Electric water heater power saving method and system based on particle swarm optimization algorithm

Abstract

The invention discloses an electric water heater power saving method and system based on a particle swarm optimization algorithm. The method comprises the steps of data collection, adaptive mode decomposition, parameter adaptive optimization, disturbance mode division, disturbance mode denoising, threshold adaptive correction, analysis signal construction, power smooth switching and power saving strategy design. The invention belongs to the field of power saving, and particularly relates to an electric water heater power saving method and system based on a particle swarm optimization algorithm. According to the scheme, higher heat efficiency and lower energy consumption fluctuation are achieved by introducing self-adaptive VMD and chaos T distribution optimization based on the energy loss ratio; an actual heating stage, a temperature falling stage and environment heat loss are distinguished through disturbance mode division, and power disturbance caused by noise or power grid fluctuation is measured; wavelet threshold de-noising is carried out on all disturbance modes, a specific threshold function is selected, self-adaptive correction is carried out on the threshold, and then the power saving effect of the electric water heater is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power saving, and in particular to a method and system for saving power of an electric water heater based on a particle swarm optimization algorithm. Background Art

[0002] Electric water heater power conservation methods employ a series of technologies and strategies to improve efficiency and reduce energy waste, thereby lowering energy consumption and saving operating costs. However, typical electric water heater power conservation methods suffer from signal non-stationarity and noise interference, which can easily lead to controller misjudgments and parameter jitter, impacting stability. These methods also fail to distinguish the actual heating and insulation process from ambient heat loss and grid noise, making it difficult for the controller to accurately compensate for temperature fluctuations and prone to over-response to noise. Summary of the Invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an electric water heater power saving method and system based on particle swarm optimization algorithm. In view of the problem that the general electric water heater power saving method has signal non-stationarity and noise interference, which easily leads to controller misjudgment and parameter jitter, affecting stability, this solution introduces adaptive VMD and chaotic T based on energy loss ratio to save power of electric water heaters. Distributed optimization can accurately separate various physical processes in the complex and non-stationary operating environment of electric water heaters, and can also automatically search for the optimal decomposition parameters online, thereby achieving higher thermal efficiency and lower energy consumption fluctuations; the general power saving method of electric water heaters is unable to distinguish the actual heating and insulation process from environmental heat loss and grid noise, which makes it difficult for the controller to accurately compensate for the temperature and is prone to over-response to noise. This solution uses disturbance mode division to distinguish the actual heating stage, temperature drop stage and power disturbances caused by environmental heat loss, measurement noise or grid fluctuations; wavelet threshold denoising is performed on all disturbance modes, a specific threshold function is selected, and the threshold is adaptively corrected so that the retained dominant mode more accurately reflects the actual heating and insulation dynamics of the electric water heater; modal reconstruction is used to smooth power switching, generate a smooth phase switching signal, and reduce damage to electrical equipment, thereby improving the power saving effect of the electric water heater.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides a method for saving power of an electric water heater based on a particle swarm optimization algorithm, the method comprising the following steps:

[0005] Step S1: data collection;

[0006] Step S2: adaptive modal decomposition;

[0007] Step S3: parameter adaptive optimization;

[0008] Step S4: disturbance mode division;

[0009] Step S5: disturbed mode denoising;

[0010] Step S6: threshold adaptive correction;

[0011] Step S7: constructing an analytical signal;

[0012] Step S8: smooth power switching;

[0013] Step S9: Power saving strategy design.

[0014] Furthermore, in step S1, the data collection is to collect the original signal of the electric water heater operation and perform preprocessing.

[0015] Furthermore, in step S2, the adaptive modal decomposition is to decompose the original signal f(t) of the electric water heater into K intrinsic mode functions, and the VMD objective function is expressed as: ; ;in, is the kth mode function; is the central angular frequency of the mode; K is the mode number; is the Dirac distribution; j is the imaginary unit; is the first derivative with respect to time t.

