Parallel control system and method for UPS (Uninterrupted Power Supply)

Abstract

The invention relates to the technical field of power supply control, in particular to a UPS parallel control system and method, and the system comprises two UPS modules, two controllers, a processor, a state monitoring unit, a balance control module, a fault detection unit, an automatic switching module, a communication module and an intelligent energy-saving module. The two controllers are connected with the corresponding UPS modules respectively, the processor is connected with the two controllers through the communication module, the state monitoring unit and the fault detection unit are connected with the processor, the balance control module is connected with the state monitoring unit and the processor, the automatic switching module is connected with the fault detection unit and the two controllers, and the power supply module is connected with the power supply module. And the intelligent energy-saving module is connected with the processor, so that the technical problems that the load is difficult to balance accurately, the system efficiency is reduced, the aging of a part of modules is accelerated and the maintenance cost is increased due to the difference of module performance parameters and the difference of element aging degrees of a parallel UPS system in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of power control technology, and in particular to a UPS power supply parallel control system and method. Background Technology

[0002] In today's society, uninterruptible power supply (UPS) systems, as key equipment for ensuring the stability of power supply, are widely used in many fields with extremely high requirements for power reliability, such as data centers, communication base stations, and industrial control. With the rapid development of information technology and the increasing reliance of various critical businesses on continuous power supply, the performance and reliability of UPS systems have become a focus of attention. An efficient and reliable UPS system can quickly provide stable power support to the load when the mains power is interrupted or abnormal, ensuring the continuous operation of critical equipment and avoiding serious consequences such as data loss and business interruption caused by power failures.

[0003] Existing parallel UPS systems suffer from differences in module performance parameters and varying degrees of component aging, making it difficult to accurately balance the load, which reduces system efficiency, accelerates the aging of some modules, and increases maintenance costs. Summary of the Invention

[0004] The purpose of this invention is to provide a UPS power supply parallel control system and method, which aims to solve the technical problems in the prior art where parallel UPS systems are difficult to balance accurately due to differences in module performance parameters and different degrees of component aging, resulting in reduced system efficiency, accelerated aging of some modules, and increased maintenance costs.

[0005] To achieve the above objectives, the present invention employs a UPS power supply parallel control system, comprising two UPS modules, two controllers, a processor, a status monitoring unit, a balancing control module, a fault detection unit, an automatic switching module, a communication module, and an intelligent energy-saving module;

[0006] The two controllers are respectively connected to the corresponding UPS modules. The processor is connected to both controllers through the communication module. The status monitoring unit and the fault detection unit are both connected to the processor. The equalization control module is connected to the status monitoring unit and the processor. The automatic switching module is connected to the fault detection unit and the two controllers. The intelligent energy-saving module is connected to the processor.

[0007] The controller is used to receive and send control commands and status information to the UPS module connected to it;

[0008] The processor is used for data processing and decision-making, and the status monitoring unit is used to monitor the operating status of the UPS module in real time.

[0009] The equalization control module calculates the power adjustment of each UPS module based on the data from the status monitoring unit using a dynamic load balancing algorithm. The dynamic load balancing algorithm takes the output voltage deviation and battery health score as inputs and generates power compensation commands through a PID control algorithm.

[0010] The fault detection unit is used to detect faults in the UPS modules. When a fault is detected in one of the UPS modules, the fault type is determined according to the fault level classification table, and a switching command is sent to the automatic switching module. The automatic switching module automatically switches the load to the normally operating UPS module.

[0011] The intelligent energy-saving module dynamically adjusts the number of UPS modules in operation according to real-time load demand. When the total load rate is continuously lower than the first threshold T1 for a time Δt1, and the battery health score of all UPS modules is greater than Smin, the redundant UPS modules are controlled to enter sleep mode. When the total load rate is greater than the second threshold T2 or the failure rate of the operating module is greater than Fmax, the sleep UPS modules are woken up and put into operation synchronously.

