Power system control parameter identification method and device and electronic equipment

Abstract

The embodiment of the invention provides a power system control parameter identification method and device and electronic equipment, and the method comprises the steps: constructing and generating a test working condition curve signal which comprises a sine frequency sweep signal segment with the frequency increasing according to a fixed step length and a fixed amplitude, the amplitude is determined according to the operation range of the power system, and the fixed step length is determined according to the bandwidth and the identification precision of the power system; controlling the power system to operate according to the test working condition curve signal, and collecting input data and output data of the power system; frequency response estimation is carried out on the input data and the output data to obtain the frequency response number of the power system, and the identification precision of the control parameters of the power system is low.

Description

Methods, devices and electronic equipment for identifying control parameters of power systems Technical Field

[0001] This application relates to the field of power system testing technology, and in particular to a method, device and electronic equipment for identifying power system control parameters. Background Technology

[0002] In the development and commissioning of propulsion systems (such as rotor propulsion systems for UAVs or electric vertical takeoff and landing aircraft), obtaining accurate control parameters is crucial for ensuring system stability and response performance. Currently, in the field of propulsion system control parameter identification, the frequency response characteristics of the system are typically obtained by performing spectrum analysis on the input and output data. However, existing data processing and analysis methods are often sensitive to noise during the testing process. Under actual operating conditions, sensor noise or environmental interference can easily infiltrate the test data, leading to significant errors in the obtained frequency response data. Consequently, the identified control parameters are not accurate enough and cannot meet the commissioning requirements of high-precision control systems. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and computer-readable storage medium for identifying control parameters of a power system, aiming to improve the problem of low identification accuracy caused by insufficient noise resistance in existing power system parameter identification methods.

[0004] In a first aspect, embodiments of this application provide a method for identifying control parameters of a power system, comprising: constructing and generating a test operating condition curve signal, the test operating condition curve signal including a sinusoidal frequency sweep signal segment with a fixed amplitude and an increasing frequency at a fixed step size; controlling the power system to operate according to the test operating condition curve signal, and collecting input data and output data of the power system; performing frequency response estimation on the input data and the output data to obtain frequency response data of the power system; and determining the control parameters of the power system based on the frequency response data.

[0005] In the above embodiments, the amplitude is determined based on the operating range of the power system, and the step size is determined based on the bandwidth and identification accuracy of the power system. The power system is controlled to operate according to the test condition curve signal for parameter identification. Unlike traditional broadband random signals that disperse energy across the entire frequency band, or step signals that concentrate energy at low frequencies, fixed-amplitude sinusoidal frequency sweeps can concentrate the excitation energy at a specific single frequency point at a certain moment. The concentrated energy single-frequency excitation ensures that the system response amplitude is much higher than the background noise. The fixed step size ensures that the distribution of test points is uniform within the frequency range of interest, without missing key resonance or anti-resonance points. Therefore, this method solves the problem of low identification accuracy in strong noise environments found in existing technologies (such as FFT analysis or random signal identification). It improves the signal-to-noise ratio and anti-interference capability of frequency response data, achieving high-precision identification of power system control parameters.

[0006] In one embodiment, the sinusoidal sweep frequency signal segment is composed of multiple sinusoidal signals of different frequencies spliced ​​together in time sequence. Each sinusoidal signal has the same amplitude, the frequency difference between adjacent sinusoidal signals is the fixed step size, and each sinusoidal signal contains a preset number of signal periods.

[0007] In the above embodiments, by designing the sinusoidal sweep signal as a sine wave splicing of discrete frequency points, the frequency coverage and resolution of the test can be precisely controlled; the setting of fixed step size and preset number of cycles makes the amount of test data for each frequency point controllable and sufficient, which not only ensures the identification accuracy, but also makes the signal design logic simple and clear.

[0008] In one embodiment, acquiring the test condition curve signal includes: generating a speed increase segment signal, a pre-sweep stable segment signal, a sinusoidal sweep signal segment, a post-sweep stable segment signal, and a speed decrease segment signal; and splicing the speed increase segment signal, the pre-sweep stable segment signal, the sinusoidal sweep signal segment, the post-sweep stable segment signal, and the speed decrease segment signal according to their timing sequence to generate the test condition curve signal.

[0009] In the above embodiments, by adding rising, falling, and steady-state segments before and after the frequency sweep, a complete closed-loop test condition is formed. This design not only protects the power system hardware but also ensures that the system is in a stable operating state before entering the frequency sweep test, thereby eliminating the interference of transient processes on the identification results and improving the reliability of the data.

[0010] In one embodiment, when generating the sinusoidal sweep frequency signal segment, the termination frequency of the sinusoidal sweep frequency signal segment is set to be greater than the estimated cutoff frequency of the power system.