[0016] Furthermore, in step S3, the parameter adaptive optimization is to automatically determine the modal number K and the penalty factor α using a particle swarm optimization algorithm; specifically, the following steps are included:

[0017] Step S31: Tent chaotic mapping, select the chaotic sequence length as , initialize the seed ; Iteratively generate chaotic series , the formula used is: ; The first N1 Map to [K min ,K max ] and [α min ,α max ], the formula used is: ; ; The individual particle position is expressed as ;in, and are the i-th and i+1-th chaotic numbers respectively; rand~U(0,1); and are the minimum and maximum mode numbers, respectively; and are the minimum penalty factor and the maximum penalty factor respectively; is the i+Nth chaotic number; the chaotic number corresponds to the index of the individual particle;

[0018] Step S32: Determine the individual fitness of the particle; calculate the energy loss ratio using the formula: ; ; ;Particle individual fitness value Expressed as: ; Where E is the total energy of the original signal; is the sum of all modal energies; is the kth mode function at time t; is the energy loss ratio; is a smoothing term;

[0019] Step S33: Particle individual movement; randomly select half of the particle individuals and apply the standard particle swarm optimization algorithm to move their positions; for the remaining particle individuals, calculate the probability of the particle individual being selected , the formula used is as follows: ; When generating random numbers, the current chaotic sequence is given priority; the selected particle individuals are subjected to a crossover operation to generate offspring, which is expressed as: ; ; ; Mutation operation, the formula used is: ; ; ; ;in, is the individual fitness value of the nth particle; N2 is the population size; is the individual position of the daughter particle; and are the individual positions of the two parent particles, and is the chaotic number corresponding to the two parents; is the cross weight; and are the individual positions of particles before and after mutation, respectively; yes Obey T-distributed random disturbance; is the factor of variation; is the basic variability factor; is the growth rate;

[0020] Step S34: Additional adjustment: additionally using T distribution for adaptive global-local adjustment, the formula used is: ;in, , and Independent of each other; and are the positions after and before additional adjustment, respectively;

[0021] Step S35: Parameter determination; set the maximum number of iterations. If the maximum number of iterations is reached or the objective function obtained by the optimal individual particle position converges, the modal number K and penalty factor α represented by the optimal individual particle position are set as parameters to perform adaptive modal decomposition.

[0022] Furthermore, in step S4, the disturbance mode partitioning is to calculate the Pearson correlation coefficient of each IMF with the original signal. , the formula used is: ; Define the dominant mode: ; Perturbation mode: ;in, is the covariance; and The original signal and The standard deviation of is the correlation coefficient threshold.

[0023] Furthermore, in step S5, the disturbance mode denoising is to perform wavelet threshold denoising on all disturbance modes, and the threshold function is selected as: ;in, is the wavelet threshold denoising result of the g-th disturbance mode; is the sign function; d is the signal amplitude; T is the basic threshold; is the threshold offset parameter; is the shape parameter.

[0024] Furthermore, in step S6, the threshold adaptive correction is to perform wavelet decomposition in advance before performing wavelet threshold denoising on all disturbance modes, and use the adaptive correction threshold for the disturbance mode of the u-th layer wavelet decomposition. Instead of T, if the noise level of the electric water heater control system is low, the correction formula is expressed as: If the sensor noise and the external disturbance of the inlet water temperature fluctuation are large, the correction formula is expressed as: ;in, is the noise standard deviation estimate of the g-th disturbance mode; is the mean noise standard deviation; is the length of the u-th layer wavelet decomposition coefficient; u is the wavelet decomposition level index; e is a natural constant.

[0025] Furthermore, in step S7, the analytical signal is constructed for each mode, and the formula used is: ; ;in, is the analytical signal; is the Hilbert transform operator; is the instantaneous amplitude; The peak value corresponds to the heating start power.

[0026] Furthermore, in step S8, the power smooth switching is smoothed by modal reconstruction, and the modal reconstruction is expressed as: ;in, is the reconstructed smoothed power reference signal; D is the dominant mode set; N3 is the disturbance mode set; is the mode function of the dominant mode; is the mode function of the disturbance mode; it generates a smooth phase switching signal in the controller.

[0027] Furthermore, in step S9, the power saving strategy is designed based on the extracted features to design a real-time control strategy: intelligent preheating, which is gentle heating in advance during non-peak hours according to usage habits and ambient temperature trends; dynamic heat preservation, which is When the temperature is lower than the set threshold, short-term temperature supplementation is performed; otherwise, low-frequency inspection is initiated.

