A safety control method, system and device of a multi-energy system

Abstract

The application discloses a kind of safety control method, system and equipment of multi-energy system, it is characterized in that, comprising: obtaining the operation data corresponding to each energy in multi-energy system respectively;According to operation data, the operation state corresponding to each energy is obtained respectively;Each operation state is compared with physical threshold respectively, to determine the number of abnormal energy and target energy that does not occur abnormality based on comparison result;Each target energy is obtained respectively corresponding target operation data, and the available output power corresponding to multi-energy system is calculated according to each target operation data;Each operation state, abnormal energy quantity and available output power are input to Markov model, to determine the reliability state level corresponding to multi-energy system, to control multi-energy system into normal operating state, reduced power operating state, alarm operating state or emergency shutdown state according to reliability state level.The application can improve the operation reliability of multi-energy system under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of distributed energy, and in particular to a method, system and device for the safety control of multi-energy systems. Background Technology

[0002] With the rapid development of new energy technologies and the continuous expansion of distributed energy applications, various energy forms such as photovoltaic power generation, wind power generation, energy storage batteries, and fuel cells are gradually being integrated into scenarios such as microgrids, off-grid power supply, island power supply, and emergency power supplies. Compared with single-energy power supply methods, multi-energy systems can utilize the complementary characteristics between different energy sources to improve energy utilization efficiency and power supply stability. However, multi-energy systems contain various types of energy modules, power conversion devices, and control devices. There are complex energy interactions and operational coupling relationships between the energy modules, making them susceptible to factors such as energy module anomalies, environmental changes, and control link failures during actual operation, leading to changes in the system's operating status.

[0003] Existing multi-energy system safety control technologies typically protect individual energy modules independently, lacking comprehensive analysis and overall reliability assessment of the operating status of multiple energy modules. This makes it difficult for the system to adjust its operating strategy in a timely manner when multiple modules malfunction or experience coupled faults. Furthermore, existing technologies lack mechanisms for dynamic coordinated control based on changes in energy module status, system power supply capacity, and environmental factors. When some energy modules cease operation or experience performance degradation, it is difficult to guarantee a continuous and stable power supply. In addition, existing solutions are insufficiently capable of handling fault switching, adjusting operating status, and adapting to extreme environmental conditions during the control process, which can easily lead to reduced system reliability. Summary of the Invention

[0004] This invention provides a safety control method, system, and device for multi-energy systems, which can improve the operational reliability of multi-energy systems under complex operating conditions.

[0005] In a first aspect, embodiments of the present invention provide a safety control method for a multi-energy system, comprising: Obtain the operating data corresponding to each energy source in the multi-energy system; The operating status of each energy source is obtained based on the operating data. Each of the aforementioned operating states is compared with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced any abnormalities based on the comparison results. Obtain the target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. The operating states, the number of abnormal energy sources, and the available output power are input into a preset Markov model to determine the reliability status level of the multi-energy system. Based on the reliability status level, the multi-energy system is controlled to enter a normal operating state, a derated operating state, an alarm operating state, or an emergency shutdown state.

[0006] This invention improves the operational reliability of multi-energy systems under complex conditions by achieving comprehensive perception of the current operating status of each energy module, providing an accurate data foundation for subsequent energy status identification and system reliability assessment. It also improves the operational reliability of multi-energy systems under complex conditions by accurately identifying the operating capabilities and anomaly risks of different energy sources, avoiding misjudgments caused by changes in the state of a single energy source. Furthermore, by identifying abnormal energy modules exceeding safety boundaries, counting the number of abnormal energy sources, and selecting target energy sources that still possess normal operating capabilities, it provides a basis for subsequent system power reconfiguration and reliability status assessment, preventing system instability caused by the continuous participation of faulty energy sources in power supply, thus improving the operational reliability of multi-energy systems under complex conditions. Finally, it combines the remaining capacity of lithium batteries and methanol fuel cells... The system calculates its current available output power based on factors such as pool fuel supply capacity, photovoltaic maximum power point tracking results, and wind power generation capacity. This enables the system to dynamically assess power based on actual available energy capacity, avoiding the risk of insufficient energy or overload caused by operating at rated power, thereby improving the operational reliability of multi-energy systems under complex conditions. By comprehensively considering the probability of energy failure, state change trends, and environmental impact factors, the system dynamically assesses the future reliability status of multi-energy systems and selects normal operation, derating operation, alarm operation, or emergency shutdown control strategies according to the reliability status level. This achieves closed-loop safety control from fault detection to operational adjustment, reducing the operational risks of multi-energy systems caused by single energy failure, environmental changes, or load fluctuations, thereby improving the operational reliability of multi-energy systems under complex conditions.

[0007] Furthermore, the multi-energy system includes lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind turbine generator energy; acquiring the operating data corresponding to each energy source in the multi-energy system includes: The module temperature, cell voltage, and thermal runaway gas concentration of the lithium battery energy source are obtained as operating data of the lithium battery energy source. The methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure of the methanol fuel cell energy are obtained as operating data of the methanol fuel cell energy. The string current signal of the photovoltaic cell energy is acquired as the operating data of the photovoltaic cell energy. The wind speed and generator output power of the wind turbine generator set are obtained as operating data of the wind turbine generator set.

[0008] The above steps collect key operating parameters corresponding to lithium battery energy, methanol fuel cell energy, photovoltaic energy, and wind turbine energy respectively, so as to realize real-time perception of the electrical status, thermal safety status, fuel supply status, and environmental adaptability of each energy module. This provides an accurate data foundation for subsequent abnormal status identification, fault diagnosis, and energy power regulation, avoids the increase in system operation risk caused by a single energy abnormality, and thus improves the operational reliability of multi-energy systems under complex operating conditions.

[0009] Furthermore, obtaining the operating status corresponding to each energy source based on the operating data includes: Feature extraction is performed on the operating data corresponding to each energy source in the multi-energy system to obtain the operating status features corresponding to each energy source. The operating state of each energy source is determined based on its respective operating state characteristics.

[0010] The above steps extract features from the operating data of each energy source, transforming the original operating parameters into state features that can characterize the health status and operating capacity of the energy source. Based on the characteristic information corresponding to different energy sources, the current operating status of each energy source is identified, enabling accurate judgment of energy anomalies, performance changes, and operational risks. This provides a valid basis for subsequent fault identification and system reliability assessment, thereby improving the operational reliability of multi-energy systems under complex operating conditions.

[0011] Furthermore, the step of extracting features from the operating data corresponding to each energy source in the multi-energy system to obtain the operating state features corresponding to each energy source includes: The temperature change rate of the lithium battery energy is obtained based on the module temperature, the voltage deviation of the lithium battery energy is obtained based on the individual cell voltage, and the gas concentration change rate of the lithium battery energy is obtained based on the thermal runaway gas concentration. The methanol leakage characteristics of the methanol fuel cell energy are obtained based on the methanol vapor concentration, the hydrogen leakage characteristics of the methanol fuel cell energy are obtained based on the hydrogen concentration, and the pressure fluctuation characteristics of the methanol fuel cell energy are obtained based on the hydrogen buffer tank pressure. The string current signal is frequency-domain converted to obtain the frequency domain characteristics of the photovoltaic cell energy. The wind speed variation characteristics of the wind turbine generator's energy are obtained based on the wind speed, and the power fluctuation characteristics of the wind turbine generator's energy are obtained based on the generator's output power.

[0012] The above steps extract corresponding state characteristics for different energy types, quantify key risk information such as temperature changes, voltage deviations, gas leaks, frequency domain anomalies, and power fluctuations, and achieve accurate identification of the changing trends and potential fault risks of each energy source. This provides an effective basis for subsequent anomaly judgment and reliability assessment, thereby improving the operational reliability of multi-energy systems under complex operating conditions.

[0013] Furthermore, the step of comparing each of the aforementioned operating states with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced any abnormalities based on the comparison results includes: For any energy source and any operating state characteristic of that energy source, obtain the comparison result between the operating state characteristic and the corresponding preset physical threshold; The abnormal state detection result of the energy source is obtained based on the comparison result; The results of each abnormal state detection are statistically analyzed to determine the number of abnormal energy sources corresponding to the multi-energy system and the target energy sources that have not experienced abnormalities.

[0014] The above steps compare the operating status characteristics of each energy source with preset physical thresholds to independently identify abnormal states of different energy sources. By statistically analyzing the abnormal detection results of each energy source, the number of abnormal energy sources and the target energy sources that can continue to operate are accurately determined. This avoids the impact of a single energy source abnormality on the system status judgment, provides a reliable basis for subsequent energy power scheduling and safety control, and can improve the operational reliability of multi-energy systems under complex operating conditions.

[0015] Furthermore, the step of calculating the available output power corresponding to the multi-energy system based on the target operating data includes: Based on the target operating data and the preset output power calculation rules, the output power corresponding to each target energy source is determined respectively; The available output power of the multi-energy system is obtained by summing up the output power of each of the aforementioned output powers.

[0016] The above steps, by calculating the output power corresponding to each normally operating energy source and comprehensively evaluating the output capacity of multiple energy sources, can obtain the current available energy supply capacity of the multi-energy system in real time, avoid power assessment deviations caused by the anomaly of a single energy source, provide an accurate basis for subsequent system reliability judgment and energy coordination control, and improve the operational reliability of the multi-energy system under complex operating conditions.

