Efficient distributed photovoltaic power generation system construction method integrating energy storage and energy management

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to an efficient distributed photovoltaic power generation system construction method integrating energy storage and energy management, and aims to solve the problems that in the prior art, photovoltaic output fluctuation and energy storage matching are unbalanced, and energy management collaboration is insufficient. The method comprises the following four core steps: 1, constructing an optimization model based on illumination distribution and spatial constraint to realize the optimal layout of the photovoltaic module; secondly, energy storage parameters are accurately calculated through an energy balance method in combination with photovoltaic output and load demand rules, and energy storage medium and topology type selection is completed; 3, establishing a multi-level energy management strategy containing photovoltaic output combined prediction, load analysis and energy storage charge and discharge control; 4, a perception-decision-execution hierarchical cooperative control module is built, and photovoltaic-energy storage-load dynamic linkage is achieved; through precise matching and cooperative control, the energy utilization efficiency of the system is improved, the light abandoning rate is reduced, and the operation reliability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and more specifically to a method for constructing a high-efficiency distributed photovoltaic power generation system that integrates energy storage and energy management. Background Technology

[0002] With the acceleration of the global energy transition, distributed photovoltaic (PV) power generation has become an important form of new energy utilization due to its advantages such as being clean, low-carbon, and flexible in installation. It is widely used in various scenarios such as industrial plants, residential buildings, and public facilities. The core value of distributed PV power generation systems lies in achieving efficient conversion and local consumption of solar energy resources, reducing dependence on traditional power grids, and contributing to the optimization and upgrading of the energy structure.

[0003] However, existing distributed photovoltaic (PV) power generation systems still face many technical bottlenecks in actual operation. Among them, the imbalance between PV output fluctuations and energy storage system matching, and insufficient energy management coordination are particularly prominent. Existing technologies have attempted to configure energy storage units in distributed PV power generation systems, but most solutions have obvious defects: the selection of energy storage unit capacity and power parameters lacks scientific basis, relying mostly on empirical values ​​or simple estimations, and failing to fully combine the PV output fluctuation law with the load demand characteristics for precise matching. This leads to situations where the energy storage capacity is insufficient to meet the power gap compensation requirements, or the energy storage capacity is redundant, resulting in wasted equipment costs and idle resources, further reducing the operating efficiency of the energy storage system. Based on this, a method for constructing an efficient distributed PV power generation system that integrates energy storage and energy management is proposed to solve the above problems. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for constructing a highly efficient distributed photovoltaic power generation system that integrates energy storage and energy management.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management, comprising the following steps: Step 1: Optimize the layout of photovoltaic modules. Based on the light distribution characteristics and spatial constraints of the photovoltaic site, construct a module layout optimization model to determine the module installation angle, spacing, and array arrangement. Step 2: Energy storage unit matching and selection. Based on the photovoltaic power output fluctuation characteristics and load demand patterns, calculate the rated capacity and power parameters of the energy storage unit, and complete the selection of energy storage medium type and topology. Step 3: Energy management strategy construction, establishing a multi-level energy management framework that includes photovoltaic output prediction, load demand analysis, and energy storage charging and discharging control, and designing energy allocation rules based on prediction models; Step 4: System collaborative control is implemented by building a photovoltaic-energy storage-load collaborative control module. Through real-time data acquisition and feedback adjustment, the collaborative control module adopts a hierarchical control architecture of perception layer-decision layer-execution layer to achieve dynamic linkage operation of the three.

[0006] In a preferred embodiment, the process of constructing the component layout optimization model in step one is as follows: An optimization model is constructed with the objective function of maximizing the annual power generation of the photovoltaic modules, and with site space occupancy rate and module shading threshold as constraints. ; in, The annual power generation of photovoltaic modules, The effective light-receiving area of ​​the component. For the photoelectric conversion efficiency of the component, for Constant light intensity for The angle between the angle of solar incidence and the normal to the component at any given time. The total annual sunshine duration For component spacing, For the maximum allowable spacing, For the component installation angle, The optimal layout parameters are obtained by solving this model to determine the maximum allowable installation angle.

