Active power distribution method for offshore wind power cluster
By constructing optimization functions and health status screening units in offshore wind power clusters, and combining them with line loss constraints, we have achieved efficient and stable active power allocation and fault elastic recovery of offshore wind power clusters, solving the adaptability and stability problems of traditional methods.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INSTITUTE FOR SMART CITY OF CHONGQING UNIVERSITY IN LIYANG LIYANG
- Filing Date
- 2026-06-23
- Publication Date
- 2026-07-21
AI Technical Summary
The allocation of active power in offshore wind power clusters faces complexity and instability issues. Especially in the deep-sea environment, traditional methods are difficult to adapt to the randomness of wind resources and the influence of the marine environment, and fail to take into account both the health status of the units and line losses.
The central controller receives grid commands, collects wind turbine data, constructs optimization functions to minimize losses, allocates power based on health status and constraints, performs elastic recovery in case of faults, and redistributes power using the autonomous response of healthy turbines.
It significantly reduces transmission line losses, improves power allocation accuracy and cluster operation stability, and enhances the resilience and robustness of the method under fault conditions.
Smart Images
Figure CN122437180A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of offshore wind power dispatching technology, specifically to a method for allocating active power in offshore wind power clusters. Background Technology
[0002] Offshore wind power, as an important component of renewable energy, is rapidly developing globally due to its abundant resources, strong stability, and lack of land occupation. However, with the continuous growth of installed offshore wind power capacity and the expansion of wind farms, the power allocation problem in offshore wind power clusters is becoming increasingly prominent. Especially in the context of deep-sea development, wind power clusters are typically connected to the onshore power grid via flexible DC transmission systems, making their operation and control more complex. This complexity stems primarily from the randomness and volatility of offshore wind resources, as well as the significant impact of the marine environment on wind power equipment operation, making it difficult to directly apply traditional power allocation methods. The core objective of active power distribution technology is to achieve a reasonable and efficient distribution of power within a wind power cluster, thereby improving the operating efficiency, stability, and economy of the entire wind power system. In the special marine environment, the objective environment of variable wind speed and direction, influenced by factors such as sea surface friction and turbulence, poses a huge challenge to the existing active power distribution technology for onshore wind power, and there is an urgent need for a dedicated solution designed for the characteristics of the marine environment.
[0003] Currently, offshore wind power development is showing a trend towards clustering, large-scale development, and deep-sea expansion. The number of wind turbines in a single wind farm is increasing, and multiple wind farms need to coordinate their operation. This not only increases the complexity of power allocation but also places higher demands on the real-time performance and adaptability of allocation methods.
[0004] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention
[0005] The purpose of this invention is to address the problem that traditional power allocation does not take into account the health status of the generator units, line losses, and delayed fault response, and to propose a method for active power allocation in offshore wind power clusters.
[0006] The objective of this invention can be achieved through the following technical solutions: Methods for active power allocation in offshore wind power clusters include: S1. Command Reception and Data Acquisition: The central controller receives the total active power command of the cluster from the upper-level power grid dispatch automation platform through the power dispatch data network, and simultaneously sends data requests to the local controllers of the online wind turbines in the cluster through the ring network industrial Ethernet to obtain real-time operating status data packets including maximum available power, current actual power, and operating health status index; and selects the units to participate in power allocation based on the operating health status index. S2. Cluster power optimization allocation: Based on the collected data, an optimization function is constructed with the goal of minimizing the total active power loss of the power collection equipment. Combined with power balance, unit output upper and lower limits, and line power flow safety constraints, the optimization function is iteratively solved to obtain the unit's base power. If there is no solution, the standby allocation strategy is activated. S3, Reference Power Distribution and Monitoring: The reference power is distributed to the local controller, and the power tracking deviation, unit status changes, communication link and total cluster power are monitored. S4. Fault Resilient Recovery: When a unit fault is detected, a gradient task package is divided based on the power deficit, and an assistance request is broadcast. Healthy units respond autonomously based on multi-dimensional indicators and redistribute power through competitive allocation.
