Noise induction and suppression method for energy storage equipment

Abstract

The invention relates to the technical field of energy storage, in particular to a noise induction and suppression method for energy storage equipment, which comprises the following steps of: recording energy storage control characteristics and corresponding equipment noise when the energy storage equipment works, the noise response proportion of the N noise components with the strongest noise response is recorded as the noise suppression proportion of each energy storage control characteristic; n is the number of the anti-phase sound sources; for the difference A between the equipment noise corresponding to any energy storage control feature and the noise at the current moment and the difference B between any energy storage control feature and the energy storage control feature at the current moment, obtaining a prediction control feature according to A and B; the working process of the energy storage equipment is further regulated and intervened by utilizing the predictive control characteristics, and then noise suppression is carried out by utilizing an anti-phase sound source. According to the invention, the noise suppression effect is further improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a noise sensing and suppression method for energy storage equipment. Background Art

[0002] Energy storage devices such as lithium-ion batteries, supercapacitors, flywheel energy storage, and compressed air energy storage (CAES) are widely used in scenarios such as peak load regulation and frequency regulation of power systems, grid connection of new energy sources, and improvement of grid stability. However, these energy storage devices will generate significant noise problems during operation, mainly from electromagnetic interference of power electronic equipment, vibration of mechanical components, and operation of cooling systems.

[0003] At present, the noise suppression technology of energy storage equipment is mainly divided into two categories - passive sound insulation methods and active noise control methods. Among them, sound insulation enclosures and reverse sound source cancellation are the most commonly used technical means: the sound insulation enclosure method controls the noise in a closed or semi-enclosed space by installing sound insulation covers, sound insulation panels or sound insulation walls around the energy storage equipment to reduce the propagation and diffusion of noise. Sound insulation materials usually use a multi-layer structure, including sound-absorbing cotton, damping layers and sound insulation panels. The overall structural design takes into account the reflection, absorption and attenuation properties of sound waves. This method is not only costly but also occupies a large space, and it is easy to cause difficulties in equipment heat dissipation, affecting the stable operation of the energy storage equipment, and the noise suppression effect is also limited.

[0004] The reverse sound source cancellation (ANC) method (also known as the active noise control method) generates reverse sound waves with opposite phases to the noise in real time by placing speakers (called reverse phase sound source devices) on the noise propagation path to cancel out the noise energy. This method has limited suppression effect on complex sound fields (such as multi-source noise or non-steady-state noise) and it is difficult to completely eliminate the noise.

[0005] In summary, the existing noise suppression methods for energy storage devices are not effective enough in suppressing noise. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a noise sensing and suppression method for energy storage equipment.

[0007] A noise sensing and suppression method for energy storage equipment of the present invention adopts the following technical solution: An embodiment of the present invention provides a method for noise sensing and suppression of an energy storage device, the method comprising the following steps: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; f is recorded as the similarity suppression coefficient corresponding to the prediction control feature, and the initial value of f is a preset value; Obtain the noise suppression ratio of the predictive control feature obtained under different values ​​of the similarity suppression coefficient, and record the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature; The energy storage device operates with the target control characteristics at the next moment and records the device noise D0 collected in real time. The sound waves generated by the anti-phase sound source have the same amplitude and frequency and opposite phases as the N noise components with the strongest noise response in D0.

[0008] Preferably, the step of obtaining the noise suppression ratio of the prediction control feature obtained under different values ​​of the similarity suppression coefficient and recording the prediction control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature includes the following specific steps: A value range of the similarity suppression coefficient is preset. For any similarity suppression coefficient f1 within the value range, the prediction control feature corresponding to f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, q1+f1 is recorded as the evaluation index of f1, f1 with the largest evaluation index is recorded as fm, and the prediction control feature corresponding to fm is recorded as the target control feature.

[0009] Another embodiment of the present invention provides a feedback-based energy storage device noise suppression method, the method comprising: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and B is the largest is used as the prediction control feature; S1: The energy storage device operates with predictive control characteristics at the next moment and collects device noise D0; the sound waves generated by the anti-phase sound source have the same amplitude and frequency, but opposite phases as the N noise components with the strongest noise response in D0; S2: The difference between the maximum value max of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0 obtains the noise suppression error w of the predictive control feature, and obtains the similarity suppression coefficient f of the predictive control feature, and f is negatively correlated with w; S3: obtaining the prediction control feature again, wherein the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; S4: suppressing the device noise of the energy storage device by repeatedly executing S1, S2 and S3.

[0010] Preferably, the specific steps of obtaining the N noise components with the strongest noise response in the device noise corresponding to each energy storage control feature are as follows: The device noise under each energy storage control feature is decomposed into several noise components by using Fourier transform algorithm, the amplitude of the noise component is used as the noise response of the noise component, and the N noise components with the largest noise response are used as the N noise components with the strongest noise response.

[0011] Preferably, the specific steps of obtaining the noise response ratio are as follows: The N noise components with the strongest noise responses are recorded as first components; the ratio of the mean value of the noise response of the first components to the mean value of the noise response of all noise components of the device noise is recorded as the noise response ratio.

[0012] Preferably, the specific steps for obtaining the difference A between the device noise corresponding to the arbitrary energy storage control feature and the noise at the current moment are as follows: The current moment noise is the device noise collected at the current moment during the operation of the energy storage device, and the device noise is expressed as a time series; obtaining a first difference between the device noise and the average signal value of the time series corresponding to the current moment noise, and obtaining a DTW distance between the device noise and the time series corresponding to the current moment noise; The A is positively correlated with the first difference and the DTW distance respectively.

[0013] Preferably, the specific steps for obtaining the difference B between the arbitrary energy storage control characteristic and the energy storage control characteristic at the current moment are as follows: The energy storage control feature at the current moment represents the energy storage control feature at the current moment during the operation of the energy storage device, and the energy storage control feature is represented as a vector; The Euclidean distance between the energy storage control feature and the vector corresponding to the energy storage control feature at the current moment is taken as the value of B.

