Generator non-inductive access grid-connected method and system

Abstract

The invention discloses a generator non-inductive access grid connection method and system, and relates to the technical field of intelligent non-inductive grid connection, and the method comprises the steps: comparing a membrane potential value with an activation threshold value, carrying out the multi-dimensional state evolution through a quantum tunneling probability regulation and control mechanism, and generating a grid connection triggering instruction signal; carrying out mechanical vibration waveform coding on the grid-connected trigger instruction signal by adopting a stress wave carrier mechanism, and transmitting the coded signal to a grid-connected circuit breaker through a low-voltage non-inductive conversion box to obtain an instruction confirmation signal and an execution time delay parameter; generating a frequency synchronous compensation parameter and a transient suppression strategy through a magnetic flux vortex adaptive optimization algorithm based on the instruction confirmation signal and the execution time delay parameter in combination with the frequency-phase collaborative deviation value of the power grid frequency and the generator frequency; according to the invention, a biological neuron pulse control model is combined with a quantum tunneling probability regulation and control mechanism, so that efficient conversion from a mechanical vibration signal to an intelligent control instruction is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent seamless grid connection technology, and in particular to a method and system for seamless grid connection of a generator. Background Technology

[0002] In the technological development of generator grid-connected control, control strategies have evolved from solely relying on electrical parameters to comprehensively considering the characteristics of the electromechanical system. Traditional methods mainly rely on electrical signal feedback to achieve frequency tracking and power regulation, while current technologies are beginning to explore the introduction of mechanical vibration monitoring into the grid-connected control process. By extracting the dynamic characteristics of the shaft system through spectrum analysis and establishing a correlation between mechanical vibration signals and electrical control parameters, preliminary coordinated control of the electromechanical system has been achieved, providing a new technical path for reducing grid connection impact.

[0003] Existing technologies have room for optimization in dynamic coordinated control involving multi-physics coupling; the mapping relationship between generator shaft strain signals and electrical control parameters relies on empirical thresholds, making it difficult to adapt to the nonlinear dynamic characteristics of generators operating under varying conditions. Especially in scenarios with a high proportion of renewable energy integration, mechanical vibration harmonics and grid frequency disturbances can generate coupled oscillations, resulting in millisecond-level delays in grid connection commands and affecting frequency synchronization accuracy. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for sensorless grid connection of generators to solve the problem of insufficient dynamic coordinated control accuracy of mechanical-electrical systems under varying operating conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for sensorless grid connection of a generator, which includes: real-time acquisition and preprocessing of generator shaft strain data, calculation of membrane potential through a biological neuron pulse control model, and output of membrane potential value. By comparing the membrane potential value with the activation threshold, a multi-dimensional state evolution is performed through a quantum tunneling probability control mechanism to generate a grid-connected trigger command signal. The stress wave carrier mechanism is used to encode the grid connection trigger command signal with mechanical vibration waveform, and then transmits it to the grid connection circuit breaker through the low-voltage non-inductive conversion box to obtain the command confirmation signal and execution delay parameters. Based on the command confirmation signal and execution delay parameters, and combined with the frequency phase coordination deviation between the grid frequency and the generator frequency, frequency synchronization compensation parameters and transient suppression strategies are generated through the magnetic flux vortex adaptive optimization algorithm. Based on frequency synchronization compensation parameters and transient suppression strategies, the generator is dynamically and collaboratively adjusted to output a smooth and stable grid connection scheme.

[0007] As a preferred embodiment of the generator sensorless grid connection method of the present invention, the generator shaft strain data includes dynamic torque fluctuation data, axial bending stress data, radial shear strain data and torsional vibration harmonic data; The preprocessing includes wavelet denoising, signal transformation, normalization, outlier removal, and feature extraction. As a preferred embodiment of the generator sensorless grid connection method of the present invention, the specific steps for calculating the membrane potential and outputting the membrane potential value through a biological neuron impulse control model are as follows. The membrane potential integral differential equation, the ion channel dynamic characteristic equation, and the pulse firing mechanism are integrated and combined into the Hodgkin-Huxley dynamics framework to obtain the basic computational architecture. The pre-processed generator shaft strain data is input into the basic computing architecture and trained using the backpropagation algorithm to obtain the biological neuron pulse control model. The preprocessed generator shaft strain data is input into the biological neuron pulse control model for dynamic allocation of synaptic connection strength and time encoding conversion. The weighted strain input signal is output, and dynamic integral calculation of membrane potential and leakage current compensation are performed to generate membrane potential value.

[0008] As a preferred embodiment of the generator sensorless grid connection method of the present invention, the steps of comparing the membrane potential value with the activation threshold and generating a grid connection trigger command signal through multi-dimensional state evolution via a quantum tunneling probability control mechanism are as follows: Peak detection and probability density analysis of historical membrane potential values ​​were performed to obtain the activation threshold; The membrane potential value is compared with the activation threshold in real time to generate an over-limit flag signal, and the amplitude-time mapping is performed through a voltage-pulse converter to generate bias voltage parameters. By using a quantum tunneling probability control mechanism, a multi-dimensional probability simulation of the carrier tunneling trajectory is performed on the bias voltage parameters to generate tunneling probability distribution data. Then, pulse position modulation and command encoding conversion are performed to obtain the grid-connected trigger command signal.

