Multi-source cooperative transient coordination control optimization method and device, storage medium and computer equipment

Abstract

The multi-source collaborative transient coordination control optimization method, device, storage medium, and computer equipment provided in this application detect the grid connection voltage and the active and reactive currents of the wind farm, static var generator (SVM), and energy storage system during a fault. A multi-objective comprehensive evaluation function is constructed, incorporating transient voltage severity, active power deficit, and reactive power equipment call-up costs. This function is then optimized by combining the inverter current limit circle, wind power active power output logic, fault network algebraic equations, and off-grid voltage constraints to obtain optimal active and reactive current allocation commands, which are then sent to the inverter controllers of the wind farm, SVM, and energy storage system. Thus, under the premise of strictly meeting low-voltage ride-through requirements, quantitative and coordinated allocation of active and reactive currents among the wind farm, SVM, and energy storage system is achieved, solving the problem of finding the optimal balance between transient voltage support and active power recovery during a fault.

Description

Technical Field

[0001] This application relates to the field of offshore wind power grid connection technology, and in particular to a multi-source collaborative transient coordination control optimization method, device, storage medium and computer equipment. Background Technology

[0002] In recent years, the installed capacity of offshore wind power has grown rapidly. However, the regional transmission systems connected to large-scale offshore wind power have weak support and disturbance resistance capabilities, which can easily lead to transient voltage instability in the receiving-end power grid. In the event of emergencies such as grounding faults, it is difficult to effectively support the transient stable operation of the power grid by simply adjusting the output of offshore wind power, and it is also not conducive to the reliable absorption of new energy sources. Therefore, it is necessary to mobilize multiple reactive power resources in the region, such as static var generators (SVG) and energy storage systems, to improve transient voltage stability.

[0003] Current research utilizes the coordinated control of energy storage systems and SVG (Static Var Generator) to improve the high-voltage ride-through capability of wind farms, but lacks consideration for low-voltage ride-through capability. Existing studies often use grid connection voltage deviation as the sole basis for reactive power resource coordination, triggering reactive power support from energy storage, SVG, and wind turbines in stages based on the degree of voltage dips or rises. While this approach can meet basic transient voltage safety requirements, there is a strong competitive relationship between reactive power support and active power output from wind turbines during low-voltage ride-through. Existing methods neither coordinate and optimize this relationship nor consider the differences in adjustment costs and response characteristics of different reactive power resources, thus making it difficult to achieve an optimal balance between transient voltage support and active power recovery.

[0004] Therefore, when a fault occurs in the offshore wind power grid-connected system, causing a transient voltage drop, it is difficult to coordinate and optimize the distribution of active and reactive currents among multiple resources, and to achieve the optimal balance between transient voltage support and active power recovery while meeting the low voltage ride-through requirements. Summary of the Invention

[0005] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency in the prior art where, when a fault occurs in an offshore wind power grid-connected system causing a transient voltage drop, it is difficult to coordinate and optimize the distribution of active and reactive currents among multiple resources, and to achieve the optimal balance between transient voltage support and active power recovery while meeting the low voltage ride-through requirements.

[0006] Firstly, this application provides a multi-source collaborative transient coordinated control optimization method, applied to offshore wind power grid-connected systems, the method comprising:

[0007] The grid connection point voltage during the fault period is detected, as well as the active and reactive currents output by the wind farm, static var generator, and energy storage system.

[0008] A multi-objective comprehensive evaluation function is constructed, which is weighted by transient voltage severity, active power deficit and reactive power equipment call cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call cost is calculated based on each reactive current.

[0009] The optimization constraints are determined, including inverter current limit circle constraints to limit the vector magnitude of each active and reactive current, wind power active output logic constraints to constrain the upper limit of active current in the wind farm, fault network algebra equation equality constraints to force node voltage to satisfy Kirchhoff's laws during faults, and grid disconnection voltage constraints to require the grid connection point voltage to be no lower than the grid disconnection lower limit.

[0010] Under the condition of satisfying the optimization constraints, the optimal active current allocation command and the optimal reactive current allocation command that minimize the multi-objective comprehensive evaluation function are solved and sent to the inverter controllers of wind farms, energy storage systems and static var generators to regulate the active current and reactive current output during faults.

[0011] In one embodiment, the expression for the severity of the transient voltage is:

[0012]

[0013] in, The severity of transient voltage, This is the lower limit of the transient voltage safety. The voltage at the grid connection point. The duration of the fault.

[0014] In one embodiment, the expression for the active power deficit is:

[0015]

[0016] in, This is due to a shortfall in active power. This represents the steady-state active power command value of the wind farm before the fault. The voltage at the grid connection point. This represents the active current of the wind farm.

[0017] In one embodiment, the expression for the cost of reactive power equipment dispatch is:

[0018]

[0019] in, Cost of reactive power equipment dispatch, These are the reactive currents from the static var generator, energy storage system, and wind farm, respectively. These are the corresponding cost weighting coefficients, and .

[0020] In one embodiment, the expression for the inverter current limit circle constraint is:

[0021]

[0022] in, These are the maximum permissible transient currents for wind farms, energy storage systems, and static var generators, respectively. These are the active current and reactive current of the wind farm, respectively. These are the active current and reactive current of the energy storage system, respectively. This refers to the reactive current of the energy storage system.

