Intelligent management system for energy storage cabinet and method thereof

Abstract

This invention relates to the field of energy storage cabinet technology and discloses a smart management method for energy storage cabinets. The energy storage cabinet includes an external inert gas cylinder located at the top, an inert gas solenoid valve, an adjustable guide plate, and a multi-terminal circuit breaker. The smart management method includes: collecting temperature, sound, and gas parameter information inside the energy storage cabinet; determining the location and coordinates of abnormal sounds based on the sound parameter information; determining the location of the highest temperature area in the energy storage cabinet based on the temperature parameter information; analyzing the overlap rate between the high-temperature area and the abnormal sound location coordinates using a big data analysis model; and opening the inert gas solenoid valve to introduce inert gas when the overlap rate exceeds a set threshold. This invention calculates the overlap rate between the high-temperature area and the sound location, triggering inert gas release only when the overlap rate exceeds a threshold. This avoids false triggering, saves inert gas resources, and improves system response efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy storage cabinet technology, and in particular to an intelligent management system and method for energy storage cabinets. Background Technology

[0002] With the continuous development of the energy sector, energy storage technology has gradually become an important means to solve the contradiction between energy supply and demand and improve energy utilization efficiency. As an important component of energy storage technology, the operating status and management efficiency of energy storage cabinets directly affect the stability and reliability of the entire energy system.

[0003] An existing patent (CN117937766A) discloses an intelligent management system for energy storage cabinets, which addresses the problems of traditional energy storage cabinet management methods that typically rely on manual operation and periodic maintenance, making real-time monitoring and intelligent management difficult. Furthermore, these methods often generate large amounts of redundant and invalid data, negatively impacting the intelligent management of the energy storage cabinets. In the technology disclosed in this patent, after an anomaly or malfunction occurs, the system relies on a single voltage / current threshold alarm or direct power outage, and dispatching engineers to the site for maintenance. This process takes a considerable amount of time. While the incident during this time may not be a serious accident, directly cutting off the power would cause a sudden change in circuit load and result in additional economic losses. Summary of the Invention

[0004] This invention provides an intelligent management system and method for energy storage cabinets to solve the existing technical problems, thereby resolving the issues mentioned in the background section.

[0005] To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, a smart management method for an energy storage cabinet is applied to the energy storage cabinet; the energy storage cabinet includes an external inert gas cylinder located at the top of the energy storage cabinet, an inert gas solenoid valve, an adjustable guide plate, and a multi-terminal circuit breaker; the smart management method includes:

[0006] S1. Collect temperature, sound, and gas parameters inside the energy storage cabinet, and determine the location and coordinates of abnormal sounds in the energy storage cabinet based on the sound parameters.

[0007] S2. Determine the location of the highest temperature area in the energy storage cabinet based on the temperature parameter information;

[0008] S3. Analyze the overlap rate between the high temperature area and the location coordinates of the abnormal sound through big data judgment model. When the overlap rate exceeds the set threshold, open the inert gas solenoid valve to introduce inert gas.

[0009] S4. Further determine whether to activate the multi-terminal circuit breaker by matching the monitored gas composition with historical data.

[0010] The specific steps for calculating the overlap rate are as follows:

[0011] 1) Three sound sensors are arranged inside the energy storage cabinet. By measuring the time difference between the arrival of the sound source at each sensor, and combining the known sensor coordinates and sound speed, a set of nonlinear equations is constructed to solve for the three-dimensional spatial coordinates of the sound source.

[0012] 2) Use a thermal imaging probe to capture real-time thermal images of the inside of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature anomaly area, providing a reference position for subsequent overlap rate calculation;

[0013] 3) Define the overlap rate as the spatial correlation between the sound source location and the center location of the high-temperature region, and analyze the overlap rate based on the Euclidean distance between the sound source and the high-temperature region.

[0014] Furthermore, the big data judgment model determines the overlap rate between the location coordinates of the abnormal sound and the abnormal high-temperature area in the energy storage cabinet based on the distance between the location coordinates of the abnormal sound and the abnormal high-temperature area in the energy storage cabinet. The results are as follows:

[0015]

[0016] In the formula, This indicates the overlap rate between the location of the abnormally high temperature area in the energy storage cabinet and the coordinates of the abnormal sound; the location coordinates of the sound source are taken as... ; This indicates the intensity of environmental noise impact; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are taken as... .

