Fault processing method and device based on new energy station and electronic equipment

Abstract

The embodiment of the invention discloses a fault processing method and device based on a new energy station and electronic equipment. The problems of accurate identification and dynamic strategy response of equipment faults of the new energy station can be solved. The new energy station comprises an air conditioner and new energy equipment. The method comprises the following specific implementation steps that operation parameters of the air conditioner and environment parameters of the new energy station are collected; according to the operation parameters of the air conditioner, the environment parameters and the target fault detection model, an initial fault detection result is obtained; correcting the initial fault detection result according to pre-stored correlation characteristics between the air conditioner and the new energy equipment to obtain a target fault detection result; determining a target fault processing strategy according to the target fault detection result and the operation parameters of the new energy equipment; and outputting and executing the target fault processing strategy.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a fault handling method, device and electronic equipment based on new energy power stations. Background Technology

[0002] In renewable energy power plants, with the vigorous promotion of remote centralized control and unmanned operation modes, the air conditioning systems in areas such as relay protection rooms and Static Var Generator (SVG) rooms are crucial for maintaining a stable environment. Renewable energy power plants have unique environmental characteristics, including strong electromagnetic interference, large temperature variations, and wide distribution. Currently, sensors are easily affected by environmental interference, increasing the difficulty of data acquisition. Furthermore, equipment fault diagnosis mainly relies on fixed threshold triggering of corresponding fault handling strategies, limiting the monitoring dimensions of renewable energy power plant equipment. Therefore, how to accurately identify equipment faults and implement dynamic strategy responses in renewable energy power plants has become an urgent problem to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, or at least partially address them, this application provides a fault handling method, apparatus, and electronic equipment for new energy power stations, which solves the problems of accurate identification and dynamic strategy response to equipment faults in new energy power stations.

[0004] To achieve the above objectives, the technical solutions provided in this application are as follows: In a first aspect, embodiments of this application provide a fault handling method based on a new energy power station, wherein the new energy power station includes air conditioning and new energy equipment, and the fault handling method based on the new energy power station includes: Collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station; Based on the operating parameters of the air conditioner, the environmental parameters, and the target fault detection model, the initial fault detection results are obtained; Based on the pre-stored correlation characteristics between the air conditioner and the new energy equipment, the initial fault detection result is corrected to obtain the target fault detection result; Based on the target fault detection results and the operating parameters of the new energy equipment, a target fault handling strategy is determined; Output and execute the target fault handling strategy.

[0005] Secondly, embodiments of this application provide a fault handling device based on a new energy power station, wherein the new energy power station includes air conditioning and new energy equipment, and the fault handling device based on the new energy power station includes: The acquisition module is used to collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station; The processing module is used to obtain initial fault detection results based on the operating parameters of the air conditioner, the environmental parameters, and the target fault detection model; The processing module is further configured to correct the initial fault detection result according to the pre-stored correlation characteristics between the air conditioner and the new energy equipment, so as to obtain the target fault detection result; The processing module is also used to determine a target fault handling strategy based on the target fault detection results and the operating parameters of the new energy equipment. The processing module is also used to output and execute the target fault handling strategy.

[0006] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the fault handling method based on new energy power stations in the first aspect of the embodiments of this application.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that causes a computer to execute the fault handling method based on a new energy power station as described in the first aspect of embodiments of this application. The computer-readable storage medium includes ROM / RAM, a magnetic disk, or an optical disk, etc.

[0008] Fifthly, embodiments of this application provide a computer program product that, when run on a computer, causes the computer to perform some or all of the steps of any of the methods of the first aspect.

[0009] Sixthly, embodiments of this application provide an application publishing platform for publishing computer program products, wherein when the computer program product is run on a computer, the computer performs some or all of the steps of any of the methods of the first aspect.

[0010] Compared with the prior art, the embodiments of this application have the following beneficial effects: This application provides a fault handling method, device, and electronic equipment based on a new energy power station. The new energy power station includes air conditioning and new energy equipment. The method involves collecting the operating parameters of the air conditioning and the environmental parameters of the new energy power station; obtaining an initial fault detection result based on the operating parameters, environmental parameters, and a target fault detection model; correcting the initial fault detection result according to pre-stored correlation characteristics between the air conditioning and new energy equipment to obtain a target fault detection result; determining a target fault handling strategy based on the target fault detection result and the operating parameters of the new energy equipment; and outputting and executing the target fault handling strategy. In this scheme, during the fault detection process of the air conditioning, in addition to the conventional pre-processed fault detection model, the output result of the basic model can be corrected by combining the correlation characteristics between the air conditioning and new energy equipment. This ensures that the fault detection of the air conditioning is not only based on the air conditioning parameters and environmental parameters but also considers the operating conditions of the new energy equipment, effectively improving the accuracy of fault detection in complex scenarios of new energy power stations. Simultaneously, by executing a more accurate and effective fault handling strategy, the normal operation of the new energy equipment can be guaranteed. Attached Figure Description

[0011] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0012] To more clearly illustrate the technical solutions in the embodiments 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 based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a fault handling method based on a new energy power station provided in an embodiment of this application. Figure 1 ; Figure 2 This is a flowchart illustrating a fault handling method based on a new energy power station provided in an embodiment of this application. Figure 2 ; Figure 3 This is a schematic diagram of the structure of a fault handling device based on a new energy power station provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] To better understand the above-mentioned objectives, features, and advantages of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features of this application can be combined with each other. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, rather than to describe a specific order of objects.