[0023] In one embodiment, the expression for the wind power active power output logic constraint is:

[0024]

[0025] in, This represents the active current of the wind farm. This represents the initial active current of the wind farm before the fault. This is the maximum permissible transient current of the wind farm. This refers to the reactive current of the wind farm.

[0026] In one embodiment, the expression for the equality constraints of the algebraic equations of the fault network is:

[0027]

[0028] in, This is a vector of node voltage changes. To account for the system's equivalent impedance matrix under fault boundary conditions, The vector of sudden current injected into each station and fault point. and These are the node voltage vectors before and during the fault, respectively.

[0029] Secondly, this application provides a multi-source collaborative transient coordinated control optimization device, applied to an offshore wind power grid-connected system, the device comprising:

[0030] The voltage and current detection module is used to detect the grid connection point voltage during a fault, as well as the active and reactive currents output by the wind farm, static var generator, and energy storage system.

[0031] The multi-objective comprehensive evaluation function construction module is used to construct a multi-objective comprehensive evaluation function weighted by transient voltage severity, active power deficit, and reactive power equipment call cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call cost is calculated based on each reactive current.

[0032] The optimization constraint determination module is used to determine the optimization constraints. The optimization constraints include inverter current limit circle constraints to limit the vector magnitude of each active and reactive current, wind power active output logic constraints to constrain the upper limit of active current in the wind farm, fault network algebraic equation constraints to force node voltage to satisfy Kirchhoff's laws during faults, and grid disconnection voltage constraints to require the grid connection point voltage to be no lower than the grid disconnection lower limit.

[0033] The optimal current distribution command solution module is used to solve for the optimal active current distribution command and the optimal reactive current distribution command that minimize the multi-objective comprehensive evaluation function under the condition of satisfying the optimization constraints, and send them to the inverter controllers of wind farms, energy storage systems and static var generators to adjust the active current and reactive current output during faults.

[0034] Thirdly, this application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of any of the multi-source collaborative transient coordination control optimization methods described in the above embodiments.

[0035] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0036] The memory stores computer-readable instructions, which, when executed by one or more processors, perform the steps of any of the multi-source cooperative transient coordination control optimization methods described in the above embodiments.

[0037] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0038] This application provides a multi-source collaborative transient coordination control optimization method, device, storage medium, and computer equipment. By detecting the grid connection point voltage and the active and reactive currents of each resource during a fault, it constructs a multi-objective comprehensive evaluation function that includes the severity of transient voltage, active power deficit, and reactive power equipment call-up cost. Specifically, the transient voltage severity is calculated based on the grid connection point voltage to quantify the threat of voltage drop to stability; the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, reflecting the urgency of active power recovery; and the reactive power equipment call-up cost is calculated based on each reactive current, reflecting the economic efficiency of regulation. Based on this, and combining inverter current limit circle constraints to limit the amplitude of each current vector to no more than the physical capacity, wind power active power output logic constraints to ensure the actual generating capacity of the wind farm, fault network algebraic equation constraints to enforce Kirchhoff's laws during the fault, and off-grid voltage constraints to maintain the voltage floor for low-voltage ride-through, the optimal active and reactive current allocation command is obtained through optimization and sent to the inverter controller. Therefore, under the premise that the grid connection point is not lower than the grid disconnection limit, that is, strictly meeting the low voltage ride-through requirements, the quantitative and coordinated allocation of active and reactive currents among wind farms, static var generators and energy storage systems is realized, solving the problem of the difficulty in achieving optimal balance between transient voltage support and active power recovery during faults; at the same time, by minimizing the multi-objective function, the system stability, power recovery speed and reactive power equipment call-up cost are taken into account, effectively improving the transient response performance and operational economy of offshore wind power grid connection systems. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic diagram of an offshore wind power grid connection system provided in an embodiment of this application;

[0041] Figure 2 A flowchart illustrating the multi-source collaborative transient coordination control optimization method provided in this application embodiment;

[0042] Figure 3 A schematic diagram of the structure of the multi-source collaborative transient coordination control optimization device provided in the embodiments of this application;

[0043] Figure 4 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] This application provides a multi-source collaborative transient coordinated control optimization method, applied to offshore wind power grid-connected systems, such as... Figure 1 As shown, offshore wind farms and energy storage power stations are first connected via 220kV AC, then via a step-up transformer to the 500kV bus, and finally integrated into the 500kV grid along with conventional thermal power units (i.e., traditional synchronous generators and static var generators, SVG), and finally connected to the infinite power system via transmission lines. In the analysis, a second-order classical model is used for the conventional generators, and the role of the synchronous generator governor is not considered during transient processes. When a severe short-circuit fault occurs in a grid with a high proportion of renewable energy, the system's transient stability faces the dual threats of voltage drop and active power loss. However, limited by inverter capacity, there is a strong competitive relationship between reactive power support and active power output of offshore wind turbines during low voltage ride-through (LVRT). To achieve the optimal balance between transient voltage support and active power recovery, a multi-source collaborative transient coordination control optimization method is proposed. Figure 2 As shown, the method includes:

[0046] S101: Detects the grid connection point voltage during a fault, as well as the active and reactive currents output by the wind farm, static var generator, and energy storage system.