[0011] In the above embodiments, by setting the termination frequency to cover the estimated cutoff frequency, it is ensured that the identified frequency response data can fully reflect the bandwidth characteristics of the system, and key control parameters cannot be accurately obtained due to insufficient test range.

[0012] In one embodiment, the frequency response estimation of the input data and the output data includes: encapsulating the input data and the output data into a system identification object; and using a spectrum analysis algorithm to calculate the complex frequency response based on the input data and the output data of the encapsulated system identification object to obtain the frequency response data.

[0013] In the above embodiments, by encapsulating the raw data into standardized system identification objects and processing them using professional spectrum analysis algorithms, all information in the time and frequency domains can be effectively utilized, significantly reducing the complexity of data processing and improving the accuracy of the calculation results.

[0014] In one embodiment, the step of using a spectrum analysis algorithm to calculate the complex frequency response based on the input data and output data of the encapsulated system identification object to obtain the frequency response data includes: analyzing the input data and output data of the encapsulated system identification object based on a generalized linear model to calculate the complex frequency response of the power system within a preset frequency range.

[0015] In the above embodiments, an analysis method based on a generalized linear model is adopted. This method has extremely high frequency resolution and excellent noise resistance. In particular, it can significantly reduce identification errors when processing test data of power systems with environmental noise.

[0016] In one embodiment, the acquisition of input and output data of the power system includes: acquiring segmented input data and segmented output data corresponding to each frequency segment in the sinusoidal sweep frequency signal segment; concatenating each segmented input data in frequency order to form continuous input data; and concatenating each segmented output data in frequency order to form continuous output data.

[0017] The above embodiments allow for segmented data collection and subsequent splicing, making data processing simpler and testing more flexible.

[0018] In one embodiment, determining the control parameters of the power system based on the frequency response data includes: calculating amplitude-frequency characteristic data and phase-frequency characteristic data based on the frequency response data; and determining the control parameters based on the amplitude-frequency characteristic data and the phase-frequency characteristic data, wherein the control parameters include cutoff frequency, gain, or phase margin.

[0019] In the above embodiments, by converting the complex frequency response into intuitive amplitude and phase frequency characteristic data, engineers can quickly and intuitively read the key performance indicators of the system, providing a direct basis for the parameter tuning of the controller.

[0020] Secondly, embodiments of this application provide a power system control parameter identification device, comprising: a signal generation module for acquiring a test condition curve signal, the test condition curve signal including a sinusoidal sweep frequency signal segment with a fixed amplitude and an increasing frequency at a fixed step size; a test control module for controlling the power system to operate according to the test condition curve signal and collecting input data and output data of the power system; and a calculation and analysis module for performing frequency response estimation on the input data and the output data to obtain frequency response data of the power system, and determining the control parameters of the power system based on the frequency response data.

[0021] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the program stored in the memory to implement the method described in any embodiment of this application.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described in any embodiment of this application. Attached Figure Description

[0023] Figure 1 is a flowchart of a power system control parameter identification method according to an embodiment of this application; Figure 2 is a schematic diagram of a test condition curve generation interface according to an embodiment of this application; Figure 3 is a schematic diagram of a power system frequency response Bode plot according to an embodiment of this application; Figure 4 is a structural diagram of a power system control parameter identification device according to an embodiment of this application; Figure 5 is a structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0024] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] In the parameter identification of existing propulsion systems (such as electric drive systems for aircraft), it is usually necessary to input specific signals and analyze the output response. However, existing frequency response analysis methods often process data through simple spectral transformations. Although the calculation process is relatively straightforward, this approach is highly sensitive to noise in the test data. In actual propulsion system testing environments, the operation of the motor itself generates electromagnetic interference and mechanical vibration noise. This noise can be superimposed on the effective response signal, resulting in a less smooth or even distorted frequency response curve. Consequently, the identification accuracy often fails to meet the design requirements of high-performance controllers.

[0026] For example, traditional methods often employ step signals, random signals, or Fast Fourier Transform (FFT) analysis. These methods have limitations in signal design and data processing. For instance, FFT analysis is limited by spectral leakage and the picket fence effect, is sensitive to noise, has low frequency resolution, and cannot effectively handle non-stationary signals. There are also system identification methods based on pseudo-random binary sequences (PRBS), which use PRBS as input signals and estimate system parameters through correlation analysis or least squares methods. These methods involve complex signal design and lack sufficient identification accuracy at high frequencies. To address the aforementioned issues of low identification accuracy, this application proposes an identification scheme based on a specific sinusoidal frequency sweep and noise immunity analysis model.

