Fault diagnosis and early warning method, system and medium for energy storage management system

Abstract

The present application provides a fault diagnosis and early warning method, system and medium for an energy storage management system, which belongs to the field of energy storage management and big data technology. The method includes: obtaining the operating parameter monitoring information of the energy storage device, extracting the voltage change rate data, the current change rate data and the voiceprint data respectively, and then processing to obtain the voltage change rate abnormal data, the current change rate abnormal data and the voiceprint abnormal data, and processing to obtain the energy storage device failure rate evaluation index, and then combining the historical energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index, and judge the probability level of the energy storage device failure. The present application conducts real-time monitoring and in-depth analysis of the key parameters of the energy storage device, realizes comprehensive, accurate and intelligent monitoring and fault prediction of the energy storage device, provides strong technical support and decision-making basis for operation and maintenance personnel, and helps to improve the operating efficiency and reliability of the energy storage equipment.

Description

Technical Field

[0001] The present application relates to the field of energy storage management and big data technology, and more specifically, to a fault diagnosis and early warning method, system and medium for an energy storage management system. Background Art

[0002] As an important part of energy storage system, energy storage management system has made remarkable development in recent years driven by both technological progress and market demand, as the world pays more attention to renewable energy and builds new power systems. Although energy storage management system and its related technologies have great potential and application prospects, they still face many limitations and challenges in practical applications.

[0003] The current energy storage management system still has technical problems caused by unstable operation and sudden failures of energy storage equipment, which makes it impossible to establish a complete and effective maintenance strategy. There is a lack of a deep and comprehensive analysis method for the factors that cause energy storage equipment failures.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of this application is to provide a fault diagnosis and early warning method, system and medium for an energy storage management system, which can obtain the operating parameter monitoring information of the energy storage device, extract the voltage change rate data, current change rate data and voiceprint data respectively, and then process them respectively to obtain the abnormal voltage change rate data, abnormal current change rate data and abnormal voiceprint data, and process them through a preset fault prediction model to obtain the energy storage device failure rate evaluation index, and then combine the historical energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index, and compare it with the preset failure rate index threshold to determine the probability level of failure of the energy storage device. This application conducts real-time monitoring and in-depth analysis of the key parameters of the energy storage device, realizes comprehensive, accurate and intelligent monitoring and fault prediction of the energy storage device, provides strong technical support and decision-making basis for operation and maintenance personnel, and helps to improve the operating efficiency and reliability of the energy storage device.

[0006] The present application provides a fault diagnosis and early warning method for an energy storage management system, which is characterized by comprising the following steps:

[0007] Obtaining the operating parameter monitoring information of the energy storage device within a preset time period, and extracting the voltage change rate data, current change rate data and voiceprint data respectively;

[0008] Obtain voltage change rate abnormal data according to the voltage change rate data, obtain current change rate abnormal data according to the current change rate data, and obtain voiceprint abnormal data according to the voiceprint data;

[0009] The abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data are processed by a preset fault prediction model to obtain an energy storage device failure rate evaluation index;

[0010] Acquire historical fault information of the energy storage device to correct the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index;

[0011] The energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability level of failure of the energy storage device.

[0012] Among them, in the fault diagnosis and early warning method of the energy storage management system described in the present application, it is characterized in that the voltage change rate abnormal data is obtained according to the voltage change rate data, specifically:

[0013] Comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate;

[0014] Comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold;

[0015] If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormality alarm message is issued;

[0016] If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable.

[0017] Among them, in the fault diagnosis and early warning method of the energy storage management system described in the present application, it is characterized in that the current change rate abnormal data is obtained according to the current change rate data, specifically:

[0018] Comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate;

[0019] Comparing the current change rate deviation rate with a preset current change rate deviation rate threshold;

[0020] If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, current change rate abnormal data is obtained and a current abnormality alarm message is issued;

[0021] If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable.

[0022] Among them, in the fault diagnosis and early warning method of the energy storage management system described in the present application, it is characterized in that the voiceprint abnormality data is obtained according to the voiceprint data, specifically:

[0023] Extracting voiceprint feature data according to the voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data;

[0024] According to the abnormal sound data, spectrum distribution data and time domain feature data, recognition processing is performed through a preset fault voiceprint recognition database to obtain voiceprint abnormality data.

[0025] Among them, in a fault diagnosis and early warning method of an energy storage management system described in the present application, it is characterized in that the acquisition of historical fault information of energy storage equipment corrects the energy storage equipment failure rate evaluation index to obtain the energy storage equipment failure rate correction index, specifically:

[0026] Obtaining historical fault information of the energy storage device within the preset time period;

[0027] Extracting an average index of energy storage device failure rate evaluation based on historical failure information of the energy storage device;

[0028] The energy storage device failure rate evaluation index is corrected according to the energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index.