[0014] Preferably, the specific steps for obtaining the maximum value max of the noise suppression ratio of the energy storage control feature are as follows: The noise suppression ratios of all energy storage control features are clustered by mean shift to obtain all categories. The mean of all noise suppression ratios in each category is calculated to obtain the category with the smallest difference between the mean of all noise suppression ratios and F0. The maximum value of the noise suppression ratio in this category is recorded as max; F0 represents the noise suppression ratio corresponding to D0.

[0015] Preferably, the noise suppression error w=(max-F0) / max.

[0016] Preferably, the similarity suppression coefficient f=1-w.

[0017] The beneficial effects of the technical solution of the present invention are: In one embodiment of the present invention, the energy storage control feature with the largest difference between A and f×B is used as the predictive control feature, the noise suppression ratio of the predictive control feature obtained under different values ​​of the similar suppression coefficient is obtained, and the predictive control feature with the largest noise suppression ratio and similar suppression coefficient is recorded as the target control feature; the energy storage device works and suppresses noise with the target control feature at the next moment. This process first appropriately regulates the working process of the energy storage device, so that the device noise generated by the regulated energy storage device is reduced as much as possible, and the reduced device noise can be suppressed as much as possible by the anti-phase sound source. By dynamically suppressing the noise from both the control of the energy storage device and the control of the anti-phase sound source, the noise suppression effect is improved; in addition, the noise can be suppressed to a large extent at each adjustment.

[0018] In another embodiment of the present invention, the energy storage control feature when the difference between A and B is the largest is used as the predictive control feature, and the energy storage device is adjusted and noise is suppressed based on the predictive control feature, and the predictive control feature is dynamically updated through the noise suppression error during the next noise suppression. This process also realizes the appropriate regulation of the working process of the energy storage device, so that the device noise generated by the regulated energy storage device is reduced as much as possible, and the reduced device noise can be suppressed as much as possible by the anti-phase sound source. By dynamically suppressing the noise from both the control of the energy storage device and the control of the anti-phase sound source, the noise suppression effect is improved; in addition, it can ensure fast response and good overall suppression effect in the entire noise suppression process.

[0019] In general, the present invention further improves the noise suppression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a noise sensing and suppression method for energy storage equipment provided by one embodiment of the present invention; Figure 2 A flowchart of a method for suppressing noise of an energy storage device based on feedback is provided in accordance with another embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the noise sensing and suppression method of an energy storage device proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The specific scheme of the noise sensing and suppression method for energy storage equipment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0025] Embodiment 1: See also Figure 1 , which shows a flowchart of a noise sensing and suppression method for energy storage equipment provided by an embodiment of the present invention, the method comprising the following steps: Step S101, recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device.

[0026] During the operation of energy storage equipment (such as lithium batteries, flywheel energy storage equipment, compressed air energy storage equipment, etc.), certain equipment noise will be generated. The sources of this noise include but are not limited to the following aspects: (1) Mechanical vibration noise: thermal expansion / contraction during battery charging and discharging, rotating parts of flywheel energy storage, compressors of compressed air energy storage, etc. Long-term vibration may cause structural fatigue and produce low-frequency noise pollution. (2) Electromagnetic noise: high-frequency noise pollution is generated by high-frequency switching actions of power electronic equipment (such as inverters and converters) during operation. (3) Cooling system noise: medium and high-frequency noise generated during the operation of cooling fans, water pumps, etc.

[0027] Existing noise suppression methods usually involve covering with sound insulation materials, enclosing sound insulation boards, etc. to reduce the impact of noise, but these methods are costly on the one hand, and cannot completely suppress all noise on the other hand. All embodiments of the present invention are implemented on the basis of existing noise suppression methods, so that equipment noise is further suppressed.

[0028] In this embodiment, a microphone is placed around each energy storage device to collect and record the device noise at every moment when the energy storage device is working in real time.

[0029] Furthermore, when the energy storage device is working, its charging and discharging process is regulated by the energy storage control system. The energy storage control system is a subsystem in the power grid system and is a well-known technology. The specific working principle thereof is not described in this embodiment. When the energy storage control system regulates the energy storage device, it calculates the charging and discharging rate for regulating the energy storage device in real time, controls the switching of the charging state and the discharging state of the energy storage device, etc., so that the energy storage device can maintain the safe and stable operation of the power grid (for example, maintaining the stability of the power grid through the peak shaving and valley filling effect of the energy storage device).

[0030] In this embodiment, the charge and discharge rate of the energy storage device when it is in a charge and discharge state under the regulation of the energy storage control system is used as the energy storage control feature when the energy storage device is working.

[0031] In some embodiments, the energy storage control system can not only adjust the charge and discharge rate of the energy storage device, but also adjust the SOC (State of Charge) target value, DOD (Depth of Discharge) limit, target terminal voltage or output voltage of the energy storage device, etc. Therefore, in other embodiments, these values ​​can also be used as part of the energy storage control feature. In other embodiments, the energy storage control feature can be set according to the specific energy storage device, and this embodiment does not specifically limit it.

[0032] Further consideration is that when the energy storage control system adjusts the operation of the energy storage device, the energy storage device will generate different degrees of noise interference under different energy storage control characteristics. For example, changes in the size of the charge and discharge rate will change the switching action of the power electronic equipment (such as inverters and converters) during operation, thereby generating different noise interference. This embodiment reduces noise interference by regulating the energy storage control characteristics of the energy storage device. Prior to this, this embodiment first obtains and records the energy storage control characteristics of the energy storage device at each moment in the historical working process; note that the energy storage control characteristics recorded in this embodiment refer to the energy storage control characteristics calculated or allocated by the energy storage control system during the working process of the energy storage device, and the energy storage control characteristics are determined by the power grid operating environment where the energy storage device is located (this embodiment will later record the energy storage control characteristics, and the energy storage control characteristics adjusted in this embodiment are not recorded).

