Active power distribution network SVC reactive voltage control method and system based on improved particle swarm

Abstract

The invention discloses an SVC reactive voltage control method and system based on an improved particle swarm active power distribution network, and relates to the technical field of power distribution network planning, and the method comprises the steps: building a distributed photovoltaic output model, and designing a first strategy; establishing a first constraint by taking the minimum voltage deviation of the power distribution network as a target function; and calculating the optimal position and the maximum objective function value of the particles, verifying the number of iterations and outputting the number of iterations. According to the SVC reactive voltage control method based on the improved particle swarm active power distribution network, detailed modeling is carried out on the output characteristics of a distributed photovoltaic power generation system, the adaptability of the whole power distribution network under the photovoltaic access condition is improved, the voltage deviation of the power distribution network is reduced through accurate adjustment of reactive compensation, and the stability of the power distribution network is improved. By improving the random weight in the particle swarm algorithm, the search capability of the algorithm between the global optimal solution and the local optimal solution is improved, and the method achieves better effects in the aspects of adaptability, robustness and response capability.

Description

Technical Field

[0001] The invention relates to the technical field of distribution network planning, and in particular to a reactive voltage control method of an SVC active distribution network based on an improved particle swarm. Background Art

[0002] In the 21st century with the rapid development of science and technology, energy utilization has become a hot topic today. The extensive application of fossil energy has led to serious environmental problems. In order to change the current energy structure, new energy has been vigorously advocated. Distributed photovoltaics, as a renewable energy source, has the advantages of flexible installation, clean and environmentally friendly. It adheres to the principles of local power generation, grid connection, conversion, and utilization, and can effectively increase power generation. It changes the traditional unidirectional power supply mode and makes the flow of electricity more flexible and efficient. However, due to the randomness and uncertainty of distributed photovoltaic output, after being incorporated into the distribution network, the direction of the power grid flow will change. In addition, it may also increase the node voltage level and even cause voltage over-limit, thus affecting the safe and stable operation of the distribution network. Therefore, the voltage quality of the distribution network has always been the focus of people's attention, and reactive power optimization can solve the voltage over-limit problem caused by the access of distributed photovoltaics to the distribution network.

[0003] Reactive power optimization is to adjust the active and reactive power of distributed power sources by adjusting the reactive power compensator. Its purpose is to improve the power flow distribution and voltage quality in the distribution network, which has a good effect on improving the voltage level of the distribution network. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing reactive voltage control method has the randomness and uncertainty of distributed photovoltaic output, the reactive power optimization is insufficient, and how to reduce the optimization problem of distribution network voltage deviation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: based on an improved particle swarm active distribution network SVC reactive voltage control method, the method includes establishing a distributed photovoltaic output model and designing a first strategy; taking minimizing the distribution network voltage deviation as the objective function and establishing a first constraint; calculating the optimal position of the particle and the maximum objective function value, verifying the number of iterations and outputting them.

[0007] As a preferred solution of the improved particle swarm active distribution network SVC reactive voltage control method described in the present invention, wherein: the establishment of a distributed photovoltaic output model includes using the distributed photovoltaic output model to analyze voltage fluctuations and power flow reversal conditions under high photovoltaic penetration, and adjusting the model parameters to reflect the photovoltaic output characteristics under different meteorological conditions.

[0008] As a preferred solution of the improved particle swarm active distribution network SVC reactive voltage control method described in the present invention, wherein: the design of the first strategy includes adjusting the distribution of reactive power in real time to cope with voltage fluctuations in the distribution network, and using the first device to dynamically adjust the reactive power between nodes according to the load conditions of different nodes.

[0009] As a preferred solution of the reactive voltage control method of SVC active distribution network based on improved particle swarm active distribution network described in the present invention, wherein: the objective function of minimizing the voltage deviation of the distribution network includes the objective function combined with the impact of distributed photovoltaic access to the distribution network on the voltage, and the voltage fluctuation is controlled by the constraint conditions of the voltage difference of each node.