[0028] The electric water heater power saving system based on the particle swarm optimization algorithm provided by the present invention includes a data acquisition module, an adaptive mode decomposition module, a parameter adaptive optimization module, a disturbance mode division module, a disturbance mode denoising module, a threshold adaptive correction module, an analytical signal construction module, a power smooth switching module and a power saving strategy design module;

[0029] The data acquisition module collects and pre-processes the original operation signal of the electric water heater;

[0030] The adaptive mode decomposition module uses variational mode decomposition to adaptively decompose the original signal;

[0031] The parameter adaptive optimization module adaptively optimizes VMD parameters based on T distribution variation and crossover operation;

[0032] The disturbance mode division module calculates the Pearson correlation coefficient between each mode and the original signal, and defines the dominant mode and the disturbance mode;

[0033] The disturbance mode denoising module applies a denoising function based on a wavelet threshold to all disturbance modes;

[0034] The threshold adaptive correction module adaptively adjusts the wavelet denoising threshold according to different decomposition levels and noise levels;

[0035] The analytical signal construction module performs Hilbert transform on each mode and extracts the instantaneous amplitude and mutation time point as features;

[0036] The power smooth switching module generates a smooth power reference signal through modal reconstruction;

[0037] The power saving strategy design module formulates intelligent preheating and dynamic heat preservation strategies based on the extracted features.

[0038] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0039] (1) In view of the problem that the general electric water heater power saving method has signal non-stationarity and noise interference, which easily leads to controller misjudgment and parameter jitter, affecting stability, this scheme introduces adaptive VMD and chaotic T distribution optimization based on energy loss ratio. It can accurately separate the various physical processes in the complex and non-stationary electric water heater operating environment, and can also automatically search for the optimal decomposition parameters online, thereby achieving higher thermal efficiency and lower energy consumption fluctuations.

[0040] (2) In view of the fact that the general power saving method of electric water heaters cannot distinguish the actual heating and insulation process from the environmental heat loss and grid noise, which makes it difficult for the controller to accurately compensate for the temperature and is prone to over-response to noise, this scheme uses disturbance mode division to distinguish the actual heating stage, the temperature drop stage and the power disturbance caused by the environmental heat loss, measurement noise or grid fluctuation; all disturbance modes are subjected to wavelet threshold denoising, a specific threshold function is selected, and the threshold is adaptively corrected so that the retained dominant mode can more accurately reflect the actual heating and insulation dynamics of the electric water heater; the power switching is smoothed by modal reconstruction, and a smooth phase switching signal is generated to reduce damage to electrical equipment; thereby improving the power saving effect of the electric water heater. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic flow chart of a method for saving power of an electric water heater based on a particle swarm optimization algorithm provided by the present invention;

[0042] Figure 2 This is a schematic diagram of the electric water heater power saving system based on the particle swarm optimization algorithm provided by the present invention.

[0043] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] In the description of the present invention, it should be understood that terms such as "up", "down", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the system or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0046] Example 1, see Figure 1 The present invention provides a method for saving power of electric water heaters based on particle swarm optimization algorithm, which comprises the following steps:

[0047] Step S1: Data acquisition: collecting and preprocessing the original signal of the electric water heater operation;

[0048] Step S2: Adaptive modal decomposition: Adaptively decompose the original signal using variational modal decomposition;

[0049] Step S3: Parameter adaptive optimization: Based on T distribution variation and crossover operation, adaptively optimize VMD parameters;

[0050] Step S4: Disturbance mode division; calculate the Pearson correlation coefficient between each mode and the original signal, and define the dominant mode and disturbance mode;

[0051] Step S5: disturbance mode denoising: applying a denoising function based on wavelet threshold to all disturbance modes;

[0052] Step S6: threshold adaptive correction; adaptively adjust the wavelet denoising threshold according to different decomposition levels and noise levels;

[0053] Step S7: Construct analytical signals; perform Hilbert transform on each mode, and extract the instantaneous amplitude and mutation time point as features;

[0054] Step S8: Power smooth switching; generating a smooth power reference signal through modal reconstruction;

[0055] Step S9: Power saving strategy design; formulate intelligent preheating and dynamic heat preservation strategies based on the extracted features.

