High-power UPS system fault early warning method and system based on data analysis

Abstract

The present application provides a high-power UPS system fault warning method and system based on data analysis. The electrical performance characteristic data of the UPS system is extracted, including voltage characteristic data, current characteristic data and power characteristic data, and corresponding processing is performed respectively to obtain the voltage fluctuation abnormality index, the current response abnormality index and the power deviation abnormality index, the temperature characteristic data of the UPS system is extracted, and the temperature state abnormality index is obtained by processing, the load characteristic data is obtained, and the load state abnormality index is obtained by processing, the battery characteristic data is obtained, and the battery performance abnormality index is obtained by processing according to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and the corresponding fault warning processing is performed, thereby realizing the technology of high-power UPS system fault warning based on data analysis.

Description

Technical Field

[0001] The present application relates to the technical field of high-power UPS systems, and in particular to a high-power UPS system fault warning method and system based on data analysis. Background Art

[0002] UPS (Uninterruptible Power Supply), as a core IT equipment, plays a vital role in ensuring the power supply security required for the normal operation of the data center.

[0003] However, various faults may occur in the UPS system during operation. Traditional fault detection methods often issue an alarm only when a fault has occurred or is about to cause the system to shut down, which may cause damage to equipment that relies on UPS power supply.

[0004] It has become an urgent problem to realize early warning of failure occurrence by analyzing the UPS system operation data. Summary of the invention

[0005] The purpose of the present application is to provide a high-power UPS system fault warning method and system based on data analysis. The method and system can extract the electrical performance characteristic data of the UPS system and perform corresponding processing respectively to obtain the voltage fluctuation abnormality index, the current response abnormality index and the power deviation abnormality index, extract the temperature characteristic data of the UPS system, process to obtain the temperature state abnormality index, obtain the load characteristic data, process to obtain the load state abnormality index, obtain the battery characteristic data, process to obtain the battery performance abnormality index, and process according to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index in combination with the battery performance abnormality index to obtain the system state abnormality index, and perform corresponding fault warning processing, thereby realizing the technology of high-power UPS system fault warning based on data analysis.

[0006] The present application also provides a high-power UPS system fault early warning method based on data analysis, comprising the following steps:

[0007] Monitor the operating status information of the preset UPS system within a preset time period, and extract electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data;

[0008] Processing the voltage characteristic data to obtain a voltage fluctuation abnormality index;

[0009] Processing the current characteristic data to obtain a current response abnormality index;

[0010] Processing the power characteristic data to obtain a power deviation abnormality index;

[0011] Collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index;

[0012] Acquire load characteristic data of the preset UPS system within the preset time period, and process to obtain a load state abnormality index;

[0013] Acquire battery characteristic data of the preset UPS system within the preset time period, and process to obtain a battery performance abnormality index;

[0014] According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and corresponding fault warning processing is performed.

[0015] Optionally, in the high-power UPS system fault early warning method based on data analysis described in the present application, the processing according to the voltage characteristic data to obtain the voltage fluctuation abnormality index includes:

[0016] The voltage characteristic data includes input voltage characteristic data and output voltage characteristic data;

[0017] Performing statistical processing on the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data;

[0018] Performing extraction processing according to the output voltage characteristic data to obtain output voltage harmonic content data;

[0019] The input voltage standard deviation data and the input voltage peak-to-peak value data are processed in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

[0020] Optionally, in the high-power UPS system fault early warning method based on data analysis described in the present application, the collecting of temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index includes:

[0021] Collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components;

[0022] The temperature characteristic data includes inverter temperature maximum value data, rectifier temperature maximum value data, transformer temperature maximum value data and battery temperature maximum value data;

[0023] The temperature state abnormality index is obtained by processing the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data.

[0024] Optionally, in the high-power UPS system fault early warning method based on data analysis described in the present application, the step of acquiring the load characteristic data of the preset UPS system within the preset time period and processing to obtain the load state abnormality index includes:

[0025] Obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data;

[0026] The load mutation characteristic data includes load mutation amplitude data and load mutation frequency data;

[0027] The load power factor data, the load mutation amplitude data and the load mutation frequency data are processed to obtain a load state abnormality index.

[0028] Optionally, in the high-power UPS system fault warning method based on data analysis described in the present application, the acquiring of battery characteristic data of the preset UPS system within the preset time period and processing to obtain the battery performance abnormality index includes:

[0029] Acquire battery characteristic data of the preset UPS system within the preset time period, including internal resistance data, capacity data, charging current data, and discharging current data corresponding to each single battery;

[0030] Extracting corresponding internal resistance difference maximum value data, capacity difference maximum value data, charging current maximum value data and discharging current maximum value data according to the internal resistance data, capacity data, charging current data and discharging current data;

[0031] The battery performance abnormality index is obtained by processing the internal resistance difference maximum value data, the capacity difference maximum value data, the charging current maximum value data and the discharging current maximum value data.

[0032] Optionally, in the high-power UPS system fault early warning method based on data analysis described in the present application, the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index are processed in combination with the battery performance abnormality index to obtain the system state abnormality index, and corresponding fault early warning processing is performed, including:

[0033] According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained;

[0034] Comparing the system status abnormality index with a preset threshold value to obtain a threshold comparison result;

[0035] The abnormal level of the system status is obtained according to the threshold comparison result, and corresponding fault warning processing is performed.