[0017] Furthermore, determining the output power corresponding to each target energy source based on the target operating data and the preset output power calculation rules includes: When the target energy source is a lithium battery energy source, the remaining capacity, charge / discharge state and allowable charge / discharge current of the lithium battery energy source are obtained, so as to determine the current allowable output power of the lithium battery energy source based on the remaining capacity, charge / discharge state and allowable charge / discharge current; When the target energy source is methanol fuel cell energy, the fuel supply status, fuel cell output power and stack operation status corresponding to the methanol fuel cell energy are obtained, so as to determine the stable output power corresponding to the methanol fuel cell energy based on the fuel supply status, the fuel cell output power and the stack operation status. When the target energy source is photovoltaic cell energy, the string output power, solar irradiance and maximum power point tracking status corresponding to the photovoltaic cell energy are obtained, so as to determine the maximum output power corresponding to the photovoltaic cell energy based on the string output power, the solar irradiance and the maximum power point tracking status; When the target energy source is wind turbine energy, the wind speed, generator output power and generator speed corresponding to the wind turbine energy are obtained, so as to determine the allowable output power corresponding to the wind turbine energy based on the wind speed, the generator output power and the generator speed.

[0018] The above steps calculate the actual output power for different energy types by combining corresponding operating parameters. This fully considers the operating characteristics and state changes of lithium batteries, methanol fuel cells, photovoltaic cells, and wind turbines, improving the accuracy of energy output capacity assessment. It provides a valid basis for subsequent system reliability analysis and power scheduling, and can improve the operational reliability of multi-energy systems under complex operating conditions.

[0019] Furthermore, the step of inputting each of the aforementioned operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level corresponding to the multi-energy system includes: The reliability status of the multi-energy system is determined based on each of the aforementioned operating states and the number of abnormal energy sources, wherein the reliability status includes normal state, derating state, alarm state, and emergency shutdown state; The reliability state, the available output power, and the preset energy failure rate, energy repair rate, and environmental stress factor are input into the Markov model to update the state transition probability matrix based on the energy failure rate, the energy repair rate, and the environmental stress factor, thereby obtaining the state transition probability between each reliability state. The state probability distribution in the Markov model is updated based on the state transition probabilities of each state to determine the state probability of the multi-energy system being in the normal state, the derated state, the alarm state, or the emergency shutdown state. The reliability status level of the multi-energy system is calculated based on the state probabilities and preset reliability weight values.

[0020] The above steps, by constructing a Markov reliability assessment model based on operating status, number of anomalies, output capacity, and environmental factors, enable dynamic prediction of the state evolution process of multi-energy systems. The system reliability level is calculated based on the probability distribution of different reliability states, thereby improving the accuracy of system state judgment and fault response capability, and enhancing the operational reliability of multi-energy systems under complex operating conditions.

[0021] Secondly, embodiments of the present invention provide a safety control system for a multi-energy system, comprising: an acquisition module, an identification module, a detection module, a calculation module, and a control module; The acquisition module is used to acquire the operating data corresponding to each energy source in the multi-energy system. The identification module is used to obtain the operating status corresponding to each energy source based on the operating data; The detection module is used to compare each of the operating states with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities based on the comparison results. The calculation module is used to acquire target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. The control module is used to input the operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level of the multi-energy system, so as to control the multi-energy system to enter the normal operation state, the derated operation state, the alarm operation state, or the emergency shutdown state according to the reliability status level.

[0022] This invention, through the collaborative processing of acquisition, identification, detection, calculation, and control modules, achieves a comprehensive analysis of the operating status, abnormal conditions, available energy supply capacity, and reliability level of a multi-energy system. Based on the reliability status level, it executes corresponding safety control strategies, thereby improving the system's adaptability to complex fault states and enhancing the operational reliability of multi-energy systems under complex operating conditions.

[0023] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of a security control method for a multi-energy system as described in this application.

[0024] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0025] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating one embodiment of a safety control method for a multi-energy system provided in this application; Figure 2 This is a flowchart illustrating a method for extracting operating state features of a multi-energy system provided in this application; Figure 3 This is a flowchart illustrating a method for assessing the reliability status of a multi-energy system based on a Markov model, as provided in this application. Figure 4 This is a schematic diagram of the structure of one embodiment of a safety control system for a multi-energy system provided in this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, 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.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0029] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0032] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0033] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0034] With the development of new energy technologies, multi-energy systems are widely used in microgrids, off-grid power supply, and other scenarios. However, due to their inclusion of multiple types of energy modules and complex coupling relationships, they are susceptible to factors such as module anomalies, environmental changes, and control failures. Existing safety control methods lack comprehensive assessment and dynamic coordinated control of the operating status of multiple energy sources, making it difficult to adapt to energy failures, power supply changes, and complex operating conditions in a timely manner, resulting in insufficient system reliability.

[0035] See Figure 1In order to improve the operational reliability of multi-energy systems under complex operating conditions, an embodiment of the present invention provides a safety control method for multi-energy systems, including steps S101 to S105. Step S101: Obtain the operating data corresponding to each energy source in the multi-energy system; In some embodiments, the multi-energy system includes lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind turbine energy; acquiring the operating data corresponding to each energy source in the multi-energy system includes: acquiring the module temperature, single-cell voltage, and thermal runaway gas concentration of the lithium battery energy as operating data; acquiring the methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure of the methanol fuel cell energy as operating data; acquiring the string current signal of the photovoltaic cell energy as operating data; and acquiring the wind speed and generator output power of the wind turbine energy as operating data.

[0036] In some embodiments, the module temperature, individual cell voltage, and thermal runaway gas concentration of the lithium battery energy source are acquired as operating data of the lithium battery energy source. Specifically, this involves: acquiring the operating data of the battery module corresponding to the lithium battery energy source; acquiring the module temperature of each battery module through a temperature acquisition sensor installed inside the lithium battery energy source; acquiring the individual cell voltage of each battery cell through the battery management system (BMS) configured in the lithium battery energy source; and acquiring the thermal runaway gas concentration generated during the operation of the lithium battery energy source through a VOC sensor (volatile organic compound detection sensor, used to detect changes in the concentration of characteristic gases generated during thermal runaway of the lithium battery) installed on the top of the battery box. The lithium battery energy source uses a lithium iron phosphate battery pack, and each battery module is equipped with multiple temperature acquisition points to acquire real-time temperature information at different locations of the battery module; the battery management system (BMS) includes a battery management unit (BMU), a battery cluster management unit (BCU), and a battery stack management unit (BAU), and the battery management system (BMS) acquires the voltage data corresponding to each battery cell according to a preset sampling period; the VOC sensor is used to detect the characteristic gases generated during thermal runaway, including hydrogen, carbon monoxide, and methane.

[0037] In some embodiments, the methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure of the methanol fuel cell energy are acquired as operating data of the methanol fuel cell energy. Specifically, this involves acquiring fuel supply status data corresponding to the methanol fuel cell energy, collecting methanol vapor concentration using methanol leak detection sensors located at the bottom of the methanol storage tank and at the fuel delivery pipeline, collecting hydrogen concentration using hydrogen concentration detection sensors located inside the methanol fuel cell energy, and collecting hydrogen buffer tank pressure using pressure sensors located at the outlet of the hydrogen buffer tank. The methanol fuel cell energy adopts the methanol reforming-high temperature proton exchange membrane fuel cell technology route, including a methanol storage tank, a methanol reformer, a hydrogen buffer tank, and a fuel cell stack; the methanol leak detection sensor is used to detect methanol vapor leakage, with a detection range of 0 ppm (parts per million) to 5000 ppm; the hydrogen concentration detection sensor is used to detect hydrogen leakage generated during the fuel reforming process, with a detection range of 0%VOL (volume percentage concentration) to 4%VOL; and the pressure sensor is used to detect pressure changes inside the hydrogen buffer tank.

[0038] In some embodiments, the string current signal of the photovoltaic energy source is acquired as the operating data of the photovoltaic energy source. Specifically, this involves acquiring the operating data of the photovoltaic string corresponding to the photovoltaic energy source by acquiring the string current signal at the output side of each photovoltaic string through a current acquisition device installed in the DC combiner box of the photovoltaic energy source. The photovoltaic energy source uses monocrystalline silicon photovoltaic modules and is divided into multiple independent photovoltaic strings. Each photovoltaic string is configured with an independent string-level power optimizer and a fast shutdown device. The current acquisition device continuously acquires the current change data corresponding to each photovoltaic string according to a preset sampling period to obtain the string current signal during the operation of the photovoltaic energy source.

[0039] In some embodiments, the wind speed and generator output power of the wind turbine energy are acquired as operating data of the wind turbine energy. Specifically, this involves acquiring the wind turbine operating status data corresponding to the wind turbine energy, collecting the current ambient wind speed using a wind speed and direction sensor installed at the bottom of the wind turbine tower, and collecting the generator output power using a power detection device installed on the output side of the wind turbine. The wind turbine energy includes a permanent magnet synchronous wind turbine, a three-phase uncontrolled rectifier bridge, a Boost converter (step-up DC converter), and a load shedding controller. The wind speed and direction sensor is used to detect the wind speed information of the environment where the wind turbine is located in real time, with a detection range of 0 m / s to 60 m / s. The power detection device determines the generator output power by collecting voltage and current data on the output side of the wind turbine.