[0007] As a further improvement of the present invention, the specific operation steps of S1 are as follows: During the data preparation phase of the wind farm central controller, the central controller receives the cluster total active power command issued by the upper-level power grid dispatch automation platform through the power dispatch data network at a preset communication cycle; upon receiving the command, the central controller immediately initiates a round of cluster internal data acquisition polling. The central controller sends data request frames to the local controllers of each online wind turbine in the cluster via a ring network industrial Ethernet. Upon receiving the request, each local controller returns a set of structured real-time operating status data packets to the central controller. The data packets include the maximum available power, the current actual power, and the operating health status index. The operational health status index is a quantitative coefficient obtained by comprehensively evaluating unit fault codes, cumulative fatigue damage, and monitoring data of key components. Wind turbines participating in power allocation were selected based on their operational health status index.
[0008] As a further improvement of the present invention, the specific operation steps of S2 include: Based on the collected real-time operating status data of wind turbine units, an optimization function is constructed with the goal of minimizing the total active power loss of the power collection equipment. The optimization function includes the active power loss of transmission lines and the power deviation weighting coefficient. The power deviation weighting coefficient is determined by the grid dispatch requirements and the wind farm operation and maintenance strategy. The optimization function satisfies three types of constraints: Power balance constraint: The difference between the total active power of the cluster and the sum of the reference power of each unit shall not exceed 0.5% of the total active power of the cluster; Unit output upper and lower limit constraints: The lower limit of unit output is the minimum stable operating technical output, and the upper limit is the product of the maximum available power and the dynamic adjustment coefficient of health status. The dynamic adjustment coefficient of health status is a monotonically increasing function of the operating health status index. Line power flow safety constraints: The power transmitted by the submarine cable shall not exceed the line thermal stability limit determined based on the rated voltage and cable type; For wind turbine units, a health weight vector is constructed based on the corresponding operational health status index to obtain the corresponding unit weight coefficient; The virtual resistance of the line is adjusted based on the status of the units or combiner boxes connected at both ends of the line to optimize the power transmission path.
[0009] As a further improvement of the present invention, the specific operation steps of S2 also include: Based on the electrical wiring diagram of offshore wind power clusters, three types of nodes are divided: The unit node is the PQ node, the active power is equal to the reference power, and the reactive power is obtained by the reference power and the tangent of the power factor angle. The combiner box node is an intermediate node that only undertakes power aggregation and transmission, and its power is the sum of the power of its subordinate unit nodes. The converter station node is a balanced node, with the voltage amplitude set to the rated voltage and the phase angle set to 0°. Based on the node connection relationships in the preset cluster topology parameter library, a complete node association matrix including generator units, combiner boxes, and converter stations is generated; Power flow calculations are performed based on node information and actual line resistance. The calculation results are used only for line power flow safety constraint verification. The optimization function is solved iteratively. In each iteration, a candidate baseline power vector is generated and the power flow safety constraints are verified. Convergence is judged by the improvement of the optimization function, the change of the optimization variables, and the constraint satisfaction. When there is no solution or the optimization fails to converge, the backup allocation strategy is activated, and a suboptimal solution is generated based on the proportional allocation method of health weight.
[0010] As a further improvement of the present invention, the specific operation steps of S3 are as follows: After the cluster power optimization allocation solution converges, the central controller encapsulates the final reference power of each unit into a standardized power command frame and sends it to the local controller of the corresponding wind turbine unit. After receiving the command, the local controller verifies its validity. If the verification is successful, it writes the target power value into the setpoint register of the power control loop and returns a command confirmation frame to the central controller. After the central controller issues power commands to all units, it initiates periodic monitoring, which includes the following: Power tracking deviation monitoring compares the actual power of the unit with the reference power in real time. An alarm is triggered if the deviation exceeds the preset tracking tolerance threshold. Unit status changes are monitored, and the unit's operational health status index is continuously updated. Units whose index declines for multiple consecutive cycles or enters a warning state are marked as potential fault risk units. Communication link monitoring records local controller data response latency and packet loss rate; if a communication link is found to be abnormal, a fault handling process is initiated. Cluster total power monitoring: Real-time collection of cluster total output power and comparison with power dispatch command from the power grid.