[0010] As a preferred solution of the improved particle swarm active distribution network SVC reactive voltage control method described in the present invention, wherein: the establishment of the first constraint includes managing the voltage and power in the system under the first constraint condition, and stabilizing the system voltage by regulating and limiting the voltage level and output power range.

[0011] As a preferred solution of the reactive voltage control method of the active distribution network SVC based on improved particle swarm described in the present invention, the calculation of the optimal position of the particle and the maximum objective function value includes solving the voltage control model through the improved particle swarm algorithm, increasing the random weight to improve the global search capability, obtaining the optimal solution through multiple iterations, and updating the particle position and speed until the target conditions are met.

[0012] As a preferred solution of the reactive voltage control method of the SVC active distribution network based on the improved particle swarm according to the present invention, the verification of the number of iterations and the output thereof include verifying whether the number of iterations meets the requirements, if so, outputting the final result, if not, repeating the iterative calculation until the number of iterations is met, and comparing the voltage levels of the distribution network before and after participating in the reactive compensation, respectively, to verify whether the reactive compensation strategy improves the voltage level.

[0013] Another object of the present invention is to provide a reactive voltage control system based on an improved particle swarm active distribution network SVC, which can set constraints with the goal of minimizing voltage deviation through a function constraint module to ensure the optimal allocation of reactive power compensation, solving the current problem of being unable to effectively deal with voltage fluctuations and over-limit caused by high photovoltaic penetration rates.

[0014] As a preferred solution of the improved particle swarm active distribution network SVC reactive voltage control system described in the present invention, it includes: a model strategy module, a function constraint module, and a calculation verification module; the model strategy module is used to establish a distributed photovoltaic output model and design a first strategy; the function constraint module is used to establish a first constraint with minimizing the distribution network voltage deviation as the objective function; the calculation verification module is used to calculate the optimal position of the particle and the maximum objective function value, verify the number of iterations and output.

[0015] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor executes the computer program to implement the steps of a reactive voltage control method based on an improved particle swarm active distribution network SVC.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a reactive voltage control method based on an improved particle swarm active distribution network SVC are implemented.

[0017] Beneficial effects of the present invention: The reactive voltage control method of the SVC active distribution network based on the improved particle swarm algorithm provided by the present invention improves the adaptability of the entire distribution network under the condition of photovoltaic access by modeling the output characteristics of the distributed photovoltaic power generation system in detail, reduces the voltage deviation of the distribution network through precise adjustment of reactive compensation, makes the voltage of the system more stable when photovoltaic access is applied, reduces the risk of equipment loss and grid failure caused by voltage fluctuations, and improves the flexibility and robustness of the voltage control of the distribution network by formulating a mathematical optimization model with the goal of minimizing the voltage deviation. By improving the random weights in the particle swarm algorithm, the algorithm's search capability between the global and local optimal solutions is improved. By dynamically adjusting and optimizing the particle swarm solution, the adaptability and responsiveness of the grid are improved. The actual effect of the SVC reactive compensation strategy in optimizing the voltage of the distribution network is verified through simulation and comparative analysis. The present invention achieves better results in terms of adaptability, robustness and responsiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0019] Figure 1 The overall flow chart of the reactive voltage control method of SVC in the active power distribution network based on the improved particle swarm is provided for the first and second embodiments of the present invention.

[0020] Figure 2 A schematic diagram of a distributed photovoltaic access distribution network based on an improved particle swarm active distribution network SVC reactive voltage control method provided in the first and second embodiments of the present invention.

[0021] Figure 3 A schematic diagram of a calculation model of an improved particle swarm active power distribution network SVC reactive power and voltage control method provided for the first and second embodiments of the present invention.

[0022] Figure 4 A schematic diagram of an IEEE 33-node system based on an improved particle swarm active power distribution network SVC reactive voltage control method provided in the third embodiment of the present invention.

