Photovoltaic power generation grid-connected performance optimization system based on edge calculation

Abstract

The invention relates to the technical field of grid-connected performance optimization, and discloses a photovoltaic power generation grid-connected performance optimization system based on edge calculation, and the system comprises a data collection module which is used for collecting power data from a photovoltaic power generation system; the edge control module is used for locally processing the power data in real time through edge calculation, executing a control algorithm and sending an instruction to an inverter; the grid-connected optimization module is used for adjusting the voltage, the frequency and the active power of the power grid according to the power data; the filtering control module is used for filtering higher harmonics in the output current of the inverter; and the monitoring operation and maintenance module is used for carrying out real-time monitoring and intelligent management on the photovoltaic power generation system. According to the invention, the grid-connected performance and the operation and maintenance efficiency are improved, and high-precision current control and dynamic harmonics are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid-connected performance optimization, and specifically relates to a photovoltaic power generation grid-connected performance optimization system based on edge computing. Background Art

[0002] Currently, photovoltaic power generation technology has been widely used in the global energy market, but its grid-connected performance still faces many technical challenges, especially in the scenario of large-scale photovoltaic power generation connected to the grid. These problems mainly focus on the inverter's grid-connected adaptability, system stability, and responsiveness to grid fluctuations. Traditional photovoltaic inverters often fail to meet complex grid requirements during the grid-connected process, resulting in poor system reliability and grid-connected safety. In particular, in the face of grid voltage fluctuations, frequency offsets, harmonic interference, etc., existing technologies are difficult to provide sufficient regulation and adaptability.

[0003] Therefore, there is an urgent need for a photovoltaic power generation grid-connected performance optimization system to solve the above problems. Summary of the invention

[0004] The present invention provides a photovoltaic power generation grid-connected performance optimization system based on edge computing, which solves the technical problems of insufficient grid-connected performance of inverters, low output current quality and system instability in related technologies.

[0005] The present invention provides a photovoltaic power generation grid-connected performance optimization system based on edge computing, comprising: A data acquisition module, which is used to collect power data from the photovoltaic power generation system, wherein the power data includes: inverter voltage, inverter current, grid connection point voltage, grid connection point current and grid frequency; An edge control module, which is used to process the power data locally in real time through edge computing, execute a control algorithm and send instructions to the inverter; A grid-connected optimization module, which is used to adjust the grid voltage, frequency and active power according to the power data; A filter control module, which is used to filter out high-order harmonics in the inverter output current through a filter; The monitoring and operation module is used for real-time monitoring and intelligent management of the photovoltaic power generation system.

[0006] Furthermore, the edge control module is composed of: a dual DSP redundant control system, an edge computing unit, a PI+repetitive control algorithm and a communication interface; The dual DSP redundant control system is a master-slave DSP architecture. The master DSP is responsible for the main control tasks during daily operation, including grid-connected control, power regulation and algorithm execution. The slave DSP serves as a backup system and automatically takes over the control tasks when the master DSP fails. The edge computing unit performs local calculations through a high-speed processor, is responsible for analyzing the collected data, and executing the control algorithm. The PI+repetitive control algorithm combines PI control and repetitive control for grid-connected current control and grid-connected optimization. The communication interface is used to exchange data and instructions with the data acquisition module and the filtering control module.

[0007] Furthermore, the dual DSP redundant control system adopts a fault detection and switching mechanism, that is, the operating status of the main DSP is monitored in real time by the slave DSP. When the slave DSP detects that the main DSP is abnormal, the switching mechanism will be immediately started, and the slave DSP will automatically take over the control task to ensure that the system control is not interrupted; the detection criteria include: long-term unresponsiveness, overload or overheating and calculation errors, and the switching process is implemented through hardware interrupts or dedicated communication interfaces; when the main DSP fault is repaired, the system will be restored to the initial state, and the recovery steps include: Step S201, the slave DSP monitors the status of the master DSP to confirm that it is operating normally and all modules have been restored; Step S202, the slave DSP performs data synchronization with the master DSP; Step S203, the slave DSP verifies each module of the master DSP; Step S204, the slave DSP gradually returns the tasks and control signals to the master DSP; Step S205: the main DSP takes over all control tasks, and the system re-enters the normal working mode to continue to perform the control and optimization tasks of the photovoltaic system.