[0016] The terms “comprising” and “having”, and any variations thereof, in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0017] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0018] In new energy power plants, with the vigorous promotion of remote centralized control and unmanned operation modes, the air conditioning systems in areas such as relay protection rooms and SVG rooms are crucial for maintaining a stable environment. Current technologies have several shortcomings. For example, the monitoring dimensions are limited, fault diagnosis is not precise enough, the linkage mechanism is imperfect, and it is difficult to fully adapt to the special environment of new energy power plants, characterized by strong electromagnetic interference, large temperature variations, and wide distribution. Specific problems are as follows: Poor environmental adaptability: New energy power stations are usually subject to a lot of sand and dust, salt spray (such as coastal or plateau power stations), and sudden voltage surges. These environmental factors have a significant impact on the operation of air conditioning, and sensors are easily interfered with, resulting in inaccurate data collection.

[0019] Lack of operational coupling: Air conditioner malfunctions are closely related to the operating status of new energy equipment (such as SVG equipment) (for example, a high load on SVG equipment will increase the load on the air conditioners between its equipment). Currently, there is no correlation analysis model between the two. Most of the fault diagnosis models mentioned in the existing technology rely solely on the data of the air conditioner itself and do not take into account the operating parameters of the equipment.

[0020] Inflexible linkage strategies: Most current linkage protection measures are triggered when a fixed threshold is reached (such as simply reducing the load or shutting down the unit), without dynamically adjusting the strategy based on the real-time power generation efficiency of the renewable energy power plant, the remaining energy storage capacity, weather forecasts, and other factors.

[0021] Insufficient self-healing capability: The existing system can only issue alarms or execute some simple commands, and cannot achieve cross-device collaborative diagnosis of faults and resource scheduling, nor can it autonomously arrange repair work.

[0022] To address the technical challenges in existing technologies regarding environmental adaptability, operational condition coupling, linkage flexibility, and self-healing capabilities of new energy power stations, this application provides a fault handling method, device, and electronic equipment based on new energy power stations. The new energy power station includes air conditioning and new energy equipment. The specific implementation steps are as follows: Collecting the operating parameters of the air conditioning and the environmental parameters of the new energy power station; obtaining initial fault detection results based on the air conditioning operating parameters, environmental parameters, and a target fault detection model; correcting the initial fault detection results according to pre-stored correlation characteristics between the air conditioning and new energy equipment to obtain a target fault detection result; determining a target fault handling strategy based on the target fault detection result and the operating parameters of the new energy equipment; and outputting and executing the target fault handling strategy. In this solution, during the fault detection process of the air conditioning, in addition to the conventional pre-processed fault detection model, the output results of the basic model can be corrected by combining the correlation characteristics between the air conditioning and new energy equipment. This ensures that the fault detection of the air conditioning is not only based on air conditioning and environmental parameters but also considers the operating conditions of the new energy equipment, effectively improving the accuracy of fault detection in complex scenarios of new energy power stations. Simultaneously, by executing a more accurate and effective fault handling strategy, the normal operation of the new energy equipment can be guaranteed.

[0023] like Figure 1 As shown, Figure 1 A flowchart of a fault handling method based on a new energy power station provided in this application embodiment is included. The method may include the following steps: 101. Collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station.

[0024] It should be noted that a renewable energy power station is a collection of power facilities that integrate renewable energy power generation facilities such as wind power and photovoltaics, and connect them to the power grid through a grid connection point. A renewable energy power station can include renewable energy equipment and air conditioning systems. Renewable energy equipment can include: wind turbines, photovoltaic power generation equipment, transformers, converters, energy storage devices, SVG equipment, etc. A renewable energy power station mainly includes wind farms and photovoltaic power stations. A wind farm can be understood as a wind farm built using wind resources, connected to the power grid through high-voltage lines to generate wind power. A photovoltaic power station mainly uses photovoltaic panels and other equipment to convert solar energy into electrical energy to generate solar power. Air conditioning in a renewable energy power station is essential for controlling the temperature of renewable energy equipment. During operation, renewable energy equipment may reach high temperatures due to high power loads or environmental influences. To protect the renewable energy equipment from overheating failures, air conditioning is needed to control the temperature of the renewable energy equipment.

[0025] In this embodiment of the application, the collected operating parameters of the air conditioner may include compressor current, fan speed, compressor winding temperature, condenser pressure, etc.; the environmental parameters of the new energy power station may include the characteristic frequency of the equipment's electromagnetic radiation (range of 10kHz - 1GHz), dust concentration (0-500μg / m³), salt spray content (0-1000ppm), temperature and humidity of the equipment room, etc.

