Energy storage power station warning and early warning method and system based on cloud platform

Abstract

The embodiment of the invention relates to the technical field of energy storage power station warning and early warning, and provides an energy storage power station warning and early warning method and system based on a cloud platform, and the method comprises the steps: obtaining the single information of an energy storage system, uploading the single information to the cloud platform, and storing the single information in a database; the monomer information comprises operation data of each energy storage power station monomer in the energy storage system within a period of time; and based on the single information, determining actual values corresponding to the plurality of alarm indexes, and generating alarm early warning information according to the situation that the actual value corresponding to each alarm index triggers an alarm threshold value of a corresponding level. According to the embodiment of the invention, the alarm and early warning information can be generated through a data-driven grading strategy and a multi-source information fusion technology, the early recognition, precise grading and quick response of the operation state of each energy storage power station monomer are realized, the safety and operation and maintenance efficiency of an energy storage system can be improved, and the safety of the energy storage system is improved. And the design of the energy storage system can be reversely guided according to the warning and early warning information, and reasonable and scientific suggestions are provided for research and development personnel.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy storage power station alarm and early warning, and in particular to a cloud platform-based energy storage power station alarm and early warning method and system. Background Art

[0002] With the large-scale integration of renewable energy and the increasing complexity of power systems, energy storage power stations are playing an increasingly prominent role in peak load regulation, frequency regulation, and backup power. However, energy storage systems are susceptible to factors such as battery aging, overcharge and over-discharge, temperature anomalies, and equipment failure during operation. Failure to promptly provide early warnings and address these influencing factors can lead to safety incidents or impact grid stability.

[0003] Although traditional energy storage power station monitoring systems also have alarm mechanisms, they can only issue alarm information after an alarm event, that is, an event that affects the energy storage power station, occurs. They have problems such as information lag and high false alarm rates, and cannot meet the modern power grid's requirements for high reliability and intelligent operation and maintenance. Summary of the Invention

[0004] The present disclosure aims to solve at least one of the problems existing in the prior art and provide a cloud platform-based energy storage power station alarm and early warning method and system.

[0005] In one aspect of the present disclosure, a cloud platform-based energy storage power station alarm and early warning method is provided. The cloud platform-based energy storage power station alarm and early warning method includes:

[0006] Obtaining cell information of the energy storage system, uploading the cell information to the cloud platform and storing it in the database; the cell information includes operating data of each energy storage power station cell in the energy storage system over a period of time;

[0007] Based on the monomer information, actual values ​​corresponding to several alarm indicators are determined respectively, and according to the situation where the actual value corresponding to each of the alarm indicators triggers the alarm threshold of the corresponding level, alarm warning information is generated.

[0008] Optionally, the operating data includes voltage data; the alarm indicators include maximum voltage, minimum voltage, and voltage difference;

[0009] The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including:

[0010] Reading the first voltage data of each of the energy storage power station cells within a first preset time period from the database;

[0011] Setting the alarm thresholds corresponding to the maximum voltage, the minimum voltage, and the pressure difference respectively;

[0012] Based on the first voltage data, counting actual values ​​corresponding to the maximum voltage, the minimum voltage, and the voltage difference, respectively, and counting the number of times the actual values ​​corresponding to the maximum voltage, the minimum voltage, and the voltage difference respectively reach corresponding alarm thresholds as actual voltage alarm frequencies;

[0013] Determine the voltage alarm level at which the actual voltage alarm frequency lies according to the voltage alarm frequency thresholds corresponding to different alarm levels;

[0014] The energy storage power station units are sorted according to their corresponding voltage alarm levels to generate voltage alarm warning information.

[0015] Optionally, the operating data further includes current data; the alarm indicator further includes internal resistance;

[0016] The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including:

[0017] Reading the second voltage data and the first current data of each energy storage power station cell during discharge within a second preset time period in the database, and calculating the corresponding internal resistance value according to the second voltage data and the first current data;

[0018] Calculating a standard score Z-Score of the internal resistance value within the second preset time period;

[0019] Setting the alarm threshold corresponding to the standard score Z-Score of the internal resistance value;

[0020] Counting the number of times the standard score Z-Score of the internal resistance value reaches the corresponding alarm threshold as the actual internal resistance alarm frequency;

[0021] Determining the internal resistance alarm level at which the actual internal resistance alarm frequency lies according to internal resistance alarm frequency thresholds corresponding to different alarm levels;

[0022] The energy storage power station cells are sorted according to their corresponding internal resistance alarm levels to generate internal resistance alarm warning information.