[0033] So far, this embodiment has obtained the energy storage control characteristics of the energy storage device at each moment in the historical working process, and the device noise corresponding to each energy storage control characteristic. The historical working process described in this embodiment refers to the working process of the most recent month, and other embodiments may refer to the working process of the most recent quarter, which is not specifically limited in this embodiment.

[0034] As an example, a specific method for obtaining the device noise at each moment includes: In this embodiment, every two seconds is a moment (in other embodiments, every five seconds can be a moment). At any time point in the historical working process of the energy storage device, the signal timing sequence output by the microphone in the time period T before the time point is obtained. The signal timing sequence output by the microphone is used as the device noise at each moment. The microphone used in this embodiment outputs a signal every 0.1 second (this embodiment collects a voltage signal).

[0035] In this embodiment, the time period T refers to the time period consisting of the 10 moments before the time point (including the time point). In other embodiments, the time period T can be set to other values, which are not specifically limited in this implementation.

[0036] Step S102: In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources.

[0037] Using an inverted sound source to suppress noise is a common method. This method allows the inverted sound source to generate a sound wave signal with the same amplitude and opposite phase as the equipment noise to offset the equipment noise, thereby achieving the effect of noise suppression. This method is used in the fields of automobiles, industrial pipelines, etc. It should be noted that the inverted sound source described in this embodiment is essentially a sound wave generating device that can generate sound waves of the required amplitude, frequency and phase. This device is a well-known technology and is not specifically limited in this embodiment.

[0038] However, the equipment noise contains many noise signal components that change dynamically. It is difficult to achieve a good noise suppression effect when conventional, multiple anti-phase sound sources work in a fixed phase.

[0039] The noise suppression ratio of each energy storage control feature in this embodiment is used to describe the N noise components with the strongest noise response in the device noise under each energy storage control feature. The larger the noise response ratio of these N noise components (that is, the noise suppression ratio), the greater the impact of the device noise at each moment (or under each energy storage control feature) can be reduced when these N noise components are suppressed (for example, after being suppressed using N anti-phase sound sources).

[0040] As an example, the specific steps for obtaining the N noise components with the strongest noise response in the device noise under each energy storage control feature are as follows: For each device noise under the energy storage control characteristic, the device noise is decomposed into several noise components using Fourier transform algorithm, each noise component has a single frequency and amplitude; The amplitude of the noise component is taken as the noise response of the noise component, and the N noise components with the largest noise response are taken as the N noise components with the strongest noise response.

[0041] In this example, the amplitude is used to measure the strength of the noise response, that is, the louder the sound, the stronger the noise response.

[0042] As another example, the specific steps for obtaining the N noise components with the strongest noise response in the device noise under each energy storage control feature are as follows: The product of the amplitude and frequency of the noise component is taken as the noise response of the noise component, and the N noise components with the largest noise responses are taken as the N noise components with the strongest noise responses.

[0043] In this example, both amplitude and frequency are used to measure the strength of the noise response. That is, the louder and harsher the sound, the stronger the noise response.

[0044] As an example, the specific method for obtaining the noise response ratio of the N noise components with the strongest noise response in the device noise under each energy storage control feature is: The N noise components with the strongest noise responses are recorded as first components. The mean of the noise responses of all first components is obtained and recorded as the first mean. For all noise components of device noise, the mean of the noise responses of these noise components is recorded as the second mean. The ratio of the first mean to the second mean is recorded as the noise response ratio.

[0045] In this embodiment, the noise response proportion focuses on describing the proportion of the noise response of the noise component with a stronger noise response in the overall noise response.

[0046] As another example, the specific method for obtaining the noise response ratio of the N noise components with the strongest noise response in the device noise under each energy storage control feature is as follows: The N noise components with the strongest noise response are recorded as the first component; all noise components other than the first component are recorded as the second component. The mean of the noise response of all first components is recorded as x1, the mean of the noise response of all second components is recorded as x2, and (x1-x2) / (x1+x2) is recorded as the noise response ratio. The noise response ratio obtained by this method focuses on describing the difference between the noise components with stronger noise response and those with weaker noise response.

[0047] In other examples, the specific method for obtaining the noise response ratios of the N noise components with the strongest noise response in the device noise under each energy storage control feature is: averaging the noise response ratios obtained in the above two examples to obtain the noise response ratio obtained in this example.

[0048] In this embodiment, N=5 is used as an example, and N anti-phase sound sources are placed around the microphone. This embodiment does not limit the placement and number of the anti-phase sound sources. In some embodiments, the N anti-phase sound sources can be integrated into one device for easy carrying and placement.

[0049] As a comparative embodiment, the method of performing noise suppression based on the N noise components with the strongest noise response is: The amplitude and phase of N anti-phase sound sources are changed in real time, so that at each moment, the amplitudes of the N anti-phase sound sources are respectively the same as the amplitudes and frequencies of the N noise components, but the phases are opposite.

[0050] Step S103: Record the energy storage control feature whose difference A between the corresponding device noise and the current noise is the largest and whose difference B with the energy storage control feature at the current moment is the smallest as the prediction control feature.