[0026] Among them, the collection of air conditioning operating parameters and environmental parameters of new energy power stations can be achieved by using specific sensors. For example, anti-environmental interference sensors can be used to collect parameters such as the characteristic frequency of electromagnetic radiation of equipment, dust concentration, and salt spray content unique to new energy power stations; and fiber optic grating sensors can replace traditional electrical sensors: taking advantage of the characteristic of optical fibers that are not afraid of electromagnetic interference, key parameters such as compressor winding temperature (accuracy up to ±0.5℃) and condenser pressure (accuracy ±0.2kPa) can be accurately collected.

[0027] In some embodiments, sensors for collecting the operating parameters of the air conditioner and the environmental parameters of the new energy power station can be set at multiple locations in the environment where the air conditioner and the new energy power station are located. This allows for cross-device verification when abnormal sensor data is detected. In other words, after collecting the operating parameters of the air conditioner and the environmental parameters of the new energy power station, the process may also include: performing threshold detection on the operating parameters of the air conditioner and the environmental parameters to identify abnormal data; determining multiple sensor data collected by other sensors adjacent to the sensor location based on the sensor location corresponding to the abnormal data; and if multiple sensor data are all normal data, then determining that the sensor corresponding to the abnormal data is in an abnormal state and deleting the abnormal data.

[0028] It should be noted that if a sensor detects abnormal data, it may indeed be that the parameter corresponding to that sensor data is abnormal, or it may be that the sensor itself is malfunctioning, causing abnormal data acquisition. Therefore, it can be verified by using sensors at adjacent locations.

[0029] Here, the anomaly detection of sensor data can be a preliminary judgment or a specific anomaly detection based on corresponding thresholds. That is, corresponding thresholds are set based on various sensor data. If the sensor parameter exceeds the corresponding threshold, then the sensor parameter can be considered to be abnormal.

[0030] It should be noted that, based on the specific location of the sensor in the new energy power station, other sensors adjacent to the sensor can be identified, and multiple sensor data collected by other sensors can be obtained. If multiple sensor data are all normal, that is, multiple sensor data are less than or equal to the sensor parameters exceeding the corresponding threshold, then it can be considered that the current abnormal sensor data is caused by sensor abnormality, and therefore the data collected by that sensor can be discarded.

[0031] 102. Based on the air conditioner's operating parameters, environmental parameters, and the target fault detection model, the initial fault detection results are obtained.

[0032] In this embodiment of the application, after obtaining the operating parameters and environmental parameters of the air conditioner, the operating parameters and environmental parameters of the air conditioner can be input into the target fault detection model. The target fault detection model can be a model for detecting equipment faults obtained through model training. The target fault detection model can calculate the operating parameters and environmental parameters of the air conditioner to obtain the initial fault detection result. The input items of the target fault detection model are the operating parameters and environmental parameters of the air conditioner, and the output item is the initial fault detection result.

[0033] It should be noted that the initial fault detection result can include: faulty equipment, fault type, fault cause, etc. The specific content of this initial fault detection result can be determined based on the sample data used during model training of the target fault detection model. In other words, the content of the initial fault detection result can be understood as the task of the target fault detection model. During model training, the sample data needs to include specific fault conditions so that the target fault detection model can learn the correlation between fault conditions and parameters such as operating data. Therefore, if the fault conditions in the sample data include faulty equipment, then the output initial fault detection result can include faulty equipment; similarly, if the fault conditions in the sample data include fault type, then the output initial fault detection result can include fault type; similarly, if the fault conditions in the sample data include fault cause, then the output initial fault detection result can include fault cause; and so on for other fault conditions.

[0034] 103. Based on the pre-stored correlation characteristics between air conditioners and new energy equipment, correct the initial fault detection results to obtain the target fault detection results.

[0035] In this embodiment of the application, after obtaining the initial fault detection result, the initial fault detection result actually indicates the fault condition of the air conditioner. However, since there are certain correlation characteristics between the air conditioner and the new energy equipment in the new energy power station, that is, the change of the parameters of the new energy equipment may lead to the change of the relevant parameters of the air conditioner or the change of the air conditioner parameter requirements. Therefore, in order to further improve the accuracy of the fault detection result, the initial fault detection result can be corrected based on the correlation characteristics between the air conditioner and the new energy equipment to obtain the target fault detection result.

[0036] It should be noted that the process of correcting the initial fault detection result based on the correlation characteristics between the air conditioner and the new energy equipment can be as follows: First, the initial fault detection result is corrected after the target fault detection model outputs the initial fault detection result, thus obtaining the target fault detection result. Second, the initial fault detection result is obtained through the target fault detection model, and then the initial fault detection result is further corrected based on the correlation characteristics, thus obtaining the target fault detection result, which is then output through the target fault detection model. Third, a fault detection correction model is introduced on the basis of the target fault detection model, which is used to correct the output result of the target fault detection model based on the correlation characteristics between the air conditioner and the new energy equipment. The embodiments of this application do not impose specific limitations on this method.

