Voltage support SVG regulation and control method and system of new energy weak sending end

Abstract

The invention relates to the technical field of voltage support in a power system, in particular to a voltage support SVG regulation and control method and system for a new energy weak transmission end, and the method comprises the steps: determining and optimizing key parameters of an SVG device through a multi-dimensional voltage support optimization algorithm, and meeting the voltage support requirements; through a dynamic working condition self-adaptive voltage regulation and control algorithm, a control strategy is optimized, and real-time output of the SVG is automatically adjusted; an intelligent SVG multi-criterion type selection algorithm is utilized to select equipment, and system integration and debugging are completed; an SVG device is continuously optimized through real-time data monitoring and a global feedback optimization algorithm, and real-time voltage support is achieved; according to the optimized SVG model selection and control strategy, the voltage supporting capacity of a new energy weak sending end system is improved, it is ensured that the voltage is kept stable under the large disturbance and complex working conditions, the equipment requirement and cost are reduced, the resource utilization efficiency is improved, the dynamic response speed is improved, and guarantee is provided for stable operation of the new energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage support in power systems, and in particular to a voltage support SVG control method and system for a new energy weak sending end. Background Art

[0002] With the rapid development of renewable energy around the world, a high proportion of new energy systems have gradually replaced traditional large power grids and become an important part of power supply. However, compared with traditional power grids, new energy systems use a large number of power electronic equipment to connect to the grid, resulting in a relatively weak grid structure and poor dynamic response capabilities of the system. Especially when a large disturbance occurs in the system, the existing static VAR generator (SVG) device is difficult to meet the voltage support requirements and cannot effectively respond to the voltage fluctuations of the system in a short time. In this case, the new energy system not only faces voltage fluctuations and overvoltage problems, but may also cause system stability to decrease due to delayed equipment response.

[0003] Existing SVG technology is mainly used for voltage support in traditional power grids, and its design and control strategies are usually aimed at strong grid structures. However, when this technology is directly applied to the weak sending-end system of new energy, the SVG device shows many shortcomings when facing complex operating conditions. For example, under large disturbance conditions, the SVG device may not be able to adjust the output in a timely and effective manner, resulting in an increase in the system overvoltage level after the short-circuit fault is cleared. In this case, in order to meet the requirements of transient overvoltage, some schemes introduce phase-shifting configurations to enhance the voltage support capability of the system. However, although the phase-shifting scheme performs well in tolerating system transient overvoltage, it has a high investment cost and its dynamic response speed is not as fast as SVG. In addition, the scheme using phase-shifting may cause the SVG devices that have been put into operation to be idle, resulting in low resource utilization efficiency.

[0004] In summary, the voltage support SVG control method and system for the weak-transmission end of new energy is intended to solve the problem in the prior art that the SVG device in the weak-transmission end system of new energy is difficult to effectively cope with voltage fluctuations under large disturbance conditions. Specifically, the invention optimizes the equipment selection and control strategy of SVG, improves the response speed and voltage support capability of SVG under transient and dynamic conditions, and thus improves the reliability and economy of the system, ensuring that the weak-transmission end system of new energy can maintain a stable voltage level under complex operating conditions. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the technical problem to be solved by the present invention is: by optimizing equipment selection and control strategy, the dynamic response speed and stability of the SVG device under large disturbances and complex working conditions are enhanced, ensuring that the system can maintain a stable voltage level under different working conditions, thereby improving the reliability and economy of the entire new energy system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions, a voltage support SVG control method for the weak sending end of new energy, including: determining and optimizing the key parameters of the SVG device to meet the voltage support requirements through a multi-dimensional voltage support optimization algorithm; optimizing the control strategy through a dynamic condition adaptive voltage control algorithm, and automatically adjusting the output of the SVG under real-time conditions; utilizing an intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging; and continuously optimizing the SVG device through real-time data monitoring and a global feedback optimization algorithm to achieve optimized SVG control of real-time voltage support.

[0008] As a preferred solution of the voltage support SVG control method for the weak sending end of new energy described in the present invention, wherein: the multi-dimensional voltage support optimization algorithm includes initialization parameter setting, calculation of objective function, checking constraint conditions, parameter adjustment and iteration, and determination of final parameters to complete the optimization of key parameters of the SVG device to meet the voltage support requirements;

[0009] The computational objective function is expressed as:

[0010]

[0011] in, represents the objective function, ω i Represents the weight coefficient, V ref Indicates the reference voltage value, V i represents the actual voltage value of the i-th node, λ represents the penalty coefficient, ΔP j represents the active power adjustment of the jth SVG device, ΔQ j represents the reactive power adjustment of the jth SVG device, n represents the number of nodes, and m represents the number of SVG devices; if the objective function value exceeds 20% of the preset threshold, it means that the parameter setting of the current SVG device cannot meet the voltage support requirement, and optimization is performed;

[0012] The constraints are expressed as:

[0013]

[0014] in, represents the sensitivity of node voltage to active power, Indicates the sensitivity of voltage to reactive power; if the constraints are not met, the voltage support requirement cannot be met;

[0015] The initialization parameter setting sets the initial active power adjustment amount and reactive power adjustment amount of the SVG device; wherein the initialization parameters include a voltage reference value, a node voltage value, a regulation coefficient, an active power adjustment amount, and a reactive power adjustment amount;

[0016] The parameter adjustment and iteration are based on the objective function and constraints, and the deviation of the node voltage from the reference voltage. j and ΔQ j If the node voltage deviation exceeds 5% of the preset deviation, the SVG device power is adjusted to reduce the deviation. By calculating the adjusted objective function value, an optimal parameter set is obtained in which the objective function value is less than 1% of the preset threshold and the constraint condition is satisfied.