[0011] As a further improvement of the present invention, the specific operation steps of S4 include: When a wind turbine is detected to be out of service during periodic monitoring, the central controller marks the faulty turbine as an uncontrollable power unit and removes it from the allocation list. There is no need to immediately start global optimization calculation. Power redistribution is achieved through a distributed and collaborative approach. Based on the difference between the original allocated baseline power of the faulty unit and the actual output power at the time of the fault, the power deficit or surplus is determined. When the difference is negative, the surplus power is directly added to the total output of the cluster without the need to activate the assistance mechanism. When the difference is positive, it is determined to be a power deficit. Based on the principle of decreasing capacity, several tiered task packages are divided. The first task package has the largest capacity. Each task package includes a number, capacity, priority identifier, generation timestamp, and associated faulty unit number. The central controller constructs a power assistance request data frame and broadcasts it to the local controllers of all healthy and participating units. The request includes information about the faulty unit, a list of gradient task packages, total capacity, and a response deadline timestamp.
[0012] As a further improvement of the present invention, the specific operation steps of S4 also include: After receiving a power assistance request, the local controller of the healthy unit responds to the decision autonomously through the built-in decision agent unit. Based on the difference between the maximum available power and the actual output power under the current wind speed conditions, the power adjustability margin is obtained. The response capability coefficient is obtained by combining the real-time wind speed with the cut-in, rated, and cut-out wind speeds. The penalty coefficient is generated by referring to the current power of the unit and the Euclidean distance of the fatigue sensitive zone. Based on power adjustability margin, response capability coefficient and penalty coefficient, the unit basic response coefficient is introduced to obtain the assistance willingness value and feed it back to the central controller. After the central controller screens valid feedback, it initiates a competitive allocation process, allocating tiered task packages based on the weight ratio of healthy units, with allocation priority from high to low, and simultaneously deducting the corresponding unit's adjustable power margin. If the margin is exhausted, the weight is reset to zero in subsequent allocations. After all task packages are allocated, the allocation amounts for each unit are summed to generate the final incremental power command. The total allocation amount is verified to be consistent with the total capacity of the task packages. If there are no errors, the command is sent to the local controller of the corresponding unit.
[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention receives the total active power command of the power grid cluster through a central controller, collects the maximum available power, current actual power and operating health status index of wind turbines, constructs an optimization function with the goal of minimizing the loss of power collection equipment, and combines power balance, unit output limit and line power flow safety as constraints. The benchmark power is solved by optimizing the optimization function and then distributed. At the same time, power tracking, unit status, communication link and total cluster power are periodically monitored, which significantly reduces the active power loss of transmission lines and improves the accuracy of power allocation and the stability of cluster operation. This invention marks faulty units when a fault occurs, divides gradient task packages based on power deficit, and broadcasts assistance requests to healthy units. Healthy units autonomously generate assistance willingness based on power adjustability margin, wind speed response capability, and fatigue penalty coefficient, and then complete power redistribution through competitive allocation. Without initiating global optimization, it significantly improves the elastic recovery capability and response efficiency under fault conditions. At the same time, it uses a backup allocation strategy to deal with scenarios where optimization is unsolvable, enhancing the robustness of the method and its adaptability to complex operating scenarios. Attached Figure Description
[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] Example: like Figure 1 As shown, the active power allocation method for offshore wind power clusters includes command reception and data acquisition, cluster power optimization allocation, baseline power distribution and monitoring, and fault elastic recovery.
[0017] S1. Command Reception and Data Acquisition: During the data preparation phase of the wind farm central controller, the central controller receives the cluster total active power command from the upper-level power grid dispatch automation platform through the power dispatch data network at a preset communication cycle; the cluster total active power is the target value of the total active power that the entire offshore wind farm needs to transmit to the grid in the next control cycle; after receiving the command, the central controller immediately starts a round of cluster internal data acquisition polling. The central controller sends data request frames to the local controllers of each online wind turbine in the cluster via a ring network industrial Ethernet. Upon receiving the request, each local controller returns a set of structured real-time operating status data packets to the central controller. The data packets include the maximum available power, the current actual power, and the operating health status index. The operational health status index is a quantitative coefficient obtained by comprehensively evaluating unit fault codes, cumulative fatigue damage, and monitoring data of key components; key components include main bearings and gearbox tooth surfaces. Through formula Calculations were performed to obtain the unit operational health status index ;in, This represents the fault severity penalty factor, generated based on the unit's current highest-level fault code, reflecting the direct impact of fault severity on the unit's health status. This represents a fatigue health factor, derived from the mapping of fatigue damage in key components, reflecting the degree of component lifespan loss. The condition monitoring coefficient is derived from multi-dimensional monitoring data, including gearbox oil temperature, generator winding temperature, and vibration amplitude of key bolts, through standardization and weighted averaging. These are the influence weighting factors of the fault level penalty factor, fatigue health factor, and condition monitoring coefficient, respectively. If the operating health status index is greater than the upper limit of the preset range, it indicates that the current wind turbine is in a healthy state and is suitable to undertake the baseline or even additional power tasks. If the operating health status index is within the preset range, it indicates that the current wind turbine is in a warning state. When participating in power allocation, the upper limit of the allocated incremental power is reduced. If the operating health status index is less than the lower limit of the preset range, the central controller directly removes the current wind turbine from the qualification to participate in power allocation.
