A method, device and equipment for connecting a generator to the grid without sensing

Abstract

The present application discloses a method, device and equipment for grid-connected access of a generator without sensing. By collecting the grid state and generator operation monitoring parameters in real time, a reliable data basis is provided for subsequent operations. The grid frequency is calculated at multiple time points to construct a real-time frequency curve, which is used to analyze stability, predict changes, compare the generator current output frequency to identify frequency differences, and evaluate coordination. Frequency synchronization is achieved by using the minimum difference time point detection, and this time point is marked to optimize frequency control. Combining dynamic frequency adjustment and synchronous frequency error estimation ensures grid-connected stability and security. In terms of power control, transient power suppression optimization reduces power fluctuations, and dynamic feedforward compensation control enhances the adaptability of the generator. In addition, intelligent non-sensing grid connection optimization reduces manual intervention, constructs an intelligent grid connection engine, realizes seamless connection between the generator and the grid, comprehensively improves grid connection efficiency and security, and reduces system complexity and risk.

Description

Technical Field

[0001] The present application relates to the field of power grid connection, and more specifically, to a method, device and equipment for non-inductive grid connection of a generator. Background Art

[0002] As the world actively promotes the large-scale use of renewable energy and the power system accelerates towards intelligence and automation, the connection technology between generators and power grids is in the process of continuous innovation and optimization. In the past, the connection of generators to the grid mainly relied on the perception of key parameters such as frequency and voltage to complete the synchronous grid-connected operation. However, in real operating scenarios, subtle differences in frequency and unstable fluctuations in voltage frequently cause unstable conditions during grid connection, greatly increasing the complexity of power system operation and correspondingly increasing potential risks. Especially in renewable energy power generation systems such as wind power and solar power, where the power generation power fluctuates significantly, the moment the generator is connected to the grid is often accompanied by violent transient power fluctuations and significant frequency errors, which undoubtedly brings severe challenges to the power grid that already requires highly stable operation.

[0003] At present, traditional means of connecting generators to the grid mostly rely on induction components and require manual adjustments to a large extent. Although this method can ensure the basic stability of generator grid connection under certain conditions, due to its high dependence on manual intervention and cumbersome system adjustment process, it often seems powerless in the face of rapidly changing grid conditions. Moreover, during long-term continuous operation, traditional grid-connected methods are prone to adverse phenomena such as frequency deviation from preset values ​​and excessive power shocks. In summary, traditional generator grid-connected technology has many shortcomings such as slow response speed, poor frequency synchronization accuracy, and excessive power fluctuations. It is difficult to meet the stringent requirements of modern power systems for efficient, safe, and stable grid connection.

[0004] Based on this, the present application provides a solution for the non-sensing grid connection of a generator, which effectively makes up for the deficiencies of the prior art. Summary of the invention

[0005] The present application provides a method, device and equipment for non-sensing grid-connection of generators. Through a series of technologies such as real-time data collection, multi-time point frequency calculation, minimum difference time point detection, dynamic frequency adjustment, etc., frequency synchronization optimization and precise power control are achieved, effectively improving the efficiency, safety and stability of generator grid-connection.

[0006] A method for connecting a generator to a grid without induction, comprising:

[0007] Acquire real-time state parameters of the power grid, and perform multi-time point calculation of the power grid operation frequency on the real-time state parameters of the power grid to construct a real-time frequency curve of the power grid;

[0008] Acquire generator operation monitoring parameters, and perform real-time current output frequency statistics according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics;

[0009] Performing minimum difference time point detection on the real-time frequency curve of the power grid based on the real-time current output frequency to determine the minimum frequency difference time point;

[0010] Perform dynamic frequency adjustment and synchronous frequency error estimation according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency to generate accurate synchronous frequency parameters;

[0011] Based on the precise synchronous frequency parameters, the generator is connected to the grid, grid response data is collected, and a transient power correction strategy is constructed through transient power suppression optimization;

[0012] According to the current delay parameters obtained by performing a lag delay analysis on the corresponding grid-connected data, dynamic feedforward compensation control is performed on the generator, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy.

[0013] Optionally, performing multi-time point calculation of the grid operation frequency on the real-time state parameter of the grid to construct a real-time frequency curve of the grid includes:

[0014] Performing multi-time point calculation of the grid operation frequency on the real-time state parameters of the grid to obtain the grid operation frequency at multiple time points;

[0015] Generate a power grid frequency fluctuation node by identifying frequency fluctuations of the power grid operating frequencies at the multiple time points;

[0016] Conduct frequency fluctuation time series distribution analysis on power grid frequency fluctuation nodes to generate frequency fluctuation time series distribution characteristics;

[0017] Based on the time series distribution characteristics of the frequency fluctuations, a discrete fitting of the frequency fluctuations is performed to construct a real-time frequency curve of the power grid.

[0018] Optionally, generator operation monitoring parameters are obtained, and real-time current output frequency statistics are performed according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics, including:

[0019] Calculating the instantaneous angular acceleration according to the generator operation monitoring parameters to obtain the instantaneous angular acceleration of the generator;

[0020] Performing mechanical inertia analysis based on the instantaneous angular acceleration of the generator to obtain the mechanical inertia characteristics of the generator;

[0021] Perform a correlation analysis of the current output frequency and the rotational speed on the operating monitoring parameters of the generator to obtain damping characteristic data;

[0022] Perform real-time statistical analysis of the current output frequency on the operating monitoring parameters of the generator to generate the real-time current output frequency;

[0023] Perform frequency instant response evolution on the real-time current output frequency based on the mechanical inertia characteristics of the generator and the damping characteristic data to generate the engine frequency instant response characteristics.

[0024] Optionally, perform minimum difference time point detection on the real-time grid frequency curve based on the real-time current output frequency to determine the minimum frequency difference time point, including:

[0025] Perform multi-point frequency difference calculation on the real-time grid frequency curve based on the real-time current output frequency to obtain multi-point frequency difference data;

[0026] Perform difference trend time series change analysis on the multi-point frequency difference data to determine the frequency difference change trend;

[0027] Perform future period rolling prediction based on the frequency difference change trend to generate rolling window frequency difference prediction data;

[0028] Perform minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

[0029] Optionally, perform dynamic frequency adjustment and synchronous frequency error estimation based on the minimum frequency difference time point and the engine frequency instant response characteristics to generate precise synchronous frequency parameters, including:

[0030] Determine the minimum time point grid frequency corresponding to the minimum frequency difference time point in the real-time grid frequency curve;

[0031] Perform frequency difference compensation calculation based on the minimum time point grid frequency to obtain frequency difference compensation parameters;

[0032] Perform dynamic frequency adjustment on the engine frequency instant response characteristics based on the frequency difference compensation parameters to generate the engine dynamically adjusted frequency;

[0033] Perform synchronous frequency error estimation on the engine dynamically adjusted frequency and perform precise synchronous correction on the generated synchronous frequency error parameters to generate precise synchronous frequency parameters.

[0034] Optionally, perform grid connection processing on the generator based on the precise synchronous frequency parameters, collect grid connection response data and construct a transient power correction strategy through transient power suppression optimization, including:

[0035] Performing grid-connection processing on the generator based on the precise synchronous frequency parameters and collecting grid-connection response data;

[0036] Calculating the transient power fluctuation amplitude of the grid-connected response data to generate transient power fluctuation data;

[0037] Performing multi-power range analysis on the transient power fluctuation data to extract power fluctuation data in different ranges;

[0038] Performing adaptive power correction calculation on the power fluctuation data in different ranges to generate transient power fluctuation correction parameters;

[0039] By performing transient power suppression optimization on the transient power fluctuation correction parameters, a transient power correction strategy is constructed.

[0040] Optionally, according to the current delay parameter obtained by performing a lag delay analysis on the grid-connected corresponding data, dynamic feedforward compensation control is performed on the generator, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy to build an intelligent grid-connected engine, including:

[0041] Performing current hysteresis effect analysis on the grid-connected response data to obtain current hysteresis effect characteristics of the motor grid-connected;

[0042] Performing current time delay calculation on the current hysteresis effect characteristics of the motor grid connection to generate current time delay parameters;

[0043] Based on the current time delay parameter, dynamic feedforward compensation control is performed on the generator to generate a current lag compensation strategy;

[0044] Intelligent non-inductive incorporation optimization is performed according to the current lag compensation strategy and the transient power correction strategy.

