Generator non-inductive access grid-connected method, device and equipment

Abstract

The invention discloses a generator non-inductive access grid-connected method, device and equipment, and provides a reliable data basis for subsequent operation by collecting a power grid state and generator operation monitoring parameters in real time. Power grid frequency is calculated at multiple time points, and a real-time frequency curve is constructed and used for analyzing stability, predicting changes, comparing generator current output frequencies to identify frequency differences and evaluating coordination. Frequency synchronization is realized by using minimum difference time point detection, the time point is marked to optimize frequency control, and grid-connected stability and safety are guaranteed by combining dynamic frequency adjustment and synchronous frequency error estimation. In the aspect of power control, power fluctuation is reduced through transient power suppression optimization, and the adaptability of the generator is enhanced through dynamic feed-forward compensation control. In addition, manual intervention is reduced through intelligent non-inductive grid-connected optimization, an intelligent grid-connected engine is constructed, seamless butt joint of a generator and a power grid is achieved, grid-connected efficiency and safety are comprehensively improved, and system complexity and risks are reduced.

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] Performing correlation analysis between current output frequency and speed on the generator operation monitoring parameters to obtain damping characteristic data;

[0022] Performing real-time current output frequency statistics on the generator operation monitoring parameters to generate real-time current output frequency;

[0023] The instantaneous frequency response evolution of the real-time current output frequency is performed based on the mechanical inertia characteristics of the generator and the damping characteristic data to generate an instantaneous frequency response characteristic of the engine.

[0024] Optionally, 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:

[0025] 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;

[0026] 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;

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

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

[0029] Optionally, 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:

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

[0031] Perform frequency difference compensation calculation according to the power grid frequency at the minimum time point to obtain a frequency difference compensation parameter;

[0032] 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;

[0033] The synchronous frequency error of the engine dynamic adjustment frequency is estimated, and the generated synchronous frequency error parameter is accurately synchronized and corrected to generate accurate synchronous frequency parameters.

[0034] Optionally, the generator is connected to the grid based on the precise synchronous frequency parameters, grid connection response data is collected, and a transient power correction strategy is constructed 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 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.

[0062] 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.

[0063] 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.

[0064] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0066] Figure 1 A flow chart of a method for non-sensing grid connection of a generator disclosed in an embodiment of the present application;

[0067] Figure 2 A schematic diagram of a generator non-sensing grid-connected device disclosed in an embodiment of the present application;

[0068] Figure 3 This is a hardware structure block diagram of a generator non-sensing grid-connected device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The present application example provides a method, device and equipment for non-inductive connection of generators to the grid. The execution subjects of the method, device and equipment for non-inductive connection of generators to the grid include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. equipped with the system can be regarded as the general computing nodes of the present application, and 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 present application scheme is introduced. The present application proposes the following technical scheme, please see below for details.

[0072] Figure 1 The present invention is a flowchart of a method for non-inductively connecting a generator to the grid disclosed in an embodiment of the present application.

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

[0074] Step S1, obtaining real-time state parameters of the power grid, and performing 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.

[0075] Specifically, first of all, voltage and current sensors with high sampling accuracy need to be accurately deployed in the power grid infrastructure. Among them, current transformers (CT) and voltage transformers (VT) are commonly used devices, and their sampling frequency must be as low as 1kHz to ensure that dynamic changes in power grid operation can be accurately captured. At the same time, a variety of monitoring sensors, including speed sensors, power sensors, and temperature sensors, need to be installed on the generator side to monitor the operating 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 generator are collected on the same platform and uploaded to the central database in a timely manner for storage and in-depth analysis.

[0076] Next, the real-time state parameters of the power grid are quickly obtained from the established data acquisition system. These parameters cover key indicators such as voltage (V), current (I), power (P), and frequency (f). 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 calculations and comprehensive analysis. Simultaneously, the operating parameters of the generator, such as speed, power generation, load conditions, and temperature, are obtained from the generator monitoring system, and the timestamp corresponding to each parameter is also recorded in detail to ensure that the data of the generator and the power grid can be accurately matched in the time dimension.

[0077] Then, based on the voltage and current data collected in real time, the sliding window method is used to calculate the operating frequency of the power grid. Specifically, a time window of 5 minutes is set. Within this window, the voltage and current data are averaged first, and then the frequency value is calculated based on this. After that, according to the set window size, the time window is gradually moved, the frequency value of each time period is calculated in turn, and the results are stored in the database in order.

