Station service power recovery control method based on generator set

Abstract

The invention provides a station service power recovery control method based on a generator set, and relates to the technical field of black start of photovoltaic power stations, and the method comprises the steps: obtaining the operation data and access process of core equipment according to an emergency plan, and evaluating the actual station service power recovery capability of the generator set according to the impact on the generator set in the access process. The method comprises the following steps: selecting a photovoltaic panel subsystem which preferentially recovers operation according to the impact possibly caused by each photovoltaic panel subsystem on a generator set and a value coefficient of power supply recovery of the photovoltaic panel subsystem, and enhancing the power supply capability of the generator set by the photovoltaic panel subsystem which recovers operation, thereby recovering all station service power. The hidden danger that the generator set collapses due to the access impact of equipment is avoided, and the plant power recovery capability of a power plant is enhanced.

Description

Technical Field

[0001] This invention relates to the field of black start technology for photovoltaic power plants, specifically to a method for restoring plant power supply based on generator sets. Background Technology

[0002] To meet electricity demand, power plants need to have the ability to cope with emergencies, that is, to have a certain self-starting capability in the event of an unexpected power outage. Therefore, many power plants use diesel generators as black start generators to gradually restore the plant's power supply and thus restore the overall function of the power plant. However, as a small power generation device that operates through mechanical movement, diesel generators often have limited stability and shock resistance. During the process of connecting other equipment to the generator set, the generator set is prone to shutdown due to excessive impact, which affects the power plant's recovery and operation and reduces the power plant's emergency response capability. The "Regulations on Emergency Response and Investigation of Power Safety Accidents" and the "Regulations on the Management of Power Plant Grid Connection Operation" require that power plants must formulate comprehensive emergency plans to reduce the harm of emergencies to the production process. In photovoltaic power plants, the electricity generated by the photovoltaic panel subsystem can be directly used within the plant, which helps improve the plant's black start capability.

[0003] In the prior art, CN117293998A discloses a method and device for restoring power supply to a generator set. The method involves obtaining the detection results of the hydropower station based on the first and second power supply transformers. If the detection results meet the conditions for power supply restoration, the method sequentially disconnects the circuits of the hydropower station's first-stage power supply busbar, the diesel generator, and the first power supply transformer. Then, it sequentially connects the circuits of the second power supply transformer and the first-stage power supply busbar. Power is supplied to the hydropower station through the second power supply transformer, and the diesel generator's power supply is stopped.

[0004] While the publicly available technical documents enabled the restoration of power to the plant, they did not take into account the impact of equipment on the generator set, and could not prevent the generator set from collapsing due to the impact.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a plant power restoration control method based on generator sets to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The plant power restoration control method based on generator sets includes the following specific steps: Step 1: Obtain the operating data of each core device, analyze the operating data of each core device to form the load index of each core device, and connect the core devices to the bus of the generator set in sequence; Step 2: During the process of core equipment access, the impact of each core equipment on the generator set is acquired synchronously, and the impact coefficient is obtained by analysis and quantification. At the same time, the load occupancy rate of the generator set when each core equipment is accessed is recorded. The impact estimation model is obtained by using the load index and load occupancy rate as samples and the impact coefficient as labels. Step 3: Obtain the real-time operating data of all photovoltaic subsystems in the bus that need to be connected to the generator set to form a load index, and input it together with the load occupancy rate of the generator set at this time into the impact estimation model to obtain the estimated impact coefficient of each photovoltaic subsystem if connected to the bus. Step 4: Calculate the value coefficient based on the power generation of each photovoltaic subsystem before the power outage, sort all photovoltaic subsystems according to the value coefficient, and select the photovoltaic panel subsystem with the highest value coefficient and the estimated impact coefficient below a certain value for restoration after all core equipment is restored; Step 5: Integrate the restored photovoltaic subsystem into the generator set, adjust the rated operating power of the generator set according to the real-time power generation of the photovoltaic subsystem, and update the load occupancy rate of the generator set according to the adjusted rated operating power of the generator set. Step 6: Repeat steps 3 to 5 to restore other photovoltaic panel subsystems in sequence until all photovoltaic panel subsystems are connected to the generator bus.