[0045] A generator non-inductive grid-connected device, comprising:

[0046] A real-time power grid frequency module is used to obtain real-time power grid status parameters, and perform multi-time point calculation of power grid operation frequency on the real-time power grid status parameters to construct a real-time power grid frequency curve;

[0047] An output frequency statistics module is used to obtain generator operation monitoring parameters, and perform real-time current output frequency statistics according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics;

[0048] A minimum frequency difference module, used to detect the minimum difference time point of the real-time frequency curve of the power grid based on the real-time current output frequency, and determine the minimum frequency difference time point;

[0049] A synchronous frequency adjustment module, used to perform dynamic frequency adjustment and synchronous frequency error estimation according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency, and generate accurate synchronous frequency parameters;

[0050] A transient power suppression module, for performing grid-connection processing on the generator based on the precise synchronous frequency parameters, collecting grid-connection response data and constructing a transient power correction strategy through transient power suppression optimization;

[0051] The intelligent grid-connected optimization module is used to perform dynamic feedforward compensation control on the generator according to the current delay parameters obtained by performing lag delay analysis on the corresponding grid-connected data, and to perform intelligent non-inductive connection optimization based on the transient power correction strategy.

[0052] Optionally, the power grid real-time frequency module includes:

[0053] A multi-time point difference unit, used for performing multi-time point frequency difference calculation on the real-time frequency curve of the power grid based on the real-time current output frequency, to obtain multi-time point frequency difference data;

[0054] A change trend unit, used to perform a time series change analysis of the difference trend on the frequency difference data at multiple time points to determine the frequency difference change trend;

[0055] A rolling window unit, used to perform rolling prediction for future time periods based on the frequency difference change trend, and generate rolling window frequency difference prediction data;

[0056] The minimum difference unit is used to perform minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

[0057] A generator non-sensing grid-connected device, comprising a memory and a processor;

[0058] The memory is used to store programs;

[0059] The processor is used to execute the program to implement each step of the method for non-sensing grid connection of a generator as described in any one of the above items.

[0060] It can be seen from the above technical solutions that the method, device and equipment for non-inductive grid connection of a generator provided in the embodiments of the present application have significant beneficial effects in many aspects.

[0061] In terms of frequency analysis, the present invention adopts a multi-time-point method to calculate the grid frequency, which can accurately construct the real-time frequency curve of the grid. With the help of this curve, not only can the stability of the grid be deeply analyzed, but also the changing trend of the frequency can be clearly observed, so as to accurately predict the future frequency changes and provide strong support for taking adjustment measures in advance. At the same time, the current output frequency of the generator is statistically calculated in real time, which can intuitively reflect its actual working state. By comparing this frequency with the grid frequency, the frequency difference between the generator and the grid can be quickly identified. The accurate statistical data of the current output frequency provides a key basis for evaluating the coordination between the generator and the grid, and points out the direction for subsequent targeted adjustments.

[0062] In terms of frequency synchronization optimization, the present invention can efficiently and accurately find the moment with the smallest difference between the grid frequency and the generator output frequency through the minimum difference time point detection technology, so as to successfully achieve frequency synchronization. Marking the minimum frequency difference time point provides a clear timing node for the optimization decision, which helps to greatly reduce the frequency deviation and significantly optimize the frequency control effect of the grid. In addition, the dynamic frequency adjustment function can track the changes of the grid frequency in real time and accurately adjust the generator output accordingly, effectively reducing the adverse impact of frequency fluctuations on the grid stability. The synchronous frequency error estimation provides accurate error data for further optimizing the frequency adjustment strategy, strongly ensuring the stability of the grid connection process. Precise synchronous frequency parameters ensure a highly accurate frequency synchronization between the generator and the grid, greatly improving the safety during the grid connection process and effectively preventing system instability problems caused by frequency differences.

[0063] Regarding power control, the transient power suppression optimization technology of the present invention can significantly reduce the power fluctuations during the grid connection process of the generator, avoid instantaneous power surges, thereby reducing the negative impact on the grid and strongly enhancing the overall reliability of the system. The dynamic feedforward compensation control technology can compensate the influence of grid frequency fluctuations on the generator in real time, significantly enhancing the adaptability of the generator and greatly improving the system's response ability to emergencies.

[0064] In terms of the intelligence of grid connection control, the intelligent non-sensing grid connection optimization technology enables the generator to achieve a more efficient and intelligent grid connection control without frequent manual intervention during grid connection, while effectively reducing the complexity and uncertainty of system regulation. By constructing an intelligent grid connection engine, seamless connection between the generator and the grid can be effectively achieved, significantly improving the grid connection efficiency and safety, and minimizing the risks during the grid connection process to the greatest extent. Description of the Drawings

[0065] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0066] Figure 1 It is a flowchart of a method for the induction connection of a generator to the grid without sensing disclosed in an embodiment of the present application;

[0067] Figure 2 It is a schematic diagram of a device for the induction connection of a generator to the grid without sensing disclosed in an embodiment of the present application;

[0068] Figure 3 It is a hardware structure block diagram of a device for the induction connection of a generator to the grid without sensing disclosed in an embodiment of the present application. Detailed implementation manners

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of them. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0070] The embodiments of the present application provide a method, device, and equipment for the induction connection of a generator to the grid without sensing. The execution subjects of the method, device, and equipment for the induction connection of a generator to the grid without sensing include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry this system, which can be regarded as general computing nodes of the present application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0071] Next, the solution of the present application will be introduced. The present application proposes the following technical solutions. Please refer to the following text for details.

[0072] Figure 1 It is a flowchart of a method for the induction connection of a generator to the grid without sensing disclosed in an embodiment of the present application.

[0073] As Figure 1 shown, the method may include:

[0074] Step S1: Obtain the real-time state parameters of the power grid, perform multi-point calculation of the power grid operation frequency on the real-time state parameters of the power grid, and construct a real-time frequency curve of the power grid.

[0075] Specifically, first in the power grid infrastructure, voltage and current sensors with high sampling accuracy need to be accurately deployed. Among them, current transformers (CTs) and voltage transformers (VTs) are commonly used devices, and their sampling frequency needs to reach at least 1 kHz to ensure that the dynamic changes in the power grid operation can be accurately captured. At the same time, at the generator end, various monitoring sensors also need to be installed, including speed sensors, power sensors, and temperature sensors, to monitor the operation status of the generator in real time. Using real-time data acquisition systems such as SCADA or DCS, various relevant parameters from the power grid and the generator are uniformly collected onto the same platform and promptly uploaded to the central database for storage and in-depth analysis.

[0076] Immediately afterwards, quickly obtain the real-time state parameters of the power grid from the established data acquisition system. These parameters cover key indicators such as voltage (V), current (I), power (P), frequency (f), etc. In this process, it is particularly important to ensure that each data point carries accurate timestamp information to provide an accurate time reference for subsequent frequency calculation and comprehensive analysis. Synchronously, obtain the operation parameters of the generator from the generator monitoring system, such as speed, power generation, load condition, and temperature, and also record the timestamp corresponding to each parameter in detail to ensure that the data of the generator and the power grid can be accurately matched in the time dimension.

[0077] Subsequently, based on the real-time collected voltage and current data, use the sliding window method to calculate the operating frequency of the power grid. Specifically, set a time window with a duration of 5 minutes. Within this window, first calculate the average values of the voltage and current data, and then calculate the frequency value based on this. After that, move the time window step by step according to the set window size, calculate the frequency values of each time period in turn, and store the obtained results in the database in an orderly manner.

[0078] Finally, with the help of professional data analysis and visualization tools such as Matplotlib or Plotly, using the timestamp as the abscissa and the frequency as the ordinate, visualize the frequency data calculated at multiple time points, and then generate a clear and intuitive real-time frequency curve of the power grid. This frequency curve is stored together with the real-time monitoring data to provide core data support for subsequent in-depth analysis. These data not only help to achieve the seamless connection of the generator to the grid and optimize the real-time control strategy, but also can timely identify potential frequency fluctuation problems through the regular analysis of the frequency curve, providing an effective early warning for ensuring the stable operation of the power grid.

[0079] Step S2: Obtain the generator operation monitoring parameters, and perform real-time current output frequency statistics based on the generator operation monitoring parameters to generate the real-time current output frequency and the engine frequency instant response characteristics.

[0080] Specifically, at the generator end, a series of necessary monitoring devices need to be installed. Among them, the current sensor is crucial. A current transformer (CT) with high precision and fast response characteristics should be selected, and the sampling frequency should be set at least at 1 kHz to accurately capture the transient changes and dynamic characteristics of the current. At the same time, a speed sensor and a power sensor are also required. Using a data acquisition system such as SCADA or DCS, various operating monitoring parameters of the generator are collected in real time and uploaded to the central database in a timely manner. This system needs to have stable real-time monitoring and efficient data storage capabilities to lay a foundation for subsequent analysis.

[0081] During the data acquisition process, the output current of the generator is monitored in real time through the current sensor. When recording the current data, it is necessary to mark each data point with an accurate timestamp to ensure the timing of the data. The parameters monitored in real time include not only the current amplitude (A), but also the power factor and the speed (RPM) of the generator. These parameters are the key basis for calculating the output frequency of the current.

