Reliability evaluation and planning method for offshore wind farm power collection system

Abstract

The application discloses a kind of offshore wind farm power collection system reliability evaluation and planning method, reliability evaluation method includes the following steps: S1: establishing offshore wind farm power collection system model and offshore wind farm power collection system fault network reconfiguration strategy model;S2: build reliability evaluation model, the reliability evaluation model includes the objective function of minimization and constraint condition;S3: solving the optimization model, obtains the reliability evaluation result of offshore wind farm power collection system, the network reconfiguration process after the present application considers the power collection system failure in the reliability evaluation process, can make evaluation result more close to actual situation, obtain more accurate reliability evaluation result, the offshore wind farm power collection system planning method of the present application is established power collection system planning model by power collection system network planning model and reliability evaluation model, and the planning of offshore wind farm power collection system can be quickly and accurately obtained by solving model, overall decision.

Description

Technical Field

[0001] This invention relates to the field of power system planning, and in particular to a method for reliability assessment and planning of offshore wind farm collection systems. Background Technology

[0002] my country has proposed adhering to a balanced approach of land and sea development, promoting the coordinated and rapid development of wind power, improving the offshore wind farm (OWF) industry chain, and encouraging the construction of offshore wind power bases. In recent years, global offshore wind power has shown a sustained growth trend. By the end of 2021, the global cumulative installed capacity of offshore wind power reached 48.59 million kilowatts, with China accounting for 20.69 million kilowatts, or 42.59%. In 2021, the global newly installed offshore wind power capacity was approximately 13.4 million kilowatts, of which China accounted for approximately 10.8 million kilowatts, or 80%, ranking first. According to statistics and forecasts from the Global Wind Energy Council, the planned installed capacity of global offshore wind power will grow by 13% annually over the next 20 years. Future growth in offshore wind power will mainly be concentrated in the EU, China, and India. However, with the advancement of related technology research and application, the construction of offshore wind farms will develop towards deep-sea, large-scale, and multi-site grid connection, which presents challenges in many aspects, including economic planning, reliable power supply, and safe and stable operation.

[0003] The electrical structure of an offshore wind farm mainly consists of three parts: wind turbine generators, a power collection system, and a transmission system. The wind farm's power collection system connects the wind turbine generators via submarine cables, transmitting the generated electricity to an offshore substation for centralized voltage boosting and grid connection. While these cables are typically located on the seabed, their failure rate is lower than overhead lines, but they still present challenges in construction, operation, and maintenance, posing more safety hazards and technical difficulties compared to overhead lines. To better develop abundant wind resources, wind farms are expanding into deep-sea areas. However, the maintenance and repair of submarine cables in deep-sea environments are exceptionally difficult, with average repair times potentially exceeding two months. The operating environment of deep-sea wind farms is complex, and the normal operation of submarine cables is threatened by external damage and corrosion. Failures can cause severe economic losses and even social impacts. Under these circumstances, the reliability of the power system is paramount, creating an urgent need for reliability assessment. Since the investment cost and reliability of offshore wind farm projects largely depend on the layout design of the submarine cables connecting the wind turbines and offshore substations, it is necessary to accurately assess reliability and optimize the topology design of the power collection system to meet the operational reliability requirements of offshore wind farms, thereby maximizing the economic benefits and reliability of wind farm projects.

[0004] Currently, reliability assessment methods can be divided into time series simulation methods and analytical methods. Among them, the most commonly used simulation method is the Monte Carlo method. However, when applying simulation methods to calculate the reliability of large-scale offshore wind farm power collection systems, it is necessary to generate tens of thousands of Monte Carlo time series sample states, which greatly reduces the computational efficiency of this method. Existing analytical methods often use approximate calculations to improve algorithm efficiency when solving the reliability of large-scale offshore wind farm power collection systems, making it difficult to obtain accurate calculation results in a short time.

[0005] When planning and designing the topology of offshore wind farm collection systems, decision-makers can only rely on reliability assessments as a post-hoc step to check whether the planning results meet reliability requirements before making a decision. Therefore, the final planning scheme usually requires manual decision-making. Under this model, there is a possibility of over-investment in order to meet reliability requirements, resulting in poor project economics.

[0006] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to solve the problem of obtaining more accurate reliability assessment results for offshore wind farm power collection systems, and to provide a reliability assessment and planning method for offshore wind farm power collection systems.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A reliability assessment method for an offshore wind farm power collection system includes the following steps:

[0010] S1: Establish a model of the offshore wind farm power collection system and a model of the network reconfiguration strategy after the failure of the offshore wind farm power collection system;

[0011] S2: Construct a reliability assessment model, which includes a minimum objective function and constraints on the objective function;

[0012] S3: Solve the reliability assessment model to obtain the reliability assessment results of the offshore wind farm power collection system.