[0036] In a second aspect, the present application provides a high-power UPS system fault warning system based on data analysis, the system comprising: a memory and a processor, the memory comprising a program of a high-power UPS system fault warning method based on data analysis, and the program of the high-power UPS system fault warning method based on data analysis is executed by the processor to implement the following steps:

[0037] Monitor the operating status information of the preset UPS system within a preset time period, and extract electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data;

[0038] Processing the voltage characteristic data to obtain a voltage fluctuation abnormality index;

[0039] Processing the current characteristic data to obtain a current response abnormality index;

[0040] Processing the power characteristic data to obtain a power deviation abnormality index;

[0041] Collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index;

[0042] Acquire load characteristic data of the preset UPS system within the preset time period, and process to obtain a load state abnormality index;

[0043] Acquire battery characteristic data of the preset UPS system within the preset time period, and process to obtain a battery performance abnormality index;

[0044] According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and corresponding fault warning processing is performed.

[0045] Optionally, in the high-power UPS system fault early warning system based on data analysis described in the present application, the processing according to the voltage characteristic data to obtain the voltage fluctuation abnormality index includes:

[0046] The voltage characteristic data includes input voltage characteristic data and output voltage characteristic data;

[0047] Performing statistical processing on the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data;

[0048] Performing extraction processing according to the output voltage characteristic data to obtain output voltage harmonic content data;

[0049] The input voltage standard deviation data and the input voltage peak-to-peak value data are processed in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

[0050] Optionally, in the high-power UPS system fault early warning system based on data analysis described in the present application, the collecting of temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index includes:

[0051] Collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components;

[0052] The temperature characteristic data includes inverter temperature maximum value data, rectifier temperature maximum value data, transformer temperature maximum value data and battery temperature maximum value data;

[0053] The temperature state abnormality index is obtained by processing the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data.

[0054] Optionally, in the high-power UPS system fault warning system based on data analysis described in the present application, the step of acquiring the load characteristic data of the preset UPS system within the preset time period and processing to obtain the load state abnormality index includes:

[0055] Obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data;

[0056] The load mutation characteristic data includes load mutation amplitude data and load mutation frequency data;

[0057] The load power factor data, the load mutation amplitude data and the load mutation frequency data are processed to obtain a load state abnormality index.

[0058] As can be seen from the above, the high-power UPS system fault warning method and system based on data analysis provided by the present application monitors the operating status information of the UPS system within a time period, and extracts electrical performance characteristic data, including voltage characteristic data, current characteristic data and power characteristic data, processes according to the voltage characteristic data to obtain a voltage fluctuation abnormality index, processes according to the current characteristic data to obtain a current response abnormality index, processes according to the power characteristic data to obtain a power deviation abnormality index, collects temperature monitoring information of the UPS system within a time period, and extracts temperature characteristic data of internal components, processes to obtain a temperature state abnormality index, obtains load characteristic data of the UPS system within a time period, processes to obtain a load state abnormality index, obtains battery characteristic data of the UPS system within a time period, processes to obtain a battery performance abnormality index, processes according to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, obtains a system state abnormality index, and performs corresponding fault warning processing, thereby realizing the technology of high-power UPS system fault warning based on data analysis.

[0059] 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

[0060] 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.

[0061] Figure 1 A flow chart of a high-power UPS system fault warning method based on data analysis provided in an embodiment of the present application;

[0062] Figure 2 A flow chart of obtaining a voltage fluctuation abnormality index in a high-power UPS system fault warning method based on data analysis provided in an embodiment of the present application;

[0063] Figure 3 A flow chart of obtaining an abnormal temperature state index of a high-power UPS system fault warning method based on data analysis provided in an embodiment of the present application;

[0064] Figure 4A flow chart of obtaining a load status abnormality index for a high-power UPS system fault warning method based on data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] 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.

[0066] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. 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.

[0067] Please refer to Figure 1 , Figure 1 The flowchart of the high-power UPS system fault early warning method based on data analysis in some embodiments of the present application. The high-power UPS system fault early warning method based on data analysis is used in terminal devices, such as computers, mobile phone terminals, etc. The high-power UPS system fault early warning method based on data analysis includes the following steps:

[0068] S11, monitoring the operating status information of a preset UPS system within a preset time period, and extracting electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data;

[0069] S12, processing the voltage characteristic data to obtain a voltage fluctuation abnormality index;

[0070] S13, processing the current characteristic data to obtain a current response abnormality index;

[0071] S14, processing the power characteristic data to obtain a power deviation abnormality index;

[0072] S15, collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index;

[0073] S16, acquiring load characteristic data of the preset UPS system within the preset time period, and processing to obtain a load state abnormality index;

[0074] S17, acquiring battery characteristic data of the preset UPS system within the preset time period, and processing to obtain a battery performance abnormality index;

[0075] S18. According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained, and corresponding fault warning processing is performed.