[0047] In the offshore wind power grid-connected system involved in this application, the fault period refers to the time when the grid connection point voltage drops below the low voltage crossing trigger threshold due to a short circuit or severe disturbance in the system. This period typically begins from the moment the voltage drops until the fault is cleared and the voltage returns to the normal range. The grid connection point voltage refers to the effective value of the three-phase voltage at the common connection point where the offshore wind farm, energy storage system, and static var generator are connected. The wind farm is a power generation unit composed of multiple offshore wind turbine generators, and its output current includes two orthogonal components: active current and reactive current. The static var generator is a dynamic reactive power compensation device based on a voltage source inverter, used to quickly adjust the grid connection point voltage. The energy storage system refers to a battery energy storage device capable of storing electrical energy and bidirectional charging and discharging, and its inverter also has the ability to independently adjust the active current and reactive current. The active current refers to the current component in phase with the grid connection point voltage, which directly determines the output of active power. The reactive current refers to the current component orthogonal to the grid connection point voltage, used to adjust the amplitude of the grid connection point voltage.

[0048] In the actual operation of offshore wind power grid-connected systems, voltage transformers installed at the grid connection point first continuously collect three-phase voltage signals. The secondary output of the voltage transformers is then isolated and converted before being sent to an analog-to-digital converter, which converts the analog voltage into a digital quantity at a sampling rate of thousands to tens of thousands of times per second. Simultaneously, three-phase current transformers are installed on the output side of the wind farm's transmission line, the output side of the static var generator (SVM), and the AC side of the energy storage system to synchronously collect their respective instantaneous three-phase current values.

[0049] When the system is operating normally and the grid connection voltage is within the rated range, the collected data is used only for routine monitoring. In the event of a short circuit in the offshore transmission line or a severe disturbance on the grid side, the grid connection voltage will drop rapidly. At this time, a fault detection logic embedded in the controller calculates the effective voltage value in real time and compares it with a preset low-voltage ride-through threshold. If the effective voltage value is detected to be below this threshold for three consecutive sampling points, the system is determined to have entered a fault period. Within each control cycle after the determination, the controller latches the current voltage sampling value and the current sampling values ​​of the three devices.

[0050] For each set of sampled data, the controller employs a coordinate transformation algorithm based on instantaneous reactive power theory to transform the voltage and current in the three-phase stationary coordinate system to a synchronous rotating coordinate system, thereby separating the active current component and the reactive current component. Specifically, using the phase of the grid-connected voltage as the reference phase, the current vector is projected onto the voltage direction to obtain the active current, and projected onto the orthogonal direction to obtain the reactive current. This calculation is repeated independently for the wind farm, static var generator, and energy storage system to obtain the active current and reactive current values ​​output by each of the three devices.

[0051] By detecting the grid connection point voltage during a fault, it is possible to accurately identify whether the system is in a fault state requiring transient control, avoiding invalid optimization calculations during normal operation. Simultaneously, the active and reactive currents output by the wind farm, static var generator (SVM), and energy storage system are detected separately, providing fundamental data for subsequently differentiating the current contributions of different resources, making multi-source coordinated allocation possible. Synchronizing the detection of the grid connection point voltage with the active and reactive currents of the three types of equipment ensures the temporal consistency of the input parameters required for subsequent optimization solutions, avoiding distortion of the optimal solution due to data asynchrony.

[0052] S102: Construct a multi-objective comprehensive evaluation function that is weighted by transient voltage severity, active power deficit, and reactive power equipment call-up cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call-up cost is calculated based on each reactive current.

[0053] Among them, the severity of transient voltage is used to quantify the degree to which the grid connection point voltage deviates from the rated value during a fault; the active power deficit is used to reflect the urgency of active power recovery; the reactive power equipment call cost is used to measure the economic cost or equipment loss caused by the reactive power capacity consumed to support the voltage; the multi-objective comprehensive evaluation function is a scalar function obtained by multiplying the above three indicators by their respective weight coefficients and then adding them together. The smaller the value, the better the comprehensive control effect.

[0054] After detecting the grid connection point voltage and the active and reactive currents of each device during the fault, the first step is to calculate the severity of the transient voltage using the detected grid connection point voltage. Specifically, the effective value of the grid connection point voltage is compared to the rated voltage, and the squared error or piecewise linear function is used to amplify the impact of the low-voltage region. For example, when the voltage drops to 0.5 times the rated value, the transient voltage severity is set to 1.0; when the voltage drops to 0.2 times the rated value, the value rapidly rises to 2.5, thus highlighting the urgency of supporting voltage under severe fault conditions.

[0055] Subsequently, the active power deficit is calculated based on the grid connection point voltage and the current active current output by the wind farm. A voltage-related expected active power recovery curve can be established; for example, when the voltage drops to 0.5 times the rated value, the expected active power should not be less than 0.3 times the pre-fault command; when the voltage rises back to 0.8 times the rated value, the expected active power should reach 0.7 times the pre-fault command. The expected active power is obtained by looking up the table based on the current grid connection point voltage, and then subtracted from the actual active power to obtain the active power deficit.