[0029] Among them, in a fault diagnosis and early warning method for an energy storage management system described in the present application, it is characterized in that the energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability of failure of the energy storage device, specifically:

[0030] Comparing the energy storage device failure rate correction index with a preset first failure rate index threshold and a second failure rate index threshold, respectively, the first failure rate index threshold being less than the second failure rate index threshold;

[0031] If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low;

[0032] If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium;

[0033] If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

[0034] In a second aspect, the present application provides a fault diagnosis and early warning system for an energy storage management system, the system comprising: a memory and a processor, the memory comprising a program of a fault diagnosis and early warning method for an energy storage management system, the program of the fault diagnosis and early warning method for an energy storage management system being executed by the processor to implement the following steps:

[0035] Obtaining the operating parameter monitoring information of the energy storage device within a preset time period, and extracting the voltage change rate data, current change rate data and voiceprint data respectively;

[0036] Obtain voltage change rate abnormal data according to the voltage change rate data, obtain current change rate abnormal data according to the current change rate data, and obtain voiceprint abnormal data according to the voiceprint data;

[0037] The abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data are processed by a preset fault prediction model to obtain an energy storage device failure rate evaluation index;

[0038] Acquire historical fault information of the energy storage device to correct the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index;

[0039] The energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability level of failure of the energy storage device.

[0040] Among them, in the fault diagnosis and early warning system of the energy storage management system described in the present application, it is characterized in that the voltage change rate abnormal data is obtained according to the voltage change rate data, specifically:

[0041] Comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate;

[0042] Comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold;

[0043] If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormality alarm message is issued;

[0044] If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable.

[0045] Among them, in the fault diagnosis and early warning system of the energy storage management system described in the present application, it is characterized in that the current change rate abnormal data is obtained according to the current change rate data, specifically:

[0046] Comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate;

[0047] Comparing the current change rate deviation rate with a preset current change rate deviation rate threshold;

[0048] If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, current change rate abnormal data is obtained and a current abnormality alarm message is issued;

[0049] If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable.

[0050] In a third aspect, the present application also provides a computer-readable storage medium, which includes a fault diagnosis and early warning method program for an energy storage management system. When the fault diagnosis and early warning method program for an energy storage management system is executed by a processor, the steps of the fault diagnosis and early warning method for an energy storage management system as described in any one of the above items are implemented.

[0051] As can be seen from the above, the fault diagnosis and early warning method, system and medium of an energy storage management system provided by the embodiment of the present application obtain the operating parameter monitoring information of the energy storage device within a preset time period, respectively extract the voltage change rate data, the current change rate data and the voiceprint data, obtain the voltage change rate abnormal data according to the voltage change rate data, obtain the current change rate abnormal data according to the current change rate data, obtain the voiceprint abnormal data according to the voiceprint data, and process the voltage change rate abnormal data, the current change rate abnormal data and the voiceprint abnormal data through a preset fault prediction model to obtain the energy storage device failure rate evaluation index, obtain the energy storage device historical fault information, extract the energy storage device failure rate evaluation average index, correct the energy storage device failure rate evaluation index, obtain the energy storage device failure rate correction index, compare the energy storage device failure rate correction index with the preset failure rate index threshold, and judge the probability level of failure of the energy storage device. This application conducts real-time monitoring and in-depth analysis of key parameters of energy storage equipment, realizes comprehensive monitoring of energy storage equipment, improves the comprehensiveness of fault detection, and realizes comprehensive, accurate, and intelligent monitoring and fault prediction of energy storage equipment, providing strong technical support and decision-making basis for operation and maintenance personnel, which helps to improve the operating efficiency and reliability of energy storage equipment.