[0009] As a preferred embodiment of the generator sensorless grid connection method described in this invention, the following steps are employed: A stress wave carrier mechanism is used to encode the grid connection trigger command signal using mechanical vibration waveform encoding, and this encoded signal is propagated to the grid connection circuit breaker via a low-voltage sensorless conversion box to obtain the command confirmation signal and execution delay parameters. Based on the grid-connected trigger command signal, frequency shift keying modulation is performed through the stress wave carrier mechanism to obtain the coded stress wave signal; The piezoelectric transducer array of the low-voltage inductive converter box is used to perform carrier stripping and time-domain correlation demodulation on the coded stress wave signal to generate a standardized command signal. The transmission delay of the standardized command signal is calculated and the command integrity is verified to obtain the command confirmation signal and execution delay parameters.

[0010] As a preferred embodiment of the generator sensorless grid connection method of the present invention, the specific steps for generating frequency synchronization compensation parameters and transient suppression strategies are as follows: Nanosecond-level clock calibration is performed on the command confirmation signal and execution delay parameters to obtain the control command; Real-time acquisition of power grid frequency and generator frequency and high-precision deviation analysis are performed to obtain the frequency-phase coordination deviation. By using the magnetic flux vortex adaptive optimization algorithm, the dual constraint problem of frequency tracking and transient suppression is solved for the control command and frequency phase coordination deviation, and frequency synchronization compensation parameters and transient suppression strategy are generated.

[0011] As a preferred embodiment of the generator seamless grid connection method of the present invention, the specific steps for dynamically and collaboratively adjusting the generator based on frequency synchronization compensation parameters and transient suppression strategies to output a smooth and stable grid connection scheme are as follows. Based on frequency synchronization compensation parameters and transient suppression strategies, a cooperative control instruction set is obtained by performing multi-objective rolling time-domain optimization through a dynamic matrix predictive control architecture. Through a multi-physics field collaborative optimization mechanism, the electromagnetic field adjustment, mechanical dynamics matching and energy flow balance of the collaborative control command set are calculated collaboratively to generate a smooth and stable grid connection scheme.

[0012] Secondly, the present invention provides a generator-to-grid connection system without sensor input, comprising, The acquisition module is used to acquire generator shaft strain data in real time and preprocess it, calculate membrane potential through a biological neuron pulse control model, and output membrane potential value. The biomimetic module is used to compare the membrane potential value with the activation threshold, and generate a grid-connected trigger command signal through a quantum tunneling probability control mechanism to perform multi-dimensional state evolution. The communication module is used to encode the grid-connection trigger command signal into a mechanical vibration waveform using a stress wave carrier mechanism, and then transmit it to the grid-connected circuit breaker through a low-voltage non-inductive conversion box to obtain the command confirmation signal and execution delay parameters. The optimization module is used to generate frequency synchronization compensation parameters and transient suppression strategies based on the instruction confirmation signal and execution delay parameters, combined with the frequency phase coordination deviation between the grid frequency and the generator frequency, through the magnetic flux vortex adaptive optimization algorithm. The output module is used to dynamically and collaboratively adjust the generator based on frequency synchronization compensation parameters and transient suppression strategies, and output a smooth and stable grid connection scheme.

[0013] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the generator sensorless grid connection method as described in the first aspect of the present invention.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the generator sensorless grid connection method as described in the first aspect of the present invention.

[0015] The beneficial effects of this invention are as follows: By combining a biological neuron pulse control model with a quantum tunneling probability modulation mechanism, efficient conversion of mechanical vibration signals into intelligent control commands is achieved. Utilizing biomimetic neural network processing technology, generator shaft strain data is transformed into membrane potential signals with time-series characteristics, effectively capturing the dynamic characteristics of the mechanical system. Through a quantum modulation mechanism, electrical signals are converted into highly sensitive grid-connection commands, and lossless transmission is achieved using stress waves. This overcomes the limitations of generator electrical signal processing, constructing a seamless connection from mechanical state perception to electrical control execution, realizing smooth and stable grid connection of the generator under various operating conditions, and significantly improving the reliability and adaptability of the grid connection process. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the generator sensorless grid connection method.

[0018] Figure 2 This is a schematic diagram of a generator being connected to the grid without sensor input.

[0019] Figure 3 A flowchart for generating the grid connection trigger signal.

[0020] Figure 4 A flowchart for generating frequency synchronization compensation parameters and transient suppression strategies. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for sensorless grid connection of a generator, comprising the following steps: S1. Real-time acquisition and preprocessing of generator shaft strain data.

[0025] S1.1 The generator shaft strain data includes dynamic torque fluctuation data, axial bending stress data, radial shear strain data, and torsional vibration harmonic data.

[0026] S1.2 Preprocessing includes wavelet denoising, signal conversion, normalization, outlier removal, and feature extraction.