[0012] The state detection unit includes a data acquisition module and a balance analysis module. The data acquisition module is connected to the processor, and the balance analysis module is connected to both the data acquisition module and the balance control unit.

[0013] The data acquisition module is used to collect the output voltage, current, frequency, temperature and battery health parameters of the two UPS modules in real time.

[0014] The load balance analysis module analyzes the load status and battery health status of each UPS module based on the collected parameters, uses a machine learning-based predictive model to assess the remaining battery life, and provides adjustment suggestions to the load balance control module.

[0015] The fault detection unit includes a fault detection module and a fault determination module. The fault detection module is connected to the processor, and the fault determination modules are connected to the fault detection module and the two controllers.

[0016] The fault detection module continuously monitors the operating status of the two UPS modules, including output voltage, current, frequency, temperature and communication status.

[0017] When the fault detection module detects an anomaly, the fault determination module confirms whether a fault has occurred based on the preset fault determination logic and fault level classification table, and sends a fault signal and fault type information to the automatic switching module.

[0018] The fault level classification table includes:

[0019] Level 1 fault: Output voltage exceeds limit by 5% or temperature > Tmax, triggering immediate switching;

[0020] Level 2 fault: Current fluctuation rate > 30% for 10 seconds, triggering an early warning and initiating equalization compensation;

[0021] Level 3 fault: Battery health score is too low, triggering standby replacement process.

[0022] The automatic switching module includes a static switch and switching control logic. The static switch is connected between the two controllers. The switching control logic controls the on / off state of the static switch based on fault signals and fault type information to achieve seamless load switching.

[0023] The UPS power parallel control system further includes a dynamic adjustment module, which is connected to the equalization control module.

[0024] The dynamic adjustment module works in conjunction with the equalization control module to dynamically optimize the PID control algorithm parameters based on the real-time status of the system and historical operating data.

[0025] The dynamic adjustment module adopts a fuzzy rule base and corrects parameters in real time according to the error magnitude and rate of change. Through simulation and experimental verification, the fuzzy rule base and parameter adjustment strategy are continuously optimized to ensure that the algorithm has high efficiency and stability under different load conditions, and dynamic iterative optimization is performed based on actual running results.

[0026] The communication module supports dual redundant communication links. Both the main link and the backup link support multiple communication protocols, including CAN, Ethernet and RS485, with a communication rate of ≥100Mbps.

[0027] This invention also provides a UPS power supply parallel control method, applied to the UPS power supply parallel control system as described above.

[0028] Includes the following steps:

[0029] The processor completes hardware self-test and communication link establishment, and starts the synchronous calibration program to ensure that the output parameters of the UPS module are consistent.

[0030] The status monitoring unit collects data in real time, and the equalization control module calculates the power adjustment amount based on the improved PID algorithm;

[0031] The fault detection module continuously monitors for anomalies. After identifying an anomaly, the fault determination module determines a handling plan based on the level classification table. The automatic switching module performs seamless load transfer.

[0032] Meanwhile, the intelligent energy-saving module dynamically adjusts the number of UPS modules in operation based on the load rate.

[0033] The dynamic adjustment module corrects the PID parameters in real time based on the system status and historical data using a fuzzy rule base.

[0034] Simultaneously, an energy-saving optimization report is generated; the report data is synchronously stored in a distributed system, and historical energy efficiency curves and energy-saving suggestions are displayed through a human-machine interface.

[0035] When the fault detection module identifies an anomaly, it also stores the fault log and processing log within the distributed system.

[0036] This invention discloses a parallel UPS power supply control system and method. In practical use, two controllers interact with the corresponding UPS modules, exchanging commands and status information. The processor processes the data and makes decisions. The status monitoring unit monitors the operating status of the UPS modules in real time. The balancing control module calculates the power adjustment based on the data using a dynamic load balancing algorithm (taking output voltage deviation and battery health score as inputs, and generating compensation commands through PID control). The fault detection unit detects faults, determines the type, and then the automatic switching module switches the load. The intelligent energy-saving module dynamically adjusts the number of UPS modules in operation according to real-time load demand and set thresholds and conditions. This approach solves the technical problems in existing parallel UPS systems where differences in module performance parameters and component aging levels make precise load balancing difficult, reducing system efficiency, accelerating the aging of some modules, and increasing maintenance costs. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the first embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the second embodiment of the present invention.