[0078] Finally, with the help of professional data analysis and visualization tools such as Matplotlib or Plotly, the frequency data calculated at multiple time points are visualized with the timestamp as the horizontal axis and the frequency as the vertical axis, thereby generating 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 senseless access to the grid for generators and optimize real-time control strategies, but also can timely identify potential frequency fluctuation problems through regular analysis of the frequency curve, providing effective early warning to ensure the stable operation of the power grid.

[0079] Step S2: 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.

[0080] Specifically, a series of necessary monitoring equipment needs to be installed at the generator end, among which 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 to at least 1kHz to accurately capture the transient changes and dynamic characteristics of the current. At the same time, speed sensors and power sensors are also required. Using data acquisition systems 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 must have stable real-time monitoring and efficient data storage capabilities to lay the foundation for subsequent analysis.

[0081] During the data collection process, the output current of the generator is monitored in real time through the current sensor. When recording current data, it is important to mark each data point with an accurate timestamp to ensure the timing of the data. In addition to the current amplitude (A), the real-time monitoring parameters also include the power factor and the speed (RPM) of the generator. These parameters are the key basis for calculating the current output frequency.

[0082] Since the output current frequency of the generator is closely related to the speed, the commonly used 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 speed data in real time and combining it with the known number of pole pairs, the real-time current 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 calculation operations every 1 second. In the data analysis system, a special algorithm needs to be written to first obtain the current speed, then derive the current output frequency based on the formula, and record the calculation results in the database to ensure that the timestamp of the data is consistent with other monitoring parameters, so as to facilitate subsequent in-depth analysis and visualization.

[0083] Finally, professional data visualization tools such as Matplotlib, Grafana or Tableau are used to present the real-time current output frequency in the form of a chart, with the horizontal axis set as time and the vertical axis as the real-time current output frequency. These charts can clearly and intuitively display the dynamic changes of the generator frequency, making it easier for staff to observe and analyze frequency fluctuations and their development trends.

[0084] Step S3: Detect the minimum difference time point of 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.

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

[0086] After the data integration is completed, the data needs to be cleaned. 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 is the absolute difference between the grid frequency and the generator output frequency. By traversing the integrated data, the frequency difference at each time point is calculated one by one, and the results are stored in a new column. To improve the calculation efficiency, loop operations or vectorized operations can be used.

[0088] After the frequency difference calculation is completed, the minimum value in the difference column is found with the help of data analysis tools. Using Pandas built-in functions such as min() and idxmin(), the minimum difference value and its corresponding timestamp can be quickly located. The specific information of the time point is recorded in detail, including the minimum difference value and the corresponding grid frequency and generator frequency.

[0089] Add a marker column to the data frame to mark the minimum difference time point. Use Boolean indexing to set the marker to True at the minimum difference index and False at other positions to clearly identify the minimum difference time point.

[0090] Finally, the data frame with the minimum difference time point mark is stored in the database for subsequent in-depth analysis and processing. The stored data must fully include the timestamp, grid frequency, generator frequency and their difference values. At the same time, the detailed information of the minimum difference time point, including timestamp, frequency value and difference value, is fully recorded to provide sufficient data support for subsequent real-time monitoring and analysis.

[0091] Step S4: 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.

[0092] Specifically, first, the target frequency to be adjusted is determined based on the calculation result of the minimum frequency difference. Usually, in order to ensure that the generator can be smoothly 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 50.02Hz.

[0093] Subsequently, a dynamic control algorithm, such as a PID controller, is used to perform the frequency adjustment. Before applying the PID controller, its parameters, namely the proportional, integral and differential gains, need to be accurately set to achieve real-time and precise control 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. Assuming that the current generator frequency is 50.00Hz and the target frequency is 50.02Hz, the PID controller will output a corresponding control signal to instruct the generator to increase the frequency by 0.02Hz.

