A distributed photovoltaic power station centralized collection and regulation method and device

Abstract

The application discloses a kind of distributed photovoltaic power station centralized collection and regulation method and device, method is applied to distributed photovoltaic power station centralized collection and regulation device, the device is connected multiple inverters by serial interface, also connect cloud platform by uplink communication protocol, device is with specified period polling inverter and collects the monitoring data uploaded by inverter, analysis monitoring data type and send monitoring data to cloud platform after processing;If device obtains the control instruction of cloud platform, judge instruction type, for remote control instruction then obtain monitoring data and generate corresponding remote control strategy, for remote control instruction then call corresponding preset remote control strategy, the data of remote control strategy and / or remote control strategy is converted into corresponding instruction frame and sent to corresponding inverter.The application efficiently realizes the intelligent centralized management and control to distributed photovoltaic power station.

Description

TECHNICAL FIELD

[0001] The present application relates to distributed photovoltaic power station maintenance technology, in particular to a kind of distributed photovoltaic power station centralized collection and regulation method and device. BACKGROUND

[0002] At present, in order to accelerate the construction of new power system, optimize power resource allocation, help achieve the goal of carbon emission reduction of power industry, distributed photovoltaic power station construction needs to fully meet the requirements of "observable, measurable, adjustable, controllable".

[0003] At present, photovoltaic power station control and collection device still has some problems:

[0004] 1) Some devices have data collection and regulation function, and the regulation function involves the adjustment and control strategy of the active power configuration of the distributed photovoltaic power station inverter, and the photovoltaic power station needs to be able to independently control the access, shutdown or step power regulation of the distributed photovoltaic power station connected to the power grid, and arrange the external power connection ratio from the power grid side, and the current regulation only adjusts the active power, without considering the influence of photovoltaic power station on other parameters, and the active power regulation method sets a reference inverter, if the value of the reference inverter changes, it will affect the active power regulation of the whole system.

[0005] 2) The data communication channel of the existing photovoltaic power station inverter only supports single channel, and the main technology is to realize external multiple private cloud storage data, which will occupy a lot of cloud resources, the implementation cost is high and the equipment is complex, and at present, the construction party, cloud management and control platform and other parties require the collection and control of power station data from the collection device, and the protocol of multiple platforms needs to be developed, and the technical realization is complex. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a kind of distributed photovoltaic power station centralized collection and regulation method and device to solve the above problems in the prior art, to ensure the safety and stability of large power grid as a prerequisite, to make the power grid end control the power quality of grid-connected, accurately regulate the state of photovoltaic power station inverter, realize the smooth and controllable regulation process, and realize the observability, measurability, adjustability and controllability of photovoltaic power station operation state by photovoltaic power station operation and maintenance cloud platform.

[0007] In order to solve the above technical problems, the technical scheme adopted by the present application is:

[0008] A distributed photovoltaic power station centralized collection and regulation method applied to a distributed photovoltaic power station centralized collection and regulation device, the distributed photovoltaic power station centralized collection and regulation device connects multiple inverters through an interface supporting hot plug and automatic identification, can be compatible with inverters of different models and types, and the distributed photovoltaic power station centralized collection and regulation device also connects a cloud platform through an uplink communication protocol, and the method comprises the following steps:

[0009] According to the operating state and power fluctuation of the inverter, the collection cycle is adaptively adjusted, the remote signaling data and the remote measurement data uploaded by the inverter are collected, the collected data is analyzed, verified and processed in real time, and the corresponding remote control strategy is generated or the preset remote control strategy is called according to the cloud platform control instruction;

[0010] The remote control strategy and / or remote control strategy data are sent to the corresponding inverter through an instruction frame with priority scheduling and integrity verification, so as to realize reliable regulation and control of the inverter;

[0011] When the corresponding remote control strategy is generated, the step of generating a limited power distribution strategy is included, which specifically comprises:

[0012] Firstly, the current active power and the minimum operating power of each inverter are obtained based on the monitoring data, and the maximum reducible power of each inverter is calculated therefrom, the maximum reducible power being equal to the difference between the current active power and the minimum operating power;

[0013] Secondly, based on the efficiency optimal regulation principle, the reciprocal of the efficiency of each inverter is taken as a distribution weight to construct a parameter, the weight of all inverters is normalized, so that the inverter with lower efficiency will preferentially undertake the reduction task in the subsequent power reduction process. The normalized weight of each inverter is determined according to the efficiency optimal principle, and the calculation formula is as follows:

[0014]

[0015] Among them, and are the efficiencies of the i th inverter and the j th inverter respectively;

[0016] According to the normalized efficiency weight, the total reduction power is distributed to each inverter to obtain the initial reduction power distribution value of each inverter, and the calculation formula is as follows:

[0017]

[0018] Among them, is the total reduction power;

[0019] If the initial reduction power distribution values of all inverters do not exceed their maximum reducible powers, the initial distribution values are directly taken as the final reduction powers of the inverters; if there is an out-of-limit distribution value, the distribution value of the out-of-limit inverter is corrected, and the remaining reducible capacity is redistributed to ensure that all reduction values are within the allowable range of the inverters.