[0013] In some embodiments, in step S2, the objective function minimizes the power generation loss caused by the fault by minimizing the sum of the fault impact variables and fault duration variables of all wind turbine nodes under all fault scenarios, thereby obtaining an accurate reliability index considering network reconfiguration after the fault.

[0014] In some embodiments, in step S2, the objective function to be minimized is expressed by the following formula:

[0015]

[0016] Among them, Ψ C Ψ represents the set of all submarine cable lines. N It is a set that includes all wind turbine nodes. The fault impact variable represents the scope of the impact of a power outage event. The fault persistence variable represents the scope of the impact of a power outage event.

[0017] In some embodiments, the constraints include reliability index constraints, steady-state power flow constraints under normal operation and fault conditions, and fault impact constraints. The reliability index constraints are used to describe the reliability of the power collection system, the steady-state power flow constraints under normal operation and fault conditions are used to describe the operating state of the power collection system, and the fault impact constraints are used to describe the fault impact of the power collection system.

[0018] In some embodiments, the reliability index constraints include wind turbine outage frequency constraints, wind turbine outage duration constraints, and expected untransmitted power constraints.

[0019] In some embodiments, the wind turbine power outage frequency constraint is expressed by the following formula:

[0020]

[0021] The constraint on the power outage duration of the wind turbine is expressed by the following formula:

[0022]

[0023] The expected untransmitted power constraint is expressed by the following formula:

[0024]

[0025] Where k represents the fan, Ψ C Ψ represents the set of all submarine cable lines. N Let λ be the set containing all wind turbine nodes. rs The probability of a fault occurring on cable RS. The time required to isolate a fault occurring on the cable RS line locally via a switch operation. The time required to isolate and repair a fault that occurs on the cable RS line. The fault impact variable represents the scope of the impact of a power outage event. The fault duration variable, u, represents the extent of the impact of a power outage event. d R represents the average annual effective utilization hours of the wind turbine. k This is the rated capacity of fan k.

[0026] In some embodiments, the steady-state power flow constraints under normal operation and fault conditions include: active power balance constraints in the collector system, constraints on the relationship between active power in the feeder and active power in the cable connected to the feeder, coupling constraints between active power flow in the cable and cable connection state, upper and lower limit constraints on active power flowing through the cable in the collector system, and upper limit constraints on active power flowing through the feeder in the collector system.

[0027] In some embodiments, the fault impact constraints include cable state constraints in the fault scenario, influence relationship constraints between the faulty cable and the wind turbine node belonging to the same feeder as the faulty cable, on-site scheduling logic constraints when a fault occurs, coupling constraints between the wind turbine's power generation and the fault persistence variable, system radial operation constraints, value range constraints of wind turbine-feeder dependent variables and cable-feeder dependent variables, and quantity constraints of each wind turbine or each cable and feeder.

[0028] This invention also provides a method for planning an offshore wind farm power collection system, comprising the following steps:

[0029] A1: Establish a power collection system network planning model based on the power collection system network of offshore wind farms;

[0030] A2: Based on the power collection system network planning model and the above-mentioned reliability assessment model, a power collection system planning model is established. The power collection system planning model includes topological constraints, which are used to describe the coupling relationship between decision variables in the planning model and variables in the reliability assessment model.

[0031] A3: Solve the current collector system planning model to obtain the planning results with assessed reliability.

[0032] In some embodiments, the topological constraints are expressed by the following formula:

[0033]

[0034]

[0035]

[0036]

[0037] Where M represents the Big M method, This indicates the connection status of the cable directly connected to feeder f under normal operating conditions. f This indicates the cable directly connected to feeder f. This indicates the subordinate relationship between cable i and feeder f. This indicates the subordinate relationship between cable j and feeder f. This indicates the subordinate relationship between cable ij and feeder f. Describe the subordinate relationship between the fan k and the feeder f. This indicates the cable br directly connected to feeder f. f The feeder dependent variable, This refers to the cable br that is directly connected to feeder f under normal operating conditions. f The connection status, Ψ C Ψ represents the set of all submarine cable lines. F This is the set of all feeders.

[0038] The present invention has the following beneficial effects:

[0039] This invention establishes a model of the offshore wind farm power collection system and a network reconfiguration strategy model after a power collection system failure, constructs a reliability assessment model, and solves the optimization model settings. By considering the network reconfiguration process after a power collection system failure during the reliability assessment, the assessment results are more closely aligned with actual conditions. This solves the problem of inaccurate reliability assessment results for offshore wind farm power collection systems, yielding more accurate reliability assessment results. The offshore wind farm power collection system planning method of this invention establishes a power collection system planning model through a power collection system network planning model and a reliability assessment model, and solves the model to quickly and accurately obtain the planning of the offshore wind farm power collection system, enabling comprehensive decision-making.