[0076] It should be noted that UPS (Uninterruptible Power Supply) as a core IT equipment plays a vital role in ensuring the power supply security required for the normal operation of the data center. However, various faults may occur in the UPS system during operation. Traditional fault detection methods often issue alarms only when a fault has occurred or is about to cause the system to shut down, which may cause damage to the equipment that relies on UPS power supply. The analysis of system operation data to achieve early warning of fault occurrence has become an urgent problem to be solved. In order to solve the above problems, firstly, the operation status information of the UPS system within a time period is monitored, and electrical performance characteristic data, including voltage characteristic data, current characteristic data and power characteristic data, are extracted, and the voltage characteristic data is processed to obtain the voltage fluctuation abnormality index, and the current characteristic data is processed to obtain the current response abnormality index, and the power characteristic data is processed to obtain the power deviation abnormality index. The temperature monitoring information of the UPS system within the time period is collected, and the temperature characteristic data of the internal components are extracted to obtain the temperature state abnormality index, the load characteristic data of the UPS system within the time period are obtained, and the load state abnormality index is obtained. The battery characteristic data of the UPS system within the time period is obtained, and the battery performance abnormality index is obtained. According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and corresponding fault warning processing is performed, thereby realizing the technology of high-power UPS system fault warning based on data analysis.

[0077] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of obtaining a voltage fluctuation abnormality index in a high-power UPS system fault warning method based on data analysis in some embodiments of the present invention. According to an embodiment of the present invention, the voltage characteristic data is processed to obtain a voltage fluctuation abnormality index, including:

[0078] S21, the voltage characteristic data includes input voltage characteristic data and output voltage characteristic data;

[0079] S22, performing statistical processing according to the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data;

[0080] S23, extracting and processing the output voltage characteristic data to obtain output voltage harmonic content data;

[0081] S24. Processing the input voltage standard deviation data and the input voltage peak-to-peak value data in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

[0082] It should be noted that for a running high-power UPS system, in addition to monitoring the instantaneous value of the input voltage, the amplitude, frequency and duration of its fluctuation should also be counted, and these data should be statistically analyzed and extracted to obtain the input voltage standard deviation data and the input voltage peak-to-peak value data. Under normal circumstances, the standard deviation should be within 5% of the rated voltage, and the peak-to-peak value should be between 10% and 15% of the rated voltage. In addition, the harmonic components of the output voltage are analyzed by Fourier transform. For a high-quality high-power UPS system, the total harmonic distortion (THD) of the output voltage should usually be controlled below 3% to 5%, and then the voltage fluctuation abnormality index is obtained by processing the above data;

[0083] The calculation formula of the voltage fluctuation abnormality index is:

[0084] ;

[0085] in, is the voltage fluctuation abnormality index, , They are the input voltage standard deviation data and the input voltage peak-to-peak data respectively. is the output voltage harmonic content data, , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0086] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of obtaining a temperature state abnormality index of a high-power UPS system fault warning method based on data analysis in some embodiments of the present application. According to an embodiment of the present invention, the temperature monitoring information of the preset UPS system within the preset time period is collected, and the temperature characteristic data of the internal components are extracted, and the temperature state abnormality index is obtained by processing, including:

[0087] S31, collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components;

[0088] S32, the temperature characteristic data includes inverter temperature maximum data, rectifier temperature maximum data, transformer temperature maximum data and battery temperature maximum data;

[0089] S33, processing is performed according to the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data to obtain a temperature state abnormality index.

[0090] It should be noted that temperature is another important factor of the UPS system. The temperature of different components inside the UPS is measured, such as the temperature values ​​of the inverter, rectifier, transformer and battery. During normal operation, these temperatures should be relatively stable. If they exceed a certain threshold, it may indicate poor local heat dissipation or problems with heat conduction between components. This is an early warning signal of a fault. Therefore, the temperature monitoring information of the UPS system within a time period is collected, and the temperature characteristic data of the internal components are extracted, including the maximum temperature of the inverter, the maximum temperature of the rectifier, the maximum temperature of the transformer and the maximum temperature of the battery. Then, the above data is processed to obtain the temperature status abnormality index;

[0091] The calculation formula of the temperature abnormality index is:

[0092] ;

[0093] in, is the temperature abnormality index, , , , They are the maximum temperature data of the inverter, the maximum temperature data of the rectifier, the maximum temperature data of the transformer, and the maximum temperature data of the battery. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0094] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a load state abnormality index of a high-power UPS system fault warning method based on data analysis in some embodiments of the present application. According to an embodiment of the present invention, the load characteristic data of the preset UPS system within the preset time period is obtained, and the load state abnormality index is obtained by processing, including:

[0095] S41, obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data;

[0096] S42, the load mutation characteristic data includes load mutation amplitude data and load mutation frequency data;

[0097] S43, performing processing according to the load power factor data, the load mutation amplitude data and the load mutation frequency data to obtain a load state abnormality index.