[0023] Figure 5 A schematic diagram of voltage distribution based on an improved particle swarm active power distribution network SVC reactive voltage control method provided in the third embodiment of the present invention.

[0024] Figure 6 This is an overall module diagram of the SVC reactive voltage control system based on the improved particle swarm active distribution network provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] Example 1

[0027] Reference Figure 1-Figure 3 , which is an embodiment of the present invention, provides a reactive voltage control method for SVC active power distribution network based on improved particle swarm active power distribution network, including:

[0028] S1: Establish a distributed photovoltaic output model and design the first strategy.

[0029] Furthermore, a distributed photovoltaic output model is established, including the distributed photovoltaic output model used to analyze voltage fluctuations and power flow reversal conditions under high photovoltaic penetration rates, and the photovoltaic output characteristics under different meteorological conditions are reflected by adjusting the model parameters.

[0030] It should be noted that due to the randomness and uncertainty of the output of distributed photovoltaics, when the photovoltaic penetration rate is high, it will cause the voltage in the distribution network to exceed the limit or even the power flow to reverse. In the process of converting solar energy into electrical energy, the power generation efficiency of photovoltaic energy production will be affected by many factors such as solar irradiance, installation angle and temperature. Among them, the most important factor is solar radiation, which is related to air conditions, the angle between light and the ground, the duration of radiation, geographical coordinates and many other factors. In different weather environments, light radiation and temperature will vary greatly, resulting in volatility and randomness of distributed photovoltaic power generation. The distributed photovoltaic output model is expressed as:

[0031]

[0032] Where P is the photovoltaic output active power, P m is the maximum power of photovoltaic output, α and β are shape distribution parameters.

[0033] Furthermore, designing the first strategy includes coping with voltage fluctuations in the distribution network by adjusting the distribution of reactive power in real time, and dynamically adjusting the reactive power between nodes according to the load conditions of different nodes using the first device.

[0034] In the embodiment of the present application, the first strategy is the SVC reactive power compensation strategy. Due to the high impedance ratio and typical radial structure of the distribution network, the current is large and the load is relatively dispersed. It has higher active power loss and greater voltage deviation than the transmission network. The distribution network structure containing distributed photovoltaics is as follows: Figure 2 As shown, the voltage deviation between any two nodes i and j in the distribution network is expressed as:

[0035]

[0036]

[0037] Among them, P j is the active power of node j, Q i is the reactive power of node i, U j is the voltage at node j, R ij and X ij are the resistance and reactance values ​​between lines ij respectively. Reactive power plays a vital role in determining voltage deviation and network loss in the distribution network. Changes in reactive power will change the stability of the bus voltage and even the safety function of the distribution network. Therefore, the stability of reactive power is crucial to ensuring voltage quality.

[0038] In an optional embodiment, the first strategy may also be implemented in other ways, such as a reactive power regulation strategy based on dynamic voltage feedback, where the reactive power regulation strategy based on dynamic voltage feedback uses sensors to collect voltage data of each node;

[0039] Calculate the reactive power demand of each node based on the deviation between the set voltage target and the actual voltage;

[0040] According to the reactive power demand, the trigger angle of each SVC is adjusted in real time to optimize reactive power distribution to reduce voltage fluctuations;

[0041] This strategy quickly adjusts reactive power compensation through real-time voltage feedback. It is suitable for scenarios with large load fluctuations and effectively avoids voltage fluctuations exceeding the limit due to delayed adjustment. By dynamically adjusting the voltage of each node, it keeps the power grid operating within the compliance range and reduces equipment losses and operational instability caused by voltage fluctuations.

