Micro-grid distributed new energy data processing method, system, medium and equipment

Abstract

The application relates to a micro-grid distributed new energy data processing method, system, medium and equipment, and relates to the technical field of micro-grids, wherein the method comprises the following steps: inputting each historical power generation power and historical meteorological data into a preset short-term power prediction model to obtain a power generation power prediction sequence of each new energy power generation unit within a second preset time length after the current time, and determining an initial backup capacity required by a target region according to each power generation power prediction sequence; adjusting and optimizing the initial backup capacity according to a historical fault type and a historical fault period to obtain a target backup capacity; determining whether each new energy power generation unit has a fault at the current time, and when a fault exists, adjusting and optimizing the target backup capacity according to an actual fault type that exists to obtain a final backup capacity of the target region. The application has the effect of improving the accuracy of backup capacity evaluation.

Description

Technical Field

[0001] This application relates to the field of microgrid technology, specifically to a method, system, medium, and equipment for processing distributed renewable energy data in a microgrid. Background Technology

[0002] A microgrid is a small-scale power system composed of distributed power sources (such as photovoltaic and wind power), energy storage systems, electricity loads (such as industrial, commercial, and residential loads), and monitoring and protection devices. With the increasing penetration of new energy sources, microgrids, as an important carrier for integrating distributed photovoltaic and wind power, increasingly rely on the efficient processing of massive amounts of distributed energy data for operation and management. Because photovoltaic and wind power are significantly affected by meteorological factors, their power generation fluctuates considerably, making microgrids integrating distributed new energy sources more susceptible to supply-demand imbalances in the power supply area. To mitigate this risk, a reasonable assessment of the reserve capacity of the power supply area is typically required. Reserve capacity assessment is a core component of power system planning and real-time dispatch, directly impacting power supply reliability, system stability, and energy utilization efficiency.

[0003] Currently, the common method for assessing reserve capacity in a power supply area is to acquire meteorological data from various dimensions of the area, analyze the power generation of distributed renewable energy sources and the load demand of the area based on this data, and thus determine the reserve capacity that needs to be allocated to the area. If distributed renewable energy generation is affected by meteorological factors, the assessed reserve capacity can respond quickly to maintain grid power balance. However, the factors affecting distributed renewable energy generation are not limited to meteorological factors. This method, relying solely on meteorological data to assess reserve capacity, results in relatively poor accuracy in reserve capacity assessment. Summary of the Invention

[0004] To improve the accuracy of reserve capacity assessment, this application provides a method, system, medium, and equipment for processing distributed renewable energy data in microgrids.

[0005] The first aspect of this application provides a method for processing distributed renewable energy data in microgrids, specifically including:

[0006] The historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data of the target area are obtained within a first preset time period before the current time. The target area is the electricity consumption area covered by the microgrid.

[0007] The historical power generation and historical meteorological data are input into a preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within a second preset time after the current time. Based on the power generation prediction sequence, the initial reserve capacity required for the target area is determined.

[0008] Obtain the historical fault types and historical fault periods of a single new energy power generation unit, and adjust and optimize the initial reserve capacity according to the historical fault types and historical fault periods to obtain the target reserve capacity;

[0009] Determine whether each of the aforementioned new energy power generation units is faulty at the current time. If a fault exists, adjust and optimize the target reserve capacity according to the actual fault type to obtain the final reserve capacity of the target area.

[0010] When there are no faults in any of the aforementioned new energy power generation units, the target reserve capacity is determined as the final reserve capacity of the target area.

[0011] By employing the aforementioned technical solution, after acquiring historical power generation and meteorological data, a short-term power prediction model is used to predict the power generation of new energy power generation equipment after the current time, resulting in a power generation prediction sequence. Combined with the power generation prediction sequences of each new energy power generation unit, the initial reserve capacity required for the target area is preliminarily determined from the perspective of the impact of meteorological factors on the power generation of the new energy power generation units. Furthermore, based on historical fault types and periods, the probability of new energy power generation units failing within a second preset time period after the current time is analyzed. Taking into account the impact of new energy power generation unit failures on power generation, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity, thereby improving the accuracy of reserve capacity assessment. Finally, based on the analysis of the failure situation of new energy power generation units at the current time, the target reserve capacity is optimized to obtain the final reserve capacity, further improving the accuracy of reserve capacity assessment.

[0012] In one implementation, adjusting and optimizing the initial reserve capacity based on the historical fault type and the historical fault period to obtain the target reserve capacity specifically includes:

[0013] Based on the historical fault types, at least one key fault type is identified, which is a historical fault type that is prone to occur in a single new energy power generation unit;

[0014] Based on the historical fault periods corresponding to a single key fault type, at least one key fault period is determined, wherein the key fault period is a historical fault period in which the single key fault type is prone to occur;

[0015] Determine a first risk coefficient for the occurrence of each of the key fault types, and determine a second risk coefficient for the occurrence of faults during the key fault period corresponding to each of the key fault types;

[0016] Based on the first risk coefficient and the second risk coefficient, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity.

