Intelligent management system and method for energy storage cabinet

Abstract

The invention relates to the technical field of energy storage cabinets, and discloses an intelligent management method for an energy storage cabinet, which is applied to the energy storage cabinet. The energy storage cabinet comprises an external inert gas bottle, an inert gas electromagnetic valve, an adjustable guide plate and a multi-end circuit breaker which are located at the top of the energy storage cabinet; the intelligent management method comprises the following steps: acquiring parameter information of temperature, sound and gas in the energy storage cabinet, and judging the position and coordinates of abnormal sound in the energy storage cabinet according to the parameter information of the sound; the position of the highest-temperature area in the energy storage cabinet is judged according to the parameter information of the temperature; and the coincidence rate of the high-temperature area and the abnormal sound position coordinates is analyzed through a big data judgment model, and when the coincidence rate exceeds a set threshold value, an inert gas electromagnetic valve is opened to introduce inert gas. By calculating the coincidence rate of the high-temperature area and the sound position, inert gas release is triggered only when the coincidence rate exceeds the threshold value, so that false triggering can be avoided, inert gas resources are saved, and meanwhile, the system response efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage cabinets, and in particular to an intelligent management system for energy storage cabinets and a method thereof. Background Art

[0002] With the continuous development of the energy sector, energy storage technology has gradually become an important means to resolve the contradiction between energy supply and demand and improve energy utilization efficiency. As a key 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 discloses an intelligent management system for energy storage cabinets (CN117937766A). This system addresses the problem that traditional energy storage cabinet management methods typically rely on manual operation and periodic maintenance, making real-time monitoring and intelligent management difficult to achieve. Furthermore, they tend to generate large amounts of redundant and invalid data, negatively impacting intelligent management. The technology disclosed in this patent relies on a single voltage / current threshold alarm or a direct power outage when an anomaly or fault occurs, leading to a lengthy maintenance process involving dispatching engineers. While the incident during this timeframe is typically not a serious one, a direct power outage can cause sudden changes in circuit load and result in additional economic losses. Summary of the Invention

[0004] In order to solve existing technical problems, the present invention provides an intelligent management system for energy storage cabinets and a method thereof, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a smart management method for an energy storage cabinet is provided. The method is applied to the energy storage cabinet. The energy storage cabinet includes an external inert gas bottle located on the top of the energy storage cabinet, an inert gas solenoid valve, an adjustable deflector, and a multi-terminal circuit breaker. The smart management method includes: S1. Collect temperature, sound, and gas parameter information inside the energy storage cabinet, and determine the location and coordinates of abnormal sounds in the energy storage cabinet based on the sound parameter information; S2. Determine the location of the highest temperature area in the energy storage cabinet based on the temperature parameter information; S3. Analyze the coincidence rate between the high-temperature area and the abnormal sound location coordinates through a big data judgment model. When the coincidence rate exceeds a set threshold, open the inert gas solenoid valve to introduce inert gas. S4. Matching the monitored gas composition with historical data to further determine whether to activate the multi-terminal circuit breaker; The specific steps for calculating the overlap rate are as follows: 1) Place three sound sensors inside the energy storage cabinet. By measuring the time difference between the sound source reaching each sensor, and combining the known sensor coordinates and sound speed, a nonlinear equation system is constructed to solve the three-dimensional spatial coordinates of the sound source. 2) Use a thermal imaging probe to capture real-time thermal images of the interior of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature abnormality area, providing a reference position for subsequent coincidence rate calculation; 3) Define the coincidence rate as the spatial correlation between the sound source position and the center position of the high-temperature area, and analyze the coincidence rate based on the Euclidean distance between the sound source and the high-temperature area.