[0040] In some embodiments, the multi-energy system further includes a hierarchical redundancy safety control architecture. This architecture is used for distributed safety control, regional coordination control, and remote fault diagnosis of the operating status of each energy module. The hierarchical redundancy safety control architecture includes a field control layer, a regional coordination layer, and a remote monitoring layer. The field control layer is located on each energy module side and includes multiple local safety controllers corresponding to lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind turbine energy, respectively. Each local safety controller is connected to the power conversion unit and safety detection sensors of the corresponding energy module to collect real-time operating data of the corresponding energy module and generate local control commands based on the safety control logic of the corresponding energy module. The local safety controller for lithium battery energy is used to receive module temperature, cell voltage, current, and thermal runaway gas concentration data uploaded by the battery management system (BMS), and performs battery thermal runaway detection, charge / discharge power limiting, and fault isolation control based on the operating data. The local safety controller for methanol fuel cell energy is used to perform fuel leak detection and fuel supply control based on methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure. The local safety controller for photovoltaic cell energy is used to perform arc detection and string fast shutdown control based on string current signals. The local safety controller for wind turbine energy is used to perform unloading control and mechanical braking control based on wind speed and generator output power. The regional coordination layer is located on the central control side of the multi-energy system and includes a main safety controller and a backup safety controller. The main safety controller and the backup safety controller constitute a dual-redundant control node. Both are communicatively connected to the local safety controllers corresponding to each energy module to receive operating status data uploaded by each energy module and generate energy coordination and scheduling instructions based on the overall system operating status. The primary safety controller and the backup safety controller are connected via a heartbeat line and a synchronization data link. The primary safety controller periodically synchronizes the current control status, energy operating parameters, and fault information to the backup safety controller. When a failure is detected in the primary safety controller or a preset number of heartbeat signals are lost consecutively, the backup safety controller completes the control switch based on the synchronization data, so as to replace the primary safety controller in performing system control within milliseconds.

[0041] In some embodiments, the multi-energy system further includes a multi-dimensional safety monitoring network. This network collects various operational status information from each energy module and communication link, providing real-time safety status data for the hierarchical redundant safety control architecture. The multi-dimensional safety monitoring network includes an electrical status monitoring network, a thermal status monitoring network, a chemical status monitoring network, a mechanical status monitoring network, an insulation status monitoring network, and a communication status monitoring network. The electrical status monitoring network includes voltage sensors, current sensors, and power detection devices installed on the output side of each energy module. These sensors collect charging and discharging voltage and current information of lithium-ion batteries, output power information of methanol fuel cells, string current information of photovoltaic cells, and power generation information of wind turbine generators. The thermal status monitoring network includes temperature sensors installed inside the lithium-ion battery module, fuel cell stack, photovoltaic module, and wind turbine generator. These sensors collect battery module temperature, stack operating temperature, photovoltaic module backplane temperature, and generator winding temperature. The chemical status monitoring network includes gas concentration sensors installed in the lithium-ion battery housing and the methanol fuel cell fuel supply path. These sensors detect the concentration of characteristic gases generated during thermal runaway of the lithium-ion battery and the concentration of methanol vapor and hydrogen during the operation of the methanol fuel cell. The mechanical condition monitoring network includes vibration sensors installed on the wind turbine tower, nacelle, and generator bearings to collect vibration data of the wind turbine structure and mechanical operating status data of the generator. The insulation condition monitoring network includes insulation monitoring sensors installed on the photovoltaic system and DC bus side to detect the insulation impedance between the energy modules and ground in real time, determining if there is insulation degradation or leakage risk. The communication condition monitoring network includes signal integrity monitoring modules deployed in the communication links of each controller to detect communication bus voltage, termination resistance, bit error rate, frame loss rate, and communication latency in real time, triggering communication link switching when communication quality is abnormal. Monitoring data from all dimensions is uploaded to the regional coordination layer via a dual-channel redundant communication bus. This dual-channel redundant communication bus includes a CAN FD communication link for transmitting control commands and an industrial Ethernet communication link for transmitting monitoring data. When any communication link fails, critical control information continues to be transmitted through the backup communication channel to ensure the continuous and safe operation of the multi-energy system.

[0042] Through the above steps, key operational data can be collected for lithium batteries, methanol fuel cells, photovoltaic cells, and wind turbine generators respectively. Combined with a hierarchical redundant safety control architecture and a multi-dimensional safety monitoring network, real-time acquisition and reliable transmission of multi-source status information can be achieved. This provides an accurate data foundation for subsequent energy status assessment, fault identification, and safety control, and improves the operational reliability of multi-energy systems under complex operating conditions.

[0043] Step S102: Obtain the operating status corresponding to each energy source based on the operating data; In some embodiments, obtaining the operating status corresponding to each energy source based on the operating data includes: extracting features from the operating data corresponding to each energy source in the multi-energy system to obtain operating status features corresponding to each energy source; and determining the operating status corresponding to each energy source based on the operating status features.

[0044] In some embodiments, feature extraction is performed on the operating data corresponding to each energy source in the multi-energy system to obtain the operating state features corresponding to each energy source. Specifically, the temperature change rate is calculated based on continuously collected module temperature data to characterize the temperature change trend of the battery module; the voltage deviation is calculated based on the difference between the current cell voltage and the predicted voltage obtained based on the SOC-OCV curve (the curve corresponding to the state of charge and open circuit voltage) to characterize the abnormal state of the battery cell; the gas concentration change rate is calculated based on the thermal runaway gas concentration to characterize the release trend of thermal runaway characteristic gas; at the same time, the current battery charge / discharge state is determined based on the loop current, and the operating state features corresponding to the lithium battery energy source are formed by combining the temperature change rate, voltage deviation, and gas concentration change rate. Methanol leakage in the fuel supply pipeline is determined based on methanol vapor concentration, hydrogen leakage during fuel reforming is determined based on hydrogen concentration, fuel supply pressure stability is determined based on hydrogen buffer tank pressure changes, and fuel cell operating thermal state is determined based on fuel cell stack temperature. Time synchronization processing is performed on methanol vapor concentration, hydrogen concentration, hydrogen buffer tank pressure, and fuel cell stack temperature to extract concentration change trends, pressure fluctuation characteristics, and temperature change characteristics, thus obtaining the operating state characteristics corresponding to the methanol fuel cell energy. Frequency domain conversion processing is performed on the string current signal, and Fast Fourier Transform (FFT) is used to extract the spectral characteristics corresponding to the string current signal, obtaining harmonic amplitude, frequency distribution, and current abrupt change characteristics in the 10kHz to 300kHz frequency band to characterize whether there is a risk of series or parallel arcing in the photovoltaic string. Simultaneously, photovoltaic output stability characteristics are obtained based on DC side voltage changes, and insulation state characteristics corresponding to the photovoltaic cell energy are obtained based on the system's positive and negative electrode insulation impedance to ground, forming the operating state characteristics corresponding to the photovoltaic cell energy. The current wind energy input status is determined based on wind speed data, and the changes in wind power resources are judged in combination with the rated wind speed and the cut-off wind speed. The real-time output power of the generator is calculated based on the generator output voltage and output current, and the output power change rate, power fluctuation amplitude and load change characteristics are extracted to determine whether the wind turbine generator set is operating at overpower or experiencing a sudden load drop. At the same time, the operating status characteristics of the wind turbine generator set corresponding to the energy are formed by combining the operating status of the unloading controller and the mechanical braking mechanism.

[0045] In some embodiments, the operating state of each energy source is determined according to the operating state characteristics, specifically: the lithium battery energy source is judged based on the temperature change rate, voltage deviation, and thermal runaway gas concentration; when the temperature change rate is less than 2℃ / min, the voltage deviation is less than 0.2V, and the thermal runaway gas concentration is less than 200ppm, the lithium battery energy source is determined to be in normal operating state; when the temperature change rate is between 2℃ / min and 5℃ / min, or the voltage deviation is between 0.2V and 0.5V, or the thermal runaway gas concentration is between 200ppm and 500ppm, the lithium battery energy source is determined to be in warning operating state; when the temperature change rate is not less than 5℃ / min, the voltage deviation is not less than 0.5V, and the thermal runaway gas concentration is not less than 500ppm, the lithium battery energy source is determined to be in thermal runaway risk state. The status is determined based on changes in methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure. When the methanol vapor concentration is less than 500 ppm and the hydrogen concentration is less than 0.5% VOL, the methanol fuel cell is determined to be in normal operating condition. When the methanol vapor concentration is between 500 ppm and 1000 ppm, or the hydrogen concentration is between 0.5% VOL and 1% VOL, the methanol fuel cell is determined to be in a leakage warning state. When the methanol vapor concentration is not less than 1000 ppm, the hydrogen concentration is not less than 1% VOL, or the hydrogen buffer tank pressure fluctuates abnormally by more than ±10% / s, the methanol fuel cell is determined to be in a fuel safety abnormal state. The status is determined based on the spectral characteristics, current mutation characteristics, and insulation impedance data corresponding to the string current signal. When no abnormal harmonics are detected in the string current signal spectrum, and the current change is stable with the insulation impedance higher than a preset threshold, the photovoltaic cell energy is determined to be in normal operation. When characteristic harmonics with amplitudes exceeding the threshold are detected in the 10kHz to 300kHz frequency band, the photovoltaic cell energy is determined to have a risk of series arcing. When current mutations are detected accompanied by broadband noise, the photovoltaic cell energy is determined to have a risk of parallel arcing. The status is also determined based on wind speed, output power, and power change characteristics. When the wind speed is between the cut-in wind speed and the rated wind speed, and the generator output power is stable, the wind turbine energy is determined to be in normal operation. When the wind speed exceeds 1.5 times the rated wind speed, or the load drop causes the output power change to exceed 50% of the rated power, the wind turbine energy is determined to be in an overload risk state, and unloading control is initiated. When the wind speed reaches the cut-out wind speed or the wind turbine operates abnormally, the wind turbine energy is determined to be in a shutdown protection state, and the mechanical braking mechanism is controlled to perform a safe shutdown.

