Voltage control method corresponding to magnetic circuit hybrid distribution transformer in power distribution network

Abstract

The invention discloses a voltage control method corresponding to a magnetic circuit hybrid distribution transformer in a power distribution network. The method comprises the following steps: acquiring a voltage control instruction corresponding to a magnetic circuit hybrid distribution transformer in a power distribution network; in response to the voltage control instruction, calling a voltage control function corresponding to the power distribution network; determining a target output voltage reference value according to the active power value, the reactive power value and a voltage control function; and generating a voltage control instruction corresponding to the series converter according to the target output voltage reference value, so as to control the voltage of the series converter through the voltage control instruction, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value. According to the invention, the technical problem that it is difficult to conveniently, quickly and accurately regulate the voltage of the power distribution network in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution networks, and more specifically, to a voltage control method for a magnetic circuit hybrid distribution transformer in a power distribution network. Background Technology

[0002] Active distribution networks are characterized by a high proportion of distributed renewable energy access. This high proportion of distributed renewable energy access exacerbates the volatility of the source load and increases the risk of voltage deviation and over-limit in active distribution networks.

[0003] Output voltage regulation methods based on load forecasting exhibit poor robustness to forecast deviations, making it difficult to achieve rapid response and reliable improvement under load fluctuations. Output voltage control algorithms based on local node voltage sampling can specifically address voltage offset or limit exceedance issues at local nodes in the distribution network, but their application in multi-node active distribution networks has limitations. In contrast, distribution transformer output voltage control algorithms based on network-wide sampling and centralized control can effectively solve voltage offset or limit exceedance problems in the distribution network, but they require a large number of sampling modules, communication equipment, and other hardware, resulting in high costs.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a voltage control method for a magnetic circuit hybrid distribution transformer in a power distribution network, which at least solves the technical problem in related technologies that it is difficult to conveniently, quickly and accurately regulate the voltage of the power distribution network.

[0006] According to one aspect of the present invention, a voltage control method corresponding to a magnetic circuit hybrid distribution transformer in a distribution network is provided, comprising: acquiring a voltage control command corresponding to the magnetic circuit hybrid distribution transformer in the distribution network, wherein the voltage control command carries active power values ​​and reactive power values, the magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter, the main transformer includes a primary winding, a secondary winding, and an auxiliary winding, the AC side of the series converter is connected in series with the auxiliary winding, the AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node, the DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage, the primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the power supply side of the distribution network. The load side is connected; in response to the voltage control command, a voltage control function corresponding to the distribution network is retrieved, wherein the voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. The multiple coefficient values ​​are determined based on an objective function that aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions; a target output voltage reference value is determined based on the active power value, the reactive power value, and the voltage control function; a voltage control command corresponding to the series converter is generated based on the target output voltage reference value, so as to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

[0007] Optionally, before retrieving the voltage control function corresponding to the magnetic circuit hybrid distribution transformer, the method further includes: obtaining distribution network parameters of the distribution network, wherein the distribution network parameters include distribution network topology parameters, line impedance parameters, and voltage parameters corresponding to multiple nodes respectively; establishing multiple typical operating conditions based on the power parameters corresponding to multiple power-related nodes in the distribution network, wherein the multiple power-related nodes include multiple load nodes and multiple new energy nodes, and the power parameters include at least one of the following: historical power data, rated power capacity, and the typical operating conditions represent operating conditions in which the probability of the distribution network occurring is greater than a predetermined threshold; retrieving the objective function and corresponding constraints corresponding to the target distribution network, wherein the objective function aims to minimize the overall voltage deviation index in the distribution network under the multiple typical operating conditions, and the corresponding constraints include power flow equation constraints and magnetic circuit hybrid distribution transformer constraints; solving the objective function under the corresponding constraints based on the distribution network parameters and the multiple typical operating conditions to obtain the optimal output voltage reference values ​​corresponding to the multiple typical operating conditions respectively; and determining the multiple coefficient values ​​based on the multiple optimal output voltage reference values.

[0008] Optionally, determining the plurality of coefficient values ​​based on a plurality of optimal output voltage reference values ​​includes: determining, based on a plurality of optimal output voltage reference values, the active power value and reactive power value corresponding to the plurality of typical operating conditions for the distribution transformer; and fitting the relationship between the plurality of optimal output voltage reference values, the plurality of active power values ​​and the plurality of reactive power values ​​to determine the plurality of coefficient values.

[0009] Optionally, fitting the relationship between the plurality of optimal output voltage reference values, the plurality of active power values ​​under multiple operating conditions, and the plurality of reactive power values ​​under multiple operating conditions to determine the plurality of coefficient values ​​includes at least one of the following: fitting the relationship between the plurality of optimal output voltage reference values, the plurality of active power values ​​under multiple operating conditions, and the plurality of reactive power values ​​under multiple operating conditions through a target method to determine the plurality of coefficient values, wherein the target method includes any one of the following: least squares method, neural network fitting method.

[0010] Optionally, based on the power parameters corresponding to multiple power-related nodes in the power distribution network, multiple typical operating conditions are established, including: when the power parameters include historical power data, determining the power probability distributions corresponding to multiple time periods based on the historical power data of the multiple power-related nodes; taking the same number of data points for each node in the corresponding probability distribution to obtain typical data corresponding to the multiple power-related nodes; and determining the multiple typical operating conditions based on the typical data corresponding to the multiple power-related nodes.

[0011] Optionally, before retrieving the objective function and corresponding constraints corresponding to the target distribution network, the method further includes: determining the power flow equation constraints, wherein the power flow equation constraints include line voltage drop constraints, power and current relationship constraints, node active power balance constraints, and node reactive power balance constraints.

[0012] Optionally, before retrieving the objective function and corresponding constraints corresponding to the target distribution network, the method further includes: determining the constraints of the magnetic circuit hybrid distribution transformer, wherein the constraints of the magnetic circuit hybrid distribution transformer include output voltage reference constraints, transformer active power balance constraints, transformer reactive power balance constraints, upper limit constraints of output voltage, and lower limit constraints of output voltage.

[0013] According to one aspect of the present invention, a voltage control device corresponding to a magnetic circuit hybrid distribution transformer in a distribution network is provided, comprising: an acquisition module, configured to acquire a voltage control command corresponding to the magnetic circuit hybrid distribution transformer in the distribution network, wherein the voltage control command carries active power values ​​and reactive power values, the magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter, the main transformer includes a primary winding, a secondary winding, and an auxiliary winding, the AC side of the series converter is connected in series with the auxiliary winding, the AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node, the DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage, the primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network; adjustment A retrieval module is used to retrieve a voltage control function corresponding to the distribution network in response to the voltage control command. The voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. These multiple coefficient values ​​are determined based on an objective function that minimizes the overall voltage deviation index in the distribution network under multiple typical operating conditions. A determination module is used to determine a target output voltage reference value based on the active power value, the reactive power value, and the voltage control function. A generation module is used to generate a voltage control command corresponding to the series converter based on the target output voltage reference value, so as to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement a voltage control method for a magnetic circuit hybrid distribution transformer in a power distribution network as described in any of the preceding claims.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform a voltage control method corresponding to a magnetic circuit hybrid distribution transformer in a distribution network as described in any of the preceding claims.

[0016] In this embodiment of the invention, a voltage control command corresponding to a magnetic circuit hybrid distribution transformer in the distribution network is obtained. The voltage control command carries active power and reactive power values. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network. In response to the voltage control command, the voltage control is adjusted... A voltage control function corresponding to the distribution network is selected. This voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. These coefficient values ​​are determined based on an objective function that aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions. A target output voltage reference value is determined based on the active power value, reactive power value, and voltage control function. Based on the target output voltage reference value, a voltage control command corresponding to the series converter is generated. This command controls the voltage of the series converter, adjusts the voltage of the auxiliary winding, and adjusts the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value. By employing a method based on local power information and optimized voltage control functions, this approach acquires the active and reactive power values ​​at the output of a magnetic circuit hybrid distribution transformer. Using a predefined voltage control function, it calculates the target output voltage reference value and controls the series converter to adjust the output voltage. This achieves adaptive adjustment of the distribution network voltage and optimization of voltage deviation. Furthermore, power data can be collected only at the transformer output, eliminating the need for multi-node sensor deployment and communication links, significantly reducing hardware deployment and maintenance complexity and simplifying voltage regulation. Simultaneously, the coefficients of the voltage control function have been pre-optimized under multiple typical operating conditions, eliminating the need for complex iterative calculations during real-time control. The target voltage can be quickly solved using a linear formula. Combined with the short-path execution logic of the series converter directly adjusting the auxiliary winding voltage, this significantly shortens the voltage regulation response time, achieving rapid voltage regulation. This results in convenient, rapid, and precise voltage regulation in the distribution network, thus solving the technical problem of difficulty in convenient, rapid, and accurate voltage regulation in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1This is a flowchart of a voltage control method for a magnetic circuit hybrid distribution transformer in a power distribution network according to an embodiment of the present invention;