[0056] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, data acquisition is to collect the original signal of the electric water heater and perform preprocessing; a temperature sensor is installed in the water tank of the electric water heater to collect the water tank temperature; a power meter is installed in the power supply circuit to obtain the real-time power; flow meters are installed at the water inlet and the return water inlet to obtain the water outlet flow and the return water flow; zero drift correction is performed on the sampled signal and abnormal values ​​of the current jump are removed to ensure the stability of the subsequent decomposition.

[0057] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the adaptive modal decomposition is to decompose the original signal f(t) of the electric water heater into K intrinsic mode functions, each mode corresponding to a different physical process; the VMD objective function is expressed as: ; ;in, is the kth mode function; is the central angular frequency of the mode; K is the mode number; is the Dirac distribution; j is the imaginary unit; is the first derivative with respect to time t.

[0058] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the parameter adaptive optimization uses a particle swarm optimization algorithm to automatically determine the modal number K and the penalty factor α. Specifically, the following steps are included:

[0059] Step S31: Tent chaotic mapping is used to enhance population diversity. The length of the chaotic sequence is selected as , initialize the seed ; Iteratively generate chaotic series , the formula used is: ; The first N1 Map to [K min ,K max ] and [α min ,α max ], the formula used is: ; ; The individual particle position is expressed as ;in, and are the i-th and i+1-th chaotic numbers respectively; rand~U(0,1); and are the minimum and maximum mode numbers, respectively; and are the minimum penalty factor and the maximum penalty factor respectively; the parameters will be normalized in the search space, and the specific range will be returned when the individual particle position is finally realized; is the i+Nth chaotic number; the chaotic number corresponds to the index of the individual particle;

[0060] Step S32: Determine the individual fitness of the particle; calculate the energy loss ratio using the formula: ; ; ;Particle individual fitness value Expressed as: ; Where E is the total energy of the original signal; is the sum of all modal energies; is the kth mode function at time t; is the energy loss ratio; is a smoothing term;

[0061] Step S33: Particle individual movement; randomly select half of the particle individuals and apply the standard particle swarm optimization algorithm to move their positions; for the remaining individuals, calculate the probability of the particle individual being selected , the formula used is as follows: ; When generating random numbers, the current chaotic sequence is preferred rather than a purely uniform distribution; the selected particle individuals are subjected to a crossover operation to generate offspring, which is expressed as: ; ; ; Mutation operation, the formula used is: ; ; ; ;in, is the individual fitness value of the nth particle; N2 is the population size; is the individual position of the daughter particle; and are the individual positions of the two parent particles, and is the chaotic number corresponding to the two parents; is the cross weight; and are the individual positions of particles before and after mutation, respectively; yes Obey T-distributed random disturbance; is the factor of variation; is the basic variability factor; is the growth rate;

[0062] Step S34: Additional adjustment: additionally using T distribution for adaptive global-local adjustment, the formula used is: ;in, , and Independent of each other; and are the positions after and before additional adjustment, respectively;

[0063] Step S35: Parameter determination; set the maximum number of iterations. If the maximum number of iterations is reached or the objective function obtained by the optimal individual particle position converges, the modal number K and penalty factor α represented by the optimal individual particle position are set as parameters to perform adaptive modal decomposition.

[0064] By performing the above operations, we address the problem that general electric water heater power saving methods have signal non-stationarity and noise interference, which easily lead to controller misjudgment and parameter jitter, affecting stability. By introducing adaptive VMD and chaotic T distribution optimization based on energy loss ratio, this scheme can accurately separate the various physical processes in the complex and non-stationary electric water heater operating environment, and can also automatically search for the optimal decomposition parameters online, thereby achieving higher thermal efficiency and lower energy consumption fluctuations.

[0065] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the disturbance mode partitioning is to calculate the Pearson correlation coefficient of each IMF with the original signal. , the formula used is: ; Define the dominant mode: , corresponding to the actual heating stage and the temperature drop stage; disturbance mode: , corresponding to power disturbances caused by ambient heat loss, measurement noise or grid fluctuations; among them, is the covariance; and The original signal and The standard deviation of It is the correlation coefficient threshold; it realizes the fine separation of the various physical processes of the water heater operation and provides a basis for targeted control.