[0007] In a preferred embodiment, the method for calculating the rated capacity of the energy storage unit in step two is as follows: based on the difference sequence between photovoltaic output and load demand, the energy storage capacity is calculated using the energy balance method, with the following formula: ; in, The rated capacity of the energy storage unit, For safety factors, the value of k ranges from 1.1 to 1.3, and can be adjusted according to the fluctuation range of photovoltaic output and the importance of the load. for Photovoltaic power output at all times for Constant load demand, and The calculation period covers typical power output fluctuation cycles; the rated power of the energy storage unit is determined based on the maximum charge and discharge power requirements, satisfying the following formula: ; in, This refers to the rated power of the energy storage unit.

[0008] In a preferred embodiment, the photovoltaic output prediction in step three employs a combined prediction model, which obtains the final predicted value by fusing the outputs of multiple individual prediction algorithms. The prediction model is shown in the following formula: ; in for Forecast value of photovoltaic output at any time These are the weight coefficients for each individual prediction algorithm, such as the method based on the minimum sum of squared errors or the entropy weight method, and they satisfy... Let i = 1, 2, ..., n, where n is the number of single prediction algorithms. This represents the output value at time t for each individual prediction algorithm.

[0009] In a preferred embodiment, the energy storage charging and discharging control rule in step three is as follows: when the photovoltaic output is greater than the load demand and the energy storage unit is not fully charged, the energy storage unit is controlled to charge, and the charging power is dynamically adjusted according to the output surplus; when the photovoltaic output is less than the load demand and the energy storage unit is not discharged, the energy storage unit is controlled to discharge to make up for the power gap; when the photovoltaic output and the load demand are balanced, the energy storage unit remains in standby mode, and the charging and discharging control logic satisfies the following formula: ; in, for The charging and discharging power of the energy storage unit at any given time; a positive value indicates charging, and a negative value indicates discharging. for The state of charge of the energy storage unit at all times. The maximum state of charge threshold. This is the minimum state of charge threshold.

[0010] In a preferred embodiment, the collaborative control module in step four adopts a hierarchical control architecture, including a perception layer, a decision layer, and an execution layer; the perception layer is responsible for collecting photovoltaic output, energy storage status, load demand, and grid operation parameters; the decision layer generates control commands based on the data from the perception layer and energy management strategies; and the execution layer executes the commands by controlling the photovoltaic inverter, energy storage converter, and load controller.

[0011] In a preferred embodiment, the decision-making layer uses a model predictive control algorithm to generate control commands, with the optimization objectives being the highest system operating efficiency and the lowest curtailment rate. Constraints include the energy storage SOC range and equipment power limits. The optimization model is implemented as follows: ; in, To optimize the objective function value, This is the weighting coefficient for the light rejection rate. Weighting coefficients for system operating efficiency , For light rejection rate, For system operating efficiency, for Inverter output power at all times This refers to the rated power of the inverter.

[0012] In a preferred embodiment, the energy storage medium selection in step two includes, but is not limited to, at least one of lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries; the energy storage topology adopts a centralized, distributed, or hybrid topology, with centralized topology preferred for small-scale systems and distributed topology for large-scale, high-precision systems, determined according to the system scale and control accuracy requirements.

[0013] In a preferred embodiment, the system also includes a new system operation status monitoring and fault diagnosis step. By collecting the operating parameters of each component, a fault diagnosis model is constructed to identify the fault status of photovoltaic modules, energy storage units and control equipment in real time and generate fault warning signals.

[0014] In a preferred embodiment, the fault diagnosis model employs a threshold judgment method based on feature parameters. When the monitored parameters exceed a preset normal range, the corresponding component is determined to be faulty. The fault judgment logic is as follows: ; in, The fault diagnosis result is: "1 indicates a fault, 0 indicates normal". for Monitor parameter values ​​constantly. To monitor the minimum normal threshold of the parameter, This is the maximum normal threshold for the monitored parameters.

[0015] The beneficial effects of this invention are: 1. This invention sets up a multi-level energy management strategy that integrates photovoltaic power output prediction, load demand analysis and energy storage charging and discharging control. By combining prediction models to improve the accuracy of photovoltaic power output prediction, and by combining dynamic load analysis to formulate precise charging and discharging control logic, it effectively solves the problem of energy waste caused by the mismatch between photovoltaic power output fluctuations and load demand, thereby improving the system's energy allocation efficiency, reducing the curtailment rate, and ensuring the efficient utilization of photovoltaic power.