[0019] Figure 2 This is a structural diagram of a magnetic circuit hybrid distribution transformer according to an optional embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of an adaptive voltage control method for a magnetic circuit hybrid distribution transformer according to an optional embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the optimization selection method of the first and second adaptive voltage control methods in the optional embodiments of the present invention;

[0022] Figure 5 This is a schematic diagram of the optimization function and corresponding constraints in the optimization selection method of the first and second adaptive voltage control methods in the optional embodiments of the present invention;

[0023] Figure 6 This is a schematic diagram of the optimized selection method of the third and fourth adaptive voltage control methods in the optional embodiments of the present invention;

[0024] Figure 7 This is a schematic diagram of the optimization selection method of the 5th and 6th adaptive voltage control methods in the optional embodiments of the present invention;

[0025] Figure 8 This is a schematic diagram of the optimization function and corresponding constraints in the optimization selection method of the 3rd, 4th, 5th and 6th adaptive voltage control methods in the optional embodiments of the present invention;

[0026] Figure 9 This is a structural block diagram of a voltage control device corresponding to a magnetic circuit hybrid distribution transformer in a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] According to an embodiment of the present invention, an embodiment of a voltage control method corresponding to a magnetic circuit hybrid distribution transformer in a distribution network is provided. 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] Figure 1 This is a flowchart of a voltage control method for a magnetic circuit hybrid distribution transformer in a distribution network according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S102: Obtain the voltage control command corresponding to the magnetic circuit hybrid distribution transformer in the distribution network. The voltage control command carries active power value and reactive power value. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back through DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network.

[0033] In step S102 of this application, a voltage control command corresponding to the magnetic circuit hybrid distribution transformer is obtained. This command includes active power value and reactive power value, and clarifies the structural composition and connection method of the magnetic circuit hybrid distribution transformer. This provides accurate input parameters and hardware foundation for subsequent adaptive voltage control, ensuring that the control method achieves rapid response based on local information.

[0034] This involves voltage control commands, which are control signals used to adjust the output voltage of a magnetic circuit hybrid distribution transformer. These commands include the active power value and reactive power value at the output of the magnetic circuit hybrid distribution transformer. They are used to instruct the magnetic circuit hybrid distribution transformer to adjust to the target output voltage reference value and are the core input for realizing adaptive voltage control.

[0035] This includes the active power value, which is the active power at the output of the magnetic circuit hybrid distribution transformer. It represents the actual transmitted electrical energy and is one of the key input parameters for calculating the output voltage reference value, reflecting the real-time load demand of the distribution network.

[0036] This involves reactive power values, which are the reactive power at the output of the magnetic circuit hybrid distribution transformer. Reactive power represents the power used to establish and maintain the electromagnetic field and is another key input parameter for calculating the output voltage reference value, affecting the voltage stability and power factor of the distribution network.

[0037] Among them, a magnetic circuit hybrid distribution transformer is involved. The magnetic circuit hybrid distribution transformer is a type of distribution transformer that uses a power electronic converter to regulate the output voltage based on the traditional distribution transformer. It has the ability to quickly, continuously and repeatedly adjust the output voltage and is suitable for voltage control in active distribution networks.

[0038] This involves the main transformer, which is the core component of the magnetic circuit hybrid distribution transformer. It can include a primary winding, a secondary winding, and an auxiliary winding, and is used to realize voltage transformation and power transmission. It is the basis for the magnetic circuit hybrid distribution transformer to complete the basic transformation function.

[0039] This involves a series converter, a type of power electronic converter whose AC side is connected in series with the auxiliary winding of the main transformer. By controlling the voltage of the auxiliary winding, the voltage of the auxiliary winding can be adjusted, thereby achieving fine regulation of the output voltage.

[0040] This involves a parallel converter, a type of power electronic converter whose AC side is connected in parallel with the secondary winding of the main transformer to provide or absorb reactive power, and is connected back-to-back with the series converter on the DC side to jointly support the stability of the output voltage.

[0041] This involves the primary winding, which is the input winding of the main transformer. It is used to connect to the power supply side of the distribution network, receive electrical energy, and serves as the interface between the magnetic circuit hybrid distribution transformer and the upstream power grid.

[0042] This involves the secondary winding, which is the output winding of the main transformer. It is used to connect to the load side and output electrical energy. It is the direct channel for the magnetic circuit hybrid distribution transformer to supply power to the distribution network.

[0043] This involves the auxiliary winding, which is an additional winding of the main transformer and is connected to the AC side of the series converter. By adjusting its voltage, it compensates for the output voltage and is a key part of realizing adaptive voltage regulation.

[0044] This involves the AC side, which is the part of the converter that connects to the AC circuit. The AC side of the series converter is connected in series with the auxiliary winding, and the AC side of the parallel converter is connected in parallel with the secondary winding, forming a path for voltage and power regulation.

[0045] This involves parallel nodes, which are nodes formed by connecting the AC side and the secondary winding of a parallel converter in parallel. These nodes are used to connect to the load side of the distribution network and are the final output point of the output voltage.

[0046] This involves the DC side, which is the DC section of the converter. The DC sides of the series converter and the parallel converter are connected back-to-back via DC voltage to achieve power exchange and energy balance between the two converters.

[0047] This involves the power supply side, which is the power supply side of the distribution network. It is connected to the primary winding of the main transformer and provides input electrical energy. It is the energy source of the magnetic circuit hybrid distribution transformer.

[0048] This involves the load side, which is the power consumption side of the distribution network. It connects to parallel nodes, receives the adjusted output voltage, and is the end user served by the magnetic circuit hybrid distribution transformer.

[0049] This step clarifies the input parameters (active power and reactive power values) of the voltage control command and the detailed structure of the magnetic circuit hybrid distribution transformer (main transformer, series converter, parallel converter, and their connection methods). This provides an accurate data foundation and hardware support for subsequent adaptive voltage control algorithms based on local power, avoids the use of additional sampling equipment and communication facilities, reduces system costs, and ensures rapid response to source load fluctuations. It also achieves overall optimization of the distribution network voltage, thereby effectively improving voltage deviation and over-limit problems.

[0050] Step S104: In response to the voltage control command, retrieve the voltage control function corresponding to the distribution network. The voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. The multiple coefficient values ​​are determined based on an objective function that aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions.

[0051] In step S104 of this application, in response to the received voltage control command, a voltage control function pre-set for distribution network optimization is retrieved. This function contains specific coefficient values ​​determined by a systematic offline optimization method, establishing a direct and efficient calculation relationship from locally measured power to the optimal output voltage reference value, providing a precise algorithm core for real-time voltage control.

[0052] This involves a voltage control function, which is a control law that characterizes the mathematical mapping relationship between the output voltage reference value of a magnetic circuit hybrid distribution transformer and the local active and reactive power. It is the core algorithm model for achieving fast adaptive voltage control without the need for remote communication and relying solely on local information.

[0053] This involves multiple coefficient values, which constitute a specific set of parameters for the voltage control function. These coefficient values ​​include at least constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. These coefficients together determine the regulation pattern and magnitude of the local power change on the target output voltage.

[0054] This involves constant coefficient values, which are constant terms in the voltage control function. They represent the reference value of the base output voltage when both the active and reactive power outputs of the magnetic circuit hybrid distribution transformer are zero, thus setting the reference starting point for voltage regulation.

[0055] This involves the first coefficient value, which is the coefficient multiplied by the active power in the voltage control function. It quantifies the adjustment of the output voltage reference value when the active power changes by a unit amount, reflecting the sensitivity of the active load fluctuation to the voltage level of the distribution network.

[0056] This involves a second coefficient value, which is the coefficient multiplied by reactive power in the voltage control function. This coefficient quantifies the adjustment of the output voltage reference value when the reactive power changes by a unit amount, reflecting the sensitivity of reactive power to the voltage support or suppression effect of the distribution network.

[0057] This involves an objective function, which is a mathematical optimization objective used in the optimization process of determining the multiple coefficient values. The objective is to minimize the overall voltage deviation or over-limit situation of all nodes in the distribution network under multiple typical operating conditions, and to ensure that the control coefficients determined thereby can lead to the globally optimal voltage quality.