[0008] Furthermore, the steps of using the PI+repetitive control algorithm include: Step S301, initializing the PI control parameters and calculating the first control signal output by the PI controller, wherein the PI control parameters include a proportional coefficient and an integral coefficient, and the calculation formula of the first control signal includes: ; ; in represents the first control signal output by the PI controller, represents the proportionality coefficient, represents the integral coefficient, Represents the system error, that is, the difference between the actual output current of the inverter and the target reference current. and Respectively represent the actual output current and target reference current of the inverter, represents the time integral of the PI controller error; Step S302, calculating the second control signal output by the repetitive control part, the calculation formula of the second control signal is: ; in represents the second control signal output by the repetitive control part, T represents the period of the grid current, Represents the sum of errors within a grid current cycle; Step S303, combining the first control signal output by the PI controller with the second control signal output by the repetitive control part to obtain a final control signal; the calculation formula involved is: ; Step S304: adjusting the output current of the inverter in real time through the final control signal.

[0009] Furthermore, the grid voltage regulation relies on reactive power compensation provided by the inverter. The grid-connected optimization module determines whether it is necessary to output reactive power through the inverter to stabilize the voltage by monitoring the fluctuation of the grid voltage. When the grid voltage is lower than or higher than the preset threshold, the grid-connected optimization module will start reactive power regulation. The calculation formula of the regulation is: ,in Indicates the regulation value of reactive power, represents the gain parameter of reactive power regulation, Indicates the nominal voltage, Indicates the actual measured grid voltage; The regulation of grid frequency relies on adjusting the output of active power to stabilize the frequency. The calculation formula for this regulation is: ,in Indicates the adjustment value of active power, represents the gain parameter of active power regulation, represents the reference frequency, Indicates the actual measured grid frequency; The active power is adjusted according to the load change of the power grid. The calculation formula for this adjustment is: ,in Indicates the target active power, which is set according to the grid load demand. Indicates the actual output power of the current photovoltaic power generation system.

[0010] Furthermore, an LCLR filter is used to filter out the high-order harmonics in the inverter output current, and the filtering effect is adjusted in real time according to the inverter operating status; wherein the LCLR filter adds a controllable resistor to the traditional LCL filter structure to enable the filter to have the ability of dynamic adjustment. The working process of the filter includes: Step S401, performing Fourier transform on the output current through an edge computing device to analyze the harmonic components in the output current; Step S402, according to the result of harmonic analysis, the system dynamically adjusts the damping value of the controllable resistor of the LCLR filter; Step S403, continuously optimizing the filter parameters by real-time monitoring of harmonic suppression effect and grid status.

[0011] Furthermore, the functions of the monitoring and operation and maintenance module include: real-time monitoring, fault warning and remote control; the module collects power data of the photovoltaic power generation system to monitor the operating status of the entire system in real time, including the inverter, battery pack and filter. When the equipment status or parameters in the system exceed the preset safety threshold, the module will immediately issue a fault warning and notify the operation and maintenance personnel to check and take emergency measures; the module supports remote execution of scheduling instructions and control operations by integrating with the cloud platform and scheduling system.

[0012] The beneficial effects of the present invention are as follows: the present invention adopts the redundant design of the master and slave DSPs, so that when the master DSP fails, the slave DSP automatically takes over the task, thereby ensuring the continuity of the system operation. Even if a controller fails, it will not cause the system to shut down or the performance to drop significantly, thereby greatly improving the reliability of the system; the fault detection and switching mechanism is adopted, so that the system can detect the status of the master DSP in real time, automatically determine the fault and immediately switch to the slave DSP without manual intervention, thereby ensuring the continuous and stable operation of the system; The present invention uses PI+repetitive control algorithm, so that the system can maintain high-precision current control in both dynamic and steady-state conditions, reduce harmonics and steady-state errors during grid connection, and improve the grid connection performance and grid adaptability of the system; The present invention uses the dynamically controllable resistance of the LCLR filter to enable the system to dynamically adjust the filtering effect according to the real-time analysis results of the harmonics, improve the filtering accuracy, reduce power loss, and enhance the adaptability of the system under different working conditions; The present invention uses a monitoring operation and maintenance module to enable the system to monitor the operating status of the photovoltaic power generation system in real time, and issue an early warning when a fault occurs or equipment parameters are abnormal, notifying the operation and maintenance personnel to take timely actions to prevent the fault from further deteriorating. At the same time, the system supports remote monitoring and scheduling, and the operation and maintenance personnel can manage and adjust the photovoltaic power station through a remote control platform without having to frequently visit the site, which greatly reduces the operation and maintenance costs and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a module schematic diagram of a photovoltaic power generation grid-connected performance optimization system based on edge computing of the present invention; Figure 2is a flow chart of system failure recovery of the present invention; Figure 3 is a flow chart of the PI+ repetitive control algorithm of the present invention; Figure 4 It is a working flow chart of the LCLR filter of the present invention.

