Phase modifier substituted new energy DC sending end transient overvoltage distributed control method, system and device, and storage medium

Abstract

The invention discloses a phase modifier substituted new energy direct current sending end transient overvoltage distributed control method, system and device and a storage medium, and relates to the field of new energy power system control, and the method comprises the steps: S1, carrying out the synchronous sampling of the terminal voltage and the direct current bus current of a local phase modifier, and according to the sampled terminal voltage and the direct current bus current, carrying out the synchronous sampling of the terminal voltage and the direct current bus current; adopting a fractional order PID algorithm to carry out reactive power adaptive adjustment on the new energy direct current sending end; s2, establishing information interaction between a local phase modifier and an adjacent phase modifier through a sparse communication network, and performing reactive power distribution on the plurality of phase modifiers by adopting a Gossipy consistency algorithm; s3, on the basis of the ultra-short-term illumination prediction data, a model prediction control algorithm is adopted to update a phase modifier operation point; according to the invention, rapid suppression of transient overvoltage and dynamic optimal distribution of reactive power can be realized.

Description

Technical Field

[0001] This invention relates to the field of new energy power system control, specifically to a distributed control method, system, device, and storage medium for transient overvoltage at the DC sending end of a new energy power system, which replaces a synchronous condenser. Background Technology

[0002] With the large-scale grid connection of new energy sources, photovoltaic power generation systems, as an important component of distributed energy, face a critical challenge in terms of transient overvoltage at their DC transmission end, hindering stable system operation. When photovoltaic arrays are connected to the grid via DC combiner boxes and inverters, high-frequency transient overvoltages are easily triggered on the DC side due to factors such as sudden changes in sunlight intensity, inverter switching, DC-side capacitor charging and discharging effects, and grid faults. These overvoltages are characterized by high amplitude (up to 1.5 to 2 times the rated value), rapid rise time (microseconds), and concentrated energy, threatening not only the insulation reliability of power devices but also causing the inverter to trip out of the grid for protection, further exacerbating grid frequency and voltage fluctuations.

[0003] In related technologies, centralized control strategies rely on a single controller to collect global information and issue control commands, which suffers from problems such as high communication latency, poor scalability, and single-point failure risk. As photovoltaic power plants expand to the megawatt level, the DC-side topology exhibits multi-branch, hierarchical characteristics, making traditional architectures insufficient to meet real-time requirements. Distributed control methods based on multi-agent theory have emerged, with the core idea of ​​distributing control logic to local controllers, enabling information exchange between adjacent nodes through a sparse communication network, and completing collaborative decision-making by combining local measurement data. Existing distributed control strategies mainly focus on three aspects: first, dynamic reactive power compensation, which adjusts the inverter's reactive power output to change line impedance characteristics and suppress voltage spike propagation; second, energy routing optimization, which uses DC circuit breakers and power modules to reconstruct the topology to isolate faulty branches; and third, pulse modulation coordination, which synchronizes the PWM carriers of multiple inverters and reduces overvoltage amplitude through harmonic cancellation. However, the above studies are mostly aimed at steady-state operation or single-fault scenarios, lacking collaborative control mechanisms for multi-timescale transient processes. Especially in DC grids with a high proportion of power electronic converters connected, the strong coupling between the controller and the physical system can easily lead to new problems such as subsynchronous oscillations, and there is an urgent need to establish a distributed control architecture that combines speed and robustness. Summary of the Invention

[0004] In view of this, the present invention provides a distributed control method, system, device and storage medium for transient overvoltage at the DC transmission end of a new energy source to replace the synchronous condenser, so as to solve the technical problems in the related art.

[0005] In a first aspect, the present invention provides a distributed control method for transient overvoltage at the DC transmission end of a new energy source, replacing a synchronous condenser, comprising:

[0006] S1. The terminal voltage and DC bus current of the local synchronous condenser are sampled synchronously, and the reactive power of the new energy DC transmission end is adaptively adjusted by a fractional-order PID algorithm based on the sampled terminal voltage and DC bus current.

[0007] S2. Establish information interaction between local synchronous condensers and adjacent synchronous condensers through a sparse communication network, and use the Gossipy consensus algorithm to allocate reactive power to multiple synchronous condensers.

[0008] S3. Based on ultra-short-term illumination prediction data, the operating point of the condenser is updated using a model predictive control algorithm.

[0009] In one optional implementation, the fractional-order PID algorithm uses the Grünwald-Letnikov discretization method to construct a fractional-order differential operator, with the transfer function being:

[0010]

[0011] Where λ is the order of integration, μ is the order of differentiation, and K p K is the gain coefficient of the proportional element. i K is the gain coefficient of the integrator. d is the gain coefficient of the differential element.

[0012] In one optional implementation, the reactive power adaptive adjustment process includes: increasing the derivative order μ when the rate of change of the DC bus current exceeds a threshold; and decreasing the integral order λ when the deviation of the terminal voltage persists.

[0013] In one optional implementation, the sparse communication network satisfies a first condition; the first condition includes: communication bandwidth < 5kbps, network topology entropy < 0.3, reactive power allocation consistency convergence completed within 20ms using the Gossipy algorithm, and the convergence threshold set to 0.01pu.

