Flexible DC power transmission system cooperative control method and system considering distributed power supply access, computer equipment and storage medium

Abstract

The invention belongs to the technical field of power control, and particularly relates to a flexible direct current power transmission system cooperative control method and system considering distributed power supply access, computer equipment and a storage medium. Obtaining a DC bus voltage deviation value, a distributed power supply cluster system power fluctuation quantity and a system frequency change rate; acquiring a system operation state according to a comparison relationship between the system frequency change rate and a preset threshold range; and when the frequency change rate of the system exceeds a preset threshold range, judging that the system is in an unstable running state, and starting a virtual inertia compensation mode. The method can solve the problems of asynchronous instruction execution and phase deviation of a dynamic process, is more convenient for cooperative control, can quickly calculate the optimal power distribution scheme of each converter station when the system is in unsteady-state operation, achieves synchronous execution of each converter station, effectively inhibits system fluctuation, and improves the system reliability. The problems of insufficient inertia support, unreasonable power distribution and weak fault ride-through capability are solved, so that the requirement of high-proportion distributed power supply grid connection is met.

Description

Technical Field

[0001] This invention belongs to the field of power control technology, specifically relating to a collaborative control method and system for a flexible DC transmission system considering distributed power source access, as well as computer equipment and storage media. Background Technology

[0002] Distributed generation refers to a power generation method that involves arranging relatively small power generation devices within a distribution network to directly supply power to loads or transmit electrical energy back to the distribution network. Unlike traditional centralized power generation and long-distance transmission models, distributed generation offers greater flexibility. In flexible DC transmission systems, the large-scale integration of distributed generation (such as wind power and photovoltaics) has become an important trend in power system development. However, these power sources are characterized by random and highly volatile power output, leading to decreased system voltage and frequency stability and posing challenges to grid operation.

[0003] In the centralized control architecture of traditional flexible DC transmission systems, the central controller needs to collect data from the entire network and calculate power regulation commands before sending them to each distributed power source and converter station. This results in high communication latency, asynchronous command execution, and phase deviation in the dynamic process. Furthermore, the different equipment types and control parameters of each distributed power source and converter station lead to differences in power regulation latency and rate. Therefore, traditional control methods are difficult to adapt to such dynamic changes in real time, resulting in insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, which cannot meet the requirements of high-proportion distributed power source grid connection. Summary of the Invention

[0004] The purpose of this invention is to provide a collaborative control method and system, computer equipment and storage medium for flexible DC transmission systems that consider distributed power source access, in order to solve the problem in the prior art that it is difficult to adapt to dynamic power changes in real time and cannot meet the needs of high proportion of distributed power source grid connection.

[0005] To address the aforementioned technical problems, this invention provides a technical solution for a collaborative control method for flexible DC transmission systems considering distributed power source integration, as detailed below:

[0006] A collaborative control method for a flexible DC transmission system considering distributed generation integration includes:

[0007] When the system is determined to be in an unstable operating state, a virtual inertia adjustment is generated based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate. The virtual inertia adjustment refers to a dynamic power support quantity related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is connected to the flexible DC transmission system through the converter station.

[0008] The virtual inertia contribution value of each distributed power source is obtained based on the power output level of the distributed power source; the virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide to the flexible DC transmission system by adjusting its own power output.

[0009] The power allocation of each converter station is optimized based on the virtual inertia contribution value and virtual inertia adjustment value of each converter station, and coordinated control is performed based on the power allocation of each converter station.

[0010] The beneficial effects of the above technical solution are as follows: This invention initiates virtual inertia compensation based on the system state, linearly combining the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate to generate a virtual inertia adjustment. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage, the virtual inertia adjustment standardizes the support capabilities of different power sources through a unified dimension. Combined with a dynamic consistency protocol, each device can directly exchange states with neighboring devices without relying on a central controller, synchronizing power adjustment commands in real time. This solves the problems of asynchronous command execution and phase deviation in the dynamic process, facilitating more collaborative control. Furthermore, optimizing converter station power allocation based on the virtual inertia contribution value and virtual inertia adjustment of distributed power sources considers the differences in physical characteristics and real-time operating states of different types of power sources, achieving multi-objective optimization, ensuring consistent operation of multiple converter stations, reducing power allocation errors, and quickly calculating the optimal power allocation scheme for each converter station during non-steady-state system operation. Each converter station executes synchronously, effectively suppressing system fluctuations and solving problems such as insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, thereby meeting the requirements for high-proportion distributed power grid connection.

[0011] Furthermore, the method for generating the virtual inertia adjustment is as follows:

[0012] Construct a multi-dimensional feature vector that includes voltage fluctuation rate, distributed generation penetration rate, and grid strength index;

[0013] Feature mapping is performed on the multidimensional feature vector to output the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor;

[0014] The first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are used to update the voltage deviation weight corresponding to the DC bus voltage deviation value in the linear combination, the power fluctuation weight corresponding to the power fluctuation of the distributed power cluster system, and the frequency change rate weight corresponding to the system frequency change rate, respectively.

[0015] The virtual inertia adjustment is generated by using the updated voltage deviation weight, power fluctuation weight, and frequency change rate weight, as well as the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate.

[0016] Furthermore, a fuzzy neural network is used to perform feature mapping on the multidimensional feature vectors, and the specific process includes:

[0017] Each parameter in the multidimensional feature vector is mapped to a preset set of fuzzy linguistic variables;

[0018] The fuzzy membership value of each parameter in the fuzzy linguistic variable set is calculated using the S-shaped membership function;

[0019] Based on the fuzzy membership value matching preset condition-action rule library, the condition-action rule library includes control strategies for the combined state of voltage fluctuation rate, distributed power source penetration rate and grid strength index corresponding to DC bus voltage, and outputs the fuzzy control quantity corresponding to the fuzzy membership value.

[0020] The fuzzy control quantity is defuzzified, and the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are calculated using the centroid method.

[0021] Furthermore, it also includes correcting the generated virtual inertia adjustment based on meteorological environmental data and historical fault frequency records, and the correction method is as follows:

[0022] Obtain first meteorological environmental data for a future preset time period; retrieve second meteorological environmental data for the same period and historical fault frequency records from the power grid historical database; calculate the absolute deviation between the first meteorological environmental data and the second meteorological environmental data.

[0023] The risk weight coefficients are determined based on the absolute deviation, historical failure frequency records, and the established risk weight mapping table; the risk weight mapping table stores the relationship between the absolute deviation, historical failure frequency records, and risk weight coefficients.

[0024] Calculate the risk weather impact delay coefficient based on the risk weather type, risk weather distance, and risk weather movement speed, and generate a time-varying correction term based on the risk weather impact delay coefficient;

[0025] The virtual inertia adjustment gain coefficient is generated by combining the risk weight coefficient and the time-varying correction term, and the virtual inertia adjustment amount is corrected by using the virtual inertia adjustment gain coefficient; wherein, the risk weight coefficient and the time-varying correction term are both positively correlated with the virtual inertia adjustment gain coefficient.

[0026] Furthermore, the time-varying correction term is:

[0027]

[0028]

[0029] In the formula, Indicates the time-varying correction term. This represents the proportionality coefficient. This indicates the time delay coefficient for the impact of risky weather. Indicates the distance to the risk weather. Indicates the speed of movement of hazardous weather. This indicates the safety margin in time.

[0030] Furthermore, the method for obtaining the virtual inertia contribution value of each distributed power source based on its power output level is as follows:

[0031] Based on the distributed power source type and the current power output value, the type baseline weight and power output level factor are obtained. This power output level factor reflects the degree of influence of the distributed power source on the contribution of actual power output to inertia.

[0032] The frequency response gain is generated based on the system frequency deviation. This frequency response gain reflects the degree to which the power supply responds to system frequency fluctuations.

[0033] A time-varying attenuation factor is generated based on the characteristics of the power source type. This time-varying attenuation factor is used to describe the attenuation characteristics of the support capability of the distributed power source over time when providing virtual inertia.

[0034] The virtual inertia contribution value of the distributed power source is obtained based on the type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source. The type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source are all positively correlated with the virtual inertia contribution value of the distributed power source.

[0035] Furthermore, the power output level factor is , Indicates the first The actual power output of a distributed power source. Indicates the first The rated capacity of a distributed power source, This represents the power output sensitivity coefficient associated with the type of distributed power source.

[0036] Furthermore, the frequency response gain is , Indicates the first Frequency sensitivity coefficient of a distributed power source Indicates the system frequency deviation. This represents the reference value for frequency deviation.

[0037] Furthermore, the method for optimizing the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment is as follows:

[0038] The power reference value of each converter station is obtained based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source; the virtual inertia contribution value and DC bus voltage deviation value are both positively correlated with the power reference value.

[0039] Based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of each converter station.

[0040] Furthermore, the formula for calculating the power reference value is as follows:

[0041]

[0042] In the formula, Indicates the power reference value. Indicates the reference power. Indicates the first The virtual inertia contribution value of each distributed power source. This represents the virtual inertia adjustment amount. This indicates the DC bus voltage deviation value. Indicates the rated voltage. This represents the power-inertia correlation coefficient.

[0043] Furthermore, the process of optimizing the power allocation of each converter station using model predictive control algorithms includes:

[0044] Establish an explicit relationship between system status and converter station power adjustment, and quantify the impact of converter station power adjustment on DC bus voltage;

[0045] The optimization objectives are defined as DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation. These objectives are minimized within a preset future time period using a weighted quadratic objective function, and the priority of each optimization objective is adjusted by weighting coefficients. The system power deviation is the deviation between the actual power value and the power reference value.

[0046] The weighted quadratic objective function is transformed into a quadratic form using an optimization algorithm. The optimal power adjustment is then solved until all constraints are met and the process converges.

[0047] The power allocation of each converter station is optimized on a rolling basis with a preset time period.

[0048] Furthermore, when the rate of change of the system frequency exceeds a preset threshold range, it is determined to be in a non-steady-state operating state.

[0049] To address the aforementioned technical problems, this invention provides a technical solution for a collaborative control system of a flexible DC transmission system considering distributed power source integration, as detailed below:

[0050] The present invention provides a collaborative control system for a flexible DC transmission system considering distributed power source integration, comprising:

[0051] This is used to generate a virtual inertia adjustment amount based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate when the system is determined to be in an unstable operating state. The virtual inertia adjustment amount refers to a dynamic power support amount related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is a module that connects to the flexible DC transmission system through the converter station.

[0052] This module is used to obtain the virtual inertia contribution value of each distributed power source based on the power output level of the distributed power source. The virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide for the flexible DC transmission system by adjusting its own power output.

[0053] This module is used to optimize the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment value, and to perform coordinated control based on the power allocation of each converter station.

[0054] The beneficial effects of the above technical solution are as follows: The control system of the present invention is a computer solution for implementing the method of the present invention. This method initiates virtual inertia compensation based on the system state, linearly combining the DC bus voltage deviation, the power fluctuation of the distributed power supply cluster system, and the system frequency change rate to generate a virtual inertia adjustment. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage, the virtual inertia adjustment quantity standardizes the support capabilities of different power sources through a unified dimension. Combined with a dynamic consistency protocol, each device can directly exchange status with neighboring devices without relying on a central controller, synchronizing power adjustment commands in real time. This solves the problems of asynchronous command execution and phase deviation in the dynamic process, facilitating more collaborative control. Furthermore, based on the virtual inertia contribution value and virtual inertia adjustment quantity of distributed power sources, the power allocation of converter stations is optimized, taking into account the differences in physical characteristics and real-time operating states of different types of power sources. This achieves multi-objective optimization, ensuring consistent operation of multiple converter stations, reducing power allocation errors. During non-steady-state system operation, the optimal power allocation scheme for each converter station can be quickly calculated, and all converter stations execute synchronously, effectively suppressing system fluctuations and solving problems such as insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, thereby meeting the requirements for high-proportion distributed power grid connection.