[0014] In the figure: data acquisition module 101, edge control module 102, grid connection optimization module 103, filtering control module 104 and monitoring and operation module 105. DETAILED DESCRIPTION

[0015] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0016] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] like Figure 1-Figure 4 As shown, a photovoltaic power generation grid-connected performance optimization system based on edge computing includes: A data acquisition module 101 is used to collect power data from the photovoltaic power generation system, wherein the power data includes: inverter voltage, inverter current, grid connection point voltage, grid connection point current and grid frequency; An edge control module 102, which is used to process the power data locally in real time through edge computing, execute a control algorithm and send instructions to the inverter; A grid-connected optimization module 103, which is used to adjust the grid voltage, frequency and active power according to the power data; A filter control module 104, which is used to filter out high-order harmonics in the inverter output current through a filter; The monitoring and operation module 105 is used to perform real-time monitoring and intelligent management of the photovoltaic power generation system.

[0018] In one embodiment of the present invention, the collected power data is filtered and denoised to eliminate interference. Specifically, a low-pass filter is used to eliminate high-frequency noise and retain low-frequency voltage and current signals.

[0019] In one embodiment of the present invention, the edge control module comprises: a dual DSP redundant control system, an edge computing unit, a PI+repetitive control algorithm and a communication interface; The dual DSP redundant control system is a master-slave DSP architecture, where the master DSP is responsible for the main control tasks during daily operation, including grid connection control, power regulation and algorithm execution, and the slave DSP serves as a backup system, automatically taking over the control tasks when the master DSP fails; The edge computing unit performs local calculations through a high-speed processor, is responsible for analyzing the collected data, and executes the control algorithm; The PI+repetitive control algorithm is used for grid-connected current control and grid-connected optimization. By combining PI control and repetitive control, the current output is optimized, the steady-state error during grid connection is reduced, and the dynamic performance of the system is improved; The communication interface is used to exchange data and instructions with the data acquisition module and the filter control module, and the main interface adopts the CAN bus.

[0020] In one embodiment of the present invention, the dual DSP redundant control system adopts a fault detection and switching mechanism, that is, the operating status of the main DSP is monitored in real time by the slave DSP. When the slave DSP detects that the main DSP is abnormal, the switching mechanism will be immediately started, and the slave DSP will automatically take over the control task to ensure that the system control is not interrupted; the detection criteria include: long-term no response, overload or overheating and calculation errors, and the switching process is implemented through hardware interrupts or dedicated communication interfaces; when the main DSP fault is repaired, the system will be restored to the initial state, and the recovery steps include: Step S201, the slave DSP monitors the status of the master DSP to confirm that it is operating normally and all modules have been restored; Step S202, the slave DSP synchronizes data with the master DSP to ensure that tasks, real-time data and control instructions are not lost after the master DSP takes over; Step S203, the slave DSP verifies each module of the master DSP to ensure that all sensor data and computing modules are in normal status; Step S204, the slave DSP gradually returns the tasks and control signals to the master DSP to avoid system instability caused by sudden load switching; Step S205: the main DSP takes over all control tasks, and the system re-enters the normal working mode to continue to perform the control and optimization tasks of the photovoltaic system.

[0021] By adopting the above steps, the dual DSP redundant control system can quickly recover to normal operation after the main DSP fails, ensuring the stability and reliability of the system.

[0022] In one embodiment of the present invention, the steps of using the PI+repetitive control algorithm include: Step S301, initialize the PI control parameters and calculate the first control signal output by the PI controller, wherein the PI control parameters include a proportional coefficient and an integral coefficient, which determine the response speed and steady-state error of the system; the calculation formula of the first control signal is: ; ; in represents the first control signal output by the PI controller, represents the proportionality coefficient, represents the integral coefficient, Represents the system error, that is, the difference between the actual output current of the inverter and the target reference current. and Respectively represent the actual output current and target reference current of the inverter, represents the time integral of the PI controller error; Step S302, calculating the second control signal output by the repetitive control part, the calculation formula of the second control signal is: ; in represents the second control signal output by the repetitive control part, T represents the period of the grid current, Represents the sum of errors within a grid current cycle; Step S303, combining the first control signal output by the PI controller with the second control signal output by the repetitive control part to obtain a final control signal; the calculation formula involved is: ; Step S304: adjusting the output current of the inverter in real time through the final control signal to ensure synchronization with the grid-connected current of the power grid and reduce harmonics and current fluctuations.

