Direct current power source power allocation method and system for generator status monitoring apparatus

Abstract

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

Description

A DC power distribution method and system for a generator condition monitoring device Technical Field

[0001] This invention relates to the field of power distribution technology, and in particular to a DC power distribution method and system for a generator condition monitoring device. Background Technology

[0002] The generator condition monitoring device is powered by a battery, making it more flexible and reliable in generator maintenance sites. Currently, lithium-ion batteries are widely used due to their advantages such as high energy density, low self-discharge rate, and long cycle life. In some monitoring projects, pulsed energy is required; therefore, a hybrid energy storage system using both energy-type and power-type energy storage elements can optimize the overall power supply characteristics of the generator condition monitoring device.

[0003] Reasonable power allocation is crucial for hybrid energy storage systems to leverage the complementary advantages of their energy storage components. Significant research has been conducted both domestically and internationally on power allocation methods for hybrid energy storage systems. Commonly used methods include low-pass filtering, Fourier transform, wavelet transform, and empirical mode decomposition (EMD). However, low-pass filters are prone to introducing time delays during the filtering process; wavelet decomposition results are highly dependent on the choice of basis functions; and while EMD is not affected by wavelet basis functions, it can suffer from mode aliasing. These phenomena can lead to unreasonable power allocation in energy storage systems.

[0004] Summary of the Invention

[0005] In view of the problems existing in the DC power distribution and system of existing generator condition monitoring devices, this invention is proposed.

[0006] Therefore, the problem that this invention aims to solve is that the unreasonable power distribution of the energy storage system results in a large range of changes in the state of charge and a reduced service life.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a DC power distribution method for a generator condition monitoring device, which includes the following steps:

[0009] Construct a variational problem model using system data;

[0010] The constructed variational problem model is solved, and the computational parameters of the variational problem model are optimized using the particle swarm optimization algorithm.

[0011] Based on the calculation results of the variational problem model, the total output power of the hybrid energy storage system is divided into energy-type energy storage devices and power-type energy storage devices according to the ratio.

[0012] As a preferred embodiment of the DC power allocation method for the generator condition monitoring device of the present invention, the method for constructing the variational problem model includes:

[0013] The mode function is calculated using the Hilbert transform, and the relevant analytic signal is obtained, which is expressed mathematically as follows:

[0014] In the formula, u k (t) represents the mode function;

[0015] The predicted center frequencies of the modal components are determined as follows: Then, the spectrum of the modal components is adjusted using the exponential correction method so that it falls within the corresponding fundamental frequency band, which can be expressed mathematically as:

[0016] The bandwidth prediction of the modal components can be obtained by calculating the square norm of the gradient of the analytic signal. The constrained variational model of variational mode decomposition is expressed as:

[0017] In the formula, K represents the number of modes in the decomposition, u k Let ω be the k-th modal component after decomposition. k Represented as u k The corresponding center frequency, It is expressed as the partial derivative of t.

[0018] As a preferred embodiment of the DC power allocation method for the generator condition monitoring device of the present invention, the method for solving the variational problem model includes:

[0019] The constrained variational model is transformed into an unconstrained variational model by introducing a quadratic penalty factor and a Lagrange multiplication operator. The augmented Lagrange expression is as follows:

[0020] With two variables fixed and the remaining variable updated, the solution method for the unconstrained variational model can be expressed as follows:

[0021] but The values ​​are represented as follows:

[0022] in,

[0023] Solving this problem in the frequency domain using Parseval's theorem is specifically expressed as follows:

[0024] Then use ω-ω k The substitution, expressed mathematically, is as follows:

[0025] The integral over the non-negative frequency range can be expressed as:

[0026] Differentiation yields:

[0027] In the formula, α represents the quadratic penalty factor, and λ represents the Lagrange multiplier operator.

[0028] As a preferred embodiment of the DC power distribution method for the generator condition monitoring device of the present invention, the method for solving the variational problem model further includes:

[0029] For center frequency Solve for the expression:

[0030] The integral over the non-negative frequency range can be expressed as:

[0031] In the formula, Represented as the current remaining amount Wiener filtering, Represented as the center frequency of the current modal component, for Performing an inverse Fourier transform, the resulting real part is {u} k (ω)}.