[0037] 104. Based on the target fault detection results and the operating parameters of the new energy equipment, determine the target fault handling strategy.

[0038] In this embodiment of the application, after the target fault detection result is determined, the fault condition indicated by the target fault detection result is processed. Therefore, the target fault processing strategy can be determined based on the target fault detection result and the operating parameters of the new energy equipment.

[0039] It should be noted that since air conditioners are used to dissipate heat from new energy equipment, if the new energy equipment is under heavy load and the temperature is high, the air conditioner cannot be shut down directly even in the event of a minor malfunction (such as insufficient cooling capacity). In other words, this target fault handling strategy needs to be carried out on the basis of ensuring the normal operation of the new energy equipment. Therefore, when determining the target fault handling strategy, it is necessary to combine the operating parameters of the new energy equipment.

[0040] In some embodiments, a correspondence between fault detection results and fault handling strategies is pre-stored. After determining the target fault detection result, multiple fault handling strategies corresponding to the target fault detection result can be determined from the pre-stored correspondence. Then, combined with the operating parameters of the new energy equipment, the target fault handling strategy is determined from the multiple fault handling strategies.

[0041] In some embodiments, a target fault handling strategy can be dynamically generated: based on the fault type, the current load status of the main equipment (e.g., whether the photovoltaic output is 20% or 80%), the SOC (state of charge) of the energy storage, and the weather forecast, a tiered instruction can be generated. In case of minor faults (such as insufficient cooling capacity), the backup air conditioner should be activated first. While meeting grid requirements, the main equipment can be allowed to reduce its load by about 10% simultaneously, rather than shutting down completely. In case of serious faults (such as compressor shutdown), the main equipment should be shut down immediately, and a temporary cooling system powered by energy storage should be activated. Simultaneously, it can be linked with renewable energy equipment, sending load adjustment curves (e.g., linearly reducing from 100% to 60% within 10 minutes) to renewable energy devices such as wind turbines or photovoltaic inverters to prevent sudden load drops from impacting the grid.

[0042] 105. Output and execute the target fault handling strategy.

[0043] In this embodiment of the application, after the target fault handling strategy is determined, the target fault handling strategy can be output and executed.

[0044] This application provides a fault handling method for new energy power stations. In the process of air conditioner fault detection, in addition to the conventional pre-processed fault detection model, the output results of the basic model can be corrected by combining the correlation characteristics between the air conditioner and the new energy equipment. In this way, the fault detection of the air conditioner is not only based on the air conditioner parameters and environmental parameters, but also takes into account the operating conditions of the new energy equipment, which effectively improves the accuracy of fault detection in complex scenarios of new energy power stations. At the same time, by executing a more accurate and effective fault handling strategy, the normal operation of the new energy equipment can be guaranteed.

[0045] like Figure 2 As shown, Figure 2 A flowchart of a fault handling method based on a new energy power station provided for embodiments of this application is shown. The method may further include the following steps: 201. Obtain the operating status of new energy equipment.

[0046] 202. Collect the air conditioner's operating parameters and environmental parameters according to the sampling frequency corresponding to the operating status.

[0047] In this embodiment, real-time data collection is not required when collecting the operating parameters and environmental parameters of the air conditioner. If the new energy equipment is not running or its operating power is low, its heat generation will not be significant. In other words, the air conditioner is not needed for cooling, or it only needs to work intermittently for cooling. In this case, the sampling frequency for collecting the operating parameters and environmental parameters of the air conditioner can be reduced. Therefore, the correspondence between the operating status of the new energy equipment and the sampling frequency can be pre-set. After obtaining the operating status of the new energy equipment, the sampling frequency corresponding to the operating status can be determined, and the operating parameters and environmental parameters of the air conditioner can be collected according to this sampling frequency.

[0048] It should be noted that the operating status of new energy equipment can include: idle state, low load state, high load state, and paused operation state. This means that when the new energy equipment is in a paused operation state, it is essentially turned off, so the air conditioner does not need to cool it down, and therefore the air conditioner can also be in a paused operation state without requiring fault detection. When the new energy equipment is in an idle state, it indicates that the equipment is currently powered on but not operating. The equipment will not generate heat on its own; if cooling is required, it is likely due to environmental factors. Therefore, the air conditioner's operating parameters and environmental parameters can be collected at a lower frequency, such as once per hour. When the new energy equipment is under low load, it means it is operating but not heavily. The equipment will generate heat, but the heat will not be very high, and intermittent cooling by air conditioning may be necessary. Therefore, the operating parameters of the air conditioner and the environment can be collected at a slightly higher sampling frequency, such as once every 10 minutes. When the new energy equipment is under high load, it means it is operating very heavily. The equipment will generate heat, and the heat will be high, potentially leading to overheating. Therefore, continuous cooling by air conditioning is required. In this case, when detecting air conditioning faults, the operating parameters of the air conditioner and the environment need to be collected at a very high sampling frequency, such as once every 10 seconds. It is understood that the specific sampling frequency can be set independently, and can be tiered according to the different operating states of the new energy equipment.