[0058] This involves typical operating conditions, which are a set of scenarios covering possible operating states constructed based on historical operating data of the distribution network or the rated capacity of equipment through random sampling or representative selection methods. Each operating condition specifically defines the active and reactive power values ​​of the load of each node and the distributed new energy source, which are used to simulate the diversity of source-load fluctuations faced by the distribution network in actual operation.

[0059] Among them, the overall voltage deviation index is involved. The overall voltage deviation index is a quantitative indicator used to comprehensively evaluate the voltage quality of the distribution network. It can be calculated by accumulating the sum of squares of the deviations of the voltage of all nodes from the nominal value, or by counting the number of nodes with voltage exceeding the limit. It is a key performance indicator for optimizing the driving coefficient and measuring the effectiveness of the control strategy.

[0060] This step retrieves the pre-determined voltage control function and its coefficients based on the global optimization objective of the distribution network, enabling the control method to quickly and accurately calculate the optimal output voltage reference value based on local power information. This method avoids complex online optimization calculations and significantly improves response speed. Furthermore, since the coefficients are derived from optimizations based on typical operating conditions covering various scenarios, it ensures that the control strategy can effectively improve the overall voltage level of the distribution network under various source-load fluctuations, reducing voltage deviation and over-limit risks, and achieving low-cost, high-efficiency adaptive voltage control.

[0061] Step S106: Determine the target output voltage reference value based on the active power value, reactive power value, and voltage control function;

[0062] In step S106 provided in this application, a target output voltage reference value is calculated based on the real-time acquired active power value and reactive power value, combined with a predefined voltage control function. This reference value is used to guide the output voltage adjustment of the magnetic circuit hybrid distribution transformer to ensure the stability of the distribution network voltage.

[0063] This involves the target output voltage reference value, which is the target output voltage value of the magnetic circuit hybrid distribution transformer, in volts or per unit. The voltage of the auxiliary winding is adjusted by controlling the voltage of the series converter, so that the output voltage of the transformer can reach the reference value.

[0064] This step utilizes locally available active and reactive power measurements and a pre-optimized voltage control function to quickly and accurately calculate the optimal target output voltage reference value under the current operating conditions. This method avoids centralized control that relies on load forecasting or requires full-network voltage sampling, achieving real-time voltage decision-making based on local information. This not only significantly reduces the system's dependence on communication and additional hardware, saving costs, but also enables rapid response to source-load fluctuations, fully leveraging the rapid voltage regulation capability of the magnetic circuit hybrid distribution transformer, and providing a reliable and efficient decision-making basis for achieving overall optimized control of the distribution network voltage.

[0065] Step S108: Based on the target output voltage reference value, generate a voltage control command corresponding to the series converter, so as to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

[0066] In step S108 of this application, a voltage control command for the series converter is generated based on the calculated target output voltage reference value. The output voltage of the series converter is adjusted by the command, thereby changing the voltage of the auxiliary winding, and finally making the output voltage of the magnetic circuit hybrid distribution transformer reach the target value, thus achieving precise voltage control.

[0067] This step transforms the calculated target output voltage reference value into specific voltage control commands, driving the series converter to adjust the voltage. This, in turn, regulates the output voltage of the magnetic circuit hybrid distribution transformer via the auxiliary winding, achieving rapid and precise voltage control. Furthermore, this control process is entirely based on local information, eliminating the need for remote communication and additional sampling equipment. This significantly reduces system complexity and hardware costs, ensuring the reliability and economy of the voltage control method.

[0068] Through steps S102-S108 above, a voltage control command corresponding to the magnetic circuit hybrid distribution transformer in the distribution network is obtained. This voltage control command carries active and reactive power values. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network. In response to the voltage control command... The system retrieves the voltage control function corresponding to the distribution network. This function includes multiple coefficient values, such as constant coefficients, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. These coefficient values ​​are determined based on an objective function that minimizes the overall voltage deviation index in the distribution network under multiple typical operating conditions. A target output voltage reference value is determined based on the active power value, reactive power value, and voltage control function. Based on the target output voltage reference value, a voltage control command corresponding to the series converter is generated. This command controls the voltage of the series converter, adjusts the voltage of the auxiliary winding, and adjusts the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value. By employing a method based on local power information and optimized voltage control functions, this approach acquires the active and reactive power values ​​at the output of a magnetic circuit hybrid distribution transformer. Using a predefined voltage control function, it calculates the target output voltage reference value and controls the series converter to adjust the output voltage. This achieves adaptive adjustment of the distribution network voltage and optimization of voltage deviation. Furthermore, power data can be collected only at the transformer output, eliminating the need for multi-node sensor deployment and communication links, significantly reducing hardware deployment and maintenance complexity and simplifying voltage regulation. Simultaneously, the coefficients of the voltage control function have been pre-optimized under multiple typical operating conditions, eliminating the need for complex iterative calculations during real-time control. The target voltage can be quickly solved using a linear formula. Combined with the short-path execution logic of the series converter directly adjusting the auxiliary winding voltage, this significantly shortens the voltage regulation response time, achieving rapid voltage regulation. This results in convenient, rapid, and precise voltage regulation in the distribution network, thus solving the technical problem of difficulty in convenient, rapid, and accurate voltage regulation in related technologies.

[0069] As an optional embodiment, before retrieving the voltage control function corresponding to the magnetic circuit hybrid distribution transformer, the method further includes: obtaining distribution network parameters, wherein the distribution network parameters include distribution network topology parameters, line impedance parameters, and voltage parameters corresponding to multiple nodes; establishing multiple typical operating conditions based on the power parameters corresponding to multiple power-related nodes in the distribution network, wherein the multiple power-related nodes include multiple load nodes and multiple new energy nodes, and the power parameters include at least one of the following: historical power data, rated power capacity, and the typical operating conditions represent operating conditions in which the probability of the distribution network occurring is greater than a predetermined threshold; retrieving the objective function and corresponding constraints corresponding to the target distribution network, wherein the objective function aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions, and the corresponding constraints include power flow equation constraints and magnetic circuit hybrid distribution transformer constraints; solving the objective function under the corresponding constraints based on the distribution network parameters and multiple typical operating conditions to obtain the optimal output voltage reference values ​​corresponding to the multiple typical operating conditions; and determining multiple coefficient values ​​based on the multiple optimal output voltage reference values.

[0070] This embodiment illustrates the process of optimizing and determining multiple coefficient values ​​in the voltage control function.

[0071] This involves distribution network parameters, which are a set of parameters describing the structure and electrical characteristics of the distribution network. These parameters can include topology, impedance, and node voltage information, and are the basic data for voltage control optimization.

[0072] This involves distribution network topology parameters, which define the connection relationships between nodes and lines in the distribution network, such as the connection methods between nodes and the network structure, and directly affect power flow distribution and voltage levels. Topology parameters are essential information for constructing power flow equation constraints.

[0073] This involves line impedance parameters, which are the resistance and reactance values ​​of each line in the distribution network. These parameters determine the voltage drop and power loss on the line and are key factors in calculating node voltage changes. They can be used to establish voltage drop constraints in the power flow equations.

[0074] This involves node voltage parameters, which are the voltage amplitude and phase angle information of each node in the distribution network. They reflect the voltage state of the system, are the basis for evaluating voltage deviation and over-limit situations, and are the basis for calculating voltage deviation in the optimization objective function.

[0075] This involves power-related nodes, which are nodes in the distribution network that are related to power injection or consumption. These nodes can include load nodes and new energy nodes. Their power changes directly affect the system voltage and are used to establish typical operating conditions.

[0076] This involves load nodes, which are nodes connected to electrical loads and consume active and reactive power. Their fluctuations are one of the main factors causing voltage deviation.

[0077] This involves new energy nodes, which are nodes connected to distributed new energy sources and inject active and reactive power. Their randomness and volatility exacerbate the risk of voltage instability.

[0078] This involves power parameters, which are parameters that describe the power characteristics of nodes. They can include historical power data or rated power capacity, and are used to characterize the operating status of the distribution network. They are input data for establishing typical operating conditions.

[0079] This includes historical power data, which consists of measured values ​​of active and reactive power at power-related nodes over a past period. This data reflects the actual operating patterns, fluctuation characteristics, and probability distribution of the distribution network. Historical power data is the primary data source for establishing typical operating conditions, ensuring the authenticity and representativeness of these conditions.

[0080] This includes rated power capacity, which is the rated power value of node loads or new energy equipment, representing its maximum operating capacity. It is used to describe extreme or typical operating conditions and can serve as an alternative data source for establishing typical operating conditions.