[0008] Furthermore, the rated operating power and reference response time of the generator set are obtained, wherein the reference response time is the maximum time allowed for the generator set to regain balance after being subjected to an impact. The core equipment includes: transformers, control system equipment, protection system equipment, and communication system equipment; The core equipment is connected sequentially to the generator busbars. The connection order of the core equipment is obtained according to the power plant's emergency plan, and then connected to the generator busbars sequentially according to the connection order. The operating data of each core device is obtained separately. The operating data includes rated power, peak starting current, rated current, and device start-up duration. The load index is then obtained, and the logic is as follows: The load index can be obtained by multiplying the ratio of peak starting current to rated current, the ratio of equipment start-up duration to reference response time, and the ratio of equipment rated power to generator set rated operating power.

[0009] Furthermore, the speed regulation response time of the generator set when each core device is connected to the bus is obtained. The speed regulation response time is the time it takes for the generator set to recover balance after being impacted by the connected device. The adjustment time used by each core device when connected is obtained, and the impact coefficient is constructed, with the following logic: The impact coefficient can be obtained by comparing the adjustment time with the reference response time. The longer the adjustment time or the shorter the reference response time, the larger the impact coefficient.

[0010] Furthermore, during the process of core equipment connecting to the generator set according to the connection sequence, whenever a core equipment is successfully connected and running, the total load of the generator set after each equipment is connected is obtained, and the load occupancy rate of the generator set at this time is recorded, as follows: The load occupancy rate can be obtained by dividing the total load occupancy rate of the generator set by the rated operating power of the generator set.

[0011] Furthermore, the impact coefficient, load index, and load occupancy rate of the generator set before connection of each core device are summarized to form a dataset, as follows: Each device generates data fluctuations upon connection. The collection of data fluctuations generated by each device upon connection is considered a sample. The sample contains elements such as the impact coefficient, load index, and the load occupancy rate of the generator set before the device was connected. By summing up all the samples, a dataset is formed. Using the load index and the load occupancy rate of the generator set before equipment connection as samples, and the impact coefficient as the label, the impact estimation model is obtained, with the following logic: Using the load index and the load occupancy rate of the generator set before equipment connection as independent variables, and the impact coefficient as the dependent variable, the data is solved using the bisection method to obtain the functional relationship expression between the independent and dependent variables. The mathematical model of the impact coefficient is obtained through the independent variables and the functional relationship expression, which is the impact estimation model.

[0012] Furthermore, the operating data of all photovoltaic panel subsystems is obtained, the load index of each photovoltaic panel subsystem is obtained through the operating data, the load of the generator set at this moment is obtained, and the load index of each photovoltaic panel subsystem and the load occupancy rate of the generator set at this moment are input into the impact estimation model to obtain the estimated impact coefficient of each photovoltaic panel subsystem.

[0013] Furthermore, when obtaining the power generation of all photovoltaic panel subsystems before the power outage and forming the value coefficient of each photovoltaic panel subsystem, the power generation and the estimated impact coefficient are used as the value parameters of that photovoltaic subsystem. All photovoltaic panel subsystems are sorted from largest to smallest value coefficient. The photovoltaic panel system with the highest value coefficient and an estimated impact coefficient lower than a certain value is prioritized for restoration and connected to the generator set.

[0014] Furthermore, the power generation of the connected photovoltaic panel subsystem is obtained, and the rated operating power of the generator set is corrected accordingly. The logic is as follows: The corrected rated operating power of the generator set is obtained by adding the generator power of the connected photovoltaic panel subsystem to the rated operating power of the generator set. The greater the rated operating power of the generator set or the power generation of the connected photovoltaic panel system, the greater the corrected rated operating power of the generator set.