[0040] Furthermore, the method of this embodiment minimizes the power generation loss caused by the fault by minimizing the sum of the fault impact variables and fault duration variables of all wind turbine nodes under all fault scenarios, and can obtain an accurate reliability index considering network reconfiguration after the fault.

[0041] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0042] Figure 1 This is a flowchart of the reliability assessment method for the offshore wind farm power collection system in this embodiment of the invention;

[0043] Figure 2 This is a schematic diagram of the offshore wind farm power collection system in an embodiment of the present invention;

[0044] Figure 3 These are schematic diagrams of radial offshore wind farm power collection systems in some embodiments;

[0045] Figure 4 This is a schematic diagram of a network reconfiguration strategy model after a fault in the power collection system of an offshore wind farm, as described in an embodiment of the present invention.

[0046] Figure 5This is a flowchart of the offshore wind farm power collection system planning method in the experimental example of this invention;

[0047] Figure 6 This is a schematic diagram of the topology of the offshore wind farm collection system at the Saint-Brieu 62 wind turbine node in the experimental example of this invention.

[0048] Figure 7 This is a schematic diagram illustrating the changes in reliability as the mean time to repair faults changes in the experimental examples of this invention.

[0049] Figure 8 This is a schematic diagram illustrating the changes in reliability when the cable failure rate changes in the experimental examples of this invention.

[0050] Figure 9 This is a schematic diagram illustrating the changes in reliability when the rated capacity of the fan changes in an embodiment of the present invention;

[0051] Figure 10 This is a schematic diagram illustrating the change in reliability when the active power transmission capacity of the cable changes in an embodiment of the present invention.

[0052] Explanation of reference numerals in the attached figures:

[0053] 101 - First feeder, 102 - Second feeder, 103 - First cable. Detailed Implementation

[0054] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0055] The purpose of this invention is to propose a reliability assessment method for offshore wind farm power collection systems that can be embedded in the planning process. This method is applicable to the reliability assessment of power collection systems in large-scale offshore wind farms in my country. It can assess the power loss costs caused by cable faults during the operating cycle of existing offshore wind farm power collection systems and guide the planning and design of power collection system topologies. This embodiment can provide a reference for decision-making on power collection system topology planning schemes for existing or under-construction offshore wind farm projects, and has important reference value for promoting the construction of offshore wind power bases and contributing to the achievement of the national "dual carbon" goals.

[0056] This invention relates to a reliability assessment method for offshore wind farm power collection systems that can be embedded in the planning process, such as... Figure 1 As shown, the method includes the following steps:

[0057] S1: Establish a model of the offshore wind farm power collection system and a model of the network reconfiguration strategy after the failure of the offshore wind farm power collection system;

[0058] S2: Construct a reliability assessment model, which includes a minimum objective function and constraints;

[0059] S3: Solve the optimization model to obtain the reliability assessment results of the offshore wind farm power collection system.

[0060] This embodiment specifically includes the following steps:

[0061] S1: Establish a model of the offshore wind farm's power collection system and a model of the network reconfiguration strategy after a power collection system failure. While maintaining accuracy as much as possible, simplify the complex physical system, incorporate considerations for network reconfiguration after a power collection system failure, and model and define the process.

[0062] 1) Modeling of offshore wind farm power collection system

[0063] A model of the offshore wind farm power collection system is established based on the characteristics of the offshore wind farm power collection system.

[0064] The offshore wind farm power collection system model in this embodiment is as follows: Figure 2 As shown, dashed lines represent lines that are disconnected, and solid lines represent lines that are connected. A line that is normally disconnected (…) Figure 2 The dashed lines (A3-A5) represent tie lines, and the power collection system is equivalent to a network. The network consists of nodes representing offshore substations or wind turbines, and connecting branches between nodes representing submarine cables. These include offshore substation node A1, five wind turbine nodes A2 to A6, circuit breakers B1 and B2, twelve disconnect switches S1 to S12, first feeder 101, second feeder 102, first cable 103, and the tie lines between wind turbine nodes A3 and A5. The planning and construction of the power collection system generally takes place after the micro-site selection process of the wind turbines is determined. Therefore, the coordinates of the wind turbines and substations are known, and the power generated by the wind turbines within a certain time scale is also known. The power collection system operates in an open-loop mode, in a radial pattern. Statistical data shows that the failure rate of submarine cables in offshore wind farms is mostly between 0.001 failures / km / year and 0.005 failures / km / year. Compared to the failure rate of overhead lines in power distribution systems, the failure rate of submarine cables is extremely low. The probability of multiple cables failing simultaneously is negligible. Wind turbines and cables supply power to offshore substations via feeders. The substations then centrally step up the voltage and transmit the power to the onshore power grid for grid integration. Feeders are equipped with circuit breakers near the substation, and all cables are fitted with disconnect switches at both ends to isolate faults locally. (Reference) Figure 3 In other embodiments, the offshore wind farm collection system model is radial. Figure 3 This includes offshore substation node A11 and five wind turbine nodes A21 to A61.