[0081] This involves constraints, which are conditions that must be satisfied in the optimization problem. These constraints can include power flow equation constraints and magnetic circuit hybrid distribution transformer constraints, ensuring the physical feasibility of the solution and equipment safety.

[0082] This involves power flow equation constraints, which are power balance equations based on Kirchhoff's laws. These constraints can include line voltage drop, power-current relationships, and active and reactive power balance constraints at nodes. Power flow equation constraints are fundamental to describing power flow in distribution networks.

[0083] This involves constraints on magnetic circuit hybrid distribution transformers. These constraints are limitations imposed on magnetic circuit hybrid distribution transformers and can include output voltage reference values, power balance, and upper and lower limits of output voltage constraints. These constraints ensure that the optimization results conform to the actual operating characteristics and safety range of the equipment. Magnetic circuit hybrid distribution transformer constraints are another core constraint condition in the optimization problem, besides the power flow equations.

[0084] This involves the optimal output voltage reference value, which is the best output voltage setpoint obtained by solving an optimization problem under a given typical operating condition. It can minimize the voltage deviation under that operating condition and is a key intermediate result for determining the voltage control function coefficients.

[0085] This involves coefficient values, which are specific parameters in the voltage control function. These coefficient values ​​can include constant coefficient values, the first coefficient value corresponding to the active power term, and the second coefficient value corresponding to the reactive power term. Together, they define the mathematical relationship between local power and target output voltage.

[0086] In this step, firstly, the topology, impedance, and node voltage parameters of the distribution network are obtained; then, based on the historical power data or rated power capacity of power-related nodes (load and new energy nodes), multiple typical operating conditions are established; next, an objective function aimed at minimizing the overall voltage deviation index is retrieved, and power flow equations and magnetic circuit hybrid distribution transformer constraints are applied; using the distribution network parameters and typical operating conditions, the optimization problem under constraints is solved to obtain the optimal output voltage reference value for each operating condition; finally, based on these optimal values, the coefficient values ​​of the voltage control function are determined through fitting or optimization methods.

[0087] This method enables the pre-optimization of voltage control function coefficients based on the actual structure and operation data of the distribution network, making it adaptable to various typical operating conditions. This allows for rapid and accurate response to source-load fluctuations in real-time control. Furthermore, by establishing typical operating conditions and solving optimization problems, the global optimality of coefficient values ​​is ensured, avoiding control deviations caused by relying on single operating conditions or experience-based settings, thus improving the robustness and adaptability of voltage regulation. In addition, this method only requires offline acquisition of distribution network parameters and power data, eliminating the need for online communication and additional hardware, significantly reducing system complexity and cost, and achieving cost-effective voltage control optimization.

[0088] As an optional embodiment, multiple coefficient values ​​are determined based on multiple optimal output voltage reference values, including: determining the active power value and reactive power value of the distribution transformer corresponding to multiple typical operating conditions based on multiple optimal output voltage reference values; fitting the relationship between multiple optimal output voltage reference values, multiple operating condition active power values ​​and multiple operating condition reactive power values ​​to determine multiple coefficient values.

[0089] In this embodiment, the process of determining the voltage control function coefficients by fitting method is illustrated.

[0090] This includes the active power value under operating conditions, which is the active power value at the output terminal of a magnetic circuit hybrid distribution transformer under specific typical operating conditions, reflecting the actual active load under that operating condition.

[0091] This includes the reactive power value under operating conditions, which is the reactive power value at the output terminal of a magnetic circuit hybrid distribution transformer under specific typical operating conditions, reflecting the reactive power balance state under that operating condition.

[0092] This involves fitting, which is the process of finding the optimal matching relationship between the optimal output voltage reference value and the active power value and reactive power value under operating conditions through mathematical methods. The aim is to determine the coefficient values ​​in the voltage control function. Fitting methods can include least squares method or neural network fitting.

[0093] In this step, firstly, based on the optimal output voltage reference values ​​under multiple typical operating conditions, the corresponding active power value and reactive power value under the operating conditions are determined; then, a fitting algorithm is used to establish the mathematical relationship between the optimal output voltage reference value and the active power value and reactive power value under the operating conditions; finally, multiple coefficient values ​​in the voltage control function are determined through this fitting relationship.

[0094] This method automatically determines the optimal coefficients of the voltage control function based on the optimization results, avoiding the subjectivity and inaccuracy of manual experience-based settings and improving the scientific rigor and reliability of coefficient determination. Simultaneously, the fitting process fully considers the optimal operating points under various typical operating conditions, ensuring that the coefficient values ​​can adapt to different operating states and enhancing the generalization ability and adaptability of the voltage control function. Furthermore, this method establishes concise mathematical relationships through a data-driven approach, reducing the complexity of the control algorithm and facilitating rapid implementation and stable operation in practical devices, providing a reliable parameter basis for achieving efficient and precise voltage control.

[0095] As an optional embodiment, the relationship between multiple optimal output voltage reference values, multiple active power values ​​under multiple operating conditions, and multiple reactive power values ​​under multiple operating conditions is fitted to determine multiple coefficient values, including at least one of the following: by a target method, the relationship between multiple optimal output voltage reference values, multiple active power values ​​under multiple operating conditions, and multiple reactive power values ​​under multiple operating conditions is fitted to determine multiple coefficient values, wherein the target method includes any one of the following: least squares method, neural network fitting method.

[0096] In this embodiment, the specific fitting method used to determine the coefficient values ​​is described.

[0097] This involves an objective approach, which is a mathematical method used to fit the relationship between the optimal output voltage reference value and the active and reactive power values ​​under operating conditions. The aim is to find the functional form that best reflects the inherent laws governing this relationship. Objective approaches can include least squares methods and neural network fitting methods.

[0098] This involves the least squares method, a mathematical optimization method that finds the best function match for data by minimizing the sum of squared errors. It is suitable for fitting linear or linearizable function relationships.

[0099] This involves neural network fitting methods, which are computational models that mimic the connection patterns of neurons in the human brain. These models can learn complex nonlinear mapping relationships through training and are suitable for fitting relationships that are difficult to represent with simple functions.

[0100] In this step, based on the dataset of the optimal output voltage reference value, the active power value under operating conditions, and the reactive power value under operating conditions, one of the least squares method or the neural network fitting method is selected for fitting. If the least squares method is selected, the linear equation system is solved to obtain the coefficient values ​​that minimize the sum of squared errors. If the neural network fitting method is selected, the network structure is designed and the network weights are trained to approximate the optimal mapping relationship. Finally, multiple coefficient values ​​are determined through the selected fitting method.

[0101] This approach provides flexible and adaptable methods for determining coefficients to different needs: the least squares method is computationally efficient and has a clear principle, quickly providing optimal coefficient solutions for linear or near-linear control functions, ensuring the simplicity and real-time performance of the control strategy; the neural network fitting method possesses powerful nonlinear mapping capabilities, capturing the complex nonlinear relationship between local power and optimal output voltage, thus providing a more accurate voltage control function when the distribution network operating characteristics are complex and the linear model accuracy is insufficient, enhancing the adaptability and robustness of the control method. Both methods are data-driven, ensuring the objectivity and optimality of coefficient determination.

[0102] As an optional embodiment, multiple typical operating conditions are established based on the power parameters corresponding to multiple power-related nodes in the distribution network, including: when the power parameters include historical power data, determining the power probability distribution corresponding to multiple time periods based on the historical power data of multiple power-related nodes; taking the same number of data for each node in the corresponding probability distribution to obtain typical data corresponding to multiple power-related nodes; and determining multiple typical operating conditions based on the typical data corresponding to multiple power-related nodes.

[0103] This embodiment illustrates the specific process of establishing multiple typical operating conditions based on historical power data.

[0104] This involves multiple time periods, which are several time periods divided into a day or a specific cycle. Each time period corresponds to different load and new energy operation characteristics, which is used to refine the statistics of power probability distribution and help to capture the time sequence characteristics of distribution network operation.

[0105] This involves the power probability distribution, which is the statistical distribution of active and reactive power values ​​at power-related nodes within a specific time period. It describes the probability of each power value occurring and is used to characterize the typical operating state during that period. The power probability distribution can be obtained through statistical analysis of historical data and serves as the basis for sampling typical operating conditions.

[0106] This involves typical data, which consists of representative power value combinations obtained by sampling from the power probability distribution of each power-related node. Each data point corresponds to the power state of a node under a specific operating condition. Typical data is obtained through random sampling or representative selection methods and is used to construct typical operating conditions.