[0017] The final parameters are determined by initializing parameter settings, calculating objective function values, checking constraints, adjusting parameters and iterating to determine the active power adjustment amount ΔP of the SVG device. j and reactive power adjustment ΔQ j , which is the key parameter after optimization.

[0018] As a preferred solution of the voltage support SVG control method of the new energy weak sending end described in the present invention, wherein: the dynamic working condition adaptive voltage control algorithm includes an adaptive voltage control objective function and dynamic constraints;

[0019] The adaptive voltage regulation objective function is expressed as:

[0020]

[0021] in, represents the objective function of adaptive voltage regulation;

[0022] The dynamic constraint condition is expressed as:

[0023]

[0024] Among them, γ represents the dynamic adjustment coefficient, which is used to adjust the influence of the voltage change rate over time. Represents the rate of change of node voltage over time.

[0025] As a preferred solution of the voltage support SVG control method of the new energy weak sending end described in the present invention, the optimization control strategy includes minimizing the square difference between the node voltage and the participating voltage, minimizing ΔQ j ;

[0026] Calculate the node voltage deviation (V ref -V i), when the node voltage deviation exceeds the preset threshold of 5%, the SVG device’s ΔQ j , reduce the overall voltage deviation, and limit the reactive power adjustment range by controlling the penalty coefficient λ;

[0027] Real-time monitoring of node voltage V i and the rate of change of node voltage over time The calculated objective function, if the objective function value exceeds 20% of the preset threshold, indicates that the current working condition is unstable and optimization adjustment is performed; if the voltage change exceeds the γ preset range, dynamic correction is performed by adjusting the reactive power; according to the sensitivity Determine the node voltage fluctuation caused by reactive power adjustment. If the voltage fluctuation exceeds 1% of the preset threshold, recalculate and adjust ΔQ j ; Adjust ΔQ j After that, the constraint conditions are recalculated. If the constraint conditions are not met, adjustment is performed again. When the overall voltage deviation is less than 5% of the preset threshold and the constraint conditions are met, the adjustment is completed.

[0028] As a preferred solution of the voltage support SVG control method for the weak-transmitting end of new energy described in the present invention, the intelligent SVG multi-criteria selection algorithm is expressed as:

[0029]

[0030] Among them, Z j represents the comprehensive score of the jth SVG device, α, β, ζ, δ represent weight coefficients, represents the voltage regulation objective function value of the jth SVG device under real-time working conditions, λ j represents the power adjustment penalty coefficient of the jth SVG device, κ j represents the total cost of the jth SVG device, θ j represents the reliability coefficient of the jth SVG device;

[0031] According to the voltage support requirements, a preliminary screening is carried out, and the comprehensive score Z is calculated for the SVG equipment after the preliminary screening using the intelligent SVG multi-criteria selection algorithm. j , combining key parameters to measure the performance of SVG devices, and calculating the overall score Z j , select the highest rated SVG device;

[0032] After selecting the SVG equipment, carry out system integration and debugging, connect the selected SVG equipment to the system, and adjust key parameters according to the system operating status to meet the voltage support requirements.

[0033] As a preferred solution of the voltage support SVG control method of the new energy weak sending end described in the present invention, wherein: the global feedback optimization algorithm includes a global feedback objective function and a global feedback adjustment equation;

[0034] The global feedback objective function is expressed as:

[0035]

[0036] in, represents the global feedback optimization objective function value, ΔQ j,k represents the reactive power adjustment of the jth SVG device at the kth adjustment, and φ represents the adjustment coefficient;

[0037] The global feedback adjustment equation is expressed as:

[0038]

[0039] in, Represents the node voltage V i Reactive power adjustment ΔQ j,k Sensitivity;

[0040] The calculated objective function value is used to evaluate the current operating status of the SVG device. If the calculated objective function value is When it exceeds 20% of the preset threshold, it means that the system has a voltage deviation exceeding 5% of the preset deviation and a voltage change rate exceeding the preset threshold of 0.05, then optimization adjustment is performed; ΔQ is adjusted according to the global feedback adjustment equation j,k , reduce voltage deviation and control voltage change rate;

[0041] After optimization adjustment, recalculate the global feedback optimization objective function and global feedback adjustment equation to determine the system stable state. If the stable state is not reached, continue to optimize and adjust;

[0042] When the system reaches a stable state, record the final reactive power adjustment ΔQ j,k and key parameters, applied to the system and monitored in real time.