[0056] Next, the cost of reactive power equipment deployment is calculated. Static var generators (SVMs), energy storage systems, and wind farms can be pre-classified into three tiers based on deployment priority: SVMs in the first tier, energy storage systems in the second, and wind farms in the third. When calculating costs, the total reactive current output of the first-tier equipment is first calculated. If this total reactive current meets all the reactive power requirements for the current voltage support, the cost is set to zero. If it is insufficient, the reactive current of the second-tier equipment is calculated based on the shortfall, and a fixed penalty value is assigned. If it is still insufficient, the reactive current output of the third-tier equipment is calculated, and a higher fixed penalty value is assigned.

[0057] The multi-objective comprehensive evaluation function is obtained by multiplying the severity of transient voltage, active power deficit, and reactive power equipment call-up cost by their respective weighting coefficients and then summing them. The selection of these weighting coefficients needs to be pre-tuned through offline simulation or field tests based on the actual operational requirements of the offshore wind power grid-connected system. For example, in scenarios with extremely high transient voltage stability requirements, the weight of transient voltage severity is set to 0.6, the weight of active power deficit to 0.3, and the weight of reactive power equipment call-up cost to 0.1; if rapid active power recovery is prioritized, the weight of active power deficit is adjusted primarily.

[0058] The severity of transient voltage directly reflects the risk of voltage drop. Incorporating this indicator into the function automatically tilts the optimization process towards increasing the grid connection voltage, thereby enhancing voltage support capabilities during faults. Active power deficit quantifies the urgency of active power recovery. Adding this indicator avoids excessive reactive current input leading to slow active power recovery, allowing the optimization result to find a balance between voltage support and active power recovery. Reactive power equipment call-up cost is calculated based on each reactive current, ensuring that the optimization prioritizes lower-cost reactive resources while meeting voltage recovery requirements, thus reducing losses in expensive equipment such as energy storage systems and improving overall operational economy. By weighted combining these three indicators, the previously conflicting objectives of voltage support, active power recovery, and economy are unified into a quantifiable function, providing a clear objective guide for subsequent optimization solutions and avoiding the shortcomings of relying on experience to allocate current without considering all aspects.

[0059] S103: Determine the optimization constraints, which include inverter current limit circle constraints to limit the vector magnitude of each active and reactive current, wind power active output logic constraints to constrain the upper limit of active current in the wind farm, fault network algebraic equation constraints to force node voltages to satisfy Kirchhoff's laws during faults, and grid disconnection voltage constraints to require the grid connection point voltage to be no lower than the grid disconnection lower limit.

[0060] The inverter current limit circle constraint means that the current vector formed by the active and reactive currents of each inverter in the wind farm, static var generator, and energy storage system cannot exceed the maximum physical current range allowed by the inverter itself. The wind power active power output logic constraint means that the actual active current output by the wind farm during a fault is limited by the maximum output capacity determined by the current wind conditions and unit operating status; this capacity value dynamically changes with wind speed and unit operating conditions. The fault network algebraic equation equality constraint means that during the transient process after a fault, the voltage and injected current of each node in the system must satisfy the algebraic equation relationship determined by Kirchhoff's current law and voltage law, which couples the grid connection point voltage with the active and reactive currents output by each device. The grid disconnection voltage constraint mandates that the grid connection point voltage must not be lower than the grid disconnection lower limit voltage value specified by the low voltage ride-through standard throughout the entire fault period.

[0061] After constructing the multi-objective comprehensive evaluation function, a set of optimization constraints needs to be established to ensure that the optimal current allocation command obtained from the subsequent solution meets the constraints of the actual physical system and its operating rules. These optimization constraints, in the form of equations or inequalities, together with the objective function constitute a complete optimization problem.

[0062] When establishing the inverter current limit circle constraint, the maximum allowable current value is obtained for each inverter in the wind farm, static var generator, and energy storage system. This constraint requires that the total length of the composite vector formed by the active and reactive currents of each inverter must not exceed the maximum current value of that inverter. The maximum current values ​​of the three inverters may be different, so constraints are applied independently to each, and capacity sharing is not allowed. This means that if an inverter has already output a large active current, its available reactive current margin will be reduced accordingly, and vice versa, thus reflecting the physical constraint relationship between active and reactive currents.

[0063] For the active power output logic constraint of wind power, it is necessary to read the maximum active power output capacity of the wind farm under the current wind speed and unit operating conditions. This capacity value is jointly determined by wind conditions, generator speed, and unit control mode, and changes dynamically with operating conditions. The constraint requires that the actual active current output by the wind farm must not exceed the upper limit of the current calculated based on this maximum active power capacity. This constraint ensures that the optimization command is always within the actual reach of the wind farm, avoiding the requirement for the wind farm to output active current beyond its current physical limits.

[0064] During the transient process following a fault, regardless of changes in the grid topology, the voltage at each node and the injected current at each device must strictly obey Kirchhoff's laws. Therefore, an algebraic equation is constructed based on the post-fault network structure and line parameters. Wind farms, energy storage systems, static var generators, and conventional generating units are considered as injected current sources, and the grid connection point voltage is treated as the variable to be determined. This equation is then embedded as an equality constraint in the optimization process. Any candidate current distribution command can only pass verification if it satisfies this equation, thus ensuring that the optimization results are consistent with the actual electromagnetic behavior of the power grid.

[0065] The grid disconnection voltage constraint directly references the lower grid disconnection voltage limit specified in the Low Voltage Ride-Through Technical Standard. This constraint, expressed as an inequality, requires that the grid connection point voltage remain above this lower limit throughout the entire fault period. Any current distribution scheme that causes the grid connection point voltage to fall below the lower limit is considered an infeasible solution and is automatically eliminated during the optimization process.