[0098] It should be noted that the load power factor reflects the nature of the load (inductive, capacitive or resistive). When the power factor deviates from the normal range (such as from 0.8-0.95 to below 0.7), it may be that the nature of the load equipment has changed or a fault has occurred, which will affect the power output and efficiency of the UPS and is also an important reference for fault warning. Sudden changes in the load are also an important warning indicator. If the load changes by more than 30%-50% of the rated load in a short period of time (such as a few seconds to a few minutes), it may cause an impact on the power conversion circuit of the UPS, resulting in instantaneous fluctuations in the output voltage and current. At this time, a warning signal should be issued. In addition, if the load changes frequently, it may also cause problems such as output voltage fluctuations and current shocks. These data need to be processed to obtain the load status abnormality index;

[0099] The calculation formula of the load state abnormality index is:

[0100] ;

[0101] in, is the load status abnormality index, is the load power factor data, , They are load mutation amplitude data and load mutation frequency data respectively. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0102] According to an embodiment of the present invention, the step of acquiring battery characteristic data of the preset UPS system within the preset time period and processing to obtain a battery performance abnormality index includes:

[0103] Acquire battery characteristic data of the preset UPS system within the preset time period, including internal resistance data, capacity data, charging current data, and discharging current data corresponding to each single battery;

[0104] Extracting corresponding internal resistance difference maximum value data, capacity difference maximum value data, charging current maximum value data and discharging current maximum value data according to the internal resistance data, capacity data, charging current data and discharging current data;

[0105] The battery performance abnormality index is obtained by processing the internal resistance difference maximum value data, the capacity difference maximum value data, the charging current maximum value data and the discharging current maximum value data.

[0106] It should be noted that for battery performance, battery pack consistency is critical, including internal resistance and capacity consistency. In addition, the battery charging and discharging conditions must be monitored, and then the battery performance abnormality index is obtained by processing the extracted maximum internal resistance difference, maximum capacity difference, maximum charging current, and maximum discharging current data.

[0107] The calculation formula of the battery performance abnormality index is:

[0108] ;

[0109] in, is the battery performance abnormality index, , , , They are the maximum value data of internal resistance difference, the maximum value data of capacity difference, the maximum value data of charging current, and the maximum value data of discharging current. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0110] According to an embodiment of the present invention, the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index are processed in combination with the battery performance abnormality index to obtain the system state abnormality index, and corresponding fault warning processing is performed, including:

[0111] According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained;

[0112] Comparing the system status abnormality index with a preset threshold value to obtain a threshold comparison result;

[0113] The abnormal level of the system status is obtained according to the threshold comparison result, and corresponding fault warning processing is performed.

[0114] It should be noted that it is necessary to evaluate the overall status of the UPS system to determine its abnormal situation, and then respond to the corresponding fault warning processing according to the degree of abnormality. For this, the obtained voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index are processed in combination with the battery performance abnormality index to obtain the system state abnormality index, which is then compared with the preset value to obtain the system state abnormality level;

[0115] The calculation formula of the system status abnormality index is:

[0116] ;

[0117] in, is the system status abnormality index, , , , , , They are voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index, load state abnormality index, and battery performance abnormality index. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0118] According to an embodiment of the present invention, it also includes:

[0119] The processing according to the current characteristic data to obtain the current response abnormality index specifically includes:

[0120] The current characteristic data includes input current characteristic data and output current characteristic data;

[0121] Extracting and processing the input current characteristic data to obtain input current three-phase imbalance rate data;

[0122] Extracting and processing the output current characteristic data to obtain output current three-phase imbalance rate data;

[0123] Acquire current response time data and current overshoot data of the preset UPS system when the load suddenly changes;

[0124] The input current three-phase imbalance rate data and the output current three-phase imbalance rate data are processed in combination with the current response time data and the current overshoot data to obtain a current response abnormality index.

[0125] The calculation formula of the current response abnormality index is:

[0126] ;

[0127] in, is the current response anomaly index, , They are the input current three-phase unbalance rate data and the output current three-phase unbalance rate data, , They are current response time data and current overshoot data respectively. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0128] It should be noted that for a three-phase UPS system, it is crucial to calculate the imbalance rate of the three-phase current. At the same time, it is also necessary to understand the current response time and overshoot of the UPS when the load suddenly changes. Therefore, the input current characteristic data is extracted and processed to obtain the input current three-phase imbalance rate data, and the output current characteristic data is extracted and processed to obtain the output current three-phase imbalance rate data. At the same time, the current response time data and current overshoot data of the UPS system when the load suddenly changes are obtained, and then the above data are processed to obtain the current response abnormality index.

[0129] According to an embodiment of the present invention, it also includes:

[0130] The processing according to the power characteristic data to obtain the power deviation abnormality index specifically includes:

[0131] The power characteristic data includes input power factor data and output power factor data;

[0132] Acquiring input power factor setting value data and output power factor setting value data of the preset UPS system;

[0133] The input power factor data and the output power factor data are processed in combination with the input power factor setting value data and the output power factor setting value data to obtain a power deviation abnormality index.

[0134] The calculation formula of the power deviation abnormality index is:

[0135] ;

[0136] in, is the power deviation abnormality index, , They are input power factor data and output power factor data respectively. , They are respectively the input power factor setting value data and the output power factor setting value data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0137] It should be noted that, on the other hand, for a running high-power UPS system, it is necessary to monitor the input power factor and output power factor of the UPS, which reflect the energy exchange efficiency between the UPS and the power grid and between the UPS and the load. Under normal circumstances, the input power factor should be between 0.9 and 0.99, and the output power factor should be between 0.8 and 0.95. If the power factor deviates from this range, it may be due to a fault in the UPS internal reactive compensation circuit, a change in the nature of the load (such as an increase in inductive load), or a decrease in the efficiency of the rectification / inversion link. Therefore, the input power factor data and the output power factor data are processed in combination with the input power factor setting value data and the output power factor setting value data of the system to obtain the power deviation abnormality index.