[0017] Furthermore, when If the location of the abnormally high temperature area in the energy storage cabinet coincides with the coordinates of the abnormal sound, then the inert gas solenoid valve needs to be activated.

[0018] when If the location of the abnormally high temperature area in the energy storage cabinet does not coincide with the coordinates of the abnormal sound, then there is no need to activate the inert gas solenoid valve.

[0019] Furthermore, the data acquisition in step S1 includes a temperature sensor, a sound detection sensor, and a gas detection module;

[0020] The sound detection sensors include sound sensor a, sound sensor b, and sound sensor c, which are located at the bottom of the energy storage cabinet and on the left and right sides inside, respectively.

[0021] Furthermore, the big data judgment model also determines the coordinates of the abnormal sound in the energy storage cabinet based on the time difference between the sound source arriving at the three sensors as measured by the sound detection sensors, and the position coordinates of the sound detection sensors. Therefore:

[0022]

[0023]

[0024]

[0025] Furthermore, there are:

[0026]

[0027]

[0028]

[0029] In the formula, the position coordinates of the sound source are taken as The coordinates of sound sensor a are taken as follows: The coordinates of sound sensor b are taken as follows: The coordinates of the sound sensor c are taken as follows: ; The speed of sound transmission in the energy storage cabinet is expressed in m / s. This indicates the time in seconds (s) when sound sensor a receives a sound source. This indicates the time in seconds (s) when sound sensor b receives a sound source. This indicates the time in seconds (s) when the sound sensor c receives the sound source.

[0030] Furthermore, the temperature sensor is a thermal imaging probe used to capture thermal images of the energy storage cabinet and determine the location of abnormally high-temperature areas in the energy storage cabinet based on the thermal images.

[0031] Furthermore, step S4 specifically includes:

[0032] S401. Based on the gas composition parameter information collected from the data collection, determine the degree of matching with the historical log parameters in the energy storage cabinet during the adverse events.

[0033] S402. The matching degree is determined based on the results of historical data matching, and then it is determined whether to activate the multi-terminal circuit breaker.

[0034] A smart management system includes:

[0035] The data acquisition module is used to collect parameter information such as temperature, sound, and gas composition inside the energy storage cabinet;

[0036] The judgment model is used to analyze whether the adjustable guide plate should be closed and the inert gas solenoid valve opened, based on the feedback from the data acquisition module.

[0037] The execution control module is used to receive feedback from the judgment model, open the inert gas solenoid valve and close the adjustable guide vane.

[0038] This invention provides an intelligent management system and method for energy storage cabinets. Compared with existing technologies, the advantages achieved by this method are:

[0039] 1. This invention calculates the overlap rate between the high-temperature region and the sound location, and triggers the release of inert gas only when the overlap rate exceeds a threshold. This avoids false triggering, saves inert gas resources, and improves system response efficiency.

[0040] 2. This invention protects the internal circuitry by installing an inert gas storage bottle above the energy storage cabinet and filling it with inert gas when an abnormality occurs in the energy storage cabinet. This design can avoid the additional economic losses caused by direct power outages.

[0041] 3. This invention controls the activation of multi-terminal circuit breakers by executing the control module based on feedback from the feedback optimization module, and optimizes the control strategy by combining historical data matching module, thereby enhancing the system's ability to predict adverse events, realizing self-learning function, and gradually improving the system's adaptability and reliability. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the energy storage cabinet in this invention;

[0043] Figure 2 This is a flowchart of the intelligent management system in this invention;

[0044] Figure 3 In this invention, the overlap rate d varies with the X and Y coordinates;

[0045] Figure 4 The overlap rate d and influence intensity in this invention A schematic diagram of the sampling data;

[0046] Figure 5 This is a schematic diagram of the sampling data of the overlap rate d and the sound source distance m in this invention;

[0047] Figure 6 The overlap rate d and influence intensity in this invention A model diagram of the distance m from the sound source.

[0048] 1. Energy storage cabinet; 2. External inert gas cylinder. Detailed Implementation

[0049] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 , 2 As shown, a smart management method for an energy storage cabinet is applied to energy storage cabinet 1. The energy storage cabinet 1 includes an external inert gas cylinder 2 located on the top of the energy storage cabinet 1, an inert gas solenoid valve, an adjustable guide plate, and a multi-terminal circuit breaker. The method specifically includes the following steps:

[0051] Step 1: Determine the location and coordinates of any abnormal sounds occurring in the energy storage cabinet based on the sound detection sensor.