[0051] The above steps obtain the N noise components with the strongest noise response at each moment (or under each energy storage control feature). If N anti-phase sound sources are directly used to suppress the N noise components with the strongest noise response at each moment (for example, when the comparative embodiment of step S102 is used for suppression), the suppression efficiency is not high. The reason is: considering that the energy storage device operates with different energy storage control features under the control of the energy storage control system (specifically determined by the operating state of the power grid and the power consumption of the power grid users, which is a well-known technology), different device noises may be generated at different moments or time periods. When the energy storage control features are appropriately adjusted, the interference of part of the device noise can also be eliminated. However, when only N anti-phase sound sources are used for noise suppression (for example, when the comparative embodiment of step S102 is used for suppression), the energy storage control characteristics cannot be adjusted to a suitable value, which not only affects the normal operation of the energy storage device, but also fails to further suppress the device noise on the basis of adjusting the energy storage control characteristics of the energy storage device; or in other words, when only the comparative embodiment of step S102 is used for suppression without considering the influence of the adjustment of the energy storage control characteristics of the energy storage device on the device noise, the device noise suppression effect is not optimal.

[0052] The predictive control feature described in this embodiment represents the energy storage control feature obtained after adjusting the energy storage control feature to a certain extent; the interference of the equipment noise generated by the energy storage device working under the predictive control feature will be significantly reduced; this embodiment is subsequently based on the predictive control feature and combines N anti-phase sound sources to achieve a further noise suppression effect.

[0053] The current moment noise refers to the device noise collected at the current moment during the operation of the energy storage device. The current moment energy storage control feature refers to the energy storage control feature at the current moment during the operation of the energy storage device.

[0054] As a preferred example, the energy storage control feature having the largest difference A between the corresponding device noise and the current noise and the smallest difference B between the corresponding device noise and the current energy storage control feature is recorded as the predictive control feature, specifically including: For any energy storage control feature, the difference between the device noise corresponding to the energy storage control feature and the noise at the current moment is represented as A; the difference between the energy storage control feature and the energy storage control feature at the current moment is represented as B.

[0055] The difference between A and f×B is recorded as the reference degree of the energy storage control feature, and the energy storage control feature with the largest reference degree is used as the predictive control feature; f is recorded as the similarity suppression coefficient of the predictive control feature, and the initial value of f is the preset value.

[0056] Note that in some embodiments, in order to avoid interference between the dimensions and orders of magnitude of A and B, it is necessary to remove the orders of magnitude and dimensions of A and B. One method is to perform linear normalization on the A values ​​corresponding to all energy storage control features and also perform linear normalization on the B values ​​corresponding to all energy storage control features.

[0057] In other embodiments, other methods may be used to remove the magnitude and dimension of A and B, which will not be described in detail in this embodiment.

[0058] For any energy storage control feature, the greater the reference degree of the energy storage control feature, the more it means that even if the energy storage control feature is changed by a smaller amplitude, the device noise at the current moment can be significantly changed (thereby reducing the device noise generated by the energy storage device). The smaller the reference degree, the more it means that even if the energy storage control feature is changed by a larger amplitude, it is difficult to significantly change the device noise at the current moment (that is, it is difficult to reduce the device noise generated by the energy storage device).

[0059] As an example, the difference A between the corresponding device noise and the current noise is: For any energy storage control feature, the average signal value of the device noise corresponding to the energy storage control feature is recorded as a1, the average signal value of the noise at the current moment is recorded as a2, a1-a2 is simply recorded as the first difference, and A is set equal to the first difference.

[0060] In this example, A describes the difference in the overall signal strength of the device noise. This example is used first when the signal output by the microphone is seriously interfered by noise (for example, when the microphone has insufficient noise sensing ability due to poor quality or abnormal operation).

[0061] As an example, the method for obtaining the average signal value includes: For any device noise, the device noise is a time series consisting of signals output by a microphone, and the mean of the signal values ​​in the time series is recorded as the average signal value.

[0062] If the signal value in the time series is less than 0, the average of the absolute values ​​of the signal values ​​in the time series is recorded as the average signal value.

[0063] As another example, the difference A between the corresponding device noise and the current noise is: When a1>a2, let k=1, indicating that the overall signal strength of the device noise corresponding to the energy storage control feature is greater than the overall signal strength of the noise at the current moment; when a1<=1, k=-1, indicating that the overall signal strength of the device noise corresponding to the energy storage control feature is less than or equal to the overall signal strength of the noise at the current moment.

[0064] The DTW distance between the device noise corresponding to the energy storage control feature and the timing sequence corresponding to the noise at the current moment is denoted as a, and A=k×a.

[0065] The DTW distance is obtained by the DTW algorithm, which is a well-known technology. The algorithm can describe the differences in details between time series (such as differences in local change trends, etc.). Therefore, the A obtained in this example can describe the differences in details between device noises (such as when there are local small changes). When the signal output by the microphone is less disturbed by noise (for example, when the microphone is of good quality and high precision), this embodiment is preferably used for implementation.

[0066] In some other embodiments, the difference A between the corresponding device noise and the current noise is: combining the above two examples, that is, A=(a1-a2)×a.

[0067] As an example, a method for obtaining the difference between the energy storage control feature and the energy storage control feature at the current moment (ie, B) includes: The energy storage control characteristics include charge and discharge rates, or SOC, DOD, etc. In this embodiment, the energy storage control characteristics are represented as a vector composed of these values. The difference B between the energy storage control characteristics and the energy storage control characteristics at the current moment is equal to the Euclidean distance between the energy storage control characteristics and the vectors corresponding to the energy storage control characteristics at the current moment.

[0068] It should be noted that in order to eliminate the interference of the dimension and order of magnitude of the energy storage control feature, this embodiment uses ACA whitening processing, and other embodiments may use other methods to remove the dimension and order of magnitude, which will not be described in detail in this embodiment.

[0069] The above-mentioned f is recorded as the similarity suppression coefficient of the predictive control feature. In some embodiments, f=1 is used as an example for description. In other embodiments, f may also be set to other non-zero values.

[0070] Step S104: Select the predictive control feature with the largest noise suppression ratio from the predictive control features as the target control feature.