[0018] S2, Cluster Power Optimization Allocation: Based on the real-time operating status data packets collected by S1, the central controller constructs an optimization function with the objective of minimizing the total power loss of the power collection equipment. The modified optimization function is as follows:
[0019] in, Denotes the minimum optimization function. Indicates the total number of transmission lines. Indicates the first The active power loss of the transmission line, The power deviation weighting coefficient is determined by grid dispatch requirements and wind farm operation and maintenance strategies. This indicates the total number of wind turbine units participating in power distribution. Indicates the unit The reference power, Indicates the unit The actual output power; The constraints of the optimization function include: Power balance constraints: ,in, This represents the total active power of the cluster. This represents the sum of the reference power of all generating units. This represents the allowable imbalance, and is set at 0.5% of the total active power of the cluster; for example, when hour, =1MW, meaning the total output of the power cluster needs to be controlled within Within the range; Unit output upper and lower limit constraints: Lower bound constraint: ,in, Indicates the unit Minimum stable operating technical output; Upper limit constraint: ,in, Indicates the unit Maximum available power, , representing the dynamic adjustment coefficient of health status, is a monotonically increasing function of the operating health status index; when the operating health status index is 1, the upper limit constraint is the maximum available power of the current unit; Line power flow safety constraints: The transmission power of each submarine cable shall not exceed the line thermal stability limit of the current cable; the line thermal stability limit is obtained based on the cable rated voltage and the cable thermal stability current determined by the cable type; For wind turbines participating in power allocation within offshore wind power clusters, a health weight vector is constructed by combining the corresponding operational health status index; wherein, the formula is used... Calculations were performed to obtain the unit The weighting coefficients, , represents a very small constant used to prevent division by zero; If the wind turbines connected to both ends of the line are in good condition, the corresponding weight coefficient is the smallest, the correction of the line resistance is weak, and the power transmission path prioritizes the wind turbines in good condition. If the wind turbines connected to both ends of the line are in a warning state, the formula is used. For the first offshore wind power cluster The virtual resistance is obtained by correcting the line resistance. ;in, Indicates the first The resistance per unit length of the line. Indicates the first Weighting coefficients for the generator units on each line; Indicates the first The weighting coefficient on the combiner box side of each line; Through formula The weighting coefficient of the combiner box is calculated; where, The health status index of the combiner box is obtained by referring to the calculation method of the unit operation health status index based on the combiner box temperature rise, switch status, and communication status. The wiring connections of offshore wind power clusters are divided into two categories: the wiring between the turbine and the combiner box, and the wiring between the combiner box and the converter station; the wiring between the combiner box and the converter station has combiner boxes at both ends. If both ends of the line are combiner boxes, then the corresponding virtual resistance correction formula is: ;in, These represent the weighting coefficients of the two junction boxes, respectively. Through formula The actual total active power loss of the wind power cluster was calculated based on... Indicates the first Real-time current of the line, Indicates the first The actual length of the line; Based on virtual resistance, the original total active power loss formula is replaced to obtain a revised total active power loss formula for wind power clusters. The revised total active power loss formula is as follows: ;in, This represents the total active power loss of the wind power cluster after correction by virtual resistance. Based on the modified formula for total active power loss of wind power clusters, an optimized network including virtual resistance parameters is constructed. Based on the electrical wiring diagram of offshore wind power clusters, all nodes are divided into three categories, including: Unit node: Belongs to the PQ node, i.e., the active power of the node. reactive power ,in, This represents the power factor angle, which is the phase difference angle between the unit's voltage and current. Combiner box node: It is an intermediate node, located at the power injection or consumption, and only undertakes the functions of power