[0040] 101-UPS module, 102-Controller, 103-Processor, 104-Status monitoring unit, 105-Balanced control module, 106-Fault detection unit, 107-Automatic switching module, 108-Communication module, 109-Intelligent energy-saving module, 110-Dynamic adjustment module, 111-Data acquisition module, 112-Balanced analysis module, 113-Fault detection module, 114-Fault determination module, 115-Static switch, 116-Switching control logic, 201-Remote monitoring and management module, 202-Login module, 203-Authentication module, 204-Access control module. Detailed Implementation

[0041] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0042] For the first embodiment, please refer to... Figure 1 , Figure 1 This is a schematic diagram of the first embodiment of the present invention.

[0043] This invention provides a parallel UPS power supply control system, comprising two UPS modules 101, two controllers 102, a processor 103, a status monitoring unit 104, a balancing control module 105, a fault detection unit 106, an automatic switching module 107, a communication module 108, an intelligent energy-saving module 109, and a dynamic adjustment module 110. The status detection unit includes a data acquisition module 111 and a balancing analysis module 112. The fault detection unit 106 includes a fault detection module 113 and a fault determination module 114. The automatic switching module 107 includes a static switch 115 and a switching control logic 116. The aforementioned technical solution solves the technical problems in the prior art where parallel UPS systems suffer from difficulties in accurately balancing the load due to differences in module performance parameters and varying degrees of component aging, leading to reduced system efficiency, accelerated aging of some modules, and increased maintenance costs.

[0044] In this specific embodiment, the controller 102 is used to receive and send control commands and status information to the UPS module 101 connected thereto;

[0045] The processor 103 is used for data processing and decision-making, and the status monitoring unit 104 is used to monitor the operating status of the UPS module 101 in real time.

[0046] The equalization control module 105 calculates the power adjustment amount of each UPS module 101 based on the data of the status monitoring unit 104 using a dynamic load balancing algorithm; the dynamic load balancing algorithm takes the output voltage deviation and battery health score as inputs and generates power compensation commands through a PID control algorithm.

[0047] The fault detection unit 106 is used to detect faults in the UPS module 101; when a fault is detected in one of the UPS modules 101, the fault type is determined according to the fault level classification table, and a switching command is sent to the automatic switching module 107, which automatically switches the load to the normally operating UPS module 101.

[0048] The intelligent energy-saving module 109 dynamically adjusts the number of UPS modules 101 in operation according to real-time load demand. When the total load rate is continuously lower than the first threshold T1 for a time Δt1, and the battery health score of all UPS modules 101 is greater than Smin, the redundant UPS modules 101 are controlled to enter sleep mode. When the total load rate is greater than the second threshold T2 or the failure rate of the operating module is greater than Fmax, the sleep UPS modules 101 are woken up and put into operation synchronously.

[0049] First threshold T1 = 40%, second threshold T2 = 75%, Δt1 = 5min, Smin = 0.8;

[0050] When waking up the hibernation module, a phase synchronization algorithm is used to ensure that the output voltage phase difference is less than 1°, and the parallel operation is completed within 200ms.