[0052] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of a fault diagnosis and early warning method for an energy storage management system provided in an embodiment of the present application;

[0055] Figure 2 A flowchart of obtaining abnormal voltage change rate data of a fault diagnosis and early warning method for an energy storage management system provided in an embodiment of the present application;

[0056] Figure 3 A flow chart of obtaining abnormal current change rate data of a fault diagnosis and early warning method for an energy storage management system provided in an embodiment of the present application;

[0057] Figure 4 A flowchart of obtaining abnormal voiceprint data in a method for fault diagnosis and early warning of an energy storage management system provided in an embodiment of the present application;

[0058] Figure 5 A flow chart of obtaining a failure rate correction index of an energy storage device in a fault diagnosis and early warning method of an energy storage management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0060] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0061] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for fault diagnosis and early warning of an energy storage management system in some embodiments of the present application. The method for fault diagnosis and early warning of an energy storage management system is used in a terminal device, such as a computer, a mobile phone terminal, etc. The method for fault diagnosis and early warning of an energy storage management system comprises the following steps:

[0062] S101, obtaining operating parameter monitoring information of the energy storage device within a preset time period, and extracting voltage change rate data, current change rate data and voiceprint data respectively;

[0063] S102, obtaining voltage change rate abnormal data according to the voltage change rate data, obtaining current change rate abnormal data according to the current change rate data, and obtaining voiceprint abnormal data according to the voiceprint data;

[0064] S103, processing the abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data through a preset fault prediction model to obtain an energy storage device failure rate evaluation index;

[0065] S104, acquiring historical fault information of the energy storage device and performing correction processing on the energy storage device failure rate evaluation index to obtain a correction index of the energy storage device failure rate;

[0066] S105: Compare the energy storage device failure rate correction index with a preset failure rate index threshold to determine the probability level of failure of the energy storage device.

[0067] Among them, the present application obtains the operating parameter monitoring information of the energy storage device within a preset time period, extracts the voltage change rate data, the current change rate data and the voiceprint data respectively, obtains the voltage change rate abnormal data according to the voltage change rate data, obtains the current change rate abnormal data according to the current change rate data, obtains the voiceprint abnormal data according to the voiceprint data, processes the voltage change rate abnormal data, the current change rate abnormal data and the voiceprint abnormal data through a preset fault prediction model, obtains the energy storage device failure rate evaluation index, obtains the historical fault information of the energy storage device, extracts the energy storage device failure rate evaluation average index, corrects the energy storage device failure rate evaluation index, obtains the energy storage device failure rate correction index, compares the energy storage device failure rate correction index with the preset failure rate index threshold, and determines the probability level of the energy storage device failure. The present application conducts real-time monitoring and in-depth analysis of the key parameters of the energy storage device, realizes comprehensive monitoring of the energy storage device, improves the comprehensiveness of fault detection, realizes comprehensive, accurate and intelligent monitoring and fault prediction of the energy storage device, provides strong technical support and decision-making basis for operation and maintenance personnel, and helps to improve the operating efficiency and reliability of the energy storage device;

[0068] The calculation formula of the fault prediction model is:

[0069] ;

[0070] in, is the failure rate evaluation index of energy storage equipment. , , They are abnormal voltage change rate data, abnormal current change rate data and abnormal voiceprint data. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset troubleshooting database).

[0071] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of a method for fault diagnosis and early warning of an energy storage management system in some embodiments of the present invention for obtaining abnormal voltage change rate data. According to an embodiment of the present invention, the abnormal voltage change rate data is obtained according to the voltage change rate data, specifically:

[0072] S201, comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate;

[0073] S202, comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold;

[0074] S203, if the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, obtaining voltage change rate abnormal data and issuing voltage abnormality alarm information;

[0075] S204: If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable.

[0076] Among them, in order to avoid equipment failure caused by voltage mutation, the voltage change rate data is compared with the preset voltage change rate threshold to obtain the voltage change rate deviation rate, and the voltage change rate deviation rate is compared with the preset voltage change rate deviation rate threshold. If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormal alarm message is issued. If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable. Through the above steps and logic, we can effectively monitor the voltage change rate and issue an alarm in time when the voltage changes too fast, thereby avoiding equipment failure caused by voltage mutation.

[0077] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of a method for fault diagnosis and early warning of an energy storage management system in some embodiments of the present invention for obtaining abnormal current change rate data. According to an embodiment of the present invention, the abnormal current change rate data is obtained according to the current change rate data, specifically:

[0078] S301, comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate;

[0079] S302, comparing the current change rate deviation rate with a preset current change rate deviation rate threshold;

[0080] S303, if the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, obtaining current change rate abnormal data and issuing current abnormal alarm information;

[0081] S304: If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable.

[0082] Among them, in order to avoid equipment failure caused by current mutation and overcurrent phenomenon, the current change rate data is compared with the preset current change rate threshold to obtain the current change rate deviation rate, and the current change rate deviation rate is compared with the preset current change rate deviation rate threshold. If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, the current change rate abnormal data is obtained and the current abnormal alarm information is issued. If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable. The system can respond to current abnormalities quickly, which helps to take measures in advance and reduce the risk of equipment damage. This method is more sophisticated and reliable than simply comparing the current value or the current change rate itself.