[0049] 203. Preprocess the operating parameters and environmental parameters of the air conditioner to obtain the preprocessed operating parameters and environmental parameters of the air conditioner.

[0050] In this embodiment of the application, after collecting the operating parameters and environmental parameters of the air conditioner, the operating parameters and environmental parameters of the air conditioner can be preprocessed. The preprocessing can include filtering, abnormal data removal, etc.

[0051] Among them, filtering can be used to remove environmental noise and electromagnetic noise from the air conditioner's operating parameters and environmental parameters, which can be achieved through wavelet transform; abnormal data screening can delete obviously abnormal data from the air conditioner's operating parameters and environmental parameters. That is, if the difference between a certain data point and other data points is too large, then this data point may be considered obviously abnormal and can be deleted.

[0052] 204. Upload the pre-processed air conditioning operating parameters and environmental parameters to the control center of the new energy power station.

[0053] In this embodiment of the application, the control center of the new energy power station can be a server, a cloud, or a control system, etc. After preprocessing the operating parameters and environmental parameters of the air conditioner, the preprocessed operating parameters and environmental parameters of the air conditioner can be uploaded to the control center for storage and subsequent processing.

[0054] It should be noted that, to ensure data security, the pre-processed operating and environmental parameters of the air conditioner can be encrypted before being uploaded. The control center, upon receiving the encrypted operating and environmental parameters, can decrypt and store them.

[0055] In some embodiments, during the process of uploading pre-processed air conditioner operating parameters and environmental parameters, 5G slicing and BeiDou short message dual links can be used. The specific link selection can be based on the actual environment and link communication quality. Under normal circumstances, 5G industrial slicing can ensure low data transmission latency (less than 50ms). In extreme environments (such as places with poor signal on plateaus), it can automatically switch to BeiDou short message for emergency transmission, which can well adapt to the complex communication environment of new energy power stations.

[0056] 205. Obtain historical operating data and historical fault information of the air conditioner.

[0057] During the training of the target fault detection model, historical data can be used for training. This historical data can specifically include historical operating data and historical fault conditions. The historical operating data can include normal operating data and fault operating data.

[0058] It should be noted that historical fault information can be correlated with fault operation data, that is, the fault operation data when the air conditioner malfunctions and the corresponding historical fault information are obtained; at the same time, the normal operation data when the air conditioner is running normally is also obtained.

[0059] 206. Based on historical operating data and historical fault information, train the preset model to obtain the target fault detection model.

[0060] In this embodiment of the application, after obtaining historical operating data and historical fault conditions, a preset model can be trained using the historical operating data and historical fault conditions. The preset model can be a relatively common neural network model, so that the preset model can learn the correlation between fault operating data and corresponding historical fault conditions, as well as the difference between fault operating data and normal operating data, thereby obtaining the target fault detection model.

[0061] In some embodiments, the training process of the target fault detection model can be the commonly used neural network training process.

[0062] In some embodiments, in order to improve model accuracy, when training the target fault detection model, the correlation features between air conditioners and new energy equipment can be introduced into the sample data. This allows the preset model to learn the correlation between fault operation data and corresponding historical fault conditions, and to incorporate the influence of the correlation features between air conditioners and new energy equipment on historical fault conditions. In other words, it learns the correlation between fault operation data, the correlation features between air conditioners and new energy equipment, and the corresponding historical fault conditions.

[0063] 207. Based on the air conditioner's operating parameters, environmental parameters, and the target fault detection model, the initial fault detection results are obtained.

[0064] In this embodiment, the description of step 207 is the same as the detailed description of step 102 in the above embodiments, and will not be repeated in this embodiment.

[0065] In some embodiments, after preprocessing the air conditioner's operating parameters and environmental parameters in conjunction with the above steps, initial fault detection results can be obtained based on the preprocessed air conditioner operating parameters, environmental parameters, and target fault detection model.

[0066] 208. Collect operating parameters of new energy equipment.

[0067] In this embodiment of the application, in order to determine the impact of the current operation of the new energy equipment on the demand for air conditioning, the operating parameters of the new energy equipment can be collected. These operating parameters may specifically include parameters that are related to temperature or affect the temperature of the equipment, such as photovoltaic panel temperature, irradiance, equipment speed, and equipment load.

[0068] 209. Based on the correlation characteristics between air conditioners and new energy equipment, determine the air conditioner adjustment amount corresponding to the operating parameters of new energy equipment.

[0069] It should be noted that the correlation characteristics between the air conditioner and the new energy equipment can be pre-set correspondences, or correlation characteristics obtained in advance through simulation tests. For example, for every 3°C increase in the temperature of the SVG equipment, the corresponding air conditioner cooling power demand increases by 8%; the correlation characteristics between the speed and the air conditioner heat dissipation demand, etc.