[0020] The method can improve system data acquisition accuracy, reduce communication delay, and enhance the real-time control capability of the cloud platform on the photovoltaic power station.

[0021] Further, the method further comprises a data forwarding step, specifically comprising:

[0022] An extensible register mapping table is established, and the register start address and return data length are dynamically adjusted according to different inverter models and data structures;

[0023] The collected remote signaling, remote measurement, remote control and remote adjustment data are locally classified and stored according to data types and security levels, and multi-channel upload paths are dynamically allocated; the remote signaling and remote measurement data are uploaded to the third-party platform through the interface, while the remote control and remote adjustment permissions are uploaded to the power dispatching master station only through the 5G network, realizing centralized and unified control; the method improves the data security, compatibility and expandability of the system, and supports stable and reliable data forwarding in a multi-platform and multi-protocol environment.

[0024] Further, if there is an out-of-limit distribution value, the distribution value of the out-of-limit inverter is corrected, and the remaining reducible capacity is redistributed, and the specific steps include:

[0025] If there is an inverter whose distribution value exceeds its maximum reducible power, the maximum reducible power of the inverter is taken as the final distribution value, and the difference between the original distribution value and the final distribution value of the inverter is calculated;

[0026] The normalized weights of the remaining inverters are updated, and according to the updated weights, the difference is distributed to each remaining inverter to obtain a new distribution value of the remaining inverter, and the calculation formula is as follows:

[0027]

[0028] wherein, represents the distribution value of the i-th inverter in the remaining inverters at the k-th iteration, represents the updated weight of the i-th inverter in the remaining inverters, represents the difference value corresponding to the inverter whose distribution value exceeds its maximum reducible power at the k-th iteration;

[0029] The step of checking whether the remaining inverters exceed their maximum reducible power is performed again until all the inverters do not exceed their maximum reducible power.

[0030] Further, when the corresponding remote adjustment strategy is generated, the step of generating a reactive power adjustment strategy is included, specifically comprising:

[0031] According to the monitoring data, it is judged whether the voltage of each inverter exceeds the safety range, if the voltage of the inverter exceeds the safety range, the deviation value between the actual voltage and the set safety range is calculated, and the compensation reactive power value is generated through the PI controller, the compensation reactive power value is added to the current reactive power of the corresponding inverter to obtain the reactive adjustment value of the corresponding inverter;

[0032] If the voltage of the inverter is within the safety range, and the reactive power scheduling instruction issued by the cloud platform is obtained, the reactive power target value in the reactive power scheduling instruction is taken as the reactive adjustment value of the corresponding inverter;

[0033] If the power factor instruction issued by the cloud platform is obtained, the reactive power target value is calculated according to the power factor instruction value in the power factor instruction and taken as the reactive adjustment value of the inverter.

[0034] Further, the calculation formula of the reactive power target value is as follows:

[0035]

[0036] When the power factor is less than 0, sign=-1, when the power factor is greater than or equal to 0, sign=1.

[0037] Further, after collecting the remote signaling data and the remote measurement data uploaded by the inverter, the method further comprises:

[0038] According to the content of the remote signaling data, a state report is generated, and the remote measurement data is packaged into a remote measurement data packet;

[0039] The state report and / or the remote measurement data packet are respectively encrypted and compressed, and the encrypted and compressed data is sent to the cloud platform through a main channel, if the main channel network is abnormal, the encrypted and compressed data is sent to the cloud platform through a backup channel.

[0040] The application also provides a distributed photovoltaic power station centralized collection and regulation device, comprising a processor and a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the distributed photovoltaic power station centralized collection and regulation method.

[0041] The application further provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and the computer program realizes the steps of the distributed photovoltaic power station centralized collection and regulation method when executed by a processor.

[0042] The application further provides a computer program product, comprising a computer program, which realizes the steps of the distributed photovoltaic power station centralized collection and regulation method when executed by a processor.