[0094] During the dynamic frequency adjustment process, the synchronous frequency error is calculated in real time. The synchronous frequency error is defined as the difference between the grid frequency and the generator frequency. With the help of the data acquisition system, the frequency changes of the generator are continuously monitored in real time, and the synchronous frequency error is continuously 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 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 and the grid frequency are always highly consistent. The precise synchronous frequency parameter is generated based on the synchronous frequency error calculated in real time. In this process, a threshold is set in advance, such as 1Hz. When the synchronous frequency error is less than the threshold, it is determined that the generator and the grid have reached a precise synchronization state.

[0096] After the precise synchronization frequency parameters are generated, the relevant information is recorded in the database, including key data such as timestamp, current grid frequency, generator frequency, and synchronization frequency error. At the same time, an effective feedback mechanism is established to continuously optimize the control parameters through in-depth analysis of real-time monitoring results. Specifically, the parameters of the PID controller are dynamically adjusted to significantly improve the response speed and control accuracy of the system. In addition, the actual effect of the frequency adjustment is regularly evaluated, 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 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.

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

[0099] In the generator control system, the precise synchronous frequency parameters are accurately input so that the control system can dynamically adjust the generator output frequency according to the parameters. At the same time, the data acquisition system is carefully configured to monitor the connection status and operating parameters of the generator and the power grid in real time.

[0100] The grid connection process adopts a gradual grid connection strategy. In the initial stage, the output power of the generator is slowly increased to the level required by the grid to avoid transient shocks. The initial power is set to 10% of the rated power, and then gradually increased to 50%, and finally reached 100%. In each step of the grid connection, the voltage, frequency and power of the grid are closely monitored in real time to ensure that the generator output is always synchronized with the grid. Once the frequency or voltage is abnormal, the generator output power is immediately reduced to ensure safe grid connection.

[0101] During the grid connection period, the transient power fluctuation is monitored. With the help of the collected current and power data, the transient power changes are deeply analyzed to accurately identify the fluctuation amplitude and frequency. The transient power threshold is pre-set, and when the fluctuation exceeds 5% of the rated power, the transient power suppression strategy is immediately activated.

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

[0103] The transient power correction strategy is integrated into the control system, and the generator output is dynamically regulated with the help of the power data fed back by the real-time monitoring system. If the generator output power fluctuation 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] The transient power correction strategy is organically combined with the dynamic feedforward compensation control to achieve more accurate power output control. To this end, it is necessary to set a monitoring threshold for transient power fluctuations, such as setting it to 2% of the rated power. When the power fluctuation is detected to exceed this threshold, the system will quickly start the transient power correction strategy. Through the control system, the feedback results of the transient power correction strategy are closely linked to 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 based on dynamic feedforward control.

[0110] Further integrate dynamic feedforward compensation control and transient power correction strategy to build an intelligent grid-connected engine. The engine has the ability to monitor the operating status of the power grid and generators in real time, and can dynamically adjust the generator output based on real-time data, ultimately achieving non-sensing connection. During the construction process, the engine's workflow is clarified, 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, and optimize the model's ability to predict future frequency changes through in-depth training of historical data, thereby improving the system's response to power grid frequency changes.

[0111] Implement real-time monitoring, and use the data acquisition system to feed back key information such as 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-connected engine and display the change trend of grid frequency, generator frequency, and power output in real time. In the display chart, key data such as time, frequency value, and power output are clearly presented to facilitate intuitive observation of system performance. In addition, the operation effect of the intelligent grid-connected engine is regularly evaluated, 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 strong guarantees for the reliable operation of the power system.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

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

[0118] Step S11, 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.

[0119] Specifically, high-precision sensors are installed at key nodes of the power grid, such as substations, distribution rooms, and generator sets. These sensors must be able to monitor parameters such as voltage, current, power, and frequency in real time. It is recommended that their sampling frequency be set to 1Hz or higher to ensure the real-time nature of the data. For generators, additional vibration sensors, temperature sensors, and speed sensors are configured to monitor their operating status in real time. Data transmission uses industrial communication protocols such as Modbus, DNP3, or CAN to ensure the stability and real-time nature of data transmission. Data is first preprocessed by edge computing devices and then uploaded to the central database. A special data storage strategy is set to ensure that real-time data is accurately stored according to timestamps to facilitate subsequent analysis. Time series databases such as InfluxDB can be used for efficient storage. Regularly verify 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 in the missing values ​​to ensure data continuity.