[0027] Specifically, the Daubechies wavelet basis is used to decompose the generator shaft strain data into multiple scales. The decomposed generator shaft strain data is then subjected to soft thresholding. Adaptive parameters based on noise level estimation are used to reconstruct the data and complete wavelet denoising. The processed generator shaft strain data is converted into a time-frequency matrix through short-time Fourier transform, and windowing is performed using a Hanning window. A fixed window length and sliding step size are set to generate a time-frequency energy distribution spectrum to complete the signal conversion. The maximum-minimum normalization method was used to normalize the dynamic torque fluctuation data, axial bending stress data, radial shear strain data, and torsional vibration harmonic data in the generator shaft strain data. Outlier removal employs the 3σ criterion method of statistical distribution to detect anomalies in generator shaft strain data. By determining the mean and standard deviation of generator shaft strain data, data points that deviate from the mean by more than 3 times the standard deviation are identified as outliers. Linear interpolation is then used to correct the outliers, thus completing the outlier removal process. Feature extraction employs time-frequency analysis to extract signal features from generator shaft strain data. By extracting time-domain peak characteristics and frequency-domain harmonic components, combined with energy distribution characteristics, a multi-dimensional feature vector is formed to complete feature extraction.

[0028] S2. Calculate membrane potential using a biological neuron pulse control model and output the membrane potential value.

[0029] S2.1 Integrate the membrane potential integral differential equation, the ion channel dynamic characteristic equation, and the pulse firing mechanism, and combine them into the Hodgkin-Huxley dynamic framework to obtain the basic computational architecture.

[0030] Specifically, the membrane potential variable from the membrane potential integral differential equation is output to the ion-gated variable differential equation of the ion channel dynamic characteristic equation; the conductance output value of the ion channel dynamic characteristic equation is input into the current term of the membrane potential integral differential equation; the pulse threshold of the pulse firing mechanism is set by way of example, based on the critical range of membrane potential during action potential firing in mammalian neurons (-40mV to -55mV) as the pulse threshold of the pulse firing mechanism; when the instantaneous value of the membrane potential exceeds the pulse threshold, the sodium ion channel inactivation gate variable of the ion channel dynamic characteristic equation is immediately reset to 0 and the potassium ion channel activation gate variable is set to 1; a multidimensional feature vector is added as the ion channel conductance correction coefficient. The three components—membrane potential integral differential equation, ion channel dynamic characteristic equation, and pulse firing mechanism—are combined to obtain a bidirectional data stream connection between the membrane potential variable and the ion channel gate variable differential equation. The pulse firing mechanism and gate variable reset operation are written into the state transition logic, and encapsulated to form the basic computational architecture of the Hodgkin-Huxley dynamics framework, which includes a differential equation solver, a state machine controller, and a parameter memory.

[0031] S2.2 Input the pre-processed generator shaft strain data into the basic computing architecture and train it using the backpropagation algorithm to obtain the biological neuron pulse control model.

[0032] Specifically, the pre-processed generator shaft strain data is input into the basic calculation architecture. Initial values ​​for adjustable parameters in the basic calculation architecture are set, for example, a maximum conductivity of 120 millisiemens per square centimeter for sodium ion channels and a maximum conductivity of 36 millisiemens per square centimeter for potassium ion channels. The pre-processed generator shaft strain data is then input into the basic calculation architecture in a time series to perform dynamic integration calculation of membrane potential, outputting a sequence of membrane potential values ​​and a target pulse pattern sequence. The target pulse pattern is defined as the pulse emission frequency range corresponding to the strain peak-to-peak value, for example, from 10 Hz to 100 Hz. Calculate the mean square error (MSE) between the membrane potential sequence and the target pulse pattern; based on the MSE between the membrane potential sequence and the target pulse pattern, adjust the ion channel conductivity and membrane capacitance parameters of the basic computing architecture layer by layer using a backpropagation algorithm; automatically optimize the weight ratio of the processed generator shaft strain data components (dynamic torque fluctuation data, axial bending stress data, radial shear strain data, and torsional vibration harmonic data) to the membrane potential output, with the exemplary single adjustment amplitude not exceeding 15% of the original parameter value; repeat the parameter adjustment process until the MSE between the membrane potential sequence and the target pulse pattern is lower than the exemplary 0.001 or reaches the exemplary maximum training times of 500; output the biological neuron pulse control model.

[0033] It should be noted that the expression for calculating the mean square error between the membrane potential value sequence and the target pulse pattern is as follows: ; in, It is the mean square error between the membrane potential value sequence and the target pulse pattern. It is the total number of time points in the membrane potential value sequence and the target pulse mode sequence. It is an index variable at a specific point in time. It is the first The membrane potential values ​​output by the biological neuron impulse control model at each time point. It is the first The desired amplitude membrane potential value of the preset target pulse mode at each time point.

[0034] It should also be explained that the specific steps for setting the desired amplitude membrane potential value of the target pulse mode are as follows: Based on the historical preprocessed generator shaft strain data, identify the strain peak value and peak-to-peak value in each time series; map each strain peak-to-peak value to the corresponding pulse firing frequency range, for example, 10 Hz to 100 Hz, to determine the desired pulse firing frequency; using the relationship between membrane potential and pulse firing in the basic computing architecture, convert the corresponding frequency into membrane potential amplitude to obtain the desired membrane potential amplitude sequence at each time point; use the desired membrane potential amplitude sequence as the input of the target pulse mode for subsequent dynamic integral calculation of membrane potential and error evaluation.