[0043] Compared with the prior art, the application has the following advantages:

[0044] The application designs a register mapping table, and the collected remote signaling, remote measurement, remote control and remote adjustment data are locally classified and stored according to the register mapping table, so that when multiple types of information interaction demands such as remote signaling, remote measurement, remote control and remote adjustment exist at the same time, multi-channel data forwarding is realized, and the data integrity and transmission real-time performance are improved.

[0045] When the control instruction of the cloud platform is a remote adjustment control instruction, the application acquires monitoring data and generates a corresponding remote adjustment strategy, if the remote adjustment control instruction is a limited power distribution instruction, the remote adjustment strategy adopts a limited power percentage parameter adjustment strategy based on efficiency weight iteration correction, if the remote adjustment control instruction is a reactive power adjustment instruction, the remote adjustment strategy adopts a power factor and reactive power adjustment strategy based on priority logic, multi-parameter adjustment is realized, and flexible distribution of the strategy can be realized according to the scheduling instruction issued by the cloud platform. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The figure is a system structure diagram of the embodiment of the application.

[0047] Figure 2 The figure is a method flowchart of the embodiment of the application.

[0048] Figure 3 The figure is a device hardware topology diagram of the embodiment of the application. DETAILED DESCRIPTION

[0049] The application will be further described below in combination with the accompanying drawings and specific preferred embodiments, but the protection scope of the application is not limited by this.

[0050] Embodiment one

[0051] The embodiment provides a distributed photovoltaic power station centralized collection and regulation method, which can realize multi-parameter adjustment and multi-channel forwarding to improve the control ability of the photovoltaic power station, and the method is applied to a distributed photovoltaic power station centralized collection and regulation device, such as Figure 1As shown, the distributed photovoltaic power station centralized collection and regulation device supports hot plug and automatic identification interface connection of multiple inverters, can be compatible with inverters of different models and types, and is further connected to a cloud platform through an uplink communication protocol.

[0052] As shown in the formula (I), the method of the embodiment comprises the following steps: Figure 2

[0053] S101) Centralized collection process: the distributed photovoltaic power station centralized collection and regulation device adaptively adjusts the collection period according to the operating state and power fluctuation of the inverter, and collects the remote signaling data and remote measurement data uploaded by the inverter.

[0054] In the embodiment, the monitoring data comprises remote signaling data and remote measurement data, wherein the remote signaling data refers to the remote transmission of inverter state signals such as inverter switch state, fault code, event sequence, etc., and the remote measurement data refers to the remote transmission of continuous analog quantity data of the inverter.

[0055] As shown in the formula (I), after collecting the remote signaling data and remote measurement data uploaded by the inverter, the following steps are further included: Figure 2

[0056] According to the content of the remote signaling data, a state report is generated, and the remote measurement data is packaged into a remote measurement data packet.

[0057] The state report and / or remote measurement data packet are respectively encrypted and compressed, and the encrypted and compressed data is sent to the cloud platform through a main channel, which can adopt a data transmission channel with a higher transmission rate, if the main channel network is abnormal, the encrypted and compressed data is sent to the cloud platform through a backup channel, which can adopt a data transmission channel with a transmission rate not as good as the main channel but better network stability.

[0058] S102) Centralized control process: the distributed photovoltaic power station centralized collection and regulation device performs real-time analysis, verification and processing on the collected data, and generates corresponding remote control strategies or calls preset remote control strategies according to the cloud platform control instructions, if the cloud platform control instruction is a remote control instruction, a corresponding remote control strategy is generated, if the cloud platform control instruction is a remote control instruction, a corresponding preset remote control strategy is called, in the embodiment, the remote control is the remote operation adjustment of the inverter working parameter or the set value; the remote control is the remote control equipment operation and the execution action, which can realize the equipment restart, the start of the inverter, the stop of the inverter, the limit power enablement and the limit power shutdown five functions. The realization of the five functions is through writing the inverter control corresponding to the electric meter information in the collection device, when the cloud platform issues the remote control instruction, the corresponding control function is called to realize the corresponding control of the inverter.

[0059] ​​The distributed photovoltaic power station centralized collection and regulation device sends the data of the remote control strategy and / or the remote control strategy to the corresponding inverter through an instruction frame with priority scheduling and integrity checking, to realize reliable regulation and control of the inverter.