[0120] According to the voltage and current data of the power grid, the formula f=1 / T (where T is the period) is used to calculate the power grid operating frequency. The time domain signal is converted into a frequency domain signal with the help of Fast Fourier Transform (FFT), and the frequency is then accurately calculated. Set the FFT parameters, such as setting 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 windows of 1 minute in time, perform FFT processing on the data in each window, extract the frequency components, and record the power grid operating frequency corresponding to each time window, so as to obtain the power grid operating frequency at multiple time points.

[0121] Step S12: generating a power grid frequency fluctuation node by identifying frequency fluctuations of the power grid operating frequencies at the multiple time points.

[0122] Specifically, a suitable frequency fluctuation identification algorithm is selected, such as a threshold-based detection method or dynamic time warping (DTW). A fluctuation threshold is set, such as a frequency change exceeding ±0.5 Hz, to identify obvious frequency fluctuations. The frequency data obtained in step S11 is analyzed point by point using a sliding window technique to identify frequency fluctuation nodes. The selected identification algorithm is applied to the frequency data to extract all frequency fluctuation nodes, and their occurrence time, amplitude, and duration are recorded in detail. The identification results are stored in a database, covering the characteristics of each fluctuation node, in preparation for subsequent analysis.

[0123] Step S13: Perform frequency fluctuation time series distribution analysis on the power grid frequency fluctuation nodes to generate frequency fluctuation time series distribution characteristics.

[0124] Specifically, a time series analysis is performed on the frequency fluctuation nodes extracted in step S12 to calculate the time interval, fluctuation amplitude and frequency of each node. Use Python's Statsmodels library and other time series analysis tools for data analysis, and set analysis parameters, such as a time window length of 5 minutes, to count the fluctuation characteristics of different time periods. Through calculation, the distribution characteristics of frequency fluctuations are obtained, including fluctuation frequency, amplitude distribution, etc., and corresponding statistical graphs are generated, such as histograms or box plots, to help intuitively understand the fluctuation characteristics. The analysis results are stored in a database, including statistical information on the fluctuation time series characteristics.

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

[0126] Specifically, a fitting model of frequency fluctuation is constructed through discrete fitting methods, such as 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 that the fitting effect is reliable. Use the frequency fluctuation time series distribution characteristics obtained in step S13 as input data, apply the selected fitting algorithm to build a model, and generate a real-time frequency curve of the power grid. 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 frequency curve of the power grid and its parameters in a database for subsequent application. Based on the fitting results, monitor the frequency changes of the power grid in real time, take timely countermeasures, and 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, calculating the instantaneous angular acceleration according to the generator operation monitoring parameters to obtain the instantaneous angular acceleration of the generator.

[0129] Specifically, a high-precision speed sensor is installed on the shaft of the generator. It is recommended to use a photoelectric encoder or Hall effect sensor. Such sensors must have a sampling frequency of at least 1kHz to accurately capture rapid changes in speed. While collecting speed data, other operating monitoring parameters such as current, temperature, and load are also recorded simultaneously. 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] The collected speed data is preprocessed. First, the moving average filter is used to smooth the signal. The window size is set to 5 data points to effectively reduce the interference of high-frequency noise on the results. For missing values ​​in the data, linear interpolation is used to fill them to ensure that there is valid speed data at each time point. If the proportion of missing data in a window exceeds 10%, the calculation results of the window are discarded.

[0131] The instantaneous angular acceleration (α) is calculated by the rate of change of the rotational speed using the formula: , where ω(t) is the speed. In actual calculation, the finite difference method in the numerical differentiation method is used for approximate calculation. Set the time interval to 1 second, and the calculation formula is The speed data is read by writing a Python script, and calculations are performed for each time point. The instantaneous angular acceleration at each time point is recorded and stored 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 generator's ability to respond to changes in angular acceleration, mainly including the moment of inertia, damping coefficient, and dynamic characteristics of the mechanical system. These characteristics are crucial for evaluating the stability and reaction speed of the generator when the load changes.

[0134] Based on the instantaneous angular acceleration data obtained in step S21, the mechanical inertia characteristics are mined using statistical analysis methods. A series of calculation parameters, such as mean, standard deviation, and range, are set 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. Be sure to use data at the same time point when calculating. Combined with other parameters monitored in real time, the moment of inertia and other related inertia characteristics of the generator are calculated in batches by writing data processing scripts (such as using the Pandas library), and these characteristic data are recorded and stored for subsequent in-depth analysis.