[0082] Since the output current frequency of the generator is closely related to the speed, its common calculation formula is: f = N × p / 120, where f represents the frequency (Hz), N is the speed (RPM), and p is the number of pole pairs of the generator. Based on this, by collecting the speed data in real time and combining the known number of pole pairs, the real-time output frequency of the generator can be dynamically calculated. To ensure that the frequency update can reflect the dynamic response of the generator in a timely manner, it is recommended to perform regular calculations, such as performing a calculation operation every 1 second. In the data analysis system, a special algorithm needs to be written to first obtain the current speed, then calculate the output frequency of the current according to the formula, and record the calculation result in the database to ensure that the timestamp of this data is consistent with other monitoring parameters for subsequent in-depth analysis and visualization.

[0083] Finally, using professional data visualization tools such as Matplotlib, Grafana, or Tableau, the real-time output frequency of the current is presented in the form of a chart. The abscissa is set as time, and the ordinate is the real-time output frequency of the current. Through these charts, the dynamic changes of the generator frequency can be intuitively and clearly displayed, facilitating the staff to observe and analyze the frequency fluctuations and their development trends.

[0084] Step S3: Detect the minimum difference time point of the real-time grid frequency curve based on the real-time output frequency of the current to determine the minimum frequency difference time point.

[0085] Specifically, through the previous operations, the real-time power grid frequency curve and the real-time current output frequency of the generator are generated. At this time, the primary task is to ensure that these two sets of data have the same time stamps, which is a key prerequisite for effective comparison. Using data analysis tools (such as Pandas), integrate the power grid frequency data and the generator frequency data into the same data framework to construct a table containing time stamps, power grid frequencies, and generator frequencies.

[0086] After the data integration is completed, data cleaning operations need to be performed on the data. Carefully check and remove any missing values and outliers in the data to ensure the accuracy of the data. At the same time, use methods such as median filtering or moving average to smooth the data to effectively reduce the interference of noise on the data.

[0087] The frequency difference, that is, the absolute difference between the power grid frequency and the generator output frequency. By traversing the integrated data, calculate the frequency difference at each time point one by one and store the obtained results in a new column. To improve the calculation efficiency, loop operations or vectorized operations can be used.

[0088] After completing the calculation of the frequency difference, use a data analysis tool to find the minimum value in the difference column. Using built-in functions such as min() and idxmin() in Pandas, the minimum difference value and its corresponding time stamp can be quickly located. Record the specific information of this time point in detail, which includes the minimum difference value and the corresponding power grid frequency and generator frequency.

[0089] Add a marker column in the data framework specifically for marking the minimum difference time point. Using boolean indexing, at the located minimum difference index, set the marker to True and the rest to False to clearly identify the minimum difference time point.

[0090] Finally, store the data framework with the marked minimum difference time point in the database for subsequent in-depth analysis and processing. The stored data should completely include time stamps, power grid frequencies, generator frequencies, and their difference values. At the same time, comprehensively record the detailed information of the minimum difference time point, including time stamps, frequency values, and difference values, to provide sufficient data support for subsequent real-time monitoring and analysis.

[0091] Step S4: Perform dynamic frequency adjustment and synchronous frequency error estimation based on the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency to generate accurate synchronous frequency parameters.

[0092] Specifically, first, based on the calculation results of the minimum frequency difference, determine the target frequency that needs to be adjusted. Usually, to ensure that the generator can be successfully connected to the power grid, the target frequency is set to the power grid frequency. For example, in the current example scenario, the target frequency is determined to be 50.02 Hz.

[0093] Subsequently, a dynamic control algorithm, such as a PID controller, is adopted to perform the frequency adjustment work. Before applying the PID controller, its parameters, namely the proportional, integral, and derivative gains, need to be precisely set to achieve real-time and accurate regulation of the generator output frequency. Based on the set target frequency, the frequency value that the generator needs to increase or decrease is accurately calculated. Suppose the current generator frequency is 50.00 Hz and the target frequency is 50.02 Hz, then the PID controller will output a corresponding control signal instructing the generator to increase the frequency by 0.02 Hz.

[0094] During the dynamic frequency adjustment process, the calculation of the synchronous frequency error is carried out in real time. The synchronous frequency error is defined as the difference between the grid frequency and the generator frequency. With the help of a data acquisition system, the frequency change of the generator is continuously and real-time monitored, and the synchronous frequency error is updated accordingly. The data acquisition system will continuously record the current grid frequency and generator frequency into the data framework to provide data support for subsequent in-depth analysis.

[0095] The precise synchronous frequency parameter is obtained based on the real-time monitoring and calculation of the difference between the grid frequency and the generator frequency. This parameter plays a key role in guiding the generator frequency adjustment, aiming to ensure that the generator frequency is always highly consistent with the grid frequency. According to the synchronously calculated frequency error in real time, the precise synchronous frequency parameter is generated. In this process, a threshold, such as 1 Hz, is preset. When the synchronous frequency error is less than this threshold, it is determined that the generator and the grid have achieved a precise synchronization state.

[0096] After the generation of the precise synchronous frequency parameter is completed, the relevant information is recorded in the database, which includes key data such as the timestamp, the current grid frequency, the generator frequency, and the synchronous frequency error. At the same time, an effective feedback mechanism is established. Through in-depth analysis of the real-time monitoring results, the control parameters are continuously optimized. Specifically, the parameters of the PID controller are dynamically adjusted to significantly improve the system's response speed and control accuracy. In addition, the actual effect of the frequency adjustment is evaluated regularly, and the synchronization status between the generator frequency and the grid frequency is closely monitored to ensure that the system can operate stably and efficiently in all aspects.

[0097] Step S5: Based on the precise synchronous frequency parameter, the generator is connected to the grid, and the grid connection response data is collected and a transient power correction strategy is constructed through transient power suppression optimization.

[0098] Specifically, before officially carrying out grid connection processing, it is necessary to ensure that the accurate synchronous frequency parameters of the generator have been calculated and precisely match the grid frequency. For example, if the current grid frequency is 50.02 Hz, then the accurate synchronous frequency of the generator must also be 50.02 Hz. At the same time, comprehensively check the operating status of the generator, covering key parameters such as current, voltage, and power factor, to ensure that they are all within the safe range. The output current of the generator should be lower than the rated value, and the voltage needs to be stably maintained within the specified range.

[0099] In the control system of the generator, accurately input the accurate synchronous frequency parameters so that the control system can dynamically adjust the output frequency of the generator based on this parameter. At the same time, carefully configure the data acquisition system to be used for real-time monitoring of the connection status between the generator and the grid as well as the operating parameters.

[0100] The grid connection process adopts a step-by-step grid connection strategy. In the initial stage, slowly increase the output power of the generator to the level required by the grid to avoid transient shocks. Set the initial power to 10% of the rated power, and then gradually increase it to 50%, and finally reach 100%. In each step of grid connection, closely monitor the voltage, frequency, and power of the grid in real time to ensure that the generator output is always synchronized with the grid. Once abnormal frequency or voltage is detected, immediately reduce the output power of the generator to fully ensure safe grid connection.

[0101] During grid connection, focus on monitoring the transient power fluctuation conditions. With the collected current and power data, deeply analyze the transient power changes to accurately identify the fluctuation amplitude and frequency. Preset the transient power threshold. When the fluctuation exceeds 5% of the rated power, immediately activate the transient power suppression strategy.

[0102] According to the transient power suppression requirements, carefully design the corresponding correction strategy. Use the PID control algorithm to adjust the output power of the generator in real time to effectively suppress the fluctuation. When power fluctuation is detected, the control system will automatically adjust the power output of the generator according to the PID algorithm. Determine the PID parameters, namely the proportional gain (Kp), integral time (Ti), and derivative time (Td), through experiments to obtain the best response speed and stability.

[0103] Integrate the transient power correction strategy in the control system. With the power data feedback from the real-time monitoring system, dynamically regulate the output of the generator. If the output power fluctuation of the generator exceeds the set threshold, the control system will automatically intervene to adjust the output and reduce the power fluctuation.

[0104] Continuously monitor the effectiveness of the correction strategy to ensure that the transient power suppression strategy can effectively reduce output fluctuations. Set a 1-second monitoring period and check the power output and fluctuations every 1 second. Use data visualization tools such as Matplotlib or Grafana to intuitively display the transient power fluctuations after grid connection and monitor the generator output stability in real time. The chart should clearly show the time, power output, and fluctuation amplitude to intuitively evaluate the effectiveness of the correction strategy.

[0105] Regularly record the operation results of the correction strategy, including data such as the amplitude, frequency, and duration of transient power fluctuations, to evaluate the actual effect of the strategy. Dynamically optimize the transient power correction strategy based on the monitoring results. If the strategy is not effective in some cases, readjust the PID parameters or use other control algorithms to improve system performance. In addition, regularly conduct system evaluations, optimize the grid-connected strategy and transient power correction scheme based on historical data and real-time data analysis, and ensure the stability and security of the power system in all aspects.