[0060] Through the above steps, in the centralized collection process, the data collection is started periodically, and the monitoring data is uploaded to the cloud platform at high speed and stability through the primary channel. If the primary channel has network abnormities, the backup channel is switched to, to ensure the continuity and reliability of data transmission. In the centralized control process, different strategies are generated according to the control instructions of the cloud platform, and are converted into instruction frames recognizable by the inverter, to realize remote regulation and control. The execution results of the inverter (such as the monitoring data after regulation and control) can be collected by the distributed photovoltaic power station centralized collection and regulation device in the next step S101 (centralized collection process) and fed back to the cloud platform, to form a safe closed loop. Thus, the intelligent centralized management and control of the distributed photovoltaic power station are realized efficiently. The system data collection accuracy is improved, the communication delay is reduced, and the real-time control capability of the cloud platform on the photovoltaic power station is enhanced.

[0061] In addition, the method of the embodiment further includes a data forwarding step, specifically including:

[0062] S201) An extensible register mapping table is established, and the register start address and return data length are dynamically adjusted according to different inverter models and data structures. In the embodiment, the registers are divided into four categories, respectively corresponding to switch quantity, analog quantity, set value and control quantity, to realize hierarchical management of data sources. Through the register mapping table, the register address corresponding to the type (switch quantity, analog quantity, set value and control quantity) of the data to be forwarded can be obtained;

[0063] S202) The collected remote signaling, remote measurement, remote control and remote regulation data are locally classified and stored according to the data type and security level, and a multi-channel upload path is dynamically allocated. The remote signaling and remote measurement data are uploaded to the third party platform (construction party) through the RS485 / RJ45 interface, while the remote control and remote regulation permission data are transmitted to the cloud power dispatching master station (cloud platform) through the 5G network, to realize centralized and unified control and dual permission isolation of the protocol layer and the physical layer.

[0064] Due to the introduction of classified storage + dual-channel forwarding strategy in data interaction, compared with the existing mode, the above data forwarding step not only improves the efficiency of register calling and data access, but also reduces the pressure of the communication link while ensuring the integrity of data upload, improves the real-time performance and security, and avoids congestion or security risks caused by single-channel transmission. The data security, compatibility and expandability of the system are improved, and stable and reliable data forwarding in a multi-platform and multi-protocol environment is supported.

[0065] In this embodiment, the remote control instruction includes control instructions corresponding to three parameters of limited power percentage, power factor and reactive power ratio. When the monitoring data in the register is obtained and the corresponding remote adjustment strategy is generated, the corresponding adjustment strategy is generated for the three parameters.

[0066] For the limited power percentage, the existing limited power distribution method mostly adopts fixed proportion distribution or reduction method based on capacity proportion, which fails to fully consider the efficiency difference of different inverters in actual operation, easily causing overall efficiency to decrease, and even leading to long-term low-efficiency operation of some inverters.

[0067] To solve this problem, the embodiment proposes an efficiency weight iterative correction based limited power percentage parameter adjustment strategy, the core idea of which is to preferentially adopt the most efficient distribution scheme, and when the efficiencies of all inverters are the same, the power is distributed according to the maximum capacity proportion. Correspondingly, when the corresponding remote adjustment strategy is generated, the step of generating the limited power distribution strategy specifically includes:

[0068] S300) Add all inverters to the distribution register table;

[0069] S301) Obtain the current active power and minimum operating power of each inverter based on the monitoring data, and calculate the maximum reducible power of each inverter, the efficiency of each inverter , the current active power and the minimum operating power , , the current active power is calculated as:

[0070]

[0071] S302) Based on the efficiency optimization control principle, construct the parameter by taking the reciprocal of the efficiency of each inverter as the distribution weight, and normalize the weight of all inverters, so that the inverter with lower efficiency will preferentially undertake the reduction task in the subsequent power reduction process, and the normalized weight of each inverter is determined according to the efficiency optimization principle, and the calculation formula is as follows:

[0072]

[0073] S303) According to the normalized efficiency weight, the total reduction power is distributed to each inverter to obtain the initial reduction power distribution value of each inverter, and the calculation formula is as follows:

[0074]

[0075] wherein, The system total power reduction needed for conversion of the power limit percentage instruction issued by the cloud platform.