[0066] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the disturbance mode denoising is to perform wavelet threshold denoising on all disturbance modes. The threshold function is selected as: ;in, is the wavelet threshold denoising result of the g-th disturbance mode; is the sign function; d is the signal amplitude; T is the basic threshold; is the threshold offset parameter; is the shape parameter.

[0067] Example 7, see Figure 1 This embodiment is based on the above embodiment. In step S6, the threshold adaptive correction is that the fixed basic threshold is difficult to take into account the optimal denoising effect under different decomposition levels and different noise environments; therefore, before performing wavelet threshold denoising on all disturbance modes, wavelet decomposition is performed in advance, and the adaptive correction threshold is used for the disturbance mode of the u-th layer wavelet decomposition. Instead of T, if the noise level of the electric water heater control system is low, the correction formula is expressed as: If the sensor noise and the external disturbance of the inlet water temperature fluctuation are large, the correction formula is expressed as: ;in, is the noise standard deviation estimate of the g-th disturbance mode; is the mean noise standard deviation; is the length of the u-th layer wavelet decomposition coefficient; u is the wavelet decomposition level index; e is a natural constant; it effectively suppresses high-frequency disturbances such as sensor jitter and grid harmonics, so that the retained dominant mode more accurately reflects the actual heating and insulation dynamics of the electric water heater.

[0068] Example 8, see Figure 1 This embodiment is based on the above embodiment. In step S7, constructing an analytical signal is to extract the instantaneous amplitude and key time points. The analytical signal is constructed for each mode, and the formula used is: ; ;in, is the analytical signal; is the Hilbert transform operator; is the instantaneous amplitude; The peak value corresponds to the heating start power; the mutation point corresponds to the moment when heating ends or the insulation mode is switched.

[0069] Example 9, see Figure 1 This embodiment is based on the above embodiment. In step S8, the power smooth switching is smoothed by modal reconstruction. The modal reconstruction is expressed as: ;in, is the reconstructed smoothed power reference signal; D is the dominant mode set; N3 is the disturbance mode set; is the mode function of the dominant mode; It is the modal function of the disturbance mode; it generates a smooth phase switching signal in the controller to avoid large current jumps in the relay and improve the life of the electrical equipment.

[0070] In step S9, the power saving strategy is designed based on the extracted features to design a real-time control strategy: intelligent preheating, according to the usage habits and ambient temperature trends, gentle heating in the non-peak period in advance to avoid the peak high power impact; dynamic heat preservation, when When the water temperature is lower than the set threshold, short-term heating is performed; otherwise, low-frequency inspection is performed to reduce idle consumption; short-term heating: 10 minutes of heating; low-frequency inspection: inspection is performed every 3 hours. If the water temperature drops to 5 degrees below the set threshold, short-term heating is started.

[0071] By performing the above operations, the conventional power saving method for electric water heaters cannot distinguish the actual heating and insulation process from ambient heat loss and grid noise, which makes it difficult for the controller to accurately compensate for the temperature and is prone to over-responding to noise. This solution uses disturbance mode division to distinguish the actual heating stage, the temperature drop stage, and the power disturbance caused by ambient heat loss, measurement noise, or grid fluctuations. Wavelet threshold denoising is performed on all disturbance modes, a specific threshold function is selected, and the threshold is adaptively corrected so that the retained dominant mode more accurately reflects the actual heating and insulation dynamics of the electric water heater. Modal reconstruction is used to smooth power switching, generate a smooth phase switching signal, and reduce damage to electrical equipment, thereby improving the power saving effect of the electric water heater.