[0023] Optionally, the alarm indicator also includes energy;

[0024] The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including:

[0025] Reading the third voltage data and the second current data of each of the energy storage power station cells during discharge within a third preset time period in the database, and calculating the corresponding energy value according to the third voltage data and the second current data;

[0026] Calculate a standard score Z-Score of the energy value within the third preset time period;

[0027] Setting the alarm threshold corresponding to the standard score Z-Score of the energy value;

[0028] Counting the number of times the standard score Z-Score of the energy value reaches the corresponding alarm threshold as the actual energy alarm frequency;

[0029] Determining the energy alarm level at which the actual energy alarm frequency lies according to energy alarm frequency thresholds corresponding to different alarm levels;

[0030] The energy storage power station units are sorted according to their corresponding energy alarm levels to generate energy alarm warning information.

[0031] Optionally, calculating the corresponding energy value according to the third voltage data and the second current data includes:

[0032] For a discharge segment including multiple observation time points, the product of the voltage value, the current value, and the interval time corresponding to each observation time point is calculated respectively; wherein the interval time is used to indicate the sampling time interval between two adjacent observation time points;

[0033] The product of the voltage value, the current value and the interval time corresponding to all the observation time points is added together to obtain the energy value corresponding to the discharge segment.

[0034] Optionally, the operating data includes temperature data; the alarm indicators include maximum temperature, minimum temperature, and temperature difference;

[0035] The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including:

[0036] Reading the temperature data of each of the energy storage power station cells within a fourth preset time period from the database;

[0037] Setting the alarm thresholds corresponding to the maximum temperature, the minimum temperature, and the temperature difference respectively;

[0038] Based on the temperature data of each of the energy storage power station cells, counting the actual values ​​corresponding to the maximum temperature, the minimum temperature, and the temperature difference, and counting the number of times the actual values ​​corresponding to the maximum temperature, the minimum temperature, and the temperature difference reach the corresponding alarm threshold as the actual temperature alarm frequency;

[0039] Determining the temperature alarm level at which the actual temperature alarm frequency lies according to the temperature alarm frequency thresholds corresponding to different alarm levels;

[0040] The energy storage power station cells are sorted according to their corresponding temperature alarm levels to generate temperature alarm warning information.

[0041] Optionally, the alarm warning information includes the location of each of the energy storage power station cells;

[0042] The cloud platform-based energy storage power station alarm and early warning method further includes:

[0043] The number of times each energy storage power station cell triggers the alarm threshold is counted according to the identification number, and the energy storage power station cell having a fault is located according to the statistical result.

[0044] Optionally, the cloud platform-based energy storage power station alarm and early warning method further includes:

[0045] Writing the process of generating the alarm warning information into an alarm program;

[0046] The alarm program is uploaded to the cloud platform, and a corresponding script is written to enable the alarm program to run at a certain time, and the running results are stored in the database.

[0047] Optionally, the cloud platform-based energy storage power station alarm and early warning method further includes:

[0048] The cloud platform uses front-end Web development and back-end Java development to perform data visualization; the back-end Java program is used to obtain the running results of the alarm program to form an API interface; the front-end Web is used to obtain the running results by calling the API interface and visualize the running results on the interface.

[0049] Another aspect of the present disclosure provides a cloud platform-based energy storage power station alarm warning system, the cloud platform-based energy storage power station alarm warning system includes a collection module and a cloud platform; wherein,

[0050] The acquisition module is used to obtain the monomer information of the energy storage system, upload the monomer information to the cloud platform and store it in the database; the monomer information includes the operating data of each energy storage power station monomer in the energy storage system over a period of time;

[0051] The cloud platform is used to determine the actual values ​​corresponding to several alarm indicators based on the monomer information, and generate alarm warning information according to the situation where the actual value corresponding to each alarm indicator triggers the corresponding level of alarm threshold.