[0066] When determining optimization constraints, the inverter current limit circle constraint limits the active and reactive current vector magnitudes of each inverter, ensuring that all current commands do not exceed the physical capacity of the equipment, thus avoiding control failures or equipment overloads caused by command inoperability. The wind power active power output logic constraint binds the upper limit of the wind farm's active current to real-time wind speed and unit operating status, ensuring that the optimization results always remain within the realistically achievable range of wind power generation, eliminating the deviation between theoretical optimality and actual output. The fault network algebraic equation equality constraint forces the optimization results to satisfy Kirchhoff's laws, guaranteeing electromagnetic consistency between the grid connection point voltage and the output current of each device, making the optimal solution not only mathematically feasible but also valid in actual grid behavior. The grid disconnection voltage constraint directly sets the voltage floor for low-voltage ride-through, actively eliminating any allocation schemes that might cause the grid connection point voltage to fall below the lower limit during the optimization process, thereby preventing the risk of the wind farm disconnecting from the grid due to excessively low voltage during a fault. The four constraints, from the perspectives of equipment capacity, wind power characteristics, power grid physics, and grid connection rules, jointly define the safety boundary of the feasible solution, providing a reliable guarantee for the subsequent solution of the optimal current allocation command that is truly feasible and meets the low voltage ride-through requirements.

[0067] S104: Under the condition of satisfying the optimization constraints, solve for the optimal active current allocation command and the optimal reactive current allocation command that minimize the multi-objective comprehensive evaluation function, and send them to the inverter controllers of the wind farm, energy storage system and static var generator to regulate the active current and reactive current output during the fault period.

[0068] The optimal active current allocation command refers to the target active current values ​​obtained through optimization and sent to the inverter controllers of the wind farm, energy storage system, and static var generator (SVM). These values ​​achieve overall optimality under the combined constraints of a multi-objective comprehensive evaluation function and all optimization constraints. The optimal reactive current allocation command refers to the target reactive current values ​​obtained through synchronous optimization and sent to the inverter controllers of the three devices. The inverter controller is a closed-loop control unit installed inside each power electronic conversion device, responsible for receiving externally given current commands and driving the inverter to output the corresponding actual current.

[0069] After constructing the multi-objective comprehensive evaluation function and setting the four optimization constraints, all mathematical expressions are summarized into a standard constrained optimization problem. The optimization variables of this problem include the active and reactive current values ​​of the wind farm, energy storage system, and static var generator, totaling six variables. Numerical optimization algorithms such as the interior-point method or sequential quadratic programming can be used to solve this problem. Due to the small number of variables and the fact that all constraints are convex, the solution process can be completed within tens of milliseconds.

[0070] The optimal active current allocation command and optimal reactive current allocation command obtained from the solution correspond to the target current values ​​for the wind farm, energy storage system, and static var generator, respectively. To ensure synchronous response of all devices, these three sets of commands can be packaged into a single control message. Using fiber optic communication or industrial Ethernet, the wind farm command is sent to the main controller of the wind farm cluster, the energy storage system command is sent to the local controller of the energy storage converter, and the static var generator command is sent to its own controller. The transmission time is selected at a preset point before the start of the next control cycle, ensuring that all devices update their commands simultaneously.

[0071] Upon receiving the command, each inverter controller uses it as the setpoint for the inner current loop and drives the switching devices of the inverter through pulse width modulation (PWM) technology, enabling the actual output active and reactive currents to quickly track the command values. Because communication delay and inverter response time are both controlled to sub-millisecond levels, the total time of the entire closed loop, from detection to optimization to execution, meets the real-time requirements of offshore wind power grid-connected systems for transient control. Throughout the fault duration, the solution and transmission process is repeated in each control cycle, continuously updating the optimal command based on the dynamic changes in the system state.

[0072] By solving for the instructions that minimize the multi-objective comprehensive evaluation function while satisfying all optimization constraints, the final output current distribution scheme is neither arbitrarily selected nor simply dependent on experience, but rather a mathematically rigorous optimal solution within the physically feasible region. This set of optimal instructions is sent to the inverter controllers of the wind farm, energy storage system, and static var generator, realizing a closed loop from optimization calculation to actual execution. This allows each device to coordinately adjust its active and reactive current outputs in an overall optimal manner during fault periods. Thus, the previously mutually constraining voltage support requirements, active power recovery requirements, and economic requirements are quantitatively balanced within the same framework and implemented through the controllers, thereby truly achieving a multi-source collaborative transient coordination control effect.