[0107] In this step, the time is first divided into multiple time periods based on the historical power data of the power-related nodes, and the probability distribution of the power of each node in each time period is determined. Then, in the probability distribution corresponding to each time period, the same amount of power data is randomly or representatively extracted from each node to obtain the typical data of each node. Finally, these typical data are combined by node to form multiple typical operating conditions, each of which contains the power status of all nodes at a specific time.

[0108] This approach enables the establishment of typical operating conditions based on actual operational data, ensuring that the operating conditions accurately reflect the historical operating patterns and probabilistic characteristics of the distribution network, thereby improving the practicality and adaptability of the optimization results. Simultaneously, by dividing the data into time periods and sampling using probability distributions, it covers the operating states of the distribution network at different times, avoiding the limitations of a single operating condition and enhancing the robustness of the voltage control function to time-series fluctuations and randomness. Furthermore, this method generates typical operating conditions through a data-driven approach, reducing the bias of human assumptions and providing comprehensive and representative input for subsequent optimization problems, thus improving the overall performance and reliability of the voltage control strategy.

[0109] As an optional embodiment, before retrieving the objective function and corresponding constraints corresponding to the target distribution network, the method further includes: determining the power flow equation constraints, wherein the power flow equation constraints include line voltage drop constraints, power and current relationship constraints, node active power balance constraints, and node reactive power balance constraints.

[0110] In this embodiment, the specific composition of the power flow equation constraints required to construct the optimization problem is illustrated.

[0111] This involves line voltage drop constraints, which are equations describing the relationship between the voltage amplitude difference between the beginning and end of a distribution network line and the line's transmission power and impedance, reflecting the voltage loss on the line.

[0112] This involves constraints on the relationship between power and current, which are equations describing the relationship between the apparent power of a line and current and voltage, and establishes a physical connection between power measurement and current measurement.

[0113] This involves the active power balance constraint at nodes, which is based on the active power conservation equation of Kirchhoff's current law, ensuring that the sum of active power flowing into the node is equal to the sum of power flowing out.

[0114] This involves node reactive power balance constraints, which are based on the reactive power conservation equation of Kirchhoff's current law, ensuring that the sum of reactive power flowing into the node equals the sum of power flowing out.

[0115] In this step, before taking the objective function corresponding to the target distribution network online, the power flow equation constraints that the optimization problem needs to satisfy are determined in advance; specifically, this includes: establishing constraint equations describing line voltage drop, establishing constraint equations describing the relationship between power and current, establishing constraint equations to ensure active power balance at nodes, and establishing constraint equations to ensure reactive power balance at nodes.

[0116] By introducing complete power flow equation constraints, this approach ensures that the voltage control function coefficients obtained from solving the optimization problem strictly satisfy the physical operating laws of the distribution network, avoiding solutions that are physically infeasible or conflict with the actual operating state of the power grid, thus improving the rationality and practicality of the optimization results. At the same time, the complete constraint system can more accurately describe the complex coupling relationship between power flow and voltage distribution in the distribution network, enabling the optimized voltage control strategy to more effectively coordinate the voltage level of the entire network, laying a solid physical model foundation for achieving overall voltage optimization control.

[0117] As an optional embodiment, before retrieving the objective function and corresponding constraints corresponding to the target distribution network, the method further includes: determining the constraints of the magnetic circuit hybrid distribution transformer, wherein the constraints of the magnetic circuit hybrid distribution transformer include output voltage reference constraints, transformer active power balance constraints, transformer reactive power balance constraints, upper limit constraints of output voltage, and lower limit constraints of output voltage.

[0118] This embodiment illustrates the specific components of the magnetic circuit hybrid distribution transformer constraints required to construct the optimization problem.

[0119] This involves an output voltage reference constraint, which stipulates that the voltage at the output node of the magnetic circuit hybrid distribution transformer should be equal to the target output voltage reference value in the optimization variables.

[0120] This involves the active power balance constraint of the transformer, which describes that the active power at the output of a magnetic circuit hybrid distribution transformer is equal to the sum of the active power of all its downstream lines, reflecting the active power transmission relationship of the transformer.

[0121] This involves the reactive power balance constraint of the transformer, which describes that the reactive power at the output of a magnetic circuit hybrid distribution transformer is equal to the sum of the reactive power of all its downstream lines, reflecting the reactive power transmission relationship of the transformer.

[0122] This includes an upper limit constraint on output voltage, which specifies the maximum allowable reference value for the output voltage of a magnetic circuit hybrid distribution transformer, ensuring that the equipment operates within a safe range.

[0123] This includes an output voltage lower limit constraint, which specifies the minimum allowable value of the output voltage reference value of a magnetic circuit hybrid distribution transformer to ensure that the equipment operates within a safe range.

[0124] In this step, before retrieving the objective function corresponding to the target distribution network, the constraints of the magnetic circuit hybrid distribution transformer that the optimization problem needs to satisfy are determined in advance; specifically, this includes: establishing output voltage reference constraints, establishing transformer active power balance constraints, establishing transformer reactive power balance constraints, establishing upper limit constraints of output voltage, and establishing lower limit constraints of output voltage.

[0125] By introducing a complete constraint system for the magnetic circuit hybrid distribution transformer, this approach ensures that the voltage control function coefficients obtained through optimization not only meet the operating rules of the distribution network but also strictly conform to the equipment characteristics and safe operating boundaries of the magnetic circuit hybrid distribution transformer itself. This avoids control strategies that may damage the transformer or affect its lifespan. At the same time, this constraint system organically combines the transformer's operating limitations with the optimization objectives of the distribution network, enabling the final voltage control function to maximize its voltage regulation capability under the premise of safe equipment operation. This provides a key guarantee for achieving economical, safe, and efficient voltage optimization control.

[0126] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0127] Among related technologies, active distribution networks are characterized by a high proportion of distributed renewable energy access. The high proportion of distributed renewable energy access exacerbates the volatility of source loads and increases the risk of voltage deviation and over-limit in active distribution networks.

[0128] Output voltage regulation methods based on load forecasting exhibit poor robustness to forecast deviations, making it difficult to achieve rapid response and reliable improvement under load fluctuations. Output voltage control algorithms based on local node voltage sampling can specifically address voltage offset or limit exceedance issues at local nodes in the distribution network, but their application in multi-node active distribution networks has limitations. In contrast, distribution transformer output voltage control algorithms based on network-wide sampling and centralized control can effectively solve voltage offset or limit exceedance problems in the distribution network, but they require a large number of sampling modules, communication equipment, and other hardware, resulting in high costs.

[0129] In view of this, the present invention proposes an adaptive voltage control method for a magnetic circuit hybrid distribution transformer, which can also be called a voltage control method and its optimization selection method. Figure 2 This is a structural diagram of a magnetic circuit hybrid distribution transformer according to an optional embodiment of the present invention. Figure 3 This is a schematic diagram of an adaptive voltage control method for a magnetic circuit hybrid distribution transformer according to an optional embodiment of the present invention. Figure 4 This is a schematic diagram of the optimization selection method for the first and second adaptive voltage control methods in the optional embodiments of the present invention. Figure 5 This is a schematic diagram of the optimization function and corresponding constraints in the optimization selection method of the first and second adaptive voltage control methods of the optional embodiments of the present invention. Figure 6 This is a schematic diagram illustrating the optimized selection method of the third and fourth adaptive voltage control methods in the optional embodiments of the present invention. Figure 7 This is a schematic diagram illustrating the optimized selection method of the 5th and 6th adaptive voltage control methods in the optional embodiments of the present invention. Figure 8 This is a schematic diagram of the optimization function and corresponding constraints in the optimization selection method of the 3rd, 4th, 5th, and 6th adaptive voltage control methods of the optional embodiments of the present invention, as shown below. Figures 2 to 8 As shown, it will be introduced below.

[0130] This invention proposes an adaptive voltage control method for a magnetic circuit hybrid distribution transformer. It includes:

[0131] (a) Magnetic circuit hybrid distribution transformer:

[0132] The magnetic circuit hybrid distribution transformer described in the optional embodiment of this invention refers to a distribution transformer that, based on a traditional distribution transformer, regulates the output voltage (secondary voltage) through a power electronic converter. Compared to the traditional distribution transformer, which cannot adjust the input voltage to output voltage ratio, the magnetic circuit hybrid distribution transformer can not only achieve voltage transformation and power transmission, but also regulate the output voltage by controlling the voltage, current, or power of the power electronic converter.