[0032] As a preferred embodiment of the DC power distribution method for the generator condition monitoring device described in this invention, the Lagrange multiplication operator λ is updated, and the update formula is expressed as:

[0033] For the Lagrange multiplier λ n+1 The solution is expressed as:

[0034] In the formula, the Lagrange multiplier λ n+1 The solution output consists of K modal components.

[0035] As a preferred embodiment of the DC power distribution method for the generator condition monitoring device of the present invention, the following is provided: Judgment conditions are set, expressed mathematically as follows:

[0036] In the formula, ε represents the discrimination accuracy;

[0037] When the judgment condition is met, output the IMF component; when the judgment condition is not met, input n = n + 1 and solve the variational problem model again.

[0038] As a preferred embodiment of the DC power distribution method for the generator condition monitoring device described in this invention, the total output power of the hybrid energy storage system is output with a timestamp and a power identification mechanism. The power identification mechanism tests the power of both the energy-type and power-type energy storage devices, marking the power of the energy-type device as A and the power of the power-type device as B. Through the introduction of the timestamp, power A and power B are added simultaneously, and the total output power of the hybrid energy storage system is marked as power C. Whether the condition is satisfied by the following judgment formula is then determined:

[0039] C-(A+B)≤T

[0040] In the formula, T represents the difference;

[0041] When the conditional formula is met, a normal log is output, marked with a timestamp;

[0042] If the judgment formula is not met, an exception log will be output, an alert will be issued, and a timestamp will be added.

[0043] Secondly, embodiments of the present invention provide a DC power distribution system for a generator condition monitoring device, which includes an algorithm transformation module, an exponential correction module, a frequency domain calculation module, and an information output module;

[0044] The algorithm transformation module uses Hilbert transform to process mode functions, calculates relevant analytic signals, and analyzes the frequency components of the signals in the complex plane;

[0045] The exponential correction module uses the exponential correction method to adjust the spectrum of the modal components to match the predefined fundamental frequency band and clarify the different frequency components;

[0046] The frequency domain calculation module uses Parseval's theorem to solve the model, leveraging the advantages of frequency domain analysis to simplify and accelerate the solution process.

[0047] The information output module sets specific algorithm termination conditions, such as accuracy judgment, to determine when to stop the algorithm and output the results.

[0048] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described DC power distribution method for a generator state monitoring device.

[0049] Fourthly, embodiments of the present invention provide 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 above-described DC power distribution method for a generator state monitoring device.

[0050] The beneficial effects of this invention are as follows: By ensuring the use of the convergent particle swarm optimization algorithm, it not only surpasses the solution speed but also exhibits significant advantages in computational accuracy. Furthermore, by solving the variational problem model, a more accurate allocation ratio is obtained. The required number of mode decompositions is determined according to the specific application scenario. In the subsequent search and solution process, variational mode decomposition can adaptively match the optimal center frequency and bandwidth of each mode, thereby effectively separating the intrinsic mode components, realizing the frequency domain division of the signal, obtaining the effective decomposition components of the given signal, and finally obtaining the optimal solution of the variational problem. The intrinsic mode components obtained through variational mode decomposition not only each have an independent center frequency but also exhibit significant sparsity characteristics in the frequency domain, that is, most values ​​are zero, and only a small portion are non-zero values, which makes it particularly suitable for sparsity research. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. 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. Wherein:

[0052] Figure 1 is a flowchart of the DC power distribution method of the generator condition monitoring device.

[0053] Figure 2 is a flowchart of the particle swarm algorithm parameter optimization for the DC power allocation method of the generator condition monitoring device.

[0054] Figure 3 is a schematic diagram of the variational mode decomposition results of the DC power distribution method of the generator condition monitoring device.

[0055] Figure 4 shows the variational mode decomposition spectrum of the DC power distribution method of the generator condition monitoring device.

[0056] Figure 5 is a schematic diagram of the empirical mode decomposition results of the DC power distribution method of the generator condition monitoring device.

[0057] Figure 6 shows the empirical mode decomposition spectrum of the DC power distribution method of the generator condition monitoring device.