[0083] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of a method for fault diagnosis and early warning of an energy storage management system in some embodiments of the present invention for obtaining abnormal voiceprint data. According to an embodiment of the present invention, the abnormal voiceprint data is obtained according to the voiceprint data, specifically:

[0084] S401, extracting voiceprint feature data according to the voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data;

[0085] S402: performing recognition processing on the abnormal sound data, spectrum distribution data and time domain feature data through a preset fault voiceprint recognition database to obtain abnormal voiceprint data.

[0086] Among them, in order to more intuitively and effectively discover and lock the cause of energy storage equipment failure, voiceprint feature data is extracted according to voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data. Abnormal sound data refers to sounds that are different from normal operation, such as sharp noise, abnormal vibration sound, etc. The spectrum distribution data analyzes the energy distribution of different frequency components to reveal the frequency characteristics of the sound. The time domain feature data analyzes the waveform, envelope, periodicity and other characteristics of the sound signal in the time domain to understand the law of sound change over time, and then the preset fault voiceprint recognition database is used for identification and processing to obtain voiceprint abnormal data. Through the above method, the cause of energy storage equipment failure can be more intuitively and effectively discovered and locked, the accuracy and efficiency of fault diagnosis can be improved, and maintenance costs and time can be reduced.

[0087] Please refer to Figure 5 , Figure 5 This is a flow chart of obtaining a storage device failure rate correction index of a method for fault diagnosis and early warning of an energy storage management system in some embodiments of the present application. According to an embodiment of the present invention, the energy storage device failure rate evaluation index is corrected by obtaining historical failure information of the energy storage device to obtain the energy storage device failure rate correction index, specifically:

[0088] S501, obtaining historical fault information of energy storage equipment within the preset time period;

[0089] S502, extracting an average index of energy storage device failure rate evaluation according to the historical failure information of the energy storage device;

[0090] S503: Correct the energy storage device failure rate evaluation index according to the energy storage device failure rate evaluation average index to obtain an energy storage device failure rate correction index.

[0091] Among them, in order to more accurately predict the failure rate of energy storage equipment, the historical failure information of energy storage equipment within a preset time period is obtained, the average index of energy storage equipment failure rate evaluation is extracted according to the historical failure information of energy storage equipment, and the energy storage equipment failure rate evaluation index is corrected to obtain the energy storage equipment failure rate correction index;

[0092] The calculation formula of the energy storage equipment failure rate correction index is:

[0093] ;

[0094] in, is the energy storage equipment failure rate correction index, is the failure rate evaluation index of energy storage equipment. It is the average index of energy storage equipment failure rate evaluation. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset troubleshooting database).

[0095] According to an embodiment of the present invention, the energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability of failure of the energy storage device, specifically:

[0096] Comparing the energy storage device failure rate correction index with a preset first failure rate index threshold and a second failure rate index threshold, respectively, the first failure rate index threshold being less than the second failure rate index threshold;

[0097] If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low;

[0098] If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium;

[0099] If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

[0100] Among them, in order to more intuitively reflect the probability of failure of the energy storage device, the energy storage device failure rate correction index is compared with the preset first failure rate index threshold and the second failure rate index threshold respectively. The first failure rate index threshold is less than the second failure rate index threshold. If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low. If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium. If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

[0101] According to an embodiment of the present invention, it also includes:

[0102] Extracting energy storage device temperature data according to the operating parameter information;

[0103] Comparing the temperature data of the energy storage device with a preset first temperature threshold and a second temperature threshold respectively, wherein the first temperature threshold is less than the second temperature threshold;

[0104] If the temperature data of the energy storage device is less than or equal to the preset first temperature threshold, the energy storage device is in a low temperature environment and sends a low temperature alarm message;

[0105] If the temperature data of the energy storage device is greater than a preset first temperature threshold and less than a preset second temperature threshold, the energy storage device is in a normal operating temperature environment;

[0106] If the temperature data of the energy storage device is greater than or equal to a preset second temperature threshold, the energy storage device is in a high temperature environment and sends a high temperature warning message.

[0107] Among them, in order to ensure that the energy storage device is at a normal operating temperature, the temperature data of the energy storage device is extracted according to the operating parameter information, and the temperature data of the energy storage device is compared with a preset first temperature threshold and a second temperature threshold respectively. The first temperature threshold is less than the second temperature threshold. If the temperature data of the energy storage device is less than or equal to the preset first temperature threshold, the energy storage device is in a low temperature environment and sends a low temperature alarm message. If the temperature data of the energy storage device is greater than the preset first temperature threshold and less than the preset second temperature threshold, the energy storage device is in a normal operating temperature environment. If the temperature data of the energy storage device is greater than or equal to the preset second temperature threshold, the energy storage device is in a high temperature environment and sends a high temperature alarm message.