[0072] Example 10, see Figure 2 This embodiment is based on the above embodiment. The electric water heater power saving system based on the particle swarm optimization algorithm provided by the present invention includes a data acquisition module, an adaptive mode decomposition module, a parameter adaptive optimization module, a disturbance mode division module, a disturbance mode denoising module, a threshold adaptive correction module, an analytical signal construction module, a power smooth switching module and a power saving strategy design module;

[0073] The data acquisition module collects and pre-processes the original operation signal of the electric water heater;

[0074] The adaptive mode decomposition module uses variational mode decomposition to adaptively decompose the original signal;

[0075] The parameter adaptive optimization module adaptively optimizes VMD parameters based on T distribution variation and crossover operation;

[0076] The disturbance mode division module calculates the Pearson correlation coefficient between each mode and the original signal, and defines the dominant mode and the disturbance mode;

[0077] The disturbance mode denoising module applies a denoising function based on a wavelet threshold to all disturbance modes;

[0078] The threshold adaptive correction module adaptively adjusts the wavelet denoising threshold according to different decomposition levels and noise levels;

[0079] The analytical signal construction module performs Hilbert transform on each mode and extracts the instantaneous amplitude and mutation time point as features;

[0080] The power smooth switching module generates a smooth power reference signal through modal reconstruction;

[0081] The power saving strategy design module formulates intelligent preheating and dynamic heat preservation strategies based on the extracted features.

[0082] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0083] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

[0084] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A power saving method for electric water heaters based on particle swarm optimization algorithm, characterized by: The method comprises the following steps: Step S1: Data acquisition: collecting and preprocessing the original signal of the electric water heater operation; Step S2: Adaptive modal decomposition: Adaptively decompose the original signal using variational modal decomposition; Step S3: Parameter adaptive optimization: Based on T distribution variation and crossover operation, adaptively optimize VMD parameters; Step S4: Disturbance mode division; calculate the Pearson correlation coefficient between each mode and the original signal, and define the dominant mode and disturbance mode; Step S5: disturbance mode denoising: applying a denoising function based on wavelet threshold to all disturbance modes; Step S6: threshold adaptive correction; adaptively adjust the wavelet denoising threshold according to different decomposition levels and noise levels; Step S7: Construct analytical signals; perform Hilbert transform on each mode, and extract the instantaneous amplitude and mutation time point as features; Step S8: Power smooth switching; generating a smooth power reference signal through modal reconstruction; Step S9: Power saving strategy design; formulate intelligent preheating and dynamic heat preservation strategies based on the extracted features.

2. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 1, characterized in that: In step S2, the adaptive modal decomposition is to decompose the original signal f(t) of the electric water heater into K intrinsic mode functions. The VMD objective function is expressed as: ; ;in, is the kth mode function; is the central angular frequency of the mode; K is the mode number; is the Dirac distribution; j is the imaginary unit; is the first derivative with respect to time t.

3. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 2, characterized in that: In step S4, the disturbance mode partitioning is to calculate the Pearson correlation coefficient of each IMF with the original signal. , the formula used is: ; Define the dominant mode: ; Perturbation mode: ;in, is the covariance; and The original signal and The standard deviation of is the correlation coefficient threshold.

4. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 3, characterized in that: In step S5, the disturbance mode denoising is to perform wavelet threshold denoising on all disturbance modes, and the threshold function is selected as: ;in, is the wavelet threshold denoising result of the g-th disturbance mode; is the sign function; d is the signal amplitude; T is the basic threshold; is the threshold offset parameter; is the shape parameter.

5. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 4, characterized in that: In step S6, the threshold adaptive correction is to perform wavelet decomposition in advance before performing wavelet threshold denoising on all disturbance modes, and use the adaptive correction threshold for the disturbance mode of the u-th layer wavelet decomposition. Instead of T, if the noise level of the electric water heater control system is low, the correction formula is expressed as: If the sensor noise and the external disturbance of the inlet water temperature fluctuation are large, the correction formula is expressed as: ;in, is the noise standard deviation estimate of the g-th disturbance mode; is the mean noise standard deviation; is the length of the u-th layer wavelet decomposition coefficient; u is the wavelet decomposition level index; e is a natural constant.

6. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 5, characterized in that: In step S7, the analytical signal is constructed for each mode using the formula: ; ;in, is the analytical signal; is the Hilbert transform operator; is the instantaneous amplitude; The peak value corresponds to the heating start power; In step S8, the power smooth switching is smoothed by modal reconstruction, and the modal reconstruction is expressed as: ;in, is the reconstructed smoothed power reference signal; D is the dominant mode set; N3 is the disturbance mode set; is the mode function of the dominant mode; is the mode function of the disturbance mode; it generates a smooth phase switching signal in the controller.

7. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 6, characterized in that: In step S9, the power saving strategy is designed based on the extracted features to design a real-time control strategy: intelligent preheating, which is gentle heating in advance during non-peak hours according to usage habits and ambient temperature trends; Dynamic insulation, when When the temperature is lower than the set threshold, short-term heating is applied; Otherwise, enter low-frequency inspection.

8. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 7, characterized in that: In step S3, the parameter adaptive optimization is to automatically determine the modal number K and the penalty factor α using the particle swarm optimization algorithm; specifically, the following steps are included: Step S31: Tent chaotic mapping, select the chaotic sequence length as , initialize the seed ; Iteratively generate chaotic series , the formula used is: ; The first N1 Map to [K min ,K max ] and [α min ,α max ], the formula used is: ; ; The individual particle position is expressed as ;in, and are the i-th and i+1-th chaotic numbers respectively; rand~U(0,1); and are the minimum and maximum mode numbers, respectively; and are the minimum penalty factor and the maximum penalty factor respectively; is the i+Nth chaotic number; the chaotic number corresponds to the index of the individual particle; Step S32: Determine the individual fitness of the particle; calculate the energy loss ratio using the formula: ; ; ;Particle individual fitness value Expressed as: ; Where E is the total energy of the original signal; is the sum of all modal energies; is the kth mode function at time t; is the energy loss ratio; is a smoothing term; Step S33: individual particles move; Step S34: Additional adjustment: additionally using T distribution for adaptive global-local adjustment, the formula used is: ;in, , and Independent of each other; and are the positions after and before additional adjustment, respectively; Step S35: Parameter determination; set the maximum number of iterations. If the maximum number of iterations is reached or the objective function obtained by the optimal individual particle position converges, the modal number K and penalty factor α represented by the optimal individual particle position are set as parameters to perform adaptive modal decomposition.

9. The electric water heater power saving method based on particle swarm optimization algorithm according to claim 8, characterized in that: In step S33, the particle individual movement is to randomly select half of the particle individuals and apply the standard particle swarm optimization algorithm to move the position; for the remaining particle individuals, the probability of the particle individual being selected is calculated. , the formula used is as follows: ; When generating random numbers, the current chaotic sequence is given priority; the selected particle individuals are subjected to a crossover operation to generate offspring, which is expressed as: ; ; ; Mutation operation, the formula used is: ; ; ; ;in, is the individual fitness value of the nth particle; N2 is the population size; is the individual position of the daughter particle; and are the individual positions of the two parent particles, and is the chaotic number corresponding to the two parents; is the cross weight; and are the individual positions of particles before and after mutation, respectively; yes Obey T-distributed random disturbance; is the factor of variation; is the basic variability factor; is the growth rate.

10. An electric water heater power saving system based on a particle swarm optimization algorithm, for implementing the electric water heater power saving method based on a particle swarm optimization algorithm as claimed in any one of claims 1 to 9, characterized in that: It includes data acquisition module, adaptive modal decomposition module, parameter adaptive optimization module, disturbance mode division module, disturbance mode denoising module, threshold adaptive correction module, analytical signal construction module, power smooth switching module and power saving strategy design module; The data acquisition module collects and pre-processes the original operation signal of the electric water heater; The adaptive mode decomposition module uses variational mode decomposition to adaptively decompose the original signal; The parameter adaptive optimization module adaptively optimizes VMD parameters based on T distribution variation and crossover operation; The disturbance mode division module calculates the Pearson correlation coefficient between each mode and the original signal, and defines the dominant mode and the disturbance mode; The disturbance mode denoising module applies a denoising function based on a wavelet threshold to all disturbance modes; The threshold adaptive correction module adaptively adjusts the wavelet denoising threshold according to different decomposition levels and noise levels; The analytical signal construction module performs Hilbert transform on each mode and extracts the instantaneous amplitude and mutation time point as features; The power smooth switching module generates a smooth power reference signal through modal reconstruction; The power saving strategy design module formulates intelligent preheating and dynamic heat preservation strategies based on the extracted features.

Images