[0043] As a preferred solution of the voltage support SVG control method for the weak sending end of new energy described in the present invention, wherein: the optimized SVG control of the real-time voltage support includes voltage trend prediction, real-time response triggering, distributed adjustment priority, intelligent parameter adaptation and collaborative control mechanism;

[0044] The voltage trend prediction will monitor the node voltage V in real time. i and the rate of change of node voltage over time Use the prediction algorithm to predict the voltage trend. If the prediction result exceeds the preset voltage deviation of 5% and the voltage change rate exceeds the preset threshold of 0.05, optimization adjustment is performed;

[0045] The prediction algorithm is expressed as:

[0046]

[0047] Among them, V i (t+Δt) represents the node voltage at the predicted time, V i (t) represents the node voltage at the current moment, Δt represents the prediction time step, represents the predicted adjustment coefficient;

[0048] The real-time response trigger sets a real-time response mechanism to trigger optimization adjustment of the SVG device when the voltage deviation and the voltage change rate exceed a preset threshold;

[0049] The distributed adjustment priority classifies SVG devices into high priority and low priority according to the node voltage deviation and voltage change rate. If the node voltage deviation is greater than 5% of the set deviation and the voltage change rate is greater than the preset threshold value 0.05, it is a high priority. If the node voltage deviation is equal to 5% of the set deviation and the voltage change rate is equal to the preset threshold value 0.05, it is a low priority. Optimize and adjust the high priority. If the system has not reached a stable state, continue to adjust the low priority.

[0050] The intelligent parameter adaptation automatically adjusts the coefficient according to the real-time data to adapt to the voltage deviation and voltage change rate;

[0051] The collaborative control mechanism establishes an SVG device collaborative mechanism to synchronously adjust the SVG devices.

[0052] Another object of the present invention is to provide a voltage support SVG control system for the weak sending end of new energy. Through intelligent selection and adaptive control algorithms, efficient control and real-time optimization of the SVG device can be achieved, the dynamic response speed and stability of the system can be improved, the equipment investment cost can be reduced, and the economy and reliability of the system under complex working conditions can be ensured, thereby providing a more complete and efficient voltage support solution for the weak sending end system of new energy.

[0053] In order to solve the above technical problems, the present invention provides the following technical solutions: a voltage support SVG control system for a new energy weak sending end, comprising: a voltage optimization model, a working condition control model, an equipment selection model and a feedback control model;

[0054] The voltage optimization model determines and optimizes key parameters of the SVG device to meet voltage support requirements through a multi-dimensional voltage support optimization algorithm;

[0055] The working condition control model optimizes the control strategy and automatically adjusts the output of the SVG under real-time working conditions through a dynamic working condition adaptive voltage control algorithm;

[0056] The equipment selection model uses an intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging;

[0057] The feedback control model continuously optimizes the SVG device through real-time data monitoring and a global feedback optimization algorithm, thereby achieving optimized SVG control for real-time voltage support.

[0058] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the voltage support SVG control method for the new energy weak sending end as described above are implemented.

[0059] 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 voltage support SVG control method for the new energy weak sending end as described above are implemented.

[0060] Beneficial effects of the present invention: By optimizing the selection and control strategy of the SVG device, the voltage support capacity in the weak-transmitting-end system of new energy is significantly improved, ensuring that the system maintains voltage stability under large disturbances and complex working conditions. The introduction of dynamic working condition adaptive control algorithm and real-time data monitoring continuously optimizes the SVG operating state and improves system stability. Compared with the traditional phase-shifting phase configuration scheme, the present invention reduces equipment requirements and investment costs and improves resource utilization efficiency. The intelligent SVG selection and control algorithm improves the dynamic response speed, prevents overvoltage and undervoltage problems, improves the economy and adaptability of the system, and provides reliable guarantee for the stable operation of the new energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 A flow chart of a voltage support SVG control method for a new energy weak sending end provided by an embodiment of the present invention.

[0063] Figure 2 A voltage response curve diagram of a voltage support SVG control method for a new energy weak sending end provided by an embodiment of the present invention.

[0064] Figure 3A reactive power adjustment diagram of a voltage support SVG control method for a new energy weak sending end provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Example 1, reference Figure 1 , is an embodiment of the present invention, which provides a voltage support SVG control method for a new energy weak sending end, including:

[0067] S1: Through the multi-dimensional voltage support optimization algorithm, determine and optimize the key parameters of the SVG device to meet the voltage support requirements.

[0068] It should be noted that the multi-dimensional voltage support optimization algorithm includes initialization parameter setting, calculation of objective function, checking of constraints, parameter adjustment and iteration, and determination of final parameters to complete the optimization of key parameters of the SVG device to meet the voltage support requirements;

[0069] The objective function minimizes the voltage deviation and power regulation of the system by adjusting the active power and reactive power of the SVG equipment, which is expressed as:

[0070]

[0071] in, Represents the objective function, which is used to represent the total error and adjustment cost in the optimization process, ω i Represents the weight coefficient, V ref Indicates the reference voltage value, V i represents the actual voltage value of the i-th node, λ represents the penalty coefficient, which is used to balance the relationship between voltage deviation and power adjustment, ΔP j represents the active power adjustment of the jth SVG device, ΔQ j represents the reactive power adjustment of the jth SVG device, n represents the number of nodes, and m represents the number of SVG devices; if the objective function value exceeds 20% of the preset threshold, it means that the parameter setting of the current SVG device cannot meet the voltage support requirement, and optimization is performed;