[0106] Step S6, based on the precise synchronization frequency parameters, the generator is connected to the grid, the grid response data is collected and a transient power correction strategy is constructed through transient power suppression optimization. According to the current delay parameters obtained by performing a lag delay analysis on the corresponding grid data, dynamic feedforward compensation control is performed on the generator, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy.

[0107] Specifically, dynamic feedforward compensation control aims to optimize generator output by predicting frequency changes between the power grid and the generator, thereby effectively reducing the lag effect. This control strategy relies on real-time monitoring of the power grid frequency and the generator output frequency. First, set the dynamic feedforward control algorithm, usually using the model predictive control (MPC) method. Use historical data and real-time monitoring data to predict the changing trend of the power grid frequency in the next few seconds. At the same time, accurately set the control parameters, such as setting the prediction time to 3 seconds and the feedback gain to 0.8, so as to achieve a rapid response to frequency changes.

[0108] The dynamic feedforward compensation mechanism is implemented in the control system. Once the grid frequency is detected to be rising or falling, the control system will automatically calculate the output power that the generator needs to adjust. For example, if the grid frequency is expected to rise by 0.1Hz, the system will immediately instruct the generator to increase the output power in advance to cope with the impact of the frequency increase.

[0109] Organically combine the transient power correction strategy with the dynamic feedforward compensation control to achieve more precise power output control. To this end, it is necessary to set the monitoring threshold for transient power fluctuations, such as setting it to 2% of the rated power. When it is detected that the power fluctuation exceeds this threshold, the system will quickly activate the transient power correction strategy. Through the control system, closely associate the feedback result of the transient power correction strategy with the dynamic feedforward compensation control. If the transient power fluctuation exceeds the set range, the system will automatically adjust the generator output power to ensure fine adjustment on the basis of dynamic feedforward control.

[0110] Further integrate the dynamic feedforward compensation control and the transient power correction strategy to build an intelligent grid connection engine. This engine has the ability to monitor the operating states of the power grid and the generator in real time, and can dynamically adjust the generator output according to the real-time data, and finally achieve seamless grid connection. During the construction process, clarify the working process of the engine, covering key links such as real-time data acquisition, frequency prediction, dynamic compensation, and power correction. Introduce intelligent algorithms, such as machine learning or adaptive control technology, into the control system. Through in-depth training of historical data, optimize the model's prediction ability for future frequency changes, and then improve the system's response level to power grid frequency changes.

[0111] Implement real-time monitoring. With the help of the data acquisition system, feedback key information such as the power grid frequency, generator frequency, and power output to the intelligent control system in real time. Use data visualization tools such as Grafana to monitor the operation effect of the intelligent grid connection engine, and display the change trends of the power grid frequency, generator frequency, and power output in real time. In the display chart, clearly present key data such as time, frequency value, and power output for intuitive observation of the system performance. In addition, comprehensively evaluate the operation effect of the intelligent grid connection engine regularly, including key indicators such as frequency matching accuracy, power fluctuation amplitude, and response time, so as to ensure the stability of the system and provide a strong guarantee for the reliable operation of the power system.

[0112] As can be seen from the above technical solutions, a method, device, and equipment for seamless access of a generator to the grid provided by the embodiments of the present application have many significant beneficial effects.

[0113] In terms of frequency analysis, the present invention adopts a multi-time-point method to calculate the grid frequency, which can accurately construct the real-time frequency curve of the grid. With the help of this curve, not only can the stability of the grid be deeply analyzed, but also the changing trend of the frequency can be clearly observed, and then accurate prediction of future frequency changes can be made, providing strong support for taking adjustment measures in advance. At the same time, the current output frequency of the generator is statistically calculated in real time, which can intuitively reflect its actual working state. By comparing this frequency with the grid frequency, the frequency difference between the generator and the grid can be quickly identified. The accurate statistical data of the current output frequency provides a key basis for evaluating the coordination between the generator and the grid, and points out the direction for subsequent targeted adjustments.

[0114] In terms of frequency synchronization optimization, the present invention can efficiently and accurately find the moment with the smallest difference between the grid frequency and the generator output frequency through the minimum difference time-point detection technology, thus successfully achieving frequency synchronization. Marking the minimum frequency difference time-point provides a clear timing node for optimization decisions, which helps to greatly reduce the frequency deviation and significantly optimize the frequency control effect of the grid. In addition, the dynamic frequency adjustment function can track the changes of the grid frequency in real time and accurately adjust the generator output accordingly, effectively reducing the adverse impact of frequency fluctuations on the grid stability. The synchronous frequency error estimation provides accurate error data for further optimizing the frequency adjustment strategy, strongly guaranteeing the stability of the grid connection process. Precise synchronous frequency parameters ensure highly accurate frequency synchronization between the generator and the grid, greatly improving the safety during the grid connection process and effectively preventing system instability problems caused by frequency differences.

[0115] Regarding power control, the transient power suppression optimization technology of the present invention can significantly reduce the power fluctuations during the generator grid connection process, avoid instantaneous power surges, thereby reducing the negative impact on the grid and strongly enhancing the overall reliability of the system. The dynamic feed-forward compensation control technology can compensate in real time for the impact of grid frequency fluctuations on the generator, significantly enhancing the adaptability of the generator and greatly improving the system's response ability to emergencies.

[0116] In terms of the intelligence of grid connection control, the intelligent non-sensing grid connection optimization technology enables the generator to be grid-connected without frequent manual intervention, realizing more efficient and intelligent grid connection control, while effectively reducing the complexity and uncertainty of system regulation. By constructing an intelligent grid connection engine, seamless connection between the generator and the grid can be effectively achieved, significantly improving the grid connection efficiency and safety, and minimizing the risks during the grid connection process to the greatest extent.

[0117] In some embodiments of the present application, step S1 of the present application is further introduced, which may specifically include:

[0118] Step S11: Perform multi-point calculations of the grid operating frequency on the real-time state parameters of the grid to obtain the grid operating frequencies at multiple time points.

[0119] Specifically, install sensors with high precision at key nodes of the grid, such as substations, distribution rooms, and generator sets. These sensors should be capable of real-time monitoring of parameters such as voltage, current, power, and frequency. It is recommended to set their sampling frequency to 1 Hz or higher to ensure the real-time nature of the data. For generators, additional vibration sensors, temperature sensors, and rotational speed sensors are configured to monitor their operating status in real time. Industrial communication protocols, such as Modbus, DNP3, or CAN, are used for data transmission to ensure the stability and real-time nature of data transmission. The data is first preprocessed by edge computing devices and then uploaded to the central database. A dedicated data storage strategy is set to ensure that real-time data is accurately stored according to timestamps, facilitating subsequent analysis. A time series database, such as InfluxDB, can be selected for efficient storage. Regularly perform verification on the collected data, use statistical methods, such as Z-score, to detect outliers and perform corresponding processing. For missing values in the data, interpolation methods, such as linear interpolation or spline interpolation, are used to fill them to ensure the continuity of the data.

[0120] Based on the voltage and current data of the grid, calculate the grid operating frequency using the formula f = 1 / T (where T is the period). Convert the time-domain signal to the frequency-domain signal by means of the Fast Fourier Transform (FFT) to accurately calculate the frequency. Set FFT parameters, for example, set the window size to 1024 points and the overlap rate to 50% to improve the accuracy of frequency calculation. Divide the real-time monitoring data into multiple 1-minute windows according to time, perform FFT processing on the data within each window, extract the frequency components, and record the grid operating frequency corresponding to each time window, thus obtaining the grid operating frequencies at multiple time points.

[0121] Step S12: Generate grid frequency fluctuation nodes by identifying frequency fluctuations in the grid operating frequencies at the multiple time points.

[0122] Specifically, select a suitable frequency fluctuation identification algorithm, such as threshold-based detection or Dynamic Time Warping (DTW). Set a fluctuation threshold, such as a frequency change exceeding ±0.5 Hz, to identify obvious frequency fluctuations. Use the sliding window technique to perform point-by-point analysis on the frequency data obtained in Step S11 to identify frequency fluctuation nodes. Apply the selected identification algorithm to the frequency data to extract all frequency fluctuation nodes and record their occurrence times, amplitudes, and durations in detail. Store the identification results in the database, covering the characteristics of each fluctuation node, for subsequent analysis.

[0123] Step S13: Conduct a time - series distribution analysis of the power grid frequency fluctuation nodes to generate the time - series distribution characteristics of the frequency fluctuations.

[0124] Specifically, conduct a time - series analysis on the frequency fluctuation nodes extracted in Step S12, calculate the time interval, fluctuation amplitude, and frequency of each node. Use time - series analysis tools such as the Statsmodels library in Python for data analysis, and set analysis parameters, such as a time window length of 5 minutes, to statistically analyze the fluctuation characteristics in different time periods. Through calculation, obtain the distribution characteristics of the frequency fluctuations, including the fluctuation frequency, amplitude distribution, etc., and generate corresponding statistical graphs, such as histograms or box plots, to facilitate an intuitive understanding of the fluctuation characteristics. Store the analysis results in a database, including the statistical information of the fluctuation time - series characteristics.