[0023] In one embodiment of the present invention, the grid voltage regulation relies on reactive power compensation provided by the inverter. The grid-connected optimization module determines whether it is necessary to output reactive power through the inverter to stabilize the voltage by monitoring the fluctuation of the grid voltage. When the grid voltage is lower than or higher than a preset threshold, the grid-connected optimization module starts reactive power regulation. The calculation formula of the regulation is: ,in Indicates the regulation value of reactive power, represents the gain parameter of reactive power regulation, Indicates the nominal voltage, such as 220V, Indicates the actual measured grid voltage; The regulation of grid frequency relies on adjusting the output of active power to stabilize the frequency. The calculation formula for this regulation is: ,in Indicates the adjustment value of active power, represents the gain parameter of active power regulation, represents the reference frequency. Preferably, the reference frequency is set to 50 Hz. Indicates the actual measured grid frequency; The active power is adjusted according to the load change of the power grid. The calculation formula for this adjustment is: ,in Indicates the target active power, which is set according to the grid load demand. Indicates the actual output power of the current photovoltaic power generation system.

[0024] In one embodiment of the present invention, a LCLR filter is used to filter out high-order harmonics in the inverter output current, and the filtering effect is adjusted in real time according to the inverter operation state; wherein the LCLR filter adds a controllable resistor to the traditional LCL filter structure, so that the filter has the ability of dynamic adjustment, and the working process of the filter includes: Step S401, Fourier transform the output current through the edge computing device to analyze the harmonic components in the output current. Through harmonic analysis, the system can clearly identify the main high-order harmonics in the current and determine the frequency band that the filter needs to focus on suppressing; Step S402, according to the result of harmonic analysis, the system dynamically adjusts the damping value of the controllable resistor of the LCLR filter; for example, when the harmonic content is high, the damping value of the controllable resistor is increased to enhance the filtering effect of the filter and attenuate high-frequency harmonics to the maximum extent; conversely, when the current waveform is relatively smooth and the harmonic content is low, the system can reduce the damping value of the controllable resistor to reduce the impact of the filter on the current and reduce power loss; Step S403, continuously optimizing the filter parameters by real-time monitoring of harmonic suppression effect and grid status.

[0025] Compared with the traditional LCL filter, the LCLR filter achieves dynamic filtering capability by adding controllable resistors, can optimize the filtering effect according to real-time conditions, and improve the flexibility and adaptability of the system; the filtering control module realizes real-time harmonic analysis and feedback through edge computing devices, continuously optimizes the working state of the filter, and ensures that the current quality always remains at the optimal level.

[0026] In one embodiment of the present invention, the functions of the monitoring and operation and maintenance module include: real-time monitoring, fault warning and remote control; the module collects power data of the photovoltaic power generation system to monitor the operating status of the entire system in real time, including the inverter, battery pack and filter. When the equipment status or parameters in the system exceed the preset safety threshold, the module will immediately issue a fault warning and notify the operation and maintenance personnel to check and take emergency measures; the notification method is mobile phone text messages and emails; the module supports remote execution of scheduling instructions and control operations by integrating with the cloud platform and scheduling system, so that the operation and maintenance personnel can remotely manage the photovoltaic power station.

[0027] Through the monitoring and operation and maintenance module, the operating status of the photovoltaic power generation system can be monitored in real time and intelligently managed to improve the reliability of the system, reduce maintenance costs, and optimize the overall operation of the photovoltaic power station.

[0028] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A photovoltaic power generation grid-connected performance optimization system based on edge computing, characterized in that: include: A data acquisition module, which is used to collect power data from the photovoltaic power generation system, wherein the power data includes: inverter voltage, inverter current, grid connection point voltage, grid connection point current and grid frequency; An edge control module, which is used to process the power data locally in real time through edge computing, execute a control algorithm and send instructions to the inverter; A grid-connected optimization module, which is used to adjust the grid voltage, frequency and active power according to the power data; A filter control module, which is used to filter out high-order harmonics in the inverter output current through a filter; The monitoring and operation module is used for real-time monitoring and intelligent management of the photovoltaic power generation system.

2. A photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 1, characterized in that: The edge control module comprises: a dual DSP redundant control system, an edge computing unit, a PI+repetitive control algorithm and a communication interface; The dual DSP redundant control system is a master-slave DSP architecture. The master DSP is responsible for the main control tasks during daily operation, including grid-connected control, power regulation and algorithm execution. The slave DSP serves as a backup system and automatically takes over the control tasks when the master DSP fails. The edge computing unit performs local calculations through a high-speed processor, is responsible for analyzing the collected data, and executing the control algorithm. The PI+repetitive control algorithm combines PI control and repetitive control for grid-connected current control and grid-connected optimization. The communication interface is used to exchange data and instructions with the data acquisition module and the filtering control module.