[0017] In one implementation, adjusting and optimizing the initial reserve capacity based on the first risk coefficient and the second risk coefficient to obtain the target reserve capacity specifically includes:

[0018] Based on the current time and the second preset duration, a target time period is determined, and the key fault time periods included in the target time period are determined as target fault time periods. If the target fault time period exists in each key fault time period corresponding to the key fault type, then the corresponding key fault type is determined as the target fault type.

[0019] Calculate the first product of the first risk coefficient of each target fault type and the second risk coefficient of the corresponding target fault period, and sum the first products to obtain the first summation result of a single new energy power generation unit;

[0020] If the first summation result is greater than the preset first threshold, the corresponding new energy power generation unit is determined as the target power generation unit, and the initial reserve capacity is adjusted and optimized according to each target power generation unit to obtain the target reserve capacity.

[0021] If none of the first summation results are greater than the first threshold, then the initial reserve capacity is determined as the target reserve capacity of the target area.

[0022] In one embodiment, adjusting and optimizing the initial reserve capacity according to each of the target power generation units to obtain the target reserve capacity specifically includes:

[0023] Summing the target products corresponding to the same target fault type in each target product yields a second summation result, wherein the target product is the first product corresponding to a single target power generation unit;

[0024] Select a shutdown fault type from the target fault types, wherein the shutdown fault type is the fault type that caused the shutdown;

[0025] The second summation results corresponding to each of the aforementioned shutdown fault types are summed to obtain the final summation result. If the final summation result is greater than the preset second threshold, the corresponding target power generation unit is determined as the shutdown power generation unit.

[0026] Based on the actual power generation of the shutdown power generation unit at the current time, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity.

[0027] In one implementation, determining whether each of the new energy power generation units is faulty at the current time specifically includes:

[0028] Obtain the actual electrical parameters of a single new energy power generation unit during operation at the current time, determine the actual fault type of the corresponding new energy power generation unit based on the actual electrical parameters, and identify the new energy power generation unit with the fault as a faulty power generation unit;

[0029] The key fault period containing the current time is determined as the reference fault period. If the reference fault period exists in each key fault period corresponding to the key fault type, the corresponding key fault type is determined as the reference fault type.

[0030] Calculate the second product of the first risk coefficient of each reference fault type and the second risk coefficient of the corresponding reference fault period, and sum the second products to obtain the third summation result of a single new energy power generation unit;

[0031] If the third summation result is greater than the preset third threshold, the corresponding new energy power generation unit is determined as the reference power generation unit. If the faulty power generation unit is the reference power generation unit, it is determined that the faulty power generation unit is faulty at the current time.

[0032] In one embodiment, the method further includes:

[0033] When the faulty power generation unit is the reference power generation unit, the largest second product is selected from each second product corresponding to the faulty power generation unit;

[0034] If the reference fault type corresponding to the maximum second product is the actual fault type of the faulty power generation unit, then the fault type verification of the faulty power generation unit is determined to be successful.

[0035] In one embodiment, the method further includes:

[0036] When none of the aforementioned new energy power generation units are faulty at the current time, the first inspection order of each of the aforementioned reference power generation units is determined. The larger the third summation result of the reference power generation units, the earlier the first inspection order of the corresponding reference power generation units is.

[0037] A reference product is selected from the second products corresponding to a single reference power generation unit, and a second investigation order for the corresponding reference fault type is determined based on the reference product. The larger the reference product, the earlier the corresponding second investigation order is. The reference product is a second product that is greater than a preset fourth threshold.

[0038] The first and second troubleshooting sequences corresponding to the same reference power generation unit are sent to the terminal of the troubleshooting personnel.

[0039] A second aspect of this application provides a microgrid distributed renewable energy data processing system, specifically comprising:

[0040] The data acquisition module is used to acquire the historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data of the target area within a first preset time period before the current time. The target area is the electricity consumption area covered by the microgrid.

[0041] The capacity assessment module is used to input the historical power generation and the historical meteorological data into a preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within a second preset time after the current time, and to determine the initial reserve capacity required by the target area based on the power generation prediction sequence.

[0042] The first optimization module is used to obtain the historical fault type and historical fault period of a single new energy power generation unit, and adjust and optimize the initial reserve capacity according to the historical fault type and the historical fault period to obtain the target reserve capacity.

[0043] The second optimization module is used to determine whether each of the new energy power generation units has a fault at the current time. If a fault exists, the target reserve capacity is adjusted and optimized according to the actual fault type to obtain the final reserve capacity of the target area.

[0044] The third optimization module is used to determine the target reserve capacity as the final reserve capacity of the target area when there are no faults in any of the new energy power generation units.

[0045] By adopting the above technical solution, after the data acquisition module obtains historical power generation and historical meteorological data, the capacity assessment module determines the initial reserve capacity required for the target area. Then, the first optimization module adjusts and optimizes the initial reserve capacity to obtain the target reserve capacity. Then, the second optimization module adjusts and optimizes the target reserve capacity according to the actual fault types to obtain the final reserve capacity of the target area. Finally, the third optimization module determines the target reserve capacity as the final reserve capacity of the target area when there are no faults in each new energy power generation unit.