[0055] To address the aforementioned technical problems, the present invention also provides a technical solution for a computer device, as detailed below:

[0056] A computer device according to the present invention includes a processor, wherein the processor, when executing a computer program, performs the following method steps:

[0057] When the system is determined to be in an unstable operating state, a virtual inertia adjustment is generated based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate. The virtual inertia adjustment refers to a dynamic power support quantity related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is connected to the flexible DC transmission system through the converter station.

[0058] The virtual inertia contribution value of each distributed power source is obtained based on the power output level of the distributed power source; the virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide to the flexible DC transmission system by adjusting its own power output.

[0059] The power allocation of each converter station is optimized based on the virtual inertia contribution value and virtual inertia adjustment value of each converter station, and coordinated control is performed based on the power allocation of each converter station.

[0060] The beneficial effects of the above technical solution are as follows: The computer equipment of the present invention provides basic hardware support for implementing the method of the present invention, ensuring the reliable implementation of the method. This method initiates virtual inertia compensation based on the system state, linearly combining the DC bus voltage deviation, the power fluctuation of the distributed power supply cluster system, and the system frequency change rate to generate a virtual inertia adjustment amount. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage, the virtual inertia adjustment quantity standardizes the support capabilities of different power sources through a unified dimension. Combined with a dynamic consistency protocol, each device can directly exchange status with neighboring devices without relying on a central controller, synchronizing power adjustment commands in real time. This solves the problems of asynchronous command execution and phase deviation in the dynamic process, facilitating more collaborative control. Furthermore, based on the virtual inertia contribution value and virtual inertia adjustment quantity of distributed power sources, the power allocation of converter stations is optimized, taking into account the differences in physical characteristics and real-time operating states of different types of power sources. This achieves multi-objective optimization, ensuring consistent operation of multiple converter stations, reducing power allocation errors. During non-steady-state system operation, the optimal power allocation scheme for each converter station can be quickly calculated, and all converter stations execute synchronously, effectively suppressing system fluctuations and solving problems such as insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, thereby meeting the requirements for high-proportion distributed power grid connection.

[0061] Furthermore, the method for generating the virtual inertia adjustment is as follows:

[0062] Construct a multi-dimensional feature vector that includes voltage fluctuation rate, distributed generation penetration rate, and grid strength index;

[0063] Feature mapping is performed on the multidimensional feature vector to output the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor;

[0064] The first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are used to update the voltage deviation weight corresponding to the DC bus voltage deviation value in the linear combination, the power fluctuation weight corresponding to the power fluctuation of the distributed power cluster system, and the frequency change rate weight corresponding to the system frequency change rate, respectively.

[0065] The virtual inertia adjustment is generated by using the updated voltage deviation weight, power fluctuation weight, and frequency change rate weight, as well as the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate.

[0066] Furthermore, a fuzzy neural network is used to perform feature mapping on the multidimensional feature vectors, and the specific process includes:

[0067] Each parameter in the multidimensional feature vector is mapped to a preset set of fuzzy linguistic variables;

[0068] The fuzzy membership value of each parameter in the fuzzy linguistic variable set is calculated using the S-shaped membership function;

[0069] Based on the fuzzy membership value matching preset condition-action rule library, the condition-action rule library includes control strategies for the combined state of voltage fluctuation rate, distributed power source penetration rate and grid strength index corresponding to DC bus voltage, and outputs the fuzzy control quantity corresponding to the fuzzy membership value.

[0070] The fuzzy control quantity is defuzzified, and the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are calculated using the centroid method.

[0071] Furthermore, it also includes correcting the generated virtual inertia adjustment based on meteorological environmental data and historical fault frequency records, and the correction method is as follows:

[0072] Obtain first meteorological environmental data for a future preset time period; retrieve second meteorological environmental data for the same period and historical fault frequency records from the power grid historical database; calculate the absolute deviation between the first meteorological environmental data and the second meteorological environmental data.

[0073] The risk weight coefficients are determined based on the absolute deviation, historical failure frequency records, and the established risk weight mapping table; the risk weight mapping table stores the relationship between the absolute deviation, historical failure frequency records, and risk weight coefficients.

[0074] Calculate the risk weather impact delay coefficient based on the risk weather type, risk weather distance, and risk weather movement speed, and generate a time-varying correction term based on the risk weather impact delay coefficient;

[0075] The virtual inertia adjustment gain coefficient is generated by combining the risk weight coefficient and the time-varying correction term, and the virtual inertia adjustment amount is corrected by using the virtual inertia adjustment gain coefficient; wherein, the risk weight coefficient and the time-varying correction term are both positively correlated with the virtual inertia adjustment gain coefficient.

[0076] Furthermore, the time-varying correction term is:

[0077]

[0078]

[0079] In the formula, Indicates the time-varying correction term. This represents the proportionality coefficient. This indicates the time delay coefficient for the impact of risky weather. Indicates the distance to the risk weather. Indicates the speed of movement of hazardous weather. This indicates the safety margin in time.

[0080] Furthermore, the method for obtaining the virtual inertia contribution value of each distributed power source based on its power output level is as follows:

[0081] Based on the distributed power source type and the current power output value, the type baseline weight and power output level factor are obtained. This power output level factor reflects the degree of influence of the distributed power source on the contribution of actual power output to inertia.

[0082] The frequency response gain is generated based on the system frequency deviation. This frequency response gain reflects the degree to which the power supply responds to system frequency fluctuations.

[0083] A time-varying attenuation factor is generated based on the characteristics of the power source type. This time-varying attenuation factor is used to describe the attenuation characteristics of the support capability of the distributed power source over time when providing virtual inertia.

[0084] The virtual inertia contribution value of the distributed power source is obtained based on the type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source. The type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source are all positively correlated with the virtual inertia contribution value of the distributed power source.

[0085] Furthermore, the power output level factor is , Indicates the first The actual power output of a distributed power source. Indicates the first The rated capacity of a distributed power source, This represents the power output sensitivity coefficient associated with the type of distributed power source.

[0086] Furthermore, the frequency response gain is , Indicates the first Frequency sensitivity coefficient of a distributed power source Indicates the system frequency deviation. This represents the reference value for frequency deviation.

[0087] Furthermore, the method for optimizing the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment is as follows:

[0088] The power reference value of each converter station is obtained based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source; the virtual inertia contribution value and DC bus voltage deviation value are both positively correlated with the power reference value.

[0089] Based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of each converter station.

[0090] Furthermore, the formula for calculating the power reference value is as follows:

[0091]

[0092] In the formula, Indicates the power reference value. Indicates the reference power. Indicates the first The virtual inertia contribution value of each distributed power source. This represents the virtual inertia adjustment amount. This indicates the DC bus voltage deviation value. Indicates the rated voltage. This represents the power-inertia correlation coefficient.

[0093] Furthermore, the process of optimizing the power allocation of each converter station using model predictive control algorithms includes:

[0094] Establish an explicit relationship between system status and converter station power adjustment, and quantify the impact of converter station power adjustment on DC bus voltage;

[0095] The optimization objectives are defined as DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation. These objectives are minimized within a preset future time period using a weighted quadratic objective function, and the priority of each optimization objective is adjusted by weighting coefficients. The system power deviation is the deviation between the actual power value and the power reference value.

[0096] The weighted quadratic objective function is transformed into a quadratic form using an optimization algorithm. The optimal power adjustment is then solved until all constraints are met and the process converges.

[0097] The power allocation of each converter station is optimized on a rolling basis with a preset time period.

[0098] Furthermore, when the rate of change of the system frequency exceeds a preset threshold range, it is determined to be in a non-steady-state operating state.

[0099] To address the aforementioned technical problems, the present invention also provides a technical solution for a computer-readable storage medium, as detailed below:

[0100] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the following method:

[0101] When the system is determined to be in an unstable operating state, a virtual inertia adjustment is generated based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate. The virtual inertia adjustment refers to a dynamic power support quantity related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is connected to the flexible DC transmission system through the converter station.

[0102] The virtual inertia contribution value of each distributed power source is obtained based on the power output level of the distributed power source; the virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide to the flexible DC transmission system by adjusting its own power output.

[0103] The power allocation of each converter station is optimized based on the virtual inertia contribution value and virtual inertia adjustment value of each converter station, and coordinated control is performed based on the power allocation of each converter station.

[0104] The beneficial effects of the above technical solution are as follows: the storage medium of this invention stores a computer program that implements the method of this invention, ensuring the reliable execution of the method. This method initiates virtual inertia compensation based on the system state, linearly combining the DC bus voltage deviation, the power fluctuation of the distributed power supply cluster system, and the system frequency change rate to generate a virtual inertia adjustment. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage, the virtual inertia adjustment quantity standardizes the support capabilities of different power sources through a unified dimension. Combined with a dynamic consistency protocol, each device can directly exchange status with neighboring devices without relying on a central controller, synchronizing power adjustment commands in real time. This solves the problems of asynchronous command execution and phase deviation in the dynamic process, facilitating more collaborative control. Furthermore, based on the virtual inertia contribution value and virtual inertia adjustment quantity of distributed power sources, the power allocation of converter stations is optimized, taking into account the differences in physical characteristics and real-time operating states of different types of power sources. This achieves multi-objective optimization, ensuring consistent operation of multiple converter stations, reducing power allocation errors. During non-steady-state system operation, the optimal power allocation scheme for each converter station can be quickly calculated, and all converter stations execute synchronously, effectively suppressing system fluctuations and solving problems such as insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, thereby meeting the requirements for high-proportion distributed power grid connection.

[0105] Furthermore, the method for generating the virtual inertia adjustment is as follows:

[0106] Construct a multi-dimensional feature vector that includes voltage fluctuation rate, distributed generation penetration rate, and grid strength index;

[0107] Feature mapping is performed on the multidimensional feature vector to output the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor;

[0108] The first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are used to update the voltage deviation weight corresponding to the DC bus voltage deviation value in the linear combination, the power fluctuation weight corresponding to the power fluctuation of the distributed power cluster system, and the frequency change rate weight corresponding to the system frequency change rate, respectively.

[0109] The virtual inertia adjustment is generated by using the updated voltage deviation weight, power fluctuation weight, and frequency change rate weight, as well as the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate.

[0110] Furthermore, a fuzzy neural network is used to perform feature mapping on the multidimensional feature vectors, and the specific process includes:

[0111] Each parameter in the multidimensional feature vector is mapped to a preset set of fuzzy linguistic variables;

[0112] The fuzzy membership value of each parameter in the fuzzy linguistic variable set is calculated using the S-shaped membership function;

[0113] Based on the fuzzy membership value matching preset condition-action rule library, the condition-action rule library includes control strategies for the combined state of voltage fluctuation rate, distributed power source penetration rate and grid strength index corresponding to DC bus voltage, and outputs the fuzzy control quantity corresponding to the fuzzy membership value.