[0046] Please refer to Figure 2In some embodiments, the step of extracting features from the operating data corresponding to each energy source in the multi-energy system to obtain the operating status features corresponding to each energy source includes: steps S201 to S204. Step S201: Obtain the temperature change rate of the lithium battery energy based on the module temperature, obtain the voltage deviation of the lithium battery energy based on the single cell voltage, and obtain the gas concentration change rate of the lithium battery energy based on the thermal runaway gas concentration. Step S202: Obtain the methanol leakage characteristics of the methanol fuel cell energy based on the methanol vapor concentration, obtain the hydrogen leakage characteristics of the methanol fuel cell energy based on the hydrogen concentration, and obtain the pressure fluctuation characteristics of the methanol fuel cell energy based on the hydrogen buffer tank pressure. Step S203: Perform frequency domain conversion on the string current signal to obtain the frequency domain characteristics of the photovoltaic cell energy; Step S204: Obtain the wind speed variation characteristics of the wind turbine generator set's energy based on the wind speed, and obtain the power fluctuation characteristics of the wind turbine generator set's energy based on the generator's output power.

[0047] In this first embodiment, sampling data of the lithium battery energy module temperature, individual cell voltage, and thermal runaway gas concentration are acquired. The module temperature sampling data is collected by a temperature sensor installed inside the battery module, with a sampling accuracy of ±1℃. The individual cell voltage sampling data is collected by the battery management system (BMS) to obtain the real-time voltage value of each cell, with a sampling accuracy of ±5mV. The thermal runaway gas concentration sampling data is collected by a VOC sensor installed on the top of the battery box. The VOC sensor is used to detect characteristic thermal runaway gases such as hydrogen, carbon monoxide, and methane. Based on the module temperature sampling data, the temperature change between adjacent sampling times is calculated according to a preset time window. The temperature change rate corresponding to the lithium battery energy is obtained according to the ratio of the temperature change to the corresponding time interval. The preset time window is set to 1 minute to characterize the temperature rise rate of the lithium battery energy in a short period of time. The system obtains the State of Charge (SOC) value corresponding to the current operating state of the lithium battery and determines the predicted single-cell voltage value under the current SOC state based on a pre-established SOC-OCV relationship curve. The difference between the actual single-cell voltage value and the predicted single-cell voltage value in the sampled single-cell voltage data is calculated to obtain the voltage deviation of the lithium battery, reflecting abnormal voltage changes caused by abnormal polarization, internal short circuit, or thermal runaway. Based on the thermal runaway gas concentration sampling data, the change in gas concentration between adjacent sampling times is calculated, and the ratio of the gas concentration change to the corresponding time interval is used to obtain the gas concentration change rate of the lithium battery, characterizing the rate of combustible gas release during thermal runaway. A feature set of thermal runaway states corresponding to the lithium battery is constructed based on the temperature change rate, voltage deviation, and gas concentration change rate, used for subsequent determination of whether the lithium battery is in a normal state, a warning state, or a thermal runaway state.

[0048] In this first embodiment, methanol vapor concentration data, hydrogen concentration data, and hydrogen buffer tank pressure data corresponding to the methanol fuel cell energy are acquired. The methanol vapor concentration data is collected by a methanol vapor sensor located near the methanol storage tank and fuel delivery pipeline, with a detection range of 0-5000 ppm. The hydrogen concentration data is collected by a hydrogen concentration sensor located in the fuel cell stack and hydrogen delivery area, with a detection range of 0-4% VOL. The hydrogen buffer tank pressure data is collected by a pressure sensor located at the outlet of the hydrogen buffer tank, with a pressure detection range of 0-0.5 MPa. The current methanol vapor concentration value is obtained based on the methanol vapor concentration data and compared with a preset methanol leakage concentration threshold to determine the methanol leakage characteristics corresponding to the methanol fuel cell energy. Specifically, when the methanol vapor concentration reaches 1000 ppm, it is determined that there is a methanol leakage risk to the methanol fuel cell energy. The hydrogen concentration value at the current moment is obtained based on hydrogen concentration data, and the deviation of the hydrogen concentration value from the range of hydrogen concentration under normal operating conditions is calculated to obtain the hydrogen leakage characteristics corresponding to the methanol fuel cell energy. Specifically, when the hydrogen concentration reaches 1%VOL, the methanol fuel cell energy is determined to have a hydrogen leakage risk. Based on the hydrogen buffer tank pressure data obtained from multiple consecutive sampling moments, the pressure change between adjacent sampling moments is calculated, and the pressure change rate is determined based on the pressure change amount and the sampling time interval to obtain the pressure fluctuation characteristics corresponding to the methanol fuel cell energy. Specifically, when the pressure change rate of the hydrogen buffer tank exceeds a preset fluctuation range, the hydrogen buffer tank is determined to have abnormal pressure fluctuations. The methanol leakage characteristics, hydrogen leakage characteristics, and pressure fluctuation characteristics are combined to form a fuel safety status characteristic set corresponding to the methanol fuel cell energy, which is used to identify methanol leakage, hydrogen leakage, and abnormal fuel supply conditions.

[0049] In this first embodiment, the string current signal corresponding to each photovoltaic string in the photovoltaic cell energy is acquired. The string current signal is collected by a current detection device installed in the DC combiner box and used as data input for DC arc detection. The string current signal is preprocessed, including removing the DC component, filtering low-frequency disturbances, and dividing the sampled data into time windows to obtain multiple continuous current sampling sequences. A Fast Fourier Transform (FFT) is performed on each current sampling sequence to convert the time-domain string current signal into frequency-domain current spectrum data, obtaining harmonic amplitude information corresponding to different frequencies. The frequency amplitude distribution, characteristic harmonic amplitude, and spectral energy concentration within the 10kHz~300kHz frequency band are extracted from the current spectrum data to obtain the frequency domain characteristics corresponding to the photovoltaic cell energy. The DC arc state corresponding to the photovoltaic cell energy is determined based on the frequency domain characteristics; when characteristic harmonics appear in the 10kHz~300kHz frequency band and the corresponding harmonic amplitude exceeds the preset threshold, it is determined that the photovoltaic cell energy has series arc characteristics; when the string current signal shows obvious abrupt change and broadband noise characteristics exist in the frequency domain, it is determined that the photovoltaic cell energy has parallel arc characteristics.

[0050] In this first embodiment, real-time wind speed data and generator output power data corresponding to the wind turbine generator's energy are acquired. Wind speed data is collected by a wind speed sensor installed at the bottom of the wind turbine generator tower. The wind speed sensor has a detection range of 0~60 m / s and a detection accuracy of ±0.5 m / s. Generator output power data is collected by a power detection device. Based on the wind speed data acquired at multiple consecutive sampling times, the wind speed change between adjacent sampling times is calculated. The wind speed change rate is determined based on the wind speed change and the sampling time interval, thus obtaining the wind speed change characteristics corresponding to the wind turbine generator's energy, characterizing the impact of wind environment changes on the wind turbine's operating state. Based on the generator output power data acquired at multiple consecutive sampling times, the power change between adjacent sampling times is calculated. The fluctuation amplitude and power change trend of the generator output power are determined based on the power change, thus obtaining the power fluctuation characteristics corresponding to the wind turbine generator's energy. By correlating wind speed variation characteristics with power fluctuation characteristics, it is determined whether there is an abnormal power output state of the wind turbine generator set. When the wind speed changes rapidly, the load drops suddenly, or the output power fluctuation exceeds the preset range, it is determined that there is a power stability risk of the wind turbine generator set, and this is used to trigger the unloading controller to adjust the duty cycle of the unloading switch in order to maintain the stability of the DC bus voltage.

[0051] Through the above steps, by extracting characteristics such as temperature changes, voltage deviations, gas leaks, frequency domain harmonics, wind speed changes, and power fluctuations for lithium battery energy, methanol fuel cell energy, photovoltaic energy, and wind turbine energy respectively, the abnormal characteristics of different energy operation can be accurately characterized. Combined with multi-parameter joint analysis, the misjudgment caused by a single detection index is reduced, and the accuracy of identifying the operating status of each energy is improved, thereby improving the operational reliability of multi-energy systems under complex operating conditions.

[0052] Step S103: Each of the aforementioned operating states is compared with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced any abnormalities based on the comparison results. In some embodiments, comparing each of the operating states with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities based on the comparison results includes: for any energy source and any operating state feature of that energy source, obtaining the comparison result between the operating state feature and the corresponding preset physical threshold; obtaining the abnormal state detection result of the energy source based on the comparison result; and statistically analyzing each of the abnormal state detection results to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities corresponding to the multi-energy system.