[0065] 2) Establish a network reconfiguration strategy model after a fault in the offshore wind farm's power collection system.

[0066] The network reconfiguration strategy model for the offshore wind farm collection system after a fault in this embodiment is as follows: Figure 4 As shown, the dashed line between wind turbine node A4 and wind turbine node A5 represents the line disconnected under fault conditions. When a persistent fault occurs in the first cable 103 (marked on the first cable 103 in the figure to indicate a persistent fault), the circuit breaker B2 of the second feeder 102 automatically trips, preventing wind turbine nodes A4 and A5 from continuing to supply power to substation node A1 through the second feeder 102. Disconnect switches S7 and S8 on the first cable 103 open to isolate the fault locally. After fault isolation, circuit breaker B2 on the second feeder 102 closes again, allowing wind turbine node A4 to resume supplying power to substation node A1 through the second feeder 102. Then, disconnect switches S9 and S10 on the tie line are closed, allowing wind turbine node A5 to supply power to substation node A1 through the first feeder 101. This completes the post-fault network reconfiguration process. After a period of time, the fault is repaired, and then disconnect switches S7 and S8 close, while disconnect switches S9 and S10 open, restoring the power collection system to its original normal operating state.

[0067] S2: Construct a reliability assessment model, which includes a minimum objective function and constraints:

[0068] 1) Reliability index constraints:

[0069] There are various metrics that can be used to describe the reliability of a power system. In this method, the reliability of wind turbines is characterized by the Turbine Interruption Frequency (TIF) and the Turbine Interruption Duration (TID), while the reliability of the power collection system is characterized by the Expected Energy Not Transmitted (EENT). That is, the reliability metric constraints of this embodiment include constraints on the wind turbine interruption frequency, the wind turbine interruption duration, and the expected energy not transmitted.

[0070] The traditional expressions for calculating TIF and TID are:

[0071]

[0072]

[0073] In formula (1), f k The frequency at which power is cut off to the kth wind turbine, Ψ N This is the set containing all wind turbine nodes. In formula (2), The duration of each power outage.

[0074] However, traditional calculation methods require known historical data. For power collection systems without historical data, reliability assessment should be conducted from a probabilistic and statistical perspective. Specifically, it is necessary to define a set of fault events including cable faults and analyze the probability and impact of these fault events. This method defines fault impact variables to characterize the scope of influence of power outage events. and fault persistence variables This indicates whether the wind turbine K has lost its power supply due to a fault in the cable RS. When the wind turbine K loses its power supply due to a fault in the cable RS... otherwise This indicates whether wind turbine K is still unable to restore power supply capability after a cable RS fault occurs and network reconstruction is completed. When wind turbine K is still unable to supply power to the substation after a cable RS fault occurs and network reconstruction is completed, this indicates the situation. otherwise After introducing these variables, rewrite the TIF and TID calculation expressions:

[0075]

[0076]

[0077] In formulas (3)-(4), Ψ C This represents the set of all submarine cable lines. `rs` as a superscript / subscript indicates a persistent fault was found on cable `rs`. `λ` rs For the probability of a fault occurring on cable rs, in formula (4), The time required to isolate a fault occurring on the RS line locally via a switch operation. To determine the time required to repair the fault after isolating it on the rs line, formula (3) is the wind turbine outage frequency constraint in this embodiment, and formula (4) is the wind turbine outage duration constraint.

[0078] After obtaining the node's reliability index, EENT can be further calculated using equation (5).

[0079]

[0080] In formula (5), u d R represents the average annual effective utilization hours of the wind turbine. k Let k be the rated capacity of the fan, formula (5) be the expected untransmitted power constraint, and 8760 be the number of hours in a year.

[0081] 2) Objective function

[0082] The method proposed in this embodiment performs reliability assessment from the perspective of mathematical optimization. By minimizing the sum of the failure impact variables and failure duration variables of all wind turbine nodes under all failure scenarios, the power generation loss caused by the failure is minimized, and an accurate reliability index considering network reconfiguration after failure is obtained.