[0051] In this configuration, two controllers 102 are each connected to a corresponding UPS module 101. A processor 103 is connected to both controllers 102 via a communication module 108. A status monitoring unit 104 and a fault detection unit 106 are both connected to the processor 103. A balancing control module 105 is connected to both the status monitoring unit 104 and the processor 103. An automatic switching module 107 is connected to both the fault detection unit 106 and the two controllers 102. An intelligent energy-saving module 109 is connected to the processor 103. In practical use, the two controllers 102 interact with their corresponding UPS modules to exchange commands and status information. The processor 103... The system employs data-driven decision-making. The status monitoring unit 104 monitors the UPS module's operating status in real time. The balancing control module 105 calculates the power adjustment based on this data using a dynamic load balancing algorithm (with output voltage deviation and battery health score as inputs, and compensation commands generated via PID control). The fault detection unit 106 detects faults, determines the type, and then the automatic switching module 107 switches the load. The intelligent energy-saving module 109 dynamically adjusts the number of UPS modules operating according to real-time load demands and set thresholds and conditions. This approach solves the technical problems in existing parallel UPS systems where differences in module performance parameters and component aging levels make precise load balancing difficult, reducing system efficiency, accelerating module aging, and increasing maintenance costs.

[0052] Secondly, the data acquisition module 111 is connected to the processor 103, and the equalization analysis module 112 is connected to both the data acquisition module 111 and the equalization control unit.

[0053] The data acquisition module 111 is used to collect the output voltage, current, frequency, temperature and battery health parameters of the two UPS modules 101 in real time.

[0054] The equalization analysis module 112 analyzes the load status and battery health status of each UPS module 101 based on the collected parameters, uses a machine learning-based prediction model to assess the remaining battery life, and provides adjustment suggestions to the equalization control module 105.

[0055] The data acquisition module 111 is closely connected to the processor 103, forming a basic channel for data transmission. The equalization analysis module 112 is connected to the data acquisition module 111 and the equalization control unit respectively, so as to realize data interaction and smooth transmission of control commands.

[0056] The data acquisition module 111 is responsible for collecting key data in real time. With a precise sampling frequency and reliable measurement accuracy, it continuously acquires basic operating parameters such as output voltage, current, frequency, and temperature of the two UPS modules 101. At the same time, it collects complete battery health parameters, such as battery internal resistance and charge / discharge cycles, providing comprehensive and accurate data support for subsequent analysis.

[0057] The load balancing analysis module 112, based on the collected abundant parameters, employs a multi-parameter fusion-based load balancing algorithm to deeply analyze the load status of each UPS module 101. This algorithm comprehensively considers multiple factors such as voltage, current, and power, and through weighted calculation and dynamic adjustment, accurately determines the workload of each module, avoiding errors that may arise from judging a single parameter. Simultaneously, for battery health status assessment, a machine learning-based battery health prediction model is used. This model combines a large amount of historical battery data and real-time operating data, and through feature extraction and model training, can accurately predict battery health trends. Based on this, a remaining lifespan estimation algorithm is used, combining factors such as battery charging and discharging characteristics and operating environment, to scientifically and rationally assess the remaining battery lifespan, providing a forward-looking reference for the balanced operation of the system. Based on the analysis results of this series of algorithms, highly targeted adjustment suggestions are provided to the load balancing control module 105 to ensure that the two UPS modules 101 can operate in a balanced manner, effectively extending the overall service life of the system and reducing equipment losses caused by uneven load.

[0058] Meanwhile, the fault detection module 113 is connected to the processor 103, and the fault determination module 114 is connected to both the fault detection module 113 and the two controllers 102.

[0059] The fault detection module 113 continuously monitors the operating status of the two UPS modules 101, including output voltage, current, frequency, temperature and communication status.

[0060] When the fault detection module 113 detects an abnormality, the fault determination module 114 confirms whether a fault has occurred based on the preset fault determination logic and fault level classification table, and sends a fault signal and fault type information to the automatic switching module 107.

[0061] The fault detection module 113 continuously monitors the operating status of the two UPS modules 101, focusing not only on conventional parameters such as output voltage, current, frequency, and temperature, but also closely monitoring the communication status to ensure unimpeded information transmission between the various parts of the system. Employing high-precision sensors and advanced signal processing algorithms, it can quickly and accurately capture minute changes in parameters, improving the sensitivity of fault detection.