[0053] In some embodiments, for any energy source and any operating state characteristic of that energy source, the comparison result between the operating state characteristic and the corresponding preset physical threshold is obtained. Specifically, for each energy module in the multi-energy system, the corresponding operating state characteristic is obtained, and a safety boundary judgment is performed based on the preset physical threshold corresponding to each operating state characteristic. For lithium battery energy, the operating state characteristics of the lithium battery energy, such as module temperature, temperature change rate, single cell voltage, voltage deviation, loop current, and thermal runaway gas concentration, are obtained. The module temperature is compared with a preset temperature threshold, the temperature change rate is compared with a preset temperature rise rate threshold, the voltage deviation is compared with a preset voltage deviation threshold, and the thermal runaway gas concentration is compared with a preset gas concentration threshold. When the temperature change rate corresponding to lithium battery energy is less than 2℃ / min, the voltage deviation is less than 0.2V, and the gas concentration is less than 200ppm, all operating characteristics are considered to be within the normal range. When the temperature change rate is between 2℃ / min and 5℃ / min, or the voltage deviation is between 0.2V and 0.5V, or the gas concentration is between 200ppm and 500ppm, the corresponding operating characteristics are considered to be within the warning range. When the temperature change rate is greater than or equal to 5℃ / min, the voltage deviation is greater than or equal to 0.5V, and the gas concentration is greater than or equal to 500ppm, the corresponding operating characteristics are considered to exceed the safety threshold. For methanol fuel cell energy, the operating characteristics such as methanol vapor concentration, hydrogen concentration, reformer temperature, and hydrogen buffer tank pressure are acquired. The methanol vapor concentration is compared with the first gas concentration threshold of 1000ppm, the hydrogen concentration is compared with the second gas concentration threshold of 1%VOL, and the hydrogen buffer tank pressure change is compared with the preset pressure fluctuation threshold. When the methanol vapor concentration is less than 500 ppm and the hydrogen concentration is less than 0.5% VOL, the fuel safety status is determined to be normal. When the methanol vapor concentration is between 500 ppm and 1000 ppm or the hydrogen concentration is between 0.5% VOL and 1% VOL, the fuel safety status is determined to be in a warning state. When the methanol vapor concentration is greater than or equal to 1000 ppm, the hydrogen concentration is greater than or equal to 1% VOL, or the hydrogen buffer tank pressure change exceeds ±10% / s, the corresponding operating status characteristic is determined to have exceeded the safety threshold. For photovoltaic cell energy, the operating status characteristics such as the string current signal, string current frequency domain characteristics, DC side insulation impedance, and output power of the photovoltaic cell energy are acquired. The string current frequency domain characteristics are compared with the preset arc characteristic frequency band, and the DC side insulation impedance is compared with the preset insulation impedance threshold.After frequency domain analysis of the string current signal, when characteristic harmonics are detected in the 10kHz~300kHz frequency band and the harmonic amplitude exceeds a preset threshold, it is determined that the photovoltaic energy has a series arcing anomaly; when a sudden change in string current is detected accompanied by broadband noise, it is determined that the photovoltaic energy has a parallel arcing anomaly; when the insulation impedance of the photovoltaic system to ground is lower than a preset threshold, it is determined that the photovoltaic energy has an insulation anomaly. For wind power, the operating status characteristics of the wind power generation, such as wind speed, wind speed change trend, generator output power, generator vibration state, and speed state, are obtained. The wind speed is compared with the rated wind speed threshold and the cut-off wind speed threshold, the output power fluctuation amplitude is compared with the preset power fluctuation threshold, and the vibration state is compared with the preset mechanical vibration threshold. When the wind speed exceeds 1.5 times the rated wind speed, or when a sudden load drop causes the output power fluctuation to exceed 50%, it is determined that the wind power generation has entered an overload risk state; when the wind turbine vibration amplitude exceeds the preset mechanical safety threshold, it is determined that the wind power generation has a mechanical anomaly.

[0054] In some embodiments, obtaining the abnormal state detection result of the energy source based on the comparison result specifically involves: fusing and judging each operating state feature based on the comparison results corresponding to multiple operating state features in the same energy module to avoid misjudgment caused by a single sensor malfunction. For lithium battery energy, when only a single temperature sensor data exceeds the threshold, while the temperature change rate, voltage deviation, and thermal runaway gas concentration do not exceed the corresponding thresholds, it is determined that the temperature sensor may have a drift anomaly, and it is uncertain whether the lithium battery energy has experienced thermal runaway; when the temperature change rate, voltage deviation, and thermal runaway gas concentration all exceed the corresponding fault thresholds simultaneously, it is determined that the lithium battery energy is in a thermal runaway abnormal state, and a lithium battery thermal runaway abnormal detection result is generated. For methanol fuel cell energy, when the methanol vapor concentration exceeds 1000ppm, or the hydrogen concentration exceeds 1%VOL, or the hydrogen buffer tank pressure fluctuates abnormally, it is determined that the methanol fuel cell energy has a fuel leakage risk, and a fuel safety abnormal detection result is generated; otherwise, a normal state or warning state detection result is generated based on whether each detected quantity is within the warning range. For photovoltaic (PV) energy, when abnormal harmonics appear in the frequency domain characteristics of the string current within the range of 10kHz to 300kHz, and the harmonic amplitude exceeds a preset threshold, the corresponding PV string is determined to have a series arcing abnormality; when the string current experiences a sudden change accompanied by broadband noise, the corresponding PV string is determined to have a parallel arcing abnormality; when the insulation impedance is lower than a preset threshold, the PV energy is determined to have an insulation abnormality. For wind power energy, when the wind speed exceeds the safe operating range, the output power suddenly exceeds the preset range, or the mechanical vibration state is abnormal, the wind power energy is determined to have an operational abnormality, and a wind power abnormality detection result is generated. When multiple operating state characteristics corresponding to a certain energy module do not exceed preset physical thresholds, a normal state detection result for that energy module is generated; when some operating state characteristics reach the warning threshold but do not reach the fault threshold, a warning state detection result for that energy module is generated; when a key operating state characteristic exceeds the fault threshold, an abnormal state detection result for that energy module is generated.

[0055] In some embodiments, the abnormal state detection results are statistically analyzed to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities in the multi-energy system. Specifically, this involves acquiring the abnormal state detection results for lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind power energy, and determining the current operating status of each energy module based on the abnormal state detection results. Energy status identifiers are established as follows: when the abnormal state detection result for lithium battery energy is normal, the lithium battery energy status identifier is set to 0; when the abnormal state detection result for lithium battery energy is a thermal runaway warning state or a thermal runaway abnormal state, the lithium battery energy status identifier is set to 1. When the abnormal state detection result for methanol fuel cell energy is normal, the methanol fuel cell energy status identifier is set to 0; when the abnormal state detection result for methanol fuel cell energy is a leakage abnormal state, the methanol fuel cell energy status identifier is set to 1. When the abnormal state detection result for photovoltaic cell energy is normal, the photovoltaic cell energy status identifier is set to 0; when the abnormal state detection result for photovoltaic cell energy is an arc abnormality or an insulation abnormality, the photovoltaic cell energy status identifier is set to 1. When the abnormal state detection result for wind power energy is normal, the wind power energy status flag is set to 0; when the abnormal state detection result for wind power energy is overload or mechanical abnormality, the wind power energy status flag is set to 1. The status flags corresponding to each energy module are statistically analyzed. When the status flag for a certain energy module is 1, that energy module is identified as an abnormal energy source, and the number of abnormal energy sources is accumulated. For example, if the detected status flags for lithium battery energy are 1, methanol fuel cell energy, photovoltaic cell energy, and wind power energy are 0, then it is determined that there are two abnormal energy sources in the multi-energy system: lithium battery energy and wind power energy. Simultaneously, methanol fuel cell energy and photovoltaic cell energy are identified as target energy sources that have not experienced any abnormalities.

[0056] In some embodiments, the number of abnormal energy sources is matched with the system-level safety state model: when the number of abnormal energy sources is 0, the system is determined to be in normal state S0, and all energy modules are kept running at rated capacity; when the number of abnormal energy sources is 1, the system is determined to be in derated state S1, and a dynamic energy topology reconfiguration strategy is initiated, with the target energy source that has not experienced any abnormalities taking over the power supply task after the faulty energy source is removed; when the number of abnormal energy sources is greater than or equal to 2, the system is determined to enter alarm state S2, and power supply to non-critical loads is restricted; when a risk of fire, explosion, or structural damage is detected, regardless of the number of abnormal energy sources, the system is forced to enter emergency shutdown state S3.

[0057] Through the above steps, by comparing the operating status characteristics of each energy module with the corresponding physical thresholds item by item, and combining the multi-feature fusion judgment mechanism, different types of faults such as lithium battery thermal runaway, fuel leakage, photovoltaic arcing, and wind turbine abnormalities can be accurately identified, reducing the risk of misjudgment caused by a single detection parameter. At the same time, by statistically analyzing the detection results of each energy anomaly, the number of abnormal energy sources and target energy sources can be quickly determined, providing an accurate basis for subsequent system reliability assessment and operating status switching, thereby improving the operational reliability of multi-energy systems under complex operating conditions.

[0058] Step S104: Obtain the target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. In some embodiments, calculating the available output power of the multi-energy system based on each of the target operating data includes: determining the output power of each target energy source based on each of the target operating data and a preset output power calculation rule; and accumulating each of the output powers to obtain the available output power of the multi-energy system.