collection and transmission. The power is obtained by summing the power of all subordinate unit nodes. Converter station node: It is a balancing node and is the voltage center of the entire offshore wind power cluster; the voltage amplitude is set to the rated voltage and the voltage phase angle is 0°. During node construction, the central controller automatically reads the node connection relationships in the preset cluster topology parameter library and generates a complete node association matrix including the unit, combiner box, and converter station; Power flow calculation: Based on the wiring diagram of offshore wind power cluster motors, actual line resistance, node types, and node correlation matrix, a node admittance matrix is constructed. Based on the current node voltage amplitude and phase angle, combined with the node admittance matrix, the active power and reactive power of the unit node are obtained, and compared with the given active power and reactive power of the unit node to obtain the power deviation, including active power deviation and reactive power deviation. The nodal power balance equations are linearized using Taylor series expansion, and the Jacobian matrix is constructed. Based on the current node voltage state, update the element values of the Jacobian matrix, and obtain the magnitude correction and phase angle correction of the node voltage through matrix inverse operation; The magnitude correction and phase angle correction of the node voltage are superimposed on the current node voltage to obtain a new voltage magnitude and phase angle; Based on the updated voltage amplitude and phase angle, the power flow calculation is repeated until the power deviation of all unit nodes is less than the corresponding preset threshold. The power flow is then determined to be converged, and the voltage of each node and the current of each line are output. The power flow calculation results are only used for line power flow safety constraint verification and are not used for virtual loss optimization calculation; Based on a nonlinear optimization algorithm, the optimization function is solved iteratively while satisfying all constraints; in each iteration: A nonlinear optimization algorithm generates a new set of candidate reference power vectors; the new reference power vectors are used as active power injection for unit nodes, and power flow calculations are performed based on the actual line resistance to obtain new line currents and corrected total active power losses; at the same time, line power flow safety constraints are verified to ensure that the power transmitted on each line is less than the line thermal stability limit apparent power. After each optimization iteration, the central controller calculates the following three convergence criteria: Criterion for improvement of the optimization function: obtained based on the absolute difference between the optimization function value of the current iteration and the previous iteration; Criterion for the change in optimization variables: It is obtained based on the vector norm of the current iteration value and the previous iteration value of the optimization variables of all unit baseline power. Constraint Satisfaction: Check the satisfaction status of all constraints; If the improvement of the optimization function and the change of the optimization variable are both less than the corresponding preset thresholds and all constraints are met, then the current optimization solution is determined to be converged, the unit reference power obtained in the current iteration is taken as the final valid solution, and the process is transferred to S3; if any criterion is not met, the iterative solution continues. Once the optimization solution converges, the final base power of the unit is sent to the local controller of each wind turbine as the power target value for steady-state operation. If the optimization solution fails to converge within the preset maximum number of iterations, or if there is no feasible solution due to constraint conflicts, the central controller will automatically activate the backup allocation strategy, only when the total power increment required by the wind farm needs to be supplemented. When the absolute value is greater than the balanced operation deviation, the backup allocation strategy is activated; The backup allocation strategy employs a health-weighted proportional allocation method to quickly generate a suboptimal feasible solution that satisfies power balance and basic output limits, while favoring healthier turbines to output more power. This solution represents the target power that the local controller of the wind turbines needs to track. The formula for the health-weighted proportional allocation method is as follows:
[0020] in, This represents the total power increment that the wind farm needs to replenish, calculated as the difference between the total power demand required by the grid dispatch and the current total output of the wind farm. Indicates the unit The weighting coefficients.