[0073] In the above embodiments, a multi-objective comprehensive evaluation function is constructed by detecting the grid connection point voltage and the active and reactive currents of various resources during the fault period. This function includes the severity of transient voltage, active power deficit, and reactive power equipment call-up cost. The transient voltage severity is calculated based on the grid connection point voltage to quantify the threat of voltage drop to stability; the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm to reflect the urgency of active power recovery; and the reactive power equipment call-up cost is calculated based on each reactive current to reflect the economic efficiency of regulation. Based on this, combined with inverter current limit circle constraints to limit the amplitude of each current vector to no more than the physical capacity, wind power active power output logic constraints to ensure the actual generating capacity of the wind farm, fault network algebraic equation constraints to enforce Kirchhoff's laws during the fault period, and off-grid voltage constraints to maintain the voltage floor for low-voltage ride-through, the optimal active and reactive current allocation command is obtained through optimization and sent to the inverter controller. Therefore, under the premise that the grid connection point is not lower than the grid disconnection limit, that is, strictly meeting the low voltage ride-through requirements, the quantitative and coordinated allocation of active and reactive currents among wind farms, static var generators and energy storage systems is realized, solving the problem of the difficulty in achieving optimal balance between transient voltage support and active power recovery during faults; at the same time, by minimizing the multi-objective function, the system stability, power recovery speed and reactive power equipment call-up cost are taken into account, effectively improving the transient response performance and operational economy of offshore wind power grid connection systems.

[0074] In one embodiment, the expression for the severity of the transient voltage is:

[0075]

[0076] in, The severity of transient voltage, This is the lower limit of the transient voltage safety. The voltage at the grid connection point. The duration of the fault.

[0077] In this formula, when the grid connection point voltage is lower than the transient voltage safety lower limit, the larger the difference or the longer the fault duration, the larger the value of this index; if the voltage is not lower than the safety lower limit, the index is zero. Thus, this index reflects both the depth and duration of the voltage drop, quantifying the urgency of voltage support into a single value. By incorporating it into the optimization objective, the solution process automatically imposes a greater penalty on faults with deep voltage drops and long durations, thereby prioritizing the allocation of reactive current to quickly raise the voltage. This avoids focusing solely on the instantaneous voltage amplitude while ignoring the stability risks caused by prolonged low voltage, effectively enhancing the transient voltage support capability during low voltage ride-through.

[0078] In one embodiment, the expression for the active power deficit is:

[0079]

[0080] in, This is due to a shortfall in active power. This represents the steady-state active power command value of the wind farm before the fault. The voltage at the grid connection point. This represents the active current of the wind farm.

[0081] In this formula, when the grid connection voltage drops or the active current of the wind farm is restricted, the actual active power decreases, and the deficit increases accordingly. Using the steady-state output before the fault as a reference, the degree of active power loss caused by the fault is quantified. After incorporating this into the optimization objective, the solution process seeks a balance between raising the voltage to improve transient stability and retaining active current to accelerate power recovery. Specifically, if excessive reactive current is output to support the voltage, it will crowd out the active current capacity and may indirectly affect the active power calculation due to the voltage increase, thus increasing the deficit penalty. Conversely, if a large active current is forcibly retained while ignoring voltage support, the voltage will be too low, also limiting the actual active power and resulting in a larger deficit penalty. Through this deficit term, the optimization algorithm is guided to the optimal point of simultaneously raising the voltage and retaining the active current, thereby minimizing the active power loss during the fault while meeting the low-voltage ride-through requirements. This is beneficial for the rapid recovery of system frequency and power angle after the fault is cleared.

[0082] In one embodiment, the expression for the cost of reactive power equipment dispatch is:

[0083]

[0084] in, Cost of reactive power equipment dispatch, These are the reactive currents from the static var generator, energy storage system, and wind farm, respectively. These are the corresponding cost weighting coefficients, and .

[0085] In this formula, the cost of reactive power equipment is the weighted sum of the reactive current outputs of the static var generator (SVM), energy storage system, and wind farm, multiplied by their respective cost weighting coefficients. The cost weighting coefficients are set such that the SVM is the lowest, the energy storage system is in the middle, and the wind farm is the highest. This setting reflects the principle of prioritizing dedicated reactive power equipment. The SVM is dedicated to reactive power compensation and has the lowest cost, so it is prioritized for outputting reactive current during optimization. While the energy storage system can output reactive power, it consumes electrical capacity and has a moderate cost, so it is only used when the SVM is insufficient. The wind farm's reactive power output squeezes the active current margin and affects power generation efficiency, resulting in the highest cost, and it is only used as a backup measure. Therefore, this cost-driven optimization algorithm, while meeting voltage support requirements, uses the lowest-cost equipment to provide reactive power, thereby reducing the crowding out of the wind farm's active power output and minimizing unnecessary operating losses in the energy storage system, ultimately achieving the transient voltage support target at the lowest economic cost.

[0086] In one embodiment, the expression for the inverter current limit circle constraint is:

[0087]

[0088] in, These are the maximum permissible transient currents for wind farms, energy storage systems, and static var generators, respectively. These are the active current and reactive current of the wind farm, respectively. These are the active current and reactive current of the energy storage system, respectively. This refers to the reactive current of the energy storage system.

[0089] In this formula, the inverters of the wind farm and energy storage system are each subject to current limit circle constraints, meaning the sum of the squares of the active current and the reactive current does not exceed the square of their respective maximum transient allowable current. This reflects the physical coupling relationship between the active and reactive currents of the two systems; when the output active current is large, the available reactive current margin automatically decreases, and vice versa. The static var generator (SVA) is only constrained by upper and lower limits of reactive current, as it does not output active current. Therefore, by limiting the current vector amplitude of each device separately, the optimization command is prevented from exceeding the actual overload capacity of the inverter, thus avoiding overcurrent tripping or damage to the equipment. At the same time, the circular constraint form allows for flexible configuration of the active and reactive power ratio within the capacity range, providing a feasible domain for coordinating voltage support and active power recovery. Using simple reactive power limiting for the SVA alone fully utilizes its dedicated reactive power compensation characteristics, simplifying the constraint expression without crowding out active power capacity.