[0052] Step 2: Determine the location of the highest or lowest temperature zone in the energy storage cabinet based on the temperature sensor.

[0053] Step 3: Determine the overlap rate between the high-temperature area and the location coordinates of the abnormal sound. If the overlap rate exceeds the set threshold, open the inert gas solenoid valve to introduce inert gas.

[0054] Step 4: Based on the changes in gas composition monitored by the historical data matching module, further determine whether to activate the multi-terminal circuit breaker;

[0055] The specific steps for calculating the overlap rate are as follows:

[0056] 1) Three sound sensors are arranged inside the energy storage cabinet. By measuring the time difference between the arrival of the sound source at each sensor, and combining the known sensor coordinates and sound speed, a set of nonlinear equations is constructed to solve for the three-dimensional spatial coordinates of the sound source.

[0057] 2) Use a thermal imaging probe to capture real-time thermal images of the inside of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature anomaly area, providing a reference position for subsequent overlap rate calculation;

[0058] 3) Define the overlap rate as the spatial correlation between the sound source location and the center location of the high-temperature region, and analyze the overlap rate based on the Euclidean distance between the sound source and the high-temperature region.

[0059] Example 1

[0060] like Figure 1As shown, the data acquisition system includes a temperature sensor, a sound detection sensor, and a gas detection module. The temperature sensor is a thermal imaging probe used to capture thermal images of the energy storage cabinet and determine the location of abnormally high-temperature areas within the cabinet. The sound detection sensors include sensor a, sensor b, and sensor c, located at the bottom and left / right sides of the cabinet, respectively. Real-time acquisition of temperature, sound, and gas data, combined with cloud-based analysis, enables rapid response. Furthermore, the execution control module precisely operates the inert gas solenoid valve and deflector, enhancing the safety of the energy storage cabinet and mitigating the risk of fire or explosion.

[0061] Example 2

[0062] The big data judgment model determines the coordinates of abnormal sounds within the energy storage cabinet based on the time difference between the arrival of the sound source at the three sensors measured by the sound detection sensors, and the position coordinates of the sound detection sensors. Therefore:

[0063]

[0064]

[0065]

[0066] Furthermore, there are:

[0067]

[0068]

[0069]

[0070] In the formula, the position coordinates of the sound source are taken as The coordinates of sound sensor a are taken as follows: The coordinates of sound sensor b are taken as follows: The coordinates of the sound sensor c are taken as follows: ; The speed of sound transmission in the energy storage cabinet is expressed in m / s. This indicates the time in seconds (s) when sound sensor a receives a sound source. This indicates the time in seconds (s) when sound sensor b receives a sound source. This indicates the time in seconds (s) when the sound sensor c receives the sound source.

[0071] Wherein, if the coordinates of sound sensor a are taken The coordinates of sound sensor b are taken as follows The coordinates of the sound sensor c are taken as follows The speed at which sound travels in the energy storage cabinet is taken (m / s), the event difference of the sound detected by sensor a and sensor b. (s), the event difference of the sound detected by sensor a and sensor c (s), the event difference of the sound detected by sensor b and sensor c (s), then we have:

[0072]

[0073]

[0074]

[0075] From the above formula, the position coordinates of the sound can be derived as follows: The system locates the sound source coordinates based on the time difference of three sound sensors and uses a mathematical model to achieve high-precision fault location. This reduces false positives and improves the system's accuracy in identifying abnormal sounds.

[0076] Example 3

[0077] like Figure 1-6 As shown, the big data judgment model determines the overlap rate between the location coordinates of the abnormal high-temperature area and the coordinates of the abnormal sound in the energy storage cabinet based on the distance between these coordinates and the abnormal high-temperature area within the cabinet. The results are as follows:

[0078]

[0079] In the formula, This indicates the overlap rate between the location of the abnormally high-temperature area in the energy storage cabinet and the coordinates of the abnormal sound; the location coordinates of the sound source are taken as... ; This indicates the intensity of environmental noise impact; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are taken as... .