[0083]

[0084] In formula (6), Ψ C Ψ represents the set of all submarine cable lines. N It is a set that includes all wind turbine nodes. and These are the binary decision variables defined in this method. Summing them can reflect the power loss caused by line faults in the offshore wind farm's power collection system.

[0085] 3) Operating model of the power collection system and steady-state power flow constraints under normal and fault conditions

[0086] The power collection system includes submarine cables, offshore substations, and wind turbines. Formulas (7)-(11) are used to apply steady-state power flow constraints under normal operation and fault conditions.

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] In this model, the superscripts of all variables represent a certain scenario of the collector system. rs = {NO} indicates that the collector system is in normal operating condition, and rs ∈ Ψ. C This indicates a persistent fault in cable rs. Formula (7) represents the active power balance constraint in the collector system, where Ψ i Let i represent the set of all nodes connected to node i. Let represent the active power transmitted by cable ij in scenario rs. Let be the active power supplied by wind turbine i in scenario rs. Since the voltage in the collector system is relatively constant, this embodiment uses a linearized power flow model to model the power flow under steady state, allowing for smaller errors to achieve higher computational efficiency. Equation (8) indicates that the active power in the feeder is equal to the active power in the cable connected to the feeder, that is, the relationship between the active power in the feeder and the active power in the cable connected to the feeder is constrained. Let br be the active power flowing through feeder f after network reconstruction in scenario rs. f This represents the cable directly connected to feeder f. For the network reconfiguration in scenario rs, the current flows through the cable br f The active power, Ψ F This is a set that includes all feeders. Equation (9) represents the coupling constraint between active power flow and cable connection status in the cable. This indicates the connection state of cable ij in scene rs, where M is a sufficiently large positive number. The formula means: if cable ij is in a connected state in scene rs, then... There is active power flow on cable ij. If cable ij is disconnected in scenario rs, then Because the cable is broken, no power can flow through cable ij, therefore Formula (10) represents the upper and lower limits of active power flowing through the cable in the current collector system. Let be the active power transmission capacity of cable ij. Formula (11) is the upper limit constraint on the active power flowing through the feeder of the collector system. Let be the active power transmission capacity of feeder f. In formulas (7)-(11), rs belongs to Ψ. C ∪{NO} means that the current collection system must satisfy the constraint formulas (7)-(11) in both normal operation and operation states where any cable fails.

[0094] 4) Fault impact analysis model and fault impact constraints

[0095] Formula (12) indicates that the cable rs is unavailable and in a disconnected state in the fault scenario rs, which is the cable state constraint in the fault scenario.

[0096]

[0097] Analysis of the network reconfiguration process following the fault reveals that the circuit breaker tripping on the feeder due to the fault affects the wind turbine nodes on the same feeder as the faulty cable. In other words, these wind turbine nodes... Formula (13) is proposed to describe this relationship, namely the influence relationship constraint between the faulty cable and the fan node that belongs to the same feeder as the faulty cable.

[0098]

[0099]

[0100] In formula (13-a), Represents the set of all wind turbine nodes. Describe the subordinate relationship between wind turbine k and feeder f. If wind turbine k supplies power to the substation through feeder f, then wind turbine k is said to belong to feeder f. otherwise Describe the subordinate relationship between cable rs and feeder f. If cable rs supplies power to the substation through feeder f, then cable rs is said to belong to feeder f. otherwise When the wind turbine K and cable RS both belong to feeder F, the wind turbine K will be affected by a fault on cable RS. hour, If the fan k and the cable rs do not belong to the same feeder f or

[0101] However, since the right side of equation (13-a) contains a bilinear term, this causes nonlinearity in the model and affects the solution efficiency. In order to eliminate the nonlinear term, linearization technique is used to linearize it, and the equivalent equation (13-b) can be obtained.

[0102]

[0103] Formula (14) describes the dispatch logic constraints at the site when a fault occurs, namely: wind turbines that have not lost their power supply capability due to circuit breaker tripping after a fault should be able to maintain their power supply capability after network reconfiguration. For any wind turbine node k, if due

[0104]

[0105] In formula (15), Pk is the rated active power capacity of wind turbine k, which describes the power generation of the wind turbine. and fault persistence variables Coupling constraints between them.

[0106]

[0107] Formula (16) is the radial operation constraint of the system.

[0108]

[0109]

[0110]

[0111]

[0112] Formulas (17)-(18) specify the fan-feeder dependent variables. and cable-feeder dependent variables The range of values ​​is constrained. Describe the subordinate relationship between cable ij and feeder f. If cable ij supplies power to the substation through feeder f, then cable ij is said to belong to feeder f. otherwise Formulas (19)-(20) indicate that each wind turbine or each cable can belong to at most one feeder, that is, at most one feeder supplies power to the substation, which is a constraint on the number of each wind turbine or each cable and feeder.