[0125] Step S14: Based on the time - series distribution characteristics of the frequency fluctuations, perform discrete fitting of the frequency fluctuations to construct a real - time power grid frequency curve.

[0126] Specifically, construct a fitting model for the frequency fluctuations through discrete fitting methods, such as the least - squares method or Bayesian optimization. Set fitting parameters, such as requiring the fitting accuracy R² value to be greater than 0.95 to ensure a reliable fitting effect. Use the time - series distribution characteristics of the frequency fluctuations obtained in Step S13 as input data, apply the selected fitting algorithm to construct a model, and generate a real - time power grid frequency curve. Verify the fitting results, calculate the fitting residuals, and ensure that the fitting model can effectively reflect the frequency change trend. Store the generated real - time power grid frequency curve and its parameters in a database for subsequent applications. Based on the fitting results, monitor the power grid frequency changes in real - time and take timely countermeasures to ensure the stability and reliability of the power grid.

[0127] In some embodiments of the present application, Step S2 of the present application is further introduced, which may specifically include:

[0128] Step S21: Calculate the instantaneous angular acceleration of the generator based on the generator operation monitoring parameters to obtain the generator's instantaneous angular acceleration.

[0129] Specifically, install a high - precision rotational speed sensor on the shaft of the generator. It is recommended to use an optical encoder or a Hall - effect sensor. Such sensors need to have a sampling frequency of at least 1 kHz to accurately capture the rapid changes in rotational speed. While collecting rotational speed data, simultaneously record other operation monitoring parameters such as current, temperature, and load. These data will provide comprehensive background information for subsequent analysis. Use a data acquisition system, such as NIDAQ or Arduino, to upload the data to the central control platform.

[0130] Preprocess the collected rotational speed data. First, use a moving average filter to smooth the signal, setting the window size to 5 data points to effectively reduce the interference of high-frequency noise on the results. For missing values in the data, use linear interpolation to fill them to ensure that there is valid rotational speed data at each time point. If the proportion of missing data in a certain window exceeds 10%, then discard the calculation results of that window.

[0131] The instantaneous angular acceleration (α) is calculated through the rate of change of rotational speed, and the formula is , where ω(t) is the rotational speed. In actual calculation, the finite difference method in numerical differentiation is used for approximate calculation. Set the time interval to 1 second, and the calculation formula is . Read the rotational speed data by writing a Python script, calculate for each time point, and record the instantaneous angular acceleration at each time point and store it in the database.

[0132] Step S22: Perform mechanical inertia analysis based on the instantaneous angular acceleration of the generator to obtain the mechanical inertia characteristics of the generator.

[0133] Specifically, the mechanical inertia characteristics reflect the response ability of the generator to changes in angular acceleration, mainly covering the moment of inertia, damping coefficient, and dynamic characteristics of the mechanical system. These characteristics are crucial for evaluating the stability and response speed of the generator when the load changes.

[0134] Based on the instantaneous angular acceleration data obtained in step S21, use statistical analysis methods to mine the mechanical inertia characteristics. Set a series of calculation parameters, such as the mean, standard deviation, and range, etc., to describe the mechanical inertia characteristics. The formula for calculating the moment of inertia J is J = P / α, where P is the input power and α is the instantaneous angular acceleration. Make sure to use data at the same time point during calculation. Combine other parameters monitored in real time, and by writing a data processing script (such as using the Pandas library), batch calculate the moment of inertia and other related inertia characteristics of the generator, and record and store these characteristic data for subsequent in-depth analysis.

[0135] Step S23: Perform correlation analysis between the current output frequency and the rotational speed of the generator operation monitoring parameters to obtain damping characteristic data.

[0136] Specifically, select the Pearson correlation coefficient to analyze the relationship between the current output frequency and the rotational speed. Calculate the correlation between the current output frequency (such as the number of current output changes per minute) and the rotational speed of the generator, generate a correlation matrix, and use the Seaborn library in Python to visualize it as a heat map for more intuitive result analysis.

[0137] Step S24: Statistically analyze the real-time current output frequency of the operating monitoring parameters of the generator to generate the real-time current output frequency.

[0138] Specifically, a high-precision current sensor, such as a Hall sensor or a current transformer, is used to monitor the current output of the generator in real time, ensuring that the sampling frequency is 1 Hz or higher. The collected current data is statistically analyzed to generate the real-time current output frequency. The time window is set to 5 minutes, and the average value, peak value, and fluctuation amplitude of the current output are calculated within each window. A script is written in Python to analyze the current data every 5 minutes, generate a real-time current output frequency curve, and store it in the database.

[0139] Step S25: Based on the mechanical inertia characteristics of the generator and the damping characteristic data, perform frequency instant response evolution on the real-time current output frequency to generate the engine frequency instant response characteristics.

[0140] Specifically, the damping characteristic reflects the stability of the generator when the load changes, and is mainly described by the relationship between the change rate of the current output and the rotational speed. Using real-time current and rotational speed data, a dynamic system modeling method, such as a state space model or a transfer function model, is used to describe the change law of the current output frequency, and corresponding model parameters, such as the time constant and damping ratio, are set.

[0141] Based on the real-time current output frequency and damping characteristic data, a frequency response model is established to generate an engine frequency instant response characteristic curve. In the form of a state space model: dY / dt = AY + BU, where Y is the system output, U is the input signal, and A and B are the system matrices. The generated frequency response characteristics are compared with the actual data, and the goodness of fit of the model is evaluated by calculating indexes such as the root mean square error (RMSE) to ensure the reliability of the model.

[0142] In some embodiments of the present application, step S3 of the present application is further introduced, which may specifically include:

[0143] Step S31: Calculate the multi-point frequency difference of the real-time grid frequency curve based on the real-time current output frequency to obtain multi-point frequency difference data.

[0144] Specifically, a high-precision current sensor is continuously used to monitor the current output of the generator in real time, ensuring that the sampling frequency is not less than 1 Hz to accurately capture current changes. At the same time, the real-time frequency data of the grid is collected through the grid monitoring system. It is recommended to use a time series database, such as InfluxDB, to store the current output frequency and grid frequency data, and it is necessary to ensure that the timestamps of both are exactly aligned.

[0145] The frequency difference is calculated by the formula Δf(t)=fgrid(t)-fgen(t), where Δf(t) represents the frequency difference at time t, fgrid(t) is the grid frequency, and fgen(t) is the real-time current output frequency of the generator. In a data analysis platform, such as a Python environment, write a script to read the grid frequency and generator current output frequency data. Use loops or vectorized operations to calculate the frequency difference for each time point. Store the calculation results in a database, and the stored data should include timestamps and the corresponding frequency difference values for subsequent in-depth analysis.

[0146] Step S32: Conduct a differential trend time series analysis on the multi-time point frequency difference data to determine the change trend of the frequency difference.

[0147] Specifically, use time series analysis techniques to analyze the trend of the multi-time point frequency difference data. Adopt the moving average method, set the window size to 5 data points, and smooth the data to effectively identify the data trend. In a Python environment, use the Pandas library to process the frequency difference data, calculate the average frequency difference for each time window, and record the trend changes in detail. Use the Matplotlib or Seaborn library to generate trend charts to visually present the difference change trend and help identify the periodic and sudden changes in the frequency difference. Finally, store the analysis results, such as the statistical data and charts of the trend changes, in a database or file for subsequent use and report generation.

[0148] Step S33: Based on the change trend of the frequency difference, conduct a rolling prediction for the future period to generate rolling window frequency difference prediction data.

[0149] Specifically, select a suitable time series prediction model, such as ARIMA (Autoregressive Integrated Moving Average Model) or LSTM (Long Short-Term Memory Network), to conduct the frequency difference prediction for the future period. Set the model parameters, such as the order (p, d, q) of the ARIMA model, which can be reasonably selected through AIC (Akaike Information Criterion). Divide the historical frequency difference data into a training set and a test set, for example, divide it according to the ratio of 80% for training and 20% for testing to train the prediction model. In Python, use the statsmodels library or the TensorFlow framework to implement the model construction. Conduct multiple rolling predictions, that is, update the model based on the previous observations at each time point, and then predict the future frequency difference. Set the prediction step size to 1 hour and continuously update the model through loop operations. Store the data predicted by the rolling window, that is, the predicted value of the frequency difference for the next 1 hour, in the database and establish an association with the timestamp to provide data support for subsequent analysis and decision-making.