[0059] In some embodiments, based on the target operating data and preset output power calculation rules, the output power corresponding to each target energy source is determined respectively. Specifically, the target operating data corresponding to each target energy source is obtained, wherein the target operating data includes the battery state of charge, battery terminal voltage, battery operating current and battery health status corresponding to lithium battery energy, the fuel supply status, fuel cell stack temperature, hydrogen buffer tank pressure and fuel cell output status corresponding to methanol fuel cell energy, the solar irradiance, module temperature, string voltage and string current and maximum power point tracking status corresponding to photovoltaic cell energy, and the wind speed, wind turbine speed, generator output voltage and generator output current corresponding to wind power energy. For lithium-ion batteries, the system determines whether the battery is in a permissible charge / discharge state based on its state of charge (SBC), terminal voltage, operating current, and health status. When the battery is operating normally, the system determines the battery's release capacity based on its SBC and the actual output power based on the maximum permissible charge / discharge current, terminal voltage, and the conversion efficiency of the bidirectional DC / DC converter. When a fault or out-of-service state is detected, the corresponding output power is set to zero, and the power conversion unit is prohibited from outputting power to the DC bus. When the battery is functioning normally, the system acquires SBC information from the Battery Management System (BMS) and determines a reference value for the battery's output power based on the maximum permissible discharge current and the current DC bus voltage requirement. The output current is then limited using the voltage outer loop and current inner loop control structure of the bidirectional DC / DC converter to ensure the battery's output power does not exceed the current safe operating range. For methanol fuel cell energy, the ability to continuously supply power is determined based on the fuel supply status, fuel cell stack temperature, hydrogen buffer tank pressure, and fuel cell output status. When the methanol fuel cell is operating normally, the output power is determined based on the fuel supply, rated fuel cell output power, and current stack temperature. When methanol vapor concentration exceeds a preset leakage threshold, hydrogen concentration exceeds a preset safety threshold, or abnormal fluctuations occur in the hydrogen buffer tank pressure, the methanol fuel cell is controlled to enter a protection state, and the corresponding output power is adjusted to zero. The rated output power of the methanol fuel cell is 10kW. When the fuel supply is normal and the stack temperature is within the allowable operating range, the actual output power is determined based on the current fuel supply capacity. When the methanol fuel cell enters an abnormal state, the fuel supply control path is shut down, and power output to the DC bus is stopped.

[0060] In some embodiments, for photovoltaic (PV) energy, the maximum output power corresponding to the PV energy is determined based on irradiance, module temperature, string voltage, string current, and maximum power point tracking (MPPT) status. The theoretical output capacity of the PV module is obtained based on the current irradiance and module temperature, and MPPT control is executed through a string-level power optimizer to ensure the PV energy operates at its maximum power point under current environmental conditions. The actual output power of the PV energy is calculated based on string voltage, string current, and power conversion unit efficiency. When a DC arc fault, ground insulation impedance below a preset threshold, or string-level fast shutdown is detected, the corresponding PV string is isolated from the DC bus, and the output power of the faulty string is set to zero. For PV strings without abnormalities, the corresponding power continues to be output based on the MPPT results. For wind power generation, the output power is determined based on wind speed, turbine speed, generator output voltage, and generator output current. The system checks whether the turbine is within the cut-in and cut-out wind speed range based on the current wind speed. When the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the wind power output power is set to zero. When the wind speed is within the effective power generation range, the actual output power is determined based on the turbine speed and generator output current. If an increase in output power is detected due to wind speed exceeding the rated wind speed, or if a sudden load drop causes excess power on the DC bus, the unloading controller is activated. This controller adjusts the duty cycle of the unloading switch via PWM (Pulse Width Modulation) to dissipate some of the excess power in the unloading resistor, thus maintaining stable DC bus voltage.

[0061] In some embodiments, the output power of each energy module is accumulated to obtain the available output power corresponding to the multi-energy system. Specifically, this involves acquiring the output power of lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind power energy, and unifying the output power of each energy module according to the power transmission direction between each energy module and the DC bus. When an energy module is supplying power to the DC bus, the corresponding output power is taken as the positive output power; when an energy module is in a state of limited output, fault exit, or power supply stoppage, the corresponding output power is set to zero; when the lithium battery energy is in a charging state, its power direction is marked as negative power to characterize the energy consumption within the system. The positive output power corresponding to each target energy is accumulated to obtain the total available output power that the multi-energy system can provide to the DC bus in the current state. For example, when the lithium battery energy is in a discharging state, its output power is... The methanol fuel cell operates normally, and its output power is... The photovoltaic cell energy output power after MPPT (maximum power point tracking) control is: The output power of wind power generation is calculated based on the current wind speed. The output power of the four energy sources mentioned above is superimposed to obtain the current available output power of the multi-energy system. The available output power is compared with the current load demand power. When the available output power is greater than or equal to the load demand power, each energy module is controlled to operate according to the preset power supply priority. When the available output power is less than the load demand power, the output power of each energy module is adjusted according to the dynamic energy topology reconfiguration strategy, including increasing the output power of available new energy modules, starting the methanol fuel cell to supplement the base load, and controlling the lithium battery to undertake the power fluctuation compensation task to ensure continuous power supply to critical loads.

[0062] It should be noted that the dynamic energy topology reconfiguration strategy is as follows: When a photovoltaic (PV) module malfunctions and exits, the power transmission path between the corresponding power conversion unit and the DC bus is disconnected, and the output power command of the wind turbine module is increased, allowing the wind turbine module to operate at the maximum allowable output power under the current wind speed conditions. Simultaneously, the methanol fuel cell module is activated, increasing its output power to supplement the system's basic load demand, and the lithium battery module is controlled to enter a power fluctuation suppression mode, dynamically adjusting the charging and discharging power based on the DC bus voltage deviation to absorb wind power output fluctuations. When a wind turbine module malfunctions and exits, the output enable state of the corresponding power conversion unit is reduced, and the PV module is controlled to operate in maximum power point tracking mode, allowing the PV module to output the maximum available power under the current irradiance conditions. When the PV output power is insufficient to meet the load demand, the methanol fuel cell module is activated to provide supplementary power, while the lithium battery module is controlled to perform frequency modulation control based on the DC bus frequency or power deviation to maintain DC bus power balance. When a lithium battery module malfunctions and exits, its charge / discharge control is released. The output power of the methanol fuel cell module, photovoltaic module, and wind power module is adjusted according to the current system load changes to limit the system power change rate within a preset range. Specifically, the system power fluctuation rate is limited to no more than 10% / min of the rated power by limiting the slope of the output power change of each energy module, thus preventing rapid fluctuations in the DC bus voltage due to the energy storage unit's exit. When a methanol fuel cell module malfunctions and exits, the corresponding fuel supply control path is shut down, and the photovoltaic and wind power modules are controlled as the primary energy sources, providing usable power generation based on sunlight intensity and wind speed conditions. Simultaneously, the lithium battery module is controlled to compensate for any remaining power loss, and its discharge depth is limited to no more than 50%. When insufficient fuel supply or continuous fuel cell anomalies are detected, a fuel replenishment warning is generated and sent to the remote monitoring layer. After completing the dynamic energy topology reconfiguration, the regional coordination layer continuously collects information on the output power of each energy module, DC bus voltage, and load power. Based on the reconfigured energy operating status, it performs closed-loop adjustment of the power reference values ​​of each power conversion unit to ensure that the reconfigured energy supply topology maintains stable operation.

[0063] In some embodiments, determining the output power corresponding to each target energy source based on the target operating data and a preset output power calculation rule includes: when the target energy source is a lithium battery energy source, obtaining the remaining capacity, charge / discharge state, and allowable charge / discharge current of the lithium battery energy source, and determining the current allowable output power of the lithium battery energy source based on the remaining capacity, charge / discharge state, and allowable charge / discharge current; when the target energy source is a methanol fuel cell energy source, obtaining the fuel supply state, fuel cell output power, and stack operating state of the methanol fuel cell energy source, and determining the current allowable output power of the methanol fuel cell energy source based on the fuel supply state, fuel cell output power, and stack operating state. The stack operating status determines the stable output power corresponding to the methanol fuel cell energy. When the target energy is photovoltaic cell energy, the string output power, solar irradiance, and maximum power point tracking status corresponding to the photovoltaic cell energy are obtained to determine the maximum output power corresponding to the photovoltaic cell energy based on the string output power, solar irradiance, and maximum power point tracking status. When the target energy is wind turbine energy, the wind speed, generator output power, and generator speed corresponding to the wind turbine energy are obtained to determine the allowable output power corresponding to the wind turbine energy based on the wind speed, generator output power, and generator speed.

[0064] In some embodiments, when the target energy source is a lithium battery, the remaining capacity, state of charge / discharge, and allowable charge / discharge current of the lithium battery are acquired to determine the current allowable output power of the lithium battery based on the remaining capacity, state of charge / discharge, and allowable charge / discharge current. Specifically, this involves acquiring the battery state of charge (SOC), battery terminal voltage, battery operating current, and battery state of health (SOH) collected by the battery management system (BMS), and determining the remaining releaseable capacity of the lithium battery based on the SOC; correcting the rated capacity of the lithium battery based on the SOH to obtain the effective usable capacity of the lithium battery under the current state. Based on the current battery temperature, battery terminal voltage, battery operating current, and thermal runaway monitoring results, it is determined whether the lithium battery is in an allowable charge / discharge state. Specifically, when the battery temperature change rate exceeds a preset threshold, the voltage deviation exceeds a preset threshold, or the VOC concentration rises abnormally, the lithium battery is determined to enter an abnormal protection state, and the corresponding solid-state circuit breaker is controlled to open, setting the output power of the lithium battery to zero. When the lithium battery is in normal operating condition, the current allowable output current is determined based on the current SOC range, SOH state, and the maximum allowable charge / discharge current output by the BMS. Further, considering the battery terminal voltage and the conversion efficiency of the bidirectional DC / DC converter, the corresponding current allowable output power of the lithium battery is determined. When the lithium battery is discharging, the sustainable discharge capacity is determined based on the remaining capacity, and the output current is limited to not exceed the maximum allowable discharge current. When the lithium battery is charging, the maximum charging current is limited based on the current SOC and battery health state to avoid overcharging at high SOC levels, which could lead to battery safety risks. The current allowable output power is used as a power reference value for the lithium battery, and the output power is adjusted through a dual-loop control structure of the bidirectional DC / DC converter (voltage outer loop - current inner loop) to ensure that the lithium battery output power meets the system's power requirements while maintaining the output current within a safe range.