[0006] Furthermore, the big data judgment model determines the coincidence rate between the position coordinates of the abnormal sound in the energy storage cabinet and the coordinates of the abnormal sound based on the distance between the position coordinates of the abnormal sound in the energy storage cabinet and the abnormal high temperature area in the energy storage cabinet, as follows:

[0007] Where, Indicates the coincidence rate between the abnormal high temperature area in the energy storage cabinet and the abnormal sound coordinates; the location coordinates of the sound source are ; Indicates the impact intensity of environmental noise; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are .

[0008] Furthermore, when When , it means that the abnormal high temperature area in the energy storage cabinet coincides with the abnormal sound coordinates, and the inert gas solenoid valve needs to be activated; when , it means that the abnormal high temperature area in the energy storage cabinet does not coincide with the abnormal sound coordinates. At this time, there is no need to activate the inert gas solenoid valve.

[0009] Furthermore, 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, wherein sound sensor a, sound sensor b and sound sensor c are respectively located at the bottom end and left and right sides of the interior of the energy storage cabinet.

[0010] 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 and the three sensors measured by the sound detection sensor, as well as the position coordinates of the sound detection sensor. Then, we have:

[0011]

[0012]

[0013] And, there are:

[0014]

[0015]

[0016] In the formula, the position coordinates of the sound source are ; The coordinates of the sound sensor a are taken ; The coordinates of the sound sensor b are taken ; The coordinates of the sound sensor c are taken ; Indicates the speed of sound transmission in the energy storage cabinet, unit: m / s; It indicates the time when the sound sensor a receives the sound source, in seconds; It indicates the time when the sound sensor b receives the sound source, in seconds; It indicates the time when the sound sensor c receives the sound source, in seconds.

[0017] Furthermore, the temperature measuring sensor is a thermal imaging probe, which is used to capture a thermal image of the energy storage cabinet and determine the location of the abnormally high temperature area in the energy storage cabinet based on the thermal image.

[0018] Furthermore, the step S4 specifically further includes: S401: Determine the degree of matching between the gas composition parameter information collected from the data and the historical log parameters of the energy storage cabinet malicious events; S402: Determine the matching degree based on the historical data matching result, and then determine whether to activate the multi-terminal circuit breaker.

[0019] A smart management system includes: Data acquisition module, used to collect parameter information of temperature, sound and gas composition inside the energy storage cabinet; a judgment model for analyzing whether to close the adjustable guide plate and open the inert gas solenoid valve based on 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.

[0020] The present invention provides an intelligent management system and method for energy storage cabinets. Compared with the existing technology, this method has the following effects: 1. The present invention calculates the overlap rate between the high-temperature area and the sound position, and triggers the release of inert gas only when the overlap rate exceeds a threshold. This can avoid false triggering, save inert gas resources, and improve system response efficiency.

[0021] 2. The present invention provides an inert gas storage bottle above the energy storage cabinet and fills the bottle with inert gas to protect the internal circuits when an abnormality occurs in the energy storage cabinet. This design can avoid additional economic losses caused by direct power outages.

[0022] 3. The present invention controls the activation of the multi-terminal circuit breaker according to the feedback of the feedback optimization module through the execution control module, optimizes the control strategy in combination with the historical data matching module, enhances the system's ability to predict malicious events, realizes the self-learning function, and gradually improves the adaptability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural diagram of the energy storage cabinet in the present invention; Figure 2 This is a flow chart of the smart management system of the present invention; Figure 3 The overlap rate d in the present invention changes with the X and Y coordinates; Figure 4 The overlap rate d and the influence strength in the present invention Schematic diagram of sampling data; Figure 5 Schematic diagram of sampling data of the overlap rate d and the sound source distance m in the present invention; Figure 6 The overlap rate d and the influence strength in the present invention , model diagram of sound source distance m.

[0024] 1. Energy storage cabinet; 2. External inert gas cylinder. DETAILED DESCRIPTION

[0025] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 1 、 2 As shown, a smart management method for an energy storage cabinet is applied to an energy storage cabinet 1. The energy storage cabinet 1 includes an external inert gas bottle 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: Step 1: Use the sound detection sensor to determine the location and coordinates of the abnormal sound in the energy storage cabinet.