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

[0136] Specifically, the Pearson correlation coefficient is selected to analyze the relationship between the current output frequency and the rotation speed. The correlation between the current output frequency (such as the number of current output changes per minute) and the generator rotation speed is calculated, and a correlation matrix is ​​generated. The Seaborn library of Python is used to visualize it as a heat map to analyze the results more intuitively.

[0137] Step S24: Perform real-time current output frequency statistics on the generator operation monitoring parameters to generate a 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 a real-time current output frequency. The time window is set to 5 minutes, and the average, peak, and fluctuation amplitude of the current output are calculated in 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 a database.

[0139] Step S25: 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 engine frequency instantaneous response characteristics.

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

[0141] Based on the real-time current output frequency and damping characteristic data, a frequency response model is established to generate the engine frequency instant response characteristic curve. The state space model is adopted: dY / dt=AY+BU, where Y is the system output, U is the input signal, and A and B are system matrices. The generated frequency response characteristics are compared with the actual data, and the model fit is evaluated by calculating indicators 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, 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.

[0144] Specifically, high-precision current sensors are continuously used to monitor the current output of the generator in real time, ensuring that the sampling frequency is not less than 1Hz, so as to accurately capture current changes. At the same time, the real-time frequency data of the power grid is collected with the help of the power grid monitoring system. It is recommended to use a time series database such as InfluxDB to store the current output frequency and power grid frequency data, and it is important to ensure that the timestamps of the two are completely 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. Write a script in a data analysis platform, such as Python, to read the grid frequency and generator current output frequency data. Use loops or vector operations to perform frequency difference calculations for each time point. Store the calculation results in a database. The stored data should include timestamps and corresponding frequency difference values ​​for subsequent in-depth analysis.

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

[0147] Specifically, time series analysis technology is used to analyze the trend of frequency difference data at multiple time points. The moving average method is used to set the window size to 5 data points to smooth the data, so as to effectively identify the data trend. In the Python environment, the frequency difference data is processed with the help of the Pandas library, the average frequency difference of each time window is calculated, and the trend changes are recorded in detail. The trend chart is generated using the Matplotlib or Seaborn library to visualize the difference change trend and help identify the periodic and sudden changes in frequency differences. Finally, the analysis results, such as statistical data and charts of trend changes, are stored in a database or file for subsequent use and report generation.

[0148] Step S33: Perform rolling prediction for future time periods based on the frequency difference change trend to generate rolling window frequency difference prediction data.

[0149] Specifically, select an appropriate time series forecasting model, such as ARIMA (autoregressive integrated moving average model) or LSTM (long short-term memory network), to carry out frequency difference forecasting for future periods. Set 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 training sets and test sets, for example, 80% for training and 20% for testing, to train the forecasting model. In Python, use the statsmodels library or TensorFlow framework to implement model building. Perform multiple rolling forecasts, that is, update the model based on previous observations at each time point to predict future frequency differences. Set the forecast 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 in the next 1 hour, in the database and associate it 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 set in advance, for example, the minimum difference is set to 0.1Hz, so as to eliminate the interference of small fluctuations on the detection results. In Python, the rolling forecast data is analyzed using the numpy or pandas library 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. Make charts such as line graphs to show the changes in frequency differences over time, focusing on the minimum difference point. Display the results through visualization tools such as Tableau or PowerBI, so that decision makers can intuitively understand the dynamic changes in frequency differences.

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

[0153] Step S41, determining the minimum time point grid frequency corresponding to the minimum frequency difference time point in the real-time frequency curve of the grid.

[0154] Specifically, the 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 collect the grid frequency at a high frequency at multiple time points with a sampling frequency of at least 1Hz. It is essential to ensure that the grid frequency data and the data at the time point of the minimum frequency difference have completely consistent timestamps for subsequent data matching and calculation.

[0155] Using the minimum frequency difference time point as the index, the frequency value corresponding to the time point is accurately extracted from the power grid frequency data. In this process, it is important to ensure that the extracted data accurately matches 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 power grid frequency curve. Record the extracted power grid frequency value in detail, including the timestamp and frequency value, and store it in the database to build a clear data structure for subsequent frequency compensation calculations and related analysis.