[0072] The constraint condition ensures that the voltage change of each node will not destroy the stability of the system when power adjustment is performed, which is expressed as:

[0073]

[0074] in, Indicates the sensitivity of node voltage to active power and is used to calculate the impact of each adjustment on the overall system voltage. Indicates the sensitivity of voltage to reactive power; if the constraints are not met, the voltage support requirement cannot be met;

[0075] Further, initialization parameter setting is performed to set the initial active power adjustment amount and reactive power adjustment amount of the SVG device, which are determined based on experience, wherein the initialization parameters include a voltage reference value, a node voltage value, a regulation coefficient, an active power adjustment amount, and a reactive power adjustment amount;

[0076] Calculate the objective function value, using the given initial parameters, to calculate the objective function value

[0077] Check the constraints. After each adjustment of the power parameters, check whether the constraints are met.

[0078] Parameter adjustment and iteration, according to the objective function and constraints, according to the deviation of node voltage from the reference voltage, ΔP j and ΔQ j If the node voltage deviation exceeds 5% of the preset deviation, the SVG device power is adjusted to reduce the deviation. By calculating the adjusted objective function value, an optimal parameter set is obtained in which the objective function value is less than 1% of the preset threshold and the constraint condition is satisfied.

[0079] Determine the final parameters, and determine the active power adjustment ΔP of the SVG device by initializing parameter settings, calculating the objective function value, checking constraints, parameter adjustment and iteration j and reactive power adjustment ΔQ j , i.e. the optimized key parameters;

[0080] Furthermore, the objective function value exceeds the preset threshold by 20%: the 20% threshold is to provide a sensitivity standard so that timely adjustments can be made when system performance degrades to prevent insufficient voltage support capacity; the node voltage deviation exceeds the preset deviation by 5%: the 5% threshold is a relatively commonly used standard to ensure voltage stability and avoid over-adjustment; the objective function value is less than 1% of the preset threshold: the 1% threshold is to ensure that the system can respond quickly to small voltage changes to avoid cumulative effects leading to larger voltage fluctuations.

[0081] S2: Through the dynamic working condition adaptive voltage control algorithm, the control strategy is optimized to automatically adjust the output of SVG under real-time working conditions.

[0082] It should be noted that the dynamic operating condition adaptive voltage control algorithm includes an adaptive voltage control objective function and dynamic constraints;

[0083] The adaptive voltage control objective function is expressed as:

[0084]

[0085] in, represents the objective function of adaptive voltage regulation;

[0086] The dynamic constraints are expressed as:

[0087]

[0088]

[0089] Among them, γ represents the dynamic adjustment coefficient, which controls the rate of voltage change and ensures the smoothness of system response. Represents the rate of change of node voltage over time.

[0090] Furthermore, the optimization control strategy includes minimizing the square difference between the node voltage and the participating voltage, minimizing ΔQ j ;

[0091] Calculate the node voltage deviation (V ref -V i ), when the node voltage deviation exceeds the preset threshold of 5%, the SVG device’s ΔQ j , reduce the overall voltage deviation, and limit the reactive power adjustment range by controlling the penalty coefficient λ;

[0092] Furthermore, real-time monitoring of node voltage V i and the rate of change of node voltage over time The calculated objective function, if the objective function value exceeds 20% of the preset threshold, indicates that the current working condition is unstable and optimization adjustment is performed; if the voltage change exceeds the γ preset range, dynamic correction is performed by adjusting the reactive power; according to the sensitivity Determine the node voltage fluctuation caused by reactive power adjustment. If the voltage fluctuation exceeds 1% of the preset threshold, recalculate and adjust ΔQ j ; Adjust ΔQ j After that, the constraint conditions are recalculated. If the constraint conditions are not met, adjustment is performed again. When the overall voltage deviation is less than 5% of the preset threshold and the constraint conditions are met, the adjustment is completed.

[0093] S3: Use the intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging.

[0094] It should be noted that the intelligent SVG multi-criteria selection algorithm is expressed as:

[0095]

[0096] Among them, Z j It represents the comprehensive score of the jth SVG device, which is used to evaluate the performance of each device under multiple criteria. α, β, ζ, and δ represent weight coefficients, which correspond to the relative importance of criteria such as voltage support effect, reactive power adjustment efficiency, cost-effectiveness, and reliability, respectively. They can be set according to system requirements. It represents the voltage control objective function value of the jth SVG device under real-time working conditions, reflecting the effect of the SVG device on system voltage support under dynamic conditions. The smaller the value, the better the device performance. j represents the power adjustment penalty coefficient of the jth SVG device. The smaller the penalty coefficient, the higher the adjustment efficiency. j represents the total cost of the jth SVG device. The smaller the cost, the better the cost-effectiveness. j Represents the reliability coefficient of the jth SVG device.