[0062] Once the fault detection module 113 detects an anomaly, the fault determination module 114 immediately starts operating. Based on a pre-set rigorous fault determination logic and a detailed fault level classification table, it employs a rule-based reasoning fault diagnosis algorithm to comprehensively and meticulously analyze and judge the anomaly. This algorithm compares real-time collected parameters with pre-set normal ranges and fault characteristics, accurately confirming whether a fault has actually occurred and precisely determining the fault type through logical reasoning and pattern matching. Subsequently, it quickly sends a fault signal and fault type information to the automatic switching module 107 so that the system can take timely countermeasures to ensure the stability and reliability of power supply and reduce power outage time and losses caused by faults.

[0063] The fault severity classification table includes:

[0064] Level 1 fault: Output voltage exceeds limit by 5% or temperature > Tmax, triggering immediate switching;

[0065] Level 2 fault: Current fluctuation rate > 30% for 10 seconds, triggering an early warning and initiating equalization compensation;

[0066] Level 3 fault: Battery health score is too low, triggering standby replacement process.

[0067] Furthermore, the static switch 115 is connected between the two controllers 102, and the switching control logic 116 controls the on / off state of the static switch 115 according to the fault signal and fault type information to achieve seamless load switching.

[0068] Finally, the dynamic adjustment module 110 is connected to the equalization control module 105;

[0069] The dynamic adjustment module 110 works in conjunction with the equalization control module 105 to dynamically optimize the PID control algorithm parameters based on the real-time status of the system and historical operating data.

[0070] The dynamic adjustment module 110 adopts a fuzzy rule base and corrects parameters in real time according to the error magnitude and rate of change. Through simulation and experimental verification, the fuzzy rule base and parameter adjustment strategy are continuously optimized to ensure that the algorithm has high efficiency and stability under different load conditions, and dynamic iterative optimization is performed based on actual running results.

[0071] The communication module 108 supports dual redundant communication links. Both the main link and the backup link support multiple communication protocols, including CAN, Ethernet and RS485, with a communication rate of ≥100Mbps.

[0072] In the parallel control system of a UPS power supply according to this embodiment, during specific use, the two controllers 102 respectively interact with the corresponding UPS modules to exchange commands and status information, and the processor 103 processes data for decision-making; the status monitoring unit 104 monitors the operating status of the UPS modules in real time, and the balancing control module 105 calculates the power adjustment amount based on its data using a dynamic load balancing algorithm (taking output voltage deviation and battery health score as input, and generating compensation commands through PID control); the fault detection unit 106 detects faults, determines the type, and then the automatic switching module 107 switches the load; the intelligent energy-saving module 109 dynamically adjusts the number of UPS modules in operation according to real-time load demand and set thresholds and conditions; in this way, the technical problems of existing parallel UPS systems, such as difficulty in accurately balancing the load due to differences in module performance parameters and different degrees of component aging, which reduces system efficiency, accelerates the aging of some modules, and increases maintenance costs, are solved.

[0073] For the second embodiment, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the second embodiment of the present invention.

[0074] In the first embodiment, the present invention provides a UPS power supply parallel control system, which also includes a remote monitoring and management module 201, a login module 202, an authentication module 203, and a permission management module 204.

[0075] In this specific embodiment, the remote monitoring and management module 201 is connected to the processor 103, and the login module 202 is connected to both the remote monitoring and management module 201 and the authentication module 203. The remote monitoring and management module 201, as the core remote interaction hub, is closely connected to the processor 103, enabling remote transmission of system status data and remote issuance of control commands. The login module 202 provides an entry point for users to access the remote monitoring and management module 201. It works in conjunction with the remote monitoring and management module 201 and the authentication module 203 to ensure that only legitimate users can attempt to log in. The authentication module 203 rigorously verifies the login information entered by the user to determine the legitimacy of their identity.

[0076] The permission management module 204 is connected to the authentication module 203. The permission management module 204 assigns corresponding operation permissions to users from a preset permission template based on the user's identity type (e.g., administrator, regular operator). The permission template defines the operations that users of different identity types can perform.