[0021] S3. Reference power distribution and monitoring: Once the optimization solution in S2 converges, the central controller encapsulates the final determined reference power of each unit into a standardized power command frame and sends it to the corresponding local controller of the wind turbine unit via the ring network industrial Ethernet. The power command frame includes the target power value, the command effective timestamp, and the command sequence number. After receiving the command, the local controller verifies its validity. If the verification is successful, it writes the target power value into the setpoint register of the power control loop and returns a command confirmation frame to the central controller. After issuing power commands to all units, the central controller performs periodic monitoring; the monitoring content includes: Power tracking deviation monitoring: Based on the deviation between the current actual power of each unit and the reference power; if the deviation exceeds the preset tracking tolerance threshold, a power tracking anomaly alarm will be triggered. Unit status change monitoring: Continuously update the operating health status index of each unit. If the operating health status index of any unit shows a downward trend in multiple consecutive monitoring cycles, or changes to the warning state, the current unit will be marked as a potential fault risk unit. Communication link monitoring: Record the data response latency and packet loss rate of each local controller. If any unit fails to respond to the data request normally for multiple consecutive monitoring cycles, it is determined that the current wind turbine communication link is abnormal, and the central controller initiates the communication fault handling process. Cluster total power monitoring: Real-time collection of the current total output power of the offshore wind power cluster and comparison with the total active power of the cluster issued by the power grid dispatch to ensure that the deviation of the cluster total power is always within the allowable range; S4. Fault resilience recovery: During periodic monitoring, when the central controller detects that a wind turbine has failed and needs to be taken out of service, the central controller does not immediately recalculate the global optimization. Instead, it activates the fault elastic recovery mechanism and quickly redistributes power through distributed collaboration. The central controller marks the faulty unit as a power uncontrollable unit and removes it from the list of units currently participating in power allocation. The corrected power deficit calculation formula is based on the difference between the original allocated base power of the faulty unit and the actual output power of the unit at the time of the fault. ,in, This indicates a power deficit. The original base power allocated to the faulty unit. This indicates the actual output power of the unit at the moment the fault occurred; A power deficit is determined only when the difference is positive, and a power surplus is determined when the difference is negative. The surplus power is directly added to the total output of the cluster without the need to activate the assistance mechanism. Based on the size of the power deficit, the central controller divides the power deficit into several graded task packages with decreasing capacity. The first task package has the largest capacity, and the capacity of subsequent task packages decreases step by step. The information of each task package includes the task package number, task package capacity, priority identifier, generation timestamp, and associated faulty unit number. The central controller constructs a power assistance request data frame and broadcasts it to the local controllers of all wind turbines that are in a healthy state and participating in power allocation. The power assistance request does not specify a specific compensation allocation value, but only includes the faulty turbine number and fault type code, a list of the capacity of all gradient task packages and their corresponding priority identifiers, the total capacity of the task packages, and the request response deadline timestamp. Upon receiving a power assistance request, the local controller of all wind turbines in a healthy state and participating in power distribution activates the built-in decision agent unit to make autonomous response decisions. Based on the difference between the maximum available power and the actual output power of the wind turbine under the current wind speed conditions, the power adjustability margin is obtained; the power adjustability margin reflects the upper limit of the physical capacity of the unit to undertake additional power tasks. Through formula The wind speed response coefficient is calculated, where, Indicates the unit Real-time wind speed at the location This refers to the minimum wind speed at which the wind turbine begins generating electricity. Rated wind speed refers to the wind speed at which the wind turbine achieves its rated output power. This indicates the cut-out wind speed, which refers to the maximum wind speed at which the wind turbine can operate safely. Indicates the width of the stable operating range for rated wind speed; Through formula The penalty coefficient is calculated, where, , representing the penalty intensity coefficient, This represents the Euclidean distance between the unit's current power and the fatigue-sensitive zone. The fatigue-sensitive zone is a continuous power range pre-determined through bench fatigue testing combined with on-site operation and maintenance data. Represents the distance attenuation constant. Represents the natural constant; Based on power adjustability margin, response capability coefficient, and penalty coefficient, after normalization, the formula is used... The willingness to provide assistance is calculated, where, Indicates the power adjustability margin. This represents the unit's basic response coefficient, which is determined by the unit's operation and maintenance level. The local controller of the wind turbine encapsulates the willingness to provide assistance into a response data frame, which is then uploaded to the central controller via the network through the offshore wind turbine cluster. Before the preset response deadline, the central controller filters out the valid data frames of wind turbines that are in a healthy state. After the central controller completes the screening of valid data frames, it immediately initiates a competitive power allocation process based on the virtual power bus: Through formula Calculations were performed to obtain the unit The weighting percentage on the virtual power bus contention, where... This indicates the number of healthy wind turbine units participating in the competition; The central controller allocates task packages in descending order of priority based on the gradient, and divides the task package capacity into each wind turbine based on the weight ratio, while simultaneously deducting the power adjustable margin of the corresponding turbine in real time. If the power adjustable margin of the wind turbine is exhausted, the weight ratio of the current wind turbine is set to zero in the subsequent task package allocation, and the actual allocation amount is corrected to the remaining margin value. Once all gradient task packages have been allocated, the central controller accumulates the allocation amount for each unit, generates the final incremental power command, and verifies the consistency between the total allocation amount and the total capacity of the task packages. After ensuring there is no deviation, it sends the command to the local controller of the corresponding unit.