[0058] Figure 7 is a schematic diagram of the traditional particle swarm optimization algorithm for DC power allocation in generator condition monitoring devices.

[0059] Figure 8 is a schematic diagram of the guaranteed convergence particle swarm algorithm calculation for the DC power allocation method of the generator condition monitoring device.

[0060] Figure 9 shows the state-of-charge diagram of the energy storage device after power decomposition using the DC power distribution method of the generator condition monitoring device.

[0061] Figure 10 shows the state-of-charge diagram of the energy storage device before power decomposition in the DC power allocation method of the generator condition monitoring device. Detailed Implementation

[0062] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0063] 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.

[0064] 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.

[0065] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0066] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0067] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0068] Example 1

[0069] Referring to Figures 1 to 6, the first embodiment of the present invention provides a DC power distribution method for a generator condition monitoring device, comprising the following steps.

[0070] S1. Construct a variational problem model using system data.

[0071] To obtain the one-sided spectrum, the mode function u is subjected to Hilbert transform. k (t) is calculated, and the relevant analytical signal is obtained, which is expressed by the mathematical formula:

[0072] In the formula, u k (t) represents the mode function;

[0073] The predicted center frequencies of the modal components are determined as follows: Then, the spectrum of the modal components is adjusted using the exponential correction method so that it falls within the corresponding fundamental frequency band, which can be expressed mathematically as:

[0074] The bandwidth prediction of the modal components can be obtained by calculating the square norm of the gradient of the analytic signal. The constrained variational model of variational mode decomposition is expressed as:

[0075] In the formula, K represents the number of modes in the decomposition, which is a positive integer, and u k Let ω be the k-th modal component after decomposition. k Represented as u k The corresponding center frequency, It is expressed as the partial derivative of t.

[0076] S2. Solve the constructed variational problem model and use the guaranteed convergence particle swarm optimization algorithm to optimize the computational parameters of the variational problem model.

[0077] The constrained variational model is transformed into an unconstrained variational model by introducing a quadratic penalty factor and a Lagrange multiplication operator. The augmented Lagrange expression is as follows:

[0078] With two variables fixed and the remaining variable updated, the solution method for the unconstrained variational model can be expressed as follows:

[0079] but The values ​​are represented as follows:

[0080] in,

[0081] Solving this problem in the frequency domain using Parseval's theorem is specifically expressed as follows:

[0082] Then use ω-ω k The substitution, expressed mathematically, is as follows:

[0083] The integral over the non-negative frequency range can be expressed as:

[0084] Differentiation yields:

[0085] In the formula, α represents the quadratic penalty factor, and λ represents the Lagrange multiplier operator;

[0086] For center frequency Solve for the expression:

[0087] The integral over the non-negative frequency range can be expressed as:

[0088] In the formula, Represented as the current remaining amount Wiener filtering, Represented as the center frequency of the current modal component, for Performing an inverse Fourier transform, the resulting real part is {u} k (ω)};

[0089] The update formula for the Lagrange multiplier λ is expressed as:

[0090] For the Lagrange multiplier λ n+1 The solution is expressed as:

[0091] In the formula, the Lagrange multiplier λ n+1 The solution output consists of K modal components;

[0092] The given conditions are expressed mathematically as follows:

[0093] In the formula, ε represents the discrimination accuracy;

[0094] When the judgment condition is met, output the IMF component; when the judgment condition is not met, input n = n + 1 and solve the variational problem model again.

[0095] S3. Based on the calculation results of the variational problem model, the total output power of the hybrid energy storage system is divided into energy-type energy storage devices and power-type energy storage devices according to the ratio.

[0096] When the total output power of the hybrid energy storage system is output, a timestamp and a power identification mechanism are introduced. The power identification mechanism tests the power of both the energy-type and power-type energy storage devices, marking the power of the energy-type device as A and the power of the power-type device as B. By introducing the timestamp, power A and power B are added simultaneously, and the total output power of the hybrid energy storage system is marked as power C. Whether the condition is satisfied by the following judgment formula is then determined:

[0097] C-(A+B)≤T

[0098] In the formula, T represents the difference;

[0099] When the conditional formula is met, a normal log is output, marked with a timestamp;

[0100] If the judgment formula is not met, an exception log will be output, an alert will be issued, and a timestamp will be added.