[0027] Terminology Explanation: In this application, "power system" refers to the system that generates driving force, including but not limited to components consisting of motors, electronic controls, and propellers, commonly used in eVTOL (electric vertical takeoff and landing) drones, etc.

[0028] In this application, the Bode plot, also known as the Bode diagram, is a graphical representation of the frequency response of a linear time-invariant system. It includes an amplitude-frequency response plot and a phase-frequency response plot, and is used to visually demonstrate how the system's gain and phase change with frequency.

[0029] In this application, the spafdr function is a function provided in the MATLAB software toolbox for frequency response estimation. It is based on spectrum analysis and generalized linear models, can process time-domain or frequency-domain data, and has high frequency resolution and noise resistance.

[0030] The present application provides a method for identifying control parameters of a power system. Please refer to Figure 1. The method includes the following steps: S10: Construct and generate a test condition curve signal, wherein the test condition curve signal includes a sinusoidal sweep frequency signal segment with a fixed step size and a fixed amplitude.

[0031] In this step, the test condition curve signal is the command signal used to excite the power system. In this embodiment, the test condition curve signal includes a sinusoidal frequency sweep signal segment. It should be noted that, unlike a continuously changing linear frequency sweep, this application uses a fixed-step increment frequency with a fixed amplitude. Specifically, the signal maintains a single-frequency sine wave for a period of time, and then jumps to the next frequency in a set step size (e.g., 0.1Hz), repeating this process until the termination frequency is reached. The fixed amplitude means that the amplitude of the sine wave (e.g., the amplitude of rotational speed fluctuations) remains constant throughout the frequency sweep process, ensuring that the system operates under a uniform excitation intensity.

[0032] The amplitude is determined based on the operating range of the power system, and the step size is determined based on the bandwidth and identification accuracy of the power system. The amplitude of the sinusoidal signal is set according to the operating range of the power system, and must avoid exceeding the system's control range. The frequency step size is set according to the bandwidth and identification accuracy requirements of the power system; smaller step sizes can be set in key frequency bands, and there is no specific limitation.

[0033] It's also worth noting that, for example, the operating range of a power system includes a linear operating range. A linear operating range refers to the fact that the gain of a power system (such as a motor and propeller) differs at different speeds. To obtain an accurate transfer function, the frequency sweep excitation must be limited to the range within which the system exhibits a linear response. The operating range of a power system can also include physical and safety boundaries. For example, the sum of the steady-state speed and amplitude cannot exceed the motor's maximum rated speed or the motor controller's maximum duty cycle; otherwise, signal clipping will occur, leading to nonlinear distortion. The difference between the steady-state speed and amplitude must not enter the propeller's stall region or the motor's stop / dead zone to ensure the system remains under control at all times.

[0034] By using a sinusoidal sweep signal with a fixed amplitude and increasing frequency in fixed steps, this application highly concentrates the test energy at discrete frequency points. Compared to random signals with dispersed energy, this method ensures that the dynamic system receives sufficiently strong excitation at each test point, thereby obtaining high signal-to-noise ratio response data at the output. This directly solves the technical problem in the prior art where inaccurate identification is caused by background noise drowning out the effective signal.

[0035] S20: Control the power system to operate according to the test condition curve signal, and collect the input and output data of the power system.

[0036] In this step, the generated test condition curve signal is sent to the power system as a control command. For example, on a rotor power system test bench, the test condition curve signal is sent to the motor controller as a target speed command to control the motor to drive the propeller. Simultaneously, the system's input data (i.e., the sent command signal, denoted as u(t)) and output data (i.e., the actual operating data fed back by the sensors, such as the actual speed, denoted as y(t)) are acquired. This application does not strictly limit the specific hardware implementation for data acquisition; it can be read by a host computer via CAN bus, serial port, or other communication protocols, or analog signals can be acquired by a separate data acquisition card.

[0037] By controlling the actual operation of the power system and synchronously acquiring real data, this step obtains first-hand information containing the system's nonlinearity, delay, and dynamic characteristics, which forms the basis for identification. Synchronous acquisition ensures strict alignment of input and output on the time axis, avoiding phase calculation errors and thus providing an accurate data source for subsequent calculations of key parameters such as phase margin.

[0038] S30: Perform frequency response estimation on the input data and the output data to obtain the frequency response data of the power system.

[0039] This step involves extracting frequency domain characteristics from the input / output data in the time domain. In this application, the frequency response data of the system at various test frequency points is calculated. Generally, this frequency response data typically includes real and imaginary parts, or amplitude and phase information. Through frequency response estimation, the complex time-domain waveform data is transformed into a frequency domain model describing the essential characteristics of the system. This step can extract the system's transfer function characteristics from noisy raw data, freeing engineers from focusing on complex time-domain waveform details and significantly reducing the difficulty of data analysis.