[0108] According to an embodiment of the present invention, it also includes:

[0109] Obtain maintenance and inspection information of energy storage equipment within a preset time period to extract insulation resistance data;

[0110] comparing the insulation resistance data with a preset insulation resistance threshold;

[0111] If the insulation resistance data is less than the preset insulation resistance threshold, low insulation information is obtained;

[0112] Counting is performed according to the insulation low information to obtain insulation low frequency data;

[0113] Compare the insulation low frequency data with a preset frequency threshold;

[0114] If the insulation low frequency data is greater than or equal to the frequency threshold, the insulation of the energy storage device does not meet the requirements and insulation treatment is required.

[0115] Among them, in order to ensure that the energy storage device has good insulation performance, the maintenance and detection information of the energy storage device within a preset time period is obtained to extract insulation resistance data, and the insulation resistance data is compared with a preset insulation resistance threshold. If the insulation resistance data is less than the preset insulation resistance threshold, low insulation information is obtained, and counting is performed according to the low insulation information to obtain low insulation times data, and the low insulation times data is compared with the preset times threshold. If the low insulation times data is greater than or equal to the times threshold, the insulation of the energy storage device does not meet the requirements and insulation treatment is required.

[0116] The present invention also discloses a fault diagnosis and early warning system for an energy storage management system, comprising a memory and a processor, wherein the memory comprises a fault diagnosis and early warning method program for the energy storage management system, and when the fault diagnosis and early warning method program for the energy storage management system is executed by the processor, the following steps are implemented:

[0117] Obtaining the operating parameter monitoring information of the energy storage device within a preset time period, and extracting the voltage change rate data, current change rate data and voiceprint data respectively;

[0118] Obtain voltage change rate abnormal data according to the voltage change rate data, obtain current change rate abnormal data according to the current change rate data, and obtain voiceprint abnormal data according to the voiceprint data;

[0119] The abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data are processed by a preset fault prediction model to obtain an energy storage device failure rate evaluation index;

[0120] Acquire historical fault information of the energy storage device to correct the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index;

[0121] The energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability level of failure of the energy storage device.

[0122] Among them, the present application obtains the operating parameter monitoring information of the energy storage device within a preset time period, extracts the voltage change rate data, the current change rate data and the voiceprint data respectively, obtains the voltage change rate abnormal data according to the voltage change rate data, obtains the current change rate abnormal data according to the current change rate data, obtains the voiceprint abnormal data according to the voiceprint data, processes the voltage change rate abnormal data, the current change rate abnormal data and the voiceprint abnormal data through a preset fault prediction model, obtains the energy storage device failure rate evaluation index, obtains the historical fault information of the energy storage device, extracts the energy storage device failure rate evaluation average index, corrects the energy storage device failure rate evaluation index, obtains the energy storage device failure rate correction index, compares the energy storage device failure rate correction index with the preset failure rate index threshold, and determines the probability level of the energy storage device failure. The present application conducts real-time monitoring and in-depth analysis of the key parameters of the energy storage device, realizes comprehensive monitoring of the energy storage device, improves the comprehensiveness of fault detection, realizes comprehensive, accurate and intelligent monitoring and fault prediction of the energy storage device, provides strong technical support and decision-making basis for operation and maintenance personnel, and helps to improve the operating efficiency and reliability of the energy storage device;

[0123] The calculation formula of the fault prediction model is:

[0124] ;

[0125] in, is the failure rate evaluation index of energy storage equipment. , , They are abnormal voltage change rate data, abnormal current change rate data and abnormal voiceprint data. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset troubleshooting database).

[0126] According to an embodiment of the present invention, obtaining the voltage change rate abnormal data according to the voltage change rate data is specifically:

[0127] Comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate;

[0128] Comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold;

[0129] If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormality alarm message is issued;

[0130] If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable.

[0131] Among them, in order to avoid equipment failure caused by voltage mutation, the voltage change rate data is compared with the preset voltage change rate threshold to obtain the voltage change rate deviation rate, and the voltage change rate deviation rate is compared with the preset voltage change rate deviation rate threshold. If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormal alarm message is issued. If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable. Through the above steps and logic, we can effectively monitor the voltage change rate and issue an alarm in time when the voltage changes too fast, thereby avoiding equipment failure caused by voltage mutation.