[0052] Compared with the existing technology, the present disclosure can generate alarm and warning information through data-driven classification strategies and multi-source information fusion technology, realizing early identification, accurate classification and rapid response of the operating status of each energy storage power station unit. It can not only improve the safety and operation and maintenance efficiency of the energy storage system, but also reversely guide the design of the energy storage system based on the alarm and warning information, and provide reasonable and scientific advice to R&D personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0054] Figure 1 This is a flow chart of a cloud platform-based energy storage power station alarm and early warning method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in each embodiment of the present disclosure, many technical details are provided to enable readers to better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present disclosure can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present disclosure. The various embodiments can be combined and referenced with each other under the premise that there is no contradiction.

[0056] One embodiment of the present disclosure relates to a cloud platform-based energy storage power station alarm and early warning method, the process of which is as follows: Figure 1 As shown, it includes step S110 and step S120.

[0057] Step S110, obtaining the unit information of the energy storage system, uploading the unit information to the cloud platform and storing it in the database; the unit information includes the operating data of each energy storage power station unit in the energy storage system over a period of time.

[0058] Specifically, a single energy storage power station refers to a single energy storage power station. Operational data may include, but is not limited to, voltage data, current data, and temperature data of each energy storage power station unit. For example, step S110 may utilize corresponding sensors to collect operational data for each energy storage power station unit. Subsequently, step S110 may upload the unit information of the energy storage system to the cloud platform via the Message Queuing Telemetry Transport (MQTT) protocol and store the unit information in a database.

[0059] Step S120 , based on the monomer information, respectively determine the actual values ​​corresponding to several alarm indicators, and generate alarm warning information according to the situation where the actual value corresponding to each alarm indicator triggers the corresponding level of alarm threshold.

[0060] Specifically, step S120 may generate corresponding alarm warning information for different types of operating data in the cell information.

[0061] Exemplarily, the operating data includes voltage data; the alarm indicators include maximum voltage, minimum voltage, and differential pressure. In this case, step S120 may include: reading the first voltage data of each energy storage power station cell within a first preset time period from the database; setting alarm thresholds corresponding to the maximum voltage, minimum voltage, and differential pressure, respectively; based on the first voltage data, counting the actual values ​​corresponding to the maximum voltage, minimum voltage, and differential pressure, respectively, and counting the number of times the actual values ​​corresponding to the maximum voltage, minimum voltage, and differential pressure reach the corresponding alarm thresholds as the actual voltage alarm frequency; determining the voltage alarm level corresponding to the actual voltage alarm frequency based on the voltage alarm frequency thresholds corresponding to different alarm levels; sorting the energy storage power station cells according to their corresponding voltage alarm levels, and generating voltage alarm warning information.

[0062] Specifically, the first voltage data refers to the voltage data of each energy storage power station cell stored in the database within a first preset time period. The first preset time period and the corresponding alarm thresholds for the highest voltage, lowest voltage, and voltage difference can be set according to actual needs. For example, the first preset time period can be set to the past 30 days, the past 20 days, the past 15 days, etc., and this embodiment is not limited to this.

[0063] Different alarm levels can be set based on the frequency of voltage alarms. The greater the voltage alarm frequency, the higher the alarm level. For example, alarm levels can be categorized as Level 1, Level 2, and Level 3, with Level 1 being the mildest, Level 2 being slightly more severe, and Level 3 being the most severe. In this case, Level 1 has the lowest voltage alarm frequency threshold, Level 2 has the second highest, and Level 3 has the highest. The specific values ​​for the voltage alarm frequency thresholds for Level 1, Level 2, and Level 3 can be adjusted and optimized based on actual conditions.

[0064] When sorting the individual energy storage power station units according to their corresponding voltage alarm levels, they can be sorted in descending order of severity of the alarm levels to alert relevant staff.

[0065] The voltage alarm warning information can be presented in the form of a voltage alarm table, which can be stored in a database. For example, the content in the voltage alarm table may include but is not limited to the statistical start time, statistical end time, type (highest voltage / lowest voltage / voltage difference), battery module identification, battery cell identification, number of alarms, alarm level (such as level 1, level 2, level 3, where level 3 is the most serious, level 2 is the second most serious, and level 1 is the least serious), ranking (sorted in descending order of severity of the alarm level), alarm threshold, highest voltage level 1 alarm frequency threshold, highest voltage level 2 alarm frequency threshold, highest voltage level 3 alarm frequency threshold, etc.