[0097] Furthermore, according to the voltage support requirements, preliminary screening is carried out, and the comprehensive score Z is calculated for the SVG equipment after preliminary screening using the intelligent SVG multi-criteria selection algorithm. j , combining key parameters to measure the performance of SVG devices, and calculating the overall score Z j ,Select the SVG device with the highest score, which reflects the stability of the device in long-term operation. The larger the value, the more reliable the device;

[0098] Indicates the contribution value of voltage support effect and the objective function value of voltage regulation The smaller the value, the better the device can meet the voltage support requirements, so the comprehensive score Z j The higher;

[0099] Represents the contribution value of power adjustment efficiency and the penalty coefficient λ of power adjustment j The smaller it is, the smaller the negative impact of the equipment on reactive power adjustment is, so the higher the score of the equipment is;

[0100] Reflecting the contribution value of cost-effectiveness, the total cost κ j The lower the value, the higher the cost performance of the equipment. Therefore, the overall score is Z. j Also the higher;

[0101] δ·θ j Reflects the contribution value of equipment reliability, reliability coefficient θ jThe larger the value, the higher the possibility that the device can operate stably in the system for a long time, and thus the higher the score.

[0102] Furthermore, after selecting the SVG device according to the system requirements, system integration and debugging are carried out, the selected SVG device is connected to the system, and key parameters are adjusted according to the system operating status to meet the voltage support requirements.

[0103] S4: Through real-time data monitoring and global feedback optimization algorithm, the SVG device is continuously optimized to achieve optimized SVG control with real-time voltage support.

[0104] It should be noted that the global feedback optimization algorithm includes a global feedback objective function and a global feedback adjustment equation;

[0105] The global feedback objective function is expressed as:

[0106]

[0107] in, Represents the value of the global feedback optimization objective function, which is used to evaluate the optimization effect of the jth SVG device under the current working conditions. The smaller the value, the better. ΔQ j,k It represents the reactive power adjustment of the jth SVG device at the kth adjustment, which is used to correct the voltage deviation. φ represents the adjustment coefficient, which is specifically used to regulate the rate of change of voltage over time. Prevent the system from experiencing excessively rapid voltage fluctuations;

[0108] The global feedback adjustment equation is expressed as:

[0109]

[0110] in, Represents the node voltage V i Reactive power adjustment ΔQ j,k The sensitivity is used to correct voltage deviation.

[0111] Furthermore, the operating status of the current SVG device is evaluated by calculating the objective function value. If the calculated When it exceeds 20% of the preset threshold, it means that the system has a voltage deviation exceeding the preset deviation of 5% and a voltage change rate exceeding the preset threshold of 0.05, then optimization adjustment is performed; ΔQ is adjusted according to the global feedback adjustment equation j,k , reduce voltage deviation and control voltage change rate; among them, the voltage change rate of 0.05 represents the stability requirement of system response, which is to detect and correct rapid changes that may cause large fluctuations in system voltage, so as to maintain smooth operation of the system.

[0112] After each optimization adjustment, recalculate the global feedback optimization objective function and global feedback adjustment equation to determine the system stable state. If the stable state is not reached, continue to optimize and adjust, and repeat the steps until the system is stable.

[0113] When the system reaches a stable state, record the final reactive power adjustment ΔQ j,k and key parameters, applied to the system and monitored in real time.

[0114] Furthermore, the optimized SVG regulation for real-time voltage support includes voltage trend prediction, real-time response triggering, distributed adjustment priority, intelligent parameter adaptation, and collaborative control mechanism;

[0115] The voltage trend prediction will monitor the node voltage V in real time. i and the rate of change of node voltage over time Use the prediction algorithm to predict the voltage trend. If the prediction result exceeds the preset voltage deviation of 5% and the voltage change rate exceeds the preset threshold of 0.05, optimization adjustment is performed;

[0116] The prediction algorithm is expressed as:

[0117]

[0118] Among them, V i (t+Δt) represents the node voltage at the predicted time, V i (t) represents the node voltage at the current moment, Δt represents the prediction time step, represents the predicted adjustment coefficient;

[0119] Real-time response triggering: Set up a real-time response mechanism. When the voltage deviation and voltage change rate exceed the preset threshold, the optimization adjustment of the SVG equipment is triggered, and the stability of the monitoring system after the response trigger is monitored. If the voltage change tends to be stable after the response adjustment, continue monitoring; if the voltage fluctuation cannot be effectively alleviated, continue to optimize and adjust further;

[0120] Distributed adjustment priority: SVG devices are divided into high priority and low priority according to node voltage deviation and voltage change rate. If the node voltage deviation is greater than 5% of the set deviation and the voltage change rate is greater than the preset threshold of 0.05, it is high priority. If the node voltage deviation is equal to 5% of the set deviation and the voltage change rate is equal to the preset threshold of 0.05, it is low priority. Optimize and adjust the high priority. If the system has not reached a stable state, continue to adjust the low priority.

[0121] Intelligent parameter adaptation automatically adjusts the coefficient according to real-time data to adapt to voltage deviation and voltage change rate. If the system response stability after automatic adjustment is higher, the adaptation is considered effective; otherwise, the parameter setting is re-evaluated;

[0122] Cooperative control mechanism, establish a cooperative mechanism for SVG equipment, adjust SVG equipment synchronously, observe the voltage fluctuation amplitude and system stability after cooperative control, if the cooperative control reduces the voltage deviation and change rate, then maintain the mechanism; if the cooperative control causes the system to over-adjust, then adjust the cooperative strength.