[0046] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0047] A fourth aspect of this application provides an electronic device, specifically comprising:

[0048] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0049] In summary, this application includes at least one of the following beneficial technical effects: After obtaining historical power generation and historical meteorological data, a short-term power prediction model is used to predict the power generation of new energy power generation equipment after the current time, obtaining a power generation prediction sequence. Combined with the power generation prediction sequences of each new energy power generation unit, the initial reserve capacity required for the target area is preliminarily determined from the perspective of the impact of meteorological factors on the power generation of the new energy power generation unit. Furthermore, based on historical fault types and historical fault periods, the probability of new energy power generation units failing within a second preset time period after the current time is analyzed. The impact of new energy power generation unit failures on power generation is comprehensively considered, and the initial reserve capacity is adjusted and optimized accordingly to obtain the target reserve capacity, thereby improving the accuracy of reserve capacity assessment. Finally, based on the fault situation analysis of new energy power generation units at the current time, the target reserve capacity is optimized to obtain the final reserve capacity, thereby further improving the accuracy of reserve capacity assessment. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a microgrid distributed renewable energy data processing method provided in an embodiment of this application;

[0051] Figure 2 This is a schematic diagram of the structure of a microgrid distributed new energy data processing system provided in an embodiment of this application;

[0052] Figure 3 This is a schematic diagram of another microgrid distributed new energy data processing system provided in this application embodiment.

[0053] Explanation of reference numerals in the attached diagram: 11. Data acquisition module; 12. Capacity assessment module; 13. First optimization module; 14. Second optimization module; 15. Third optimization module; 16. Fault verification module; 17. Fault troubleshooting module. Detailed Implementation

[0054] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0055] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0056] In the description of the embodiments of 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, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0057] See Figure 1 This application discloses a flowchart of a microgrid distributed renewable energy data processing method, which can be implemented using a computer program or run on a microgrid distributed renewable energy data processing system based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including:

[0058] S101: Obtain the historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data of the target area within a first preset time period before the current time.

[0059] Specifically, in this embodiment, the target area is the electricity consumption area covered by a microgrid. The electricity consumption area covered by the microgrid is at least one. A new energy power generation unit refers to a device system that uses renewable or clean energy technologies to convert natural energy into electrical energy, and is a core infrastructure for achieving low-carbon energy transition. New energy power generation units include, but are not limited to, photovoltaic inverters and wind turbines. A photovoltaic inverter is a device that converts direct current generated by solar panels into alternating current at the grid frequency, and is one of the core components of a photovoltaic power generation system. A wind turbine is a device that uses wind energy to convert into electrical energy. Power generation capacity refers to the rate at which a new energy power generation unit converts natural resources (such as wind energy, solar energy, etc.) into electrical energy at a specific moment, usually expressed in kilowatts (kW) or megawatts (MW). Meteorological data within the target area includes meteorological data in at least one dimension related to power generation, specifically dimensions such as irradiance, wind speed, and temperature.

[0060] During the power generation process of the new energy power generation units in the microgrid, the power generation capacity of each new energy power generation unit is typically monitored in real time through a grid monitoring node. This grid monitoring node can be a Point of Common Coupling (PCC), or in other embodiments, an important feeder node. Simultaneously, meteorological data of the target area is monitored in real time through a local meteorological station. Therefore, the historical power generation capacity of each new energy power generation unit within a first preset time period prior to the current time is selected from the node monitoring records of the grid monitoring node, and then historical meteorological data of the target area within the first preset time period prior to the current time is selected from the meteorological monitoring records of the local meteorological station. The node monitoring records include the power generation capacity of the new energy power generation units at different times. The meteorological monitoring records include meteorological data of the target area at different times. It should be noted that the first preset time period is 2 hours or half a day.

[0061] Furthermore, in the microgrid distributed renewable energy data processing method disclosed in this application embodiment, the execution subject is a server. The server is wirelessly connected to a terminal, which is a personal computer. The terminal has a client related to renewable energy data processing installed on it. The server is the backend server of the client. Specifically, it can be an independent physical server or a cluster composed of multiple physical servers.

[0062] S102: Input historical power generation and historical meteorological data into the preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within the second preset time after the current time, and determine the initial reserve capacity required for the target area based on each power generation prediction sequence.