[0040] S40: Based on the frequency response data, determine the control parameters of the power system.

[0041] The method provided in this application analyzes the control parameters of the power system based on the obtained frequency response data. Specifically, for example, key control parameters such as the system's cutoff frequency, gain margin, and phase margin can be identified. These parameters reflect the response speed and stability of the power system. By quantifying and determining the control parameters, this application transforms abstract data analysis results into specific engineering indicators. This enables engineers to perform targeted tuning of the power system based on these explicit parameters, thereby significantly improving the control stability of the power system.

[0042] As can be seen, this embodiment provides a method for identifying control parameters of a power system. It acquires a test condition curve signal, which includes a sinusoidal sweep signal segment with a fixed amplitude and increasing frequency at a fixed step size. The amplitude is determined based on the operating range of the power system, and the step size is determined based on the bandwidth and identification accuracy of the power system. The power system is controlled to operate according to the test condition curve signal for parameter identification. Unlike traditional broadband random signals that disperse energy across the entire frequency band, or step signals that concentrate energy at low frequencies, the fixed-amplitude sinusoidal sweep can concentrate the excitation energy at a specific single frequency point at a given moment. This concentrated single-frequency excitation ensures that the system response amplitude is much higher than the background noise. The fixed step size ensures that the distribution of test points is uniform within the frequency range of interest, preventing the omission of critical resonance or anti-resonance points. Therefore, this method solves the problem of low identification accuracy in strong noise environments found in existing technologies (such as FFT analysis or random signal identification). It improves the signal-to-noise ratio and anti-interference capability of the frequency response data, achieving high-precision identification of power system control parameters.

[0043] In one embodiment, the sinusoidal frequency sweep signal segment in step S10 is composed of multiple sinusoidal signals of different frequencies spliced ​​together in time sequence. Each sinusoidal signal has the same amplitude, the frequency difference between adjacent sinusoidal signals is the fixed step size, and each sinusoidal signal contains a preset number of signal periods.

[0044] This step describes the microstructure of the sinusoidal sweep signal segment in detail. The signal is not a smooth transition, but rather composed of consecutive segments of discrete-frequency sine waves. For example, a sine wave of a Hz runs for several cycles, followed by a sine wave of b Hz running for several cycles. The frequency change is linear (e.g., in 0.1 Hz steps), and each frequency point lasts for a fixed number of cycles (e.g., each frequency point sweeps for C cycles). This means that high-frequency signals have short durations, and low-frequency signals have long durations, but both are guaranteed to contain complete waveform information.

[0045] It should be noted that although this embodiment preferably uses a fixed step size and a preset number of cycles, in some variations, a variable step size can be used for known regions with severe nonlinearity (e.g., increasing the step size near the resonant frequency). Similarly, the number of signal cycles can be dynamically adjusted according to the frequency of the power system; these adjustments all fall within the scope of this application. The fixed step size, fixed period, and spliced ​​signal design ensures, in principle, that the power system experiences complete transient and steady-state processes at each frequency point. Compared to continuously swept signals, this discrete spliced ​​signal allows subsequent processing algorithms to accurately extract steady-state data for analysis at each frequency point, eliminating transient errors caused by rapid frequency changes. Simultaneously, the fixed number of cycles significantly shortens the testing time in the high-frequency band, avoiding unnecessary redundancy, thereby optimizing overall testing efficiency while ensuring accuracy.

[0046] In this embodiment, the splicing process simplifies signal generation mathematically, eliminating the need for complex modulation algorithms. A preset number of signal periods (rather than a fixed time length) means that the test time naturally extends in the low-frequency range (longer periods), ensuring the system has sufficient time to reach steady state; while the test time naturally shortens in the high-frequency range (shorter periods). This balances test accuracy and efficiency: avoiding excessive test time in the high-frequency range while ensuring a complete response period in the low-frequency range. The signal period can also be set according to the response period of the power system, further ensuring the rationality of the test condition curve signal.

[0047] In one embodiment, the specific generation process of obtaining the test condition curve signal in step S10 includes S11-S12: S11: Generate the speed increase segment signal, the steady segment signal before frequency sweep, the sinusoidal frequency sweep signal segment, the steady segment signal after frequency sweep, and the speed decrease segment signal.