[0150] Step S34: Perform minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

[0151] Specifically, a simple threshold detection method or a more complex peak detection algorithm, such as the find_peaks function in the SciPy library, is used to identify the time point of the minimum frequency difference. A threshold is preset, for example, the minimum difference is set to 0.1 Hz to exclude the interference of minor fluctuations on the detection results. In Python, the numpy or pandas library is used to analyze the rolling prediction data to accurately identify the time point of the minimum frequency difference. The detected minimum difference time point and its corresponding frequency difference value are recorded in the database for subsequent analysis and decision-making. At the same time, the marked minimum frequency difference time point is stored in the database and a report is generated. Line charts and other charts are made to show the change of frequency difference over time, highlighting the minimum difference point. The results are presented through visualization tools such as Tableau or PowerBI, enabling decision-makers to intuitively understand the dynamic changes of frequency difference.

[0152] In some embodiments of the present application, step S4 of the present application is further introduced, which may specifically include:

[0153] Step S41: Determine the minimum time point grid frequency corresponding to the minimum frequency difference time point in the grid real-time frequency curve.

[0154] Specifically, grid frequency curve data is obtained in real time from the grid monitoring system. To ensure the timeliness and accuracy of the data, it is recommended to perform high-frequency acquisition of the grid frequency at multiple time points with a sampling frequency of at least 1 Hz. Ensure that the grid frequency data has exactly the same timestamp as the data at the minimum frequency difference time point, which is crucial for subsequent data matching and calculation.

[0155] Using the minimum frequency difference time point as an index, accurately extract the frequency value corresponding to this time point from the grid frequency data. During this process, it is necessary to ensure that the extracted data is exactly matched with the corresponding timestamp to avoid errors caused by inconsistent data. With the help of data analysis tools, query the frequency value corresponding to the minimum frequency difference time point in the grid frequency curve. The extracted grid frequency values, including timestamps and frequency values, are recorded in detail and stored in the database to build a clear data structure for subsequent frequency compensation calculation and related analysis.

[0156] Step S42: Perform frequency difference compensation calculation based on the minimum time point grid frequency to obtain frequency difference compensation parameters.

[0157] Specifically, frequency difference compensation is a key link in improving the grid connection stability of the generator, and its core lies in reducing the frequency deviation between the two. When calculating the compensation parameters, it is necessary to comprehensively consider the difference between the current frequency of the power grid and the set frequency of the generator.

[0158] Using a simple and direct calculation formula, subtract the current frequency obtained from real-time monitoring of the power grid from the target frequency of the generator (usually set to a standard frequency value, such as 50 Hz or 60 Hz) to obtain the frequency difference that needs to be compensated, that is, the frequency difference compensation parameter. This parameter will be directly applied to subsequent frequency adjustment work, which is of great significance for achieving a stable connection between the generator and the power grid.

[0159] Step S43: Based on the frequency difference compensation parameter, perform dynamic frequency adjustment on the instantaneous response characteristics of the engine frequency to generate the dynamically adjusted engine frequency.

[0160] Specifically, dynamic frequency adjustment is the core step to achieve seamless grid connection of the generator. It aims to regulate the output frequency of the generator to highly match the grid frequency, effectively avoiding system instability problems caused by frequency mismatch.

[0161] According to the compensation parameter obtained in step S42, use the control system to dynamically adjust the output frequency of the generator. In this process, the PID control strategy is adopted to monitor the frequency change in real time and perform feedback regulation. By carefully setting appropriate proportional, integral, and derivative coefficients, ensure that the adjustment process of the generator output frequency is stable and accurate. In the control system, add the compensation parameter to the target frequency to calculate the adjusted frequency value. At the same time, fully consider the response characteristics of the generator to avoid drastic fluctuations in other operating states during the adjustment process. During the frequency adjustment, compare the output frequency of the generator with the grid frequency in real time, and through the monitoring feedback mechanism, continuously optimize the adjustment strategy to ensure that the adjustment effect meets the expectations.

[0162] Step S44: Estimate the synchronous frequency error of the dynamically adjusted engine frequency, and perform precise synchronous correction on the generated synchronous frequency error parameter to generate a precise synchronous frequency parameter.

[0163] Specifically, the synchronous frequency error is a key indicator to measure the consistency between the generator frequency and the grid frequency. Timely and accurate estimation of it is an important prerequisite for ensuring the frequency synchronization between the generator and the grid.

[0164] In the real-time monitoring system, by performing a simple subtraction operation, compare the grid frequency with the adjusted generator frequency to obtain the synchronous frequency error value. Record this error value in detail and store it in the database to provide key data support for subsequent precise synchronous correction.

[0165] Precise synchronization correction is the last key process to ensure the stable grid connection of the generator. By effectively correcting the synchronization frequency error, the stability of the power grid operation is further enhanced. According to the magnitude of the synchronization frequency error, a feedback control strategy is adopted in the control system to implement the correction. Specifically, by calculating the error value, the frequency parameter to be adjusted is determined so that the generator frequency finally agrees with the grid frequency. In actual operation, the synchronization frequency error is multiplied by a preset correction coefficient to obtain the frequency value to be adjusted. This process needs to be closely combined with the response characteristics of the system for meticulous adjustment to prevent the introduction of new unstable factors. The corrected generator frequency is monitored in real time, and the correction parameters are continuously optimized through the feedback mechanism to ensure that the generator frequency always remains consistent with the grid frequency, laying a solid foundation for the stable operation of the power system.

[0166] In some embodiments of the present application, step S5 of the present application is further introduced, which may specifically include:

[0167] Step S51: Perform grid connection processing on the generator based on the precise synchronization frequency parameter and collect grid connection response data.

[0168] Specifically, before the generator is connected to the power grid, it is necessary to ensure that the output frequency of the generator is completely synchronized with the grid frequency and the precise synchronization frequency parameter has taken effect. At the same time, comprehensively check the operating state and load condition of the generator to ensure that it is within the safe operating range.

[0169] Adopt a step-by-step grid connection strategy to connect the generator to the power grid. This strategy effectively reduces the grid connection impact by gradually increasing the generator output power to the required level of the power grid. During the grid connection process, use a data acquisition system, such as a SCADA system, to monitor the voltage and frequency of the power grid and the response data after the generator is connected to the grid, including current, voltage, power output, etc., in real time at a sampling frequency of 1 Hz or higher. Store the collected data in the database and accurately mark the time stamp to lay a foundation for subsequent analysis and processing.

[0170] Step S52: Calculate the transient power fluctuation amplitude of the grid connection response data to generate transient power fluctuation data.

[0171] Specifically, before calculating the transient power fluctuation amplitude, first preprocess the collected grid connection response data. Use a moving average filter with a window size of 5 data points to remove outliers and noise in the data and smooth the power data.

[0172] By conducting time series analysis on power data, calculate the transient power fluctuation amplitude according to the formula ΔP = P(max) - P(min), where P(max) and P(min) represent the maximum power and minimum power within the selected time period respectively. Organize and record the calculated transient power fluctuation amplitude data to generate transient power fluctuation data.

[0173] Step S53: Conduct multi-power range analysis on the transient power fluctuation data and extract power fluctuation data in different ranges.

[0174] Specifically, based on the rated power and operating characteristics of the generator, reasonably divide the power range of the transient power fluctuation data. For example, divide it into low power (0 - 50 kW), medium power (50 - 100 kW), and high power (above 100 kW).

[0175] In the data framework, use the conditional screening method to screen the transient power fluctuation data according to the set power range and extract the fluctuation data within each power range. At the same time, count the characteristics such as the fluctuation amplitude, frequency, and duration within each power range, and record the power fluctuation data in different ranges in detail to provide data support for subsequent calibration calculations.

[0176] Step S54: Determine the minimum time point corresponding to the minimum frequency difference time point in the real-time grid frequency curve, and perform adaptive power calibration calculations on the power fluctuation data in different ranges to generate transient power fluctuation calibration parameters.

[0177] Specifically, clarify the minimum time point grid frequency corresponding to the minimum frequency difference time point in the real-time grid frequency curve. According to the characteristics of the power fluctuation data in different ranges, use the weighted average method or other statistical methods, combined with machine learning algorithms such as linear regression or support vector machines, train the model through historical data to predict future power fluctuation conditions, and dynamically calculate the adaptive power calibration parameters.

[0178] During the calculation process, reasonably set the weights so that the contribution of the fluctuation characteristics in each range to the final calibration parameters meets the actual requirements. Store the calculated transient power fluctuation calibration parameters in the database to ensure the good traceability of the parameters and provide a key basis for subsequent transient power suppression optimization.

[0179] Step S55: Conduct adaptive power calibration calculations on the power fluctuation data in different ranges to generate transient power fluctuation calibration parameters.

[0180] Specifically, according to the transient power fluctuation correction parameters, a targeted correction strategy is designed. By adjusting the generator output power curve, the generator outputs more smoothly during power fluctuations. Implement transient power suppression optimization in the generator control system, and adjust the generator output in real time according to the transient changes in the grid frequency and power.