[0076] S304)Compare the initial reduction power allocation value of each inverter with its maximum reducible power, if the initial allocation value does not exceed the corresponding maximum reducible power, the initial allocation value is directly used as the final reduction power of each inverter;

[0077]

[0078]

[0079] Since the initial allocation may cause the allocation of some inverters to exceed its maximum reducible power, further, if the allocation value is out of limit, the allocation value of the out-of-limit inverter is corrected, and the specific steps of re-allocation based on the remaining reducible capacity include:

[0080] S305)If there is an inverter with allocation value exceeding the limit ( ), the maximum reducible power of the inverter is used as the final allocation value, the difference between the original allocation value and the final allocation value of the inverter is calculated, and the register is removed from the allocation register table; Specifically, for the out-of-limit inverter, the maximum reduction power is fixed as:

[0081]

[0082] The remaining unallocated power calculation formula is:

[0083] Wherein, represents the allocation value of the i-th inverter in the remaining inverters at the k-th iteration, represents the updated weight of the i-th inverter in the remaining inverters, represents the difference value corresponding to the inverter whose allocation value exceeds its maximum reducible power at the k-th iteration;

[0084] S306)According to the weight, the difference value is allocated to each remaining inverter in the allocation register table that does not exceed the limit, to obtain the new allocation value of the remaining inverters, and the step of checking whether the allocation value exceeds the limit is executed again in step S304 for the remaining inverters until all inverters do not exceed the limit. Specifically, remove the out-of-limit inverter from the allocation table, and re-normalize the weight of the remaining inverters as:

[0085]

[0086] The remaining power is re-allocated:

[0087]

[0088] Through the above strategy, the efficiency optimal power reduction of each inverter under the condition of meeting the operation constraint can be realized, and the economy and stability of the overall operation of the system are ensured.

[0089] For power factor and reactive power ratio, the existing reactive power regulation mode of photovoltaic power station usually only adopts a single mode, such as voltage priority control or reactive power fixed instruction control, which lacks dynamic switching capability in different operation scenarios, and is easy to cause problems such as large grid voltage deviation, reactive power response lag or power factor unable to meet the scheduling requirements.

[0090] In order to solve this problem, the embodiment proposes a power factor and reactive power regulation strategy based on priority logic. All kinds of monitoring data (including active power, reactive power, power factor, voltage and frequency) are uniformly processed, and the power factor and the total value of active and reactive power of the photovoltaic power station are obtained after preprocessing, uploaded to the cloud platform for display, and the total value of reactive power and voltage are input to the data buffer area for calling by the preset control mode. Then, according to the preset control mode priority, the corresponding regulation mode is dynamically selected (in the control cycle, the system sequentially performs logical judgment) and executed. In the embodiment, the regulation mode priority is in turn:

[0091] 1) Voltage control mode: when the voltage is monitored to be out of the safe range, the deviation value between the actual voltage and the set safe range is calculated, and the compensation reactive power value is generated through the PI controller. The compensation value is added to the current reactive power to obtain the final reactive power regulation value, so as to realize the rapid correction of the voltage.

[0092] 2) Reactive power control mode: when the voltage is in the normal range, the target value is written into the inverter reactive power regulation node table according to the reactive power scheduling instruction issued by the cloud platform, and is directly executed to realize the accurate control of the reactive power.

[0093] 3) Power factor control mode: when the cloud platform issues the power factor instruction, the reactive power target value is calculated according to the following formula:

[0094]

[0095] When the power factor is less than 0, sign=-1, and when the power factor is greater than or equal to 0, sign=1.

[0096] Based on the above content, when the monitoring data is obtained and the corresponding remote regulation strategy is generated, the step of generating the reactive power regulation strategy is included, which specifically includes:

[0097] S401) judging whether the voltage of each inverter exceeds the safety range according to the voltage value in the monitoring data, if the voltage of the inverter exceeds the safety range, calculating the deviation value between the actual voltage and the set safety range, and generating a compensation reactive power value through a PI controller, adding the compensation reactive power value to the current reactive power of the corresponding inverter to obtain the reactive power adjustment value of the corresponding inverter;

[0098] S402) if the voltage of the inverter is within the safety range and the reactive power scheduling instruction issued by the cloud platform is obtained, taking the reactive power target value in the reactive power scheduling instruction as the reactive power adjustment value of the corresponding inverter;

[0099] S403) if the power factor instruction issued by the cloud platform is obtained, calculating the reactive power target value according to the power factor instruction value in the power factor instruction and taking the reactive power target value as the reactive power adjustment value of the inverter.

[0100] Compared with the existing single control mode, the above strategy can dynamically switch modes according to the operating environment and external instructions, which not only ensures the safe operation of the power grid, but also improves the support ability of the photovoltaic power station to the power grid. Through the combination of the priority fusion control logic and the specific power factor conversion formula, the conflict between voltage regulation and power factor control is solved.

[0101] Embodiment Two

[0102] The embodiment provides a distributed photovoltaic power station centralized collection and regulation device, which comprises a processor and a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the distributed photovoltaic power station centralized collection and regulation method in the embodiment one.