[0133] Optionally, the magnetic circuit hybrid distribution transformer includes Figure 2The components shown include: a main transformer, a series converter, and a parallel converter. The main transformer comprises a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected to the auxiliary winding, and the AC side of the parallel converter is connected in parallel with the secondary winding. The DC sides of the series and parallel converters are connected back-to-back via DC voltage. By controlling the voltage of the series converter, the voltage of the auxiliary winding is adjusted, ultimately achieving the regulation of the output voltage. Optionally, the topology of the series and parallel converters is a three-phase two-level half-bridge converter (similar to the above-mentioned magnetic circuit hybrid distribution transformer, which includes a main transformer, a series converter, and a parallel converter; the main transformer comprises a primary winding, a secondary winding, and an auxiliary winding; the AC side of the series converter is connected in series with the auxiliary winding; the AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node; the DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage; the primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network).

[0134] (II) Mathematical expression for the adaptive voltage control method of magnetic circuit hybrid distribution transformer:

[0135] The adaptive voltage control method for the magnetic circuit hybrid distribution transformer described in the optional embodiment of the present invention calculates the output voltage reference value based on the local power. The mathematical expression of this algorithm is as follows (same as the voltage control function corresponding to the distribution network mentioned above):

[0136]

[0137] in, It is the output voltage of the magnetic circuit hybrid distribution transformer. These are the local active and reactive power of the magnetic circuit hybrid distribution transformer, that is, the active and reactive power at the output end of the magnetic circuit hybrid distribution transformer.

[0138] (III) Optimization selection method for adaptive voltage control of magnetic circuit hybrid distribution transformer:

[0139] The mathematical expression of the adaptive voltage control method for the magnetic circuit hybrid distribution transformer described in the optional embodiment of the present invention is determined through the optimization selection method in this section:

[0140] 1. The optimal mathematical expression for the adaptive voltage control method of the magnetic circuit hybrid distribution transformer is:

[0141]

[0142] The coefficients a, b, and c (which are respectively the constant coefficients mentioned above, the first coefficient corresponding to the active power term, and the second coefficient corresponding to the reactive power term) are determined through the following steps.

[0143] (1) Obtain the topology information of the distribution network, line impedance, and historical data of load and new energy power of each node;

[0144] (2) Establish several typical operating conditions for loads and renewable energy power. Each operating condition must include the active and reactive power consumed or output by the loads and renewable energy at each node. The frequency of occurrence of different typical operating conditions in all operating conditions must basically match the frequency of occurrence of the loads and renewable energy power specified for that operating condition in the actual distribution network. The information included in each typical operating condition is as follows (same as the above multiple typical operating conditions):

[0145]

[0146] in,

[0147]

[0148] Indicates the first Under these working conditions, the node The load consumption, or the active and reactive power output of photovoltaic power generation and wind power generation, This indicates the number of nodes; if a node has no load, such as photovoltaic or wind power generation, then the corresponding power of that node is 0; if other types of new energy are connected to the grid, the power information should also be included in the typical operating conditions.

[0149] Typical operating conditions are established as follows: Based on historical data of load and renewable energy power at each node of the distribution network, a day is divided into several time periods, and the probability distribution of load and renewable energy power at each node in each time period is determined. Optimally, a random sampling method is used to obtain a large number of typical operating conditions for each node's load and renewable energy power in each time period, which are then combined to form all typical operating conditions. Alternatively, one of the most representative typical operating conditions is selected from each time period and combined to form all typical operating conditions. Optionally, the number of time periods is 24.

[0150] (3) Based on the distribution network topology information and line impedance obtained in (1), and several typical operating conditions established in (2), construct a multi-operating-condition optimization problem.

[0151] The optimal objective function for the optimization problem is:

[0152]

[0153] Optionally, the objective function of the optimization problem is:

[0154]

[0155] or

[0156]

[0157] in, These are typical operating condition numbers. It is the distribution network node number. This refers to the voltage magnitude at node j under the i-th typical operating condition. It is a node voltage out-of-bounds function. and These are the set upper and lower bounds of the node voltage. The objective function described above aims to minimize the overall voltage deviation or out-of-bounds situation at each node of the distribution network under various operating conditions.

[0158] The optimization problem considers the following constraints:

[0159] (a) Power flow equation constraints:

[0160] The output node of the magnetic circuit hybrid distribution transformer is node 1. For all nodes in the active distribution network... The lines between Establish the following power flow equation constraints:

[0161]

[0162]

[0163]

[0164]

[0165] in, It is a line The sequence numbers of the starting and ending nodes, It is the first Node under various working conditions The voltage magnitude, It is the first Line under various working conditions The current, It is a line impedance, It is the first Line under various working conditions At the node Apparent power on the side, It is all of The terminal node of the line that is the starting node.

[0166] (b) Constraints of hybrid magnetic circuit distribution transformers:

[0167] The following constraints are established for the output voltage of the magnetic circuit hybrid distribution transformer:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] in, These are the rated active and reactive power outputs of a magnetic circuit hybrid distribution transformer. These are the maximum and minimum output voltage values ​​of a magnetic circuit hybrid distribution transformer.

[0174] (4) Solve the above optimization problem to obtain The value of .

[0175] 2. Optionally, the mathematical expression for the adaptive voltage control method of the magnetic circuit hybrid distribution transformer is:

[0176]

[0177] coefficient Determine this by following these steps.

[0178] (1) Obtain the topology information of the distribution network, line impedance, and rated or installed capacity of load and new energy power at each node.

[0179] (2) Establish several typical operating conditions of load and new energy power. The information contained in each typical operating condition is the same as in 1.

[0180] Typical operating conditions are established in the following way: Based on the rated or installed capacity of the load and renewable energy power at each node of the distribution network, random numbers are used to describe the ratio of the actual power of each load and renewable energy to the rated or installed capacity. The actual power of each node load or renewable energy under each combination of random numbers is taken as a typical operating condition. A large number of typical operating conditions are obtained by random sampling.

[0181] (3) Based on the distribution network topology information and line impedance obtained in (1), and the several typical operating conditions established in (2), construct a multi-operating-condition optimization problem. The objective function and constraints of the optimization problem are the same as in 1.

[0182] (4) Solve the above optimization problem to obtain the values ​​of a, b, and c.

[0183] 3. Optionally, the mathematical expression for the adaptive voltage control method of the magnetic circuit hybrid distribution transformer is:

[0184]

[0185] The coefficients a, b, and c are determined through the following steps.

[0186] (1) Obtain the topology information of the distribution network, line impedance, and historical data of load and new energy power at each node;

[0187] (2) Establish several typical operating conditions of load and new energy power. The information contained in each typical operating condition is the same as in 1.

[0188] The method for establishing typical operating conditions is the same as in section 1.

[0189] (3) Based on the distribution network topology information and line impedance obtained in (1), each typical working condition established in (2) is considered to construct a single working condition optimization problem; the number of optimization problems is the same as the number of typical working conditions.

[0190] The optimal objective function for each optimization problem is:

[0191]

[0192] Optionally, the objective function for each optimization problem is:

[0193]

[0194] or

[0195]

[0196] in, This is the output voltage of the magnetic circuit hybrid distribution transformer under typical operating condition i. The objective function described above aims to minimize the overall voltage deviation or out-of-bounds situation at each node of the distribution network under a single operating condition.

[0197] The optimization problem considers the following constraints:

[0198] (a) Power flow equation constraints:

[0199] The output node of the magnetic circuit hybrid distribution transformer is node 1. For all nodes in the active distribution network... The lines between Establish the following power flow equation constraints:

[0200]

[0201]

[0202]

[0203]

[0204] in, It is a line The sequence numbers of the starting and ending nodes, It is the first Node under various working conditions The voltage magnitude, It is the first Line under various working conditions The current, It is a line impedance, It is the first Line under various working conditions At the node Apparent power on the side, It is all of The terminal node of the line that is the starting node.

[0205] (b) Output voltage constraints of magnetic circuit hybrid distribution transformers (same as the constraints of magnetic circuit hybrid distribution transformers described above):

[0206] The following constraints are established for the output voltage of the magnetic circuit hybrid distribution transformer:

[0207]

[0208]

[0209]

[0210]

[0211]

[0212] in, These are the maximum and minimum output voltage values ​​of a magnetic circuit hybrid distribution transformer.

[0213] (4) Solve the above optimization problem to obtain the optimal solution under each working condition. And the local power of the distribution transformer under optimal conditions. and ;

[0214] (5) Use the least squares method to fit the data. and and The relationship, to obtain The value of .

[0215] 4. Optionally, the mathematical expression for the adaptive voltage control method of the magnetic circuit hybrid distribution transformer is:

[0216]

[0217] coefficient Determine this by following these steps.