[0090] In one embodiment, the expression for the wind power active power output logic constraint is:

[0091]

[0092] in, This represents the active current of the wind farm. This represents the initial active current of the wind farm before the fault. This is the maximum permissible transient current of the wind farm. This refers to the reactive current of the wind farm.

[0093] In this formula, the upper limit of the active current of the wind farm is determined by two factors: the initial active current before the fault and the remaining capacity of the inverter. Taking the smaller of the two as the upper limit means that during a fault, the active current of the wind farm cannot exceed the normal output level before the fault, nor can it exceed the current limit circle limit if reactive current has already been output. The reason for this setting is that the mechanical inertia of the wind turbine and the converter control usually do not allow the active current to exceed the steady-state value during the transient process, otherwise it may cause abnormal speed or excessive mechanical stress. At the same time, when the wind farm is required to output reactive current to support the voltage, its remaining current capacity will naturally decrease, and the active current must be adjusted accordingly. This constraint ensures that the optimization instruction respects the physical operating logic of the wind farm. Reactive power priority is the basic requirement for low voltage ride-through. After satisfying the reactive power output, active power is retained as much as possible within the remaining capacity, and the retained active power does not exceed the level before the fault. Thus, it not only meets the grid's urgent need for voltage support, but also avoids the wind turbine from disconnecting from the grid due to overcurrent or overspeed, making the optimization solution truly executable on actual wind turbines.

[0094] In one embodiment, the expression for the equality constraints of the algebraic equations of the fault network is:

[0095]

[0096] in, This is a vector of node voltage changes. To account for the system's equivalent impedance matrix under fault boundary conditions, The vector of sudden current injected into each station and fault point. and These are the node voltage vectors before and during the fault, respectively.

[0097] This formula employs the fault component method, expressing the post-fault node voltage as the sum of the pre-fault voltage and the voltage change. The voltage change is obtained by multiplying the system's equivalent impedance matrix by the sudden current vector injected by each device. The equivalent impedance matrix incorporates fault boundary conditions, and the sudden current vector includes the current changes injected by the wind farm, energy storage system, static var generator, and the fault point. This equality constraint essentially transforms Kirchhoff's laws into a computable algebraic relationship, forcing the grid connection point voltage and the active and reactive currents output by each device to satisfy the electromagnetic coupling law of the power grid. Thus, by embedding the real fault network equations into the optimization problem, the grid connection point voltage corresponding to the solved current distribution command is no longer an independent variable, but a physical quantity jointly determined by the impedance matrix and the injected current. This avoids giving a mathematically optimal but practically unrealistic current command due to neglecting the power grid structure. Simply put, this constraint ensures that the optimization result is not an ideal value calculated out of thin air, but a state that can truly be achieved under the current fault network.

[0098] The following describes the multi-source collaborative transient coordination control optimization device provided in the embodiments of this application. The multi-source collaborative transient coordination control optimization device described below can be referred to in correspondence with the multi-source collaborative transient coordination control optimization method described above. Figure 3 As shown, this application provides a multi-source collaborative transient coordinated control optimization device, applied to an offshore wind power grid-connected system. The device includes:

[0099] The voltage and current detection module 201 is used to detect the grid connection point voltage during a fault, as well as the active and reactive currents output by the wind farm, static var generator and energy storage system respectively.

[0100] The multi-objective comprehensive evaluation function construction module 202 is used to construct a multi-objective comprehensive evaluation function weighted by transient voltage severity, active power deficit and reactive power equipment call cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call cost is calculated based on each reactive current.

[0101] The optimization constraint determination module 203 is used to determine the optimization constraints, which include inverter current limit circle constraints for limiting the vector magnitude of each active and reactive current, wind power active output logic constraints for constraining the upper limit of active current in the wind farm, fault network algebraic equation constraints for forcing node voltage to satisfy Kirchhoff's laws during faults, and grid disconnection voltage constraints for requiring the grid connection point voltage to be no lower than the grid disconnection lower limit.

[0102] The optimal current distribution instruction solving module 204 is used to solve for the optimal active current distribution instruction and the optimal reactive current distribution instruction that minimize the multi-objective comprehensive evaluation function under the condition of satisfying the optimization constraints, and send them to the inverter controllers of the wind farm, energy storage system and static var generator to adjust the active current and reactive current output during the fault period.

[0103] In one embodiment, the expression for the severity of the transient voltage is:

[0104]

[0105] in, The severity of transient voltage, This is the lower limit of the transient voltage safety. The voltage at the grid connection point. The duration of the fault.

[0106] In one embodiment, the expression for the active power deficit is:

[0107]

[0108] in, This is due to a shortfall in active power. This represents the steady-state active power command value of the wind farm before the fault. The voltage at the grid connection point. This represents the active current of the wind farm.

[0109] In one embodiment, the expression for the cost of reactive power equipment dispatch is:

[0110]

[0111] in, Cost of reactive power equipment dispatch, These are the reactive currents from the static var generator, energy storage system, and wind farm, respectively. These are the corresponding cost weighting coefficients, and .