[0077] In this embodiment of the UPS power supply parallel control system, the remote monitoring and management module 201, acting as the core remote interaction hub, is closely connected to the processor 103 to realize the remote transmission of system status data and the remote issuance of control commands. The login module 202 provides an entry point for users to access the remote monitoring and management module 201. It works in conjunction with the remote monitoring and management module 201 and the authentication module 203 to ensure that only legitimate users can attempt to log in. The authentication module 203 strictly verifies the login information entered by the user to determine the legitimacy of their identity. The permission management module 204 assigns corresponding operation permissions to the user from a preset permission template based on the user's identity type (such as administrator, ordinary operator, etc.). The permission template defines the operations that users of different identity types can perform.

[0078] This invention also provides a UPS power supply parallel control method, applied to the UPS power supply parallel control system as described above.

[0079] Includes the following steps:

[0080] The processor 103 completes hardware self-test and communication link establishment, and starts the synchronous calibration program to ensure that the output parameters of the UPS module 101 are consistent.

[0081] The status monitoring unit 104 collects data in real time, and the equalization control module 105 calculates the power adjustment amount based on the improved PID algorithm;

[0082] The fault detection module 113 continuously monitors for anomalies. After the fault detection module 113 identifies an anomaly, the fault determination module 114 determines a handling plan based on the level classification table. The automatic switching module 107 performs seamless load transfer.

[0083] Meanwhile, the intelligent energy-saving module 109 dynamically adjusts the number of UPS modules 101 in operation according to the load rate.

[0084] The dynamic adjustment module 110 corrects the PID parameters in real time based on the system status and historical data using a fuzzy rule base.

[0085] Simultaneously, an energy-saving optimization report is generated; the report data is synchronously stored in a distributed system, and historical energy efficiency curves and energy-saving suggestions are displayed through a human-machine interface.

[0086] When the fault detection module 113 identifies an anomaly, it also stores the fault log and processing log in the distributed system.

[0087] Furthermore, by employing LSTM neural networks through data mining algorithms to extract time-series features from fault logs in distributed storage, the periodic change patterns of key parameters such as voltage fluctuations and temperature anomalies are identified. The Apriori algorithm is then used to mine potential association rules between faults (e.g., "when the battery health score is below the threshold, the risk of output voltage exceeding the limit increases by 3 times"). Subsequently, a deep Q-network (DQN) reinforcement learning model is used to dynamically optimize the PID parameter adjustment strategy and fault level classification threshold based on historical processing results.

[0088] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A UPS power supply parallel control system, characterized in that, It includes two UPS modules, two controllers, a processor, a status monitoring unit, a balancing control module, a fault detection unit, an automatic switching module, a communication module, and an intelligent energy-saving module; The two controllers are respectively connected to the corresponding UPS modules. The processor is connected to both controllers through the communication module. The status monitoring unit and the fault detection unit are both connected to the processor. The equalization control module is connected to the status monitoring unit and the processor. The automatic switching module is connected to the fault detection unit and the two controllers. The intelligent energy-saving module is connected to the processor. The controller is used to receive and send control commands and status information to the UPS module connected to it; The processor is used for data processing and decision-making, and the status monitoring unit is used to monitor the operating status of the UPS module in real time. The equalization control module calculates the power adjustment of each UPS module based on the data from the status monitoring unit using a dynamic load balancing algorithm. The dynamic load balancing algorithm takes the output voltage deviation and battery health score as inputs and generates power compensation commands through a PID control algorithm. The fault detection unit is used to detect faults in the UPS modules. When a fault is detected in one of the UPS modules, the fault type is determined according to the fault level classification table, and a switching command is sent to the automatic switching module. The automatic switching module automatically switches the load to the normally operating UPS module. The intelligent energy-saving module dynamically adjusts the number of UPS modules in operation according to real-time load demand. When the total load rate is continuously lower than the first threshold T1 for a time Δt1, and the battery health score of all UPS modules is greater than Smin, the redundant UPS modules are controlled to enter sleep mode. When the total load rate is greater than the second threshold T2 or the failure rate of the operating module is greater than Fmax, the sleep UPS modules are woken up and put into operation synchronously.