[0218] (1) Obtain the topology information of the distribution network, line impedance, load of each node and rated or installed capacity of new energy sources;

[0219] (2) Establish several typical operating conditions of load and new energy power. The information contained in each typical operating condition is the same as in 1.

[0220] The method for establishing typical operating conditions is the same as in section 2.

[0221] (3) Based on the distribution network topology information and line impedance obtained in (1), consider each typical operating condition established in (2) and construct a single-operating-condition optimization problem; the number of optimization problems is the same as the number of typical operating conditions. The objective function of the optimization problem is the same as in 3, and the constraints are the same as in 1;

[0222] (4) Solve the above optimization problem to obtain the optimal solution under each working condition. And the local power of the distribution transformer under optimal conditions. and ;

[0223] (5) Use the least squares method to fit the data. and and The relationship, to obtain The value of .

[0224] 5. Optional, adaptive voltage control method for magnetic circuit hybrid distribution transformers. Determine this by following these steps.

[0225] (1) Obtain the topology information of the distribution network, line impedance, and historical data of load and new energy power at each node;

[0226] (2) Establish several typical operating conditions of load and new energy power. The information contained in each typical operating condition is the same as in 1.

[0227] The method for establishing typical operating conditions is the same as in section 1.

[0228] (3) Based on the distribution network topology information and line impedance obtained in (1), consider each typical operating condition established in (2) and construct a single-operating-condition optimization problem; the number of optimization problems is the same as the number of typical operating conditions. The objective function of the optimization problem is the same as in 3, and the constraints are the same as in 1:

[0229] (4) Solve the above optimization problem to obtain the optimal solution under each working condition. And the local power of the distribution transformer under optimal conditions. and ;

[0230] (5) Use a neural network to fit and and The relationship, to obtain The functional relationship.

[0231] 6. Optional, adaptive voltage control method for magnetic circuit hybrid distribution transformers. Determine this by following these steps.

[0232] (1) Obtain the topology information of the distribution network, line impedance, load of each node and rated or installed capacity of new energy sources;

[0233] (2) Establish several typical operating conditions of load and new energy power. The information contained in each typical operating condition is the same as in 1.

[0234] The method for establishing typical operating conditions is the same as in section 2.

[0235] (3) Based on the distribution network topology information and line impedance obtained in (1), consider each typical operating condition established in (2) and construct a single-operating-condition optimization problem; the number of optimization problems is the same as the number of typical operating conditions. The objective function of the optimization problem is the same as in 3, and the constraints are the same as in 1:

[0236] (4) Solve the above optimization problem to obtain the optimal solution under each working condition. And the local power of the distribution transformer under optimal conditions. and ;

[0237] (5) Use a neural network to fit and and The relationship, to obtain The functional relationship.

[0238] Optionally, this invention proposes an adaptive voltage control method for a magnetic circuit hybrid distribution transformer. It includes:

[0239] 1. Input and output quantities of adaptive voltage control method:

[0240] The adaptive voltage control method for a magnetic circuit hybrid distribution transformer proposed in the optional embodiment of the present invention has the input being the active power and reactive power at the output terminal of the magnetic circuit hybrid distribution transformer, and the output being the reference value of the output voltage of the magnetic circuit hybrid distribution transformer.

[0241]

[0242] 2. Mathematical expression of adaptive voltage control method:

[0243] The adaptive voltage control method for a magnetic circuit hybrid distribution transformer proposed in an optional embodiment of this invention has the following mathematical expression:

[0244]

[0245] That is, the output voltage reference value as a linear function of local active power and local reactive power, or a more complex expression obtained using neural network methods:

[0246]

[0247] However, there is no identical mathematical expression for the voltage control algorithms proposed in existing technologies.

[0248] 3. Hardware implementation method of adaptive voltage control:

[0249] The adaptive voltage control method for magnetic circuit hybrid distribution transformers proposed in the optional embodiments of the present invention consists of a voltage sampling unit, a current sampling unit, and a control unit located at the output end of the magnetic circuit hybrid distribution transformer, without the need to use other sampling units at other locations in the active distribution network.

[0250] Existing hardware implementation methods require sampling units or control units located in other locations, making it impossible to implement control algorithms that are entirely based on local information.

[0251] 4. Optimization selection method for adaptive voltage control:

[0252] The adaptive voltage control method for magnetic circuit hybrid distribution transformers proposed in the optional embodiments of this invention, the method for optimizing the selection of its mathematical expression, refers to the mathematical expression... Medium parameters The selection method, or mathematical expression The optimization selection method in the form of a function should include the following steps: obtaining relevant information of the distribution network; establishing several typical operating conditions of load and new energy power; constructing and solving the optimization problem; if step (3) constructs and solves the single-condition optimization problem under each typical operating condition, fitting the solution result of the optimization problem; and obtaining the mathematical expression of the adaptive voltage control method.

[0253] However, there are no existing voltage control methods that have adopted the above steps to achieve an optimized selection method.

[0254] 5. Methods and content for obtaining distribution network related information:

[0255] According to the optimization selection method described in point 4, the distribution network-related information mentioned in step (1) should be obtained before the operation of the magnetic circuit hybrid distribution transformer adaptive voltage control method, and does not need to be obtained online during the operation of the magnetic circuit hybrid distribution transformer adaptive voltage control method. The distribution network-related information should include the distribution network topology information, line impedance, historical data of load and renewable energy power at each node, or the rated or installed capacity of load and renewable energy power at each node.

[0256] Existing voltage control methods require online acquisition of distribution network information during operation, or the acquired distribution network information may contain content different from that described above.

[0257] 6. Methods for establishing typical operating conditions for load and renewable energy power:

[0258] According to the optimization selection method described in point 4, the typical operating conditions of several loads and new energy power mentioned in step (2) should be achieved through one of the following two methods:

[0259] (1) Based on the historical data of load and renewable energy power of each node in the distribution network, the day is divided into several time periods. The probability distribution of load and renewable energy power of each node in each time period is determined, and a random sampling method is adopted. A large number of typical operating conditions of load and renewable energy power of each node are obtained in each time period, and they are combined to form all typical operating conditions; or one of the most representative typical operating conditions is selected in each time period and combined to form all typical operating conditions.

[0260] (2) Based on the rated or installed capacity of the load and new energy power of each node in the distribution network, random numbers are used to describe the ratio of the actual power of each load and new energy to the rated or installed capacity. The actual power of each node load or new energy under each combination of random numbers is taken as a typical operating condition. A large number of typical operating conditions are obtained by random sampling.

[0261] 7. Optimize the problem-solving approach:

[0262] According to the optimization selection method described in point 4, the optimization problem described in step (3) should be established by one of the following two methods:

[0263] (1) The objective function of the optimization problem should minimize the overall voltage deviation or out-of-bounds situation of each node in the distribution network under each operating condition. Possible forms include:

[0264]

[0265]

[0266]

[0267] in, These are typical operating condition numbers. It is the distribution network node number. This refers to the voltage magnitude at node j under the i-th typical operating condition. It is a node voltage out-of-bounds function. and These are the set upper and lower bounds of the node voltage.

[0268] The constraints of an optimization problem must include at least:

[0269] (a) Power flow equation constraints:

[0270] The output node of the magnetic circuit hybrid distribution transformer is node 1. For all nodes in the active distribution network... The lines between Establish the following power flow equation constraints:

[0271]

[0272]

[0273]

[0274]

[0275] in, It is a line The sequence numbers of the starting and ending nodes, It is the first Node under various working conditions The voltage magnitude, It is the first Line under various working conditions The current, It is a line impedance, It is the first Line under various working conditions At the node Apparent power on the side, It is all of The terminal node of the line that is the starting node.

[0276] For the node where the output terminal of the magnetic circuit hybrid distribution transformer is located, the following constraints are established:

[0277]

[0278]

[0279]

[0280] (b) Output voltage constraint of magnetic circuit hybrid distribution transformer:

[0281] The following constraints are established for the output voltage of the magnetic circuit hybrid distribution transformer:

[0282]

[0283]

[0284] in, These are the rated active and reactive power outputs of a magnetic circuit hybrid distribution transformer. These are the maximum and minimum output voltage values ​​of a magnetic circuit hybrid distribution transformer.

[0285] (2) For each typical operating condition, construct independent single-condition optimization problems. The objective function of each optimization problem should minimize the overall voltage deviation or out-of-bounds situation of each node in the distribution network under that operating condition. Possible forms include:

[0286]

[0287]

[0288]

[0289] in, It is the output voltage of a magnetic circuit hybrid distribution transformer under typical operating condition i.

[0290] The constraints of the optimization problem are the same as those in (1).

