A comprehensive control system for UPS power supplies based on big data

Abstract

The present invention belongs to the field of UPS power supply control, relates to parallel networking configuration technology, and is used to solve the problem that the prior art cannot perform configuration analysis on UPS power supplies before parallel networking. Specifically, it is a comprehensive control system for UPS power supplies based on big data, including a comprehensive control platform, which is communicatively connected to a networking analysis module, a redundant configuration module, an operation control module, and a database; the networking analysis module is used to perform parallel networking analysis on the UPS power supply: mark the UPS power supply that has completed parallel networking as the analysis object, generate an analysis period, obtain the load value when the analysis object has an operation failure during the analysis period, form an analysis set from the load values of all analysis objects, and perform cleaning processing on the analysis set to obtain the load critical value FL; the present invention can perform parallel networking analysis on the UPS power supply, and thus screen out the load critical value through the elements retained in the analysis set to ensure the accuracy of the setting of the basic configuration value.

Description

Technical Field

[0001] The present invention belongs to the field of UPS power supply control, and relates to parallel networking configuration technology. Specifically, it is a comprehensive control system for UPS power supply based on big data. Background Art

[0002] ‌An UPS power supply‌ is a device that can provide backup power when the mains power is interrupted to ensure that critical devices are not affected by power fluctuations or interruptions. The UPS power supply mainly consists of a battery pack, a rectifier, an inverter, a static switch, etc., and can charge the battery when the mains power is normal and provide stable power supply when the mains power is interrupted.

[0003] ‌The redundant parallel structure of the UPS power supply‌ refers to connecting multiple UPSs of the same model and the same capacity in parallel to jointly provide power for the load. Although the redundant parallel structure improves the reliability and fault tolerance of the system, it also brings disadvantages such as complex system, reduced efficiency, difficult fault troubleshooting, and uneven load distribution. The prior art cannot perform configuration analysis on the UPS power supply before parallel networking, resulting in the failure rate of a single UPS power supply not being controllable and the performance of the overall power supply system not being guaranteed.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a comprehensive control system for UPS power supply based on big data, which is used to solve the problem that the prior art cannot perform configuration analysis on the UPS power supply before parallel networking;

[0006] The technical problem to be solved by the present invention is: how to provide a comprehensive control system for UPS power supply based on big data that can perform configuration analysis on the UPS power supply before parallel networking.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A comprehensive control system for UPS power supply based on big data includes a comprehensive control platform, and the comprehensive control platform is communicatively connected with a networking analysis module, a redundant configuration module, an operation control module, and a database;

[0009] The networking analysis module is used to perform parallel networking analysis on the UPS power supply: mark the UPS power supply that has completed parallel networking and is put into use as the analysis object, generate an analysis period, and obtain the load value when the analysis object has an operation failure during the analysis period; form an analysis set from the load values of all analysis objects, and perform cleaning processing on the analysis set to obtain the load critical value FL; obtain the maximum power consumption of the current load object and mark it as the power consumption value HD, and obtain the basic configuration value JP through the formula JP = ceil(HD / FL);

[0010] The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: mark the redundant parallel structure that has completed parallel networking and is in use as the configuration object, generate the power consumption range, mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as the screening object, obtain the voltage stability data YW, voltage distortion data YJ, and efficiency data XL of the screening object during the analysis period, and perform numerical calculations to obtain the performance coefficient XN. Mark the screening object with the largest numerical value of the performance coefficient XN as the priority object, mark the total number of UPS power supplies configured by the priority object as the priority configuration value YP, and obtain the redundant configuration value RP through the formula RP = YP - JP; build a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP to obtain the configuration structure;

[0011] The operation control module is used to perform operation environment control analysis on the configuration structure.

[0012] Further, the specific process of cleaning the analysis set includes: calculating the concentration coefficient of the analysis set, obtaining the concentration threshold through the database, and comparing the concentration coefficient with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, then remove the maximum and minimum elements in the analysis set, and then recalculate the concentration coefficient, and so on until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, then mark the minimum element in the analysis set as the load critical value FL.