[0035] S2.3. Input the preprocessed generator shaft strain data into the biological neuron pulse control model to perform dynamic allocation of synaptic connection strength and time encoding conversion, output the weighted strain input signal, and perform dynamic integral calculation of membrane potential and leakage current compensation processing to generate membrane potential value.

[0036] Specifically, the preprocessed generator shaft strain data is input into the biological neuron pulse control model. Using the weight ratio relationship between dynamic torque fluctuation data, axial bending stress data, radial shear strain data and torsional vibration harmonic data determined during the training process, and according to the amplitude-time mapping rule established by the historical preprocessed generator shaft strain data, the strain data of each generator shaft is converted into a pulse sequence with a specific time interval, and the weighted strain input signal is output. The weighted strain input signal is input into the biological neuron pulse control model, and discrete membrane potential value sequence calculation is performed according to a fixed time step. The fixed time step is 0.1 milliseconds for example. The ion channel state is updated and the leakage current component is obtained at each fixed time step. The reference leakage current component is subtracted from the total current. The calculation is iterated until the membrane potential change tends to stabilize, and the final membrane potential value is output. The membrane potential change tends to stabilize when the absolute value of the incremental change of the membrane potential within several consecutive fixed time steps is less than, for example, 0.001 millivolts, that is, the membrane potential basically no longer fluctuates.

[0037] It should be noted that the weight ratio refers to the relative contribution of dynamic torque fluctuation data, axial bending stress data, radial shear strain data, and torsional vibration harmonic data to the membrane potential output during the training process. For example, at a certain time point, dynamic torque fluctuation data accounts for 40% of the total input weight, axial bending stress data accounts for 30%, radial shear strain data accounts for 20%, and torsional vibration harmonic data accounts for 10%. The specific process of constructing the amplitude-time mapping rule is as follows: Based on the historical preprocessed generator shaft strain data, the amplitude distribution range of the processed generator shaft strain data components is statistically analyzed to establish the amplitude-time mapping rule between the strain amplitude and the pulse firing time interval. An exemplary correspondence is that for every 1 MPa increase in strain amplitude, the pulse firing interval is shortened by 0.5 milliseconds. It should be noted that the expression for calculating the discrete membrane potential value sequence according to a fixed time step is as follows: ; in, It is a time step The discrete membrane potential value sequence, It is a discrete membrane potential value sequence The time step index variable, It is the initial membrane potential. The fixed time step is exemplified by a value of 0.1 milliseconds. It is an internal accumulation index variable in the process of calculating discrete membrane potential value sequences. yes External input current at all times yes Sodium ion current at any given time yes Potassium ion current at time, yes Leaking current at all times.

[0038] S3. Compare the membrane potential value with the activation threshold, and generate a grid-connected trigger command signal by performing multi-dimensional state evolution through a quantum tunneling probability control mechanism.

[0039] S3.1. Peak detection and probability density analysis are performed on historical membrane potential values ​​to obtain the activation threshold.

[0040] Specifically, a sliding window peak detection is performed on the historical membrane potential value sequence. The sliding window width is set to 5 milliseconds for example. Local maxima are identified, and the amplitude of all sliding window peaks is recorded. The amplitude of the sliding window peaks is divided into 100 equal-width intervals for example. The frequency of the sliding window peaks in each equal-width interval is counted to obtain the probability density distribution of the sliding window peak amplitude. The membrane potential value corresponding to the inflection point of the probability density distribution curve is selected as the candidate value of the activation threshold. The rationality of the candidate value of the activation threshold is verified in combination with the typical activation characteristics of biological neurons, and the activation threshold is finally determined.

[0041] S3.2. The membrane potential value is compared with the activation threshold in real time to generate an over-limit flag signal, and the amplitude-time mapping is performed through a voltage-pulse converter to generate bias voltage parameters.

[0042] Specifically, the real-time membrane potential value is compared point by point with the activation threshold. When the instantaneous value of the membrane potential exceeds the activation threshold, a logic high-level over-limit flag signal is generated; otherwise, the logic low level is maintained. The rising edge trigger time of the over-limit flag signal is recorded as a pulse event timestamp. The over-limit amplitude of the membrane potential is converted into a pulse time interval parameter through a voltage-to-pulse converter (an exemplary conversion ratio of 1mV corresponds to a 0.2ms interval), and the output is used as a bias voltage parameter.

[0043] S3.3. Through the quantum tunneling probability control mechanism, the bias voltage parameters are simulated in a multi-dimensional probability manner to generate tunneling probability distribution data, and pulse position modulation and command encoding conversion are performed to obtain the grid-connected trigger command signal.

[0044] Specifically, the bias voltage parameter is input into the quantum tunneling probability control mechanism, and the carrier tunneling barrier height is calculated based on the bias voltage parameter. For example, a scaling factor of 0.5 electron volts / volt is used to convert the voltage value into a potential energy value. A discretized computational grid is established in three-dimensional space, and for example, a resolution of 1 nanometer × 1 nanometer × 1 nanometer is used to divide the multi-dimensional probability simulation region of the carrier tunneling trajectory. The wave function amplitude of each discretized computational grid point is calculated by solving the time-state Schrödinger equation to obtain the carrier occurrence probability density. The probability density of all discretized computational grid points is normalized to output the tunneling probability distribution data.