[0027] Step 2: Use the temperature sensor to determine the location of the highest or lowest temperature area in the energy storage cabinet.

[0028] Step 3: Determine the coincidence rate between the high-temperature area and the abnormal sound location coordinates. When the coincidence rate exceeds a set threshold, open the inert gas solenoid valve to introduce inert gas.

[0029] Step 4: further determine whether to activate the multi-terminal circuit breaker based on the gas composition changes monitored by the historical data matching module; The specific calculation steps of the overlap rate are: 1) Place three sound sensors inside the energy storage cabinet. By measuring the time difference between the sound source reaching each sensor, and combining the known sensor coordinates and sound speed, a nonlinear equation system is constructed to solve the three-dimensional spatial coordinates of the sound source. 2) Use a thermal imaging probe to capture real-time thermal images of the interior of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature abnormality area, providing a reference position for subsequent coincidence rate calculation; 3) Define the coincidence rate as the spatial correlation between the sound source position and the center position of the high-temperature area, and analyze the coincidence rate based on the Euclidean distance between the sound source and the high-temperature area.

[0030] Example 1 like Figure 1 As shown, data collection specifically includes temperature sensors, sound detection sensors, and a gas detection module. The temperature sensor is a thermal imaging probe used to capture thermal images of the energy storage cabinet and locate abnormally high-temperature areas within the cabinet based on the thermal images. The sound detection sensors include sound sensors a, b, and c, located at the bottom and left and right sides of the cabinet, respectively. Real-time temperature, sound, and gas data are collected and combined with cloud-based analysis for rapid response. The execution control module precisely operates the inert gas solenoid valve and deflector, improving the cabinet's safety and preventing fire or explosion risks.

[0031] Example 2 The big data judgment model determines the coordinates of the abnormal sound in the energy storage cabinet based on the time difference between the sound source reaching the three sensors and the location coordinates of the sound detection sensors. Then, we have:

[0032]

[0033]

[0034] And, there are:

[0035]

[0036]

[0037] In the formula, the position coordinates of the sound source are ; The coordinates of the sound sensor a are taken ; The coordinates of the sound sensor b are taken ; The coordinates of the sound sensor c are taken ; Indicates the speed of sound transmission in the energy storage cabinet, unit: m / s; It indicates the time when the sound sensor a receives the sound source, in seconds; It indicates the time when the sound sensor b receives the sound source, in seconds; It indicates the time when the sound sensor c receives the sound source, in seconds.

[0038] Among them, if the coordinates of the sound sensor a are , the coordinates of the sound sensor b are , the coordinates of the sound sensor c are The speed of sound transmission in the energy storage cabinet is (m / s), the event difference between the sound detected by sensor a and sensor b is (s), the event difference between the sound detected by sensor a and sensor c is (s), the event difference between the sound detected by sensor b and sensor c is (s), then we have:

[0039]

[0040]

[0041] According to the above formula, the position coordinates of the sound can be deduced as The system locates the sound source coordinates based on the time difference between three sound sensors and uses mathematical models to achieve high-precision fault location, reducing misjudgments and improving the system's accuracy in identifying abnormal sounds.

[0042] Example 3 like Figure 1-6 As shown in FIG, the big data judgment model determines the coincidence rate between the position coordinates of the abnormal sound in the energy storage cabinet and the coordinates of the abnormal sound based on the distance between the position coordinates of the abnormal high temperature area in the energy storage cabinet and the abnormal high temperature area in the energy storage cabinet, which is:

[0043] Where, Indicates the coincidence rate between the abnormal high temperature area in the energy storage cabinet and the abnormal sound coordinates; the location coordinates of the sound source are ; Indicates the impact intensity of environmental noise; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are .