[0022] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for allocating active power in offshore wind power clusters, characterized in that, include: S1. Command Reception and Data Acquisition: The central controller receives the total active power command of the cluster from the upper-level power grid dispatch automation platform through the power dispatch data network, and simultaneously sends data requests to the local controllers of the online wind turbines in the cluster through the ring network industrial Ethernet to obtain real-time operating status data packets including maximum available power, current actual power, and operating health status index. The units participating in power allocation are selected based on the operational health status index; S2. Cluster power optimization allocation: Based on the collected data, an optimization function is constructed with the goal of minimizing the total active power loss of the power collection equipment. Combined with power balance, unit output upper and lower limits, and line power flow safety constraints, the optimization function is iteratively solved to obtain the unit's base power. If there is no solution, the standby allocation strategy is activated. S3, Reference Power Distribution and Monitoring: The reference power is distributed to the local controller, and the power tracking deviation, unit status changes, communication link and total cluster power are monitored. S4. Fault Resilient Recovery: When a unit fault is detected, a gradient task package is divided based on the power deficit, and an assistance request is broadcast. Healthy units respond autonomously based on multi-dimensional indicators and redistribute power through competitive allocation.
2. The active power allocation method for offshore wind power clusters according to claim 1, characterized in that, The specific operation steps of S1 are as follows: During the data preparation phase of the wind farm central controller, the central controller receives the cluster total active power command issued by the upper-level power grid dispatch automation platform through the power dispatch data network at a preset communication cycle; upon receiving the command, the central controller immediately initiates a round of cluster internal data acquisition polling. The central controller sends data request frames to the local controller of each online wind turbine in the cluster via a ring network industrial Ethernet. Upon receiving a request, each local controller returns a set of structured real-time operating status data packets to the central controller; the data packets include maximum available power, current actual power, and operating health status index. The operational health status index is a quantitative coefficient obtained by comprehensively evaluating unit fault codes, cumulative fatigue damage, and monitoring data of key components. Wind turbines participating in power allocation were selected based on their operational health status index.
3. The active power allocation method for offshore wind power clusters according to claim 1, characterized in that, The specific operation steps of S2 include: Based on the collected real-time operating status data of wind turbine units, an optimization function is constructed with the goal of minimizing the total active power loss of the power collection equipment. The optimization function includes the active power loss of transmission lines and the power deviation weighting coefficient. The power deviation weighting coefficient is determined by the grid dispatch requirements and the wind farm operation and maintenance strategy. The optimization function satisfies three types of constraints: Power balance constraint: The difference between the total active power of the cluster and the sum of the reference power of each unit shall not exceed 0.5% of the total active power of the cluster; Unit output upper and lower limit constraints: The lower limit of unit output is the minimum stable operating technical output, and the upper limit is the product of the maximum available power and the dynamic adjustment coefficient of health status. The dynamic adjustment coefficient of health status is a monotonically increasing function of the operating health status index. Line power flow safety constraints: The power transmitted by the submarine cable shall not exceed the line thermal stability limit determined based on the rated voltage and cable type; For wind turbine units, a health weight vector is constructed based on the corresponding operational health status index to obtain the corresponding unit weight coefficient; The virtual resistance of the line is adjusted based on the status of the units or combiner boxes connected at both ends of the line to optimize the power transmission path.