[0123] The above is a schematic scheme of a voltage support SVG control method for a new energy weak sending end of this embodiment. It should be noted that the technical solution of the system of the voltage support SVG control method for the new energy weak sending end and the technical solution of the voltage support SVG control method for the new energy weak sending end belong to the same concept, and the details of the technical solution of the voltage support SVG control system for the new energy weak sending end in this embodiment that are not described in detail can all be referred to the description of the technical solution of the voltage support SVG control method for the new energy weak sending end.

[0124] The voltage support SVG control system at the weak sending end of new energy in this embodiment is characterized by comprising: a voltage optimization model, a working condition control model, an equipment selection model and a feedback control model;

[0125] The voltage optimization model determines and optimizes key parameters of the SVG device to meet voltage support requirements through a multi-dimensional voltage support optimization algorithm;

[0126] The working condition control model automatically adjusts the output of the SVG under real-time working conditions through a dynamic working condition adaptive voltage control algorithm and an optimized control strategy;

[0127] The equipment selection model uses an intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging;

[0128] The feedback control model continuously optimizes the SVG device through real-time data monitoring and a global feedback optimization algorithm, thereby achieving optimized SVG control for real-time voltage support.

[0129] This embodiment further provides a computing device, which is applicable to the voltage support SVG control method at the weak sending end of new energy, including:

[0130] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the voltage support SVG control method for the weak sending end of new energy as proposed in the above embodiment.

[0131] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the voltage support SVG control method for the new energy weak sending end proposed in the above embodiment is implemented.

[0132] The storage medium proposed in this embodiment and the voltage support SVG control method for the new energy weak sending end proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0133] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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, a server, or a network device, etc.) to perform all or part of the steps of the methods described in 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., which can store program codes.

[0134] 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.

[0135] 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, a plurality of 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.

[0136] Example 2, reference Figure 2-Figure 3, which is an embodiment of the present invention, provides a voltage support SVG control method for the weak sending end of new energy. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0137] Experimental setup

[0138] Experimental group: The new energy weak sending end voltage support SVG control method of the present invention is adopted, including a multi-dimensional voltage support optimization algorithm, a dynamic operating condition adaptive voltage control algorithm, an intelligent SVG multi-criteria selection algorithm and a global feedback optimization algorithm.

[0139] Control group: adopting the traditional phase-shifting configuration scheme, using only the standard SVG control strategy, excluding the optimization and intelligent control strategy proposed in the present invention.

[0140] Experimental procedures

[0141] 1. System modeling:

[0142] System modeling was carried out for the experimental group and the control group respectively, and a simulation environment was established.

[0143] Input the same grid structure, load variation and external disturbance conditions.

[0144] 2. Parameter settings:

[0145] Set the initial parameters of the SVG device, including active power adjustment, reactive power adjustment and dynamic adjustment coefficient.

[0146] Set voltage reference value V ref , penalty coefficient λ, weight coefficient ω i And other key parameters.

[0147] 3. Experimental Execution:

[0148] Experimental group: The dynamic working condition adaptive voltage control algorithm is used to monitor and adjust the output of the SVG equipment in real time. The global feedback optimization algorithm is used for continuous optimization to maintain voltage stability.

[0149] Control group: Use the traditional phase adjustment scheme or standard SVG control strategy to adjust the SVG device output.

[0150] 4. Data Collection:

[0151] Collect the node voltage V of the experimental group and the control group under different disturbance conditions i , reactive power adjustment Q j Voltage change rate And other data.

[0152] Special attention is paid to the system's voltage deviation, voltage recovery time and SVG regulation efficiency under large disturbances.

[0153] 5. Data Analysis:

[0154] The voltage fluctuation amplitude, voltage recovery time, system stability and other indicators were compared between the experimental group and the control group.

[0155] Calculate the objective function value And the optimized key parameter ΔQ j,k , analyze the performance of the system under different working conditions.

[0156] 6. Result verification:

[0157] The superiority of the method of the present invention is verified through experimental results, and it is analyzed whether the voltage support capability of the system is significantly improved and the equipment requirements and investment costs are reduced.

[0158] The experimental data are shown in Table 1 below:

[0159] Table 1 Experimental data table

[0160]

[0161] The experimental results are as follows Figure 2 , Figure 3 As shown:

[0162] Figure 2 : Displays the voltage response curves of the experimental group and the control group under external disturbance, and compares the voltage recovery time of the two.

[0163] The voltage recovery time of the experimental group after external disturbance is shorter and the voltage response curve is smoother, which shows that the new SVG control method adopted by the experimental group can restore the system voltage faster and has higher stability.

[0164] The voltage in the control group recovered more slowly after being disturbed and had larger voltage fluctuations, indicating that the traditional regulation strategy was inferior to the experimental group in terms of voltage recovery capability and system stability.

[0165] Voltage Response Curve: Voltage Response Curve

[0166] Title: Voltage Response Curve

[0167] Experiment Group: Experimental Group - This refers to the group to which the new method is applied for experimentation and is represented by a circular marker in the figure.