[0070] In this embodiment of the application, after determining the operating parameters of the new energy equipment, the air conditioning adjustment amount corresponding to the operating parameters can be determined according to the pre-stored correlation characteristics between the air conditioner and the new energy equipment. The air conditioning adjustment amount can be understood as the parameter adjustment requirement of the air conditioner when the new energy equipment works according to the current operating parameters.

[0071] 210. Based on the air conditioning adjustment amount, correct the initial fault detection results to obtain the target fault detection results.

[0072] In this embodiment of the application, after determining the air conditioning adjustment amount, the initial fault detection result can be corrected based on the air conditioning adjustment amount. That is, the initial fault detection result only considers the air conditioning operating parameters and the fault detection result under the influence of the environment. The target fault detection result obtained after correcting the initial fault detection result by the air conditioning adjustment amount can be considered to further consider the impact of the operation of new energy equipment on the air conditioning.

[0073] For example, suppose that when the target fault detection model detects the operating parameters and environmental parameters of the air conditioner, it detects that a certain parameter of the air conditioner greatly exceeds the ideal threshold. Then, the fault indicated by that parameter of the air conditioner can be output. However, if it is detected that the current load of the new energy equipment is too high, based on the correlation characteristics between the air conditioner and the new energy equipment, it is determined that the excessive load of the new energy equipment will cause the air conditioner's parameter to increase (for example, by 10%). In other words, the excessive air conditioner parameter is caused by the excessive load of the new energy equipment, not by a serious fault in the air conditioner. Therefore, based on the initial fault detection result, it is necessary to offset the 10% increase in the air conditioner's parameter indicated by the correlation characteristics. That is, fault detection can be performed according to the parameter value after offsetting by 10%, thereby obtaining the target fault detection result.

[0074] 211. Obtain the thermal conductivity coefficient and temperature threshold between new energy devices.

[0075] In this embodiment of the application, the number of new energy devices in the new energy power station may be more than one; there may be multiple new energy devices. During operation, heat spreads between these devices. That is, if a device experiences high temperature, the heat generated will spread, causing other new energy devices to also overheat. Therefore, the thermal conductivity coefficient and temperature threshold between the new energy devices can be obtained. The thermal conductivity coefficient can be used to indicate the heat transfer between the new energy devices, such as the heat diffusion rate from the relay protection room to the SVG room. The temperature threshold can indicate the highest temperature that each new energy device can reach under safe operation conditions; exceeding this temperature threshold may pose a danger.

[0076] 212. Determine the fault propagation prediction results based on the thermal conductivity coefficient, temperature threshold, target fault detection results, and target fault propagation prediction model.

[0077] In this embodiment of the application, after obtaining the thermal conductivity coefficient and temperature threshold between new energy devices, the thermal conductivity coefficient, temperature threshold and target fault detection result can be processed by the target fault propagation prediction model to obtain the fault propagation prediction result. The fault propagation prediction result can be understood as predicting the heat spread and fault propagation within a period of time based on the fault situation indicated by the current target fault detection result. For example, predicting the trend of environmental deterioration within 30 minutes after the fault occurs in advance can buy more time for linkage protection.

[0078] It should be noted that the target fault propagation prediction model can be a model obtained through fault simulation testing, which can continuously monitor the heat spread and fault propagation when historical faults occur, thereby training the target fault propagation prediction model; or it can be obtained by simulating the heat spread and fault propagation in fault scenarios through a simulation model, thereby training the target fault propagation prediction model.

[0079] 213. Based on the target fault detection results, the operating parameters of the new energy equipment, and the fault propagation prediction results, determine the target fault handling strategy.

[0080] In this embodiment of the application, after determining the fault propagation prediction result, when determining the target fault handling strategy, it is necessary to consider the fault propagation between devices. Therefore, the target fault handling strategy can be determined based on the target fault detection result, the operating parameters of the new energy equipment, and the fault propagation prediction result. This allows for device linkage protection before the fault propagates to other new energy equipment.

[0081] 214. Output and execute the target fault handling strategy.

[0082] In this embodiment, the description of step 214 is the same as the detailed description of step 105 in the above embodiments, and will not be repeated in this embodiment.

[0083] In some embodiments, based on the execution of the target fault handling strategy, the optimal fault handling path can be automatically planned by taking into account factors such as the site's geographical location, the drone's inspection range, and the location of nearby maintenance teams (e.g., first having the drone fly to the site to photograph the outdoor unit's condition, while the maintenance vehicle departs simultaneously).

[0084] In some embodiments, based on the fault propagation prediction results, a three-dimensional early warning information including "fault location + deterioration rate + impact range" can be pushed to the control center (e.g., SVG room air conditioning failure, the temperature is expected to rise to the upper limit that the SVG equipment can withstand in 15 minutes, causing the SVG equipment to stop operating).

[0085] In some embodiments, a BIM model of the new energy power station can be added to the human-computer interaction interface, overlaid with real-time data and simulated animations of fault propagation, so that operation and maintenance personnel can remotely guide on-site operations through AR annotation.