[0063] Specifically, after determining the historical power generation of each new energy power generation unit and the historical meteorological data of the target area, the historical power generation of a single new energy power generation unit and its historical meteorological data are input into a preset short-term power prediction model to predict the short-term power generation of that new energy power generation unit, thereby obtaining a power generation prediction sequence for that new energy power generation unit within a second preset time period after the current time. The second preset time period can be 15 minutes or 30 minutes. The power generation prediction sequence is a continuous set of power generation values ​​within the second preset time period. In other embodiments, before inputting the historical power generation of a single new energy power generation unit and its historical meteorological data into the short-term power prediction model, adaptive Kalman filtering combined with wavelet threshold denoising technology is used for filtering and noise reduction processing, and then the processed data is input into the short-term power prediction model. The short-term power prediction model is a trained Long Short-Term Memory (LSTM) network model; in other embodiments, the short-term power prediction model can also be a trained temporal convolutional network model. Long Short-Term Memory (LSTM) network models effectively capture the complex temporal dependencies and dynamic characteristics between meteorological data (irradiance, wind speed, temperature) and historical power sequences using their internal gating mechanisms (input gate, forget gate, output gate). The training process is briefly described as follows: historical power generation samples and meteorological data samples are divided into training, validation, and test sets. The model is trained using these datasets to output predicted power for future time periods. During training, the model's parameters are tuned using the backpropagation gradient descent algorithm until convergence, ultimately yielding a short-term power prediction model. This is existing technology and will not be elaborated further.

[0064] Furthermore, based on the power generation prediction sequence corresponding to each new energy power generation unit, the initial reserve capacity required for the target area is determined. Reserve capacity can be understood as the additional power regulation capability reserved to cope with various sudden disturbances, on top of meeting the current load demand of the area. It acts like an "emergency reserve" for the power system, ensuring that the system can still maintain power balance in the event of unforeseen circumstances. For example, if a region's normal photovoltaic output is 500MW, but a sudden rainstorm causes the output to drop to 200MW, the required reserve capacity would be 300MW. It should be noted that the initial reserve capacity can be non-spinning reserve capacity, i.e., quickly provided through the energy storage system of the microgrid or rapidly startable units. In other embodiments, the initial reserve capacity can also be spinning reserve capacity.

[0065] In this embodiment of the application, a feasible way to determine the initial reserve capacity is as follows: based on the power generation prediction sequence of each new energy power generation unit and the confidence interval distribution corresponding to the power generation prediction sequence (output by the short-term power prediction model), the initial reserve capacity required for the target area is calculated by using the ±3σ criterion (covering approximately 99.7% of the probability fluctuation range).

[0066] S103: Obtain the historical fault types and historical fault periods of a single new energy power generation unit, and adjust and optimize the initial reserve capacity based on the historical fault types and historical fault periods to obtain the target reserve capacity.

[0067] Specifically, after the initial reserve capacity of the target area is determined, based on the fault monitoring records of the new energy power generation units in the target area, multiple historical fault types and historical fault periods for each new energy power generation unit are obtained. Historical fault periods are the times during which faults have occurred in the past. The fault monitoring records include the types of faults that have occurred in the past for different new energy power generation units, as well as the times during which the faults occurred. Next, the number of recurrences of a single historical fault type is counted. The higher the number of recurrences, the more likely that historical fault type was to occur in the past. If the number of recurrences exceeds a preset threshold, the corresponding historical fault type is identified as a key fault type, i.e., a historical fault type that a single new energy power generation unit is prone to experiencing.

[0068] Furthermore, from the historical fault periods of a single new energy power generation unit, the historical fault periods corresponding to a single key fault type are selected, and the number of repeated occurrences of a single historical fault period is counted among all historical fault periods corresponding to a single key fault type. If the number of repeated occurrences exceeds a preset threshold, then the historical fault period is determined as a key fault period, that is, a historical fault period in which a single key fault type is prone to occur.

[0069] Furthermore, a first risk coefficient is determined for each key fault type. The larger the first risk coefficient, the greater the probability of the corresponding key fault type occurring. The first risk coefficient is the ratio of the number of recurrences of each key fault type to the sum of the number of recurrences of all key fault types. Then, a second risk coefficient is determined for each key fault type corresponding to a key fault period. The larger the second risk coefficient, the greater the probability of a fault occurring during that key fault period. The second risk coefficient is the ratio of the number of recurrences of a key fault period to the sum of the number of recurrences of all key fault periods. Finally, based on the first and second risk coefficients corresponding to a single new energy power generation unit, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity. One feasible implementation method is as follows:

[0070] The time period from the current time to the target time is defined as the target time period, and the target time is the time node corresponding to the second preset duration after the current time. Key fault periods included within the target time period are defined as target fault periods. If a target fault period exists among the key fault periods corresponding to a key fault type, then that key fault type is defined as the target fault type. The first risk coefficient of each target fault type is calculated as the first product of the second risk coefficient of the corresponding target fault period. The larger the first product, the greater the probability that the new energy power generation unit will experience the target fault type within the target fault period after the current time. The first products are summed to obtain the first summation result for a single new energy power generation unit. The larger the first summation result, the greater the overall probability that the new energy power generation unit will experience a fault within the target time period. If the first summation result is greater than a preset first threshold, it indicates that the overall probability of the new energy power generation unit experiencing a fault within the target time period is relatively high. Therefore, this new energy power generation unit is defined as the target power generation unit. Based on each target power generation unit, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity. One feasible adjustment and optimization method is as follows:

[0071] The target products corresponding to the same target fault type in each target product are summed to obtain a second summation result. The target product is the first product corresponding to a single target power generation unit. The larger the second summation result, the greater the probability of a fault of the corresponding target fault type during the target time period. Then, based on a preset outage fault statistics table, outage fault types, i.e., the fault types that cause the new energy power generation unit to shut down, are selected from each target fault type. The outage fault statistics table includes different outage fault types. Further, the second summation results corresponding to each outage fault type are summed to obtain a final summation result. The larger the final summation result, the more likely the corresponding target power generation unit is to shut down due to a fault during the target time period, causing a sudden drop in the actual output of new energy units in the target area. This sudden fluctuation in actual output is likely to disrupt the supply and demand balance of the microgrid in the target area. If the final summation result is greater than a preset second threshold, it indicates that the corresponding target power generation unit is more likely to shut down due to a fault during the target time period, and then the target power generation unit is identified as a shutdown power generation unit. Finally, the actual power generation of the shutdown generation unit at the current time is obtained through the power grid monitoring node, and the actual power generation of the shutdown generation unit is added to the initial reserve capacity to obtain the target reserve capacity.

[0072] In other embodiments, for a single out-of-service generation unit, the second summation results corresponding to at least one remaining fault type are summed to obtain a summation result. The remaining fault types are the target fault types other than outage fault types. The final summation result is compared with the summation result. If the final summation result is greater than the summation result, it indicates that the probability of an outage fault occurring within the target time period is greater than the probability of a non-outage fault occurring, thereby verifying the outage generation unit and making the determined target reserve capacity more accurate.

[0073] In one embodiment, the priority order of attention for the operation status of each shutdown power generation unit is determined. The larger the final sum of the shutdown power generation units, the greater the possibility of shutdown failure, and the higher the corresponding priority order. Based on the priority order, the display area of ​​the operating parameters of each shutdown power generation unit in the terminal is determined. The higher the priority order, the closer the corresponding display area is to the central area of ​​the terminal that conforms to the human's visual habits, and the easier it is for the human to pay attention to it. In another embodiment, based on the final summation result of the shutdown power generation units, the frequency and duration of each inspection of the operation status of each shutdown power generation unit within the target time period are determined. The larger the final summation result, the more frequent the inspections and the longer the duration of each inspection. Then, based on the inspection frequency and duration of each inspection, at least one inspection period is determined, that is, a sub-period for checking operating parameters within the target time period. Further, the target fault period corresponding to the shutdown fault type of a single shutdown power generation unit that exists in a single inspection period is determined as the inspection fault period. The first products corresponding to each inspection fault period are summed to obtain the sum of the products of the corresponding inspection periods. The larger the sum of the products, if the sum of the products is greater than a preset threshold, then an extended inspection duration reminder is issued for the corresponding inspection period. It should be noted that the corresponding inspection frequency and duration can be matched from a preset inspection matching table based on the final summation result. The matching table includes different summation result ranges and corresponding investigation frequencies and single investigation durations, all set based on human experience. For example, if the final summation result of a shutdown power generation unit falls within a certain range, then the investigation frequency and single investigation duration corresponding to that range are determined as the investigation frequency and single investigation duration for the shutdown power generation unit. In another embodiment, if different shutdown power generation units share the same investigation period, then that investigation period is designated as a key investigation period. The maximum sum of products corresponding to this key investigation period is selected, and the shutdown power generation unit corresponding to the maximum sum of products is determined as the final investigation target for this key investigation period.

[0074] S104: Determine whether there is a fault in each new energy power generation unit at the current time. If there is a fault, adjust and optimize the target reserve capacity according to the actual fault type to obtain the final reserve capacity of the target area.

[0075] S105: When there are no faults in any of the new energy power generation units, the target reserve capacity will be determined as the final reserve capacity of the target area.

[0076] Specifically, after determining the target reserve capacity, the next step is to determine whether any new energy power generation units are faulty at the current time. One feasible method is to acquire the actual electrical parameters of each new energy power generation unit during operation at the current time using various sensors. These actual electrical parameters include, but are not limited to, voltage harmonic distortion rate, output power, and power factor. A state-space model is then constructed based on a dynamic Bayesian network, integrating the actual electrical parameters. Through probabilistic reasoning, such as particle filtering or variational inference, the fault types of the new energy power generation units are identified. If a fault exists, the actual fault type of the new energy power generation unit can be identified, and the faulty new energy power generation unit is designated as a faulty power generation unit.

[0077] Furthermore, the key fault period including the current time is determined as the reference fault period. If a reference fault period exists among the key fault periods corresponding to a key fault type, then that key fault type is determined as the reference fault type. The second product of the first risk coefficient of each reference fault type and the second risk coefficient of the corresponding reference fault period is calculated, and then the second products are summed to obtain the third summation result for a single new energy power generation unit. The larger the third summation result, the greater the probability that the corresponding new energy power generation unit will experience a fault at the current time. If the third summation result is greater than a preset third threshold, it indicates that the probability of the corresponding new energy power generation unit experiencing a fault at the current time is relatively high, so the corresponding new energy power generation unit is determined as a reference power generation unit. If the faulty power generation unit is a reference power generation unit, the accuracy of the fault existence of the faulty power generation unit is verified again, thus confirming that the faulty power generation unit has a fault at the current time, thereby more accurately determining whether a fault exists. Furthermore, when the faulty power generation unit is a reference power generation unit, the largest second product is selected from the various second products corresponding to the faulty power generation unit. If the reference fault type corresponding to the largest second product is the actual fault type of the faulty power generation unit, then the fault type verification of the faulty power generation unit is passed, thereby improving the accuracy of fault type determination.