[0015] Furthermore, using the updated rated power of the generator set, the load index of the remaining photovoltaic panel subsystems is recalculated according to step 3, and the value coefficient and impact coefficient are re-obtained based on the total load of all connected devices and photovoltaic panel subsystems. All photovoltaic panel subsystems are then gradually restored according to steps 4 and 5. Each time a new photovoltaic panel subsystem is connected, steps 3 through 5 are repeated until all plant power is restored.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention obtains the operating data and connection process of core equipment according to the emergency plan, assesses the actual ability of the generator set to restore power supply based on the impact of the connection process, and forms an impact estimation model. Then, based on the potential impact of each photovoltaic panel subsystem on the generator set and its power restoration value coefficient, the photovoltaic panel subsystems that should be prioritized for restoration are selected. The restored photovoltaic panel systems are then used to enhance the power supply capacity of the generator set, thereby restoring all power supply to the plant. This avoids the hidden danger of generator set collapse caused by equipment connection impact and enhances the power plant's power restoration capability. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example: Please see Figure 1 The present invention provides a technical solution: The plant power restoration control method based on generator sets includes the following specific steps: Step 1: Obtain the operating data of each core device, analyze the operating data of each core device to form the load index of each core device, and connect the core devices to the bus of the generator set in sequence; Step 1 includes the following: Obtain the rated operating power and reference response time of the generator set, wherein the reference response time is the maximum time allowed for the generator set to regain balance after being subjected to an impact; The core equipment and its connection sequence are obtained through the emergency plan. The core equipment is then connected to the generator set's busbar according to this sequence. During the connection process, the operating data of each core device is acquired, including rated power, peak starting current, rated current, and device start-up duration. This operating data is obtained from the device nameplate. The load index is then calculated using the following logic: The load index can be obtained by multiplying the ratio of peak starting current to rated current, the ratio of equipment start-up duration to reference response time, and the ratio of equipment rated power to generator set rated operating power.

[0021] As a preferred embodiment, the formula for obtaining the load index is as follows: in, Indicates the load index. This is the peak starting current. Rated current, For the duration of device startup, The rated power of the equipment. The rated operating power of the generator set. This is the baseline response time.

[0022] The load index specifically reflects the comprehensive impact on system stability when a single core device is connected to a generator set. It quantifies the relative burden on the device in three dimensions: power demand, current transients, and startup timing. This allows it to reflect, in a real-world environment, issues such as generator set overload, voltage fluctuations, or response delays that may occur during device startup. The issue of scaling up the rated power of proportional capture equipment in relation to the capacity occupancy of generator sets. The ratio reveals the transient overload risk when the peak starting current exceeds the rated current, while The proportions highlight the timing bottlenecks of equipment startup duration and baseline response time. These independent variables together simulate the dynamic interference scenario of equipment access during plant power restoration. The correlation with these independent variables stems from the physical coupling effect of device access, namely, power load directly amplifies system pressure, current peaks induce transient instability, and startup time prolongs response lag. The product of these three factors ensures a nonlinear superposition of multiple interactive factors. right It has a positive impact (the impact intensifies as the equipment power increases). It has a negative impact (the impact is relatively reduced when the power generation capacity increases). Positive amplification of transient risks Negative buffer peak effect Positive extension of the disturbance duration, Negative expansion of the fault tolerance window; therefore, when the numerator independent variable is such as , or When it increases Subsequently, it increases linearly; conversely, the denominator and independent variable are as follows: , or Increase The linear decrease in magnitude highlights the sensitivity of equipment parameter optimization to shock control, facilitating the prioritization of low-impact equipment to maintain system balance.

[0023] Raw operating data such as rated power, peak starting current, rated current, and equipment start-up duration are extracted from emergency plans and equipment nameplates. These data are then normalized and calculated using the generator set's rated operating power and baseline response time to generate a load index. This data processing logic quantifies the relative impact of equipment on the system through proportional relationships, avoiding biases in absolute value comparisons. It establishes a standardized data benchmark for impact assessment, ensuring the consistency and comparability of inputs for subsequent model training. Simultaneously, it connects upstream static data acquisition with downstream dynamic monitoring processes in the overall solution, providing a reliable load index label basis for the impact coefficient and load occupancy rate samples in step 2. This improves the accuracy and robustness of data-driven plant power restoration decisions.

[0024] Step 2: During the process of core equipment access, the impact of each core equipment on the generator set is acquired synchronously, and the impact coefficient is obtained by analysis and quantification. At the same time, the load occupancy rate of the generator set when each core equipment is accessed is recorded. The impact estimation model is obtained by using the load index and load occupancy rate as samples and the impact coefficient as labels. Step 2 includes the following: Step 201: Obtain the speed regulation response time of the generator set when each core device is connected to the bus. The speed regulation response time is the time it takes for the generator set to regain balance after being impacted by the connected device. The adjustment time used by each core device when it connects is obtained, and the impact coefficient is constructed. The logic is as follows: The impact coefficient can be obtained by comparing the adjustment time with the reference response time. The longer the adjustment time or the shorter the reference response time, the larger the impact coefficient.