[0045] It should be noted that the expression for calculating the wavefunction amplitude of each discretized computational grid point by solving the stationary Schrödinger equation is as follows: ; in, It is a discretization of the computational grid points Wave function amplitude, The incident wave amplitude coefficient is exemplarily set to (1.0). It is the reflection wave amplitude coefficient, with an example value of (0.3). It is the phase term of a plane wave propagating in the positive direction. It is the imaginary unit. It's a wave arrow. It is the three-dimensional coordinate of the position. It is the phase term of a plane wave propagating in the opposite direction; Extract the three-dimensional coordinates of peak points exceeding 0.7 in the tunneling probability distribution data, and record the corresponding time positions as candidate pulse moments; according to the grid synchronization signal period, the candidate pulses are phase aligned in a 20-millisecond time window for example; the phase-modulated pulse moment sequence is encoded into a rising edge representing a grid connection enable signal, and the pulse width is fixed at an example of 200 microseconds to obtain the grid connection trigger command signal.

[0046] S4. Using a stress wave carrier mechanism, the grid-connection trigger command signal is mechanically vibrated and encoded into a waveform. This waveform is then transmitted to the grid-connected circuit breaker via a low-voltage non-inductive transfer box to obtain the command confirmation signal and execution delay parameters.

[0047] It should be noted that the low-voltage sensorless transfer box mainly includes: Grid-connected controller, circuit breaker, cloud monitoring unit, data acquisition unit and quick connector; The main function of the low-voltage sensorless transfer box is to enable rapid, safe, and precise grid connection and disconnection between the generator vehicle and the distribution network without interrupting power supply. By integrating functional modules such as grid connection control, parameter detection, status judgment, and control execution, it can automatically complete the judgment of power synchronization conditions, the execution of circuit breaker control logic, and the real-time adjustment of phase frequency, avoiding human judgment errors and operational delays, and ensuring the continuity and safety of the power switching process in various power supply scenarios. By cooperating with the grid-connected controller to identify and adjust parameters such as voltage, frequency, and phase, the low-voltage sensorless transfer box can quickly perform switching operations after meeting grid connection conditions. This achieves precise grid connection requirements, such as a phase difference within an exemplary 5 degrees and a frequency difference less than 0.1 Hz. Simultaneously, the closing process achieves zero arcing, avoiding arcing impacts and electrical damage. Equipped with multiple redundant protections and remote cloud monitoring capabilities, it can complete power switching without power outages, improving the stability, safety, and response efficiency of the power supply system.

[0048] S4.1 Based on the grid-connected trigger command signal, frequency shift keying modulation is performed through the stress wave carrier mechanism to obtain the coded stress wave signal.

[0049] Specifically, the grid-connected trigger command signal is input into the stress wave carrier mechanism. The voltage transition of the command signal is monitored in real time by a digital signal processor. When the voltage value exceeds the exemplary 2.5V and the slope is greater than 0.5V / μs, it is recorded as the rising edge moment. These moments are extracted to form a time reference sequence. A direct digital frequency synthesizer is used to generate a carrier signal. The initial digital frequency is set to the exemplary 1MHz. The frequency offset is calculated based on the time interval between adjacent rising edges. The modulated digital signal is converted into an analog voltage by a digital-to-analog converter, which drives the piezoelectric transducer crystal to generate mechanical vibration. The amplitude of the mechanical vibration is proportional to the voltage amplitude (exemplary proportionality coefficient 2nm / V), and the encoded stress wave signal is output.

[0050] S4.2. Through the piezoelectric transducer array of the low-voltage inductive converter box, the coded stress wave signal is subjected to carrier stripping and time-domain correlation demodulation to generate a standardized command signal.

[0051] Specifically, the encoded stress wave signal is input into the piezoelectric transducer array of the low-voltage inductive conversion box. The piezoelectric transducer array converts mechanical vibration into an electrical signal based on the piezoelectric effect, with an exemplary conversion sensitivity of 5 mV / Pascal. The encoded stress wave signal is subjected to an eighth-order Chebyshev bandpass filter, with the center frequency fixed at 1 MHz and the stopband attenuation set to an exemplary 60 dB, filtering out carrier frequency components. The filtered encoded stress wave signal is compared with the reference pulse waveform to generate a similarity index sequence. When the similarity index sequence exceeds the judgment threshold (exemplarily 0.8) and the duration exceeds an exemplary 1.2 microseconds, the time period of the similarity index sequence exceeding the judgment threshold is marked as a valid encoded stress wave signal component. The valid encoded stress wave signal segment is dynamically compressed to limit the maximum peak-to-peak value to an exemplary 5 volt range, and a standardized command signal with stable output voltage is generated.