[0156] Step S42: performing frequency difference compensation calculation according to the power grid frequency at the minimum time point to obtain a frequency difference compensation parameter.

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

[0158] A simple and direct calculation formula is used to 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 50Hz or 60Hz), thereby obtaining 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 to achieving a stable connection between the generator and the power grid.

[0159] Step S43: dynamically adjust the engine frequency instant response characteristic based on the frequency difference compensation parameter to generate a dynamic adjustment frequency of the engine.

[0160] Specifically, dynamic frequency adjustment is the core step to achieve seamless grid connection of generators. It aims to adjust the output frequency of the generator to make it highly consistent with the grid frequency, thereby effectively avoiding system instability problems caused by frequency mismatch.

[0161] According to the compensation parameters obtained in step S42, the output frequency of the generator is dynamically adjusted with the help of the control system. In this process, the PID control strategy is adopted to monitor the frequency changes in real time and perform feedback adjustment. By carefully setting appropriate proportional, integral and differential coefficients, the generator output frequency adjustment process is ensured to be smooth and accurate. In the control system, the compensation parameters are added to the target frequency to calculate the adjusted frequency value. At the same time, the response characteristics of the generator are fully considered to avoid drastic fluctuations in other operating states caused by the adjustment process. During the frequency adjustment period, the generator output frequency is compared with the grid frequency in real time, and the adjustment strategy is continuously optimized through the monitoring feedback mechanism to ensure that the adjustment effect meets expectations.

[0162] Step S44: estimating the synchronous frequency error of the engine dynamic adjustment frequency, and performing 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 the synchronous frequency error is an important prerequisite for ensuring the synchronization between the generator and the grid frequency.

[0164] In the real-time monitoring system, the grid frequency is compared with the adjusted generator frequency through a simple subtraction operation to obtain the synchronous frequency error value. The error value is recorded in detail and stored in the database to provide key data support for subsequent precise synchronization correction.

[0165] Accurate synchronization correction is the last key process to ensure the stable grid connection of the generator. By effectively correcting the synchronous frequency error, the stability of the power grid operation is further enhanced. According to the size of the synchronous frequency error, the feedback control strategy is used in the control system to implement the correction. Specifically, by calculating the error value, the frequency parameters that need to be adjusted are determined so that the generator frequency and the grid frequency are finally consistent. In actual operation, the synchronous frequency error is multiplied by the preset correction coefficient to obtain the frequency value that needs to be adjusted. This process needs to be closely combined with the response characteristics of the system, and meticulous adjustments must be made to prevent the introduction of new unstable factors. Real-time monitoring of the corrected generator frequency, and continuous optimization of the correction parameters through the feedback mechanism to ensure that the generator frequency is always 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: grid-connecting the generator based on the precise synchronous frequency parameters, and collecting grid-connection response data.

[0168] Specifically, before the generator is connected to the grid, it is important to ensure that the generator output frequency is fully synchronized with the grid frequency and that the precise synchronization frequency parameters are in effect. At the same time, the operating status and load conditions of the generator should be fully checked to ensure that it is within the safe operating range.

[0169] The generator is connected to the grid using a gradual grid connection strategy. This strategy effectively reduces the impact of grid connection by gradually increasing the generator output power to the grid requirement level. During the grid connection process, a data acquisition system, such as a SCADA system, is used to monitor the voltage and frequency of the grid and the response data of the generator after grid connection, including current, voltage, power output, etc., in real time at a sampling frequency of 1Hz or higher. The collected data is stored in a database and accurately marked with a timestamp, laying the foundation for subsequent analysis and processing.

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

[0171] Specifically, before calculating the transient power fluctuation amplitude, the collected grid-connected response data is preprocessed. A moving average filter is used 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, the transient power fluctuation amplitude is calculated according to the formula ΔP=P(max)-P(min), where P(max) and P(min) represent the maximum power and minimum power in the selected time period, respectively. The calculated transient power fluctuation amplitude data is collated and recorded to generate transient power fluctuation data.

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

[0174] Specifically, according to the rated power and operating characteristics of the generator, the power range of the transient power fluctuation data is reasonably divided, for example, into low power (0-50kW), medium power (50-100kW), and high power (above 100kW).