[0114] The fuzzy control quantity is defuzzified, and the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are calculated using the centroid method.

[0115] Furthermore, it also includes correcting the generated virtual inertia adjustment based on meteorological environmental data and historical fault frequency records, and the correction method is as follows:

[0116] Obtain first meteorological environmental data for a future preset time period; retrieve second meteorological environmental data for the same period and historical fault frequency records from the power grid historical database; calculate the absolute deviation between the first meteorological environmental data and the second meteorological environmental data.

[0117] The risk weight coefficients are determined based on the absolute deviation, historical failure frequency records, and the established risk weight mapping table; the risk weight mapping table stores the relationship between the absolute deviation, historical failure frequency records, and risk weight coefficients.

[0118] Calculate the risk weather impact delay coefficient based on the risk weather type, risk weather distance, and risk weather movement speed, and generate a time-varying correction term based on the risk weather impact delay coefficient;

[0119] The virtual inertia adjustment gain coefficient is generated by combining the risk weight coefficient and the time-varying correction term, and the virtual inertia adjustment amount is corrected by using the virtual inertia adjustment gain coefficient; wherein, the risk weight coefficient and the time-varying correction term are both positively correlated with the virtual inertia adjustment gain coefficient.

[0120] Furthermore, the time-varying correction term is:

[0121]

[0122]

[0123] In the formula, Indicates the time-varying correction term. This represents the proportionality coefficient. This indicates the time delay coefficient for the impact of risky weather. Indicates the distance to the risk weather. Indicates the speed of movement of hazardous weather. This indicates the safety margin in time.

[0124] Furthermore, the method for obtaining the virtual inertia contribution value of each distributed power source based on its power output level is as follows:

[0125] Based on the distributed power source type and the current power output value, the type baseline weight and power output level factor are obtained. This power output level factor reflects the degree of influence of the distributed power source on the contribution of actual power output to inertia.

[0126] The frequency response gain is generated based on the system frequency deviation. This frequency response gain reflects the degree to which the power supply responds to system frequency fluctuations.

[0127] A time-varying attenuation factor is generated based on the characteristics of the power source type. This time-varying attenuation factor is used to describe the attenuation characteristics of the support capability of the distributed power source over time when providing virtual inertia.

[0128] The virtual inertia contribution value of the distributed power source is obtained based on the type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source. The type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source are all positively correlated with the virtual inertia contribution value of the distributed power source.

[0129] Furthermore, the power output level factor is , Indicates the first The actual power output of a distributed power source. Indicates the first The rated capacity of a distributed power source, This represents the power output sensitivity coefficient associated with the type of distributed power source.

[0130] Furthermore, the frequency response gain is , Indicates the first Frequency sensitivity coefficient of a distributed power source Indicates the system frequency deviation. This represents the reference value for frequency deviation.

[0131] Furthermore, the method for optimizing the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment is as follows:

[0132] The power reference value of each converter station is obtained based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source; the virtual inertia contribution value and DC bus voltage deviation value are both positively correlated with the power reference value.

[0133] Based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of each converter station.

[0134] Furthermore, the formula for calculating the power reference value is as follows:

[0135]

[0136] In the formula, Indicates the power reference value. Indicates the reference power. Indicates the first The virtual inertia contribution value of each distributed power source. This represents the virtual inertia adjustment amount. This indicates the DC bus voltage deviation value. Indicates the rated voltage. This represents the power-inertia correlation coefficient.

[0137] Furthermore, the process of optimizing the power allocation of each converter station using model predictive control algorithms includes:

[0138] Establish an explicit relationship between system status and converter station power adjustment, and quantify the impact of converter station power adjustment on DC bus voltage;

[0139] The optimization objectives are defined as DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation. These objectives are minimized within a preset future time period using a weighted quadratic objective function, and the priority of each optimization objective is adjusted by weighting coefficients. The system power deviation is the deviation between the actual power value and the power reference value.

[0140] The weighted quadratic objective function is transformed into a quadratic form using an optimization algorithm. The optimal power adjustment is then solved until all constraints are met and the process converges.

[0141] The power allocation of each converter station is optimized on a rolling basis with a preset time period.

[0142] Furthermore, when the rate of change of the system frequency exceeds a preset threshold range, it is determined to be in a non-steady-state operating state. Attached Figure Description

[0143] Figure 1 This is a flowchart of the collaborative control method for flexible DC transmission systems considering distributed power source access according to the present invention;

[0144] Figure 2 This is a structural diagram of the collaborative control system for a flexible DC transmission system considering distributed power source access, as per the present invention. Detailed Implementation

[0145] The core processing procedure of this invention is as follows: when the system is determined to be in an unstable operating state, a virtual inertia adjustment amount is generated based on a linear combination of the DC bus voltage deviation value, the power fluctuation amount of the distributed power generation cluster system, and the system frequency change rate. The virtual inertia adjustment amount refers to a dynamic power support amount related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is connected to the flexible DC transmission system through the converter station. The virtual inertia contribution value of each distributed power generation is obtained based on the power output level of the distributed power generation. The virtual inertia contribution value refers to the virtual inertia support amount that a single distributed power generation can provide to the flexible DC transmission system by adjusting its own power output. The power allocation of each converter station is optimized based on the virtual inertia contribution value and the virtual inertia adjustment amount, and coordinated control is performed based on the power allocation of each converter station.

[0146] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings.

[0147] An implementation method for a collaborative control method of a flexible DC transmission system considering distributed power source integration:

[0148] like Figure 1 As shown, this invention provides a method for coordinated control of distributed power sources integrated into a flexible DC transmission system. The method specifically includes:

[0149] S1, obtain the DC bus voltage deviation value, the power fluctuation of the distributed power supply cluster system and the system frequency change rate;

[0150] S2, obtain the system operating status based on the comparison relationship between the system frequency change rate and the preset threshold range;

[0151] S3: When the system frequency change rate is within the preset threshold range, it is determined to be in steady-state operation; when the system frequency change rate exceeds the preset threshold range, it is determined to be in unsteady-state operation and the virtual inertia compensation mode is enabled.

[0152] S4. Based on the unsteady operating state, a virtual inertia adjustment is generated according to a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate.

[0153] S5, obtain the power output level factor of the distributed power source, and obtain the virtual inertia contribution value of the distributed power source based on the power output level factor.

[0154] S6. Based on the virtual inertia contribution value and the virtual inertia adjustment amount of the distributed power source, optimize the power allocation of several converter stations, and perform coordinated control based on the power allocation of the several converter stations, wherein the distributed power source is connected to the flexible DC transmission system through the converter station.

[0155] As described in steps S1-S6 above, this invention achieves coordinated control of the flexible DC transmission system by acquiring key parameters such as DC bus voltage deviation, distributed power cluster system power fluctuation, and system frequency change rate, combined with a series of steps including system operation status determination, virtual inertia adjustment generation and correction, distributed power virtual inertia contribution value calculation, and converter station power optimization allocation. This aims to improve the system's stability, dynamic response capability, and fault handling capability in scenarios with a high proportion of distributed power access.

[0156] When distributed power sources (such as wind and solar power) are connected to the grid, their power output fluctuates and becomes intermittent due to factors such as wind speed and sunlight. This can lead to DC bus voltage deviations and abnormal system frequency change rates, thus affecting the stable operation of the power grid. In the centralized control architecture of traditional flexible DC transmission systems, the central controller needs to collect data from the entire network and calculate power regulation commands before issuing them to each distributed power source and converter station. This results in high communication delays, asynchronous command execution, phase deviations in dynamic processes, and differences in equipment types and control parameters among distributed power sources and converter stations, leading to differences in the delay and rate of power regulation. For example, when a converter station receives a power regulation command, other equipment may have already completed regulation, creating a "regulation time lag." For instance, when the system frequency drops, the central controller issues an "increase power generation" command. Converter station A executes the command 100ms later, by which time converter station B has already increased power generation. This causes the system frequency to begin to rise again when station A increases power generation, resulting in power reversal and causing oscillations. In power systems, frequency and voltage fluctuations are periodic (commonly 50Hz with a period of 20ms). If the communication delay is close to or exceeds the fluctuation period, it will cause a misalignment between the command phase and the actual system state. For example, with a 20ms delay, the system state corresponding to the command has already passed one cycle, and the adjustment direction may be opposite to the current demand (e.g., instead of increasing power generation, it becomes decreasing power generation), exacerbating oscillations. Therefore, traditional control methods are difficult to adapt to such dynamic changes in real time, resulting in insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, failing to meet the needs of high-proportion renewable energy grid connection. Therefore, a control method that can dynamically sense the system state, accurately adjust virtual inertia, and optimize multi-device collaboration is needed to solve the grid stability problem caused by distributed power source integration. This solution dynamically generates virtual inertia adjustment values ​​and optimizes converter station power allocation based on the virtual inertia contribution value of distributed power sources, achieving collaborative control of multiple devices and improving the overall stability and reliability of the system.

[0157] Specifically, this invention uses a voltage sensor to collect DC bus voltage in real time and calculates the DC bus voltage deviation based on the deviation between the DC bus voltage and the rated voltage. A power meter is used to sample the output power of the distributed power source at high frequency. Combined with the rated power or historical steady-state power of the distributed power source, the power fluctuation of each individual power source is calculated. The individual fluctuations of all distributed power sources are then superimposed to form a system-level comprehensive fluctuation, yielding the power fluctuation of the distributed power source cluster system. A phase-locked loop (PLL) is used to track the system frequency in real time, and the system frequency change rate is calculated based on the rate of change over time. The system frequency change rate is compared with a preset threshold range. When it is within the threshold range, the system is considered to be in a steady-state operation, where the load and power supply are basically balanced, and a relatively stable control strategy can be adopted. When it exceeds the preset threshold range, it is considered to be in a non-steady-state operation, such as when a system fault occurs or the power output of the distributed power source changes abruptly. In this case, a virtual inertia compensation mode needs to be activated to quickly adjust the system inertia to suppress drastic fluctuations in frequency and voltage. For example, when the wind power output suddenly drops, causing the system frequency change rate to exceed the preset threshold, timely activation of virtual inertia compensation can prevent a significant frequency drop.

[0158] Based on the unsteady-state operation, the DC bus voltage deviation, the power fluctuation of the distributed power supply cluster system, and the system frequency change rate are linearly combined to generate a virtual inertia adjustment. This virtual inertia adjustment refers to the dynamic power support provided by the converter station equipment in response to changes in system frequency, which is related to the frequency change rate. This suppresses rapid frequency fluctuations and enhances system stability and dynamic response capabilities. The generation of this virtual inertia adjustment takes into account the current power balance and frequency dynamic characteristics of the system, serving as a core reference for system inertia regulation. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage (e.g., photovoltaic response is fast but lacks inertia, while energy storage power is limited), the virtual inertia adjustment standardizes the support capabilities of different power sources through a unified dimension, facilitating collaborative control. Combined with a dynamic consistency protocol, each device can directly exchange status with neighboring devices and synchronize power adjustment commands in real time without relying on a central controller.

[0159] The virtual inertia contribution value of distributed power sources, obtained based on the power output level factor and the virtual inertia adjustment, refers to the amount of virtual inertia support a single distributed power source can provide to the flexible DC transmission system by adjusting its own power output when the system frequency changes. This process considers the physical characteristics and real-time operating status of different types of power sources, making the inertia contribution calculation of each power source more accurate. For example, energy storage devices with a high power output level factor have a larger virtual inertia contribution value, and can quickly provide a larger inertia contribution when the system frequency fluctuates.