[0086] In this embodiment, an anti-interference sensing architecture integrating environmental parameters unique to new energy power stations (electromagnetic intensity, dust concentration, etc.) is adopted. Fiber optic sensors and electromagnetic interference compensation algorithms are used, employing a dynamic sampling mechanism. This achieves data accuracy exceeding 98% in extreme environments, successfully overcoming the unique interference challenges of new energy power stations. A dual-model diagnostic mechanism based on "air conditioning-main equipment" operating condition coupling improves fault identification accuracy by 30%–50% compared to existing technologies in complex scenarios. An adaptive, linked, tiered protection strategy combining power generation efficiency, energy storage status, and weather forecasts generates tiered load adjustments and emergency cooling commands, reducing unnecessary downtime losses. Cross-equipment collaborative diagnosis (multi-sensor cross-verification) and self-healing scheduling (UAV + maintenance path planning) mechanisms shorten fault handling response time by approximately 50%, making it particularly suitable for unattended operation. Linked energy storage and main equipment load adjustments achieve a balance between "fault protection" and "energy utilization." Dual-link transmission using 5G slicing and BeiDou short messages adapts to the distributed communication environment of new energy power stations.

[0087] In some embodiments, an energy efficiency optimization module can be added to use photovoltaic power generation to drive the air conditioner. When the sunlight is strong, the cooling capacity is stored, and when the sunlight is weak, the cooling capacity is released to reduce the load on the power grid. A digital twin model of the air conditioner, equipment room, and main equipment can also be established to simulate the optimal protection strategy under different faults through historical data and continuously optimize the linkage logic. In addition, edge nodes with independent decision-making capabilities can be deployed in remote sites to autonomously execute basic protection commands when the network is interrupted, thereby enhancing the stability of the system.

[0088] like Figure 3 As shown in the figure, this application embodiment provides a fault handling device based on a new energy power station. The new energy power station includes air conditioning and new energy equipment. The fault handling device based on the new energy power station may include: The acquisition module 301 is used to collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station; The processing module 302 is used to obtain the initial fault detection result based on the air conditioner's operating parameters, environmental parameters, and the target fault detection model; The processing module 302 is also used to correct the initial fault detection result according to the pre-stored correlation characteristics between air conditioners and new energy equipment to obtain the target fault detection result; The processing module 302 is also used to determine the target fault handling strategy based on the target fault detection results and the operating parameters of the new energy equipment; The processing module 302 is also used to output and execute the target fault handling strategy.

[0089] In some embodiments, the acquisition module 301 is specifically used to acquire the operating status of the new energy equipment; The acquisition module 301 is specifically used to collect the operating parameters and environmental parameters of the air conditioner according to the sampling frequency corresponding to the operating status.

[0090] In some embodiments, the processing module 302 is further configured to preprocess the operating parameters and environmental parameters of the air conditioner to obtain preprocessed operating parameters and environmental parameters of the air conditioner. The processing module 302 is also used to upload the pre-processed air conditioning operating parameters and environmental parameters to the control center of the new energy power station.

[0091] In some embodiments, the acquisition module 301 is further configured to acquire historical operating data and historical fault information of the air conditioner, wherein the historical operating data includes normal operating data and fault operating data; The processing module 302 is also used to train the preset model based on historical operating data and historical fault conditions to obtain the target fault detection model.

[0092] In some embodiments, the acquisition module 301 is specifically used to collect the operating parameters of new energy equipment; The processing module 302 is specifically used to determine the air conditioning adjustment amount corresponding to the operating parameters of the new energy equipment based on the correlation characteristics between the air conditioner and the new energy equipment. The processing module 302 is specifically used to correct the initial fault detection result based on the air conditioning adjustment amount to obtain the target fault detection result.

[0093] In some embodiments, the acquisition module 301 is further configured to acquire the thermal conductivity coefficient and temperature threshold between new energy devices; The processing module 302 is also used to determine the fault propagation prediction result based on the thermal conductivity coefficient, temperature threshold, target fault detection result and target fault propagation prediction model; The processing module 302 is also used to determine the target fault handling strategy based on the target fault detection results, the operating parameters of the new energy equipment, and the fault propagation prediction results.

[0094] In some embodiments, the processing module 302 is further configured to perform threshold detection on the operating parameters and environmental parameters of the air conditioner to determine abnormal data; The processing module 302 is also used to determine multiple sensor data collected by other sensors adjacent to the sensor location based on the sensor location corresponding to the abnormal data. The processing module 302 is also used to determine that the sensor corresponding to the abnormal data is in an abnormal state and delete the abnormal data if multiple sensor data are all normal data.

[0095] In this embodiment, each module can implement the fault handling method based on new energy power stations provided in the above method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0096] like Figure 4 As shown in the embodiments of this application, an electronic device is also provided, which may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; Specifically, the processor 402 calls the executable program code stored in the memory 401 to execute the fault handling method based on the new energy power station executed by the electronic device in the above method embodiments.