[0048] A complete test condition includes not only the frequency sweep but also the start and end processes. Before generating the signals mentioned above, precise settings are required for the speed command signal parameters and the sinusoidal frequency sweep parameters. Specifically: the speed command signal parameters define how the power system enters the test state, under what reference conditions it operates, and how it safely exits. These parameters include the initial speed, stable speed, final speed, rising and falling slopes, and the settling time before and after the sinusoidal frequency sweep. The initial speed is the motor's initial speed at the start of the test (usually 0); the stable speed refers to the base operating point speed for the sinusoidal frequency sweep, determining the center of the system's operating range. The final speed is the command value after the test ends, typically returning to a safe stopping state. The rising and falling slopes determine the rate of change of speed from the starting point to the stable operating point, and from the operating point to the final speed, respectively. The settling time before the frequency sweep refers to maintaining the motor at a stable speed for a period before applying the frequency sweep excitation to eliminate transient interference during startup. The settling time after the frequency sweep refers to maintaining a steady state after the frequency sweep is completed, ensuring that data acquisition covers the complete system response process.

[0049] The sinusoidal sweep parameters determine the resolution and accuracy of frequency response analysis. These parameters include signal amplitude, signal period, initial frequency, termination frequency, frequency step size, and the number of sweeps per frequency. The signal amplitude is set according to the system's operating range. A fixed amplitude design is simple and provides uniform frequency coverage, improving the accuracy and stability of parameter identification, while avoiding exceeding the control range to maintain the system's linear characteristics. The signal period is set according to the response period of the control system design, ensuring that the generated discrete sinusoidal signal points match the execution frequency of the host computer. The initial and termination frequencies are set according to the required test frequency range; for example, the termination frequency must be greater than the -3dB cutoff frequency in the amplitude-frequency response plot, otherwise the complete dynamic characteristics of the system cannot be identified. The frequency step size is set according to the system bandwidth and identification accuracy requirements, for example, 0.1Hz. Increasing fixed step sizes provide uniform frequency resolution; the smaller the step size, the more accurately the feature points (such as cutoff frequency and phase margin) on the Bode plot are identified. The number of frequency sweeps per single frequency refers to the number of times the sine waveform is repeated at each fixed frequency point. By repeatedly sweeping the frequency, the power system can be ensured to reach a steady-state response at that frequency, thereby improving the noise resistance and data reliability during subsequent analysis.

[0050] In step S11, the five signal segments mentioned above are generated based on the speed command signal parameters and the sinusoidal sweep frequency parameters. The speed increase segment accelerates from 0 to the reference speed, usually with a set rise slope; the pre-sweep smooth segment maintains the reference speed for a period of time to allow the power system to reach thermal equilibrium and mechanical stability; the sinusoidal sweep frequency signal segment is the multi-frequency spliced ​​sine wave mentioned above; the post-sweep smooth segment is used to eliminate the oscillations caused by the sweep frequency; and the speed decrease segment decelerates from the reference speed to 0.

[0051] S12: The test condition curve signal is generated by splicing the signals of the speed increase segment, the pre-sweep stable segment, the sinusoidal sweep frequency signal segment, the post-sweep stable segment, and the speed decrease segment according to their timing sequence.

[0052] In this step, the generated array segments are merged in chronological order to form a complete long-term sequence array.

[0053] In this embodiment, this five-segment curve design mechanism, in principle, constructs a safe test closed loop. The slope control of the rising and falling segments avoids the instantaneous high current surges and mechanical damage caused by sudden motor starts and stops; the introduction of the steady segment ensures the system enters a stable operating region before frequency sweeping, eliminating the influence of unsteady-state data on the identification of sinusoidal frequency sweep parameters, and ensuring the system is in a stable linear operating region when entering the core frequency sweep segment. This directly improves the reliability of the test data.

[0054] For example, as shown in Figure 2, Figure 2 illustrates the change of the speed command signal received by the system during the test over time.

[0055] Speed ​​increase segment signal and frequency sweep before steady segment signal (approximately 0s-10s): The speed increases from 0 to a stable speed (e.g., 1200rpm) at a fixed slope and is maintained for a short period of time to ensure that the rotor system enters a stable operating state.

[0056] Sine sweep frequency signal segment (the core middle part): This is crucial for parameter identification. The command superimposes a sine wave of fixed amplitude onto a stable rotational speed. Observing the waveform, it can be seen that the sine wave becomes increasingly dense over time, indicating that the frequency increases in fixed steps.

[0057] The frequency sweep followed by a stable phase signal and the speed decrease phase signal: After the frequency sweep is completed, the speed decreases to 0 at a slope after the speed runs smoothly.

[0058] This signal design is simple and has uniform frequency coverage, which can fully stimulate the dynamic characteristics of the power system at different frequencies, providing basic data for subsequent high-precision identification.

[0059] As can be seen, this embodiment can eliminate the interference of non-steady initial conditions on the identification results.

[0060] In one embodiment, regarding the setting of the frequency range: when generating the sinusoidal sweep frequency signal segment, the termination frequency of the sinusoidal sweep frequency signal segment is set to be greater than the estimated cutoff frequency of the power system.