[0160] The power allocation of several converter stations is optimized based on the virtual inertia contribution value and the virtual inertia adjustment value of the distributed power source, and coordinated control is performed based on the power allocation of the several converter stations to achieve multi-objective optimization. It can also ensure that the actions of multiple converter stations are consistent, reduce power allocation errors, and quickly calculate the optimal power allocation scheme of each converter station when the system is not in steady state. The converter stations execute synchronously, effectively suppressing system fluctuations.

[0161] This invention uses the aforementioned technical means to linearly combine the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate to generate a virtual inertia adjustment. Given the different dynamic response characteristics of photovoltaic, wind power, and energy storage, the virtual inertia adjustment standardizes the support capabilities of different power sources through a unified dimension. Combined with a dynamic consistency protocol, each device can directly exchange status with neighboring devices without relying on a central controller, synchronizing power adjustment commands in real time. This solves the problems of asynchronous command execution and phase deviation in dynamic processes, facilitating more collaborative control. Furthermore, based on the virtual inertia contribution value of the distributed power source and the virtual inertia adjustment, the power allocation of several converter stations is optimized, considering the differences in physical characteristics and real-time operating states of different types of power sources. This achieves multi-objective optimization, ensuring consistent operation of multiple converter stations, reducing power allocation errors. During system non-steady-state operation, the optimal power allocation scheme for each converter station can be quickly calculated, and each converter station executes synchronously, effectively suppressing system fluctuations and solving problems such as insufficient inertia support, unreasonable power allocation, and weak fault ride-through capability, thereby meeting the requirements for high-proportion distributed power grid connection.

[0162] In one embodiment of the present invention, the step of generating a virtual inertia adjustment based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate includes:

[0163] S41, obtain the voltage deviation weight corresponding to the DC bus voltage deviation value (also known as the DC bus voltage deviation value weight), the power fluctuation weight corresponding to the power fluctuation of the distributed power cluster system (also known as the distributed power cluster system power fluctuation weight), and the frequency change rate weight corresponding to the system frequency change rate (also known as the system frequency change rate weight).

[0164] S42, obtain the voltage fluctuation rate, distributed power penetration rate and grid strength index corresponding to the DC bus voltage;

[0165] S43, construct a multi-dimensional feature vector that includes the voltage fluctuation rate corresponding to the DC bus voltage, the penetration rate of distributed power sources, and the grid strength index;

[0166] S44 uses a fuzzy neural network to perform feature mapping on the multidimensional feature vector and outputs the first weight adjustment factor, the second weight adjustment factor and the third weight adjustment factor;

[0167] S45, update the voltage deviation weight, power fluctuation weight and frequency change rate weight in real time according to the first weight adjustment factor, the second weight adjustment factor and the third weight adjustment factor respectively;

[0168] S46, based on the updated voltage deviation weight, power fluctuation weight, and frequency change rate weight, as well as the DC bus voltage deviation value, the distributed power supply cluster system power fluctuation, and the system frequency change rate, a virtual inertia adjustment is generated. The formula is as follows:

[0169]

[0170] In the formula, This represents the virtual inertia adjustment amount. Indicates the first weighting adjustment factor. This represents the first weighting coefficient (i.e., voltage deviation weight). This indicates the DC bus voltage deviation value. This represents the second weighting adjustment factor. This represents the second weighting coefficient (i.e., the power fluctuation weight). This represents the power fluctuation in a distributed power supply cluster system. This represents the third weighting adjustment factor. This represents the third weighting coefficient (i.e., the frequency change rate weight). This indicates the rate of change of the system frequency.

[0171] S47, acquire meteorological environment data and historical fault frequency records, and correct the virtual inertia adjustment amount based on the meteorological environment data and the historical fault frequency records.

[0172] As described in steps S41-S47 above, the present invention uses a fuzzy neural network to process multi-dimensional feature vectors, dynamically generates weight adjustment factors, and updates the weights of DC bus voltage deviation, distributed power cluster system power fluctuation, and system frequency change rate in real time. This enables more accurate generation of virtual inertia adjustment, improves the adaptive adjustment capability of flexible DC transmission systems to system state changes in distributed power access scenarios, and enhances system stability and dynamic response performance.

[0173] When distributed generation (DG) is connected to the grid, the system's operating state exhibits complex dynamic characteristics due to factors such as DC bus voltage fluctuations, DG penetration rate, and grid strength. Traditional fixed-weight control methods cannot adapt to these changes in real time, resulting in insufficient precision in virtual inertia adjustment and compromised system stability. For example, when DC bus voltage fluctuations are severe or grid strength is weak, fixed weights cannot be adjusted in time to provide sufficient inertia support, potentially leading to frequency collapse or voltage instability. Therefore, a method is needed to dynamically optimize weight coefficients based on real-time operating characteristics to address the insufficient adaptability of traditional control strategies under multi-dimensional parameter coupling.

[0174] Traditional solutions often use fixed weights or simple linear adjustments to determine the weighting coefficients in the calculation of virtual inertia adjustment, making it difficult to capture the nonlinear coupling relationship between voltage fluctuation rate corresponding to DC bus voltage, distributed generation penetration rate, and grid strength parameters. For example, existing technologies adjust weights based on voltage deviation, ignoring the comprehensive influence of other parameters. This results in weighting coefficients failing to accurately reflect the actual needs of the system under complex operating conditions, leading to lagging or excessive virtual inertia adjustment and affecting system stability. This proposed solution, however, constructs a vector containing the aforementioned multidimensional features and uses a fuzzy neural network for feature mapping to achieve dynamic updates of the weighting coefficients, thereby improving the overall accuracy of virtual inertia adjustment calculation and the speed of system response.

[0175] The specific steps and technical implementation are as follows:

[0176] The system acquires the voltage fluctuation rate corresponding to the DC bus voltage (calculated from high-frequency sampling data of the DC bus voltage sensor, showing the rate of voltage change between adjacent moments), the distributed generation penetration rate (calculated as the ratio of total distributed generation power output to total system load, data from the energy management system), and grid strength indicators (determined as the ratio of system short-circuit capacity to baseline capacity, with short-circuit capacity based on historical data from fault recorders), and constructs a multi-dimensional feature vector. These parameters reflect the system's current voltage dynamic characteristics, the scale of new energy integration, and the grid's anti-interference capability, serving as key criteria for weight adjustment. For example, when the voltage fluctuation rate corresponding to the DC bus voltage increases and the distributed generation penetration rate is high, the system inertia requirement increases significantly, necessitating a focused adjustment of the relevant weights.

[0177] A fuzzy neural network is used to perform feature mapping on the multidimensional feature vectors, outputting a first weight adjustment factor, a second weight adjustment factor, and a third weight adjustment factor. These are used to correct the weights of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate, respectively. The initial weight coefficients for these three factors are preset through system stability analysis. Based on the updated weight coefficients and the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate, a virtual inertia adjustment is generated according to the aforementioned formula.

[0178] The following is a detailed explanation of the aforementioned formula: The DC bus voltage deviation reflects the state of power balance in the DC system. When active power is excessive, the DC bus voltage will be higher than the rated value; conversely, when active power is insufficient, the DC bus voltage will be lower than the rated value. From a control logic perspective, the larger the absolute value of the DC bus voltage deviation, the more severe the power imbalance. In this case, a larger virtual inertia adjustment is urgently needed to simulate the inertia response of the synchronous machine. One of the core functions of the virtual inertia adjustment is to drive the converter station to quickly adjust active power when the system encounters power disturbances, thereby maintaining voltage stability. In the calculation relationship of the virtual inertia adjustment, the DC bus voltage deviation and the virtual inertia adjustment are directly positively correlated. An increase in the DC bus voltage deviation directly leads to an increase in the virtual inertia adjustment. Its significance lies in accurately quantifying the "degree of power imbalance" of the system using the intuitive physical quantity of voltage deviation, making the response of the virtual inertia adjustment more closely match the system's emergency requirements.

[0179] The analysis of distributed generation power fluctuations reflects the random fluctuation characteristics of renewable energy (such as photovoltaic and wind power) output. Situations like rapid cloud cover causing photovoltaic power jumps or gusts altering wind power output can be described by the difference between the current power and the previous power (which can also be defined as the deviation from the rated power). From the perspective of the impact on virtual inertia adjustment, power fluctuations not only have instantaneous jumps but also a "long-term cumulative effect"—when renewable energy output fluctuates continuously and frequently, the integral of the power fluctuation over a period of time accumulates continuously, representing the "long-term impact" of the power fluctuation. The larger the integral value, the higher the severity and the longer the duration of the power fluctuation, and the greater the need for integral term compensation in the system. For example, when wind power encounters continuous gusts, without strengthening the integral term compensation, voltage and frequency may continue to deteriorate. Introducing the integral term allows the virtual inertia adjustment to cover such "slow-changing disturbances," compensating for the inherent "inertia deficiency" problem in a grid with a high proportion of renewable energy. The underlying logic is to use the cumulative effect of the integral to simulate the continuous adjustment process of the synchronous machine rotor kinetic energy.

[0180] The rate of change of system frequency is a core early warning indicator of system dynamic stability. Rapid changes in system frequency often foreshadow impending system collapse due to power imbalance; for example, under large disturbances, the system frequency may rapidly drop or surge. The larger the absolute value of the rate of change of system frequency (e.g., an extremely rapid rate of frequency drop), the more urgently the system needs to increase the virtual inertia adjustment. In the calculation of virtual inertia adjustment, the derivative term of the system rate of change makes the virtual inertia adjustment particularly sensitive to "rapid disturbances," enabling a faster emergency response. Using this key indicator of the rate of change of system frequency to trigger changes in the virtual inertia adjustment buys a valuable adjustment window to avoid system collapse.

[0181] The construction of multi-dimensional feature vectors (including DC bus voltage fluctuation rate, distributed generation penetration rate, and grid strength indicators) further enriches the perception dimensions of system operating conditions. DC bus voltage fluctuation rate represents the rate of change of voltage deviation. For example, within a specific time period, the DC bus voltage deviation changes from one value to another; its fluctuation rate reflects the "development trend" of voltage instability—the greater the fluctuation rate, the faster the voltage collapse. In the control logic, to quickly suppress this deteriorating trend, it is necessary to increase the corresponding voltage deviation weight coefficient, making the virtual inertia adjustment more sensitive to the DC bus voltage deviation. By mapping the feature vectors through a fuzzy neural network, when a large voltage fluctuation rate is detected, a larger DC bus voltage deviation weight adjustment factor is output, thereby increasing the weight coefficient of the DC bus voltage deviation and ultimately strengthening the response capability of the virtual inertia adjustment to the DC bus voltage deviation. Distributed generation penetration rate, which is the ratio of new energy installed capacity to the total system capacity, directly reflects the degree of inadequacy of the system's virtual inertia adjustment. The higher the distributed generation penetration rate, the more necessary it is to strengthen the role of the integral term, that is, to increase the weight coefficient of the integral term. In scenarios with high distributed power penetration, the random fluctuations in renewable energy output impact system inertia more frequently and persistently. Strengthening the integral term allows the virtual inertia adjustment to better compensate for these long-term fluctuations. The fuzzy neural network outputs a corresponding integral term weight adjustment factor based on the real-time distributed power penetration rate, dynamically adjusting the integral term weight coefficient to compensate for the deficiencies caused by the low virtual inertia adjustment of renewable energy sources. Grid strength indicators are comprehensive indicators reflecting the grid's ability to withstand disturbances, such as the ratio of system short-circuit capacity to baseline capacity. The lower this indicator, the "weaker" the grid, and the more prone it is to instability when faced with disturbances. In this case, the system's response to frequency mutations is more demanding, thus requiring an increase in the weight coefficient of the frequency change rate differential term to make the virtual inertia more sensitive to frequency mutations. The fuzzy neural network outputs an appropriate frequency change rate differential term weight adjustment factor based on the real-time grid strength indicator, dynamically changing the weight coefficient of this differential term to ensure that, under weak grid conditions, the virtual inertia can quickly respond to frequency mutations and cope with the risk of rapid system instability.