[0071] For the similar suppression coefficient mentioned above, the larger the value is set, the less likely the energy storage control characteristics (i.e., the calculated prediction control characteristics) obtained when adjusting the energy storage control characteristics of the energy storage device will cause the energy storage device to fail to work properly due to excessive adjustment of the energy storage control characteristics, such as failure to maintain the steady state of the large power grid, or failure to improve the power utilization rate of the power grid (e.g., weakening its peak-shaving and valley-filling capabilities). The smaller the value is, the more obvious the energy storage control characteristics (i.e., the calculated prediction control characteristics) obtained after adjusting the energy storage control characteristics of the energy storage device can change the working conditions of the energy storage device, and at this time, the more obvious the prediction control characteristics that can suppress the noise of the device can be found by adjusting the energy storage device.

[0072] The target control feature described in this embodiment represents an energy storage control feature that can suppress the device noise as much as possible (using N anti-phase sound sources) while minimizing the impact on the operation of the energy storage device.

[0073] As an example, the predictive control feature with the largest noise suppression ratio is selected from the predictive control features as the target control feature, including the steps of: A value range of the similarity suppression coefficient is preset, and all values ​​of the similarity suppression coefficient within the value range are obtained. A predictive control feature is obtained corresponding to each value of the similarity suppression coefficient, and the predictive control feature with the maximum noise suppression ratio and the maximum similarity suppression coefficient is recorded as the target control feature.

[0074] As an example, a method for obtaining the value range of the similarity suppression coefficient is: setting the value range of the similarity suppression coefficient to a fixed value range, for example, the value range of the similarity suppression coefficient is: 0.5, 0.6, 0.7, ..., 1.5.

[0075] As another example, a method for obtaining a value range of a similar suppression coefficient is: setting the value range of the similar suppression coefficient to a dynamic value range, specifically including: For all vectors corresponding to the energy storage control features (described in step S103), the cosine similarity between any two vectors is obtained, and the mean of all cosine similarities is recorded as m1. Several values ​​(for example, 10 values) are selected at equal intervals in the interval [1-m1, 1+m1] as the value range of the similarity suppression coefficient. In this method, the larger the m1, the larger the variation range of the energy storage control feature. At this time, the obtained value range can allow the energy storage device to be adjusted with a relatively large adjustment range; when m1 is smaller, it means that the variation range of the energy storage control feature is smaller. At this time, the obtained value range does not allow the energy storage device to be adjusted with a relatively large adjustment range, so as to avoid the energy storage device working under energy storage control features that have never appeared.

[0076] As an example, the predictive control feature with the largest noise suppression ratio and the largest similarity suppression coefficient is recorded as the target control feature, including the following methods: For any similar suppression coefficient f1 within the value range, the prediction control feature obtained based on f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, q1+f1 is recorded as the evaluation index of f1, and f1 with the largest evaluation index is recorded as fm. The prediction control feature obtained based on fm is recorded as the target control feature.

[0077] As another example, the predictive control feature with the largest noise suppression ratio and the largest similarity suppression coefficient is recorded as the target control feature, including the method of: Let y1×q1+y2×f1 be recorded as the evaluation index of f1, where y1 and y2 are preset values. In this embodiment, y1=0.4 and y2=0.6. In other embodiments, y1 and y2 can be set to other values. This embodiment does not make specific limitations. It only needs to satisfy that y1 and y2 are both greater than or equal to 0 and y1+y2=1.

[0078] In particular, in some embodiments, y2 can be set to 0, that is, the evaluation index of f1 is equal to q1. In these embodiments, arbitrary adjustment of the energy storage device can be achieved, thereby suppressing noise to a greater extent, but there may be excessive intervention in the regulation of the energy storage device by the energy storage control system.

[0079] Step S105: The device noise generated by the energy storage device operating under the target control characteristic operation is suppressed by using an anti-phase sound source.

[0080] The energy storage device operates with the target control characteristics at the next moment, collects the device noise, obtains the N noise components with the strongest noise response in the device noise, and then makes N anti-phase sound sources generate sound waves, the phases of these sound waves are opposite to the phases of the N noise components with the strongest noise response in the device noise; the amplitude and frequency are the same as these N noise components.

[0081] In this embodiment, the working process of the energy storage device is properly regulated first, so that the device noise generated by the regulated energy storage device is reduced as much as possible, and the reduced device noise can be suppressed as much as possible by the anti-phase sound source. This embodiment improves the noise suppression effect by dynamically suppressing noise from both the control of the energy storage device and the control of the anti-phase sound source.

[0082] Embodiment 2: See also Figure 2 This embodiment provides a feedback-based energy storage device noise suppression method. The method includes: S201. Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device.

[0083] This step is the same as step S101 in the first embodiment.

[0084] S202. In the device noise corresponding to each energy storage control feature, the noise response proportion of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources.

[0085] This step is the same as step S102 in the first embodiment.

[0086] Step S203: Record the energy storage control feature whose difference A between the corresponding device noise and the current noise is the largest and whose difference B with the energy storage control feature at the current moment is the smallest as the prediction control feature.

[0087] The specific methods for obtaining predictive control features include: Obtain the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and obtain the difference B between any energy storage control feature and the energy storage control feature at the current moment.

[0088] The difference between A and B is obtained, and the difference is recorded as the reference degree of the energy storage control feature, and the energy storage control feature with the largest reference degree is used as the prediction control feature.

[0089] Step S204: suppress the equipment noise generated by the energy storage device operating under the predictive control feature by using an anti-phase sound source.

[0090] The predictive control feature described in this embodiment represents the energy storage control feature obtained after fine-tuning the energy storage control feature. The interference of the equipment noise of the energy storage device working under the predictive control feature is significantly reduced.