[0103] As Figure 3As shown, the device hardware level of the embodiment mainly consists of a T113 core board CPU, a 5G full-network module, an adaptive network port, a WiFi module, an RS485 serial port, a watchdog circuit, a reset module, and a power input, etc. The T113 board drives the RS485 serial port to connect the field inverters and the smart meters through the UART interface; meanwhile, it manages the connections with the 5G module, the adaptive network port, and the WiFi module respectively to form redundant communication channels; the watchdog and the reset circuit directly monitor the CPU running state, and the power module supplies power to all components. In the centralized collection process, the device periodically starts data collection. The T113 board CPU first polls each inverter according to the Modbus and other industrial protocols through the RS485 serial port to collect key data such as power generation power and voltage. After the data is parsed and packaged by the CPU, it is preferentially uploaded to the cloud platform at high speed and stability through the 5G module as the main link; if the 5G signal is not good, it is automatically switched to the adaptive network port or the WiFi module as a backup channel for transmission, ensuring the continuity and reliability of the data. The whole process is monitored by the watchdog circuit to prevent software deadlock. In the centralized control process, the device receives cloud instructions. The control command is sent to the T113 CPU through any of the 5G, network port, or WiFi links, which is parsed and verified, and then converted into an instruction frame recognizable by the field device through the RS485 serial port, and accurately issued to the specified inverter to realize remote control of the device start-stop, power regulation, etc. The execution result is then collected through the RS485 and fed back to the cloud through the communication module, forming a safe closed loop.

[0104] In the embodiment, the T113 core board CPU includes a main control module carrying a dual-core Cortex-A7@1.2GHz processor, adopts a low-power ARM architecture, supports a Linux operating system, can process Modbus RTU and MQTT protocols in parallel, realizes efficient protocol conversion and data interaction. The main control module runs a customized Linux version 4.9.65-rt23 kernel, integrates PREEMPT_RT real-time patches based on Linux 4.9.65, optimizes system functions and disk occupancy, significantly improves the data transmission rate of the photovoltaic power station and reduces communication delay, thereby enhancing the overall operation efficiency.

[0105] The embodiment is based on the centralized control requirements of photovoltaic power stations, and the driver layer of various peripheral chips such as 5G modules and RS485 serial ports is developed to prevent hardware damage caused by application layer misoperation and to have stronger stability; the driver layer directly controls the hardware through interrupt response, memory mapping, etc., with lower response delay.

[0106] In addition, the embodiment also adds an LORA module to increase the wireless network coverage area, reduce data packet loss, and make information transmission more stable.

[0107] In addition, the embodiment further adds a 10M / 10M adaptive network port connected with the inverter through a network cable, the system can automatically identify the inverter parameters and generate a visual Web configuration interface, realize the intelligent conversion of protocol configuration; at the same time, a high-performance WiFi module is built-in, supporting wireless access of mobile terminals, so that operation and maintenance personnel can quickly complete equipment configuration and state monitoring through mobile phones or tablet computers.

[0108] In addition, the embodiment further generates a fixed frequency pulse signal through a special timing chip, and the CPU performs timing inspection, if the program crashes and causes interruption, the hardware circuit immediately triggers CPU reset, through hardware and software double protection, the system reliability is enhanced.

[0109] The device software level of the embodiment is divided into system layer, driver layer and application layer, and the core regulation and control is deployed in the application layer. The CPU of the core board serves as the "brain" of the application, and calls other function modules by burning software code therein. The device is based on a high-performance processor, and realizes an observable, measurable, adjustable and controllable instruction set through the joint action of software and hardware, establishes a classified register table for double-channel data forwarding to support multi-platform data monitoring. On the communication level, the device mainly realizes data aggregation, protocol conversion and cloud data forwarding through the 5G network to ensure real-time transmission under wide-area coverage; through the hardware encryption chip, the cloud platform is encrypted for transmission to ensure data security, and at the same time, to cope with network instability or remote deployment requirements, the system also supports multi-mode redundant communication, and is built-in with data caching and breakpoint resume mechanism, further improving the communication reliability.

[0110] The system layer and the driver layer of the device software level are mainly responsible for the original system construction and the hardware driver setting, and the application layer includes the main remote signaling, remote measurement, remote adjustment and remote control software configuration. The remote adjustment and remote control functions of the software level are based on the control instructions issued under the MQTT protocol, and the remote adjustment and remote control functions are realized through instruction configuration.

[0111] The device of the embodiment supports double-channel data forwarding: 1) data can be uploaded to the State Grid platform through the 5G APN encrypted network; 2) serial port mapping forwarding.