[0182] When the system frequency and voltage are stable (i.e., the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate are all small), the virtual inertia adjustment is maintained at a base value to avoid excessive equipment adjustment and ensure economical system operation. However, when the system encounters instability (such as short-circuit faults or large-scale grid disconnection of new energy sources), the virtual inertia adjustment will increase rapidly, driving the converter to increase or decrease active power generation, thus buying time for system stabilization. This formula comprehensively considers the dynamic changes in system voltage, power, and frequency, and achieves accurate response to different operating conditions through dynamic weights. For example, when the system frequency drops rapidly, the weight of the frequency change rate increases, and the virtual inertia adjustment increases rapidly, providing timely inertia support.

[0183] To further improve control precision, the system retrieves historical meteorological data and fault frequency records from the power grid database for the same period to correct the virtual inertia adjustment. This allows for advance prediction of the system state over a future period. By considering equipment response delays and characteristics, the power regulation timing is optimized, ensuring that regulation commands from different devices are staggered and complementary in time (e.g., photovoltaic power is adjusted first, followed by wind power, but the overall effect is synchronized). For example, before a typhoon, based on meteorological forecast data and historical fault frequency records, the virtual inertia adjustment is increased in advance to enhance the system's inertia support capacity and cope with potential large fluctuations in wind power output caused by the typhoon.

[0184] This method leverages the nonlinear mapping capability of fuzzy neural networks to effectively handle the coupling relationship of multidimensional feature parameters, achieve adaptive optimization of weight coefficients, and improve the calculation accuracy of virtual inertia adjustment.

[0185] In one embodiment of the present invention, the step of using a fuzzy neural network to perform feature mapping on a multidimensional feature vector and outputting a first weight adjustment factor, a second weight adjustment factor, and a third weight adjustment factor includes:

[0186] S411, each parameter in the multidimensional feature vector is mapped to a preset set of fuzzy linguistic variables;

[0187] S412, The fuzzy membership value of each parameter in the fuzzy linguistic variable set is calculated using the S-type membership function;

[0188] S413, based on the fuzzy membership value matching the preset condition-action rule library, the condition-action rule library includes control strategies for the combined state of voltage fluctuation rate, distributed power source penetration rate and grid strength index corresponding to DC bus voltage, and outputs the fuzzy control quantity corresponding to the fuzzy membership value.

[0189] S414, the fuzzy control quantity is defuzzified, and the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are calculated by the centroid method.

[0190] As described in steps S411-S414 above, the present invention uses a fuzzy neural network to process multidimensional feature vectors, thereby achieving accurate output of the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor. This provides reliable weight parameter support for the dynamic calculation of virtual inertia adjustment, thereby improving the adaptive adjustment capability of the flexible DC transmission system under complex operating conditions in distributed power source access scenarios and enhancing the system's stability and dynamic response performance.

[0191] When distributed generation (DG) is connected to the grid, the dynamic changes in parameters such as the voltage fluctuation rate corresponding to the DC bus voltage, the DG penetration rate, and the grid strength lead to nonlinear characteristics in the system inertia demand. Traditional fuzzy neural networks, which use fixed membership functions or simple rule bases, struggle to accurately capture the complex mapping relationships between these parameters. This results in insufficient accuracy in generating weight adjustment factors, lagging virtual inertia adjustment, and an inability to effectively cope with rapid changes in system state. For example, in situations with weak grid strength and high DG penetration, traditional methods cannot adjust weight factors in a timely manner, leading to insufficient support for virtual inertia adjustment and slow voltage fluctuation recovery. Therefore, a fuzzy neural network method that can adaptively handle multi-dimensional features and optimize membership calculation and rule mapping is needed to address the insufficient adaptability of traditional algorithms in multi-parameter coupled scenarios.

[0192] Traditional solutions often rely on empirical presuppositions, lacking self-learning capabilities and struggling to adapt to dynamic changes in power grid operation. For instance, when the voltage fluctuation rate corresponding to the DC bus voltage changes abruptly, the membership degree calculation error is significant, leading to inaccurate weight adjustment factor output. This solution, however, introduces an S-shaped membership function, optimizes the fuzzification process, uses a dynamic matching rule base, and employs the centroid method for defuzzification, constructing a more accurate feature mapping mechanism. This comprehensively improves the generation accuracy and adaptability of the weight adjustment factor.

[0193] The specific steps and technical implementation are as follows:

[0194] The voltage fluctuation rate, distributed generation penetration rate, and grid strength index parameters corresponding to the DC bus voltage in the multidimensional feature vector are mapped to a pre-defined set of fuzzy linguistic variables (such as "extremely high", "high", "medium", "low", and "extremely low"). For example, a voltage fluctuation rate greater than 0.5 pu / s corresponding to the DC bus voltage is mapped to "extremely high", and 0.2-0.5 pu / s is mapped to "high", using an S-shaped membership function. Calculate the membership degree of each parameter in each fuzzy linguistic variable. Indicates input value For fuzzy sets The membership degree value ranges from [0,1]. Indicates shape parameters, This represents the value corresponding to the fuzzy center. Each parameter in the multidimensional feature vector, including the shape parameter and fuzzy center, is generated through training with historical fault data. First, fault data from the historical operation of the power grid is collected, including parameters such as different fault types, voltage fluctuation rates corresponding to the DC bus voltage at the time of the fault, distributed generation penetration, and grid strength indicators, as well as corresponding system response data. This historical fault data comes from records of power grid fault recorders and energy management system equipment, covering operational information under various typical operating conditions. Then, this data is used as training samples and input into an improved fuzzy neural network. During training, with the optimization objectives of system stability and the accuracy of virtual inertia adjustment calculations, the values ​​of the shape parameter and fuzzy center are adjusted so that the weight adjustment factor output by the neural network can more accurately reflect the system requirements under actual fault scenarios. For example, for a specific type of fault, when the voltage fluctuation rate corresponding to the DC bus voltage is large, by training and adjusting the shape parameter and fuzzy center, the slope and center position of the S-shaped membership function in that parameter range can more sensitively capture changes in voltage fluctuations, thereby improving the accuracy of membership calculation. The training algorithm employs a supervised learning method, using feature parameters from historical fault data as input and actual effective weight adjustment factors as the desired output. It iteratively updates the shape parameters and fuzzy centers through optimization algorithms such as backpropagation until the error between the neural network output and the desired output meets a preset threshold. The resulting shape parameters and fuzzy centers adapt to the fault characteristics in actual power grid operation, ensuring that the S-shaped membership function accurately fuzzifies the input multidimensional feature parameters during real-time control, providing a reliable foundation for subsequent rule reasoning and weight adjustment factor generation. For example, training on historical power grid data yields the membership function parameters for the voltage fluctuation rate corresponding to the DC bus voltage. When the real-time fluctuation rate is 0.3 pu / s, its membership degree in the "high" fuzzy set is calculated to be 0.75, and in the "medium" fuzzy set, it is 0.25, achieving smooth fuzzification of the parameters.

[0195] Based on the calculated fuzzy membership values, a pre-defined condition-action rule base is used for matching. This rule base contains control strategies for different combinations of characteristic parameters, such as "if the voltage fluctuation rate corresponding to the DC bus voltage is 'high', the distributed generation penetration rate is 'high', and the grid strength is 'low', then the first weight adjustment factor increases by 20%, and the third weight adjustment factor increases by 15%." The establishment of the condition-action rule base combines the experience of grid operation experts and historical fault data analysis to ensure the rationality and effectiveness of the rules. When the membership value of the input characteristic parameter activates the corresponding rule, the fuzzy control quantity is calculated through fuzzy inference. For example, when the above rule is activated, the fuzzy control quantity of the first weight adjustment factor is "increased by approximately 20%".

[0196] The centroid method is used to defuzzify the fuzzy control quantity, converting it into precise first, second, and third weight adjustment factors. The calculation process of the centroid method is as follows: First, the fuzzy control quantity is discretized into several discrete points. Each discrete point corresponds to a possible adjustment factor value and its corresponding membership degree. These discrete points are determined based on the quantification of fuzzy linguistic variables. For example, the fuzzy control quantity "increase the first weight adjustment factor by 10% to 20%" is discretized into specific numerical points such as 10%, 12%, 14%, 16%, 18%, and 20%. Then, for each discrete point, a weighted summation operation is performed using the membership degree values ​​previously calculated using the S-shaped membership function. Specifically, the adjustment factor value of each discrete point is multiplied by its corresponding membership degree to obtain a weighted value. The weighted values ​​of all discrete points are then summed to obtain the numerator; simultaneously, the membership degrees of all discrete points are summed to obtain the denominator. Finally, the numerator is divided by the denominator, and the result is the centroid value, which is the precise weight adjustment factor. For example, if the membership degrees corresponding to discrete points 10%, 15%, and 20% are 0.3, 0.7, and 0.4 respectively, then the numerator of the weighted sum is 0.3 × 10% + 0.7 × 15% + 0.4 × 20%, and the denominator is 0.3 + 0.7 + 0.4. The calculated centroid value is approximately 15.36%, i.e., the first weight adjustment factor γ1 = 1.1536. This process transforms the fuzzy control quantity into a precise value, providing a reliable basis for subsequent updates to the weight coefficients and ensuring the accuracy of the virtual inertia adjustment calculation.

[0197] This method improves the processing capability of multi-dimensional feature parameters by refining the algorithm structure of the fuzzy neural network. The sigmoid membership function, compared to traditional functions, more smoothly describes the fuzzy characteristics of parameters, improving the accuracy of membership calculation. The dynamic rule base, combined with expert experience and data training, enhances the accuracy of rule matching. The centroid method for defuzzification ensures the accurate output of the adjustment factor. For example, in a real-world power grid scenario, when the voltage fluctuation rate corresponding to the DC bus voltage is 0.4 pu / s, the distributed power penetration rate is 60%, and the grid strength is 1.8, the improved algorithm outputs a first weight adjustment factor of 1.3, which is more accurate than the traditional triangular membership function method. This reduces the calculation error of the virtual inertia adjustment, improving the system's response speed and control accuracy to transient disturbances.

[0198] In one embodiment of the present invention, the step of acquiring meteorological environmental data and historical fault frequency records, and correcting the virtual inertia adjustment amount based on the meteorological environmental data and the historical fault frequency records is as follows:

[0199] S51, acquire the first meteorological environment data for a future preset time period in real time, the meteorological environment data including wind speed prediction value and light intensity prediction value;

[0200] S52, synchronously retrieve the second meteorological environment data and historical fault frequency records of the same period from the power grid historical database;

[0201] S53. Based on the first meteorological environment data and the second meteorological environment data, the absolute deviation of wind speed and the absolute deviation of illumination are obtained. The absolute deviation of wind speed reflects the deviation between the predicted wind speed value and the wind speed of the same period in the historical database. The absolute deviation of illumination reflects the deviation between the predicted illumination intensity value and the illumination intensity of the same period in the historical database.