[0016] 2. This invention establishes a precise matching and selection mechanism for energy storage units based on the fluctuation pattern of photovoltaic output and the characteristics of load demand. It calculates the rated capacity through the energy balance method, determines the rated power by combining the maximum charge and discharge demand, and adapts the selection of energy storage medium and topology. This avoids the problems of insufficient energy storage capacity or redundant waste caused by traditional experience-based selection, so as to achieve efficient matching between energy storage system and photovoltaic system, improve operation efficiency, and reduce system equipment cost and operation and maintenance burden.

[0017] 3. This invention sets up a hierarchical collaborative control architecture of perception layer, decision layer, and execution layer, combined with model predictive control algorithm and real-time monitoring and diagnosis module, to realize dynamic linkage operation of photovoltaic, energy storage and load and timely early warning of system faults. It solves the problems of poor coordination of control architecture and delayed fault response, which significantly improves the stability and reliability of system operation and extends the service life of equipment, thus providing a guarantee for the long-term stable operation of distributed photovoltaic power generation system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall architecture of the present invention; Figure 2 This is a schematic diagram of the hierarchical architecture of the collaborative control module of the present invention; Figure 3 This is a flowchart illustrating the energy management strategy logic of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0022] like Figure 1-3As shown, this embodiment provides a method for constructing a high-efficiency distributed photovoltaic power generation system that integrates energy storage and energy management, including the following steps: Step 1: First, conduct an on-site survey of the target photovoltaic site, collect data on the site's solar radiation distribution characteristics, and clarify the site's spatial boundaries, usable area, and other spatial constraints. Based on the survey data, construct a module layout optimization model with the objective function of maximizing the annual power generation of photovoltaic modules, and with site space occupancy rate and module shading threshold as constraints. The objective function of this optimization model is set as follows: ,in Clearly define the spacing between related components Installation angle With time It accurately reflects the influence of layout parameters on the angle between the solar incidence angle and the component normal; the constraint conditions are set as follows: To avoid shading problems caused by excessively small component spacing and space waste caused by excessively large spacing, while limiting the installation angle within a reasonable range to ensure structural safety; by solving this model, the optimal component installation angle, spacing and array arrangement are obtained, and the layout planning of photovoltaic modules is completed; Step Two: First, collect historical power output data and load demand data of the photovoltaic system in the target scenario, analyze the fluctuation characteristics of photovoltaic power output and the patterns of load demand, and generate a difference sequence between photovoltaic power output and load demand; based on this difference sequence, calculate the rated capacity of the energy storage unit using the energy balance method, the calculation formula is as follows: By introducing absolute values, coverage is ensured for both surplus and insufficient photovoltaic output scenarios. Simultaneously, a safety factor k is introduced to guarantee redundant reserves of energy storage capacity, avoiding capacity shortages under extreme operating conditions. The rated power of the energy storage unit is determined based on the maximum charge and discharge power requirements, meeting... To ensure that the energy storage unit can fully compensate for the maximum power difference between photovoltaic output and load demand; after completing the calculation of rated capacity and power parameters, and considering the system scale and control accuracy requirements, the selection of energy storage medium and topology is completed: if the system has high requirements for charge and discharge efficiency and cycle life, lithium-ion batteries can be selected; if cost control is a high priority, lead-acid batteries can be selected; if the system is large-scale and requires long-term energy storage, vanadium redox flow batteries can be selected, or a combination of multiple energy storage media can be used; in terms of topology, small-scale systems can adopt centralized topology to simplify control, while large-scale systems with high-precision control requirements can adopt distributed or hybrid topologies; Step 3: Establish a multi-level energy management framework encompassing photovoltaic power output forecasting, load demand analysis, and energy storage charging and discharging control. The photovoltaic power output forecasting employs a combined forecasting model, integrating the outputs of multiple individual forecasting algorithms to obtain the final predicted value. The forecasting model is as follows: By reasonably allocating the weight coefficients of each individual prediction algorithm ,satisfy This will fully leverage the predictive advantages of different algorithms under various operating conditions and improve the accuracy of photovoltaic power output prediction. In the load demand analysis phase, historical load data and scenario-specific energy consumption characteristics are combined to analyze the dynamic changes in load demand, providing a basis for energy allocation. The energy storage charging and discharging control rules are designed as follows: when photovoltaic output exceeds load demand and the energy storage unit is not fully charged: At that time, control the energy storage unit to The charging power is dynamically adjusted according to the output surplus; when the photovoltaic output is less than the load demand and the energy storage unit is not depleted: At that time, control the energy storage unit to The power discharge supplements the power gap; when the photovoltaic output is in balance with the load demand, the energy storage unit remains in standby mode to ensure the rational allocation and efficient utilization of energy. Step 4: Construct a photovoltaic-energy storage-load coordinated control module, adopting a hierarchical control architecture of perception layer, decision layer, and execution layer. The perception layer deploys various sensors and data acquisition devices to collect real-time photovoltaic output, energy storage status, load demand, and grid operating parameters, and transmits the collected data to the decision layer. The decision layer uses a model predictive control algorithm to generate control commands based on the data transmitted from the perception layer and the energy management strategy constructed in Step 3. This algorithm aims to maximize system operating efficiency and minimize curtailment rate, and the optimization model is as follows: ,in Weighting coefficients: It can be adjusted according to actual operational needs; constraints include , This ensures the safe operation of the energy storage unit and that the equipment power does not exceed the limit; the execution layer receives control commands from the decision layer and executes the commands by controlling the photovoltaic inverter, energy storage converter and load controller to achieve dynamic linkage operation of photovoltaic, energy storage and load; Step 5: Add a system operation status monitoring and fault diagnosis step. Simultaneously collect operating parameters of photovoltaic modules, energy storage units, and control equipment through the sensing layer's acquisition devices, and construct a fault diagnosis model based on a threshold judgment method using characteristic parameters. The fault judgment logic of this model is as follows: ;in Preset normal thresholds for each monitoring parameter; real-time monitoring parameters Compared with the preset threshold, when When =1, a fault is detected in the corresponding component, a fault warning signal is immediately generated and fed back to the decision-making level, so that staff can promptly investigate and repair it to prevent the fault from escalating; when When the system is in operation, it is determined that the components are functioning normally and the system continues to operate stably. Through the above implementation steps, the present invention can achieve optimal layout of photovoltaic modules, precise matching of energy storage units, efficient energy management and coordinated and stable operation of the system, effectively solve the problems of photovoltaic output fluctuation and energy storage matching imbalance and insufficient energy management coordination in the prior art, and significantly improve the operating efficiency and reliability of distributed photovoltaic power generation system.