[0066] This implementation utilizes voltage data from operational data to generate voltage warning information, effectively enabling early identification and accurate classification of the voltage status of individual cells in each energy storage power station, helping staff to quickly respond to related faults.

[0067] Exemplarily, the operating data includes not only voltage data but also current data; the alarm indicator also includes internal resistance. In this case, step S120 may include: reading the second voltage data and first current data of each energy storage power station cell during discharge in a second preset time period from the database, calculating the corresponding internal resistance value based on the second voltage data and the first current data; calculating the standard score Z-Score of the internal resistance value in the second preset time period; setting an alarm threshold corresponding to the standard score Z-Score of the internal resistance value; counting the number of times the standard score Z-Score of the internal resistance value reaches the corresponding alarm threshold as the actual internal resistance alarm frequency; determining the internal resistance alarm level corresponding to the actual internal resistance alarm frequency based on the internal resistance alarm frequency thresholds corresponding to different alarm levels; sorting each energy storage power station cell according to its corresponding internal resistance alarm level, and generating internal resistance alarm warning information.

[0068] Specifically, the second voltage data refers to the voltage data of each energy storage power station cell stored in the database during the second preset time period. The first current data refers to the current data of each energy storage power station cell stored in the database during the second preset time period. The second preset time period and the alarm threshold corresponding to the standard score Z-Score of the internal resistance value can be set according to actual needs. For example, the second preset time period can be set to the previous day, the previous two days, the previous three days, etc.

[0069] The Z-Score is a statistical metric that measures the degree of deviation of a data point from its mean. The Z-Score for the internal resistance value can be calculated by dividing the difference between the internal resistance value at a certain point in the second preset time period and the mean of the internal resistance values ​​at all points in the second preset time period by the standard deviation of the internal resistance values ​​at all points in the second preset time period.

[0070] Different alarm levels can be set based on the frequency of voltage alarms. The greater the internal resistance alarm frequency, the higher the alarm level. For example, the alarm levels can be categorized as Level 1, Level 2, and Level 3, with Level 1 being the mildest, Level 2 being slightly more severe, and Level 3 being the most severe. In this case, Level 1 corresponds to the lowest internal resistance alarm frequency threshold, Level 2 the second highest, and Level 3 the highest. The specific values ​​for the internal resistance alarm frequency thresholds for Level 1, Level 2, and Level 3 can be adjusted and optimized based on actual conditions.

[0071] When sorting the individual energy storage power station units according to their corresponding internal resistance alarm levels, they can be sorted in descending order of severity of the alarm levels to alert relevant staff.

[0072] The internal resistance alarm warning information can be presented in the form of an internal resistance alarm table, which can be stored in a database. For example, the content of the internal resistance alarm table may include but is not limited to the date, battery module ID, battery cell ID, number of alarms, alarm level (such as level 1, level 2, and level 3, where level 3 is the most serious, level 2 is the second most serious, and level 1 is the mildest), the standard score Z-Score of the internal resistance value, the level 1 alarm frequency threshold, the level 2 alarm frequency threshold, the level 3 alarm frequency threshold, etc.

[0073] By using voltage and current data to generate internal resistance alarm warning information, the internal resistance status of each cell in each energy storage power station can be effectively identified early and accurately classified, helping staff to respond quickly to related faults.

[0074] Exemplarily, the alarm indicator also includes energy. In this case, step S120 may further include: reading the third voltage data and second current data of each energy storage power station cell during discharge in a third preset time period from the database, calculating the corresponding energy value based on the third voltage data and second current data; calculating the standard score Z-Score of the energy value in the third preset time period; setting an alarm threshold corresponding to the standard score Z-Score of the energy value; counting the number of times the standard score Z-Score of the energy value reaches the corresponding alarm threshold as the actual energy alarm frequency; determining the energy alarm level of the actual energy alarm frequency based on the energy alarm frequency thresholds corresponding to different alarm levels; and sorting each energy storage power station cell according to its corresponding energy alarm level to generate energy alarm warning information.