[0138] In a second aspect, the present invention further discloses a high-power UPS system fault warning system based on data analysis, including a memory and a processor, wherein the memory includes a high-power UPS system fault warning method program based on data analysis, and when the high-power UPS system fault warning method program based on data analysis is executed by the processor, the following steps are implemented:

[0139] Monitor the operating status information of the preset UPS system within a preset time period, and extract electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data;

[0140] Processing the voltage characteristic data to obtain a voltage fluctuation abnormality index;

[0141] Processing the current characteristic data to obtain a current response abnormality index;

[0142] Processing the power characteristic data to obtain a power deviation abnormality index;

[0143] Collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index;

[0144] Acquire load characteristic data of the preset UPS system within the preset time period, and process to obtain a load state abnormality index;

[0145] Acquire battery characteristic data of the preset UPS system within the preset time period, and process to obtain a battery performance abnormality index;

[0146] According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and corresponding fault warning processing is performed.

[0147] It should be noted that UPS (Uninterruptible Power Supply) as a core IT equipment plays a vital role in ensuring the power supply security required for the normal operation of the data center. However, various faults may occur in the UPS system during operation. Traditional fault detection methods often issue alarms only when a fault has occurred or is about to cause the system to shut down, which may cause damage to the equipment that relies on UPS power supply. The analysis of system operation data to achieve early warning of fault occurrence has become an urgent problem to be solved. In order to solve the above problems, firstly, the operation status information of the UPS system within a time period is monitored, and electrical performance characteristic data, including voltage characteristic data, current characteristic data and power characteristic data, are extracted, and the voltage characteristic data is processed to obtain the voltage fluctuation abnormality index, and the current characteristic data is processed to obtain the current response abnormality index, and the power characteristic data is processed to obtain the power deviation abnormality index. The temperature monitoring information of the UPS system within the time period is collected, and the temperature characteristic data of the internal components are extracted to obtain the temperature state abnormality index, the load characteristic data of the UPS system within the time period are obtained, and the load state abnormality index is obtained. The battery characteristic data of the UPS system within the time period is obtained, and the battery performance abnormality index is obtained. According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, the system state abnormality index is obtained, and corresponding fault warning processing is performed, thereby realizing the technology of high-power UPS system fault warning based on data analysis.

[0148] According to an embodiment of the present invention, the processing according to the voltage characteristic data to obtain the voltage fluctuation abnormality index includes:

[0149] The voltage characteristic data includes input voltage characteristic data and output voltage characteristic data;

[0150] Performing statistical processing on the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data;

[0151] Performing extraction processing according to the output voltage characteristic data to obtain output voltage harmonic content data;

[0152] The input voltage standard deviation data and the input voltage peak-to-peak value data are processed in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

[0153] It should be noted that for a running high-power UPS system, in addition to monitoring the instantaneous value of the input voltage, the amplitude, frequency and duration of its fluctuation should also be counted, and these data should be statistically analyzed and extracted to obtain the input voltage standard deviation data and the input voltage peak-to-peak value data. Under normal circumstances, the standard deviation should be within 5% of the rated voltage, and the peak-to-peak value should be between 10% and 15% of the rated voltage. In addition, the harmonic components of the output voltage are analyzed by Fourier transform. For a high-quality high-power UPS system, the total harmonic distortion (THD) of the output voltage should usually be controlled below 3% to 5%, and then the voltage fluctuation abnormality index is obtained by processing the above data;

[0154] The calculation formula of the voltage fluctuation abnormality index is:

[0155] ;

[0156] in, is the voltage fluctuation abnormality index, , They are the input voltage standard deviation data and the input voltage peak-to-peak data respectively. is the output voltage harmonic content data, , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0157] According to an embodiment of the present invention, the collecting of temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index includes:

[0158] Collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components;

[0159] The temperature characteristic data includes inverter temperature maximum value data, rectifier temperature maximum value data, transformer temperature maximum value data and battery temperature maximum value data;

[0160] The temperature state abnormality index is obtained by processing the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data.

[0161] It should be noted that temperature is another important factor of the UPS system. The temperature of different components inside the UPS is measured, such as the temperature values ​​of the inverter, rectifier, transformer and battery. During normal operation, these temperatures should be relatively stable. If they exceed a certain threshold, it may indicate poor local heat dissipation or problems with heat conduction between components. This is an early warning signal of a fault. Therefore, the temperature monitoring information of the UPS system within a time period is collected, and the temperature characteristic data of the internal components are extracted, including the maximum temperature of the inverter, the maximum temperature of the rectifier, the maximum temperature of the transformer and the maximum temperature of the battery. Then, the above data is processed to obtain the temperature status abnormality index;

[0162] The calculation formula of the temperature abnormality index is:

[0163] ;

[0164] in, is the temperature abnormality index, , , , They are the maximum temperature data of the inverter, the maximum temperature data of the rectifier, the maximum temperature data of the transformer, and the maximum temperature data of the battery. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0165] According to an embodiment of the present invention, the step of acquiring load characteristic data of the preset UPS system within the preset time period and processing to obtain the load state abnormality index includes:

[0166] Obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data;

[0167] The load mutation characteristic data includes load mutation amplitude data and load mutation frequency data;

[0168] The load power factor data, the load mutation amplitude data and the load mutation frequency data are processed to obtain a load state abnormality index.