[0097] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the fault handling method based on new energy power stations in the above-described method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0098] This application also provides a computer program product, which stores a computer program. When the computer program is executed by a processor, it implements the various processes of the fault handling method based on new energy power stations in the above method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0099] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0100] It should be understood, in the several embodiments provided in this application, that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0101] In this application, 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. The general-purpose processor can be a microprocessor or any conventional processor.

[0102] In this application, memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0103] In this application, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including permanent and non-permanent, removable and non-removable storage media. The storage medium can implement information storage by any method or technology, and the information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), other types of random access memory (RAM), read-only memory (ROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device. As defined in this document, computer-readable media do not include transient media, such as modulated data signals and carrier waves.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0105] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application. The above-described multiple embodiments are not necessarily multiple independent embodiments; they are divided into multiple embodiments only to highlight different technical features in different embodiments. Those skilled in the art should understand that the above-described multiple embodiments can also be combined arbitrarily.

[0106] In the various embodiments of this application, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0107] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-accessible memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of this application.

[0110] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fault handling method based on new energy power stations, characterized in that, The new energy power station includes air conditioning and new energy equipment, and the method includes: Collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station; Based on the operating parameters of the air conditioner, the environmental parameters, and the target fault detection model, the initial fault detection results are obtained; Based on the pre-stored correlation characteristics between the air conditioner and the new energy equipment, the initial fault detection result is corrected to obtain the target fault detection result; Based on the target fault detection results and the operating parameters of the new energy equipment, a target fault handling strategy is determined; Output and execute the target fault handling strategy.

2. The method according to claim 1, characterized in that, The collection of the operating parameters of the air conditioner and the environmental parameters of the new energy power station includes: Obtain the operating status of the new energy equipment; The operating parameters of the air conditioner and the environmental parameters are collected according to the sampling frequency corresponding to the operating state.

3. The method according to claim 1, characterized in that, After collecting the operating parameters of the air conditioner and the environmental parameters of the new energy power station, the method further includes: The operating parameters of the air conditioner and the environmental parameters are preprocessed to obtain the preprocessed operating parameters and environmental parameters of the air conditioner; The preprocessed operating parameters and environmental parameters of the air conditioner are uploaded to the control center of the new energy power station.

4. The method according to claim 1, characterized in that, Before obtaining the initial fault detection result based on the air conditioner's operating parameters, the environmental parameters, and the target fault detection model, the method further includes: Acquire historical operating data and historical fault information of the air conditioner, wherein the historical operating data includes normal operating data and fault operating data; Based on the historical operating data and the historical fault conditions, the preset model is trained to obtain the target fault detection model.

5. The method according to claim 1, characterized in that, The step of correcting the initial fault detection result according to the pre-stored correlation characteristics between the air conditioner and the new energy equipment to obtain the target fault detection result includes: Collect the operating parameters of the new energy equipment; Based on the correlation characteristics between the air conditioner and the new energy equipment, determine the air conditioner adjustment amount corresponding to the operating parameters of the new energy equipment; Based on the air conditioning adjustment amount, the initial fault detection result is corrected to obtain the target fault detection result.

6. The method according to claim 1, characterized in that, After correcting the initial fault detection result according to the pre-stored correlation characteristics between the air conditioner and the new energy equipment to obtain the target fault detection result, the method further includes: Obtain the thermal conductivity coefficient and temperature threshold between the new energy devices; The fault propagation prediction result is determined based on the thermal conductivity coefficient, the temperature threshold, the target fault detection result, and the target fault propagation prediction model. The step of determining the target fault handling strategy based on the target fault detection results and the operating parameters of the new energy equipment includes: Based on the target fault detection results, the operating parameters of the new energy equipment, and the fault propagation prediction results, the target fault handling strategy is determined.

7. The method according to claim 1, characterized in that, After collecting the operating parameters of the air conditioner and the environmental parameters of the new energy power station, the method further includes: Threshold detection is performed on the operating parameters of the air conditioner and the environmental parameters to identify abnormal data; Based on the sensor location corresponding to the abnormal data, determine multiple sensor data collected by other sensors adjacent to the sensor location; If all the sensor data are normal, then the sensor corresponding to the abnormal data is determined to be in an abnormal state, and the abnormal data is deleted.

8. A fault handling device based on a new energy power station, characterized in that, The new energy power station includes air conditioning and new energy equipment, including: The acquisition module is used to collect the operating parameters of the air conditioner and the environmental parameters of the new energy power station; The processing module is used to obtain initial fault detection results based on the operating parameters of the air conditioner, the environmental parameters, and the target fault detection model; The processing module is further configured to correct the initial fault detection result according to the pre-stored correlation characteristics between the air conditioner and the new energy equipment, so as to obtain the target fault detection result; The processing module is also used to determine a target fault handling strategy based on the target fault detection results and the operating parameters of the new energy equipment. The processing module is also used to output and execute the target fault handling strategy.

9. An electronic device, characterized in that, include: Memory containing executable program code; and the processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the fault handling method based on new energy power stations as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, include: The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the fault handling method based on any one of claims 1 to 7.

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