[0132] According to an embodiment of the present invention, obtaining the abnormal current change rate data according to the current change rate data is specifically:

[0133] Comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate;

[0134] Comparing the current change rate deviation rate with a preset current change rate deviation rate threshold;

[0135] If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, current change rate abnormal data is obtained and a current abnormality alarm message is issued;

[0136] If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable.

[0137] Among them, in order to avoid equipment failure caused by current mutation and overcurrent phenomenon, the current change rate data is compared with the preset current change rate threshold to obtain the current change rate deviation rate, and the current change rate deviation rate is compared with the preset current change rate deviation rate threshold. If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, the current change rate abnormal data is obtained and the current abnormal alarm information is issued. If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable. The system can respond to current abnormalities quickly, which helps to take measures in advance and reduce the risk of equipment damage. This method is more sophisticated and reliable than simply comparing the current value or the current change rate itself.

[0138] According to an embodiment of the present invention, obtaining abnormal voiceprint data according to the voiceprint data is specifically:

[0139] Extracting voiceprint feature data according to the voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data;

[0140] According to the abnormal sound data, spectrum distribution data and time domain feature data, recognition processing is performed through a preset fault voiceprint recognition database to obtain voiceprint abnormality data.

[0141] Among them, in order to more intuitively and effectively discover and lock the cause of energy storage equipment failure, voiceprint feature data is extracted according to voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data. Abnormal sound data refers to sounds that are different from normal operation, such as sharp noise, abnormal vibration sound, etc. The spectrum distribution data analyzes the energy distribution of different frequency components to reveal the frequency characteristics of the sound. The time domain feature data analyzes the waveform, envelope, periodicity and other characteristics of the sound signal in the time domain to understand the law of sound change over time, and then the preset fault voiceprint recognition database is used for identification and processing to obtain voiceprint abnormal data. Through the above method, the cause of energy storage equipment failure can be more intuitively and effectively discovered and locked, the accuracy and efficiency of fault diagnosis can be improved, and maintenance costs and time can be reduced.

[0142] According to an embodiment of the present invention, the acquisition of historical fault information of the energy storage device performs correction processing on the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index, specifically:

[0143] Obtaining historical fault information of the energy storage device within the preset time period;

[0144] Extracting an average index of energy storage device failure rate evaluation based on historical failure information of the energy storage device;

[0145] The energy storage device failure rate evaluation index is corrected according to the energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index.

[0146] Among them, in order to more accurately predict the failure rate of energy storage equipment, the historical failure information of energy storage equipment within a preset time period is obtained, the average index of energy storage equipment failure rate evaluation is extracted according to the historical failure information of energy storage equipment, and the energy storage equipment failure rate evaluation index is corrected to obtain the energy storage equipment failure rate correction index;

[0147] The calculation formula of the energy storage equipment failure rate correction index is:

[0148] ;

[0149] in, is the energy storage equipment failure rate correction index, is the failure rate evaluation index of energy storage equipment. It is the average index of energy storage equipment failure rate evaluation. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset troubleshooting database).

[0150] According to an embodiment of the present invention, the energy storage device failure rate correction index is compared with a preset failure rate index threshold to determine the probability of failure of the energy storage device, specifically:

[0151] Comparing the energy storage device failure rate correction index with a preset first failure rate index threshold and a second failure rate index threshold, respectively, the first failure rate index threshold being less than the second failure rate index threshold;

[0152] If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low;

[0153] If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium;

[0154] If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

[0155] Among them, in order to more intuitively reflect the probability of failure of the energy storage device, the energy storage device failure rate correction index is compared with the preset first failure rate index threshold and the second failure rate index threshold respectively. The first failure rate index threshold is less than the second failure rate index threshold. If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low. If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium. If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

[0156] According to an embodiment of the present invention, it also includes:

[0157] Extracting energy storage device temperature data according to the operating parameter information;

[0158] Comparing the temperature data of the energy storage device with a preset first temperature threshold and a second temperature threshold respectively, wherein the first temperature threshold is less than the second temperature threshold;

[0159] If the temperature data of the energy storage device is less than or equal to the preset first temperature threshold, the energy storage device is in a low temperature environment and sends a low temperature alarm message;

[0160] If the temperature data of the energy storage device is greater than a preset first temperature threshold and less than a preset second temperature threshold, the energy storage device is in a normal operating temperature environment;

[0161] If the temperature data of the energy storage device is greater than or equal to a preset second temperature threshold, the energy storage device is in a high temperature environment and sends a high temperature warning message.