[0065] In some embodiments, when the target energy source is methanol fuel cell energy, the fuel supply status, fuel cell output power, and stack operating status corresponding to the methanol fuel cell energy are acquired to determine the stable output power corresponding to the methanol fuel cell energy based on the fuel supply status, fuel cell output power, and stack operating status. Specifically, the remaining methanol fuel, fuel supply pressure, hydrogen buffer tank pressure, fuel cell output power, and fuel cell stack temperature corresponding to the methanol fuel cell energy are acquired, and it is determined whether the current methanol fuel supply meets the continuous operating conditions based on the fuel supply status. It is determined whether the fuel cell is within its normal operating temperature range based on the fuel cell stack temperature; when the fuel supply is normal, the hydrogen buffer tank pressure is stable, and the fuel cell stack temperature is within the allowable operating range, the methanol fuel cell energy is determined to be in a stable power supply state. When the methanol fuel cell energy is in a stable power supply state, the corresponding stable output power is determined based on the current fuel supply capacity, the fuel cell rated output power, and the current stack operating status; wherein, the rated output power of the methanol fuel cell energy is 10kW, and when the fuel supply capacity is insufficient or the stack temperature deviates from the normal range, the output power is reduced according to the corresponding status. When the methanol vapor concentration exceeds 1000 ppm, the hydrogen concentration exceeds 1% VOL, or abnormal fluctuations occur in the hydrogen buffer tank pressure, a safety risk is identified in the methanol fuel cell energy supply. The fuel supply control path is shut down, the emergency shut-off valve is activated, and the fuel cell's power output to the DC bus is stopped. During normal operation of the methanol fuel cell, a fuel cell power control command is generated based on the stable output power, and the fuel cell output is adjusted through the corresponding power conversion unit to provide continuous and stable base load power to the system.

[0066] In some embodiments, when the target energy source is photovoltaic (PV) cell energy, the string output power, solar irradiance, and maximum power point tracking (MPPT) status corresponding to the PV cell energy are acquired. Based on these parameters, the maximum output power of the PV cell energy is determined. Specifically, this involves acquiring the string voltage, string current, string output power, module temperature, solar irradiance, and the MPPT status output by the string-level power optimizer for each PV string in the PV cell energy. The theoretical output capacity of the PV module under the current environmental conditions is determined based on the solar irradiance and module temperature, and the MPPT status is used to determine whether the PV module is operating at its maximum power point. When the MPPT status is normal, the operating point of the PV module is adjusted by the string-level power optimizer so that each PV string operates at its maximum power point under the current illumination conditions. The maximum output power of the corresponding string is determined based on the string voltage, string current, and power conversion unit efficiency. The maximum output power of each normally operating PV string is accumulated to obtain the maximum output power currently available from the PV cell energy. When a DC arc fault, ground insulation impedance below a preset threshold, or fast shutdown device action is detected in a photovoltaic string, the corresponding faulty string is isolated from the DC bus, and the output power of the faulty string is set to zero. For photovoltaic strings without abnormalities, the maximum output power is determined based on the maximum power point tracking results.

[0067] In some embodiments, when the target energy source is wind turbine energy, the wind speed, generator output power, and generator speed corresponding to the wind turbine energy are acquired to determine the allowable output power corresponding to the wind turbine energy. Specifically, this involves acquiring the real-time wind speed, turbine speed, generator output voltage, generator output current, and current power generation corresponding to the wind turbine, and determining whether the wind turbine is within its effective power generation operating range based on the wind speed. When the current wind speed is detected to be 3 m / s lower than the cut-in wind speed or 25 m / s higher than the cut-out wind speed, it is determined that the wind turbine cannot generate electricity stably, and the allowable output power corresponding to the wind turbine energy is set to zero. When the current wind speed is within the effective power generation range, the allowable output power that the current wind turbine can provide is determined based on the wind speed, turbine speed, and generator output power; wherein, the current actual output power is calculated based on the generator output voltage and output current, and the turbine speed is used to determine whether the current operating state meets the safety operation requirements. When a wind speed exceeding the rated wind speed is detected, causing a rapid increase in power generation, or when a sudden load drop is detected, resulting in a power surplus on the DC bus, the unloading controller in the wind power generation module is activated. This controller uses PWM to adjust the duty cycle of the unloading switch, dissipating the excess power in the unloading resistor to limit DC bus voltage fluctuations. When the wind speed exceeds the safe operating range, the system further controls the wind turbine to perform pitch control speed limiting or electromagnetic power-off braking, bringing the wind turbine generator set into a safe operating state.

[0068] Through the above steps, the corresponding allowable output power is calculated according to the actual operating status of each target energy source. The output capacity of each energy source is dynamically corrected in combination with the energy failure status, environmental changes and operating constraints. This can avoid the system power supply imbalance caused by abnormal energy withdrawal or output fluctuations. At the same time, the system power supply stability is improved by multi-energy power coordination and dynamic topology reconfiguration, thereby improving the operational reliability of multi-energy systems under complex operating conditions.

[0069] Step S105: Input each of the operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level corresponding to the multi-energy system, so as to control the multi-energy system to enter the normal operation state, the derated operation state, the alarm operation state, or the emergency shutdown state according to the reliability status level.

[0070] Please refer to Figure 3 In some embodiments, the step of inputting each of the operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level corresponding to the multi-energy system includes: steps S301 to S304. Step S301: Determine the reliability status of the multi-energy system based on each of the operating states and the number of abnormal energy sources, wherein the reliability status includes normal state, derating state, alarm state and emergency shutdown state; Step S302: Input the reliability state, the available output power, and the preset energy failure rate, energy repair rate, and environmental stress factor into the Markov model to update the state transition probability matrix according to the energy failure rate, the energy repair rate, and the environmental stress factor, and obtain the state transition probability between each reliability state. Step S303: Update the state probability distribution in the Markov model based on each of the state transition probabilities, and determine the state probability of the multi-energy system being in the normal state, the derated state, the alarm state, or the emergency shutdown state; Step S304: Calculate the reliability status level of the multi-energy system based on the state probabilities and preset reliability weight values.

[0071] In this first embodiment, the operating states of lithium battery energy, methanol fuel cell energy, photovoltaic energy, and wind power energy are obtained respectively, and the number of abnormal energy sources is determined based on each operating state. It is determined whether each energy source is in normal operation, derated operation, fault exit, or protection shutdown state. When an energy module is detected to be in a fault state, exit state, or unable to meet preset power supply requirements, the corresponding energy module is marked as an abnormal energy source, and the number of abnormal energy sources is counted. The reliability state of the multi-energy system is determined based on the number of abnormal energy sources and the current safety risk status of the system. When all energy modules are in normal operation and there are no abnormal energy sources, the multi-energy system is determined to be in a normal state. When an energy module is detected to have failed or its output capacity has decreased, and the remaining energy modules can maintain system operation through dynamic energy topology reconfiguration, the multi-energy system is determined to be in a derated state. When two or more energy modules are detected to have failed, or a key sensor fails, causing the system to be unable to maintain its rated operating state, the multi-energy system is determined to be in an alarm state. When lithium battery thermal runaway, methanol leakage, hydrogen concentration exceeding limits, fire risk, or structural damage risk is detected, the multi-energy system is determined to be in an emergency shutdown state. The reliability state space is represented as follows: ,in, Indicates the normal state. Indicates a reduction in the amount, Indicates an alarm status. This indicates an emergency shutdown status.

[0072] In this first embodiment, the current available output power of each energy module is obtained, and combined with the reliability state determined in step S301, the preset energy failure rate, energy repair rate, and environmental stress factor, it is input into a pre-established Markov model. Available output power includes the allowable output power corresponding to lithium battery energy, the stable output power corresponding to methanol fuel cell energy, the maximum allowable output power corresponding to photovoltaic cell energy, and the allowable output power corresponding to wind power energy. Based on the deviation between the current available output power of each energy module and the system's rated power, the state transition trend between each reliability state is determined. For example, when the output power of a certain energy module decreases, leading to a reduction in the system's power supply capacity, the probability of transitioning from the normal state to a derating state or an alarm state is increased; when abnormal energy resumes operation or backup energy provides compensation, the probability of recovery from the derating state to the normal state is increased. The state transition probability matrix in the Markov model is updated based on the energy failure rate, energy repair rate, and environmental stress factor. The state transition probability matrix is ​​expressed as: ; in, This indicates that the multi-energy system has a reliability state. Transition to reliability state The probability (e.g.) This indicates that the multi-energy system has a reliability state. Transition to reliability state The probability of energy failure is dynamically adjusted based on historical failure data of the energy module and current environmental stress factors. Environmental stress factors include ambient temperature, humidity, altitude, and salt spray. For example, when the ambient temperature exceeds 40°C, the lithium battery failure rate is increased; when the ambient temperature is below -10°C, the lithium battery charge / discharge limitation influence coefficient is increased; and when the altitude exceeds 3000m, the corresponding state transition probability of wind power generation is adjusted according to the decrease in wind turbine output.

[0073] In this first embodiment, the state probability distribution in the Markov model is iteratively updated based on the state transition probability matrix obtained in step S302. The initial state probability distribution of the multi-energy system is obtained as follows: ,in, This represents the probability that the system is initially in the third reliability state. The state probability is updated based on the state transition probability matrix: ,in, Indicates after the first System state probability distribution after one running cycle This represents the initial state probability distribution. This indicates that the state transition probability matrix is ​​continuously processed. The result obtained after matrix multiplication The state transition matrix of order 1. Specifically: ,in, This represents the probability that a multi-energy system is in a normal state. This represents the probability that a multi-energy system is in a depletion state. This indicates the probability that a multi-energy system is in an alarm state. This represents the probability that a multi-energy system is in an emergency shutdown state.