[0044] Therefore, to solve this practical problem, the company can only analyze the coincidence rate between the location of the abnormally high temperature area and the coordinates of the abnormal sound during the production, use, and testing processes. This analysis to solve the company's practical problem is called empirical analysis, and the data obtained and the characteristic relationship between the data are the external manifestation of the empirical formula. The reasoning process is as follows: S1. Fitting a formula for the coincidence rate d between the position of the abnormally high temperature area in the energy storage cabinet and the coordinates of the abnormal sound.

[0045] The overlap rate d between the abnormally high temperature area and the coordinates of the abnormal sound in the energy storage cabinet can be expressed as the number of devices in the sample energy storage cabinets whose abnormal areas coincide with the sound-generating areas. For example, if data from 100 energy storage cabinets is collected and the manual evaluation shows that the overlap rate between the abnormal sound-generating area and the heat location in a particular energy storage cabinet exceeds that of the data from the other 50 samples, then the overlap rate d for that energy storage cabinet is 50%.

[0046] Other coordinate data and the impact intensity of environmental noise are calculated using data collected by the device itself.

[0047] S2, overlap rate d and impact intensity Establish a mathematical model of the relationship between Figure 4 As shown), then: (Formula 1) In the above formula 1, k represents an empirical constant for adjusting the sensitivity of the above model.

[0048] S3. Establish a mathematical model for the relationship between the coincidence rate d and the sound source distance m (e.g. Figure 5 As shown), then: (Formula 2) In the above formula 2, k represents the empirical constant for adjusting the sensitivity of the above model. express .

[0049] S4, overlap rate d and impact intensity , the relationship between the sound source distance m is established to establish a mathematical model (such as Figure 6 As shown), and combined with the characteristic relationship of the above formula 1 and formula 2, we have: (Formula 3) Will Substituting in, we have:

[0050] Among them, if the calculated sound position coordinates are , and based on thermal imaging, the center coordinates of the abnormally high temperature area in the energy storage cabinet can be known. .

[0051] Moreover, the impact of environmental noise is (Influence intensity = the maximum value of white noise in the external environment (dB) ÷ the minimum value of the noise produced in the energy storage cabinet (dB), then:

[0052] From the above calculations, we can know that the coordinates of this abnormal sound are 、 The coordinates are consistent with those collected by thermal imaging. And compared with one set of data, we have: Table 1 Partial implementation data parameters and coincidence relationships From the data in Table 1 above, we can know that when the sample number range is infinite, there will be a dividing line that determines whether the energy storage cabinet is abnormal or not based on the overlap rate d, that is, when When , it means that the abnormal high temperature area in the energy storage cabinet coincides with the abnormal sound coordinates, and the inert gas solenoid valve needs to be activated; when When the abnormal high temperature area in the energy storage cabinet does not coincide with the coordinates of the abnormal sound, there is no need to activate the inert gas solenoid valve. By calculating the overlap ratio between the high temperature area and the sound location, the inert gas release is triggered only when the overlap ratio exceeds the threshold. This avoids false triggering, conserves inert gas resources, and improves system response efficiency.

[0053] Example 4 like Figure 1 、 2 As shown, the intelligent management system includes a data acquisition module for collecting parameter information on temperature, sound, and gas composition within the energy storage cabinet; a judgment model for analyzing whether to close the adjustable deflector 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 and opening the inert gas solenoid valve and closing the adjustable deflector. The system calculates the overlap rate between the high-temperature area and the sound location and triggers the inert gas release only when the overlap rate exceeds a threshold. This avoids false triggering, conserves inert gas resources, and improves system response efficiency.