[0078] Furthermore, it is determined whether any of the existing fault types are shutdown faults. If not, the target reserve capacity is directly determined as the final reserve capacity. If so, the actual power generation of the renewable energy power generation units with shutdown faults is added to the target reserve capacity to obtain the final reserve capacity. If none of the renewable energy power generation units are fault-free, the target reserve capacity is determined as the final reserve capacity for the target area.

[0079] In one embodiment, when none of the new energy power generation units are fault-free at the current time, a first investigation order for each reference power generation unit is determined. The larger the sum of the third values ​​of the reference power generation units, the greater the probability of a fault, and the earlier the reference power generation unit is in the first investigation order. Then, a reference product is selected from the second products corresponding to a single reference power generation unit, that is, a second product greater than a preset fourth threshold. Based on the reference product, a second investigation order for the corresponding reference fault type is determined. The larger the reference product, the greater the probability of the corresponding reference fault type, and the earlier the corresponding second investigation order is. Finally, the first and second investigation orders corresponding to the same reference power generation unit are sent to the terminal of the fault investigator, enabling the fault investigator to conduct targeted investigations of the reference power generation units and their reference fault types, improving investigation efficiency and the timeliness of fault detection.

[0080] The implementation principle of the microgrid distributed renewable energy data processing method in this application embodiment is as follows: After acquiring historical power generation and historical meteorological data, a short-term power prediction model is used to predict the power generation of renewable energy power generation equipment after the current time, obtaining a power generation prediction sequence. Combined with the power generation prediction sequences of each renewable energy power generation unit, the initial reserve capacity required for the target area is preliminarily determined from the perspective of the impact of meteorological factors on the power generation of the renewable energy power generation unit. Furthermore, based on historical fault types and historical fault periods, the probability of renewable energy power generation units failing within a second preset time period after the current time is analyzed. The impact of renewable energy power generation unit failures on power generation is comprehensively considered, and the initial reserve capacity is adjusted and optimized accordingly to obtain the target reserve capacity, thereby improving the accuracy of reserve capacity assessment. Finally, based on the fault situation analysis of renewable energy power generation units at the current time, the target reserve capacity is optimized to obtain the final reserve capacity, thereby further improving the accuracy of reserve capacity assessment.

[0081] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0082] Please see Figure 2 This is a schematic diagram of the structure of a microgrid distributed renewable energy data processing system provided in an embodiment of this application. This microgrid distributed renewable energy data processing system can be implemented as all or part of a system through software, hardware, or a combination of both. The system includes a data acquisition module 11, a capacity assessment module 12, a first optimization module 13, a second optimization module 14, and a third optimization module 15.

[0083] Data acquisition module 11 is used to acquire the historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data in the target area within a first preset time period before the current time. The target area is the electricity consumption area covered by the microgrid.

[0084] The capacity assessment module 12 is used to input historical power generation and historical meteorological data into a preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within a second preset time after the current time, and to determine the initial reserve capacity required for the target area based on each power generation prediction sequence.

[0085] The first optimization module 13 is used to obtain the historical fault types and historical fault periods of a single new energy power generation unit, and adjust and optimize the initial reserve capacity according to the historical fault types and historical fault periods to obtain the target reserve capacity.

[0086] The second optimization module 14 is used to determine whether there is a fault in each new energy power generation unit at the current time. If there is a fault, the target reserve capacity is adjusted and optimized according to the actual fault type to obtain the final reserve capacity of the target area.

[0087] The third optimization module 15 is used to determine the target reserve capacity as the final reserve capacity of the target area when there are no faults in any of the new energy power generation units.

[0088] Optionally, the first optimization module 13 is specifically used for:

[0089] Based on historical fault types, at least one key fault type is identified. The key fault type is a historical fault type that is prone to occur in a single new energy power generation unit.

[0090] Based on the historical fault periods corresponding to a single key fault type, at least one key fault period is determined. The key fault period is the historical fault period during which a single key fault type is likely to occur.

[0091] Determine the first risk coefficient for each key fault type and the second risk coefficient for the key fault period corresponding to each key fault type.

[0092] Based on the first and second risk coefficients, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity.

[0093] Optionally, the first optimization module 13 is specifically used for:

[0094] Based on the current time and the second preset duration, determine the target time period, and identify the key fault time periods contained within the target time period as the target fault time period. If there is a target fault time period among the key fault time periods corresponding to the key fault type, then the corresponding key fault type is identified as the target fault type.