[0075] Specifically, the third voltage data refers to the voltage data of each energy storage power station cell stored in the database within the third preset time period. The second current data refers to the current data of each energy storage power station cell stored in the database within the third preset time period. The third preset time period and the alarm threshold corresponding to the standard score Z-Score of the energy value can be set according to actual needs. For example, the third preset time period can be set to the previous day, the previous two days, the previous three days, etc.

[0076] The standard score Z-Score of the energy value here can be obtained by dividing the difference between the energy value corresponding to a certain time point in the third preset time period and the mean of the energy values ​​corresponding to all time points in the third preset time period by the standard deviation of the energy values ​​corresponding to all time points in the third preset time period.

[0077] Different alarm levels can be set based on the frequency of voltage alarms. The greater the internal resistance alarm frequency, the higher the alarm level. For example, the alarm levels can be divided into Level 1, Level 2, and Level 3, where Level 1 is the mildest, Level 2 is slightly more severe, and Level 3 is the most severe. In this case, the energy alarm frequency threshold corresponding to Level 1 is the smallest, the energy alarm frequency threshold corresponding to Level 2 is the second largest, and the energy alarm frequency threshold corresponding to Level 3 is the largest. The specific values ​​of the energy alarm frequency thresholds corresponding to Level 1, Level 2, and Level 3 can be adjusted and optimized based on actual conditions.

[0078] When sorting the individual energy storage power station units according to their corresponding energy alarm levels, they can be sorted in descending order of severity of the alarm levels to alert relevant staff.

[0079] Energy alarm warning information can be presented in the form of an energy alarm table, which can be stored in a database. For example, the contents of the energy alarm table may include but are not limited to the date, battery module ID, battery cell ID, number of alarms, alarm level (such as level 1, level 2, and level 3, where level 3 is the most serious, level 2 is the second most serious, and level 1 is the mildest), standard score Z-score of energy value, level 1 alarm frequency threshold, level 2 alarm frequency threshold, level 3 alarm frequency threshold, etc.

[0080] By using voltage and current data to generate energy alarm warning information, the early identification and accurate classification of the energy status of each single unit of each energy storage power station can be effectively achieved, helping staff to respond quickly to related faults.

[0081] Exemplarily, calculating the corresponding energy value based on the third voltage data and the second current data includes: for a discharge segment including multiple observation time points, respectively calculating the product of the voltage value corresponding to each observation time point, the current value, and the interval time; wherein the interval time is used to indicate the sampling time interval between two adjacent observation time points; adding the product of the voltage value corresponding to all observation time points, the current value, and the interval time to obtain the energy value corresponding to the discharge segment.

[0082] Specifically, assuming that a discharge segment includes n observation time points, the voltage value and current value corresponding to the i-th observation time point are U and i , I i , the value range of i is 1 to n, when the interval time corresponding to the i-th observation time point is Δt i When , the energy value W corresponding to the discharge segment can be expressed as

[0083] Exemplarily, the operating data includes temperature data; the alarm indicators include maximum temperature, minimum temperature, and temperature difference. In this case, step S120 may include: reading the temperature data of each energy storage power station cell within a fourth preset time period from the database; setting alarm thresholds corresponding to the maximum temperature, minimum temperature, and temperature difference; based on the temperature data of each energy storage power station cell, counting the actual values ​​corresponding to the maximum temperature, minimum temperature, and temperature difference, and counting the number of times the actual values ​​corresponding to the maximum temperature, minimum temperature, and temperature difference reach the corresponding alarm thresholds as the actual temperature alarm frequency; determining the temperature alarm level corresponding to the actual temperature alarm frequency based on the temperature alarm frequency thresholds corresponding to different alarm levels; sorting each energy storage power station cell according to its corresponding temperature alarm level, and generating temperature alarm warning information.

[0084] Specifically, the fourth preset time period and the alarm thresholds corresponding to the maximum temperature, minimum temperature, and temperature difference can be set according to actual needs. For example, the fourth preset time period can be set to nearly 30 days, nearly 20 days, or nearly 15 days, etc., and this embodiment is not limited to this.