[0080] Therefore, when enterprises need to solve this practical problem, they can only analyze the overlap rate between the location of abnormal high-temperature areas and the coordinates of abnormal sounds in the energy storage cabinet during production, use, and testing. This analysis, conducted to solve practical problems for enterprises, is called empirical analysis, and the data obtained accordingly, as well as the characteristic relationships between the data, are the external manifestations of empirical formulas. The reasoning process is as follows:

[0081] S1. Fit the formula for the overlap rate d between the location of the abnormal high temperature area and the coordinates of the abnormal sound in the energy storage cabinet.

[0082] The overlap rate d between the location of the abnormal high-temperature area and the coordinates of the abnormal sound in the energy storage cabinet can be represented by the number of devices in the sample energy storage cabinets whose abnormal area matches the sound-emitting area. For example, if data is collected from 100 energy storage cabinet samples, and after manual evaluation, the overlap rate between the abnormal sound-emitting area and the heat-generating location of a certain energy storage cabinet exceeds that of the data in the other 50 samples, then the overlap rate d of that energy storage cabinet is 50%.

[0083] The impact intensity of other coordinate data and environmental noise was calculated using data collected by the device itself.

[0084] S2, the overlap rate d and the intensity of influence Establish mathematical models for the relationships between them (such as...) Figure 4 As shown), then:

[0085] (Formula 1)

[0086] In Formula 1 above, k represents an empirical constant for adjusting the sensitivity of the model.

[0087] S3. Establish a mathematical model for the relationship between the coincidence rate d and the distance to the sound source m (e.g.) Figure 5 As shown), then:

[0088] (Formula 2)

[0089] In Formula 2 above, k represents an empirical constant for adjusting the sensitivity of the model. express .

[0090] S4. The overlap rate d and the intensity of influence A mathematical model is established to establish the relationship between the distance m from the sound source (e.g.) Figure 6 As shown), and combining the characteristic relationship between Formula 1 and Formula 2 above, we have:

[0091] (Formula 3)

[0092] Will Substituting, we have:

[0093]

[0094] Wherein, if the calculated sound position coordinates are Furthermore, based on thermal imaging, the center coordinates of the abnormally high-temperature area in the energy storage cabinet can be determined. .

[0095] Furthermore, the intensity of the impact of environmental noise is taken as... (Influence intensity) =Maximum white noise in the external environment (dB) ÷Minimum noise generated in the energy storage cabinet (dB)), then:

[0096]

[0097] The above calculations show that the coordinates of this sound anomaly are... , The coordinates coincide with those acquired by thermal imaging. Furthermore, comparing a set of data yields the following:

[0098] Table 1. Partial Implementation Data Parameters and Overlapping Relationships

[0099]

[0100] As shown in Table 1 above, when the sample size range is infinite, a dividing line will appear where the overlap rate d delineates whether the energy storage cabinet is abnormal. When this occurs, it indicates that the location of the abnormally high-temperature area in the energy storage cabinet coincides with the coordinates of the abnormal sound, at which point the inert gas solenoid valve needs to be activated; when If the location of the abnormally high-temperature area in the energy storage cabinet does not coincide with the coordinates of the abnormal sound, then there is no need to activate the inert gas solenoid valve. By calculating the overlap rate between the high-temperature area and the sound location, inert gas release is triggered only when the overlap rate exceeds a threshold. This avoids false triggering, saves inert gas resources, and improves system response efficiency.

[0101] Example 4

[0102] like Figure 1 , 2 As shown, the intelligent management system includes: a data acquisition module for collecting parameter information such as temperature, sound, and gas composition inside the energy storage cabinet; a judgment model for analyzing whether to close the adjustable guide vane and open the inert gas solenoid valve based on feedback from the data acquisition module; and an execution control module for receiving feedback from the judgment model, opening the inert gas solenoid valve, and closing the adjustable guide vane. This system calculates the overlap rate between the high-temperature area and the sound location, triggering inert gas release only when the overlap rate exceeds a threshold. This avoids false triggering, saves inert gas resources, and improves system response efficiency.