[0202] S54. Establish a risk weight mapping table based on the absolute wind speed deviation, absolute light deviation and historical fault frequency records, and dynamically obtain the risk weight coefficient based on the risk weight mapping table.

[0203] S55, obtain the risk weather type, risk weather distance and risk weather movement speed, calculate the risk weather impact delay coefficient based on the risk weather type, risk weather distance and risk weather movement speed, and generate a time-varying correction term based on the impact delay;

[0204] S56, combine the risk weight coefficient and the time-varying correction term to generate a virtual inertia adjustment gain coefficient, and correct the virtual inertia adjustment amount by multiplying the virtual inertia adjustment amount by (1+ The corrected virtual inertia adjustment amount is obtained. Both the risk weighting coefficient and the time-varying correction term are positively correlated with the virtual inertia adjustment gain coefficient. The specific formula for calculating the virtual inertia adjustment gain coefficient is as follows:

[0205]

[0206] In the formula, This represents the virtual inertia adjustment gain coefficient. This represents the risk weighting coefficients corresponding to historical fault frequency records, absolute wind speed deviation, and absolute illumination deviation. The proportional coefficient (refers to the degree of influence of risky meteorological factors on the virtual inertia adjustment gain. For example, if a typhoon moves quickly and has a wide impact range, it needs to be quickly compensated for inertia, so the proportional coefficient is set to be larger. If cloud activity is slow and affects photovoltaics, the proportional coefficient is set to be smaller accordingly.) This indicates the time delay coefficient for the impact of risky weather. Indicates the time-varying correction term;

[0207] The formula for calculating the time delay coefficient of risky weather impact is:

[0208]

[0209] In the formula, This indicates the time delay coefficient for the impact of risky weather. Indicates the distance to the risk weather. Indicates the speed of movement of hazardous weather. This indicates the safety margin in time.

[0210] As described in steps S51-S56 above, the present invention dynamically generates a virtual inertia adjustment gain coefficient by integrating meteorological environmental data and historical fault frequency records, and corrects the virtual inertia adjustment amount, so as to improve the early warning and adaptive adjustment capability of the flexible DC transmission system in the face of external disturbances such as meteorological disasters, and enhance the stability and reliability of system operation.

[0211] It should be noted that the meteorological data in S51 and S52 above includes wind speed and solar irradiance. This is related to the type of distributed power source. If wind power and photovoltaic power generation are included, the corresponding meteorological data includes wind speed and solar irradiance. If only wind power is included, the corresponding meteorological data does not include solar irradiance. If there are other types of distributed power sources, the meteorological data can be adjusted accordingly.

[0212] Because the power output of distributed power sources is affected by meteorological conditions (such as changes in wind speed and light intensity), and meteorological disasters (such as typhoons and severe sandstorms) can cause sudden changes in power output, leading to drastic fluctuations in system inertia demand. Traditional control methods do not consider the impact of meteorological factors on virtual inertia and cannot adjust inertia support in advance before disasters occur. They often only respond passively after a fault occurs, resulting in decreased system stability. For example, when a typhoon passes through, wind power output may drop significantly, and traditional methods, lacking advance adjustment, are prone to frequency collapse. Therefore, it is necessary to establish a correlation mechanism between meteorological risk and virtual inertia adjustment by combining meteorological forecasts and historical fault data to solve the problem of lag in system inertia adjustment under external environmental disturbances.

[0213] Traditional solutions typically adjust inertia based solely on real-time system data, lacking the ability to anticipate potential external risks. They can only react passively to power output fluctuations, leading to adjustment delays and system instability. This solution, however, acquires meteorological forecast data and historical fault frequency records to establish a risk weight mapping table. By combining this with time-varying correction terms calculated based on the impact delay of meteorological disasters, it enables proactive correction of virtual inertia adjustments, thereby comprehensively improving the system's ability to respond to external environmental disturbances.

[0214] The specific steps and technical implementation are as follows:

[0215] The system acquires real-time primary meteorological environmental data for a preset future time period (e.g., 15 minutes), including predicted wind speed and solar intensity, sourced from a professional meteorological forecasting platform. Simultaneously, it retrieves secondary meteorological environmental data (e.g., historical wind speed and solar intensity data) and historical fault frequency records from the power grid's historical database. This historical data is stored in the power grid operation management system. For example, if the predicted wind speed for the next 15 minutes is 20 m / s, historical wind speed data for the same period is 10 m / s, and historical fault frequency records show five equipment overload faults occurring within that wind speed range, the system can be configured to perform these measures.

[0216] Based on the first and second meteorological data, the absolute deviation of wind speed (e.g., 20 m / s - 10 m / s = 10 m / s) and the absolute deviation of sunlight are calculated. A risk weight mapping table is then established. This table, based on historical data statistics, associates different deviation values ​​with the corresponding fault risks and dynamically obtains the risk weight coefficients. For example, for the first deviation value, the historical fault frequency record is 5 times, while for the second deviation value, the historical fault frequency record is 6 times, indicating that the second deviation value has a higher fault risk. Specifically, for example, a wind speed deviation of 10 m / s corresponds to a risk weight coefficient... =1.2 indicates that the risk of failure is high under the current meteorological conditions.

[0217] Obtain the risk weather type (e.g., typhoon, sandstorm), risk weather distance (e.g., 200km from the typhoon center to the power grid), and risk weather movement speed (e.g., 40km / h), and use the formula... Calculate the time delay coefficient for the impact of hazardous weather, where D is the distance, V is the speed of movement of the hazardous weather, and X is the safety time margin (preset 30 minutes). Generate a time-varying correction term based on the impact time delay. , where s is the proportionality coefficient (preset 0.2), and this correction term reflects the degree of impact of meteorological disasters as time approaches.

[0218] By combining the risk weight coefficient and the time-varying correction term, a virtual inertia adjustment gain coefficient is generated, which is used to correct the virtual inertia adjustment amount in order to enhance inertia support in advance.

[0219] This method incorporates meteorological forecasts and historical fault data to proactively adjust the virtual inertia. A risk weighting coefficient quantifies the meteorological risk level, while a time-varying correction term considers the time effect of disaster approach. The combination of these two factors allows the gain coefficient to dynamically reflect potential risks. For example, 4.5 hours before a typhoon, this method can increase the virtual inertia adjustment in advance, reserving sufficient inertia buffer for the system. Compared to traditional methods, this reduces the frequency deviation amplitude and shortens the voltage recovery time when a fault occurs.

[0220] In one embodiment of the present invention, the step of obtaining the power output level factor of the distributed power source and obtaining the virtual inertia contribution value of the distributed power source based on the power output level factor includes:

[0221] S61, Obtain the type baseline weight, power output sensitivity coefficient and frequency sensitivity coefficient of the distributed power source according to the type of the distributed power source;

[0222] S62 collects the current power output value percentage, rated capacity value, and system frequency deviation of the distributed power source;

[0223] S63, Obtain the power output level factor based on the current power output value ratio and the power output sensitivity index;

[0224] S64, Obtain the frequency response gain based on the system frequency deviation and frequency sensitivity coefficient;

[0225] S65 generates a time-varying attenuation factor based on the characteristics of the power supply type;

[0226] S66, obtain the reference inertia value of the distributed power source;

[0227] S67, the virtual inertia contribution value of the distributed power source is obtained based on the type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source. The type reference weight, power output level factor, frequency response gain, time-varying attenuation factor, and the reference inertia value of the distributed power source are all positively correlated with the virtual inertia contribution value of the distributed power source. The specific calculation formula is as follows:

[0228]

[0229] In the formula, Indicates the first The virtual inertia contribution value of each distributed power source. Indicates the first The type of distributed power source has a baseline weight. Indicates the first The baseline inertia value of a distributed power source. Indicates the first The actual power output of a distributed power source. Indicates the first The rated capacity of a distributed power source, Indicates the power output sensitivity coefficient. Indicates the power output level factor. Indicates the first Frequency sensitivity coefficient of a distributed power source Indicates the system frequency deviation. Indicates the reference value for frequency deviation. Indicates frequency response gain. Indicates the first The time-varying decay factor of a distributed power source.

[0230] As described in steps S61-S67 above, the present invention provides a more accurate basis for the power allocation and coordinated control of converter stations in flexible DC transmission systems by accurately calculating the virtual inertia contribution value of distributed power sources, thereby improving the inertia adaptability and stability of the system in the scenario of distributed power source access.

[0231] Because distributed power sources (such as wind power, photovoltaics, and energy storage) have different characteristics, their power output fluctuates dynamically due to environmental and operating conditions, causing changes in system inertia requirements. Traditional methods often ignore differences in power source type and real-time operating status, uniformly processing operating data from different power sources. This leads to a mismatch between power allocation and actual inertia support requirements, easily causing system power imbalance and frequency / voltage fluctuations. Therefore, it is necessary to accurately quantify the virtual inertia contribution based on factors such as power source type, real-time power output, and frequency deviation to solve the problem of inaccurate inertia assessment causing system control failure.

[0232] Traditional solutions often allocate inertia support tasks according to fixed rules or general proportions, which cannot accurately reflect the actual inertia support capability of the power supply. This step specifically addresses the characteristics of the power supply type, collects multi-dimensional operational data, and constructs a calculation model that integrates type benchmarks, power output levels, frequency response, and time-varying characteristics. This enables the accurate quantification of the virtual inertia contribution value, providing a foundation for subsequent power allocation and coordinated control.