[0091] At the next moment, the energy storage device performs charging and discharging with the predictive control characteristics and collects the device noise, which is recorded as D0.

[0092] N anti-phase sound sources are made to generate sound waves, the phases of these sound waves are opposite to the phases of the N noise components with the strongest noise response in the device noise D0; the amplitude and frequency are the same as these N noise components.

[0093] In the above process, the working process of the energy storage device is fine-tuned so that the equipment noise generated by the regulated energy storage device is reduced as much as possible. On this basis, the anti-phase sound source is used to suppress noise, which has a high noise suppression efficiency.

[0094] Step S205: Re-acquire the predicted control feature by using the maximum value of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0, and perform noise suppression.

[0095] (1) The maximum value of the noise suppression ratio for all energy storage control features is denoted as max.

[0096] The noise suppression ratio corresponding to D0 is obtained and recorded as F0, and (max-F0) / max is recorded as the noise suppression error w of the predictive control feature. In other embodiments, w=(max-F0) / M can also be set, where M is a preset value, such as M=F0. This embodiment does not limit the value of M, and it only needs to ensure that w is positively correlated with (max-F0).

[0097] In some other examples, the method for obtaining max also includes: In recent days (for example, in recent week), the maximum value of the noise suppression ratio of all energy storage control features is recorded as max.

[0098] As a preferred example, the method for obtaining max also includes: The noise suppression ratios of all energy storage control features are clustered by mean shift to obtain all categories. The mean of all noise suppression ratios in each category is calculated to obtain the category with the smallest difference between the mean and F0 (that is, the smallest absolute value of the difference). The maximum value of the noise suppression ratio in this category is recorded as max.

[0099] Using the max described in this preferred example to obtain the noise suppression error w can solve the following problems to a certain extent: For all energy storage control features collected from the historical working process of the energy storage device, the energy storage control feature with the largest noise suppression ratio may not be applicable to the current working condition of the energy storage device. For example, the working time period of the energy storage device (or the seasonal weather that affects the power consumption of the power grid, etc.) is different, resulting in the energy storage control feature with the largest noise suppression ratio being not applicable to the current working condition of the energy storage device. At this time, directly taking the maximum value of the noise suppression ratio of all energy storage control features as max may cause errors in the calculation results of the noise suppression error w. For example, the energy storage control features of the energy storage device caused by sudden changes in weather and sudden changes in the steady state of the power grid (or power grid failure) within a period of time also undergo abnormal changes. Such abnormal changes lead to errors in the calculation results of the noise suppression error w.

[0100] For the first two examples of obtaining the max, when implemented under the condition of continuous and stable operation of the power grid, both accurate and fast results can be achieved.

[0101] For the noise suppression error of the above-mentioned predictive control characteristics, the larger the noise suppression error, the more it indicates that although the predictive control characteristics obtained in step S203 can reduce the equipment noise generated by the regulated energy storage device as much as possible through fine-tuning, the predictive control characteristics cannot optimize the noise suppression effect, that is, the noise suppression effect when the noise suppression ratio is the largest is not achieved (or the noise suppression effect can continue to be optimized).

[0102] (2) Let f = 1-w be the similarity suppression coefficient of the predictive control feature.

[0103] In other embodiments, f=1-g×w may be set, where g is a preset value, such as g=0.8. In other embodiments, g may also be set to other values ​​as long as f is greater than 0 and negatively correlated with w.

[0104] The similarity suppression coefficient of other energy storage control features other than the predictive control feature defaults to 1. This process is equivalent to updating the similarity suppression coefficient of the predictive control feature.

[0105] For the current moment of the energy storage device operation process (i.e., the current moment after the next moment described in step S204), the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment are re-obtained.

[0106] The difference between A and f×B is obtained, and the difference is recorded as the reference degree of the energy storage control feature. The energy storage control feature with the largest reference degree is used as the prediction control feature.

[0107] (3) According to the method of step S204, the device noise generated by the energy storage device operating under the predictive control characteristics is suppressed by using an anti-phase sound source.

[0108] (4) Then, the method in S205 is used again to obtain the noise suppression error w of the predictive control feature. And the similarity suppression coefficient of the predictive control feature is updated. The predictive control feature is further obtained again. Thereafter, according to the method in step S204, the device noise generated by the energy storage device operating under the predictive control feature is suppressed by using the anti-phase sound source.

[0109] By repeatedly executing the methods (1), (2), (3) and (4) in S205, the noise suppression error of the last noise suppression is fed back to the next noise suppression process, so that the similar suppression coefficient (or prediction control feature) of the prediction control feature is updated in real time according to the noise suppression error of the prediction control feature, that is, when the noise suppression error is larger (the similar suppression coefficient is smaller), the energy storage device can be adjusted to find a prediction control feature that makes the device noise suppression effect more obvious; when the noise suppression error is smaller (that is, the similar suppression coefficient is larger), the energy storage control feature (that is, the calculated prediction control feature) obtained after adjusting the energy storage control feature of the energy storage device can be further suppressed, and the energy storage device will not be unable to work normally due to excessive adjustment of the energy storage control feature. With the continuous operation of the energy storage device and the update of the similar suppression coefficients of all energy storage control features, the noise can be suppressed efficiently when noise suppression is performed, and the working condition of the energy storage device can be minimized.

[0110] In summary, both Embodiment 1 and Embodiment 2 of the present invention utilize the automatic control technology of the reverse phase sound source and the automatic control technology of the energy storage device to suppress noise, and these two technologies are reasonably coordinated, wherein the automatic control technology of the energy storage device can preliminarily reduce the generation of noise, and the automatic control technology of the reverse phase sound source can further suppress noise on this basis.

[0111] In addition, according to step S104 of embodiment 1 and step S205 of embodiment 2, it can be seen that compared with the automatic control technology using only the anti-phase sound source or the automatic control technology of the energy storage device, the noise suppression efficiency and effect of all embodiments of the present invention are better.