[0025] As a preferred embodiment, the impact coefficient is based on the following formula: in, Indicates the impact coefficient. Adjust the time. Indicates the baseline response time.

[0026] Real-time acquisition of speed regulation response time data at the moment of access, and proportional calculation using the baseline response time as the denominator to generate the impact coefficient. This processing logic captures the transient dynamic effects of the access event through time ratio, filters out noise interference, and transforms discrete response data into continuous impact quantification indicators, which facilitates subsequent statistical analysis and model fitting. At the same time, it connects the load index calculation in step 1 and the load recording in step 202 in the upper and lower steps, injecting time-series accuracy into the impact label of the dataset, thereby supporting the model training process in step 203. This ensures seamless data flow from the measured data of the core equipment access to the predictive extension of photovoltaic estimation, improving the timeliness and accuracy of the overall data processing.

[0027] Step 202: During the process of core devices connecting to the generator set according to the connection sequence, whenever a core device completes connection and runs smoothly, obtain the total load of the generator set after each device completes connection, and record the load occupancy rate of the generator set at this time. The logic is as follows: The load occupancy rate can be obtained by dividing the total load occupancy rate of the generator set by the rated operating power of the generator set. The larger the total load of the generator set or the smaller the rated operating power of the generator set, the larger the load occupancy rate.

[0028] As a preferred embodiment, the formula for determining the load utilization rate of the generator set is as follows: in, Indicates load utilization rate. This indicates the total load of the generator set.

[0029] After each access event is completed, the total load data is accumulated and divided by the rated operating power of the generator set to calculate the load occupancy rate. This cumulative-proportional processing logic realizes incremental tracking of load status, prevents data omission or duplication, provides real-time snapshots of load variations before and after access, supports early warning of load accumulation risks, and connects the impact time data in step 201 with the sample summary in step 203 in the preceding and following steps, injecting historical context into the load occupancy rate variable input to the model, thereby bridging the local data of single device access with the global view of multi-device sequences, ensuring the continuity of data processing and dynamic optimization of load balancing.

[0030] Step 203: Summarize the impact coefficient, load index, and generator load occupancy rate before connection of each core device to form a dataset, as follows: Each device generates data fluctuations upon connection. The collection of data fluctuations generated by each device upon connection is considered a sample. The sample contains elements such as the impact coefficient, load index, and the load occupancy rate of the generator set before the device was connected. By summing up all the samples, a dataset is formed.

[0031] As a preferred embodiment, the formula upon which the dataset is based is as follows: in, Indicates the first Data set when each device connects. The retrieval variable is the ID of the connected device. , , This indicates the total number of connected devices. Indicates the impact coefficient. Indicates the load index. This indicates the load occupancy rate of the generator set before the equipment was connected; All datasets are input into a data tool for calculation. Using the load index and the load occupancy rate of the generator set before equipment connection as samples, and the impact coefficient as the label, an impact estimation model is obtained. The logic is as follows: Using the load index and the load occupancy rate of the generator set before equipment connection as independent variables, and the impact coefficient as the dependent variable, the data is solved using the bisection method to obtain the functional relationship expression between the independent and dependent variables. The mathematical model of the impact coefficient is obtained through the independent variables and the functional relationship expression, which is the impact estimation model.

[0032] As a preferred embodiment, the formula upon which the impact estimation model is based is as follows: in, This represents the functional relationship of the impact estimation model.

[0033] The triplet of impact coefficient, load index, and pre-connection load occupancy rate is organized into a structured dataset by device serial number and input into a data tool to train a functional relationship model in a supervised learning manner. This multivariate association-label prediction processing logic realizes the knowledge extraction from historical data to generalization function through sample-label pairing, reducing the dependence on actual measurements for each connection and instead using data-driven estimation to reduce computational overhead and risk. At the same time, it connects the load record in step 202 with the photovoltaic input in step 3, providing a reusable parameterized framework for the impact prediction of the photovoltaic subsystem, thereby realizing the transfer learning from the core equipment measured dataset to the photovoltaic estimation dataset, improving the generalization ability of data processing and the intelligence level of the solution.