[0169] It should be noted that the load power factor reflects the nature of the load (inductive, capacitive or resistive). When the power factor deviates from the normal range (such as from 0.8-0.95 to below 0.7), it may be that the nature of the load equipment has changed or a fault has occurred, which will affect the power output and efficiency of the UPS and is also an important reference for fault warning. Sudden changes in the load are also an important warning indicator. If the load changes by more than 30%-50% of the rated load in a short period of time (such as a few seconds to a few minutes), it may cause an impact on the power conversion circuit of the UPS, resulting in instantaneous fluctuations in the output voltage and current. At this time, a warning signal should be issued. In addition, if the load changes frequently, it may also cause problems such as output voltage fluctuations and current shocks. These data need to be processed to obtain the load status abnormality index;

[0170] The calculation formula of the load state abnormality index is:

[0171] ;

[0172] in, is the load status abnormality index, is the load power factor data, , They are load mutation amplitude data and load mutation frequency data respectively. , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0173] According to an embodiment of the present invention, the step of acquiring battery characteristic data of the preset UPS system within the preset time period and processing to obtain a battery performance abnormality index includes:

[0174] Acquire battery characteristic data of the preset UPS system within the preset time period, including internal resistance data, capacity data, charging current data, and discharging current data corresponding to each single battery;

[0175] Extracting corresponding internal resistance difference maximum value data, capacity difference maximum value data, charging current maximum value data and discharging current maximum value data according to the internal resistance data, capacity data, charging current data and discharging current data;

[0176] The battery performance abnormality index is obtained by processing the internal resistance difference maximum value data, the capacity difference maximum value data, the charging current maximum value data and the discharging current maximum value data.

[0177] It should be noted that for battery performance, battery pack consistency is critical, including internal resistance and capacity consistency. In addition, the battery charging and discharging conditions must be monitored, and then the battery performance abnormality index is obtained by processing the extracted maximum internal resistance difference, maximum capacity difference, maximum charging current, and maximum discharging current data.

[0178] The calculation formula of the battery performance abnormality index is:

[0179] ;

[0180] in, is the battery performance abnormality index, , , , They are the maximum value data of internal resistance difference, the maximum value data of capacity difference, the maximum value data of charging current, and the maximum value data of discharging current. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0181] According to an embodiment of the present invention, the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index are processed in combination with the battery performance abnormality index to obtain the system state abnormality index, and corresponding fault warning processing is performed, including:

[0182] According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained;

[0183] Comparing the system status abnormality index with a preset threshold value to obtain a threshold comparison result;

[0184] The abnormal level of the system status is obtained according to the threshold comparison result, and corresponding fault warning processing is performed.

[0185] It should be noted that it is necessary to evaluate the overall status of the UPS system to determine its abnormal situation, and then respond to the corresponding fault warning processing according to the degree of abnormality. For this, the obtained voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index are processed in combination with the battery performance abnormality index to obtain the system state abnormality index, which is then compared with the preset value to obtain the system state abnormality level;

[0186] The calculation formula of the system status abnormality index is:

[0187] ;

[0188] in, is the system status abnormality index, , , , , , They are voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index, load state abnormality index, and battery performance abnormality index. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0189] According to an embodiment of the present invention, it also includes:

[0190] The processing according to the current characteristic data to obtain the current response abnormality index specifically includes:

[0191] The current characteristic data includes input current characteristic data and output current characteristic data;

[0192] Extracting and processing the input current characteristic data to obtain input current three-phase imbalance rate data;

[0193] Extracting and processing the output current characteristic data to obtain output current three-phase imbalance rate data;

[0194] Acquire current response time data and current overshoot data of the preset UPS system when the load suddenly changes;

[0195] The input current three-phase imbalance rate data and the output current three-phase imbalance rate data are processed in combination with the current response time data and the current overshoot data to obtain a current response abnormality index.

[0196] The calculation formula of the current response abnormality index is:

[0197] ;

[0198] in, is the current response anomaly index, , They are the input current three-phase unbalance rate data and the output current three-phase unbalance rate data, , They are current response time data and current overshoot data respectively. , , , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0199] It should be noted that for a three-phase UPS system, it is crucial to calculate the imbalance rate of the three-phase current. At the same time, it is also necessary to understand the current response time and overshoot of the UPS when the load suddenly changes. Therefore, the input current characteristic data is extracted and processed to obtain the input current three-phase imbalance rate data, and the output current characteristic data is extracted and processed to obtain the output current three-phase imbalance rate data. At the same time, the current response time data and current overshoot data of the UPS system when the load suddenly changes are obtained, and then the above data are processed to obtain the current response abnormality index.

[0200] According to an embodiment of the present invention, it also includes:

[0201] The processing according to the power characteristic data to obtain the power deviation abnormality index specifically includes:

[0202] The power characteristic data includes input power factor data and output power factor data;

[0203] Acquiring input power factor setting value data and output power factor setting value data of the preset UPS system;

[0204] The input power factor data and the output power factor data are processed in combination with the input power factor setting value data and the output power factor setting value data to obtain a power deviation abnormality index.