[0181] With the help of a feedback mechanism, continuously monitor the matching degree between the generator output and the grid, and optimize the correction strategy in real time. Use real-time data analysis tools to evaluate the implemented transient power correction strategy, and monitor the grid stability and the generator operating status. According to the evaluation results, further adjust the correction strategy to ensure that it can effectively play its role under different operating conditions and guarantee the stable operation of the grid.

[0182] In some embodiments of the present application, step S6 of the present application is further introduced, which may specifically include:

[0183] Step S61: Analyze the current lag effect of the grid connection response data to obtain the current lag effect characteristics of the motor grid connection.

[0184] Specifically, accurately extract the current data from the grid connection response data to ensure that the data completely covers the current changes during the grid connection process. To effectively capture the transient characteristics of the current, the sampling frequency of the current data should be not less than 1 Hz. Thoroughly clean the extracted data, and use a smoothing filter, such as a moving average filter or a median filter, to remove the outliers and noise in the data, and effectively ensure the accuracy of the analysis.

[0185] The current lag effect refers to the phenomenon that when the current changes, the output current of the motor lags behind the input current in time. Its causes include electrical characteristics, control delay, and mechanical inertia and other factors. Use the cross-correlation analysis method to quantitatively analyze the current lag effect. Specifically, set multiple time delay values, for example, ranging from 0 to 5 seconds, and calculate the correlation coefficient of the current signal for each delay value. By comparing the correlation coefficients under different delay values, find the delay value with the highest correlation, and thus accurately judge the amplitude and direction of the current lag. Record the analyzed current lag effect characteristics and correlation data in the database in detail to ensure good traceability of the data and provide a reliable basis for subsequent analysis.

[0186] Step S62: Calculate the current time delay of the current lag effect characteristics of the motor grid connection to generate a current time delay parameter.

[0187] Specifically, the current delay parameter is a key quantitative indicator for measuring the hysteresis effect between the motor output current and the grid current, and is used to accurately describe the delay of the current response time. Based on the cross-correlation analysis results of step S61, the time delay value of the current lag is determined, and the delay value with the highest correlation is selected as the current delay parameter. The delay parameter is recorded and correlated with other relevant parameters such as current amplitude and frequency to lay a solid foundation for the subsequent implementation of compensation control.

[0188] Step S63: Perform dynamic feedforward compensation control on the generator based on the current time delay parameter to generate a current lag compensation strategy.

[0189] Specifically, dynamic feedforward compensation control aims to correct the current hysteresis effect and significantly improve the synchronization between the generator and the power grid. Through the feedforward control mechanism, the current change is predicted in advance, thereby effectively reducing the adverse effects of hysteresis. Dynamic feedforward compensation is implemented using control algorithms such as proportional-integral controllers. According to the current delay parameters, the generator output is precisely adjusted to compensate for the hysteresis effect. During the implementation process, the controller parameters, such as proportional gain, integral time constant, etc., are carefully set to achieve the best compensation effect. The controller needs to have the ability to respond to current changes in real time and can be flexibly adjusted according to the current delay parameters.

[0190] In the control system, the current delay parameters are input into the control algorithm, and the generator output current is adjusted according to the preset compensation strategy. For example, in the current rising stage, the generator output is increased in advance to ensure that it is synchronized with the grid current. At the same time, the matching status of the generator output current and the grid current is monitored in real time to evaluate the effectiveness of dynamic feedforward compensation. If the hysteresis effect is still found, the control parameters are readjusted in time until the ideal compensation effect is achieved.

[0191] Step S64: performing intelligent non-inductive incorporation optimization according to the current lag compensation strategy and the transient power correction strategy.

[0192] Specifically, dynamic feedforward compensation control aims to correct the current hysteresis effect and significantly improve the synchronization between the generator and the power grid. Through the feedforward control mechanism, the current change is predicted in advance, thereby effectively reducing the adverse effects of hysteresis. Dynamic feedforward compensation is implemented using control algorithms such as proportional-integral controllers. According to the current delay parameters, the generator output is precisely adjusted to compensate for the hysteresis effect. During the implementation process, the controller parameters, such as proportional gain, integral time constant, etc., are carefully set to achieve the best compensation effect. The controller needs to have the ability to respond to current changes in real time and can be flexibly adjusted according to the current delay parameters.

[0193] In the control system, the current delay parameters are input into the control algorithm, and the generator output current is adjusted according to the preset compensation strategy. For example, in the current rising stage, the generator output is increased in advance to ensure that it is synchronized with the grid current. At the same time, the matching status of the generator output current and the grid current is monitored in real time to evaluate the effectiveness of dynamic feedforward compensation. If the hysteresis effect is still found, the control parameters are readjusted in time until the ideal compensation effect is achieved.

[0194] A generator non-inductive grid-connected device provided in an embodiment of the present application is described below. The generator non-inductive grid-connected device described below and the generator non-inductive grid-connected method described above can be referenced to each other.

[0195] See also Figure 2 , Figure 2 A schematic diagram of a generator non-inductively connected to the grid device disclosed in an embodiment of the present application.

[0196] like Figure 2 As shown, the generator non-sensing grid-connected device may include:

[0197] The power grid real-time frequency module 110 is used to obtain the real-time state parameters of the power grid, and perform multi-time point calculation of the power grid operation frequency on the real-time state parameters of the power grid to construct a real-time frequency curve of the power grid;

[0198] The output frequency statistics module 120 is used to obtain generator operation monitoring parameters, and perform real-time current output frequency statistics according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics;

[0199] A minimum frequency difference module 130 is used to detect a minimum difference time point on the real-time frequency curve of the power grid based on the real-time current output frequency, and determine a minimum frequency difference time point;

[0200] A synchronous frequency adjustment module 140, configured to perform dynamic frequency adjustment and synchronous frequency error estimation according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency, and generate accurate synchronous frequency parameters;

[0201] A transient power suppression module 150 is used to perform grid connection processing on the generator based on the precise synchronous frequency parameters, collect grid connection response data and construct a transient power correction strategy through transient power suppression optimization;

[0202] The intelligent grid-connected optimization module 160 is used to perform dynamic feedforward compensation control on the generator according to the current delay parameters obtained by performing lag delay analysis on the corresponding grid-connected data, and to perform intelligent non-inductive connection optimization based on the transient power correction strategy.

[0203] It can be seen from the above technical solutions that the method, device and equipment for non-inductive grid connection of a generator provided in the embodiments of the present application have significant beneficial effects in many aspects.

[0204] In terms of frequency analysis, the present invention adopts a method of calculating the frequency of the power grid at multiple time points, which can accurately construct a real-time frequency curve of the power grid. With the help of this curve, not only can the stability of the power grid be deeply analyzed, but also the changing trend of the frequency can be clearly understood, and then accurate predictions can be made for future frequency changes, providing strong support for taking adjustment measures in advance. At the same time, the real-time statistics of the current output frequency of the generator can intuitively reflect its actual working status. By comparing this frequency with the power grid frequency, the frequency difference between the generator and the power grid can be quickly identified. Accurate current output frequency statistical data provides a key basis for evaluating the coordination between the generator and the power grid, and points out the direction for the subsequent implementation of targeted adjustments.

[0205] In terms of frequency synchronization optimization, the present invention can efficiently and accurately find the moment when the difference between the grid frequency and the generator output frequency is the smallest through the minimum difference time point detection technology, thereby smoothly realizing frequency synchronization. Marking the minimum frequency difference time point provides a clear timing node for optimization decision-making, which helps to significantly reduce frequency deviation and significantly optimize the frequency control effect of the power grid. In addition, the dynamic frequency adjustment function can track the changes in the grid frequency in real time, and accurately adjust the generator output accordingly, effectively reducing the adverse effects of frequency fluctuations on grid stability. The synchronous frequency error estimation provides accurate error data for further optimizing the frequency adjustment strategy, and effectively guarantees the stability of the grid connection process. Accurate synchronization of frequency parameters ensures that the generator and the grid achieve highly accurate frequency synchronization, greatly improves the safety during the grid connection process, and effectively prevents system instability problems caused by frequency differences.

[0206] For power control, the transient power suppression optimization technology of the present invention can significantly reduce power fluctuations during the process of generator grid connection, avoid instantaneous power shocks, thereby reducing the negative impact on the power grid and effectively improving the overall reliability of the system. The dynamic feedforward compensation control technology can compensate for the impact of grid frequency fluctuations on the generator in real time, significantly enhance the adaptability of the generator, and greatly improve the system's ability to respond to emergencies.

[0207] In terms of intelligent grid-connected control, the intelligent non-sensing optimization technology eliminates the need for frequent manual intervention when connecting generators to the grid, achieving more efficient and intelligent grid-connected control, while effectively reducing the complexity and uncertainty of system regulation. By building an intelligent grid-connected engine, it is possible to achieve seamless connection between generators and the grid, significantly improve grid-connected efficiency and safety, and minimize risks in the grid-connected process.