[0052] It should be noted that the process of setting the judgment threshold is as follows: based on the statistical characteristic analysis of the reference pulse waveform and noise, the judgment threshold is obtained by calculating the inflection point of the signal-to-noise ratio separation degree and the bit error rate curve, and then performing optimal detection threshold fitting. For example, a judgment threshold of 0.8 with a correlation coefficient corresponds to an optimized balance point where the bit error rate is less than 10^-6 when the signal-to-noise ratio is 15dB. The reference pulse waveform is obtained by extracting the average characteristics of historical grid-connected trigger command signals. Specifically, this includes statistically analyzing the pulse rise time, pulse width, and amplitude distribution of historical commands, and then performing averaging to generate a standard pulse waveform that represents typical triggering behavior. This waveform is then used for subsequent similarity comparison with the filtered and encoded stress wave signal.

[0053] S4.3 Calculate the transmission delay and verify the integrity of the standardized command signal to obtain the command confirmation signal and execution delay parameters.

[0054] Specifically, the standardized command signal is input into a high-precision timestamp marking unit, and the arrival time of the rising edge of the standardized command signal is recorded as the transmission start time; the synchronous receiver records the demodulation completion time of the standardized command signal as the reception time; the difference between the transmission start time and the reception time is calculated to obtain the execution delay parameter, with an exemplary time resolution of 10 nanoseconds; cyclic redundancy check is performed on the standardized command signal, and the specific check process is as follows: a 32-bit generator polynomial (exemplary polynomial 0x04C11DB7) is used to perform a modulo-2 division operation on the standardized command signal data stream to calculate the remainder check code. The remainder check code is compared with the check code carried in the remainder check code. When the remainder check code is 0, the check is considered passed; when the remainder check code is not 0, the check is considered failed; the frame header identifier and payload data are extracted, and when the check is passed and the integrity of the standardized command signal reaches an exemplary 99.9%, a command confirmation signal is generated.

[0055] S5. Based on the instruction confirmation signal and execution delay parameters, and combined with the frequency-phase coordination deviation between the grid frequency and the generator frequency, frequency synchronization compensation parameters and transient suppression strategies are generated through the magnetic flux vortex adaptive optimization algorithm.

[0056] S5.1 Perform nanosecond-level clock calibration on the instruction confirmation signal and execution delay parameters to obtain the control instruction.

[0057] Specifically, the instruction confirmation signal and execution delay parameters are time-calibrated. The execution delay parameters are input into a digital time compensator, and the standard clock signal of the atomic clock is read as a reference to obtain the deviation value between the execution delay parameters and the reference time. The instruction confirmation signal is time-compensated by a programmable delay line, with the delay line step resolution set to an exemplary 10 picoseconds. The delay amount is dynamically adjusted according to the deviation value. The delay line parameters are recalibrated every exemplary 1 second to ensure that the time error of the control command after compensation is continuously less than 50 nanoseconds, and a control command with a time error of less than the exemplary 50 nanoseconds is generated.

[0058] Real-time acquisition of power grid frequency and generator frequency, followed by high-precision deviation analysis, yields the frequency-phase coordination deviation.

[0059] Specifically, the system acquires the grid voltage signal and the generator output voltage signal in real time, uses a frequency measurement circuit to synchronously obtain the fundamental frequency values ​​of the grid voltage signal and the generator output voltage signal, obtains the instantaneous difference between the grid frequency and the generator frequency through continuous sampling, and sets the sampling interval to an example of 1 millisecond; it smooths the difference between the grid frequency and the generator frequency for multiple consecutive sampling periods and outputs a stable frequency-phase coordination deviation.

[0060] S5.2 Utilize the magnetic flux vortex adaptive optimization algorithm to solve the dual constraint problem of frequency tracking and transient suppression for control commands and frequency-phase coordination deviation, and generate frequency synchronization compensation parameters and transient suppression strategies.

[0061] It should be noted that the control command and frequency-phase coordination deviation are input into the flux vortex adaptive optimization algorithm to establish a dual-objective cost function: the weight of the frequency tracking error objective term is set to an exemplary 0.7, and the weight of the transient oscillation amplitude objective term is set to an exemplary 0.3. Through the vortex search mechanism, the inertial weighted particle swarm optimization algorithm is adopted, with an exemplary swarm size of 50 particles and 100 iterations to generate a candidate solution set in the parameter space. Each candidate solution includes a frequency compensation gain (exemplary range 0.1-10) and a damping coefficient (exemplary range 0.05-0.5). Using the Lyapunov stability criterion, the real part of the eigenvalues ​​of the state matrix is ​​obtained, requiring the maximum real part of the eigenvalues ​​to be less than the exemplary -0.5 to screen the dynamic performance constraint solution. When the frequency tracking error is less than the exemplary 0.01Hz, the judgment logic is: the deviation of 10 consecutive exemplary 1ms sampling periods is ≤0.01Hz, and the transient overshoot is less than 5%. The judgment logic is: the exemplary instantaneous frequency offset peak value is ≤nominal frequency × 5%. When these conditions are met, the frequency synchronization compensation parameters and transient suppression strategy combination are output.

[0062] S6. Based on frequency synchronization compensation parameters and transient suppression strategies, the generator is dynamically and collaboratively adjusted to output a smooth and stable grid connection scheme.

[0063] S6.1 Based on frequency synchronization compensation parameters and transient suppression strategies, a multi-objective rolling time-domain optimization is performed through a dynamic matrix predictive control architecture to obtain a cooperative control instruction set.