[0205] The calculation formula of the power deviation abnormality index is:

[0206] ;

[0207] in, is the power deviation abnormality index, , They are input power factor data and output power factor data respectively. , They are respectively the input power factor setting value data and the output power factor setting value data. , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset UPS system fault warning database).

[0208] It should be noted that, on the other hand, for a running high-power UPS system, it is necessary to monitor the input power factor and output power factor of the UPS, which reflect the energy exchange efficiency between the UPS and the power grid and between the UPS and the load. Under normal circumstances, the input power factor should be between 0.9 and 0.99, and the output power factor should be between 0.8 and 0.95. If the power factor deviates from this range, it may be due to a fault in the UPS internal reactive compensation circuit, a change in the nature of the load (such as an increase in inductive load), or a decrease in the efficiency of the rectification / inversion link. Therefore, the input power factor data and the output power factor data are processed in combination with the input power factor setting value data and the output power factor setting value data of the system to obtain the power deviation abnormality index.

[0209] The present invention discloses a high-power UPS system fault early warning method and system based on data analysis. The method and system monitor the operation status information of the UPS system within a time period, extract electrical performance characteristic data, including voltage characteristic data, current characteristic data and power characteristic data, process the voltage characteristic data to obtain a voltage fluctuation abnormality index, process the current characteristic data to obtain a current response abnormality index, process the power characteristic data to obtain a power deviation abnormality index, collect temperature monitoring information of the UPS system within a time period, extract temperature characteristic data of internal components, process to obtain a temperature state abnormality index, obtain load characteristic data of the UPS system within a time period, process to obtain a load state abnormality index, obtain battery characteristic data of the UPS system within a time period, process to obtain a battery performance abnormality index, process the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index in combination with the battery performance abnormality index to obtain a system state abnormality index, and perform corresponding fault early warning processing, thereby realizing the technology of high-power UPS system fault early warning based on data analysis.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0214] 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 high-power UPS system fault early warning method based on data analysis, characterized in that: The following steps are involved: Monitor the operating status information of the preset UPS system within a preset time period, and extract electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data; Processing the voltage characteristic data to obtain a voltage fluctuation abnormality index; Processing the current characteristic data to obtain a current response abnormality index; Processing the power characteristic data to obtain a power deviation abnormality index; Collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index; Acquire load characteristic data of the preset UPS system within the preset time period, and process to obtain a load state abnormality index; Acquire battery characteristic data of the preset UPS system within the preset time period, and process to obtain a battery performance abnormality index; According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained, and corresponding fault warning processing is performed; The processing according to the power characteristic data to obtain the power deviation abnormality index specifically includes: The power characteristic data includes input power factor data and output power factor data; Acquiring input power factor setting value data and output power factor setting value data of the preset UPS system; According to the input power factor data and the output power factor data, combined with the input power factor setting value data and the output power factor setting value data, a power deviation abnormality index is obtained; The calculation formula of the power deviation abnormality index is: ； in, is the power deviation abnormality index, , They are input power factor data and output power factor data respectively. , They are respectively the input power factor setting value data and the output power factor setting value data. , is the preset characteristic coefficient; The step of acquiring the load characteristic data of the preset UPS system within the preset time period and processing to obtain the load state abnormality index includes: Obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data; The load mutation characteristic data includes load mutation amplitude data and load mutation frequency data; Processing the load power factor data, the load mutation amplitude data, and the load mutation frequency data to obtain a load state abnormality index; The calculation formula of the load state abnormality index is: ； in, is the load status abnormality index, is the load power factor data, , They are load mutation amplitude data and load mutation frequency data respectively. , , is the preset characteristic coefficient; The step of acquiring battery characteristic data of the preset UPS system within the preset time period and processing to obtain a battery performance abnormality index includes: Acquire battery characteristic data of the preset UPS system within the preset time period, including internal resistance data, capacity data, charging current data, and discharging current data corresponding to each single battery; Extracting corresponding internal resistance difference maximum value data, capacity difference maximum value data, charging current maximum value data and discharging current maximum value data according to the internal resistance data, capacity data, charging current data and discharging current data; Processing is performed according to the internal resistance difference maximum value data, the capacity difference maximum value data, the charging current maximum value data, and the discharging current maximum value data to obtain a battery performance abnormality index; The calculation formula of the battery performance abnormality index is: ； in, is the battery performance abnormality index, , , , They are the maximum value data of internal resistance difference, the maximum value data of capacity difference, the maximum value data of charging current, and the maximum value data of discharging current. , , , is the preset characteristic coefficient.

2. The high-power UPS system fault early warning method based on data analysis according to claim 1 is characterized in that: The processing according to the voltage characteristic data to obtain the voltage fluctuation abnormality index includes: The voltage characteristic data includes input voltage characteristic data and output voltage characteristic data; Performing statistical processing on the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data; Performing extraction processing according to the output voltage characteristic data to obtain output voltage harmonic content data; The input voltage standard deviation data and the input voltage peak-to-peak value data are processed in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

3. The high-power UPS system fault early warning method based on data analysis according to claim 2 is characterized in that: The collecting of the temperature monitoring information of the preset UPS system within the preset time period, extracting the temperature characteristic data of the internal components, and processing to obtain the temperature state abnormality index includes: Collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components; The temperature characteristic data includes inverter temperature maximum value data, rectifier temperature maximum value data, transformer temperature maximum value data and battery temperature maximum value data; The temperature state abnormality index is obtained by processing the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data.