2. The UPS power supply parallel control system as described in claim 1, characterized in that, The state detection unit includes a data acquisition module and a balance analysis module. The data acquisition module is connected to the processor, and the balance analysis module is connected to both the data acquisition module and the balance control unit. The data acquisition module is used to collect the output voltage, current, frequency, temperature and battery health parameters of the two UPS modules in real time. The load balance analysis module analyzes the load status and battery health status of each UPS module based on the collected parameters, uses a machine learning-based predictive model to assess the remaining battery life, and provides adjustment suggestions to the load balance control module.

3. The UPS power supply parallel control system as described in claim 2, characterized in that, The fault detection unit includes a fault detection module and a fault determination module. The fault detection module is connected to the processor, and the fault determination modules are all connected to the fault detection module and the two controllers. The fault detection module continuously monitors the operating status of the two UPS modules, including output voltage, current, frequency, temperature and communication status. When the fault detection module detects an anomaly, the fault determination module confirms whether a fault has occurred based on the preset fault determination logic and fault level classification table, and sends a fault signal and fault type information to the automatic switching module.

4. The UPS power supply parallel control system as described in claim 3, characterized in that, The fault severity classification table includes: Level 1 fault: Output voltage exceeds limit by 5% or temperature > Tmax, triggering immediate switching; Level 2 fault: Current fluctuation rate > 30% for 10 seconds, triggering an early warning and initiating equalization compensation; Level 3 fault: Battery health score is too low, triggering standby replacement process.

5. The UPS power supply parallel control system as described in claim 4, characterized in that, The automatic switching module includes a static switch and switching control logic. The static switch is connected between the two controllers. The switching control logic controls the on / off state of the static switch according to the fault signal and fault type information to achieve seamless load switching.

6. The UPS power supply parallel control system as described in claim 5, characterized in that, The UPS power parallel control system also includes a dynamic adjustment module, which is connected to the equalization control module. The dynamic adjustment module works in conjunction with the equalization control module to dynamically optimize the PID control algorithm parameters based on the real-time status of the system and historical operating data. The dynamic adjustment module adopts a fuzzy rule base and corrects parameters in real time according to the error magnitude and rate of change. Through simulation and experimental verification, the fuzzy rule base and parameter adjustment strategy are continuously optimized to ensure that the algorithm has high efficiency and stability under different load conditions, and dynamic iterative optimization is performed based on actual running results.

7. The UPS power supply parallel control system as described in claim 1, characterized in that, The communication module supports dual redundant communication links. Both the main link and the backup link support multiple communication protocols, including CAN, Ethernet and RS485, with a communication rate of ≥100Mbps.

8. A UPS power supply parallel control method, applied to the UPS power supply parallel control system as described in claim 7, characterized in that, Includes the following steps: The processor completes hardware self-test and communication link establishment, and starts the synchronous calibration program to ensure that the output parameters of the UPS module are consistent. The status monitoring unit collects data in real time, and the equalization control module calculates the power adjustment amount based on the improved PID algorithm; The fault detection module continuously monitors for anomalies. After identifying an anomaly, the fault determination module determines a handling plan based on the level classification table. The automatic switching module performs seamless load transfer. Meanwhile, the intelligent energy-saving module dynamically adjusts the number of UPS modules in operation based on the load rate.

9. The UPS power supply parallel control method as described in claim 8, characterized in that, The dynamic adjustment module corrects the PID parameters in real time based on the system status and historical data using a fuzzy rule base. Simultaneously, an energy-saving optimization report is generated; the report data is synchronously stored in a distributed system, and historical energy efficiency curves and energy-saving suggestions are displayed through a human-machine interface.

10. The UPS power supply parallel control method as described in claim 9, characterized in that, Once the fault detection module identifies an anomaly, it also stores the fault log and processing log within the distributed system.

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