[0112] Embodiment three: This embodiment provides another noise suppression method, which is a combination of the methods described in Embodiment 1 and Embodiment 2 (this embodiment provides two combination methods).

[0113] First of all, it should be explained that the noise suppression method described in Example 1 obtains the target control characteristics through similarity suppression coefficients with different values, and then performs noise suppression. One of the advantages of this method is that it can limit the adjustment range of the energy storage device by specifying the value range of the similarity suppression coefficient. In addition, it can suppress noise to a large extent during each adjustment. The disadvantage is that when the value range of the similarity suppression coefficient is not accurately determined, it will affect the final effect of noise suppression and the response speed is slow.

[0114] In comparison, the noise suppression method described in Example 2 continuously updates the similar suppression coefficient and then selects a suitable predictive control feature for noise suppression. The advantage of this method is that it can determine the value of the similar suppression coefficient by error feedback, and determine the next noise suppression method by the noise suppression situation at the previous moment. This method has a fast response speed (i.e., less calculation) when it is carried out for a long time. The disadvantage is that it cannot limit the adjustment range of the energy storage device. In addition, although the noise can be suppressed to a large extent on a long-term scale, the noise suppression effect may not be optimal at a specific local time (for example, within a number of local moments).

[0115] (1) The first combination method: During peak hours of electricity consumption, the power grid is prone to imbalance, and energy storage devices (such as the peak-shaving and valley-filling effect of energy storage devices) are needed to maintain the stability of the power grid. At this time, the method in Example 1 is used to suppress noise. The reason is that this process avoids excessive adjustment of energy storage devices and affects the steady-state operation of the power grid by limiting the value range of the similar suppression coefficient.

[0116] During the low-peak electricity consumption phase, the power grid is not prone to imbalance. At this time, the method in Example 2 is used to suppress noise, with a faster response speed (that is, when every 0.5 second or every 0.1 second is a moment, noise suppression can also be performed once at each moment), and the noise suppression effect is better.

[0117] The method of dividing the peak / off-peak period of electricity consumption is well known. For example, if the average daytime temperature is greater than 28 degrees Celsius and the average nighttime temperature is less than 15 degrees Celsius, then the day is regarded as the peak period of electricity consumption, otherwise it is regarded as the off-peak period of electricity consumption. Other division methods may be used in other embodiments, and this embodiment does not specifically limit them.

[0118] (2) The second combination method: The target control feature is obtained according to the method of step S104 in the first embodiment (in this process, the similarity suppression coefficients of all energy storage control features are set to 1).

[0119] The energy storage device performs charging and discharging operation with the target control feature at the next moment, and the collected device noise is recorded as D0. And according to the method of step S105 in the first embodiment, the device noise generated by the energy storage device operating under the target control feature is suppressed.

[0120] It should be noted that the target control feature at this time is equivalent to the prediction control feature described in step S205 in the second embodiment.

[0121] Then, the noise suppression error w of the target control feature is obtained by using the method of step S205 in the second embodiment, and the similarity suppression coefficient of the target control feature is updated to f=1-w.

[0122] When performing noise suppression next time (or at the next moment), the target control feature is obtained again according to the method of step S104 in embodiment 1. Note that at this time, the value range of the similarity suppression coefficient in step S104 in embodiment 1 is set to r×f, where r is any value in the preset value range, and the preset value range is the same as the value range set in step S104 in embodiment 1, for example, 0.5, 0.6, 0.7, ..., 1.5 constitute a value range (discrete value range).

[0123] Then, according to the method of step S105 in the first embodiment, the device noise generated by the energy storage device operating under the target control characteristic operation is suppressed.

[0124] Then, the noise suppression error w of the target control feature is obtained by using the method of step S205 in the second embodiment, and the similarity suppression coefficient of the target control feature is updated to f=1-w.

[0125] By analogy, the noise suppression effect is further improved by continuously updating the value range of the similar suppression coefficient.

[0126] Compared with the first embodiment, the second combination method performs reasonable dynamic update on the value range of the similarity suppression coefficient, thereby further improving the noise suppression effect.

[0127] Embodiment 4: Based on the second combination method described in the third embodiment, a method for obtaining target control features is provided.

[0128] In the second combination method described in the third embodiment, the method for obtaining the target control feature includes: The value range of the similar suppression coefficient can be obtained by using the method described in the second combination method in Example 3, or by using the method in Example 1, and this embodiment does not limit it.

[0129] For any similar suppression coefficient f1 within the value range, the prediction control feature obtained based on f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, y1×q1+y2×f1 is recorded as the evaluation index of f1, and the f1 with the largest evaluation index is recorded as fm. The prediction control feature obtained based on fm is recorded as the target control feature.

[0130] Wherein y1 is positively correlated with the noise suppression error w of the target control feature obtained in the second combination method in the third embodiment, for example, y1=kk×w, y2=1-y1; in addition, when y2 is less than 0, y1=1, y2=0. This embodiment is described by taking kk=0.5 as an example, and other embodiments may set kk to other values, which are not specifically limited in this embodiment.

[0131] In this embodiment, when w is larger, y1 is larger. This means that when the energy storage device is adjusted based on the target control feature, although the device noise generated by the regulated energy storage device can be reduced as much as possible through adjustment, the predictive control feature cannot optimize the noise suppression effect, that is, the noise suppression effect when the noise suppression ratio is the largest is not achieved. At this time, the evaluation index of f1 pays more attention to the noise suppression ratio, rather than the size of the similar suppression coefficient f1. Ultimately, it is achieved that when the energy storage device is fine-tuned within a given range of values ​​(or on the premise of ensuring that no excessive adjustment is made), if the optimal noise suppression effect cannot be achieved, then a slightly larger adjustment of the energy storage device is allowed (that is, the constraint of the condition of not making excessive adjustments is weakened) to achieve a better noise suppression effect.