4. The active power allocation method for offshore wind power clusters according to claim 3, characterized in that, The specific operation steps of S2 also include: Based on the electrical wiring diagram of offshore wind power clusters, three types of nodes are divided: The unit node is the PQ node, the active power is equal to the reference power, and the reactive power is obtained by the reference power and the tangent of the power factor angle. The combiner box node is an intermediate node that only undertakes power aggregation and transmission, and its power is the sum of the power of its subordinate unit nodes. The converter station node is a balanced node, with the voltage amplitude set to the rated voltage and the phase angle set to 0°. Based on the node connection relationships in the preset cluster topology parameter library, a complete node association matrix including generator units, combiner boxes, and converter stations is generated; Power flow calculations are performed based on node information and actual line resistance. The calculation results are used only for line power flow safety constraint verification. The optimization function is solved iteratively. In each iteration, a candidate baseline power vector is generated and the power flow safety constraints are verified. Convergence is judged by the improvement of the optimization function, the change of the optimization variables, and the constraint satisfaction. When there is no solution or the optimization fails to converge, the backup allocation strategy is activated, and a suboptimal solution is generated based on the proportional allocation method of health weight.
5. The active power allocation method for offshore wind power clusters according to claim 1, characterized in that, The specific operation steps of S3 are as follows: After the cluster power optimization allocation solution converges, the central controller encapsulates the final reference power of each unit into a standardized power command frame and sends it to the local controller of the corresponding wind turbine unit. After receiving the command, the local controller verifies its validity. If the verification is successful, it writes the target power value into the setpoint register of the power control loop and returns a command confirmation frame to the central controller. After the central controller issues power commands to all units, it initiates periodic monitoring, which includes the following: Power tracking deviation monitoring compares the actual power of the unit with the reference power in real time. An alarm is triggered if the deviation exceeds the preset tracking tolerance threshold. Unit status changes are monitored, and the unit's operational health status index is continuously updated. Units whose index declines for multiple consecutive cycles or enters a warning state are marked as potential fault risk units. Communication link monitoring records local controller data response latency and packet loss rate; if a communication link is found to be abnormal, a fault handling process is initiated. Cluster total power monitoring: Real-time collection of cluster total output power and comparison with power dispatch command from the power grid.
6. The active power allocation method for offshore wind power clusters according to claim 1, characterized in that, The specific operation steps of S4 include: When a wind turbine is detected to be out of service during periodic monitoring, the central controller marks the faulty turbine as an uncontrollable power unit and removes it from the allocation list. There is no need to immediately start global optimization calculation. Power redistribution is achieved through a distributed and collaborative approach. Based on the difference between the original allocated baseline power of the faulty unit and the actual output power at the time of the fault, the power deficit or surplus is determined. When the difference is negative, the surplus power is directly added to the total output of the cluster without the need to activate the assistance mechanism. When the difference is positive, it is determined to be a power deficit. Based on the principle of decreasing capacity, several tiered task packages are divided. The first task package has the largest capacity. Each task package includes a number, capacity, priority identifier, generation timestamp, and associated faulty unit number. The central controller constructs a power assistance request data frame and broadcasts it to the local controllers of all healthy and participating units. The request includes information about the faulty unit, a list of gradient task packages, total capacity, and a response deadline timestamp.
7. The active power allocation method for offshore wind power clusters according to claim 6, characterized in that, The specific operation steps of S4 also include: After receiving a power assistance request, the local controller of the healthy unit responds to the decision autonomously through the built-in decision agent unit. Based on the difference between the maximum available power and the actual output power under the current wind speed conditions, the power adjustability margin is obtained. The response capability coefficient is obtained by combining the real-time wind speed with the cut-in, rated, and cut-out wind speeds. The penalty coefficient is generated by referring to the current power of the unit and the Euclidean distance of the fatigue sensitive zone. Based on power adjustability margin, response capability coefficient and penalty coefficient, the unit basic response coefficient is introduced to obtain the assistance willingness value and feed it back to the central controller. After the central controller screens valid feedback, it initiates a competitive allocation process, allocating tiered task packages based on the weight ratio of healthy units, with allocation priority from high to low, and simultaneously deducting the corresponding unit's adjustable power margin. If the margin is exhausted, the weight is reset to zero in subsequent allocations. After all task packages are allocated, the allocation amounts for each unit are summed to generate the final incremental power command. The total allocation amount is verified to be consistent with the total capacity of the task packages. If there are no errors, the command is sent to the local controller of the corresponding unit.