[0162] Among them, in order to ensure that the energy storage device is at a normal operating temperature, the temperature data of the energy storage device is extracted according to the operating parameter information, and the temperature data of the energy storage device is compared with a preset first temperature threshold and a second temperature threshold respectively. The first temperature threshold is less than the second temperature threshold. If the temperature data of the energy storage device is less than or equal to the preset first temperature threshold, the energy storage device is in a low temperature environment and sends a low temperature alarm message. If the temperature data of the energy storage device is greater than the preset first temperature threshold and less than the preset second temperature threshold, the energy storage device is in a normal operating temperature environment. If the temperature data of the energy storage device is greater than or equal to the preset second temperature threshold, the energy storage device is in a high temperature environment and sends a high temperature alarm message.

[0163] According to an embodiment of the present invention, it also includes:

[0164] Obtain maintenance and inspection information of energy storage equipment within a preset time period to extract insulation resistance data;

[0165] comparing the insulation resistance data with a preset insulation resistance threshold;

[0166] If the insulation resistance data is less than the preset insulation resistance threshold, low insulation information is obtained;

[0167] Counting is performed according to the insulation low information to obtain insulation low frequency data;

[0168] Compare the insulation low frequency data with a preset frequency threshold;

[0169] If the insulation low frequency data is greater than or equal to the frequency threshold, the insulation of the energy storage device does not meet the requirements and insulation treatment is required.

[0170] Among them, in order to ensure that the energy storage device has good insulation performance, the maintenance and detection information of the energy storage device within a preset time period is obtained to extract insulation resistance data, and the insulation resistance data is compared with a preset insulation resistance threshold. If the insulation resistance data is less than the preset insulation resistance threshold, low insulation information is obtained, and counting is performed according to the low insulation information to obtain low insulation times data, and the low insulation times data is compared with the preset times threshold. If the low insulation times data is greater than or equal to the times threshold, the insulation of the energy storage device does not meet the requirements and insulation treatment is required.

[0171] A third aspect of the present invention provides a computer-readable storage medium, which includes a fault diagnosis and early warning method program for an energy storage management system. When the fault diagnosis and early warning method program for an energy storage management system is executed by a processor, the steps of the fault diagnosis and early warning method for an energy storage management system as described in any one of the above items are implemented.

[0172] The present invention discloses a fault diagnosis and early warning method, system and medium for an energy storage management system. The method obtains operating parameter monitoring information of an energy storage device within a preset time period, extracts voltage change rate data, current change rate data and voiceprint data respectively, obtains abnormal voltage change rate data according to the voltage change rate data, obtains abnormal current change rate data according to the current change rate data, obtains abnormal voiceprint data according to the voiceprint data, processes the abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data through a preset fault prediction model, obtains an energy storage device failure rate evaluation index, obtains historical fault information of the energy storage device, extracts an average energy storage device failure rate evaluation index, corrects the energy storage device failure rate evaluation index, obtains a corrected energy storage device failure rate index, compares the corrected energy storage device failure rate index with a preset failure rate index threshold, and determines the probability level of failure of the energy storage device. This application conducts real-time monitoring and in-depth analysis of key parameters of energy storage equipment, realizes comprehensive monitoring of energy storage equipment, improves the comprehensiveness of fault detection, and realizes comprehensive, accurate, and intelligent monitoring and fault prediction of energy storage equipment, providing strong technical support and decision-making basis for operation and maintenance personnel, which helps to improve the operating efficiency and reliability of energy storage equipment.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0174] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0175] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0176] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0177] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A fault diagnosis and early warning method for an energy storage management system, characterized in that: The following steps are involved: Obtaining the operating parameter monitoring information of the energy storage device within a preset time period, and extracting the voltage change rate data, current change rate data and voiceprint data respectively; Obtain voltage change rate abnormal data according to the voltage change rate data, obtain current change rate abnormal data according to the current change rate data, and obtain voiceprint abnormal data according to the voiceprint data; The abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data are processed by a preset fault prediction model to obtain an energy storage device failure rate evaluation index; Acquire historical fault information of the energy storage device to correct the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index; Comparing the energy storage device failure rate correction index with a preset failure rate index threshold to determine the probability level of failure of the energy storage device; The obtaining of abnormal voltage change rate data according to the voltage change rate data is specifically: comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate; Comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold; If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormality alarm message is issued; If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable; The obtaining of abnormal current change rate data according to the current change rate data is specifically: comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate; Comparing the current change rate deviation rate with a preset current change rate deviation rate threshold; If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, current change rate abnormal data is obtained and a current abnormality alarm message is issued; If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable; The obtaining of abnormal voiceprint data according to the voiceprint data specifically comprises: extracting voiceprint feature data according to the voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data; According to the abnormal sound data, spectrum distribution data and time domain feature data, identification processing is performed through a preset fault voiceprint recognition database to obtain voiceprint abnormality data; The acquiring of historical fault information of the energy storage device and performing correction processing on the energy storage device fault rate evaluation index to obtain the energy storage device fault rate correction index specifically comprises: acquiring historical fault information of the energy storage device within the preset time period; Extracting an average index of energy storage device failure rate evaluation based on historical failure information of the energy storage device; The energy storage device failure rate evaluation index is corrected according to the energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index.