[0103] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A smart management method for energy storage cabinets, characterized in that, Applied to an energy storage cabinet (1); the energy storage cabinet (1) includes an external inert gas cylinder (2) located on the top of the energy storage cabinet (1), an inert gas solenoid valve, an adjustable guide plate, and a multi-terminal circuit breaker; the intelligent management method includes: S1. Collect temperature, sound, and gas parameters inside the energy storage cabinet, and determine the location and coordinates of abnormal sounds in the energy storage cabinet based on the sound parameters. S2. Determine the location of abnormally high-temperature areas in the energy storage cabinet based on temperature parameter information; S3. Analyze the overlap rate between the abnormal high temperature area and the location coordinates of the abnormal sound through big data judgment model. When the overlap rate exceeds the set threshold, open the inert gas solenoid valve to introduce inert gas. S4. Further determine whether to activate the multi-terminal circuit breaker by matching the monitored gas composition with historical data. The specific steps for calculating the overlap rate are as follows: 1) Three sound sensors are arranged inside the energy storage cabinet. By measuring the time difference between the arrival of the sound source at each sensor, and combining the known sensor coordinates and sound speed, a set of nonlinear equations is constructed to solve for the three-dimensional spatial coordinates of the sound source. 2) Use a thermal imaging probe to capture real-time thermal images of the inside of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature anomaly area, providing a reference position for subsequent overlap rate calculation; 3) Define the overlap rate as the spatial correlation between the sound source location and the center location of the high-temperature region, and analyze the overlap rate based on the Euclidean distance between the sound source and the high-temperature region; The big data judgment model determines the overlap rate between the location coordinates of the abnormal sound and the abnormal high-temperature area in the energy storage cabinet based on the distance between the location coordinates of the abnormal sound and the abnormal high-temperature area in the energy storage cabinet. The results are as follows: ； In the formula, This indicates the overlap rate between the location of the abnormally high-temperature area in the energy storage cabinet and the coordinates of the abnormal sound; the location coordinates of the sound source are taken as... ; This indicates the intensity of environmental noise impact; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are taken as... .

2. The intelligent management method for energy storage cabinets according to claim 1, characterized in that: when If the location of the abnormally high temperature area in the energy storage cabinet coincides with the coordinates of the abnormal sound, then the inert gas solenoid valve needs to be activated. when If the location of the abnormally high temperature area in the energy storage cabinet does not coincide with the coordinates of the abnormal sound, then there is no need to activate the inert gas solenoid valve.

3. The intelligent management method for energy storage cabinets according to claim 1, characterized in that: The data acquisition in step S1 includes a temperature sensor, a sound detection sensor, and a gas detection module. The sound detection sensors include sound sensor a, sound sensor b, and sound sensor c, which are located at the bottom of the energy storage cabinet and on the left and right sides inside, respectively.

4. The intelligent management method for energy storage cabinets according to claim 3, characterized in that: The big data judgment model also determines the coordinates of the abnormal sound in the energy storage cabinet based on the time difference between the arrival of the sound source at the three sensors measured by the sound detection sensors, and the position coordinates of the sound detection sensors. Therefore: ； ； ； Furthermore, there are: ； ； ； In the formula, the position coordinates of the sound source are taken as ; The coordinates of sound sensor a are taken The coordinates of sound sensor b are taken as follows: The coordinates of the sound sensor c are taken as follows: ; The speed of sound transmission in the energy storage cabinet is expressed in m / s. This indicates the time in seconds (s) when sound sensor a receives a sound source. This indicates the time in seconds (s) when sound sensor b receives a sound source. This indicates the time in seconds (s) when the sound sensor c receives the sound source.

5. The intelligent management method for energy storage cabinets according to claim 3, characterized in that: The temperature sensor is a thermal imaging probe used to capture thermal images of the energy storage cabinet and determine the location of abnormally high-temperature areas in the energy storage cabinet based on the thermal images.

6. The intelligent management method for energy storage cabinets according to claim 1, characterized in that: Step S4 specifically also includes: S401. Based on the gas composition parameter information collected from the data collection, determine the degree of matching with the historical log parameters in the energy storage cabinet during the adverse events. S402. The matching degree is determined based on the results of historical data matching, and then it is determined whether to activate the multi-terminal circuit breaker.

7. A smart management system for energy storage cabinets, characterized in that, The intelligent management system, applied to the intelligent management method according to any one of claims 1-6, comprises: The data acquisition module is used to collect parameter information such as temperature, sound, and gas composition inside the energy storage cabinet; The judgment model is used to analyze whether the adjustable guide plate should be closed and the inert gas solenoid valve opened, based on the feedback from the data acquisition module. The execution control module is used to receive feedback from the judgment model, open the inert gas solenoid valve and close the adjustable guide plate.

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