[0061] In this step, for example, if the expected bandwidth of the power system is around 2Hz, then the termination frequency of the sinusoidal sweep signal segment should be set to a value greater than 2Hz (e.g., 3Hz). The initial frequency of the sinusoidal sweep signal segment can be set empirically. As a preferred implementation, the termination frequency should be set greater than the -3dB cutoff frequency in the amplitude-frequency response diagram to ensure complete capture of the system's roll-off characteristics. If the estimated cutoff frequency is unknown, a wide-range coarse sweep can be performed first to determine the approximate range, and then the accurate termination frequency can be set in the fine test.

[0062] By forcibly setting a termination frequency to cover the estimated cutoff frequency, the integrity of the Nyquist plot or Bode plot in the critical region (gain attenuation region) is guaranteed in principle. If the test frequency is insufficient, the phase crossover frequency and gain crossover frequency cannot be accurately determined, resulting in the inability to evaluate the phase margin. This effectively avoids the risk of invalid data caused by traditional blind testing and ensures that complete bandwidth and stability parameters can be obtained through testing.

[0063] The core indicators in the control parameters (such as gain margin, phase margin, and bandwidth) all depend on the attenuation characteristics of the power system in the high-frequency range (i.e., the falling segment of the amplitude-frequency curve). If the scan range does not cover the cutoff frequency (usually the -3dB point), the resulting Bode plot will be incomplete, making it impossible to calculate the aforementioned key parameters. In this embodiment, the completeness of the identification results is ensured: all necessary data characterizing the system bandwidth and stability can be obtained through testing, and invalid testing is effectively avoided.

[0064] In one embodiment, step S30, namely frequency response estimation of the input data and the output data, includes the following steps: S31: Encapsulating the input data and the output data into a system identification object.

[0065] This step utilizes the dedicated toolboxes of data processing software. For example, in the MATLAB environment, the `iddata` function is used to encapsulate the acquired input array `u`, output array `y`, and sampling time `Ts` into a standard object recognition data packet. Directly using the `iddata` function automates the data encapsulation, reducing manual processing steps.

[0066] S32: Using a spectrum analysis algorithm, calculate the complex frequency response based on the input data and output data of the encapsulated system identification object to obtain the frequency response data.

[0067] In this step, the encapsulated object is processed directly. It should be noted that although this embodiment preferably uses a standardized encapsulated object for processing, in computing environments without relevant software toolkits (such as embedded real-time computing), the original array can also be solved directly for each frequency point by writing a correlation analysis algorithm of Discrete Fourier Transform (DFT), which is also within the scope of protection of this application.

[0068] By encapsulating data, the binding between data attributes and the data itself is achieved in principle. This avoids calculation errors caused by incorrect parameter passing (such as incorrect sampling time) in subsequent complex algorithm calls. At the same time, standardized objects enable subsequent batch processing and algorithm reuse, greatly reducing the amount of code and manual operation costs, and realizing automation, standardization, and efficiency in data processing.

[0069] In one embodiment, step S32 further includes: analyzing the input data and output data of the encapsulated system identification object based on a generalized linear model to calculate the complex frequency response of the power system within a preset frequency range.

[0070] The core algorithm used in this step is spectral analysis based on a generalized linear model. As a specific and preferred implementation, the `spafdr` function in MATLAB can be used. `spafdr` allows specifying the frequency points of interest (i.e., the sweep points during testing) and performing more refined estimations in the vicinity of these frequencies. This algorithm is better able to separate noise signals when calculating the input-output frequency response.

[0071] This analysis method employs a generalized linear model (such as SPAFDR), which, in principle, seeks the optimal linear mapping between input and output through a statistical model, rather than simply performing frequency domain transformation. Compared to traditional FFT, which is susceptible to spectral leakage and the picket fence effect, this method can accurately pinpoint the test frequency and effectively suppress aperiodic noise. Experiments demonstrate that this method can control parameter identification errors to a significantly lower level than traditional methods, thus solving the problem of obtaining high-precision Bode plots in low signal-to-noise ratio environments.

[0072] It should be noted that, in addition to using the `spafdr` function, the `tfestimate` function (a transfer function estimate based on the Welch method) or other spectral analysis functions can be used as alternatives in scenarios where accuracy requirements are not extremely stringent. However, `spafdr` offers higher frequency resolution and noise immunity, and is therefore the preferred solution in this application.

[0073] As can be seen, in this embodiment, unlike FFT (Fast Fourier Transform) which directly transforms time-domain data to the frequency domain, FFT is susceptible to spectral leakage and the picket fence effect, and is sensitive to noise. The generalized linear model, through statistical methods, finds the optimal linear mapping relationship between input and output near a preset sweep frequency point, effectively separating random noise from a deterministic sinusoidal response. It achieves higher frequency resolution and noise robustness: under the same test noise environment, it can obtain a smoother and more accurate Bode plot than FFT, significantly improving the accuracy of parameter identification.