[0112] The existing data acquisition and forwarding mode generally uses fixed register addresses and single forwarding paths, which is difficult to simultaneously consider the real-time and security of multiple types of information such as remote signaling, remote measurement, remote control and remote adjustment, and is easy to cause excessive communication pressure and insufficient interaction efficiency.

[0113] The device proposed in the embodiment adopts a dynamic register partitioning and mapping mechanism when implementing serial port mapping forwarding: the device automatically determines the register starting address according to an external sending command, sets the return data length in the initialization stage, and then establishes an extensible register mapping table. The registers are divided into four categories, respectively corresponding to switch quantity, analog quantity, set value and control quantity, to realize hierarchical management of data sources. In data interaction, the device introduces a classified storage + double-channel forwarding strategy: the collected telesignaling, telemetering, remote control and remote adjustment data are first locally classified and stored, and then only the telesignaling and telemetering data are uploaded to a third-party platform (construction party) through an RS485 / RJ45 interface, while the remote control and remote adjustment permissions are limited to be transmitted to a cloud power dispatching master station through a 5G network, to realize double permission isolation of the protocol layer and the physical layer. Compared with the existing method, the method not only improves the efficiency of register calling and data access, but also reduces the pressure of the communication link while ensuring the integrity of data upload, improves the real-time performance and security, and avoids congestion or security risks caused by single-channel transmission.

[0114] The process of centralized collection and regulation of the device of the embodiment is as follows:

[0115] When the telesignaling and telemetering collection work starts, the system first performs security authentication, establishes a communication channel for analog quantity and state quantity, and after initialization data collection, periodically polls the inverter data based on the Modbus RTU communication protocol through the RS485 interface. The collected inverter state and continuous analog quantity information are respectively encapsulated to generate telemetering data packets and state reports, and data is uploaded once every 10 minutes. The data is reported to the cloud platform through the MQTT protocol. Different device IDs are set in the software to collect different inverter data; the remote control and remote adjustment functions realize cloud instruction interaction based on the MQTT protocol. The specific process is as follows: first, establish a secure communication connection between the collection device and the cloud platform and subscribe to the control topic. After completing the security verification through the two-way authentication mechanism, the cloud platform issues the coded control instruction to the device. The device analyzes the instruction content and performs the corresponding operation: 1) when the power percentage, power factor and reactive power adjustment parameters issued by the cloud management and control platform are recognized, the device automatically generates a parameter adjustment frame that meets the Modbus-RTU specification, configures the parameters of the inverter through the RS485 interface, and realizes accurate remote adjustment; 2) when the device receives the device restart, inverter start, inverter stop, limit power enable and limit power off control instructions issued by the cloud management and control platform, it generates the corresponding Modbus control frame, and completes the inverter state control through the RS485 interface, to realize reliable remote control.

[0116] Embodiment three

[0117] The embodiment provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the distributed photovoltaic power station centralized collection and regulation method in the embodiment one.

[0118] The embodiment further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the steps of the distributed photovoltaic power station centralized collection and regulation method in the embodiment one.

[0119] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code. The present application is described with reference to flowcharts and / or block diagrams of the methods, apparatus (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including an instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including an instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including an instruction means, which implements the functions specified in the flowcharts and / or block diagrams.

[0120] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A method for centralized data acquisition and control of distributed photovoltaic power stations, characterized in that, A centralized data acquisition and control device for distributed photovoltaic power stations, wherein the device connects to multiple inverters via a hot-swappable and automatically identified interface, and is compatible with different models and types of inverters, the method comprising: Based on the inverter's operating status and power fluctuations, the acquisition cycle is adaptively adjusted to collect remote signaling data and telemetry data uploaded by the inverter; the collected data is analyzed, verified, and processed in real time, and corresponding remote adjustment strategies are generated or preset remote control strategies are invoked according to the cloud platform control commands; Remote control strategy and / or remote control strategy data are sent to the corresponding inverter through instruction frames with priority scheduling and integrity verification to achieve reliable control of the inverter; When generating the corresponding remote control strategy, the step of generating a limited power allocation strategy is included, specifically: The current active power and minimum operating power of each inverter are obtained based on monitoring data, and the maximum power that can be reduced for each inverter is calculated accordingly. The maximum power that can be reduced is equal to the difference between the current active power and the minimum operating power. Based on the principle of optimal efficiency control, the reciprocal of the efficiency of each inverter is used as the parameter to construct the allocation weights. The weights of all inverters are normalized so that inverters with lower efficiency are given priority in power reduction during subsequent power reduction processes. The normalized weights of each inverter are determined according to the principle of optimal efficiency, and the calculation formula is as follows: in, and These are the efficiencies of the i-th inverter and the j-th inverter, respectively. Based on the normalized efficiency weights, the total power reduction is allocated to each inverter to obtain the initial power reduction allocation value for each inverter. The calculation formula is as follows: in, Total power reduction; The initial power reduction allocation value of each inverter is compared with its maximum power reduction. If the initial allocation value does not exceed the corresponding maximum power reduction, the initial allocation value is directly used as the final power reduction of each inverter. If the allocation value exceeds the limit, the allocation value of the inverter that exceeds the limit is corrected and redistributed based on the remaining reduction capacity to ensure that all reduction values ​​are within the allowable range of each inverter.