[0113] The reliability assessment model proposed in this invention can be written in the following compact form:

[0114]

[0115] st(3)-(5),(7)-(24)

[0116] S3: Solve the optimization model to obtain the reliability assessment results of the offshore wind farm power collection system:

[0117] The implementation of the reliability assessment method in this embodiment depends on solving the proposed mathematical model. The reliability assessment model in this embodiment has been processed by linearization techniques, and therefore its form is a large-scale mixed-integer linear programming model. For this type of model, there are already mature solution techniques available.

[0118] This embodiment provides a method for solving a reliability assessment model: a mathematical model is established by programming using the YALMIP toolbox in MATLAB (a commercial mathematical software), and the commercial solver Gurobi is called to solve it, thereby obtaining the reliability index of each wind turbine node in the power collection system and the system reliability index.

[0119] This embodiment also provides a planning method for offshore wind farm power collection systems. It embeds a reliability assessment model into the power collection system network planning model, obtaining an explicit expression of the reliability of the power collection system with a given topology. This allows designers to more easily seek a balance between economy and reliability. Figure 5 As shown, it includes the following steps:

[0120] A1: Establish a power collection system network planning model based on the power collection system network of offshore wind farms;

[0121] A2: Establish a power collection system planning model based on the power collection system network planning model and reliability assessment model. The power collection system planning model includes topological constraints, which are used to describe the coupling relationship between decision variables in the planning model and variables in the reliability assessment model.

[0122] A3: Solve the power collection system planning model to obtain the power collection system investment and construction scheme that minimizes the total cost.

[0123] In the reliability analysis and evaluation model of this embodiment, and These are two relatively important physical quantities, and the reliability expression can be directly calculated using constraint formula (13). Furthermore, the reliability expression is calculated using a series of other constraint formulas (7), (14)-(16). This step yields reliability metrics, enabling reliability assessment. If the topology of the collector system is known, then... and These are all parameters that can be directly obtained from the system architecture diagram; however, during the planning phase, the topology of the collector system is unknown. and When parameters are transformed into variables, additional topological constraint formulas (21)-(24) are needed to describe them. and Cable connection status s with planning model variables ij The coupling relationship between them.

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Formulas (21)-(22) reveal the relationship between the dependent variables of the cable feeder and the dependent variables of the feeders at adjacent nodes. This relationship indicates that if cable ij is in a connected state, s ij=1, then the feeders belonging to the fan nodes at both ends of the cable and the feeders belonging to the cable are consistent. However, since the right side of equations (21-a) and (22-a) contains bilinear terms, this causes nonlinearity in the model and affects the solution efficiency. In order to eliminate the nonlinear terms, the Big M method is used to linearize them, and the equivalent equations (21-b) and (22-b) can be obtained.

[0132] In formula (24), This indicates the cable br directly connected to feeder f. f The feeder dependent variable, This refers to the cable br that is directly connected to feeder f under normal operating conditions. f The connection state, this formula represents the connection state of the cable br directly connected to the feeder f. f In a connected state, Then the cable br f Belongs to feeder f, It provides the "source" of the value relationships between variables.

[0133] By adding the above constraints, the reliability assessment model proposed in this embodiment can be applied in the field of power collection system network planning and operation.

[0134] The offshore wind farm power collection system planning method in this embodiment specifically includes the following steps:

[0135] A1: Establish a power collection system network planning model based on the power collection system network of offshore wind farms. The power collection system network planning model uses the construction cost of all lines as the objective function. Its constraints include: power flow constraints, equipment / line capacity constraints, investment and construction constraints (e.g., only one type of model can be selected for the location of a candidate line), radial constraints, etc.

[0136] A2: Based on the power collection system network planning model and reliability assessment model, a power collection system planning model is established. The power collection system planning model includes topological constraints, which are used to describe the coupling relationship between decision variables in the planning model and variables in the reliability assessment model (in order to describe the coupling relationship between decision variables in the planning model and variables in the reliability assessment model, corresponding topological constraints need to be introduced to express them explicitly, and the constraint set is expanded to include topological constraint formulas (21)-(24)). At this time, the constraints of the reliability assessment model include variables. and (In the current collection system to be planned, the feeder is a dependent variable) and Since it cannot be directly derived, it is transformed into a variable.

[0137] A3: Solving the power collection system planning model that considers reliability can yield the power collection system investment and construction scheme that minimizes the total cost and its corresponding reliability. This model can achieve overall optimization of the economic efficiency of power collection system construction and operational reliability, ensuring the optimality of the decision.

