number of data points in the training dataset.

2. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The stability analysis results include fuel flow stability assessment, fuel quality impact assessment and analysis of the impact on battery performance; the load adjustment strategy includes load demand prediction, load response optimization and load management adjustment measures; the power regulation parameters include power adjustment amplitude, real-time current adjustment value and threshold setting update; the potential fault prediction results include fault point identification, performance degradation index and matching failure mode; the health assessment results include component life prediction, performance degradation rate calculation and key component status assessment; the energy scheduling strategy includes operating parameter synchronization, maintenance plan formulation and component status adjustment strategy.

3. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The energy input module includes: The data recording submodule collects fuel cell performance parameters of the isolated island microgrid, records fuel flow and quality data, updates the data synchronously, verifies the consistency of data records, and generates fuel quality records; The quality assessment submodule analyzes the fuel quality records, compares the real-time data with the usage data, identifies the fluctuation range of the fuel quality, and obtains the quality stability test results; The performance impact submodule evaluates the fuel performance based on the quality stability test results, compares and analyzes the relationship between fuel quality and battery output performance, evaluates the impact of performance changes on fuel cell performance, and generates stability analysis results.

4. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The load monitoring module includes: The real-time monitoring submodule uses an anomaly detection method based on the stability analysis results to monitor the current and voltage fluctuations of the isolated island microgrid in real time, synchronizes the real-time load data to the monitoring center, and generates real-time grid status; The pattern recognition submodule analyzes the periodic fluctuations and changes of the grid load based on the real-time grid status, identifies the load pattern, including the daily use pattern and the abnormal pattern, and obtains the load pattern change record; The response mechanism adjustment submodule adjusts the power distribution and load management strategy of the isolated island microgrid according to the load pattern change record, optimizes the power output to match the differentiated load requirements, and generates a load adjustment strategy.

5. The fuel cell energy storage device for an isolated island microgrid according to claim 4, characterized in that: The formula of the anomaly detection method is as follows: in, is the deviation index of the power grid state, are the current and voltage values, is the average value of historical data, is the standard deviation, is the temperature influence coefficient, is the real-time temperature, is the reference temperature, is the frequency deviation coefficient, is the frequency deviation.

6. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The energy output regulation module includes: The power adjustment submodule adjusts the output power setting of the fuel cell according to the load adjustment strategy, monitors the real-time power and current data of the isolated island microgrid, verifies that the fuel cell output matches the real-time microgrid load demand, and generates adjusted output parameters; The threshold adjustment submodule sets the safety and efficiency thresholds of the fuel cell energy storage power output based on the adjusted output parameters, and periodically evaluates and adjusts the thresholds to obtain updated threshold settings; The load response change submodule adjusts the power output of the fuel cell energy storage in real time according to the updated threshold setting, responds to the load changes of the isolated island microgrid, optimizes energy distribution and power utilization, verifies the operating efficiency and stability of the fuel cell energy storage, and generates power regulation parameters.

7. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The health management module includes: The life analysis submodule analyzes the usage records and operating data of the fuel cell components based on the potential fault prediction results, estimates the service life of multiple components, and generates component life assessment results; The degradation rate calculation submodule uses the component life assessment results to calculate the performance degradation rate of key components of the fuel cell, classifies and analyzes differentiated components, and obtains performance degradation rate analysis results; The health analysis submodule evaluates the health of the entire fuel cell based on the performance degradation rate analysis results, analyzes factors affecting operating efficiency and stability, formulates preventive maintenance measures, and generates health assessment results.

8. The fuel cell energy storage device for an isolated island microgrid according to claim 1, characterized in that: The energy storage coordination module includes: The parameter update submodule adjusts and synchronizes the operating parameters of the fuel cell energy storage based on the health assessment results, verifies that the parameter settings match the real-time health status and efficiency requirements, and generates synchronized operating parameters; The planning submodule uses the synchronous operation parameters to analyze the maintenance requirements of the fuel cell energy storage, formulates regular and temporary maintenance strategies based on the life and performance degradation of the components, and obtains a fuel cell maintenance plan; The energy adjustment submodule adopts the fuel cell maintenance plan, optimizes the energy output and load management of the fuel cell according to the operating parameters of the fuel cell energy storage, adjusts the energy distribution to match the expected operating status and load demand, and generates an energy scheduling strategy.

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