[0074] In one embodiment, for cases involving large amounts of data or long-term testing, the data acquisition in step S20 can be performed in a segmented manner: acquiring segmented input data and segmented output data corresponding to each frequency segment in the sinusoidal sweep frequency signal segment.

[0075] The segmented input data are concatenated in frequency order to form continuous input data; and the segmented output data are concatenated in frequency order to form continuous output data.

[0076] This embodiment illustrates that the method supports breakpoint continuation testing or segmented testing. For high-power power systems, prolonged continuous operation may cause the motor or motor controller to overheat, triggering protection or altering system characteristics. By acquiring data in segments (e.g., first measuring 0.1Hz-5Hz, then stopping and cooling before measuring 5Hz-10Hz), data from multiple tests can be stitched together on the time axis. Since subsequent frequency response estimation is based on frequency points, as long as the stitched data contains all the frequency components of interest, intermediate physical time breakpoints will not affect the identification results.

[0077] In this embodiment, the physical continuity of the testing process and the logical continuity of data processing are decoupled through segmented acquisition and logical splicing. This means that testers can flexibly arrange the testing rhythm according to the equipment's heat dissipation capacity, effectively preventing equipment overheating damage or data distortion caused by temperature drift. This feature greatly expands the applicability of this method, enabling it to be applied to high-power, high-heat-generating power systems such as megawatt-level systems, especially for rotor power system testing. Moreover, the data from segmented frequency sweep tests can be directly spliced ​​without removing data from non-sinusoidal frequency sweep bands, making data processing simpler and testing more flexible.

[0078] In one embodiment, step S40, namely determining the control parameters of the power system based on the frequency response data, includes: S41: calculating amplitude-frequency characteristic data and phase-frequency characteristic data based on the frequency response data.

[0079] S42: Determine the control parameters based on the amplitude-frequency characteristic data and the phase-frequency characteristic data. The control parameters include cutoff frequency, gain, or phase margin.

[0080] This step typically involves plotting a Bode plot. The amplitude-frequency response shows the gain as a function of frequency, while the phase-frequency response shows the phase as a function of frequency. From the Bode plot, the cutoff frequency (the frequency point where the amplitude-frequency curve drops to -3dB, representing bandwidth), phase margin (the difference between the phase and -180 degrees when the gain is 0dB, representing stability), and low-frequency amplitude gain can be read automatically or manually. By calculating and displaying the amplitude-frequency and phase-frequency characteristics, complex complex domain data is theoretically mapped to an intuitive logarithmic coordinate system. This allows engineers to immediately identify system bottlenecks (such as insufficient bandwidth or phase margin) and directly read specific values ​​(such as a 45-degree phase margin). This visualized method of parameter determination significantly lowers the barrier to data interpretation and provides clear and quantifiable guidance for subsequent controller optimization.

[0081] For example, as shown in Figure 3, the top chart represents the magnitude response curve, illustrating how the system gain changes with frequency. Key metric f -3dB The area marked on the top chart is the cutoff frequency (approximately 2.35Hz). At this point, the gain drops by 3dB, typically used to measure system bandwidth or response speed. The bottom chart shows the phase response curve, illustrating the lag (phase difference) of the system output data relative to the input data. Key metric f -90° The chart below shows the frequency (approximately 2.52 Hz) when the phase reaches -90°. Compared to traditional FFT analysis, the curve generated using the spafdr function has better noise resistance and higher frequency resolution. The shaded area represents the confidence interval, demonstrating the reliability of the identification results. This chart allows engineers to directly obtain key control parameters of the powertrain system, such as cutoff frequency, gain margin, and phase margin, enabling precise optimization of the powertrain system's control algorithm.

[0082] Finally, it is worth noting that the method provided in this application is mainly applied to rotor power systems with speed loop control composed of motor, electronic control and propeller. It can be extended to power system parameter identification of torque loop and current loop, and even some power control systems with voltage and current control, etc. This application does not limit the specific application. By adjusting the amplitude, frequency range and frequency step size of the sinusoidal signal, the dynamic characteristics of the uncontrolled system can be adapted to improve the test accuracy.

[0083] This application embodiment also provides a power system control parameter identification device 40, please refer to Figure 4, including: a signal generation module 410, used to acquire a test condition curve signal for exciting the power system, the test condition curve signal including a sinusoidal sweep frequency signal segment with a fixed step size and a fixed amplitude, wherein the amplitude is determined according to the operating range of the power system, and the fixed step size is determined according to the bandwidth and identification accuracy of the power system.