[0175] In the data framework, the conditional screening method is used to screen the transient power fluctuation data according to the set power range, and the fluctuation data in each power range is extracted. At the same time, the characteristics such as the fluctuation amplitude, frequency and duration in each power range are counted, and the power fluctuation data in different ranges are recorded in detail to provide data support for subsequent correction calculations.

[0176] Step S54, determining the minimum time point grid frequency corresponding to the minimum frequency difference time point in the real-time frequency curve of the power grid, performing adaptive power correction calculation on the power fluctuation data in different ranges, and generating transient power fluctuation correction parameters.

[0177] Specifically, the minimum frequency difference time point corresponds to the minimum grid frequency at the real-time grid frequency curve. According to the power fluctuation data characteristics in different ranges, the weighted average method or other statistical methods are used, combined with machine learning algorithms such as linear regression or support vector machines, and the model is trained through historical data to predict future power fluctuations and dynamically calculate adaptive power correction parameters.

[0178] During the calculation process, the weights are set reasonably so that the contribution of the fluctuation characteristics in each range to the final correction parameters meets the actual needs. The calculated transient power fluctuation correction parameters are stored in the database to ensure that the parameters have good traceability, providing a key basis for subsequent transient power suppression optimization.

[0179] Step S55: performing adaptive power correction calculation on the power fluctuation data in different ranges to generate transient power fluctuation correction parameters.

[0180] Specifically, according to the transient power fluctuation correction parameters, a targeted correction strategy is designed to adjust the generator output power curve to make the generator output more stable when the power fluctuates. Transient power suppression optimization is implemented in the generator control system to adjust the generator output in real time according to the transient changes of grid frequency and power.

[0181] With the help of feedback mechanism, the matching degree between generator output and power grid is continuously monitored, and the correction strategy is optimized in real time. Real-time data analysis tools are used to evaluate the transient power correction strategy after implementation, monitor the stability of power grid and the operating status of generators. Based on the evaluation results, the correction strategy is further adjusted to ensure that it can play an effective role under different operating conditions and ensure the stable operation of the power 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, performing current hysteresis effect analysis on the grid-connected response data to obtain current hysteresis effect characteristics of the motor grid-connected.

[0184] Specifically, current data is accurately extracted from the grid-connected response data to ensure that the data fully covers the current changes during the grid-connected process. In order to effectively capture the transient characteristics of the current, the sampling frequency of the current data should be no less than 1Hz. The extracted data is fully cleaned and smoothing filters, such as moving average filtering or median filtering, are used to remove outliers and noise in the data to ensure the accuracy of the analysis.

[0185] The current hysteresis effect refers to the phenomenon that when the current changes, there is a time delay in the motor output current compared to the input current. The causes include electrical characteristics, control delays, and mechanical inertia. The cross-correlation analysis method (Cross-Correlation) is used to quantify the current hysteresis effect. Specifically, multiple time delay values ​​are set, ranging from 0 to 5 seconds, for example, and the correlation coefficient of the current signal is calculated for each delay value. By comparing the correlation coefficients under different delay values, the delay value with the highest correlation is found to accurately determine the magnitude and direction of the current hysteresis. The current hysteresis effect characteristics and correlation data obtained by the analysis are recorded in detail in the database to ensure that the data has good traceability and provide a reliable basis for subsequent analysis.

[0186] Step S62: performing current time delay calculation on the current hysteresis effect characteristics of the motor grid-connected to generate current delay parameters.

[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 sentence "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 may 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 will conform 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 according to the generator operation monitoring parameters to generate real-time current output frequency and engine frequency instant response characteristics; 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; 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; 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 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; The instantaneous frequency response evolution of the real-time current output frequency is performed based on the mechanical inertia characteristics of the generator and the damping characteristic data to generate an instantaneous frequency response characteristic of the engine.

4. 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.

5. The method according to claim 1, characterized in that 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; The synchronous frequency error of the engine dynamic adjustment frequency is estimated, and the generated synchronous frequency error parameter is accurately synchronized and corrected to generate accurate synchronous frequency parameters.

6. 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.

7. 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.

8. A generator non-inductive access grid-connected device, characterized in that: include: A power grid real-time frequency module 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; 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.

9. The device according to claim 8, 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.

10. 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 7.

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