[0042] In an optional embodiment, the first strategy may also be implemented in other ways, such as a reactive compensation pre-regulation strategy based on predictive analysis to collect information such as historical power generation data based on the photovoltaic system, weather forecast (such as sunshine intensity, temperature, etc.), and perform data preprocessing and analysis;

[0043] Use historical data to train a prediction model (such as a support vector regression model or an artificial neural network) to predict voltage trends over a period of time in the future;

[0044] According to the reactive power demand output by the prediction model, the trigger settings of the SVC are adjusted before the change occurs to cope with possible future voltage changes;

[0045] By predicting future voltage fluctuations and performing reactive power compensation adjustments in advance, the impact on the power grid caused by sudden fluctuations can be effectively reduced. This strategy is suitable for areas with a high proportion of photovoltaic power generation access, and can complete pre-adjustment before fluctuations occur, thereby reducing the impact of photovoltaic output changes on the stability of the power grid voltage.

[0046] In the embodiment of the present application, the first device is an SVC (static VAR compensator), which is one of the most advanced reactive power compensation devices in current technology. SVC does not rely on capacitors to provide reactive power, but achieves reactive power compensation through high-frequency switching of power electronic devices. SVC is a reactive power compensation device composed of capacitors and reactors. By adjusting the trigger delay angle of the thyristor, the purpose of continuously adjusting reactive power can be achieved. This form of compensation is mainly used in medium and high voltage distribution systems, and is particularly suitable for situations such as large loads and severe harmonic problems.

[0047] In an optional embodiment, the first device may also be implemented in other ways, such as an SVC device based on a thyristor-controlled reactor (TCR), in which a thyristor is connected in series with the reactor to control the equivalent reactance value of the reactor, and the TCR may control the time for the current to flow through the reactor by adjusting the conduction angle of the thyristor;

[0048] During the operation of the distribution network, the changes in reactive power demand are monitored through voltage sensors and reactive power measurement devices;

[0049] According to the measured reactive power demand, the trigger angle of the thyristor is adjusted, thereby dynamically adjusting the current of the reactor to achieve continuous regulation of reactive power;

[0050] The TCR device can quickly respond to changes in reactive power demand by adjusting the trigger angle of the thyristor. It is suitable for scenarios that require continuous reactive power regulation. It can maintain a stable grid voltage level by adjusting the reactance value in real time. It is suitable for distribution networks with large load fluctuations and frequent changes in reactive power demand.

[0051] In an optional embodiment, the first device may also be implemented in other ways, such as a SVC device based on a parallel controllable capacitor bank (TSC), in which multiple capacitor banks are configured in the SVC, and each group of capacitors is connected in parallel with a thyristor to achieve rapid switching on or off of the capacitor;

[0052] According to the measured reactive power demand, capacitor banks of different capacities are switched in or out in sequence to achieve the target reactive power compensation value;

[0053] The control system automatically determines the switching status of each group of capacitors to achieve accurate regulation of reactive power and reduce voltage fluctuations;

[0054] TSC can quickly realize hierarchical control of reactive power compensation and effectively avoid the problems of over-compensation and under-compensation of reactive power. It is suitable for occasions where the load changes infrequently but reactive power needs to be adjusted quickly. The reactive power compensation effect of this method is relatively direct and rapid, and it is very suitable for scenarios where the load does not fluctuate frequently but requires rapid compensation.

[0055] Example 2

[0056] Reference Figure 1-Figure 3 , which is an embodiment of the present invention, provides a reactive voltage control method for SVC active power distribution network based on improved particle swarm active power distribution network, including:

[0057] S2: Establish the first constraint with minimizing the voltage deviation of the distribution network as the objective function.

[0058] Furthermore, the objective function is to minimize the voltage deviation of the distribution network, including the impact of the distributed photovoltaic access to the distribution network on the voltage, and the voltage fluctuation is controlled by the constraint conditions of the voltage difference of each node.

[0059] It should be noted that the objective function of minimizing the voltage deviation of the distributed photovoltaic distribution network is expressed as:

[0060]

[0061] Among them, U i is the voltage at node i, is the reference voltage, U imax and U imin are the maximum and minimum voltages of node i, respectively, and n is the total number of nodes.