[0014] In one optional implementation, the process of acquiring the ultra-short-term illumination prediction data includes:

[0015] An LSTM neural network is used to process satellite cloud images and meteorological data to output a voltage output prediction curve for the target time period in the future, with a time resolution of ≤1min.

[0016] In one optional implementation, the rolling optimization objective function of the model predictive control algorithm is:

[0017]

[0018] Where, N P For prediction in the time domain, Nc To control the time domain, W V and W Q Let V be the weight matrix. t|k Let V be the predicted terminal voltage at time t. ref For the rated voltage, ΔQ t|k To control the increment.

[0019] In one optional implementation, the method further includes:

[0020] When the communication between the target synchronous condenser and the target synchronous condenser is interrupted for more than 3 cycles, the system automatically switches to the local fractional-order PID independent control mode and broadcasts an alarm signal to the adjacent synchronous condenser.

[0021] In a second aspect, the present invention provides a distributed control system for transient overvoltage at the DC transmission end of a new energy source, which replaces synchronous condensers, comprising: multiple synchronous condensers connected in parallel, a sparse communication network device, a synchronous sampling device, and a processor, wherein the processor is configured to execute the control method according to any one of claims 1-7.

[0022] Thirdly, the present invention provides a computer device, comprising:

[0023] A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the control method of any one of claims 1 to 7.

[0024] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a computer to perform the control method according to any one of claims 1 to 7.

[0025] This invention provides an embodiment that replaces the traditional SVG (Static Var Generator) with a group of synchronous condensers. By integrating fractional-order PID adaptive regulation, Gossipy consensus algorithm and model predictive control, distributed collaborative control is achieved, which can ensure rapid suppression of transient overvoltage and dynamic optimal allocation of reactive power. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1This is a flowchart illustrating a distributed control method for transient overvoltage at the DC transmission end of a new energy source, which replaces a synchronous condenser, according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Figure 1 This is a distributed control method for transient overvoltage at the DC transmission end of a new energy source according to an embodiment of the present invention. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] like Figure 1 As shown, the process includes the following steps:

[0032] S1. The terminal voltage and DC bus current of the local synchronous condenser are sampled synchronously, and the reactive power of the new energy DC transmission end is adaptively adjusted by a fractional-order PID algorithm based on the sampled terminal voltage and DC bus current.

[0033] Specifically, synchronous sampling is used to obtain terminal voltage and DC bus current. Compared with traditional PID, fractional PID provides a more refined dynamic response through non-integer order calculus operators, which can better adapt to the strong nonlinear characteristics of new energy power plants.

[0034] Among them, the adaptive adjustment of reactive power can be achieved by adjusting the fractional order or gain parameters online to cope with the rapid voltage changes at the DC sending end caused by wind and solar fluctuations.

[0035] S2. Establish information exchange between local synchronous condensers and adjacent synchronous condensers through a sparse communication network, and use the Gossipy consensus algorithm to allocate reactive power among multiple synchronous condensers.

[0036] Specifically, the sparse communication network reduces the bandwidth requirements of traditional centralized control and achieves global coordination through local information exchange (such as reactive power output of adjacent nodes).

[0037] The introduction of the Gossipy algorithm (random gossip communication) enhances the system's fault tolerance, enabling convergence even if some communication links are interrupted, making it suitable for distributed architectures in high-proportion new energy scenarios.

[0038] S3. Based on ultra-short-term illumination prediction data, the operating point of the condenser is updated using a model predictive control algorithm.

[0039] Specifically, by combining the model predictive control algorithm (MPC) with ultra-short-term illumination prediction (usually with an accuracy of 5-15 minutes) with rolling optimization, the operating point of the synchronous condenser can be adjusted in advance to prevent transient overvoltage caused by a sudden drop in photovoltaic output.

[0040] In this process, steps S1 and S2 form a "feedforward-feedback" synergy: S3 handles predictive global optimization, while steps S1 and S2 achieve real-time local correction.

[0041] In summary, the embodiments of this invention can achieve multi-timescale control, with ultra-short-term prediction (minute-level), MPC (second-level), and fractional-order PID (millisecond-level) forming a hierarchical control system. Simultaneously, the distributed architecture and consensus algorithm avoid single points of failure, making it more suitable for large-scale renewable energy clusters compared to traditional SVG centralized control. Furthermore, in transient overvoltage suppression, fractional-order PID control exhibits superior dynamic performance compared to integer-order PID, such as a smoother reactive power compensation curve.

[0042] It should be noted that the embodiments of the present invention can also be applied to high-proportion wind and solar DC transmission systems (such as UHV-supported new energy bases) to solve the problem of overvoltage at the sending end caused by DC blocking or sudden changes in wind and solar power under weak grid conditions.

[0043] In one alternative implementation, the fractional-order PID algorithm uses the Grünwald-Letnikov discretization method to construct a fractional-order differential operator, with the transfer function being:

[0044]

[0045] Where λ is the order of integration, μ is the order of differentiation, and K p K is the gain coefficient of the proportional element. i K is the gain coefficient of the integrator. d is the gain coefficient of the differential element.