[0112] In one embodiment, the expression for the inverter current limit circle constraint is:

[0113]

[0114] in, These are the maximum permissible transient currents for wind farms, energy storage systems, and static var generators, respectively. These are the active current and reactive current of the wind farm, respectively. These are the active current and reactive current of the energy storage system, respectively. This refers to the reactive current of the energy storage system.

[0115] In one embodiment, the expression for the wind power active power output logic constraint is:

[0116]

[0117] in, This represents the active current of the wind farm. This represents the initial active current of the wind farm before the fault. This is the maximum permissible transient current of the wind farm. This refers to the reactive current of the wind farm.

[0118] In one embodiment, the expression for the equality constraints of the algebraic equations of the fault network is:

[0119]

[0120] in, This is a vector of node voltage changes. To account for the system's equivalent impedance matrix under fault boundary conditions, The vector of sudden current injected into each station and fault point. and These are the node voltage vectors before and during the fault, respectively.

[0121] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the multi-source collaborative transient coordination control optimization method as described in any of the above embodiments.

[0122] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the multi-source collaborative transient coordination control optimization method as described in any of the above embodiments.

[0123] Indicatively, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 4The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the multi-source collaborative transient coordination control optimization method of any of the above embodiments.

[0124] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0125] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0127] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0128] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-source collaborative transient coordination control optimization method, characterized in that, The method, applied to offshore wind power grid-connected systems, includes: The grid connection point voltage during the fault period is detected, as well as the active and reactive currents output by the wind farm, static var generator, and energy storage system. A multi-objective comprehensive evaluation function is constructed, which is weighted by transient voltage severity, active power deficit, and reactive power equipment call-up cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call-up cost is calculated based on each reactive current. The optimization constraints are determined, including inverter current limit circle constraints for limiting the vector magnitude of each active and reactive current, wind power active output logic constraints for constraining the upper limit of active current in the wind farm, fault network algebraic equation constraints for forcing node voltages to satisfy Kirchhoff's laws during faults, and grid disconnection voltage constraints for requiring the grid connection point voltage to be no lower than the grid disconnection lower limit. Under the optimization constraints, the optimal active current allocation command and the optimal reactive current allocation command that minimize the multi-objective comprehensive evaluation function are solved and sent to the inverter controllers of the wind farm, the energy storage system and the static var generator to adjust the active current and reactive current output during the fault period.

2. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the severity of the transient voltage is: in, The severity of the transient voltage, This is the lower limit of the transient voltage safety. The grid connection point voltage, The duration of the fault.

3. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the active power deficit is: in, This refers to the active power deficit. This represents the steady-state active power command value of the wind farm prior to the fault. The grid connection point voltage, The active current of the wind farm is denoted as .

4. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the cost of utilizing reactive power equipment is: in, The cost of calling up the reactive power equipment. These are the reactive currents of the static var generator, the energy storage system, and the wind farm, respectively. These are the corresponding cost weighting coefficients, and .

5. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the inverter current limit circle constraint is: in, These are the maximum permissible transient currents of the wind farm, the energy storage system, and the static var generator, respectively. These are the active current and reactive current of the wind farm, respectively. These are the active current and reactive current of the energy storage system, respectively. The reactive current of the energy storage system is denoted as .

6. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the logic constraint of the active power output of the wind power is: in, The active current of the wind farm is denoted as . This represents the initial active current of the wind farm before the fault. This is the maximum permissible transient current of the wind farm. The reactive current of the wind farm is denoted as .

7. The multi-source collaborative transient coordination control optimization method according to claim 1, characterized in that, The expression for the equality constraints of the algebraic equations of the fault network is as follows: in, This is a vector of node voltage changes. To account for the system's equivalent impedance matrix under fault boundary conditions, The vector of sudden current injected into each station and fault point. and These are the node voltage vectors before and during the fault, respectively.

8. A multi-source collaborative transient coordination control optimization device, characterized in that, The device, applied to offshore wind power grid connection systems, includes: The voltage and current detection module is used to detect the grid connection point voltage during a fault, as well as the active and reactive currents output by the wind farm, static var generator, and energy storage system. The multi-objective comprehensive evaluation function construction module is used to construct a multi-objective comprehensive evaluation function weighted by transient voltage severity, active power deficit, and reactive power equipment call cost. The transient voltage severity is calculated based on the grid connection point voltage, the active power deficit is calculated based on the grid connection voltage and the active current of the wind farm, and the reactive power equipment call cost is calculated based on each reactive current. The optimization constraint determination module is used to determine the optimization constraints, which include inverter current limit circle constraints for limiting the vector magnitude of each active current and reactive current, wind power active output logic constraints for constraining the upper limit of the active current of the wind farm, fault network algebraic equation constraints for forcing the node voltage to satisfy Kirchhoff's laws during a fault, and grid disconnection voltage constraints for requiring the grid connection point voltage to be no lower than the grid disconnection lower limit. The optimal current allocation instruction solving module is used to solve for the optimal active current allocation instruction and the optimal reactive current allocation instruction that minimize the multi-objective comprehensive evaluation function under the optimization constraints, and send them to the inverter controllers of the wind farm, the energy storage system and the static var generator to adjust the active current and reactive current output during faults.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the multi-source cooperative transient coordination control optimization method as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the multi-source collaborative transient coordination control optimization method as described in any one of claims 1 to 7.

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