[0013] Further, the generation process of the power consumption range includes: obtaining the low power consumption value HDmin and the high power consumption value HDmax through the formulas HDmin = t1 × HD and HDmax = t2 × HD, where t1 and t2 are both proportionality coefficients, and 0.85 ≤ t1 ≤ 0.95, 1.05 ≤ t2 ≤ 1.15; the power consumption range is composed of the low power consumption value HDmin and the high power consumption value HDmax.

[0014] Further, the process of obtaining the voltage stability data YW includes: obtaining the proportion of the duration during which the output voltage value of the analysis object is within the output voltage range during the analysis period and marking it as the voltage stability value, and summing and averaging the voltage stability values of all analysis objects in the screening object to obtain the voltage stability data YW; the process of obtaining the voltage distortion data YJ includes: obtaining the proportion of the non-linear distortion contained in the output voltage of the analysis object during the analysis period and marking it as the voltage distortion value, and summing and averaging the voltage distortion values of all analysis objects in the screening object to obtain the voltage distortion data YJ; the process of obtaining the efficiency data XL includes: obtaining the maximum value of the ratio of the output power to the input power of the analysis object during the analysis period and marking it as the efficiency value, and summing and averaging the efficiency values of all analysis objects in the screening object to obtain the efficiency data XL.

[0015] Further, the specific process of the operation control module for performing operation environment control and analysis on the configuration structure includes: generating a control period and dividing the control period into several control time periods, obtaining the air temperature value of the operation environment where the configuration structure is located at the end of each control time period and marking it as the air temperature value, obtaining the air temperature range through the database, and determining whether the air temperature value is within the air temperature range: if so, it indicates that the operation environment temperature of the configuration structure meets the requirements; if not, it indicates that the operation environment temperature of the configuration structure does not meet the requirements, generating a temperature adjustment signal and sending the temperature adjustment signal to the mobile terminal of the management personnel through the comprehensive control platform.

[0016] Further, the specific process of the operation control module for performing operation environment control and analysis on the configuration structure also includes: obtaining the generation times of the temperature adjustment signal and the failure times of the UPS power supply in the configuration structure at the end of the control time period and respectively marking them as the adjustment data TS and the failure data GS, obtaining the maintenance coefficient BY through the formula BY = c1×TS + c2×GS, where both c1 and c2 are proportionality coefficients, and c2 > c1 > 1; determining the necessity of battery maintenance for the configuration structure through the maintenance coefficient BY.

[0017] Further, the specific process of determining the necessity of battery maintenance for the configuration structure includes: obtaining the maintenance threshold BYmax through the database, comparing the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configuration structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configuration structure requires battery maintenance, generating a maintenance signal and sending the maintenance signal to the mobile terminal of the management personnel through the comprehensive control platform.

[0018] The present invention has the following beneficial effects:

[0019] 1. Through the networking analysis module, parallel networking analysis can be performed on the UPS power supply, basic configuration can be carried out from the perspective of the failure rate of a single UPS power supply, feature screening of the load value of the UPS power supply during a failure can be performed in the way of data cleaning, and thus the load critical value can be screened out through the elements retained in the analysis set, ensuring the accuracy of the setting of the basic configuration value;

[0020] 2. Through the redundant configuration module, the redundant parallel structure of the UPS power supply can be analyzed for redundant configuration, the redundant parallel structure that has completed parallel networking and is in use can be marked, and then screened according to the maximum power consumption. Combining multiple performance parameters of the screening object, comprehensive analysis and calculation are carried out to obtain the performance coefficient. The redundant configuration value of the configuration structure is marked through the performance coefficient, so that both the basic configuration quantity and the redundant configuration quantity of the configuration structure are set to the optimal values, taking into account the operation failure rate of a single UPS power supply and the operation performance of the overall power supply system;

[0021] 3. Through the operation control module, the operation environment control and analysis of the completed configuration structure can be carried out. The air temperature value of the operation environment of the configuration structure is monitored in a periodic time-sharing analysis manner. Combining the temperature adjustment times and the UPS power supply failure times, the necessity of maintenance is analyzed. While optimizing the networking rules, the actual operation configuration structure is monitored in real time and dynamically. Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is the system block diagram of Embodiment 1 of the present invention;

[0024] Figure 2 It is the method flowchart of Embodiment 2 of the present invention. Detailed Embodiments

[0025] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0026] Embodiment 1: As Figure 1 shown, a comprehensive management and control system for UPS power supply based on big data includes a comprehensive management and control platform, and the comprehensive management and control platform is communicatively connected with a networking analysis module, a redundant configuration module, an operation control module, and a database.