[0085] Different alarm levels can be set based on the frequency of temperature alarms. The greater the frequency of temperature alarms, the higher the alarm level. For example, alarm levels can be categorized as Level 1, Level 2, and Level 3, with Level 1 being the mildest, Level 2 being slightly more severe, and Level 3 being the most severe. In this case, Level 1 has the lowest temperature alarm frequency threshold, Level 2 has the second highest, and Level 3 has the highest. The specific values ​​for the temperature alarm frequency thresholds for Level 1, Level 2, and Level 3 can be adjusted and optimized based on actual conditions.

[0086] When sorting the individual energy storage power station units according to their corresponding temperature alarm levels, they can be sorted in descending order of severity of the alarm levels to alert relevant staff.

[0087] The temperature alarm warning information can be presented in the form of a temperature alarm table, which can be stored in a database. For example, the contents of the temperature alarm table may include but are not limited to the statistical start time, statistical end time, type (maximum temperature / minimum temperature / temperature difference), battery module identification, battery cell identification, number of alarms, alarm level (such as level 1, level 2, level 3, where level 3 is the most serious, level 2 is the second most serious, and level 1 is the least serious), ranking (sorted in descending order of severity of the alarm level), alarm threshold, maximum temperature level 1 alarm frequency threshold, maximum temperature level 2 alarm frequency threshold, maximum temperature level 3 alarm frequency threshold, etc.

[0088] This embodiment uses temperature data from operational data to generate temperature alarm warning information, which can effectively achieve early identification and accurate classification of the temperature status of each cell in each energy storage power station, helping staff to quickly respond to related faults.

[0089] Exemplarily, the alarm information includes the location of each energy storage power station cell. The cloud platform-based energy storage power station alarm and warning method further includes: counting the number of times each energy storage power station cell triggers an alarm threshold according to its identification number, and locating the faulty energy storage power station cell based on the statistical results.

[0090] Specifically, the alarm warning information includes voltage alarm warning information, internal resistance alarm warning information, energy alarm warning information, and internal resistance temperature alarm warning information, and may include the location of each energy storage power station cell.

[0091] After counting the number of times each energy storage power station cell triggers the alarm threshold, the energy storage power station cell with a number of times the alarm threshold is triggered greater than 0 can be regarded as a faulty energy storage power station cell. Based on its identification number and location, the faulty energy storage power station cell can be quickly located to help staff quickly respond to the corresponding fault.

[0092] Exemplarily, the cloud platform-based energy storage power station alarm and warning method also includes: writing the process of generating alarm and warning information into an alarm program; uploading the alarm program to the cloud platform, writing corresponding scripts to enable the alarm program to run at a certain time, and storing the running results in a database.

[0093] For example, the process of generating alarm warning information can be written as a Python program as an alarm program. The process of generating voltage alarm warning information, internal resistance alarm warning information, energy alarm warning information, and temperature alarm warning information can each be a separate module in the alarm program. The running results of each module are the corresponding voltage alarm warning information, internal resistance alarm warning information, energy alarm warning information, and temperature alarm warning information. This information can all be stored in a database, such as a MySQL database.

[0094] By writing an alarm program and uploading it to the cloud platform for execution, we can make up for the deficiency of device-side alarms in not being able to consider time accumulation due to computing power limitations, and increase the certainty and effectiveness of long-term fault warnings.

[0095] Exemplarily, a cloud platform-based energy storage power station alarm and warning method also includes: the cloud platform uses front-end Web development and back-end Java development to perform data visualization; wherein, the back-end Java program is used to obtain the running results of the alarm program to form an API interface; the front-end Web is used to obtain the running results by calling the API interface, and the running results are visualized on the interface.

[0096] Specifically, the front-end web interface can visualize the voltage, internal resistance, energy, and temperature alarm information generated by the alarm program through four modules: voltage, internal resistance, energy, and temperature. For example, the visualization content displayed in each module can include battery module identification, battery cell identification, alarm frequency (number of times an alarm is generated), alarm level, alarm threshold, and time.

[0097] By adopting front-end Web development and back-end Java development on the cloud platform, the cloud platform can be used to monitor energy storage system faults and achieve local deployment, which is beneficial to the system maintenance of the cloud platform.