[0034] Step 3: Obtain the real-time operating data of all photovoltaic subsystems in the bus that need to be connected to the generator set to form a load index, and input it together with the load occupancy rate of the generator set at this time into the impact estimation model to obtain the estimated impact coefficient of each photovoltaic subsystem if connected to the bus. Step 3 includes the following: Obtain the operating data of all photovoltaic panel subsystems, obtain the load index of each photovoltaic panel subsystem from the operating data, obtain the load of the generator set at this moment, input the load index of each photovoltaic panel subsystem and the load occupancy rate of the generator set at this moment into the impact estimation model, and obtain the estimated impact coefficient of each photovoltaic panel subsystem.

[0035] As a preferred embodiment, although the photovoltaic panel subsystem can generate electricity, its electrical energy is unusable before it is connected to the generator set. It requires a series of operations such as frequency regulation. Therefore, before being connected, it is still a load device rather than an energy device for the generator set. The connection will cause an impact on the generator set. Therefore, the operating data of all electrical devices in the photovoltaic panel subsystem should be used as an impact factor like other loads. Thus, the impact estimation model can effectively predict the impact factor of the photovoltaic panel system.

[0036] The system collects photovoltaic subsystem operation data to calculate the load index, and then inputs it into the pre-trained model one by one after combining it with the current generator load to generate the occupancy rate, thereby estimating the impact coefficient. This parallel input-batch output processing logic utilizes the model's batch inference efficiency, unifies the photovoltaic data format to match core equipment samples, extends historical models to new energy scenarios, avoids blind experimentation with photovoltaic access, and connects the model output of step 2 with the value ranking of step 4 in the preceding and following steps. This injects photovoltaic-specific data adjustments into the estimation of the impact coefficient, thereby bridging the training data of the core stable phase and the prediction data of the photovoltaic dynamic phase, ensuring the cross-domain adaptability of data processing and the gradual safety of the recovery process.

[0037] Step 4: Calculate the value coefficient based on the power generation of each photovoltaic subsystem before the power outage, sort all photovoltaic subsystems according to the value coefficient, and select the photovoltaic panel subsystem with the highest value coefficient and the estimated impact coefficient below a certain value for restoration after all core equipment is restored; Step 4 includes the following: The power generation of all photovoltaic panel subsystems before the power outage is obtained, and the value coefficient of each photovoltaic panel system is formed. The logic is as follows: The value parameter is obtained by dividing the power generation by the estimated impact coefficient. The larger the power generation or the smaller the estimated impact coefficient, the larger the value parameter.

[0038] As a preferred embodiment, the formula used to form the value coefficient of each photovoltaic panel subsystem is as follows: in, As a value parameter, For power generation, To estimate the impact coefficient; All photovoltaic panel subsystems are sorted from largest to smallest value coefficient. The photovoltaic panel system with the highest value coefficient and an estimated impact coefficient of less than 1 is prioritized for restoration and connected to the generator set.

[0039] By using the power generation data before the power outage and the estimated impact coefficient to calculate the inverse ratio, a value coefficient is generated. The coefficients are then sorted in descending order to select the maximum values ​​within the threshold. This value-risk trade-off processing logic achieves priority optimization through a sorting algorithm. It integrates multi-source data to support decision-making, maximizes the contribution of photovoltaics while minimizing system disturbances, and improves the economic benefits of data utilization. At the same time, it connects the impact estimation in step 3 and the power iteration in step 5, providing an initial access sequence for subsequent corrections. This forms a data closed loop from assessment data to access feedback, ensuring the efficiency of photovoltaic recovery and the optimization orientation of data processing.

[0040] Step 5: Integrate the restored photovoltaic subsystem into the generator set, adjust the rated operating power of the generator set according to the real-time power generation of the photovoltaic subsystem, and update the load occupancy rate of the generator set according to the adjusted rated operating power of the generator set. Step 5 includes the following: The power generation of the connected photovoltaic panel subsystem is obtained, and the rated operating power of the generator set is adjusted accordingly. The logic is as follows: The corrected rated operating power of the generator set is obtained by adding the generator power of the connected photovoltaic panel subsystem to the rated operating power of the generator set. The greater the rated operating power of the generator set or the power generation of the connected photovoltaic panel system, the greater the corrected rated operating power of the generator set.