[0062] Furthermore, establishing the first constraint includes managing the voltage and power in the system under the first constraint condition, and stabilizing the system voltage by regulating and limiting the voltage level and the output power range.

[0063] In the embodiment of the present application, the first constraint is the reactive power output constraint of the SVC, the node voltage constraint, and the distributed photovoltaic output constraint;

[0064] The reactive power output constraint of SVC is expressed as:

[0065] Q SVC,jmin ≤Q SVC,jt ≤Q SVC,jmax

[0066] Among them, Q SVC,jmin is the minimum reactive power output by SVC, Q SVC,jmax is the maximum reactive power output by SVC, Q SVC,jt is the reactive power output by SVC at node j;

[0067] The node voltage constraint is expressed as:

[0068] U imin ≤U≤U imax

[0069] Among them, U imin is the minimum voltage of node i, U imax is the maximum value of the voltage at node i;

[0070] The distributed photovoltaic output constraint is expressed as:

[0071] P PV,min ≤P PV ≤P PV,max

[0072] Among them, P PV,min and P PV,max are the minimum and maximum values ​​of distributed photovoltaic output respectively.

[0073] S3: Calculate the optimal particle position and the maximum objective function value, verify the number of iterations and output them.

[0074] Furthermore, the calculation of the optimal particle position and the maximum objective function value includes solving the voltage control model by improving the particle swarm algorithm, increasing the random weight to enhance the global search capability, obtaining the optimal solution through multiple iterations, and updating the particle position and velocity until the target conditions are met.

[0075] It should be noted that in order to avoid the dilemma of local optimal solution in particle swarm algorithm, the improved particle swarm algorithm is used as follows: Figure 3 As shown in the figure, the random weight ω is introduced to improve the local and global search capabilities of the algorithm. The optimal solution can be found in the process of randomly generating ω, which is expressed as:

[0076]

[0077] Among them, rand(0,1) is a randomly generated number between 0 and 1, N(0,1) is the standard normal distribution function, μ is the average random weight, and μ min and μ max are the minimum and maximum values ​​of the random weights, σ ​​is the variance of the random weights, the algorithm parameters are initialized, the number of iterations is determined, the initial particle swarm and their positions are generated, the optimal solution is determined according to the algorithm formula, the position and velocity of each particle are updated according to the optimal solution, and the probability flow calculation is performed again to obtain the objective function value of the particle, which is compared with the result of the previous iteration to obtain the optimal position and maximum objective function value of the particle.

[0078] Furthermore, verifying the number of iterations and outputting the verification includes verifying whether the number of iterations meets the requirements.

[0079] It should be noted that if it meets the requirements, the final result will be output; if it does not meet the requirements, the iterative calculation will be repeated until the number of iterations is met. The voltage levels of the distribution network before and after reactive compensation are compared to verify whether the reactive compensation strategy improves the voltage level.

[0080] Example 3

[0081] Reference Figure 4-Figure 5 , which is an embodiment of the present invention, provides a reactive voltage control method for SVC active distribution network based on improved particle swarm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0082] like Figure 4 and Figure 5 As shown, Figure 4 This is a schematic diagram of the IEEE33 node system. Figure 5This is a schematic diagram of the voltage deviation distribution of the present invention. According to the simulation results, when distributed photovoltaics are not connected, it can be seen that the voltage level of the distribution network is obviously low. After distributed photovoltaics are connected, the problem of low voltage at the end of the distribution network is partially solved. However, some busbars have voltage limits that threaten the safe and stable operation of the distribution network. After adopting the SVC reactive compensation strategy for reactive power optimization, the voltage quality is significantly improved. At the same time, the voltage deviation is also reduced. The problem of voltage limit exceeding the distribution network caused by distributed photovoltaic access is effectively solved. The voltage improvement effect of the method of the present invention is proved.