[0098] Compared to existing technologies, the cloud-based energy storage power station alarm and warning method provided by the embodiments of the present disclosure can generate alarm and warning information through data-driven classification strategies and multi-source information fusion technology, achieving early identification, accurate classification, and rapid response to the operating status of each energy storage power station unit. This can not only improve the safety and operation and maintenance efficiency of the energy storage system, but also reversely guide the design of the energy storage system based on the alarm and warning information, providing reasonable and scientific advice to R&D personnel.

[0099] Another embodiment of the present disclosure relates to a cloud platform-based energy storage power station alarm and warning system. The cloud platform-based energy storage power station alarm and warning system includes a collection module and a cloud platform.

[0100] The acquisition module is used to obtain the single-unit information of the energy storage system, upload the single-unit information to the cloud platform and store it in the database; the single-unit information includes the operating data of each energy storage power station unit in the energy storage system over a period of time.

[0101] The cloud platform is used to determine the actual values ​​corresponding to several alarm indicators based on the individual information, and generate alarm warning information according to the situation where the actual value corresponding to each alarm indicator triggers the corresponding level of alarm threshold.

[0102] The specific implementation method of the cloud platform-based energy storage power station alarm and warning system provided in the embodiment of the present disclosure can be found in the cloud platform-based energy storage power station alarm and warning method provided in the embodiment of the present disclosure, and will not be repeated here.

[0103] Compared to existing technologies, the cloud-based energy storage power station alarm and warning system provided by the embodiments of the present disclosure can generate alarm and warning information through data-driven classification strategies and multi-source information fusion technology, achieving early identification, accurate classification, and rapid response to the operating status of each energy storage power station unit. This not only improves the safety and operation and maintenance efficiency of the energy storage system, but also can reversely guide energy storage system design based on the alarm and warning information, providing reasonable and scientific advice to R&D personnel.

[0104] Those skilled in the art will appreciate that the above-mentioned embodiments are specific embodiments for implementing the present disclosure, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present disclosure.

Claims

1. A cloud platform-based energy storage power station alarm and early warning method, characterized in that: The cloud platform-based energy storage power station alarm and early warning method includes: Obtaining cell information of the energy storage system, uploading the cell information to the cloud platform and storing it in the database; the cell information includes operating data of each energy storage power station cell in the energy storage system over a period of time; Based on the monomer information, actual values ​​corresponding to several alarm indicators are determined respectively, and according to the situation where the actual value corresponding to each of the alarm indicators triggers the alarm threshold of the corresponding level, alarm warning information is generated.

2. The cloud platform-based energy storage power station alarm and early warning method according to claim 1 is characterized in that: The operating data includes voltage data; the alarm indicators include maximum voltage, minimum voltage, and voltage difference; The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including: Reading the first voltage data of each of the energy storage power station cells within a first preset time period from the database; Setting the alarm thresholds corresponding to the maximum voltage, the minimum voltage, and the pressure difference respectively; Based on the first voltage data, counting actual values ​​corresponding to the maximum voltage, the minimum voltage, and the voltage difference, respectively, and counting the number of times the actual values ​​corresponding to the maximum voltage, the minimum voltage, and the voltage difference respectively reach corresponding alarm thresholds as actual voltage alarm frequencies; Determine the voltage alarm level at which the actual voltage alarm frequency lies according to the voltage alarm frequency thresholds corresponding to different alarm levels; The energy storage power station units are sorted according to their corresponding voltage alarm levels to generate voltage alarm warning information.

3. The cloud platform-based energy storage power station alarm and early warning method according to claim 2 is characterized in that: The operation data also includes current data; the alarm indicator also includes internal resistance; The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including: Reading the second voltage data and the first current data of each energy storage power station cell during discharge within a second preset time period in the database, and calculating the corresponding internal resistance value according to the second voltage data and the first current data; Calculating a standard score Z-Score of the internal resistance value within the second preset time period; Setting the alarm threshold corresponding to the standard score Z-Score of the internal resistance value; Counting the number of times the standard score Z-Score of the internal resistance value reaches the corresponding alarm threshold as the actual internal resistance alarm frequency; Determining the internal resistance alarm level at which the actual internal resistance alarm frequency lies according to internal resistance alarm frequency thresholds corresponding to different alarm levels; The energy storage power station cells are sorted according to their corresponding internal resistance alarm levels to generate internal resistance alarm warning information.