2. A method for fault diagnosis and early warning of an energy storage management system according to claim 1, characterized in that: The comparing the energy storage device failure rate correction index with a preset failure rate index threshold to determine the probability of failure of the energy storage device is specifically as follows: comparing the energy storage device failure rate correction index with a preset first failure rate index threshold and a second failure rate index threshold, respectively, wherein the first failure rate index threshold is less than the second failure rate index threshold; If the energy storage device failure rate correction index is less than the first failure rate index threshold, the probability of failure of the energy storage device is low; If the energy storage device failure rate correction index is greater than or equal to the first failure rate index threshold and less than the second failure rate index threshold, the probability of failure of the energy storage device is medium; If the energy storage device failure rate correction index is greater than or equal to the second failure rate index threshold, the probability of failure of the energy storage device is high.

3. A fault diagnosis and early warning system for an energy storage management system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a fault diagnosis and early warning method program of an energy storage management system, and the fault diagnosis and early warning method program of the energy storage management system is implemented as follows when executed by the processor: Obtaining the operating parameter monitoring information of the energy storage device within a preset time period, and extracting the voltage change rate data, current change rate data and voiceprint data respectively; Obtain voltage change rate abnormal data according to the voltage change rate data, obtain current change rate abnormal data according to the current change rate data, and obtain voiceprint abnormal data according to the voiceprint data; The abnormal voltage change rate data, the abnormal current change rate data and the abnormal voiceprint data are processed by a preset fault prediction model to obtain an energy storage device failure rate evaluation index; Acquire historical fault information of the energy storage device to correct the energy storage device failure rate evaluation index to obtain the energy storage device failure rate correction index; Comparing the energy storage device failure rate correction index with a preset failure rate index threshold to determine the probability level of failure of the energy storage device; The obtaining of abnormal voltage change rate data according to the voltage change rate data is specifically: comparing the voltage change rate data with a preset voltage change rate threshold to obtain a voltage change rate deviation rate; Comparing the voltage change rate deviation rate with a preset voltage change rate deviation rate threshold; If the voltage change rate deviation rate is greater than or equal to the voltage change rate deviation rate threshold, the voltage change rate abnormal data is obtained and a voltage abnormality alarm message is issued; If the voltage change rate deviation rate is less than the voltage change rate deviation rate threshold, the voltage change rate is stable; The obtaining of abnormal current change rate data according to the current change rate data is specifically: comparing the current change rate data with a preset current change rate threshold to obtain a current change rate deviation rate; Comparing the current change rate deviation rate with a preset current change rate deviation rate threshold; If the current change rate deviation rate is greater than or equal to the current change rate deviation rate threshold, current change rate abnormal data is obtained and a current abnormality alarm message is issued; If the current change rate deviation rate is less than the current change rate deviation rate threshold, the current change rate is stable; The obtaining of abnormal voiceprint data according to the voiceprint data specifically comprises: extracting voiceprint feature data according to the voiceprint data, including abnormal sound data, spectrum distribution data and time domain feature data; According to the abnormal sound data, spectrum distribution data and time domain feature data, identification processing is performed through a preset fault voiceprint recognition database to obtain voiceprint abnormality data; The acquiring of historical fault information of the energy storage device and performing correction processing on the energy storage device fault rate evaluation index to obtain the energy storage device fault rate correction index specifically comprises: acquiring historical fault information of the energy storage device within the preset time period; Extracting an average index of energy storage device failure rate evaluation based on historical failure information of the energy storage device; The energy storage device failure rate evaluation index is corrected according to the energy storage device failure rate evaluation average index to obtain the energy storage device failure rate correction index.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a fault diagnosis and early warning method program for an energy storage management system. When the fault diagnosis and early warning method program for an energy storage management system is executed by a processor, the steps of a fault diagnosis and early warning method for an energy storage management system as described in any one of claims 1 to 2 are implemented.

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