[0074] In this first embodiment, a comprehensive reliability evaluation of the multi-energy system is performed based on the reliability state probabilities obtained in step S303 and preset reliability weight values. Corresponding reliability weight values ​​are set for each reliability state: , , , ,in, Represents the reliability weight in the normal state. Indicates the reliability weight in the derating state. Indicates the reliability weight of the alarm state. This indicates the reliability weight for the emergency stop state. The system reliability state level is calculated based on the state probability. ,in, Indicates the reliability status index value. Indicates that multi-energy systems are in the first The current operating cycle is in the first... The state probability of a reliability state. Indicates the first Each reliability state corresponds to a preset reliability weight value; that is: ,in, This indicates the current reliability status level of the multi-energy system. The corresponding control strategy is determined based on the reliability status level: when... When the system is deemed to be in normal operating condition, the current energy dispatch strategy is maintained; when When the system is determined to be in a derated operation level, an energy topology reconfiguration strategy is executed; when When the system is in an alarm state, power supply to non-critical loads is restricted; when When the system is deemed to be in an emergency shutdown state, a full system safety shutdown is initiated.

[0075] Through the above steps, the operating status, abnormal energy quantity, and available output power of the multi-energy system are introduced into the Markov model. By comprehensively considering energy failure, recovery capability, and environmental impact factors, dynamic probabilistic assessment of the system's reliability status is achieved. Based on the reliability level, corresponding control strategies are matched, which can identify potential operational risks in advance and improve the system's safety control capabilities, thereby improving the operational reliability of the multi-energy system under complex operating conditions.

[0076] like Figure 4As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a schematic diagram of the structure of a safety control system for a multi-energy system, including: an acquisition module 100, an identification module 200, a detection module 300, a calculation module 400, and a control module 500; The acquisition module 100 is used to acquire the operating data corresponding to each energy source in the multi-energy system. The identification module 200 is used to obtain the operating status corresponding to each energy source based on the operating data. The detection module 300 is used to compare each of the operating states with a preset physical threshold, so as to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities based on the comparison results. The calculation module 400 is used to acquire target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. The control module 500 is used to input the operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level of the multi-energy system, so as to control the multi-energy system to enter the normal operation state, the derated operation state, the alarm operation state, or the emergency shutdown state according to the reliability status level.

[0077] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the safety control method for a multi-energy system provided by any of the above-described method embodiments of the present invention. More detailed workflows and principles of this system can be found, but are not limited to, the relevant descriptions of the above methods.

[0078] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0079] Based on the above-described embodiment of a security control method for a multi-energy system, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a security control method for a multi-energy system according to any embodiment of the present invention.

[0080] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0081] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0082] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0083] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a security control method for a multi-energy system as described in any of the above-described method embodiments of the present invention.

[0084] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0085] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A safety control method for a multi-energy system, characterized in that, The method includes: Obtain the operating data corresponding to each energy source in the multi-energy system; The operating status of each energy source is obtained based on the operating data. Each of the aforementioned operating states is compared with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced any abnormalities based on the comparison results. Obtain the target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. The operating states, the number of abnormal energy sources, and the available output power are input into a preset Markov model to determine the reliability status level of the multi-energy system. Based on the reliability status level, the multi-energy system is controlled to enter a normal operating state, a derated operating state, an alarm operating state, or an emergency shutdown state.

2. The safety control method for a multi-energy system as described in claim 1, characterized in that, The multi-energy system includes lithium battery energy, methanol fuel cell energy, photovoltaic cell energy, and wind turbine generator energy; acquiring the operating data corresponding to each energy source in the multi-energy system includes: The module temperature, cell voltage, and thermal runaway gas concentration of the lithium battery energy source are obtained as operating data of the lithium battery energy source. The methanol vapor concentration, hydrogen concentration, and hydrogen buffer tank pressure of the methanol fuel cell energy are obtained as operating data of the methanol fuel cell energy. The string current signal of the photovoltaic cell energy is acquired as the operating data of the photovoltaic cell energy. The wind speed and generator output power of the wind turbine generator set are obtained as operating data of the wind turbine generator set.

3. The safety control method for a multi-energy system as described in claim 1, characterized in that, The step of obtaining the operating status corresponding to each energy source based on the operating data includes: Feature extraction is performed on the operating data corresponding to each energy source in the multi-energy system to obtain the operating status features corresponding to each energy source. The operating state of each energy source is determined based on its respective operating state characteristics.

4. The safety control method for a multi-energy system as described in claim 2, characterized in that, The step of extracting features from the operating data corresponding to each energy source in the multi-energy system to obtain the operating state features corresponding to each energy source includes: The temperature change rate of the lithium battery energy is obtained based on the module temperature, the voltage deviation of the lithium battery energy is obtained based on the individual cell voltage, and the gas concentration change rate of the lithium battery energy is obtained based on the thermal runaway gas concentration. The methanol leakage characteristics of the methanol fuel cell energy are obtained based on the methanol vapor concentration, the hydrogen leakage characteristics of the methanol fuel cell energy are obtained based on the hydrogen concentration, and the pressure fluctuation characteristics of the methanol fuel cell energy are obtained based on the hydrogen buffer tank pressure. The string current signal is frequency-domain converted to obtain the frequency domain characteristics of the photovoltaic cell energy. The wind speed variation characteristics of the wind turbine generator's energy are obtained based on the wind speed, and the power fluctuation characteristics of the wind turbine generator's energy are obtained based on the generator's output power.

5. The safety control method for a multi-energy system as described in claim 3, characterized in that, The step of comparing each of the aforementioned operating states with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced any abnormalities based on the comparison results includes: For any energy source and any operating state characteristic of that energy source, obtain the comparison result between the operating state characteristic and the corresponding preset physical threshold; The abnormal state detection result of the energy source is obtained based on the comparison result; The results of each abnormal state detection are statistically analyzed to determine the number of abnormal energy sources corresponding to the multi-energy system and the target energy sources that have not experienced abnormalities.

6. The safety control method for a multi-energy system as described in claim 1, characterized in that, The step of calculating the available output power of the multi-energy system based on the target operating data includes: Based on the target operating data and the preset output power calculation rules, the output power corresponding to each target energy source is determined respectively; The available output power of the multi-energy system is obtained by summing up the output power of each of the aforementioned output powers.

7. The safety control method for a multi-energy system as described in claim 6, characterized in that, The step of determining the output power corresponding to each target energy source based on the target operating data and preset output power calculation rules includes: When the target energy source is a lithium battery energy source, the remaining capacity, charge / discharge state and allowable charge / discharge current of the lithium battery energy source are obtained, so as to determine the current allowable output power of the lithium battery energy source based on the remaining capacity, charge / discharge state and allowable charge / discharge current; When the target energy source is methanol fuel cell energy, the fuel supply status, fuel cell output power and stack operation status corresponding to the methanol fuel cell energy are obtained, so as to determine the stable output power corresponding to the methanol fuel cell energy based on the fuel supply status, the fuel cell output power and the stack operation status. When the target energy source is photovoltaic cell energy, the string output power, solar irradiance and maximum power point tracking status corresponding to the photovoltaic cell energy are obtained, so as to determine the maximum output power corresponding to the photovoltaic cell energy based on the string output power, the solar irradiance and the maximum power point tracking status; When the target energy source is wind turbine energy, the wind speed, generator output power and generator speed corresponding to the wind turbine energy are obtained, so as to determine the allowable output power corresponding to the wind turbine energy based on the wind speed, the generator output power and the generator speed.

8. The safety control method for a multi-energy system as described in claim 1, characterized in that, The step of inputting each of the aforementioned operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level corresponding to the multi-energy system includes: The reliability status of the multi-energy system is determined based on each of the aforementioned operating states and the number of abnormal energy sources, wherein the reliability status includes normal state, derating state, alarm state, and emergency shutdown state; The reliability state, the available output power, and the preset energy failure rate, energy repair rate, and environmental stress factor are input into the Markov model to update the state transition probability matrix according to the energy failure rate, the energy repair rate, and the environmental stress factor, so as to obtain the state transition probability between each reliability state. The state probability distribution in the Markov model is updated based on the state transition probabilities of each state to determine the state probability of the multi-energy system being in the normal state, the derated state, the alarm state, or the emergency shutdown state. The reliability status level of the multi-energy system is calculated based on the state probabilities and preset reliability weight values.

9. A safety control system for a multi-energy system, characterized in that, The system includes: an acquisition module, an identification module, a detection module, a calculation module, and a control module; The acquisition module is used to acquire the operating data corresponding to each energy source in the multi-energy system. The identification module is used to obtain the operating status corresponding to each energy source based on the operating data; The detection module is used to compare each of the operating states with a preset physical threshold to determine the number of abnormal energy sources and the target energy sources that have not experienced abnormalities based on the comparison results. The calculation module is used to acquire target operating data corresponding to each of the target energy sources, and calculate the available output power of the multi-energy system based on the target operating data. The control module is used to input the operating states, the number of abnormal energy sources, and the available output power into a preset Markov model to determine the reliability status level of the multi-energy system, so as to control the multi-energy system to enter the normal operation state, the derated operation state, the alarm operation state, or the emergency shutdown state according to the reliability status level.

10. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a safety control method for a multi-energy system as described in any one of claims 1-8.

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