[0168] Control Group: Control Group-This refers to the group in which the experiment is conducted using traditional methods and is represented by square marks in the figure.

[0169] Time(s): The X-axis represents time in seconds.

[0170] Voltage (V): The Y-axis represents voltage in volts.

[0171] Figure 3 :To demonstrate the reactive power adjustment of SVG equipment in the experimental group and verify the effectiveness of intelligent regulation.

[0172] The SVG equipment in the experimental group was able to adjust reactive power faster, and the adjustment range of reactive power was larger during the entire experimental process, indicating that the experimental group's method was more effective in dynamically adjusting reactive power, and could better cope with voltage changes and improve system stability.

[0173] The reactive power adjustment of the control group was relatively slow and the adjustment amplitude was small, indicating that it was not as sensitive as the experimental group in responding to voltage disturbances.

[0174] Reactive Power Adjustment: Reactive power adjustment

[0175] Title: Reactive power regulation

[0176] Experiment Group: Experimental Group - This refers to the group to which the new method is applied for experimentation and is represented by a circular marker in the figure.

[0177] Control Group: Control Group-This refers to the group in which the experiment is conducted using traditional methods and is represented by square marks in the figure.

[0178] Time(s): The X-axis represents time in seconds.

[0179] Reactive Power (MVar): The Y-axis represents reactive power in megavars (MVar).

[0180] The experimental results show that the optimized SVG control method used by the experimental group is superior to the traditional method of the control group in terms of voltage recovery speed, reactive power adjustment capability and overall system stability. This shows that the optimization method can significantly improve the voltage support capability and stability of the new energy sending-end system.

[0181] 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. A voltage support SVG control method for a new energy weak sending end, characterized by: include: Through the multi-dimensional voltage support optimization algorithm, the key parameters of the SVG device are determined and optimized to meet the voltage support requirements; Through the dynamic working condition adaptive voltage control algorithm, the control strategy is optimized to automatically adjust the output of SVG under real-time working conditions; Use intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging; Through real-time data monitoring and global feedback optimization algorithms, the SVG device is continuously optimized to achieve optimized SVG regulation with real-time voltage support.

2. The voltage support SVG control method for the new energy weak sending end according to claim 1 is characterized in that: The multi-dimensional voltage support optimization algorithm includes initialization parameter setting, calculation of objective function, checking of constraint conditions, parameter adjustment and iteration, and determination of final parameters to complete optimization of key parameters of the SVG device to meet voltage support requirements; The computational objective function is expressed as: in, represents the objective function, ω i Represents the weight coefficient, V ref Indicates the reference voltage value, V i represents the actual voltage value of the i-th node, λ represents the penalty coefficient, ΔP j represents the active power adjustment of the jth SVG device, ΔQ j represents the reactive power adjustment of the jth SVG device, n represents the number of nodes, and m represents the number of SVG devices; if the objective function value exceeds 20% of the preset threshold, it means that the parameter setting of the current SVG device cannot meet the voltage support requirement, and optimization is performed; The constraints are expressed as: in, represents the sensitivity of node voltage to active power, Indicates the sensitivity of voltage to reactive power; if the constraints are not met, the voltage support requirement cannot be met; The initialization parameter setting sets the initial active power adjustment amount and reactive power adjustment amount of the SVG device; wherein the initialization parameters include a voltage reference value, a node voltage value, a regulation coefficient, an active power adjustment amount, and a reactive power adjustment amount; The parameter adjustment and iteration are based on the objective function and constraints, and the deviation of the node voltage from the reference voltage. j and ΔQ j If the node voltage deviation exceeds 5% of the preset deviation, the SVG device power is adjusted to reduce the deviation. By calculating the adjusted objective function value, an optimal parameter set is obtained in which the objective function value is less than 1% of the preset threshold and the constraint condition is satisfied. The final parameters are determined by initializing parameter settings, calculating objective function values, checking constraints, adjusting parameters and iterating to determine the active power adjustment amount ΔP of the SVG device. j and reactive power adjustment ΔQ j , which is the key parameter after optimization.

3. The voltage support SVG control method for the new energy weak sending end according to claim 2 is characterized in that: The dynamic operating condition adaptive voltage control algorithm includes an adaptive voltage control objective function and dynamic constraints; The adaptive voltage regulation objective function is expressed as: in, represents the objective function of adaptive voltage regulation; The dynamic constraint condition is expressed as: Among them, γ represents the dynamic adjustment coefficient, Represents the rate of change of node voltage over time.

4. The voltage support SVG control method for the weak-transmitting end of new energy as claimed in claim 3 is characterized by: The optimization control strategy includes minimizing the square difference between the node voltage and the participating voltage, minimizing ΔQ j ; Calculate the node voltage deviation (V ref -V i ), when the node voltage deviation exceeds the preset threshold of 5%, the SVG device’s ΔQ j , reduce the overall voltage deviation, and limit the reactive power adjustment range by controlling the penalty coefficient λ; Real-time monitoring of node voltage V i and the rate of change of node voltage over time The calculated objective function, if the objective function value exceeds 20% of the preset threshold, indicates that the current working condition is unstable and optimization adjustment is performed; if the voltage change exceeds the γ preset range, dynamic correction is performed by adjusting the reactive power; according to the sensitivity Determine the node voltage fluctuation caused by reactive power adjustment. If the voltage fluctuation exceeds 1% of the preset threshold, recalculate and adjust ΔQ j ; Adjust ΔQ j After that, the constraint conditions are recalculated. If the constraint conditions are not met, adjustment is performed again. When the overall voltage deviation is less than 5% of the preset threshold and the constraint conditions are met, the adjustment is completed.