[0101] The optimal parameter combination [K, α] = [10, 513] was obtained through iterative optimization calculation using the particle swarm optimization algorithm. The power required by the hybrid energy storage system after the first stage of scheduling was decomposed using the variational mode decomposition algorithm, as shown in Figure 3. The time-domain waveforms of the 10 intrinsic mode functions obtained after parameter optimization by the variational mode decomposition are shown. In order to analyze the frequency characteristics of these mode functions in more depth, the intrinsic mode functions were subjected to Hilbert transform, and the corresponding marginal spectrum was obtained, as shown in Figure 4. The frequency differentiation characteristics of different intrinsic mode functions are significant, and their distribution shows a certain regularity. The power required by the hybrid energy storage system was decomposed using the empirical mode decomposition algorithm, as shown in Figure 5. The time-domain waveforms of the 10 intrinsic mode functions obtained after empirical mode decomposition are shown. Then, the intrinsic mode functions were subjected to Hilbert transform, and the corresponding marginal spectrum was obtained, as shown in Figure 6. There is a serious aliasing phenomenon between different frequency components, especially IMF1, which almost covers the entire frequency band, making it difficult to distinguish between high and low frequency components. In contrast, the variational mode decomposition algorithm with optimized parameters shows superior performance in the decomposition of power signals.

[0102] Example 2

[0103] Based on the first embodiment, this embodiment further provides a DC power distribution system for a generator condition monitoring device, including an algorithm transformation module, an exponential correction module, a frequency domain calculation module, and an information output module;

[0104] The algorithm transformation module uses Hilbert transform to process mode functions, calculates relevant analytic signals, and analyzes the frequency components of the signals in the complex plane;

[0105] The exponential correction module uses the exponential correction method to adjust the spectrum of the modal components to match the predefined fundamental frequency band and clarify the different frequency components;

[0106] The frequency domain calculation module uses Parseval's theorem to solve the model, leveraging the advantages of frequency domain analysis to simplify and accelerate the solution process.

[0107] The information output module sets specific algorithm termination conditions, such as accuracy judgment, to determine when to stop the algorithm and output the results.

[0108] This embodiment also provides a computer device applicable to the DC power distribution method of a generator condition monitoring device, 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 DC power distribution method of the generator condition monitoring device as proposed in the above embodiment.

[0109] 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.

[0110] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the DC power distribution method for the generator condition monitoring device as proposed in the above embodiments.

[0111] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0112] Example 3

[0113] Based on the previous two embodiments, this embodiment provides a DC power distribution method for a generator condition monitoring device. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0114] As shown in Figures 7 and 8, the parameters of variational mode decomposition were optimized using the guaranteed convergence particle swarm optimization algorithm of Example 1 and the traditional particle swarm optimization algorithm, respectively. The guaranteed convergence particle swarm optimization algorithm of Example 1 reached the minimum fitness function value of -0.968489 in the 14th iteration, while the traditional particle swarm optimization algorithm reached its minimum fitness function value of -0.967331 in the 18th iteration. The results demonstrate that Example 1 has a significant advantage in terms of computational accuracy.

[0115] As shown in Figures 9 and 10, power allocation is performed using variational mode decomposition with optimized parameters, which reduces the range of changes in the state of charge of the energy storage system. This reduced range of changes not only helps to improve the cycle life of the energy storage system but also enhances the overall electrical performance. Furthermore, this power allocation method has strong versatility and can be applied to the mixed use of any two energy storage technologies.

[0116] 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 DC power distribution method for a generator condition monitoring device, characterized in that: Includes the following steps, Construct a variational problem model using system data; The constructed variational problem model is solved, and the computational parameters of the variational problem model are optimized using the particle swarm optimization algorithm. Based on the calculation results of the variational problem model, the total output power of the hybrid energy storage system is divided into energy-type energy storage devices and power-type energy storage devices according to the ratio.