[0027] The networking analysis module is used to perform parallel networking analysis on UPS power supplies: mark the UPS power supplies that have completed parallel networking and are in use as the analysis objects, generate an analysis period, and obtain the load values when the analysis objects have operating failures during the analysis period. The operating failures include power supply failures, battery failures, output failures, and input failures; form an analysis set from the load values of all analysis objects, and perform cleaning processing on the analysis set: calculate the variance of the analysis set to obtain the concentration coefficient, obtain the concentration threshold through the database, and compare the concentration coefficient with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, then eliminate the maximum element and the minimum element in the analysis set, and then recalculate the concentration coefficient, and so on, until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, then mark the minimum element in the analysis set as the load critical value FL; obtain the maximum power consumption of the current load object and mark it as the power consumption value HD, and obtain the basic configuration value JP through the formula JP = ceil(HD / FL); perform basic configuration from the perspective of the failure rate of a single UPS power supply, and perform feature screening on the load values of the UPS power supply during failure in the way of data cleaning, so as to screen out the load critical value through the elements retained in the analysis set and ensure the accuracy of the setting of the basic configuration value.

[0028] The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: Mark the redundant parallel structure that has completed parallel networking and is in use as the configuration object. Obtain the low power consumption value HDmin and the high power consumption value HDmax through the formulas HDmin = t1×HD and HDmax = t2×HD, where t1 and t2 are both proportionality coefficients, and 0.85 ≤ t1 ≤ 0.95, 1.05 ≤ t2 ≤ 1.15; The power consumption range is composed of the low power consumption value HDmin and the high power consumption value HDmax. Mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as the screening object. Obtain the voltage stability data YW, voltage distortion data YJ, and efficiency data XL of the screening object within the analysis period. The process of obtaining the voltage stability data YW includes: Obtain the proportion of the duration during which the output voltage value of the analysis object is within the output voltage range within the analysis period and mark it as the voltage stability value. Sum and average the voltage stability values of all analysis objects within the screening object to obtain the voltage stability data YW; The process of obtaining the voltage distortion data YJ includes: Obtain the proportion of the non-linear distortion contained in the output voltage of the analysis object within the analysis period and mark it as the voltage distortion value. Sum and average the voltage distortion values of all analysis objects within the screening object to obtain the voltage distortion data YJ; The process of obtaining the efficiency data XL includes: Obtain the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period and mark it as the efficiency value. Sum and average the efficiency values of all analysis objects within the screening object to obtain the efficiency data XL; Obtain the performance coefficient XN of the screening object through the formula XN = k1×YW + k2×XL - k3×YJ, where k1, k2, and k3 are all proportionality coefficients, and k1 > k2 > k3 > 1; Mark the screening object with the largest numerical value of the performance coefficient XN as the priority object, mark the total number of UPS power supplies configured by the priority object as the priority configuration value YP, and obtain the redundant configuration value RP through the formula RP = YP - JP; Build a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP to obtain the configuration structure; Mark the redundant parallel structure that has completed parallel networking and is in use, then screen according to the maximum power consumption, and perform comprehensive analysis and calculation in combination with multiple performance parameters of the screening object to obtain the performance coefficient. Mark the redundant configuration value of the configuration structure through the performance coefficient, so that both the basic configuration quantity and the redundant configuration quantity of the configuration structure are set to the optimal values, taking into account the operation failure rate of a single UPS power supply and the operation performance of the overall power supply system.