[0084] The test control module 420 is used to control the power system to operate according to the test condition curve signal and to collect the input and output data of the power system.

[0085] The calculation and analysis module 430 is used to perform frequency response estimation on the input data and the output data to obtain the frequency response data of the power system, and to determine the control parameters of the power system based on the frequency response data.

[0086] For details regarding the power system control parameter identification device, please refer to the description of the aforementioned method embodiments. This application does not limit the specific implementation of the method and will not repeat the description hereafter.

[0087] This application also provides an electronic device 50, as shown in FIG5, including a memory 510 and a processor 520. The memory 510 is used to store computer programs; the processor 520 is used to execute the programs stored in the memory 510 to implement the power system control parameter identification method described in any embodiment of this application.

[0088] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power system control parameter identification method described in any embodiment of this application.

[0089] In this application, "multiple" refers to two or more.

[0090] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0091] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0092] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0093] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0094] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying control parameters of a power system, characterized in that, include: A test condition curve signal for exciting the power system is constructed and generated. The test condition curve signal includes a sinusoidal frequency sweep signal segment with a fixed amplitude and an increasing frequency at a fixed step size. The amplitude is determined according to the operating range of the power system, and the fixed step size is determined according to the bandwidth and identification accuracy of the power system. The power system is controlled to operate according to the test condition curve signal, and the input data and output data of the power system are collected. Frequency response estimation is performed on the input data and the output data to obtain the frequency response data of the power system. Based on the frequency response data, the control parameters of the power system are determined.

2. The method according to claim 1, characterized in that, The sinusoidal sweep frequency signal segment is composed of multiple sinusoidal signals of different frequencies spliced ​​together in time sequence. Each sinusoidal signal has the same amplitude, the frequency difference between adjacent sinusoidal signals is the fixed step size, and each sinusoidal signal contains a preset number of signal periods.

3. The method according to claim 2, characterized in that, The acquisition of the test condition curve signal includes: generating a speed increase segment signal, a steady segment signal before frequency sweep, a sinusoidal frequency sweep signal segment, a steady segment signal after frequency sweep, and a speed decrease segment signal; and splicing the speed increase segment signal, the steady segment signal before frequency sweep, the sinusoidal frequency sweep signal segment, the steady segment signal after frequency sweep, and the speed decrease segment signal in the time sequence to generate the test condition curve signal.

4. The method according to claim 2, characterized in that, When generating the sinusoidal sweep frequency signal segment, the termination frequency of the sinusoidal sweep frequency signal segment is set to be greater than the estimated cutoff frequency of the power system.

5. The method according to claim 1, characterized in that, The frequency response estimation of the input data and the output data includes: encapsulating the input data and the output data into a system identification object; and using a spectrum analysis algorithm to calculate the complex frequency response based on the input data and the output data of the encapsulated system identification object to obtain the frequency response data.

6. The method according to claim 5, characterized in that, The step of using a spectrum analysis algorithm to calculate the complex frequency response based on the input data and output data of the encapsulated system identification object to obtain the frequency response data includes: analyzing the input data and output data of the encapsulated system identification object based on a generalized linear model to calculate the complex frequency response of the power system within a preset frequency range.

7. The method according to claim 1, characterized in that, The acquisition of input and output data of the power system includes: acquiring segmented input data and segmented output data corresponding to each frequency segment in the sinusoidal sweep signal segment; concatenating each segmented input data in frequency order to form continuous input data; and concatenating each segmented output data in frequency order to form continuous output data.

8. The method according to any one of claims 1-7, characterized in that, The step of determining the control parameters of the power system based on the frequency response data includes: calculating amplitude-frequency characteristic data and phase-frequency characteristic data based on the frequency response data; and determining the control parameters based on the amplitude-frequency characteristic data and the phase-frequency characteristic data, wherein the control parameters include cutoff frequency, gain, or phase margin.

9. A power system control parameter identification device, characterized in that, include: A signal generation module is used to acquire a test condition curve signal for exciting the power system. The test condition curve signal includes a sinusoidal sweep frequency signal segment with a fixed amplitude and an increasing frequency at a fixed step size. The amplitude is determined according to the operating range of the power system, and the fixed step size is determined according to the bandwidth and identification accuracy of the power system. A test control module is used to control the power system to operate according to the test condition curve signal and to collect the input and output data of the power system. A calculation and analysis module is used to perform frequency response estimation on the input and output data to obtain the frequency response data of the power system, and to determine the control parameters of the power system based on the frequency response data.

10. An electronic device, characterized in that, It includes a processor and a memory, wherein: the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to implement the method described in any one of claims 1-8.

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