[0233] The specific steps and technical implementation are as follows:

[0234] First, the type of distributed power source is determined, along with its base weight (a numerical value representing the difference in the basic capabilities of different types of distributed power sources in contributing to virtual inertia), power output sensitivity coefficient, and frequency sensitivity coefficient. These parameters are preset based on the physical characteristics and historical operating data analysis of different power sources (such as wind power, photovoltaics, and energy storage). For example, photovoltaics have no mechanical inertia and need to simulate inertia through power electronic conversion, hence their base weight is relatively low; energy storage can respond quickly to power commands, so its base weight is relatively high; distributed power sources with higher rated capacity also have higher base weights. The power output sensitivity coefficient and frequency sensitivity coefficient are adjusted through a nonlinear mapping mechanism to control the degree of influence of the power output level factor and system frequency deviation on the contribution of inertia, respectively, making virtual inertia control more in line with physical reality and engineering needs. For example, when photovoltaics outputs low power (e.g., <30% of rated power), the control margin of the DC / DC converter is limited, and excessive inertia may lead to DC-side voltage collapse. When wind power output is close to the rated value, the turbine pitch angle adjustment is limited, and the mechanical inertia release capability decreases. By using differentiated settings of the power output sensitivity coefficient, equipment damage due to overload can be avoided when outputting low power or close to the rated power. The frequency sensitivity coefficient reflects the amplification effect of system frequency deviation on inertia contribution. Its value depends on the system inertia level and frequency control requirements. For example, a larger value is used for high-proportion distributed power systems, and a larger value is used for systems sensitive to frequency fluctuations (such as data center microgrids). A larger frequency sensitivity coefficient can meet the requirements of fast frequency support. Next, the current power output value percentage of the distributed power source is collected (the current power output value percentage is calculated by comparing real-time power monitored by power sensors with the rated capacity), the rated capacity value, and the system frequency deviation (measured in real-time by a phase-locked loop). Then, based on the current power output value percentage and the power output sensitivity index, the formula is used to... Calculate the power output level factor, which reflects the impact of the actual power output of the power source on the contribution of inertia. For example, when the proportion of wind power output is low, the power output level factor is small, reflecting that its inertia support capability weakens as the power output decreases. Based on the system frequency deviation and frequency sensitivity coefficient, the formula is used to... The frequency response gain is calculated; the larger the frequency deviation, the higher the gain, reflecting the power supply's responsiveness to system frequency fluctuations. For example, energy storage increases the frequency response gain when the frequency deviation is large, enabling it to quickly provide inertia support. A time-varying decay factor is then generated based on the power supply type characteristics. Different power supplies have different decay characteristics; continuously outputting the same parameters for virtual inertia adjustment may lead to equipment overheating or energy storage depletion. For instance, photovoltaic power is affected by changes in sunlight, causing the time-varying decay factor to decay rapidly over time, while energy storage, due to its energy storage characteristics, decays slowly and has strong continuous inertia support capabilities. By setting a targeted time-varying decay factor, the sustainability of virtual inertia adjustment compensation can be ensured. The time-varying decay factor is a parameter that changes over time, describing the decay characteristics of the distributed power supply's support capability in providing virtual inertia over time. Its core idea is to allow rapid action in the initial stage of virtual inertia compensation through a time-varying decay factor that decreases over time, followed by smooth convergence in the later stage, thereby balancing dynamic response speed and steady-state accuracy. Specifically, the time-varying decay factor is exponentially decaying, and the formula is... , Indicates the first The time-varying decay factor of a distributed power source The attenuation coefficient is related to the power supply type. Indicates time, for example, when the attenuation coefficient is 0.2, when When =0 (initial time), the decay factor is 1. When = 5, the attenuation factor is approximately 0.3679. When the coefficient of inertia is 0, the attenuation factor is approximately 0.1353. Finally, the virtual inertia contribution value is calculated by integrating the type benchmark weight, power output level factor, frequency response gain, and time-varying attenuation factor. This method allows for a more accurate quantification of the virtual inertia contribution of distributed power sources. By summing the virtual inertia contribution values ​​of all distributed power sources and allocating power according to the proportion of each source's virtual inertia contribution, the system can allocate power based on the actual inertia support capacity of each source. For example, in scenarios with large fluctuations in wind power output, accurate identification of changes in inertia contribution allows for timely adjustment of the power supply coordination of other sources (such as energy storage), reducing system frequency fluctuations and improving overall stability. Each calculation step is closely related to the actual operating characteristics of the power sources. From local parameter processing to overall inertia contribution quantification, the foundation for system collaborative control is gradually strengthened, ensuring that inertia regulation better meets actual needs and directly improving the operational stability of flexible DC transmission systems with distributed power source integration.

[0235] In one embodiment of the present invention, the step of optimizing the power allocation of several converter stations based on the virtual inertia contribution value of the distributed power source and the virtual inertia adjustment amount, and performing coordinated control based on the power allocation of the several converter stations includes:

[0236] S71, based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source, obtain the power reference values ​​of several converter stations; the virtual inertia contribution value and DC bus voltage deviation value are both positively correlated with the power reference values;

[0237] S72, based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of several converter stations, including: establishing an explicit relationship between the system state and the converter station power adjustment, quantifying the impact of converter station power adjustment on the DC bus voltage; defining the optimization objective as minimizing the DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation over a future period using a weighted quadratic objective function, and adjusting the priority of each optimization objective through weighting coefficients; defining constraints based on power balance and equipment limiting; using an optimization algorithm to transform the weighted quadratic objective function into a quadratic form, solving for the optimal power adjustment until all constraints are met and convergence occurs; and continuously optimizing the power allocation of the several converter stations over a preset time period.

[0238] S73, based on the optimized power allocation of the several converter stations, generates several converter station control commands, and synchronizes the control commands of each converter station through a dynamic consistency algorithm.

[0239] As described in steps S71-S73 above, this invention optimizes and coordinates the power allocation of converter stations based on the virtual inertia contribution value and virtual inertia adjustment value of distributed power sources. This enables the flexible DC transmission system to achieve more reasonable power allocation and more efficient coordination among converter stations in the scenario of distributed power source access, thereby improving the overall stability and operating performance of the system.

[0240] Because the system inertia characteristics become complex and variable after the integration of distributed power sources, the converter station, as a key link in power interaction, needs to adapt its power allocation to changes in the virtual inertia target value. Improper power allocation can lead to DC bus voltage fluctuations and power imbalances, affecting system stability. For example, if the converter station cannot adjust its power in a timely manner when the power output of the distributed power source changes abruptly, the system frequency and voltage are prone to instability. Therefore, it is necessary to accurately control the converter station power based on relevant virtual inertia parameters to solve the problem of coordinated power control under dynamic inertia changes.

[0241] Traditional methods for power allocation and coordinated control in converter stations often lack in-depth consideration of the dynamic characteristics of virtual inertia, leading to poor command synchronization during coordinated control. For example, traditional power allocation may ignore the impact of inertia changes on power demand, causing some converter stations to overload; in coordinated control, commands from different stations are prone to deviation, reducing the system's control accuracy. This step specifically integrates parameters such as virtual inertia adjustment and DC bus voltage deviation, uses model predictive control to optimize power allocation, and then uses a dynamic consistency algorithm to synchronize commands, constructing a complete control process from power reference value calculation to command coordination.

[0242] The specific implementation is as follows:

[0243] Based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source, power reference values ​​for several converter stations are obtained. The DC bus voltage deviation value is obtained by comparing the DC bus voltage with the rated voltage in real time, collected by a voltage monitoring device. The formula for calculating the power reference value is:

[0244]

[0245] In the formula, Indicates the power reference value. Indicates the reference power. Indicates the first The virtual inertia contribution value of each distributed power source. This represents the virtual inertia adjustment amount. This indicates the DC bus voltage deviation value. Indicates the rated voltage. Indicates the power-inertia correlation coefficient ( (Equals the system's rated power divided by the system's reference inertia, used to standardize the dimensions of this formula).

[0246] The reference power represents the rated power operating point of the system under undisturbed conditions. Essentially, it serves as the fundamental power support for maintaining stable DC bus voltage. The DC bus voltage deviation value... Reflecting the degree of power imbalance in the system, through Will After standardization and Multiply, then pass Standardizing the calculation results with power allows the inertia requirement to be converted into a power adjustment amount that matches voltage fluctuations. The power adjustment amount plus the reference power gives the power reference value. The power reference value can be used to determine the basic direction of power allocation for the converter station. For example, when the DC bus voltage deviation is large and the virtual inertia adjustment requirement is high, the power reference value can be adjusted to allow the converter station to provide stronger power support to cope with system changes.

[0247] Based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of several converter stations. Specifically, this involves continuously substituting different values, trying different approaches, and finally selecting the smallest summation value. Power allocation to the several converter stations is then based on this summation value. The objective function of the model predictive control algorithm is:

[0248]

[0249] In the formula, express DC bus voltage deviation at time t. express The system power deviation value at any given time is obtained based on the power reference value and the actual power value. This represents the virtual inertia weighting coefficient. express The actual value of the virtual inertia at time t. express The virtual inertia adjustment at any given moment. express The virtual inertia compensation deviation value at time t. This indicates the prediction time domain (referring to the number of time periods from the current moment onwards that require prediction and optimization).

[0250] This algorithm optimizes the control sequence over the future time domain T through rolling optimization, balancing the three major control objectives of voltage stability, power balance, and inertia tracking while satisfying system constraints (power balance and equipment limiting). For example, when a sudden drop in photovoltaic power output causes a power deficit in the system, the model predictive control algorithm will prioritize adjusting the power of converter stations with high inertia contributions while suppressing voltage fluctuations. Compared to traditional PID control, this method can shorten the frequency recovery time.

[0251] Based on the optimized power allocation, converter station control commands are generated and synchronized using a dynamic consensus algorithm. This algorithm allows converter stations to quickly correct command deviations during information exchange, ensuring command synchronization. For example, in a multi-converter station system, if a station's command deviates due to communication delays, the algorithm quickly aligns the commands across stations, ensuring coordinated power allocation and preventing power allocation chaos caused by command asynchrony. From calculating local power reference values ​​to actual inertia inversion, model prediction optimization, and command coordination, a precise and coordinated converter station power control system is gradually built. This directly improves the system's ability to cope with inertia and power changes brought about by distributed power source access, making power allocation more adaptable to system inertia requirements, improving coordinated control efficiency, ensuring the stable operation of the flexible DC transmission system, and achieving progressive optimization from local parameter processing to overall system stability.

[0252] In one embodiment of the present invention, the step of optimizing the power allocation of several converter stations using a model predictive control algorithm based on the power reference value and the virtual inertia adjustment includes:

[0253] S721 establishes an explicit relationship between system status and converter station power adjustment, quantifying the impact of converter station power adjustment on DC bus voltage.

[0254] S722, the optimization objectives are defined as DC bus voltage deviation, system power deviation and virtual inertia compensation deviation. The DC bus voltage deviation, system power deviation and virtual inertia compensation deviation within a preset time period are minimized by a weighted quadratic objective function, and the priority of each optimization objective is adjusted by a weighting coefficient.

[0255] S723, Define constraints, including power balance and device limiting;

[0256] S724, use an optimization algorithm to transform the weighted quadratic objective function into a quadratic form, solve for the optimal power adjustment amount, until all the constraints are satisfied and convergence is achieved;

[0257] S725, the power allocation of the plurality of converter stations is optimized in a rolling manner with a preset time period.

[0258] As described in steps S721-S725 above, this invention aims to solve the problems of power imbalance, voltage and frequency instability caused by output fluctuations after distributed power sources are connected to the flexible DC transmission system by constructing a closed-loop control system covering system dynamic prediction, multi-objective optimization, constraint solving and rolling execution, and ultimately realize the dynamic optimization of converter station power commands and the improvement of system stability.

[0259] The specific process is as follows: First, the model predictive control algorithm establishes an explicit relationship between the system state and the power adjustment of the converter station. For example, it establishes the relationship between the power adjustment of the converter station and the DC bus voltage deviation value through the grid sensitivity matrix or state-space equation. If converter station 1 increases its power... Converter station 2 reduces power The DC bus voltage deviation value How does the change quantify the impact of converter station power adjustment on the DC bus voltage deviation? The impact of factors such as converter station 1 increasing power generation by 500MW and converter station 2 decreasing power generation by 300MW will increase the net injected power. In the non-steady state, the capacitor will cause the voltage deviation to rise rapidly, while in the steady state, the resistor will keep the voltage at a deviation higher than the rated value. If converter station 2 decreases power generation by more, the net injected power will decrease, and the voltage deviation will fall back to the rated value or even below the rated value.

[0260] Next, the optimization objectives and constraints are defined. The objective function is a weighted quadratic form, minimizing the DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation over a future period. The priority of each optimization objective is adjusted by weighting coefficients. Constraints include power balance (the sum of the power adjustments of all converter stations equals the total system power demand) and equipment limitations (the power adjustment of a single converter station does not exceed the physical limit), ensuring the feasibility of the optimization results.