[0029] The operation control module is used to conduct operation environment control and analysis on the completed redundant parallel structure: generate a control period and divide the control period into several control time periods. At the end of each control time period, obtain the air temperature value of the operating environment where the configured structure is located and mark it as the air temperature value. Obtain the air temperature range through the database and determine whether the air temperature value is within the air temperature range: if so, it indicates that the operating environment temperature of the configured structure meets the requirements; if not, it indicates that the operating environment temperature of the configured structure does not meet the requirements, generate a temperature adjustment signal and send the temperature adjustment signal to the mobile terminal of the management personnel through the comprehensive control platform. At the same time, at the end of the control time period, obtain the generation times of the temperature adjustment signal and the failure times of the UPS power supply in the configured structure and mark them as adjustment data TS and failure data GS respectively. Obtain the maintenance coefficient BY through the formula BY = c1×TS + c2×GS, where both c1 and c2 are proportionality coefficients, and c2 > c1 > 1. Obtain the maintenance threshold BYmax through the database and compare the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configured structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configured structure requires battery maintenance, generate a maintenance signal and send the maintenance signal to the mobile terminal of the management personnel through the comprehensive control platform. Monitor the air temperature value of the operating environment of the configured structure in a periodic and time-segmented analysis manner, conduct maintenance necessity analysis by combining the temperature adjustment times and the UPS power supply failure times, optimize the networking rules, and conduct real-time dynamic monitoring of the actually operating configured structure.

[0030] Embodiment 2: As Figure 2 shown, a comprehensive control method for UPS power supply based on big data includes the following steps:

[0031] Step 1: Conduct parallel networking analysis on the UPS power supply: Mark the UPS power supply that has completed parallel networking and is in use as the analysis object, obtain the load value when the analysis object has an operating failure during the analysis period, form an analysis set from the load values of all analysis objects, and obtain the basic configuration value JP after cleaning and processing the analysis set;

[0032] Step 2: Conduct redundant configuration analysis on the redundant parallel structure of the UPS power supply: Mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as the screening object, obtain the voltage stability data YW, voltage distortion data YJ, and efficiency data XL of the screening object during the analysis period, and perform numerical calculation to obtain the performance coefficient XN, and mark the redundant configuration value RP through the performance coefficient XN;

[0033] Step 3: Build a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP to obtain a configured structure;

[0034] Step 4: Conduct an analysis of the operating environment control for the completed redundant parallel structure: Generate a control period and divide the control period into several control time periods, and determine the necessity of battery maintenance for the configured structure at the end of each control time period.

[0035] A comprehensive control system for UPS power supplies based on big data. During operation, the UPS power supplies that have completed parallel networking and are in use are marked as analysis objects, and the load values when the analysis objects have operating failures within the analysis period are obtained. An analysis set is formed by the load values of all analysis objects. After cleaning and processing the analysis set, the basic configuration value JP is obtained; the configuration objects whose maximum power consumption of the corresponding load objects is within the power consumption range are marked as screening objects, and the voltage stability data YW, voltage distortion data YJ, and efficiency data XL of the screening objects within the analysis period are obtained and numerical calculations are performed to obtain the performance coefficient XN. The redundant configuration value RP is marked through the performance coefficient XN; a redundant parallel structure is built for the current load object according to the basic configuration value JP and the redundant configuration value RP, and a configured structure is obtained.

[0036] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0037] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation; for example: the formula XN = k1×YW + k2×XL - k3×YJ; those skilled in the art collect multiple groups of sample data and set corresponding performance coefficients for each group of sample data; substitute the set performance coefficients and the collected sample data into the formula, and any three formulas form a system of linear equations with three variables. Screen the calculated coefficients and take the average value to obtain the values of k1, k2, and k3 as 3.58, 3.47, and 2.95 respectively;

[0038] The magnitude of the coefficient is a specific value obtained by quantifying each parameter, which is convenient for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the corresponding performance coefficients initially set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value, for example, the performance coefficient is directly proportional to the value of the voltage stability data.