5. The voltage support SVG control method for the new energy weak sending end according to claim 4 is characterized in that: The intelligent SVG multi-criteria selection algorithm is expressed as: Among them, Z j represents the comprehensive score of the jth SVG device, α, β, ζ, δ represent weight coefficients, represents the voltage regulation objective function value of the jth SVG device under real-time working conditions, λ j represents the power adjustment penalty coefficient of the jth SVG device, κ j represents the total cost of the jth SVG device, θ j represents the reliability coefficient of the jth SVG device; According to the voltage support requirements, a preliminary screening is carried out, and the comprehensive score Z is calculated for the SVG equipment after the preliminary screening using the intelligent SVG multi-criteria selection algorithm. j , combining key parameters to measure the performance of SVG devices, and calculating the overall score Z j , select the highest rated SVG device; After selecting the SVG equipment, carry out system integration and debugging, connect the selected SVG equipment to the system, and adjust key parameters according to the system operating status to meet the voltage support requirements.

6. The voltage support SVG control method for the new energy weak sending end according to claim 5, characterized in that: The global feedback optimization algorithm includes a global feedback objective function and a global feedback adjustment equation; The global feedback objective function is expressed as: in, represents the global feedback optimization objective function value, ΔQ j,k represents the reactive power adjustment of the jth SVG device at the kth adjustment, and φ represents the adjustment coefficient; The global feedback adjustment equation is expressed as: in, Represents the node voltage V i Reactive power adjustment ΔQ j,k Sensitivity; The calculated objective function value is used to evaluate the current operating status of the SVG device. If the calculated objective function value is When it exceeds 20% of the preset threshold, it means that the system has a voltage deviation exceeding 5% of the preset deviation and a voltage change rate exceeding the preset threshold of 0.05, then optimization adjustment is performed; ΔQ is adjusted according to the global feedback adjustment equation j,k , reduce voltage deviation and control voltage change rate; After optimization adjustment, recalculate the global feedback optimization objective function and global feedback adjustment equation to determine the system stable state. If the stable state is not reached, continue to optimize and adjust; When the system reaches a stable state, record the final reactive power adjustment ΔQ j,k and key parameters, applied to the system and monitored in real time.

7. The voltage support SVG control method for the new energy weak sending end according to claim 6, characterized in that: The optimized SVG regulation of real-time voltage support includes voltage trend prediction, real-time response triggering, distributed adjustment priority, intelligent parameter adaptation and collaborative control mechanism; The voltage trend prediction will monitor the node voltage V in real time. i and the rate of change of node voltage over time Use the prediction algorithm to predict the voltage trend. If the prediction result exceeds the preset voltage deviation of 5% and the voltage change rate exceeds the preset threshold of 0.05, optimization adjustment is performed; The prediction algorithm is expressed as: Among them, V i (t+Δt) represents the node voltage at the predicted time, V i (t) represents the node voltage at the current moment, Δt represents the prediction time step, represents the predicted adjustment coefficient; The real-time response trigger sets a real-time response mechanism to trigger optimization adjustment of the SVG device when the voltage deviation and the voltage change rate exceed a preset threshold; The distributed adjustment priority classifies SVG devices into high priority and low priority according to the node voltage deviation and voltage change rate. If the node voltage deviation is greater than 5% of the set deviation and the voltage change rate is greater than the preset threshold value 0.05, it is a high priority. If the node voltage deviation is equal to 5% of the set deviation and the voltage change rate is equal to the preset threshold value 0.05, it is a low priority. Optimize and adjust the high priority. If the system has not reached a stable state, continue to adjust the low priority. The intelligent parameter adaptation automatically adjusts the coefficient according to the real-time data to adapt to the voltage deviation and voltage change rate; The collaborative control mechanism establishes an SVG device collaborative mechanism to synchronously adjust the SVG devices.

8. A voltage support SVG control system based on any one of claims 1-7 for a new energy weak sending end, characterized in that: include: Voltage optimization model, operating condition control model, equipment selection model and feedback control model; The voltage optimization model determines and optimizes key parameters of the SVG device to meet voltage support requirements through a multi-dimensional voltage support optimization algorithm; The working condition control model optimizes the control strategy and automatically adjusts the output of the SVG under real-time working conditions through a dynamic working condition adaptive voltage control algorithm; The equipment selection model uses an intelligent SVG multi-criteria selection algorithm to select SVG equipment and complete system integration and debugging; The feedback control model continuously optimizes the SVG device through real-time data monitoring and a global feedback optimization algorithm, thereby achieving optimized SVG control for real-time voltage support.

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 voltage support SVG control method for the new energy weak sending end described in 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 voltage support SVG control method for the new energy weak sending end described in any one of claims 1 to 7 are implemented.

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