3. A photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 2, characterized in that: The dual DSP redundant control system adopts a fault detection and switching mechanism, that is, the operating status of the main DSP is monitored in real time by the slave DSP. When the slave DSP detects that the main DSP is abnormal, the switching mechanism will be immediately activated, and the slave DSP will automatically take over the control task to ensure that the system control is not interrupted; The detection criteria include: long-term unresponsiveness, overload or overheating, and calculation errors. The switching process is implemented through hardware interrupts or dedicated communication interfaces. When the main DSP fault is repaired, the system will be restored to the initial state. The recovery steps include: Step S201, the slave DSP monitors the status of the master DSP to confirm that it is operating normally and all modules have been restored; Step S202, the slave DSP performs data synchronization with the master DSP; Step S203, the slave DSP verifies each module of the master DSP; Step S204, the slave DSP returns the task and control signal to the master DSP; Step S205: the main DSP takes over all control tasks, and the system re-enters the normal working mode to continue to perform the control and optimization tasks of the photovoltaic system.

4. A photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 2, characterized in that: The steps of using the PI+repetitive control algorithm include: Step S301, initializing the PI control parameters and calculating the first control signal output by the PI controller, wherein the PI control parameters include a proportional coefficient and an integral coefficient, and the calculation formula of the first control signal includes: ； ； in represents the first control signal output by the PI controller, represents the proportionality coefficient, represents the integral coefficient, Represents the system error, that is, the difference between the actual output current of the inverter and the target reference current. and Respectively represent the actual output current and target reference current of the inverter, represents the time integral of the PI controller error; Step S302, calculating the second control signal output by the repetitive control part, the calculation formula of the second control signal is: ； in represents the second control signal output by the repetitive control part, T represents the period of the grid current, Represents the sum of errors within a grid current cycle; Step S303, combining the first control signal output by the PI controller with the second control signal output by the repetitive control part to obtain a final control signal; the calculation formula involved is: ; Step S304: adjusting the output current of the inverter in real time through the final control signal.

5. The photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 1 is characterized in that: The grid voltage regulation relies on the reactive power compensation provided by the inverter. The grid-connected optimization module determines whether it is necessary to output reactive power through the inverter to stabilize the voltage by monitoring the fluctuation of the grid voltage. When the grid voltage is lower than or higher than the preset threshold, the grid-connected optimization module will start reactive power regulation. The calculation formula of the regulation is: ,in Indicates the regulation value of reactive power, represents the gain parameter of reactive power regulation, Indicates the nominal voltage, Indicates the actual measured grid voltage; The regulation of grid frequency relies on adjusting the output of active power to stabilize the frequency. The calculation formula for this regulation is: ,in Indicates the adjustment value of active power, represents the gain parameter of active power regulation, represents the reference frequency, Indicates the actual measured grid frequency; The active power is adjusted according to the load change of the power grid. The calculation formula for this adjustment is: ,in Indicates the target active power, which is set according to the grid load demand. Indicates the actual output power of the current photovoltaic power generation system.

6. The photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 1, characterized in that: The LCLR filter is used to filter out the high-order harmonics in the inverter output current, and the filtering effect is adjusted in real time according to the inverter operating status. The LCLR filter adds a controllable resistor to the traditional LCL filter structure to enable the filter to have dynamic adjustment capabilities. The working process of the filter includes: Step S401, performing Fourier transform on the output current through an edge computing device to analyze the harmonic components in the output current; Step S402, according to the result of harmonic analysis, the system dynamically adjusts the damping value of the controllable resistor of the LCLR filter; Step S403, continuously optimizing the filter parameters by real-time monitoring of harmonic suppression effect and grid status.

7. The photovoltaic power generation grid-connected performance optimization system based on edge computing according to claim 1, characterized in that: The functions of the monitoring and operation and maintenance module include: real-time monitoring, fault warning and remote control; the module collects power data from the photovoltaic power generation system to monitor the operating status of the entire system in real time, including the inverter, battery pack and filter. When the equipment status or parameters in the system exceed the preset safety threshold, the module will immediately issue a fault warning and notify the operation and maintenance personnel to conduct inspections and take emergency measures; the module supports remote execution of scheduling instructions and control operations by integrating with the cloud platform and scheduling system.

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