2. The method for centralized data acquisition and control of distributed photovoltaic power stations according to claim 1, characterized in that, The method also includes a data forwarding step, specifically including: Establish an extensible register mapping table to dynamically adjust the register start address and return data length according to different inverter models and data structures; The collected remote signaling, telemetry, remote control, and remote adjustment data are classified and stored locally according to data type and security level, and multiple upload paths are dynamically allocated. Remote signaling and telemetry data are uploaded to a third-party platform through an interface, while remote control and remote adjustment permissions are uploaded to the power dispatching master station only through the 5G network to achieve centralized and unified control.

3. The method for centralized data acquisition and control of distributed photovoltaic power stations according to claim 1, characterized in that, If an allocation value exceeds the limit, the specific steps for correcting the allocation value of the out-of-limit inverter and reallocating it based on the remaining reduceable capacity include: If there is an inverter whose allocated value exceeds its maximum reducible power, the maximum reducible power of the inverter is used as the final allocated value, and the difference between the original allocated value and the final allocated value of the inverter is calculated. Update the normalized weights of the remaining inverters, and allocate the difference to each remaining inverter according to the updated weights to obtain the new allocation value for the remaining inverters. The calculation formula is as follows: in, This represents the allocation value of the i-th inverter in the remaining inverters during the k-th iteration. This represents the updated weight of the i-th inverter among the remaining inverters. This represents the difference between the values ​​assigned to all inverters whose maximum power reduction is exceeded at the k-th iteration. The remaining inverters are then checked again to see if their allocated power exceeds their maximum slashable power, until all inverters are no longer exceeding their maximum slashable power.

4. The method for centralized data acquisition and control of distributed photovoltaic power stations according to claim 1, characterized in that, When generating the corresponding remote control strategy, the step of generating the reactive power regulation strategy is included, specifically: Based on the monitoring data, it is determined whether the voltage of each inverter exceeds the safe range. If the voltage of the inverter exceeds the safe range, the deviation between the actual voltage and the set safe range is calculated, and a compensation reactive power value is generated through the PI controller. The compensation reactive power value is added to the current reactive power of the corresponding inverter to obtain the reactive power regulation value of the corresponding inverter. If the inverter voltage is within a safe range and a reactive power dispatch instruction is received from the cloud platform, then the reactive power target value in the reactive power dispatch instruction will be used as the reactive power adjustment value of the corresponding inverter. If a power factor command is received from the cloud platform, the reactive power target value is calculated based on the power factor command value in the command and used as the reactive power regulation value of the inverter.

5. The method for centralized data acquisition and control of distributed photovoltaic power stations according to claim 4, characterized in that, The formula for calculating the target value of reactive power is as follows: When the power factor is less than 0, sign = -1; when the power factor is greater than or equal to 0, sign = 1.

6. The method for centralized data acquisition and control of distributed photovoltaic power stations according to claim 1, characterized in that, The centralized data acquisition and control device for the distributed photovoltaic power station also connects to the cloud platform via an uplink communication protocol. After acquiring the remote signaling data and telemetry data uploaded by the inverter, it also includes: A status report is generated based on the content of the telemetry data, and the telemetry data is packaged into a telemetry data packet. The status report and / or telemetry data packet are encrypted and compressed respectively. The encrypted and compressed data is sent to the cloud platform through the primary channel. If the primary channel network is abnormal, the encrypted and compressed data is sent to the cloud platform through the backup channel.

7. A centralized data acquisition and control device for a distributed photovoltaic power station, characterized in that, The method includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which is executed by the processor to implement the steps of the centralized data acquisition and control method for a distributed photovoltaic power station as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the centralized data acquisition and control method for distributed photovoltaic power stations as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The method includes a computer program, which, when executed by a processor, implements the steps of the centralized data acquisition and control method for distributed photovoltaic power stations as described in any one of claims 1 to 6.

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