[0138] In this embodiment and Solving for reliability is crucial. When applying the reliability assessment model to the planning of power collection systems, we characterize the relationship between planning variables through (21)-(24). and After considering the impact, the influence of the planning variables on the reliability expression can be expressed by the constraint formula (13). The influence of planning decision variables on the reliability expression is reflected through a series of other constraint formulas (7), (14)-(16). This allows the reliability assessment model to be applied to the planning of the power collection system. After solving the planning model, the results can directly provide the planning scheme and reliability indicators, unlike other methods that require obtaining a definite planning scheme and then assessing reliability, which can lead to an inability to make comprehensive decisions.

[0139] Experimental Example

[0140] (1) Basic Overview

[0141] refer to Figure 6 This experimental example uses the Saint-Brieu offshore wind farm with 62 wind turbine nodes in France to verify the effectiveness of the proposed model. The farm includes wind turbine nodes 1 to 62 and offshore substation node 63. Solid lines between nodes represent normal connection lines, and dashed lines represent tie lines R1 to R7. The voltage level of the power collection system is 33kV, and the locations of the wind turbines and substation are known after micro-situation. Table 1 shows the key parameter settings. The example is modeled using the YALMIP toolbox in MATLAB R2018A (a commercial mathematical software) and solved using the commercial solver Gurobi 9.5.0. The key parameters and their values ​​are shown in Table 1 below.

[0142] Table 1

[0143]

[0144] (2) Decision Analysis

[0145] Based on the proposed model, the impact of construction structure and post-fault reconfiguration on the reliability of the collector system is compared through eight examples (the reliability calculation results are shown in Table 2): Example I represents a radial structure without redundant lines (tethers). From Example II to Example VIII, each example adds redundant lines in the order of R1-R7 based on the previous case. With this setting, the structure of Example VIII contains seven redundant lines. Examples II to Example VIII all contain a ring construction structure, which can be reconfigured after a fault.

[0146] The reliability assessment of the example was carried out using the reliability assessment model proposed in this embodiment and the sequential Monte Carlo simulation method, respectively. The results are shown in Tables 2 and 3 below. Table 2 shows the reliability assessment results obtained using the method of this embodiment, and Table 3 shows the reliability assessment results obtained using the sequential Monte Carlo simulation method.

[0147] Table 2

[0148]

[0149]

[0150] Table 3

[0151]

[0152] As can be seen, the reliability calculation results of the two methods are basically the same, but the average calculation time of the method proposed in this embodiment is 1.1 seconds, while the average time of the simulation method is 159.7 seconds. In terms of solution speed, the analysis method proposed in this embodiment is significantly better than the sequential Monte Carlo simulation method.

[0153] Analyzing the reliability index calculation results of different examples reveals that the construction topology of the power collection system has a significant impact on its reliability. With the construction of redundant lines, power collection systems have gradually evolved from simple radial structures to complex ring structures. Since the ring structure can support network reconfiguration after a fault, its reliability is thus improved. Example VIII's reliability index EENT is 7.16 MWh / year, only 2.4% of Example I. Although redundant lines are disconnected during normal operation and do not participate in normal operation, their participation in network reconfiguration after a persistent fault allows some wind turbines that were affected by the fault to regain power supply before the fault is repaired.

[0154] Furthermore, while adding extra redundant lines to a collector system typically improves reliability significantly, this is not always the case. The last three rows of Table 2 reflect the diminishing marginal returns of adding lines in terms of reliability improvement. Adding redundant line R6 has almost no effect on reliability, while adding redundant line R7 has no impact on the collector system's reliability metrics. This is because the existing redundant lines R1-R5 can adequately support network reconfiguration after a fault to restore the power supply capacity of the wind turbines. Adding R7 only increases the investment cost of the collector system without changing the optimal reconfiguration strategy, thus failing to improve system reliability. In practical engineering, the purchase and installation costs of cables are often high. Therefore, when planning a collector system without special reliability requirements, designers should seek a balance between investment cost and reliability to minimize the total cost of the entire project.

[0155] (3) Sensitivity analysis

[0156] This section performs sensitivity analysis on certain parameters. The results show that the impact of parameter changes on system reliability can be categorized as linear (see reference). Figure 7 , Figure 8 ) and nonlinear effects (reference) Figure 9 , Figure 10 ).