[0083] Table 1 Statistics of various reactive power compensation equipment

[0084]

[0085]

[0086] Example 4

[0087] Reference Figure 6 , which is an embodiment of the present invention, provides an SVC reactive voltage control system based on an improved particle swarm active distribution network, including: a model strategy module, a function constraint module, and a calculation verification module.

[0088] The model strategy module is used to establish a distributed photovoltaic output model and design the first strategy; the function constraint module is used to establish the first constraint with minimizing the voltage deviation of the distribution network as the objective function; the calculation verification module is used to calculate the optimal position of the particle and the maximum objective function value, verify the number of iterations and output it.

[0089] If the function is implemented in the form of 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0091] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0092] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. The SVC reactive voltage control method of active distribution network based on improved particle swarm is characterized by: include: Establish a distributed photovoltaic output model and design the first strategy; Taking minimizing the voltage deviation of the distribution network as the objective function, the first constraint is established; Calculate the optimal particle position and the maximum objective function value, verify the number of iterations and output them.

2. The method for controlling reactive power and voltage of SVC active power distribution network based on improved particle swarm as claimed in claim 1, characterized in that: The establishment of a distributed photovoltaic output model includes using the distributed photovoltaic output model to analyze voltage fluctuations and power flow reversal conditions under high photovoltaic penetration rates, and adjusting model parameters to reflect photovoltaic output characteristics under different meteorological conditions.

3. The method for reactive voltage control of SVC active power distribution network based on improved particle swarm as claimed in claim 2, characterized in that: The first strategy for designing includes coping with voltage fluctuations in the distribution network by adjusting the distribution of reactive power in real time, and dynamically adjusting the reactive power between nodes according to the load conditions of different nodes using the first device.

4. The method for controlling reactive power and voltage of SVC active power distribution network based on improved particle swarm as claimed in claim 3 is characterized by: The objective function of minimizing the voltage deviation of the distribution network includes combining the objective function with the impact of distributed photovoltaic access to the distribution network on the voltage, and controlling the voltage fluctuation through the constraint conditions of the voltage difference of each node.

5. The method for controlling reactive voltage of SVC active power distribution network based on improved particle swarm as claimed in claim 4 is characterized by: The establishing of the first constraint includes managing the voltage and power in the system under the first constraint condition, and stabilizing the system voltage by regulating and limiting the voltage level and the output power range.

6. The method for controlling reactive power and voltage of SVC active power distribution network based on improved particle swarm as claimed in claim 5, characterized in that: The calculation of the particle optimal position and the maximum objective function value includes solving the voltage control model by improving the particle swarm algorithm, increasing the random weight to improve the global search capability, obtaining the optimal solution through multiple iterations, and updating the particle position and speed until the target condition is met.

7. The method for controlling reactive power and voltage of SVC active power distribution network based on improved particle swarm as claimed in claim 6, characterized in that: The verification and output of the number of iterations includes whether the verification iterations meet the requirements. If so, the final result is output; if not, the iterative calculation is repeated until the number of iterations is met. The voltage levels of the distribution network before and after participating in reactive compensation are compared to verify whether the reactive compensation strategy improves the voltage level.

8. A system using the improved particle swarm active power distribution network SVC reactive power voltage control method according to any one of claims 1 to 7, characterized in that: Includes model strategy module, function constraint module, and calculation verification module; The model strategy module is used to establish a distributed photovoltaic output model and design a first strategy; The function constraint module is used to establish a first constraint by taking minimizing the voltage deviation of the distribution network as the objective function; The calculation verification module is used to calculate the optimal position of the particle and the maximum objective function value, verify the number of iterations and output them.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the reactive voltage control method based on an improved particle swarm active power distribution network SVC according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the reactive voltage control method based on an improved particle swarm active power distribution network SVC according to any one of claims 1 to 7 are implemented.

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