4. The cloud platform-based energy storage power station alarm and early warning method according to claim 3 is characterized in that: The alarm indicator also includes energy; The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including: Reading the third voltage data and the second current data of each of the energy storage power station cells during discharge within a third preset time period in the database, and calculating the corresponding energy value according to the third voltage data and the second current data; Calculate a standard score Z-Score of the energy value within the third preset time period; Setting the alarm threshold corresponding to the standard score Z-Score of the energy value; Counting the number of times the standard score Z-Score of the energy value reaches the corresponding alarm threshold as the actual energy alarm frequency; Determining the energy alarm level at which the actual energy alarm frequency lies according to energy alarm frequency thresholds corresponding to different alarm levels; The energy storage power station units are sorted according to their corresponding energy alarm levels to generate energy alarm warning information.

5. The cloud platform-based energy storage power station alarm and early warning method according to claim 4 is characterized in that: The calculating the corresponding energy value according to the third voltage data and the second current data includes: For a discharge segment including multiple observation time points, the product of the voltage value, the current value, and the interval time corresponding to each observation time point is calculated respectively; wherein the interval time is used to indicate the sampling time interval between two adjacent observation time points; The product of the voltage value, the current value and the interval time corresponding to all the observation time points is added together to obtain the energy value corresponding to the discharge segment.

6. The cloud platform-based energy storage power station alarm and early warning method according to claim 1, characterized in that: The operating data includes temperature data; the alarm indicators include maximum temperature, minimum temperature, and temperature difference; The actual values ​​corresponding to the plurality of alarm indicators are determined based on the monomer information, and alarm warning information is generated according to the situation where the actual value corresponding to each alarm indicator triggers the alarm threshold of the corresponding level, including: Reading the temperature data of each of the energy storage power station cells within a fourth preset time period from the database; Setting the alarm thresholds corresponding to the maximum temperature, the minimum temperature, and the temperature difference respectively; Based on the temperature data of each of the energy storage power station cells, counting the actual values ​​corresponding to the maximum temperature, the minimum temperature, and the temperature difference, and counting the number of times the actual values ​​corresponding to the maximum temperature, the minimum temperature, and the temperature difference reach the corresponding alarm threshold as the actual temperature alarm frequency; Determining the temperature alarm level at which the actual temperature alarm frequency lies according to the temperature alarm frequency thresholds corresponding to different alarm levels; The energy storage power station cells are sorted according to their corresponding temperature alarm levels to generate temperature alarm warning information.

7. The cloud platform-based energy storage power station alarm and early warning method according to any one of claims 1 to 6, characterized in that: The alarm warning information includes the location of each of the energy storage power station units; The cloud platform-based energy storage power station alarm and early warning method further includes: The number of times each energy storage power station cell triggers the alarm threshold is counted according to the identification number, and the energy storage power station cell having a fault is located according to the statistical result.

8. The cloud platform-based energy storage power station alarm and early warning method according to any one of claims 1 to 6, characterized in that: The cloud platform-based energy storage power station alarm and early warning method further includes: Writing the process of generating the alarm warning information into an alarm program; The alarm program is uploaded to the cloud platform, and a corresponding script is written to enable the alarm program to run at a certain time, and the running results are stored in the database.

9. The cloud platform-based energy storage power station alarm and early warning method according to claim 8, characterized in that: The cloud platform-based energy storage power station alarm and early warning method further includes: The cloud platform uses front-end Web development and back-end Java development to perform data visualization; the back-end Java program is used to obtain the running results of the alarm program to form an API interface; the front-end Web is used to obtain the running results by calling the API interface and visualize the running results on the interface.

10. A cloud platform-based energy storage power station warning system, characterized in that: The cloud platform-based energy storage power station alarm and warning system includes a collection module and a cloud platform; wherein, The acquisition module is used to obtain the monomer information of the energy storage system, upload the monomer information to the cloud platform and store it in the database; the monomer information includes the operating data of each energy storage power station monomer in the energy storage system over a period of time; The cloud platform is used to determine the actual values ​​corresponding to several alarm indicators based on the monomer information, and generate alarm warning information according to the situation where the actual value corresponding to each alarm indicator triggers the corresponding level of alarm threshold.

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