[0039] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0040] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, according to the content of this specification, many modifications and variations can be made. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A UPS power supply integrated management and control system based on big data, characterized in that: It includes an integrated management and control platform, which is communicatively connected to a networking analysis module, a redundant configuration module, an operation management and control module, and a database; The network analysis module is used to perform parallel network analysis on the UPS power supply: mark the UPS power supply that has completed parallel networking and is put into use as the analysis object, generate an analysis cycle, and obtain the load value when the analysis object has an operation failure during the analysis cycle; form an analysis set with the load values ​​of all analysis objects, and clean the analysis set to obtain the load critical value FL; obtain the maximum power consumption of the current load object and mark it as the power consumption value HD, and obtain the basic configuration value JP through the formula JP=ceil(HD / FL); The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: mark the redundant parallel structure that has completed parallel networking and put into use as a configuration object, generate a power consumption range, mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as a screening object, obtain the voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object within the analysis period and perform numerical calculation to obtain the performance coefficient XN, mark the screening object with the largest performance coefficient XN value as a priority object, mark the total number of UPS power supplies configured by the priority object as the priority configuration value YP, and obtain the redundant configuration value RP through the formula RP=YP-JP; According to the basic configuration value JP and the redundant configuration value RP, a redundant parallel structure is built for the current load object and a configuration structure is obtained; The operation control module is used to perform operation environment control analysis on the configuration structure; The process of obtaining the voltage stability data YW includes: obtaining the proportion of the time when the output voltage value of the analysis object within the analysis period is within the output voltage range and marking it as the voltage stability value, summing and averaging the voltage stability values ​​of all analysis objects in the screening object to obtain the voltage stability data YW; the process of obtaining the voltage distortion data YJ includes: obtaining the proportion of nonlinear distortion contained in the output voltage of the analysis object within the analysis period and marking it as the voltage variation value, summing and averaging the voltage variation values ​​of all analysis objects in the screening object to obtain the voltage distortion data YJ; the process of obtaining the efficiency data XL includes: obtaining the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period and marking it as the efficiency value, summing and averaging the efficiency values ​​of all analysis objects in the screening object to obtain the efficiency data XL.

2. According to the big data-based UPS power integrated management and control system of claim 1, it is characterized in that: The specific process of cleaning the analysis set includes: calculating the variance of the analysis set to obtain the concentration coefficient, obtaining the concentration threshold through the database, and comparing the concentration coefficient with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, the maximum element and the minimum element in the analysis set are eliminated, and then the concentration coefficient is recalculated, and so on, until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, the minimum element in the analysis set is marked as the load critical value FL.

3. According to the big data-based UPS power integrated management and control system of claim 2, it is characterized in that: The process of generating the power consumption range includes: obtaining the low power consumption value HDmin and the high power consumption value HDmax through the formulas HDmin=t1×HD and HDmax=t2×HD, wherein t1 and t2 are both proportional coefficients, and 0.85≤t1≤0.95, 1.05≤t2≤1.15; and the power consumption range is formed by the low power consumption value HDmin and the high power consumption value HDmax.

4. A UPS power integrated management and control system based on big data according to claim 3, characterized in that: The specific process of the operation control module to perform operation environment control analysis on the configuration structure includes: generating a control cycle and dividing the control cycle into several control periods, obtaining the air temperature value of the operating environment of the configuration structure at the end of each control period and marking it as the air temperature value, obtaining the air temperature range through the database, and determining whether the air temperature value is within the air temperature range: if so, it means that the operating environment temperature of the configuration structure meets the requirements; if not, it means that the operating environment temperature of the configuration structure does not meet the requirements, generating a temperature adjustment signal and sending the temperature adjustment signal to the mobile terminal of the manager through the comprehensive control platform.

5. A UPS power integrated management and control system based on big data according to claim 4, characterized in that: The specific process of the operation control module performing operation environment control analysis on the configuration structure also includes: obtaining the number of times the temperature control signal is generated and the number of failures of the UPS power supply in the configuration structure at the end of the control period and marking them as adjustment data TS and failure data GS respectively, and obtaining the maintenance coefficient BY through the formula BY=c1×TS+c2×GS, where c1 and c2 are both proportional coefficients, and c2>c1>1; and determining the necessity of battery maintenance for the configuration structure through the maintenance coefficient BY.

6. A UPS power integrated management and control system based on big data according to claim 5, characterized in that: The specific process of determining the necessity of battery maintenance for the configuration structure includes: obtaining the maintenance threshold BYmax through the database, and comparing the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configuration structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configuration structure requires battery maintenance, and a maintenance signal is generated and sent to the mobile terminal of the manager through the integrated management and control platform.

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