[0095] Calculate the first product of the first risk coefficient of each target fault type and the second risk coefficient of the corresponding target fault period, and sum the first products to obtain the first summation result of a single new energy power generation unit;

[0096] If the first summation result is greater than the preset first threshold, the corresponding new energy power generation unit is determined as the target power generation unit, and the initial reserve capacity is adjusted and optimized according to each target power generation unit to obtain the target reserve capacity.

[0097] If none of the first summation results are greater than the first threshold, then the initial reserve capacity is determined as the target reserve capacity of the target area.

[0098] Optionally, the first optimization module 13 is specifically used for:

[0099] Summing the target products corresponding to the same target fault type in each target product yields a second summation result. The target product is the first product corresponding to a single target power generation unit.

[0100] Select the shutdown fault type from the target fault types. The shutdown fault type is the fault type that caused the shutdown.

[0101] The second summation results corresponding to each type of shutdown fault are summed to obtain the final summation result. If the final summation result is greater than the preset second threshold, the corresponding target power generation unit is determined as the shutdown power generation unit.

[0102] Based on the actual power generation of the shutdown generating unit at the current time, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity.

[0103] Optionally, the second optimization module 14 is specifically used for:

[0104] Obtain the actual electrical parameters of a single new energy power generation unit during operation at the current time, determine the actual fault type of the corresponding new energy power generation unit based on the actual electrical parameters, and identify the new energy power generation unit with the fault as the faulty power generation unit;

[0105] The key fault period containing the current time is determined as the reference fault period. If there is a reference fault period among the key fault periods corresponding to the key fault type, the corresponding key fault type is determined as the reference fault type.

[0106] Calculate the second product of the first risk coefficient of each reference fault type and the second risk coefficient of the corresponding reference fault period, and sum the second products to obtain the third summation result of a single new energy power generation unit;

[0107] If the third summation result is greater than the preset third threshold, the corresponding new energy power generation unit is determined as the reference power generation unit. If the faulty power generation unit is the reference power generation unit, it is determined that the faulty power generation unit is faulty at the current time.

[0108] Optional, such as Figure 3 As shown, the system also includes a fault verification module 16, which is specifically used for:

[0109] When the faulty power generation unit is the reference power generation unit, the largest second product is selected from the second products corresponding to the faulty power generation unit.

[0110] If the reference fault type corresponding to the maximum second product is the actual fault type of the faulty power generation unit, then the fault type verification of the faulty power generation unit is deemed to have passed.

[0111] Optionally, the system also includes a fault diagnosis module 17, specifically used for:

[0112] When there are no faults in any of the new energy power generation units at the current time, the first inspection order of each reference power generation unit is determined. The larger the third summation result of the reference power generation unit, the earlier the first inspection order of the corresponding reference power generation unit is.

[0113] Select a reference product from the second products corresponding to a single reference power generation unit, and determine the second investigation order of the corresponding reference fault type based on the reference product. The larger the reference product, the earlier the corresponding second investigation order. The reference product is the second product that is greater than the preset fourth threshold.

[0114] The first and second troubleshooting sequences corresponding to the same reference power generation unit are sent to the terminal of the troubleshooting personnel.

[0115] It should be noted that the microgrid distributed renewable energy data processing system provided in the above embodiments is only illustrated by the division of the above functional modules when executing the microgrid distributed renewable energy data processing method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the microgrid distributed renewable energy data processing system and the microgrid distributed renewable energy data processing method embodiment provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0116] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a microgrid distributed new energy data processing method according to the above embodiments.

[0117] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0118] The microgrid distributed new energy data processing method of the above embodiment is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0119] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned microgrid distributed new energy data processing method.

[0120] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0121] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf 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, etc., and this application does not limit it.

[0122] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0123] In this electronic device, the microgrid distributed new energy data processing method of the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0124] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for processing distributed renewable energy data in a microgrid, characterized in that, The method includes: The historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data of the target area are obtained within a first preset time period before the current time. The target area is the electricity consumption area covered by the microgrid. The historical power generation and historical meteorological data are input into a preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within a second preset time after the current time. Based on the power generation prediction sequence, the initial reserve capacity required for the target area is determined. Obtaining the historical fault types and historical fault periods of a single new energy power generation unit, and adjusting and optimizing the initial reserve capacity based on the historical fault types and historical fault periods to obtain the target reserve capacity, includes: determining at least one key fault type based on the historical fault types, wherein the key fault type is a historical fault type that is prone to occur in a single new energy power generation unit; Based on the historical fault periods corresponding to a single key fault type, at least one key fault period is determined, wherein the key fault period is a historical fault period in which the single key fault type is prone to occur; Determine a first risk coefficient for the occurrence of each of the key fault types, and determine a second risk coefficient for the occurrence of faults during the key fault period corresponding to each of the key fault types; The initial reserve capacity is adjusted and optimized based on the first risk coefficient and the second risk coefficient to obtain the target reserve capacity; Determining whether each of the aforementioned new energy power generation units is faulty at the current time includes: obtaining the actual electrical parameters of a single new energy power generation unit during operation at the current time, determining the actual fault type of the corresponding new energy power generation unit based on the actual electrical parameters, and identifying the new energy power generation unit with the fault as a faulty power generation unit; The key fault period containing the current time is determined as the reference fault period. If the reference fault period exists in each key fault period corresponding to the key fault type, the corresponding key fault type is determined as the reference fault type. Calculate the second product of the first risk coefficient of each reference fault type and the second risk coefficient of the corresponding reference fault period, and sum the second products to obtain the third summation result of a single new energy power generation unit; If the third summation result is greater than the preset third threshold, the corresponding new energy power generation unit is determined as the reference power generation unit. If the faulty power generation unit is the reference power generation unit, it is determined that the faulty power generation unit is faulty at the current time. When a fault exists, the target reserve capacity is adjusted and optimized according to the actual fault type to obtain the final reserve capacity of the target area. When there are no faults in any of the aforementioned new energy power generation units, the target reserve capacity is determined as the final reserve capacity of the target area.