[0208] Optionally, the power grid real-time frequency module includes:

[0209] A multi-time point difference unit, used for performing multi-time point frequency difference calculation on the real-time frequency curve of the power grid based on the real-time current output frequency, to obtain multi-time point frequency difference data;

[0210] A change trend unit, used to perform a time series change analysis of the difference trend on the frequency difference data at multiple time points to determine the frequency difference change trend;

[0211] A rolling window unit, used to perform rolling prediction for future time periods based on the frequency difference change trend, and generate rolling window frequency difference prediction data;

[0212] The minimum difference unit is used to perform minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

[0213] The generator non-sensing grid-connected device provided in the embodiment of the present application can be applied to generator non-sensing grid-connected equipment. Figure 3 The hardware structure diagram of the generator non-sensing grid-connected equipment is shown. Figure 3 , the hardware structure of the generator non-sensing grid-connected device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0214] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;

[0215] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0216] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), etc., such as at least one disk memory;

[0217] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to:

[0218] Acquire real-time state parameters of the power grid, and perform multi-time point calculation of the power grid operation frequency on the real-time state parameters of the power grid to construct a real-time frequency curve of the power grid;

[0219] Acquire generator operation monitoring parameters, and perform real-time current output frequency statistics according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics;

[0220] Performing minimum difference time point detection on the real-time frequency curve of the power grid based on the real-time current output frequency to determine the minimum frequency difference time point;

[0221] Perform dynamic frequency adjustment and synchronous frequency error estimation according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency to generate accurate synchronous frequency parameters;

[0222] Based on the precise synchronous frequency parameters, the generator is connected to the grid, grid response data is collected, and a transient power correction strategy is constructed through transient power suppression optimization;

[0223] According to the current delay parameters obtained by performing a lag delay analysis on the corresponding grid-connected data, dynamic feedforward compensation control is performed on the generator, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy.

[0224] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0225] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0226] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0227] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for connecting a generator to the grid without induction, characterized in that: include: Acquire real-time state parameters of the power grid, and perform multi-time point calculation of the power grid operation frequency on the real-time state parameters of the power grid to construct a real-time frequency curve of the power grid; Acquire generator operation monitoring parameters, and perform real-time current output frequency statistics based on the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics, including: Calculating the instantaneous angular acceleration according to the generator operation monitoring parameters to obtain the instantaneous angular acceleration of the generator; Performing mechanical inertia analysis based on the instantaneous angular acceleration of the generator to obtain the mechanical inertia characteristics of the generator; Performing correlation analysis between current output frequency and speed on the generator operation monitoring parameters to obtain damping characteristic data; Performing real-time current output frequency statistics on the generator operation monitoring parameters to generate real-time current output frequency; Performing frequency instantaneous response evolution on the real-time current output frequency based on the mechanical inertia characteristics of the generator and the damping characteristic data to generate an engine frequency instantaneous response characteristic; Performing minimum difference time point detection on the real-time frequency curve of the power grid based on the real-time current output frequency to determine the minimum frequency difference time point; Dynamic frequency adjustment and synchronous frequency error estimation are performed according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency to generate accurate synchronous frequency parameters, including: Determine the minimum time point grid frequency corresponding to the minimum frequency difference time point in the real-time frequency curve of the grid; Perform frequency difference compensation calculation according to the power grid frequency at the minimum time point to obtain a frequency difference compensation parameter; Performing dynamic frequency adjustment on the engine frequency instant response characteristic based on the frequency difference compensation parameter to generate a dynamic adjustment frequency of the engine; Performing synchronous frequency error estimation on the engine dynamic adjustment frequency, and performing precise synchronous correction on the generated synchronous frequency error parameter to generate precise synchronous frequency parameter; Based on the precise synchronous frequency parameters, the generator is connected to the grid, grid response data is collected, and a transient power correction strategy is constructed through transient power suppression optimization; According to the current delay parameters obtained by performing a lag delay analysis on the corresponding grid-connected data, dynamic feedforward compensation control is performed on the generator, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy.

2. The method according to claim 1, characterized in that Performing multi-time point calculation of the grid operation frequency on the real-time state parameters of the grid to construct a real-time grid frequency curve includes: Performing multi-time point calculation of the grid operation frequency on the real-time state parameters of the grid to obtain the grid operation frequency at multiple time points; Generate a power grid frequency fluctuation node by identifying frequency fluctuations of the power grid operating frequencies at the multiple time points; Conduct frequency fluctuation time series distribution analysis on power grid frequency fluctuation nodes to generate frequency fluctuation time series distribution characteristics; Based on the time series distribution characteristics of the frequency fluctuations, a discrete fitting of the frequency fluctuations is performed to construct a real-time frequency curve of the power grid.

3. The method according to claim 1, characterized in that: Performing minimum difference time point detection on the real-time frequency curve of the power grid based on the real-time current output frequency to determine the minimum frequency difference time point includes: Performing multi-time-point frequency difference calculation on the real-time frequency curve of the power grid based on the real-time current output frequency to obtain multi-time-point frequency difference data; Performing time series change analysis of the difference trend on the frequency difference data at multiple time points to determine the frequency difference change trend; Perform rolling prediction for future time periods based on the frequency difference change trend to generate rolling window frequency difference prediction data; Perform a minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

4. The method according to claim 1, characterized in that The generator is connected to the grid based on the precise synchronous frequency parameters, the grid connection response data is collected, and a transient power correction strategy is constructed through transient power suppression optimization, including: Performing grid-connection processing on the generator based on the precise synchronous frequency parameters and collecting grid-connection response data; Calculating the transient power fluctuation amplitude of the grid-connected response data to generate transient power fluctuation data; Performing multi-power range analysis on the transient power fluctuation data to extract power fluctuation data in different ranges; Performing adaptive power correction calculation on the power fluctuation data in different ranges to generate transient power fluctuation correction parameters; By performing transient power suppression optimization on the transient power fluctuation correction parameters, a transient power correction strategy is constructed.

5. The method according to claim 1, characterized in that According to the current delay parameter obtained by performing a lag delay analysis on the grid-connected corresponding data, the generator is dynamically fed-forward compensated and controlled, and intelligent non-inductive connection optimization is performed based on the transient power correction strategy to construct an intelligent grid-connected engine, including: Performing current hysteresis effect analysis on the grid-connected response data to obtain current hysteresis effect characteristics of the motor grid-connected; Performing current time delay calculation on the current hysteresis effect characteristics of the motor grid connection to generate current time delay parameters; Based on the current time delay parameter, dynamic feedforward compensation control is performed on the generator to generate a current lag compensation strategy; Intelligent non-inductive incorporation optimization is performed according to the current lag compensation strategy and the transient power correction strategy.

6. A generator non-inductive grid-connected device, using the generator non-inductive grid-connected method according to any one of claims 1 to 5, characterized in that: include: A real-time power grid frequency module is used to obtain real-time power grid status parameters, and perform multi-time point calculation of power grid operation frequency on the real-time power grid status parameters to construct a real-time power grid frequency curve; An output frequency statistics module is used to obtain generator operation monitoring parameters, and perform real-time current output frequency statistics according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics; A minimum frequency difference module, used to detect the minimum difference time point of the real-time frequency curve of the power grid based on the real-time current output frequency, and determine the minimum frequency difference time point; A synchronous frequency adjustment module, used to perform dynamic frequency adjustment and synchronous frequency error estimation according to the minimum frequency difference time point and the instantaneous response characteristics of the engine frequency, and generate accurate synchronous frequency parameters; A transient power suppression module, for performing grid-connection processing on the generator based on the precise synchronous frequency parameters, collecting grid-connection response data and constructing a transient power correction strategy through transient power suppression optimization; The intelligent grid-connected optimization module is used to perform dynamic feedforward compensation control on the generator according to the current delay parameters obtained by performing lag delay analysis on the corresponding grid-connected data, and to perform intelligent non-inductive connection optimization based on the transient power correction strategy.

7. The device according to claim 6, characterized in that The power grid real-time frequency module comprises: A multi-time point difference unit, used for performing multi-time point frequency difference calculation on the real-time frequency curve of the power grid based on the real-time current output frequency, to obtain multi-time point frequency difference data; A change trend unit, used to perform a time series change analysis of the difference trend on the frequency difference data at multiple time points to determine the frequency difference change trend; A rolling window unit, used to perform rolling prediction for future time periods based on the frequency difference change trend, and generate rolling window frequency difference prediction data; The minimum difference unit is used to perform minimum difference time point detection on the rolling window frequency difference prediction data to determine the minimum frequency difference time point.

8. A generator non-inductive access grid-connected device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the method for non-sensing grid connection of a generator as described in any one of claims 1 to 5.

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