2. The DC power distribution method for the generator condition monitoring device as described in claim 1, characterized in that: The method for constructing the variational problem model includes, The mode function is calculated using the Hilbert transform, and the relevant analytic signal is obtained, which is expressed mathematically as follows: In the formula, u k (t) represents the mode function; The predicted center frequencies of the modal components are determined as follows: Then, the spectrum of the modal components is adjusted using the exponential correction method so that it falls within the corresponding fundamental frequency band, which can be expressed mathematically as: The bandwidth prediction of the modal components can be obtained by calculating the square norm of the gradient of the analytic signal. The constrained variational model of variational mode decomposition is expressed as: In the formula, K represents the number of modes in the decomposition, u k Let ω be the k-th modal component after decomposition. k Represented as u k The corresponding center frequency, It is expressed as the partial derivative of t.

3. The DC power distribution method for the generator condition monitoring device as described in claim 2, characterized in that: The methods for solving the variational problem model include, The constrained variational model is transformed into an unconstrained variational model by introducing a quadratic penalty factor and a Lagrange multiplication operator. The augmented Lagrange expression is as follows: With two variables fixed and the remaining variable updated, the solution method for the unconstrained variational model can be expressed as follows: but The values ​​are represented as follows: in, Solving this problem in the frequency domain using Parseval's theorem is specifically expressed as follows: Then use ω-ω k The substitution, expressed mathematically, is as follows: The integral over the non-negative frequency range can be expressed as: Differentiation yields: In the formula, α represents the quadratic penalty factor, and λ represents the Lagrange multiplier operator.

4. The DC power distribution method for the generator condition monitoring device as described in claim 3, characterized in that: The solution method for the variational problem model also includes, For center frequency Solve for the expression: The integral over the non-negative frequency range can be expressed as: In the formula, Represented as the current remaining amount Wiener filtering, Represented as the center frequency of the current modal component, for Performing an inverse Fourier transform, the resulting real part is {u} k (ω)}.

5. The DC power distribution method for the generator condition monitoring device as described in claim 4, characterized in that: The update formula for the Lagrange multiplier λ is expressed as: For the Lagrange multiplier λ n+1 The solution is expressed as: In the formula, the Lagrange multiplier λ n+1 The solution output consists of K modal components.

6. The DC power distribution method for the generator condition monitoring device as described in claim 5, characterized in that: The given conditions are expressed mathematically as follows: ε>0 In the formula, ε represents the discrimination accuracy; When the judgment condition is met, output the IMF component; when the judgment condition is not met, input n = n + 1 and solve the variational problem model again.

7. The DC power distribution method for the generator condition monitoring device as described in claim 6, characterized in that: When the total output power of the hybrid energy storage system is output, a timestamp and a power identification mechanism are introduced. The power identification mechanism tests the power of both energy-type and power-type energy storage devices, marking the power of the energy-type devices as A and the power of the power-type devices as B. By introducing the timestamp, power A and power B are added simultaneously, and the total output power of the hybrid energy storage system is marked as power C. The system then checks whether the following formula is satisfied: C - (A + B) ≤ T. In the formula, T represents the difference; When the conditional formula is met, a normal log is output, marked with a timestamp; If the judgment formula is not met, an exception log will be output, an alert will be issued, and a timestamp will be added.

8. A DC power distribution system for a generator condition monitoring device, based on the DC power distribution method for a generator condition monitoring device according to any one of claims 1 to 7, characterized in that: It includes an algorithm transformation module, an exponential correction module, a frequency domain calculation module, and an information output module; The algorithm transformation module uses Hilbert transform to process mode functions, calculates relevant analytic signals, and analyzes the frequency components of the signals in the complex plane; The exponential correction module uses the exponential correction method to adjust the spectrum of the modal components to match the predefined fundamental frequency band and clarify the different frequency components; The frequency domain calculation module uses Parseval's theorem to solve the model, leveraging the advantages of frequency domain analysis to simplify and accelerate the solution process. The information output module sets specific algorithm termination conditions, such as accuracy judgment, to determine when to stop the algorithm and output the results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the DC power distribution method for the generator condition monitoring device according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the DC power distribution method of the generator condition monitoring device according to any one of claims 1 to 7.

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