[0064] Specifically, the frequency compensation gain value in the frequency synchronization compensation parameters and the damping limit value in the transient suppression strategy are input into the dynamic matrix predictive control architecture. An exemplary 10×10 state transition matrix is ​​constructed based on the generator's rotational inertia. The prediction time domain is set to an exemplary 200 milliseconds and the control time domain to an exemplary 50 milliseconds. At the beginning of each rolling time domain, the real-time sampling sequence of the power grid frequency is acquired. The objective function is constructed by integrating the frequency tracking target weight (frequency synchronization compensation parameters) and the transient constraint weight (transient suppression strategy). The optimal control quantity sequence in the future time domain is calculated through a quadratic programming solver. The control quantity sequence is refreshed with an exemplary 20 millisecond period, and a discretized cooperative control instruction set is output.

[0065] It should be noted that the expression for calculating the optimal control sequence in the future time domain using a quadratic programming solver is as follows: ; in, It is the optimal control quantity sequence. It is the state transition matrix. It is a control quantity sequence The transpose of the matrix, It is a linear term vector The transpose of the matrix, It is the state transition matrix , It is the transpose symbol.

[0066] S6.2. Through a multi-physics field collaborative optimization mechanism, the electromagnetic field adjustment, mechanical dynamics matching and energy flow balance of the collaborative control command set are calculated collaboratively to generate a smooth and stable grid connection scheme.

[0067] It should be noted that the collaborative control command set is input into the multi-physics collaborative optimization mechanism to decompose the voltage amplitude command sequence and the phase adjustment command sequence; electromagnetic field finite element simulation is performed on the voltage amplitude command sequence to obtain the magnetic flux density distribution of the generator stator winding, and the voltage command is adjusted to make the magnetic flux density non-uniformity lower than the exemplary 5%; mechanical dynamics simulation is performed on the phase adjustment command sequence to verify that the rotor shaft torsional stress does not exceed the exemplary 120MPa; the active-reactive power deviation at the grid interface is calculated with an exemplary 1-second period using an energy flow balance algorithm, and the control command is dynamically corrected to make the power fluctuation rate less than the exemplary 3%; electromagnetic field adjustment, mechanical stress verification and power balance correction are iteratively executed to output a smooth and stable grid connection scheme that meets the multi-physics constraints.

[0068] This embodiment also provides a generator sensorless grid connection system, including: The acquisition module is used to acquire generator shaft strain data in real time and preprocess it, calculate membrane potential through a biological neuron pulse control model, and output membrane potential value. The biomimetic module is used to compare the membrane potential value with the activation threshold, and generate a grid-connected trigger command signal through a quantum tunneling probability control mechanism to perform multi-dimensional state evolution. The communication module is used to encode the grid-connection trigger command signal into a mechanical vibration waveform using a stress wave carrier mechanism, and then transmit it to the grid-connected circuit breaker through a low-voltage non-inductive conversion box to obtain the command confirmation signal and execution delay parameters. The optimization module is used to generate frequency synchronization compensation parameters and transient suppression strategies based on the instruction confirmation signal and execution delay parameters, combined with the frequency phase coordination deviation between the grid frequency and the generator frequency, through the magnetic flux vortex adaptive optimization algorithm. The output module is used to dynamically and collaboratively adjust the generator based on frequency synchronization compensation parameters and transient suppression strategies, and output a smooth and stable grid connection scheme.

[0069] This embodiment also provides a computer device applicable to the generator seamless grid connection method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the generator seamless grid connection method proposed in the above embodiment.

[0070] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0071] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for sensorless grid connection of a generator as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0072] In summary, this invention achieves efficient conversion of mechanical vibration signals into intelligent control commands by combining a biological neuron pulse control model with a quantum tunneling probability modulation mechanism. It utilizes biomimetic neural network processing technology to transform generator shaft strain data into membrane potential signals with time-series characteristics, effectively capturing the dynamic characteristics of the mechanical system. Furthermore, it converts electrical signals into highly sensitive grid-connection commands through a quantum modulation mechanism and achieves lossless transmission using stress waves. This overcomes the limitations of generator electrical signal processing, establishing a seamless connection from mechanical state perception to electrical control execution, enabling smooth and stable grid connection of the generator under various operating conditions, and significantly improving the reliability and adaptability of the grid connection process.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for inductive-free grid connection of a generator, characterized in that: The method comprises the steps of: Real-time acquisition and preprocessing of generator shaft strain data, membrane potential calculation through biological neuron pulse control model, and output of membrane potential value; Comparing the membrane potential value with the activation threshold, multi-dimensional state evolution through quantum tunneling probability regulation mechanism, and generation of grid-connected trigger instruction signal; Using stress wave carrier mechanism, mechanical vibration waveform coding of grid-connected trigger instruction signal, and transmission to grid-connected circuit breaker through low-voltage non-inductive conversion box to obtain instruction confirmation signal and execution time delay parameter; Based on the instruction confirmation signal and the execution time delay parameter, combined with the frequency phase deviation amount of the power grid frequency and the generator frequency, the frequency synchronization compensation parameter and the transient suppression strategy are generated through the magnetic flux vortex adaptive optimization algorithm; Based on the frequency synchronization compensation parameter and the transient suppression strategy, the generator is dynamically adjusted, and the smooth and stable access to the power grid scheme is output.