[0291] 8. Fitting methods for optimization problem solutions:

[0292] According to the optimization selection method described in point 4, the fitting method for the solution result of the optimization problem in step (4) refers to the method of fitting the output voltage of the magnetic circuit hybrid distribution transformer in the solution result of the optimization problem. The output active and reactive power of the magnetic circuit hybrid distribution transformer , The relationship was fitted using the least squares method to obtain... parameters The value; or by using a neural network to fit, obtain the value. The function form.

[0293] The above optional implementation methods can achieve at least the following beneficial effects:

[0294] (1) The present invention has the ability to quickly respond to source load fluctuations and fully utilize the voltage regulation capability of the magnetic circuit hybrid distribution transformer;

[0295] (2) This invention can achieve overall optimization in the case of voltage deviation or over-limit in active distribution networks;

[0296] (3) The present invention does not require additional sampling and communication equipment, and the cost is low.

[0297] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0298] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0299] Example 2

[0300] According to embodiments of the present invention, an apparatus is also provided for implementing the voltage control method corresponding to the above-described magnetic circuit hybrid distribution transformer in a distribution network. Figure 9 This is a structural block diagram of a voltage control device corresponding to a magnetic circuit hybrid distribution transformer in a distribution network according to an embodiment of the present invention, such as... Figure 9 As shown, the device includes: an acquisition module 902, a retrieval module 904, a determination module 906, and a generation module 908. The device will be described in detail below.

[0301] The acquisition module 902 is used to acquire the voltage control command corresponding to the magnetic circuit hybrid distribution transformer in the distribution network. The voltage control command carries active power and reactive power values. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network. The retrieval module 904, connected to the acquisition module 902, is used to retrieve the voltage control command corresponding to the distribution network. A voltage control function, comprising multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term, wherein the multiple coefficient values ​​are determined based on an objective function aimed at minimizing the overall voltage deviation index in the distribution network under multiple typical operating conditions; a determination module 906, connected to the aforementioned retrieval module 904, is used to determine a target output voltage reference value based on the active power value, the reactive power value, and the voltage control function; a generation module 908, connected to the aforementioned determination module 906, is used to generate a voltage control command corresponding to the series converter based on the target output voltage reference value, so as to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

[0302] It should be noted that the above-mentioned acquisition module 902, retrieval module 904, determination module 906 and generation module 908 correspond to steps S102 to S108 in the voltage control method for magnetic circuit hybrid distribution transformers in the distribution network. The instances and application scenarios implemented by multiple modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiment 1.

[0303] Example 3

[0304] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the voltage control method corresponding to the magnetic circuit hybrid distribution transformer in the distribution network of any of the above embodiments.

[0305] Example 4

[0306] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the voltage control method corresponding to a magnetic circuit hybrid distribution transformer in a distribution network as described above.

[0307] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0308] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0309] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0310] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0311] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0312] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0313] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A voltage control method for a magnetic circuit hybrid distribution transformer in a power distribution network, characterized in that, include: Obtain voltage control commands corresponding to the magnetic circuit hybrid distribution transformer in the distribution network. The voltage control commands carry active power and reactive power values. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network. In response to the voltage control command, the voltage control function corresponding to the distribution network is retrieved. The voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. The multiple coefficient values ​​are determined based on an objective function that aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions. Based on the active power value, the reactive power value, and the voltage control function, determine the target output voltage reference value; Based on the target output voltage reference value, a voltage control command corresponding to the series converter is generated to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

2. The method according to claim 1, characterized in that, Before retrieving the voltage control function corresponding to the magnetic circuit hybrid distribution transformer, the process also includes: Obtain the distribution network parameters of the distribution network, wherein the distribution network parameters include distribution network topology parameters, line impedance parameters, and voltage parameters corresponding to multiple nodes respectively; Based on the power parameters corresponding to multiple power-related nodes in the power distribution network, multiple typical operating conditions are established. The multiple power-related nodes include multiple load nodes and multiple new energy nodes. The power parameters include at least one of the following: historical power data and rated power capacity. The typical operating conditions represent operating conditions in which the probability of the power distribution network occurring is greater than a predetermined threshold. The objective function and corresponding constraints corresponding to the target distribution network are retrieved. The objective function aims to minimize the overall voltage deviation index in the distribution network under the multiple typical operating conditions. The corresponding constraints include power flow equation constraints and magnetic circuit hybrid distribution transformer constraints. Based on the power distribution network parameters and the multiple typical operating conditions, the objective function under the corresponding constraints is solved to obtain the optimal output voltage reference value corresponding to each of the multiple typical operating conditions; The multiple coefficient values ​​are determined based on multiple optimal output voltage reference values.

3. The method according to claim 2, characterized in that, Based on multiple optimal output voltage reference values, the multiple coefficient values ​​are determined, including: Based on multiple optimal output voltage reference values, determine the active power value and reactive power value of the power distribution transformer corresponding to the multiple typical operating conditions, respectively. By fitting the relationships between the multiple optimal output voltage reference values, the multiple active power values ​​under multiple operating conditions, and the multiple reactive power values ​​under multiple operating conditions, the multiple coefficient values ​​are determined.

4. The method according to claim 2, characterized in that, By fitting the relationships between the multiple optimal output voltage reference values, the multiple active power values ​​under multiple operating conditions, and the multiple reactive power values ​​under multiple operating conditions, the multiple coefficient values ​​are determined, including at least one of the following: By using a target method, the relationship between the multiple optimal output voltage reference values, multiple active power values ​​under multiple operating conditions, and multiple reactive power values ​​under multiple operating conditions is fitted to determine the multiple coefficient values. The target method includes any one of the following: least squares method, neural network fitting method.

5. The method according to claim 2, characterized in that, Based on the power parameters corresponding to multiple power-related nodes in the power distribution network, several typical operating conditions are established, including: When the power parameters include historical power data, the power probability distributions corresponding to multiple time periods are determined based on the historical power data of the multiple power-related nodes. In the corresponding probability distribution, the same number of data points are taken for each node to obtain typical data corresponding to the multiple power-related nodes respectively; Based on the typical data corresponding to the multiple power-related nodes, the multiple typical operating conditions are determined.

6. The method according to claim 2, characterized in that, Before retrieving the objective function and corresponding constraints for the target distribution network, the process also includes: The power flow equation constraints are determined, wherein the power flow equation constraints include line voltage drop constraints, power-current relationship constraints, node active power balance constraints, and node reactive power balance constraints.

7. The method according to claim 2, characterized in that, Before retrieving the objective function and corresponding constraints for the target distribution network, the process also includes: The constraints of the magnetic circuit hybrid distribution transformer are determined, wherein the constraints of the magnetic circuit hybrid distribution transformer include output voltage reference constraints, transformer active power balance constraints, transformer reactive power balance constraints, upper limit constraints of output voltage, and lower limit constraints of output voltage.

8. A voltage control device for a magnetic circuit hybrid distribution transformer in a power distribution network, characterized in that, include: The acquisition module is used to acquire voltage control commands corresponding to the magnetic circuit hybrid distribution transformer in the distribution network. The voltage control commands carry active power and reactive power values. The magnetic circuit hybrid distribution transformer includes a main transformer, a series converter, and a parallel converter. The main transformer includes a primary winding, a secondary winding, and an auxiliary winding. The AC side of the series converter is connected in series with the auxiliary winding. The AC side of the parallel converter is connected in parallel with the secondary winding to form a parallel node. The DC side of the series converter and the DC side of the parallel converter are connected back-to-back via DC voltage. The primary winding is used to connect to the power supply side of the distribution network, and the parallel node is used to connect to the load side of the distribution network. The retrieval module is used to retrieve the voltage control function corresponding to the distribution network in response to the voltage control command. The voltage control function includes multiple coefficient values, including constant coefficient values, a first coefficient value corresponding to the active power term, and a second coefficient value corresponding to the reactive power term. The multiple coefficient values ​​are determined based on an objective function that aims to minimize the overall voltage deviation index in the distribution network under multiple typical operating conditions. The determination module is used to determine the target output voltage reference value based on the active power value, the reactive power value, and the voltage control function; The generation module is used to generate a voltage control command corresponding to the series converter based on the target output voltage reference value, so as to control the voltage of the series converter, adjust the voltage of the auxiliary winding, and adjust the output voltage of the magnetic circuit hybrid distribution transformer to the target output voltage reference value.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the voltage control method for a magnetic circuit hybrid distribution transformer in a distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the voltage control method corresponding to the magnetic circuit hybrid distribution transformer in the distribution network as described in any one of claims 1 to 7.

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