[0041] As a preferred embodiment, the formula used to correct the rated operating power of the generator set is as follows: in, This indicates the corrected rated operating power of the generator set. This indicates the rated operating power of the generator set before the correction. This indicates the power generation capacity of the connected photovoltaic panel subsystem.

[0042] The system acquires real-time power generation data from the connected photovoltaic subsystem and adds it to the original rated operating power to generate a correction value. This incremental update processing logic maintains the dynamic consistency of the power benchmark, avoids deviations from static assumptions, reflects changes in system capacity in real time, supports benchmark adjustments for the load index, and connects the priority access in step 4 with the recalculation in step 5, injecting feedback data for power correction into the iterative process. This enhances the real-time performance and adaptability of data processing, ensuring a smooth data evolution as plant power gradually recovers.

[0043] Step 6: Repeat steps 3 to 5 to restore other photovoltaic panel subsystems in sequence until all photovoltaic panel subsystems are connected to the generator bus.

[0044] Step 6 includes the following: Using the updated rated power of the generator set, recalculate the load index of the remaining photovoltaic panel subsystems according to step 3, and re-obtain the value coefficient and estimate the impact coefficient based on the total load of all connected devices and photovoltaic panel systems, and gradually restore all photovoltaic panel systems according to steps 4 and 5. Each time a new photovoltaic panel subsystem is connected, steps 3 through 5 are repeated until all plant power is restored.

[0045] The load index of the remaining photovoltaic subsystems is recalculated based on the corrected power, and the value coefficient of the total connected load is updated and the impact estimate is integrated before the sorted connection is executed in a loop. This iterative feedback processing logic achieves adaptive optimization through cyclic resampling, accumulates data of the entire system to close the recovery path, maintains the accuracy of the model in the dynamic environment, maximizes the overall power generation efficiency, and connects the power update of step 5 with the end point of the scheme in the previous and next steps, forming a multi-round data refinement cycle for the aforementioned evaluation, thereby improving the robustness of data processing and the timeliness of comprehensive recovery.

[0046] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0047] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0048] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may 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, depending on actual needs.

[0049] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for restoring plant power supply based on generator sets, characterized in that the steps are as follows: include: Step 1: Obtain the operating data of each core device, analyze the operating data of each core device to form the load index of each core device, and connect the core devices to the bus of the generator set in sequence; Step 2: During the process of core equipment access, the impact of each core equipment on the generator set is acquired synchronously, and the impact coefficient is obtained by analysis and quantification. At the same time, the load occupancy rate of the generator set when each core equipment is accessed is recorded. The impact estimation model is obtained by using the load index and load occupancy rate as samples and the impact coefficient as labels. Step 3: Obtain the real-time operating data of all photovoltaic subsystems in the bus that need to be connected to the generator set to form a load index, and input it together with the load occupancy rate of the generator set at this time into the impact estimation model to obtain the estimated impact coefficient of each photovoltaic subsystem if connected to the bus. Step 4: Calculate the value coefficient based on the power generation of each photovoltaic subsystem before the power outage, sort all photovoltaic subsystems according to the value coefficient, and select the photovoltaic panel subsystem with the highest value coefficient and the estimated impact coefficient below a certain value for restoration after all core equipment is restored; Step 5: Integrate the restored photovoltaic subsystem into the generator set, adjust the rated operating power of the generator set according to the real-time power generation of the photovoltaic subsystem, and update the load occupancy rate of the generator set according to the adjusted rated operating power of the generator set. Step 6: Repeat steps 3 to 5 to restore other photovoltaic panel subsystems in sequence until all photovoltaic panel subsystems are connected to the generator bus.