[0023] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0024] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0025] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0026] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0027] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0028] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0029] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management, characterized in that, Includes the following steps: Step 1: Optimize the layout of photovoltaic modules. Based on the light distribution characteristics and spatial constraints of the photovoltaic site, construct a module layout optimization model to determine the module installation angle, spacing, and array arrangement. Step 2: Energy storage unit matching and selection. Based on the characteristics of photovoltaic power output fluctuation and load demand patterns, calculate the rated capacity and power parameters of the energy storage unit, and complete the selection of energy storage medium type and topology. Step 3: Energy management strategy construction, establishing a multi-level energy management framework that includes photovoltaic output prediction, load demand analysis, and energy storage charging and discharging control, and designing energy allocation rules based on prediction models; Step 4: System collaborative control is implemented by building a photovoltaic-energy storage-load collaborative control module. Through real-time data acquisition and feedback adjustment, the collaborative control module adopts a hierarchical control architecture of perception layer-decision layer-execution layer to achieve dynamic linkage operation of the three.

2. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The process of constructing the component layout optimization model described in Step 1 is as follows: Using the maximization of annual power generation of photovoltaic modules as the objective function, and site space occupancy rate and component shading threshold as constraints, an optimization model is constructed: ； in, The annual power generation of photovoltaic modules, The effective light-receiving area of ​​the component. For the photoelectric conversion efficiency of the component, for Constant light intensity for The angle between the angle of solar incidence and the normal to the component at any given moment. The total annual sunshine duration For component spacing, For the maximum allowable spacing, For the component installation angle, The optimal layout parameters are obtained by solving this model to determine the maximum allowable installation angle.

3. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The calculation method for the rated capacity of the energy storage unit in step two is as follows: Based on the difference sequence between photovoltaic output and load demand, the energy storage capacity is calculated using the energy balance method. The calculation formula is: ； in, The rated capacity of the energy storage unit, For safety factors, the value of k ranges from 1.1 to 1.3, and can be adjusted according to the fluctuation range of photovoltaic output and the importance of the load. for Photovoltaic power output at all times for Constant load demand, and The calculation period covers typical power output fluctuation cycles; the rated power of the energy storage unit is determined based on the maximum charge and discharge power requirements, satisfying the following formula: ； in, This refers to the rated power of the energy storage unit.

4. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The photovoltaic output prediction in step three adopts a combined prediction model, which obtains the final predicted value by integrating the output results of multiple individual prediction algorithms. The prediction model is shown in the following formula: ； in for Forecast value of photovoltaic output at any time These are the weight coefficients for each individual prediction algorithm, such as the method based on the minimum sum of squared errors or the entropy weight method, and they satisfy... Let i = 1, 2, ..., n, where n is the number of single prediction algorithms. This represents the output value at time t for each individual prediction algorithm.

5. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The energy storage charging and discharging control rules described in step three are as follows: When the photovoltaic output is greater than the load demand and the energy storage unit is not fully charged, the energy storage unit is controlled to charge, and the charging power is dynamically adjusted according to the output surplus; when the photovoltaic output is less than the load demand and the energy storage unit is not discharged, the energy storage unit is controlled to discharge to make up for the power gap; when the photovoltaic output and the load demand are balanced, the energy storage unit remains in standby mode, and the charging and discharging control logic satisfies the following formula: ； in, for The charging and discharging power of the energy storage unit at any given time; a positive value indicates charging, and a negative value indicates discharging. for The state of charge of the energy storage unit at all times. The maximum state of charge threshold. This is the minimum state of charge threshold.

6. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The collaborative control module described in step four adopts a hierarchical control architecture, including a perception layer, a decision layer, and an execution layer. The perception layer is responsible for collecting photovoltaic output, energy storage status, load demand, and grid operation parameters. The decision layer generates control commands based on the data from the perception layer and energy management strategies. The execution layer executes the commands by controlling the photovoltaic inverter, energy storage converter, and load controller.

7. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 6, characterized in that, The decision-making layer uses a model predictive control algorithm to generate control commands, with the optimization objectives being the highest system operating efficiency and the lowest curtailment rate. Constraints include the energy storage SOC range and equipment power limits. The optimization model is implemented as follows: ； in, To optimize the objective function value, This is the weighting coefficient for the light rejection rate. Weighting coefficients for system operating efficiency , For light rejection rate, For system operating efficiency, for Inverter output power at all times This refers to the rated power of the inverter.

8. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, The energy storage medium selection in step two includes, but is not limited to, at least one of lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries; the energy storage topology adopts a centralized, distributed, or hybrid topology, with centralized topology preferred for small-scale systems and distributed topology for large-scale, high-precision systems, determined according to the system scale and control accuracy requirements.

9. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 1, characterized in that, It also includes newly added system operation status monitoring and fault diagnosis steps. By collecting the operating parameters of each component, a fault diagnosis model is built to identify the fault status of photovoltaic modules, energy storage units and control equipment in real time and generate fault warning signals.

10. The method for constructing a high-efficiency distributed photovoltaic power generation system integrating energy storage and energy management according to claim 9, characterized in that, The fault diagnosis model adopts a threshold judgment method based on feature parameters. When the monitored parameters exceed the preset normal range, the corresponding component is determined to be faulty. The fault judgment logic is as follows: ； in, The fault diagnosis result is: "1 indicates a fault, 0 indicates normal". for Monitor parameter values ​​constantly. To monitor the minimum normal threshold of the parameter, This is the maximum normal threshold for the monitored parameters.

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