2. The method of inductive grid-connection of a generator according to claim 1, characterized in that: The generator shaft strain data includes dynamic torque fluctuation data, axial bending stress data, radial shear strain data, and torsional vibration harmonic data. The preprocessing includes wavelet denoising, signal conversion, normalization, outlier rejection, and feature extraction.

3. The method of inductive grid-connection of a generator according to claim 2, characterized in that: The membrane potential value is calculated through the biological neuron pulse control model, and the specific steps are as follows, Integrate the membrane potential integral differential equation, ion channel dynamic characteristic equation and pulse emission mechanism into the Hodgkin-Huxley dynamic framework to obtain the basic calculation architecture; The historical preprocessed generator shaft strain data is input into the basic calculation architecture, and the biological neuron pulse control model is obtained through the back propagation algorithm training; The preprocessed generator shaft strain data is input into the biological neuron pulse control model for synaptic connection strength dynamic allocation and time coding conversion, and the weighted strain input signal is output.

4. The method of inductive grid-connection of a generator according to claim 3, characterized in that: The membrane potential value is compared with the activation threshold, and multi-dimensional state evolution is performed through the quantum tunneling probability regulation mechanism to generate the grid-connected trigger instruction signal. The peak value of the historical membrane potential value is detected and the probability density is analyzed to obtain the activation threshold. The membrane potential value is compared with the activation threshold in real time to generate an out-of-limit flag signal, and the amplitude-time mapping is performed through the voltage-pulse converter to generate the bias voltage parameter. Through the quantum tunneling probability regulation mechanism, the multi-dimensional probability simulation of the carrier tunneling trajectory of the bias voltage parameter is performed to generate the tunneling probability distribution data, and the pulse position modulation and instruction coding conversion are performed to obtain the grid-connected trigger instruction signal.

5. The method of inductive grid-connection of a generator according to claim 4, characterized in that: Based on the grid-connected trigger instruction signal, the frequency shift keying modulation is performed through the stress wave carrier mechanism to obtain the coded stress wave signal. Through the piezoelectric transducer array of the low-voltage non-inductive conversion box, the carrier stripping and time domain correlation demodulation of the coded stress wave signal are performed to generate the standardized instruction signal. ​ The transmission delay of the standardized instruction signal is calculated and the instruction integrity is verified to obtain an instruction confirmation signal and an execution delay parameter.

6. The method of inductive grid-connection of a generator of claim 1, wherein: The frequency synchronization compensation parameter and the transient suppression strategy are generated by the following steps, The instruction confirmation signal and the execution delay parameter are calibrated by a nanosecond-level clock to obtain a control instruction. The grid frequency and the generator frequency are collected in real time and high-precision deviation analysis is performed to obtain a frequency-phase cooperative deviation amount. The frequency synchronization compensation parameter and the transient suppression strategy are generated by solving the dual-constraint problem of frequency tracking and transient suppression of the control instruction and the frequency-phase cooperative deviation amount by using a magnetic flux vortex adaptive optimization algorithm.

7. The method of inductive grid-connection of a generator according to claim 6, characterized in that: The generator is dynamically and cooperatively adjusted based on the frequency synchronization compensation parameter and the transient suppression strategy to output a smooth and stable grid access scheme by the following steps, The cooperative control instruction set is obtained by multi-objective rolling time domain optimization through a dynamic matrix prediction control architecture based on the frequency synchronization compensation parameter and the transient suppression strategy. The smooth and stable grid access scheme is generated by the cooperative calculation of electromagnetic field adjustment, mechanical dynamics matching and energy flow balance of the cooperative control instruction set through a multi-physical field cooperative optimization mechanism.

8. A system for inductive-free grid connection of a generator, based on the method for inductive-free grid connection of a generator according to any one of claims 1 to 7, characterized in that The method comprises the following steps: The strain data of the generator shafting are collected in real time and preprocessed, the membrane potential is calculated by a biological neuron pulse control model, and the membrane potential value is outputted; The membrane potential value is compared with an activation threshold value, multi-dimensional state evolution is performed by a quantum tunneling probability regulation mechanism, and a grid connection trigger instruction signal is generated; The communication module is used for adopting a stress wave carrier mechanism to perform mechanical vibration waveform coding on the grid connection trigger instruction signal, and transmitting the grid connection trigger instruction signal to the grid connection circuit breaker through a low-voltage non-inductive conversion box to obtain an instruction confirmation signal and an execution delay parameter; The optimization module is used for generating a frequency synchronization compensation parameter and a transient suppression strategy by a magnetic flux vortex adaptive optimization algorithm based on the instruction confirmation signal and the execution delay parameter and in combination with the frequency-phase cooperative deviation amount of the grid frequency and the generator frequency. The output module is used for dynamically and cooperatively adjusting the generator based on the frequency synchronization compensation parameter and the transient suppression strategy to output a smooth and stable grid access scheme. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the generator non-inductive grid access method according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the generator non-inductive grid access method according to any one of claims 1-7.

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