4. The high-power UPS system fault early warning method based on data analysis according to claim 3 is characterized in that: The process of processing the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index in combination with the battery performance abnormality index to obtain the system state abnormality index and perform corresponding fault warning processing includes: According to the voltage fluctuation abnormality index, the current response abnormality index, the power deviation abnormality index, the temperature state abnormality index and the load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained; Comparing the system status abnormality index with a preset threshold value to obtain a threshold comparison result; The abnormal level of the system status is obtained according to the threshold comparison result, and corresponding fault warning processing is performed.

5. A high-power UPS system fault warning system based on data analysis, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a high-power UPS system fault early warning method based on data analysis, and when the program of the high-power UPS system fault early warning method based on data analysis is executed by the processor, the following steps are implemented: Monitor the operating status information of the preset UPS system within a preset time period, and extract electrical performance characteristic data, including voltage characteristic data, current characteristic data, and power characteristic data; Processing the voltage characteristic data to obtain a voltage fluctuation abnormality index; Processing the current characteristic data to obtain a current response abnormality index; Processing the power characteristic data to obtain a power deviation abnormality index; Collecting temperature monitoring information of the preset UPS system within the preset time period, extracting temperature characteristic data of internal components, and processing to obtain a temperature state abnormality index; Acquire load characteristic data of the preset UPS system within the preset time period, and process to obtain a load state abnormality index; Acquire battery characteristic data of the preset UPS system within the preset time period, and process to obtain a battery performance abnormality index; According to the voltage fluctuation abnormality index, current response abnormality index, power deviation abnormality index, temperature state abnormality index and load state abnormality index, combined with the battery performance abnormality index, a system state abnormality index is obtained, and corresponding fault warning processing is performed; The processing according to the power characteristic data to obtain the power deviation abnormality index specifically includes: The power characteristic data includes input power factor data and output power factor data; Acquiring input power factor setting value data and output power factor setting value data of the preset UPS system; According to the input power factor data and the output power factor data, combined with the input power factor setting value data and the output power factor setting value data, a power deviation abnormality index is obtained; The calculation formula of the power deviation abnormality index is: ； in, is the power deviation abnormality index, , They are input power factor data and output power factor data respectively. , They are respectively the input power factor setting value data and the output power factor setting value data. , is the preset characteristic coefficient; The step of acquiring the load characteristic data of the preset UPS system within the preset time period and processing to obtain the load state abnormality index includes: Obtaining load characteristic data of the preset UPS system within the preset time period, including load power factor data and load mutation characteristic data; The load mutation characteristic data includes load mutation amplitude data and load mutation frequency data; Processing the load power factor data, the load mutation amplitude data, and the load mutation frequency data to obtain a load state abnormality index; The calculation formula of the load state abnormality index is: ； in, is the load status abnormality index, is the load power factor data, , They are load mutation amplitude data and load mutation frequency data respectively. , , is the preset characteristic coefficient; The step of acquiring battery characteristic data of the preset UPS system within the preset time period and processing to obtain a battery performance abnormality index includes: Acquire battery characteristic data of the preset UPS system within the preset time period, including internal resistance data, capacity data, charging current data, and discharging current data corresponding to each single battery; Extracting corresponding internal resistance difference maximum value data, capacity difference maximum value data, charging current maximum value data and discharging current maximum value data according to the internal resistance data, capacity data, charging current data and discharging current data; Processing is performed according to the internal resistance difference maximum value data, the capacity difference maximum value data, the charging current maximum value data, and the discharging current maximum value data to obtain a battery performance abnormality index; The calculation formula of the battery performance abnormality index is: ； in, is the battery performance abnormality index, , , , They are the maximum value data of internal resistance difference, the maximum value data of capacity difference, the maximum value data of charging current, and the maximum value data of discharging current. , , , is the preset characteristic coefficient.

6. The high-power UPS system fault early warning system based on data analysis according to claim 5 is characterized in that: The processing according to the voltage characteristic data to obtain the voltage fluctuation abnormality index includes: The voltage characteristic data includes input voltage characteristic data and output voltage characteristic data; Performing statistical processing on the input voltage characteristic data to obtain input voltage standard deviation data and input voltage peak-to-peak value data; Performing extraction processing according to the output voltage characteristic data to obtain output voltage harmonic content data; The input voltage standard deviation data and the input voltage peak-to-peak value data are processed in combination with the output voltage harmonic content data to obtain a voltage fluctuation abnormality index.

7. The high-power UPS system fault early warning system based on data analysis according to claim 6 is characterized in that: The collecting of the temperature monitoring information of the preset UPS system within the preset time period, extracting the temperature characteristic data of the internal components, and processing to obtain the temperature state abnormality index includes: Collecting temperature monitoring information of the preset UPS system within the preset time period, and extracting temperature characteristic data of internal components; The temperature characteristic data includes inverter temperature maximum value data, rectifier temperature maximum value data, transformer temperature maximum value data and battery temperature maximum value data; The temperature state abnormality index is obtained by processing the inverter temperature maximum value data, the rectifier temperature maximum value data, the transformer temperature maximum value data and the battery temperature maximum value data.

Images