2. The microgrid distributed renewable energy data processing method according to claim 1, characterized in that, The step of adjusting and optimizing the initial reserve capacity based on the first risk coefficient and the second risk coefficient to obtain the target reserve capacity specifically includes: Based on the current time and the second preset duration, a target time period is determined, and the key fault time periods included in the target time period are determined as target fault time periods. If the target fault time period exists in each key fault time period corresponding to the key fault type, then the corresponding key fault type is determined as the target fault type. Calculate the first product of the first risk coefficient of each target fault type and the second risk coefficient of the corresponding target fault period, and sum the first products to obtain the first summation result of a single new energy power generation unit; If the first summation result is greater than the preset first threshold, the corresponding new energy power generation unit is determined as the target power generation unit, and the initial reserve capacity is adjusted and optimized according to each target power generation unit to obtain the target reserve capacity. If none of the first summation results are greater than the first threshold, then the initial reserve capacity is determined as the target reserve capacity of the target area.

3. The microgrid distributed renewable energy data processing method according to claim 2, characterized in that, The step of adjusting and optimizing the initial reserve capacity based on each target power generation unit to obtain the target reserve capacity specifically includes: Summing the target products corresponding to the same target fault type in each target product yields a second summation result, wherein the target product is the first product corresponding to a single target power generation unit; Select a shutdown fault type from the target fault types, wherein the shutdown fault type is the fault type that caused the shutdown; The second summation results corresponding to each of the aforementioned shutdown fault types are summed to obtain the final summation result. If the final summation result is greater than the preset second threshold, the corresponding target power generation unit is determined as the shutdown power generation unit. Based on the actual power generation of the shutdown power generation unit at the current time, the initial reserve capacity is adjusted and optimized to obtain the target reserve capacity.

4. The microgrid distributed renewable energy data processing method according to claim 1, characterized in that, The method further includes: When the faulty power generation unit is the reference power generation unit, the largest second product is selected from each second product corresponding to the faulty power generation unit; If the reference fault type corresponding to the maximum second product is the actual fault type of the faulty power generation unit, then the fault type verification of the faulty power generation unit is determined to be successful.

5. The microgrid distributed renewable energy data processing method according to claim 1, characterized in that, The method further includes: When none of the aforementioned new energy power generation units are faulty at the current time, the first inspection order of each of the aforementioned reference power generation units is determined. The larger the third summation result of the reference power generation units, the earlier the first inspection order of the corresponding reference power generation units is. A reference product is selected from the second products corresponding to a single reference power generation unit, and a second investigation order for the corresponding reference fault type is determined based on the reference product. The larger the reference product, the earlier the corresponding second investigation order is. The reference product is a second product that is greater than a preset fourth threshold. The first and second troubleshooting sequences corresponding to the same reference power generation unit are sent to the terminal of the troubleshooting personnel.

6. A microgrid distributed renewable energy data processing system, used to implement the microgrid distributed renewable energy data processing method according to any one of claims 1 to 5, characterized in that, include: The data acquisition module (11) is used to acquire the historical power generation of at least one new energy power generation unit in the target area and the historical meteorological data in the target area within a first preset time period before the current time. The target area is the electricity consumption area covered by the microgrid. The capacity assessment module (12) is used to input the historical power generation and the historical meteorological data into the preset short-term power prediction model to obtain the power generation prediction sequence of each new energy power generation unit within a second preset time after the current time, and to determine the initial reserve capacity required by the target area based on the power generation prediction sequence. The first optimization module (13) is used to obtain the historical fault type and historical fault period of a single new energy power generation unit, and adjust and optimize the initial reserve capacity according to the historical fault type and the historical fault period to obtain the target reserve capacity. The second optimization module (14) is used to determine whether each of the new energy power generation units has a fault at the current time. If a fault exists, the target reserve capacity is adjusted and optimized according to the actual fault type to obtain the final reserve capacity of the target area. The third optimization module (15) is used to determine the target reserve capacity as the final reserve capacity of the target area when there are no faults in each of the new energy power generation units.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-5.

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