[0132] When w is smaller, y1 is smaller. This means that when the energy storage device is adjusted based on the target control characteristics, the noise suppression effect has approached the optimal value. At this time, we focus on the size of the similar suppression coefficient f1, and finally achieve: the energy storage device is not over-adjusted while the noise suppression effect is approaching the optimal value.

[0133] In summary, this implementation realizes the process of noise suppression, dynamically determining whether to allow the energy storage device to make excessive adjustments based on the effect of noise suppression, further improving the noise suppression effect of the entire noise suppression process. This embodiment is suitable for scenarios where the working changes of the energy storage device have little impact on the power grid when the power grid is operating stably for a long time.

[0134] It should be noted that different embodiments of the present invention may have different noise suppression effects when applied to different implementation scenarios, but all embodiments can improve the noise suppression effect compared to the existing method of using only an inverted sound source or adding sound insulation enclosures.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A noise sensing and suppression method for energy storage equipment, characterized in that: The method comprises the following steps: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; f is recorded as the similarity suppression coefficient corresponding to the prediction control feature, and the initial value of f is a preset value; Obtain the noise suppression ratio of the predictive control feature obtained under different values ​​of the similarity suppression coefficient, and record the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature; The energy storage device operates with the target control characteristics at the next moment and records the device noise D0 collected in real time. The sound waves generated by the anti-phase sound source have the same amplitude and frequency and opposite phases as the N noise components with the strongest noise response in D0.

2. A noise sensing and suppression method for energy storage equipment according to claim 1, characterized in that: The step of obtaining the noise suppression ratio of the predictive control feature obtained under different values ​​of the similarity suppression coefficient and recording the predictive control feature with the largest noise suppression ratio and similarity suppression coefficient as the target control feature includes the following specific steps: A value range of the similarity suppression coefficient is preset. For any similarity suppression coefficient f1 within the value range, the prediction control feature corresponding to f1 is recorded as F1, the noise suppression ratio of F1 is recorded as q1, q1+f1 is recorded as the evaluation index of f1, f1 with the largest evaluation index is recorded as fm, and the prediction control feature corresponding to fm is recorded as the target control feature.

3. A feedback-based energy storage device noise suppression method, characterized in that: The method includes: Recording energy storage control characteristics and corresponding device noise when the energy storage device is working, wherein the energy storage control characteristics at least include a charge and discharge rate of the energy storage device; In the device noise corresponding to each energy storage control feature, the noise response ratio of the N noise components with the strongest noise response is recorded as the noise suppression ratio of each energy storage control feature; N is the number of anti-phase sound sources; the anti-phase sound source refers to a device that can generate a specified sound wave; For the difference A between the device noise corresponding to any energy storage control feature and the noise at the current moment, and the difference B between any energy storage control feature and the energy storage control feature at the current moment, the energy storage control feature when the difference between A and B is the largest is used as the prediction control feature; S1: The energy storage device operates with predictive control characteristics at the next moment and collects device noise D0; the sound waves generated by the anti-phase sound source have the same amplitude and frequency, but opposite phases as the N noise components with the strongest noise response in D0; S2: The difference between the maximum value max of the noise suppression ratio of the energy storage control feature and the noise suppression ratio corresponding to D0 obtains the noise suppression error w of the predictive control feature, and obtains the similarity suppression coefficient f of the predictive control feature, and f is negatively correlated with w; S3: obtaining the prediction control feature again, wherein the energy storage control feature when the difference between A and f×B is the largest is used as the prediction control feature; S4: suppressing the device noise of the energy storage device by repeatedly executing S1, S2 and S3.

4. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the N noise components with the strongest noise response in the device noise corresponding to each energy storage control feature are as follows: The device noise under each energy storage control feature is decomposed into several noise components by using Fourier transform algorithm, the amplitude of the noise component is used as the noise response of the noise component, and the N noise components with the largest noise response are used as the N noise components with the strongest noise response.

5. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the noise response ratio are as follows: The N noise components with the strongest noise responses are recorded as first components; the ratio of the mean value of the noise response of the first components to the mean value of the noise response of all noise components of the device noise is recorded as the noise response ratio.

6. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the difference A between the device noise corresponding to the arbitrary energy storage control feature and the noise at the current moment are as follows: The current moment noise is the device noise collected at the current moment during the operation of the energy storage device, and the device noise is expressed as a time series; obtaining a first difference between the device noise and the average signal value of the time series corresponding to the current moment noise, and obtaining a DTW distance between the device noise and the time series corresponding to the current moment noise; The A is positively correlated with the first difference and the DTW distance respectively.

7. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the difference B between the arbitrary energy storage control characteristic and the energy storage control characteristic at the current moment are as follows: The energy storage control feature at the current moment represents the energy storage control feature at the current moment during the operation of the energy storage device, and the energy storage control feature is represented as a vector; The Euclidean distance between the energy storage control feature and the vector corresponding to the energy storage control feature at the current moment is taken as the value of B.

8. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The specific steps for obtaining the maximum value max of the noise suppression ratio of the energy storage control feature are as follows: The noise suppression ratios of all energy storage control features are clustered by mean shift to obtain all categories. The mean of all noise suppression ratios in each category is calculated to obtain the category with the smallest difference between the mean of all noise suppression ratios and F0. The maximum value of the noise suppression ratio in this category is recorded as max; F0 represents the noise suppression ratio corresponding to D0.

9. A feedback-based energy storage device noise suppression method according to claim 8, characterized in that: The noise suppression error w=(max-F0) / max.

10. The method for suppressing noise of energy storage equipment based on feedback according to claim 3, characterized in that: The similarity suppression coefficient f=1-w.

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