[0054] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A smart management method for energy storage cabinets, characterized in that: Applicable to an energy storage cabinet (1); the energy storage cabinet (1) comprises an external inert gas bottle (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 comprises: S1. Collect temperature, sound, and gas parameter information inside the energy storage cabinet, and determine the location and coordinates of abnormal sounds in the energy storage cabinet based on the sound parameter information; S2. Determine the location of the highest temperature area in the energy storage cabinet based on temperature parameter information; S3. Analyze the coincidence rate between the high-temperature area and the abnormal sound location coordinates through a big data judgment model. When the coincidence rate exceeds a set threshold, open the inert gas solenoid valve to introduce inert gas. S4. Matching the monitored gas composition with historical data to further determine whether to activate the multi-terminal circuit breaker; The specific steps for calculating the overlap rate are as follows: 1) Place three sound sensors inside the energy storage cabinet. By measuring the time difference between the sound source reaching each sensor, and combining the known sensor coordinates and sound speed, a nonlinear equation system is constructed to solve the three-dimensional spatial coordinates of the sound source. 2) Use a thermal imaging probe to capture real-time thermal images of the interior of the energy storage cabinet, and use image analysis technology to identify the center coordinates of the temperature abnormality area, providing a reference position for subsequent coincidence rate calculation; 3) Define the coincidence rate as the spatial correlation between the sound source position and the center position of the high-temperature area, and analyze the coincidence rate based on the Euclidean distance between the sound source and the high-temperature area.

2. The intelligent management method for energy storage cabinets according to claim 1, characterized in that: The big data judgment model determines the coincidence rate between the position coordinates of the abnormal sound in the energy storage cabinet and the coordinates of the abnormal sound based on the distance between the position coordinates of the abnormal sound in the energy storage cabinet and the abnormal high temperature area in the energy storage cabinet, which is: ； Where, Indicates the coincidence rate between the abnormal high temperature area in the energy storage cabinet and the abnormal sound coordinates; the location coordinates of the sound source are ; Indicates the impact intensity of environmental noise; the coordinates of the center of the abnormally high temperature area in the energy storage cabinet are .

3. The intelligent management method for energy storage cabinets according to claim 2, characterized in that: when When , it means that the abnormal high temperature area in the energy storage cabinet coincides with the abnormal sound coordinates, and the inert gas solenoid valve needs to be activated; when , it means that the abnormal high temperature area in the energy storage cabinet does not coincide with the abnormal sound coordinates. At this time, there is no need to activate the inert gas solenoid valve.

4. The intelligent management method for energy storage cabinets according to claim 2, 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, wherein sound sensor a, sound sensor b and sound sensor c are respectively located at the bottom end and left and right sides of the interior of the energy storage cabinet.

5. The intelligent management method for energy storage cabinets according to claim 4, 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 sound source and the three sensors measured by the sound detection sensor, as well as the position coordinates of the sound detection sensor. Then, we have: ； ； ； And, there are: ； ； ； In the formula, the position coordinates of the sound source are ; The coordinates of the sound sensor a are ; The coordinates of the sound sensor b are taken ; The coordinates of the sound sensor c are taken ; Indicates the speed of sound transmission in the energy storage cabinet, unit: m / s; It indicates the time when the sound sensor a receives the sound source, in seconds; It indicates the time when the sound sensor b receives the sound source, in seconds; It indicates the time when the sound sensor c receives the sound source, in seconds.

6. The intelligent management method for energy storage cabinets according to claim 4, characterized in that: The temperature measuring sensor is a thermal imaging probe, which is used to capture a thermal image of the energy storage cabinet and determine the location of an abnormally high temperature area in the energy storage cabinet based on the thermal image.

7. The intelligent management method for energy storage cabinets according to claim 1, characterized in that: The step S4 specifically also includes: S401: Determine the degree of matching between the gas composition parameter information collected from the data and the historical log parameters of the energy storage cabinet malicious events; S402: Determine the matching degree based on the historical data matching result, and then determine whether to activate the multi-terminal circuit breaker.

8. An intelligent management system for energy storage cabinets, characterized in that: The intelligent management method according to any one of claims 1 to 7, wherein the intelligent management system includes: Data acquisition module, used to collect parameter information of temperature, sound and gas composition inside the energy storage cabinet; a judgment model for analyzing whether to close the adjustable guide plate and open the inert gas solenoid valve based on 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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