[0046] It should be noted that K p K i K d The order and gain can be dynamically adjusted by identifying the impedance characteristics of new energy power plants online.

[0047] Preferably, λ = 0.8, μ = 0.7, K p∈[2.0,3.0], K i ∈[1.0,1.5], K d (∈0.5,1.0).

[0048] In one optional implementation, the reactive power adaptive regulation process includes: increasing the derivative order μ when the rate of change of the DC bus current exceeds a threshold to improve the transient response speed; and decreasing the integral order λ when the deviation of the terminal voltage persists to avoid overshoot.

[0049] In one optional implementation, the sparse communication network satisfies a first condition; the first condition includes: communication bandwidth < 5kbps, network topology entropy < 0.3, and reactive power allocation consistency convergence completed within 20ms using the Gossipy algorithm, with the convergence threshold set to 0.01pu.

[0050] In one optional implementation, the process of acquiring ultra-short-term illumination prediction data includes:

[0051] An LSTM neural network is used to process satellite cloud images and meteorological data to output a voltage output prediction curve for the target time period in the future, with a time resolution of ≤1min.

[0052] In one optional implementation, the rolling optimization objective function of the model predictive control algorithm is:

[0053]

[0054] Where, N P For prediction in the time domain, N c To control the time domain, W V and W Q Let V be the weight matrix. t|k Let V be the predicted terminal voltage at time t. ref For the rated voltage, ΔQ t|k To control the increment.

[0055] Preferably, the prediction time domain N=10, and the reactive power reference value of the synchronous condenser is updated every 100ms in combination with the illumination prediction data, and the weight coefficient of the objective function is dynamically adjusted.

[0056] In one alternative implementation, the sparse communication network may include an event-triggered communication mechanism that triggers information exchange between neighboring synchronous condensers only when the voltage deviation exceeds a preset threshold or a fault is detected.

[0057] In one optional implementation, the control method further includes:

[0058] When the communication between the target synchronous condenser and the target synchronous condenser is interrupted for more than 3 cycles, the system automatically switches to the local fractional-order PID independent control mode and broadcasts an alarm signal to adjacent nodes.

[0059] like Figure 2 As shown, embodiments of the present invention also provide a computer device, which includes one or more processors 10, a memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0060] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0061] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0062] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0063] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0064] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0065] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0066] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0067] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A distributed control method for transient overvoltage at the DC transmission end of a new energy source replacing a synchronous condenser, characterized in that, include: S1. The terminal voltage and DC bus current of the local synchronous condenser are sampled synchronously, and the reactive power of the new energy DC transmission end is adaptively adjusted by a fractional-order PID algorithm based on the sampled terminal voltage and DC bus current. S2. Establish information interaction between local synchronous condensers and adjacent synchronous condensers through a sparse communication network, and use the Gossipy consensus algorithm to allocate reactive power to multiple synchronous condensers. S3. Based on ultra-short-term illumination prediction data, the operating point of the condenser is updated using a model predictive control algorithm.

2. The method according to claim 1, characterized in that, The fractional-order PID algorithm uses the Grünwald-Letnikov discretization method to construct a fractional-order differential operator, and the transfer function is: Where λ is the order of integration, μ is the order of differentiation, and K p K is the gain coefficient of the proportional element. i K is the gain coefficient of the integrator. d is the gain coefficient of the differential element.

3. The method according to claim 2, characterized in that, The reactive power adaptive adjustment process includes: increasing the derivative order μ when the rate of change of DC bus current exceeds the threshold; and decreasing the integral order λ when the deviation of terminal voltage persists.

4. The method according to claim 1, characterized in that, The sparse communication network satisfies the first condition; the first condition includes: communication bandwidth < 5kbps, network topology entropy < 0.3, and the reactive power allocation consensus is completed within 20ms using the Gossipy algorithm, with the convergence threshold set to 0.01pu.

5. The method according to claim 1, characterized in that, The process of acquiring the ultra-short-term illumination prediction data includes: An LSTM neural network is used to process satellite cloud images and meteorological data to output a voltage output prediction curve for the target time period in the future, with a time resolution of ≤1min.

6. The method according to claim 5, characterized in that, The rolling optimization objective function of the model predictive control algorithm is: Where, N P For predicting the time domain, N c To control the time domain, W V and W Q Let V be the weight matrix. t|k Let V be the predicted terminal voltage at time t. ref For the rated voltage, ΔQ t|k To control the increment.

7. The method according to claim 1, characterized in that, Also includes: When the communication between the target synchronous condenser and the target synchronous condenser is interrupted for more than 3 cycles, the system automatically switches to the local fractional-order PID independent control mode and broadcasts an alarm signal to the adjacent synchronous condenser.

8. A distributed control system for transient overvoltage at the DC transmission end of a new energy source, replacing a synchronous condenser, characterized in that, include: A plurality of parallel-connected synchronous condensers, sparse communication network devices, synchronous sampling devices, and processors, wherein the processors are configured to perform the control method according to any one of claims 1-7.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the control method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the control method according to any one of claims 1 to 7.

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