2. The plant power restoration control method based on generator sets according to claim 1, characterized in that: Obtain the rated operating power and reference response time of the generator set, wherein the reference response time is the maximum time allowed for the generator set to regain balance after being subjected to an impact; The core equipment includes: transformers, control system equipment, protection system equipment, and communication system equipment; The core equipment is connected sequentially to the generator busbars. The connection order of the core equipment is obtained according to the power plant's emergency plan, and then connected to the generator busbars sequentially according to the connection order. The operating data of each core device is obtained separately. The operating data includes rated power, peak starting current, rated current, and device start-up duration. The load index is then obtained, and the logic is as follows: The load index can be obtained by multiplying the ratio of peak starting current to rated current, the ratio of equipment start-up duration to reference response time, and the ratio of equipment rated power to generator set rated operating power.

3. The plant power restoration control method based on generator sets according to claim 2, characterized in that: The speed regulation response time of the generator set when each core device is connected to the bus is obtained. The speed regulation response time is the time it takes for the generator set to recover balance after being impacted by the device. The adjustment time used by each core device when it is connected is obtained, and the impact coefficient is constructed. The logic is as follows: The impact coefficient can be obtained by comparing the adjustment time with the reference response time. The longer the adjustment time or the shorter the reference response time, the larger the impact coefficient.

4. The plant power restoration control method based on generator sets according to claim 3, characterized in that: During the process of core equipment connecting to the generator set in the access sequence, whenever a core equipment is connected and running smoothly, the total load of the generator set after each equipment is connected is obtained, and the load occupancy rate of the generator set at this time is recorded. The logic is as follows: The load occupancy rate can be obtained by dividing the total load occupancy rate of the generator set by the rated operating power of the generator set.

5. The plant power restoration control method based on generator sets according to claim 4, characterized in that: The impact coefficient and load index resulting from the connection of each core device, along with the load occupancy rate of the generator set before connection, are aggregated to form a dataset, as follows: Each device generates data fluctuations upon connection. The collection of data fluctuations generated by each device upon connection is considered a sample. The sample contains elements such as the impact coefficient, load index, and the load occupancy rate of the generator set before the device was connected. By summing up all the samples, a dataset is formed. Using the load index and the load occupancy rate of the generator set before equipment connection as samples, and the impact coefficient as the label, the impact estimation model is obtained, with the following logic: Using the load index and the load occupancy rate of the generator set before equipment connection as independent variables, and the impact coefficient as the dependent variable, the data is solved using the bisection method to obtain the functional relationship expression between the independent and dependent variables. The mathematical model of the impact coefficient is obtained through the independent variables and the functional relationship expression, which is the impact estimation model.

6. The plant power restoration control method based on generator sets according to claim 5, characterized in that: Obtain the operating data of all photovoltaic panel subsystems, obtain the load index of each photovoltaic panel subsystem from the operating data, obtain the load of the generator set at this moment, input the load index of each photovoltaic panel subsystem and the load occupancy rate of the generator set at this moment into the impact estimation model, and obtain the estimated impact coefficient of each photovoltaic panel subsystem.

7. The plant power restoration control method based on generator sets according to claim 6, characterized in that: When obtaining the power generation of all photovoltaic panel subsystems before the power outage and forming the value coefficient of each photovoltaic panel system, the power generation and the estimated impact coefficient are used as the value parameters of the photovoltaic subsystem. All photovoltaic panel subsystems are sorted from largest to smallest value coefficient. The photovoltaic panel system with the highest value coefficient and an estimated impact coefficient lower than a certain value is prioritized for restoration and connected to the generator set.

8. The plant power restoration control method based on generator sets according to claim 7, characterized in that: The power generation of the connected photovoltaic panel subsystem is obtained, and the rated operating power of the generator set is adjusted accordingly. The logic is as follows: The corrected rated operating power of the generator set is obtained by adding the generator power of the connected photovoltaic panel subsystem to the rated operating power of the generator set. The greater the rated operating power of the generator set or the power generation of the connected photovoltaic panel system, the greater the corrected rated operating power of the generator set.

9. The plant power restoration control method based on generator sets according to claim 8, characterized in that: Using the updated rated power of the generator set, recalculate the load index of the remaining photovoltaic panel subsystems according to step 3, and re-obtain the value coefficient and estimate the impact coefficient based on the total load of all connected devices and photovoltaic panel systems, and gradually restore all photovoltaic panel systems according to steps 4 and 5. Each time a new photovoltaic panel subsystem is connected, steps 3 through 5 are repeated until all plant power is restored.

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