[0261] Then, using optimization algorithms such as the interior-point method or the effective set method, the objective function is transformed into a quadratic form to solve for the optimal power adjustment. Within the constraints, the algorithm repeatedly explores different power combinations, calculates the system deviation, and finds the solution with the minimum total deviation. For example, when the system experiences a power deficit, the algorithm prioritizes converter stations with high inertia contributions and sufficient power margins for power adjustment, suppressing frequency drops while avoiding equipment overload. This global optimization capability overcomes the shortcomings of traditional methods in finding local optima, achieving optimal control under multi-objective constraints.

[0262] Finally, the model predictive control algorithm performs rolling optimization at preset time periods (e.g., 10-100 milliseconds), executing only the optimal command for the current moment and recalculating based on the new state in the next cycle, adapting to real-time fluctuations in distributed power sources. Compared to traditional fixed allocation, the model predictive control algorithm can shorten the frequency recovery time, reduce voltage overshoot, and control the power allocation deviation of the converter station within a smaller range, effectively improving system stability and load balance, and enabling a smoother system transition.

[0263] An implementation method for a collaborative control system of a flexible DC transmission system considering distributed power source integration:

[0264] The present invention provides a collaborative control system for a flexible DC transmission system considering distributed power source integration, comprising:

[0265] This is used to generate a virtual inertia adjustment amount based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate when the system is determined to be in an unstable operating state. The virtual inertia adjustment amount refers to a dynamic power support amount related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is a module that connects to the flexible DC transmission system through the converter station.

[0266] This module is used to obtain the virtual inertia contribution value of each distributed power source based on the power output level of the distributed power source. The virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide for the flexible DC transmission system by adjusting its own power output.

[0267] This module is used to optimize the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment value, and to perform coordinated control based on the power allocation of each converter station.

[0268] More specifically, the system is as follows Figure 2 As shown, it specifically includes:

[0269] The parameter acquisition module is used to acquire DC bus voltage deviation, power fluctuation of the distributed power supply cluster system, and system frequency change rate.

[0270] The system status assessment module is used to obtain the system operating status based on the comparison between the system frequency change rate and a preset threshold range;

[0271] The trigger module is used to determine that the system frequency change rate exceeds the preset threshold range, indicating an unsteady operating state, and to enable the virtual inertia compensation mode.

[0272] The calculation module is used to generate a virtual inertia adjustment amount based on the non-steady-state operating state, according to a linear combination of the DC bus voltage deviation value, the power fluctuation amount of the distributed power cluster system, and the system frequency change rate.

[0273] The allocation module is used to obtain the power output level factor of the distributed power source and obtain the virtual inertia contribution value of the distributed power source based on the power output level factor.

[0274] The collaborative control module is used to optimize the power allocation of several converter stations based on the virtual inertia contribution value and the virtual inertia adjustment value of the distributed power source, and to perform collaborative control based on the power allocation of the several converter stations, wherein the distributed power source is connected to the flexible DC transmission system through the converter stations.

[0275] One implementation method for a computer device:

[0276] A computer device includes a memory, a processor, an internal bus, and a computer program stored in the memory. The processor and the memory communicate and exchange data with each other via the internal bus. The processor executes the computer program to implement the steps of the method described in an embodiment of the cooperative control method for a flexible DC transmission system considering distributed power source access of the present invention. The processor can be a microprocessor (MCU), a programmable logic device (FPGA), or other processing devices; the memory can be various types of memory that store information using electrical energy, such as RAM, ROM, etc., or other types of memory.

[0277] One embodiment of a computer-readable storage medium:

[0278] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for coordinated control of distributed power sources connected to a flexible DC transmission system.

[0279] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0280] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A collaborative control method for a flexible DC transmission system considering distributed power source integration, characterized in that, include: When the system is determined to be in an unstable operating state, a virtual inertia adjustment is generated based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate. The virtual inertia adjustment refers to a dynamic power support quantity related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is connected to the flexible DC transmission system through the converter station. The virtual inertia contribution value of each distributed power source is obtained based on the power output level of the distributed power source; the virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide for the flexible DC transmission system by adjusting its own power output. The power allocation of each converter station is optimized based on the virtual inertia contribution value and virtual inertia adjustment value of each converter station, and coordinated control is performed based on the power allocation of each converter station.

2. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 1, characterized in that, The method for generating virtual inertia adjustment is as follows: Construct a multi-dimensional feature vector that includes voltage fluctuation rate, distributed generation penetration rate, and grid strength index; Perform feature mapping on the multidimensional feature vector to output the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor; The first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are used to update the voltage deviation weight corresponding to the DC bus voltage deviation value in the linear combination, the power fluctuation weight corresponding to the power fluctuation of the distributed power cluster system, and the frequency change rate weight corresponding to the system frequency change rate, respectively. The virtual inertia adjustment is generated by using the updated voltage deviation weight, power fluctuation weight, and frequency change rate weight, as well as the DC bus voltage deviation, the power fluctuation of the distributed power cluster system, and the system frequency change rate.

3. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 2, characterized in that, A fuzzy neural network is used to perform feature mapping on multidimensional feature vectors, and the specific process includes: Each parameter in the multidimensional feature vector is mapped to a preset set of fuzzy linguistic variables; The fuzzy membership value of each parameter in the fuzzy linguistic variable set is calculated using the S-shaped membership function; Based on the fuzzy membership value matching preset condition-action rule library, the condition-action rule library includes control strategies for the combined state of voltage fluctuation rate, distributed power source penetration rate and grid strength index corresponding to DC bus voltage, and outputs the fuzzy control quantity corresponding to the fuzzy membership value. The fuzzy control quantity is defuzzified, and the first weight adjustment factor, the second weight adjustment factor, and the third weight adjustment factor are calculated using the centroid method.

4. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 2 or 3, characterized in that, It also includes correcting the generated virtual inertia adjustment based on meteorological environmental data and historical fault frequency records, and the correction method is as follows: Obtain first meteorological environmental data for a future preset time period; retrieve second meteorological environmental data for the same period and historical fault frequency records from the power grid historical database; calculate the absolute deviation by analyzing the deviation between the first and second meteorological environmental data. The risk weight coefficients are determined based on the absolute deviation, historical failure frequency records, and the established risk weight mapping table; the risk weight mapping table stores the relationship between the absolute deviation, historical failure frequency records, and risk weight coefficients. Calculate the risk weather impact delay coefficient based on the risk weather type, risk weather distance, and risk weather movement speed, and generate a time-varying correction term based on the risk weather impact delay coefficient; The virtual inertia adjustment gain coefficient is generated by combining the risk weight coefficient and the time-varying correction term, and the virtual inertia adjustment amount is corrected by the virtual inertia adjustment gain coefficient; wherein, the risk weight coefficient and the time-varying correction term are both positively correlated with the virtual inertia adjustment gain coefficient.

5. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 4, characterized in that, The time-varying correction term is: ； ； In the formula, Indicates the time-varying correction term. Represents the proportionality coefficient. This indicates the time delay coefficient for the impact of hazardous weather. Indicates the distance to the risk weather. Indicates the speed at which the weather is moving in danger. This indicates the safety margin in time.

6. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 1, characterized in that, The method for obtaining the virtual inertia contribution value of each distributed power source based on its power output level is as follows: Based on the distributed power source type and the current power output value, the type baseline weight and power output level factor are obtained. This power output level factor reflects the degree of influence of the distributed power source on the contribution of actual power output to inertia. The frequency response gain is generated based on the system frequency deviation. This frequency response gain reflects the degree to which the power supply responds to system frequency fluctuations. A time-varying attenuation factor is generated based on the characteristics of the power source type. This time-varying attenuation factor is used to describe the attenuation characteristics of the support capability of the distributed power source over time when providing virtual inertia. The virtual inertia contribution value of the distributed power source is obtained based on the type reference weight, the power output level factor, the frequency response gain, the time-varying attenuation factor, and the reference inertia value of the distributed power source. The type baseline weight, power output level factor, frequency response gain, time-varying attenuation factor, and baseline inertia value of distributed power sources are all positively correlated with the virtual inertia contribution value of distributed power sources.

7. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 6, characterized in that, The power output level factor is , Indicates the first The actual power output of a distributed power source. Indicates the first The rated capacity of a distributed power source, This represents the power output sensitivity coefficient associated with the type of distributed power source.

8. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 6, characterized in that, The frequency response gain is , Indicates the first Frequency sensitivity coefficient of a distributed power source Indicates the system frequency deviation. This represents the reference value for frequency deviation.

9. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 1, characterized in that, The method for optimizing the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment amount is as follows: The power reference value of each converter station is obtained based on the virtual inertia contribution value and DC bus voltage deviation value of the distributed power source; the virtual inertia contribution value and DC bus voltage deviation value are both positively correlated with the power reference value. Based on the power reference value and the virtual inertia adjustment, a model predictive control algorithm is used to optimize the power allocation of each converter station.

10. The cooperative control method for a flexible DC transmission system considering distributed power source access according to claim 9, characterized in that, The formula for calculating the power reference value is: ； In the formula, Indicates the power reference value. Indicates the reference power. Indicates the first The virtual inertia contribution value of each distributed power source. This represents the virtual inertia adjustment amount. This indicates the DC bus voltage deviation value. Indicates the rated voltage. This represents the power-inertia correlation coefficient.

11. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 9 or 10, characterized in that, The process of optimizing the power allocation of each converter station using model predictive control algorithms includes: Establish an explicit relationship between system status and converter station power adjustment, and quantify the impact of converter station power adjustment on DC bus voltage; The optimization objectives are defined as DC bus voltage deviation, system power deviation, and virtual inertia compensation deviation. These objectives are minimized within a preset future time period using a weighted quadratic objective function, and the priority of each optimization objective is adjusted by weighting coefficients. The system power deviation is the deviation between the actual power value and the power reference value. The weighted quadratic objective function is transformed into a quadratic form using an optimization algorithm. The optimal power adjustment is then solved until all constraints are met and the process converges. The power allocation of each converter station is optimized on a rolling basis with a preset time period.

12. The cooperative control method for flexible DC transmission systems considering distributed power source access according to claim 1, characterized in that, When the rate of change of system frequency exceeds the preset threshold range, it is determined to be in an unsteady operating state.

13. A collaborative control system for a flexible DC transmission system considering distributed power source integration, characterized in that, include: This is used to generate a virtual inertia adjustment amount based on a linear combination of the DC bus voltage deviation, the power fluctuation of the distributed power generation cluster system, and the system frequency change rate when the system is determined to be in an unstable operating state. The virtual inertia adjustment amount refers to a dynamic power support amount related to the frequency change rate provided by the power control of the converter station equipment. The distributed power generation is a module that connects to the flexible DC transmission system through the converter station. This module is used to obtain the virtual inertia contribution value of each distributed power source based on the power output level of the distributed power source. The virtual inertia contribution value refers to the amount of virtual inertia support that a single distributed power source can provide to the flexible DC transmission system by adjusting its own power output. This module is used to optimize the power allocation of each converter station based on its virtual inertia contribution value and virtual inertia adjustment value, and to perform coordinated control based on the power allocation of each converter station.

14. A computer device, comprising a processor, characterized in that, When the processor executes a computer program, it implements the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 12.

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