[0157] This embodiment performs sensitivity analysis based on examples I-VI in decision analysis. Figure 7 , Figure 8 The simulation investigated the changes in reliability as the mean time to repair (MTBT) and cable failure rate changed. Figure 7 The horizontal axis represents the mean time to repair faults (days), and the vertical axis represents the reliability index EENT (MWh / year). Figure 8 The horizontal axis represents the cable failure rate (times / km / year), and the vertical axis represents the reliability index EENT (MWh / year). Figure 9 , Figure 10 The reliability changes were simulated when the rated capacity of the wind turbine and the active power transmission capacity of the cable were altered. Figure 9 The horizontal axis represents the rated capacity of the wind turbine (MW), and the vertical axis represents the reliability index EENT (MWh / year). Figure 10 The horizontal axis represents the cable's active power transmission capacity (MW), and the vertical axis represents the reliability index EENT (MWh / year).

[0158] The results show that Example I is most sensitive to changes in mean time to repair (MTTR) and cable failure rate, exhibiting the largest fluctuation in its reliability index. With the installation of redundant lines, the trend in the reliability index of the power collection system gradually slows down. Example VI's reliability index is least sensitive to changes in MTTR and cable failure rate, showing almost no change. The impact of these parameters on the reliability of the power collection system is linear. The rated capacity of the wind turbines and the active power transmission capacity of the cables have a piecewise linear impact on the reliability index, and this piecewise linearity becomes increasingly pronounced from Example I to Example VI. When the rated capacity of the wind turbines is between 1-3MW, the reliability index increases linearly. When the capacity exceeds 5.6MW, a sharp increase in the reliability index can be observed. This is because the excessively large rated capacity of the wind turbines at this point prevents some of the electricity generated by the turbines from being transmitted to the substation for voltage boosting and grid connection during normal operation, leading to increased power loss in offshore wind farms. Example I is least sensitive to changes in the active power transmission capacity of the cables, and the reliability sensitivity of the power collection system increases with the addition of redundant lines. As the number of redundant lines in a power collection system increases, its network reconfiguration capability naturally increases. However, network reconfiguration must be carried out under the condition that the cable transmission capacity does not exceed the upper limit. Therefore, generally speaking, the more redundant lines a power collection system has, the larger the cable transmission capacity, and the lower the system's reliability index EENT, the higher the reliability of the power collection system.

[0159] Some parameters, such as mean time to repair (MTBT) and cable failure rate, have a linear impact on reliability, while others, such as turbine rated capacity and cable active power transmission capacity, alter the reliability of the power collection system in a non-linear manner. Compared to linear parameters, non-linear parameters can sometimes cause drastic changes in reliability. Based on the mathematical formulas of the model, the fundamental reason for this difference in effect is that some parameters do not affect the turbine's power supply recovery strategy, thus the decision variables in the model remain unchanged; while other parameters are related to operational constraints and therefore typically affect the optimal network reconfiguration strategy after a fault. Figures 7 to 8 and Figures 9 to 10 The curve trends corresponding to different calculation examples reflect that the influence patterns of linear and nonlinearity are not static, but depend on the system's construction structure and operating status.

[0160] The numerical results demonstrate that the method proposed in this embodiment has significant application value in the reliability assessment of offshore wind farm power collection systems. Compared with the simulation method, it greatly improves the solution efficiency and provides guidance for the planning of offshore wind farm power collection systems during the construction phase. The method in this embodiment can help decision-makers pursue a balance between the economic benefits and power supply reliability of offshore wind farm projects.

[0161] Most reliability assessment methods cannot embed the reliability requirements of the power system as constraints into the optimization model of the planning process. Decision-makers can only rely on reliability assessment as a post-hoc step to check whether the planning results meet the reliability requirements and make a decision. Therefore, the final planning scheme usually requires manual decision-making. In this model, there may be situations where over-investment occurs in order to meet reliability requirements, resulting in poor project economics.

[0162] This invention proposes a reliability assessment model for offshore wind farm power collection systems that can be embedded in the planning process. The model assesses power outages under normal conditions and all possible line fault conditions, and calculates reliability indicators characterizing the turbine nodes and the power collection system by combining the fault state probabilities. This model considers the transformation of the turbine-substation power supply path after a cable fault through power collection system network reconfiguration, and can accurately and quickly assess the reliability of radial and ring-shaped offshore wind farm power collection systems.

[0163] The example section analyzes the impact of network reconfiguration and power collection system structure on reliability indicators, and compares the proposed reliability assessment method with the calculation results of Monte Carlo simulation, demonstrating the efficiency of the proposed method in the reliability assessment of large-scale offshore wind farm power collection systems.

[0164] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] The above description provides a further detailed explanation of the present invention in conjunction with specific / preferred embodiments, and it should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various substitutions or modifications can be made to these described embodiments without departing from the concept of the present invention, and all such substitutions or modifications should be considered within the scope of protection of the present invention. In the description of this specification, the reference to terms such as "an embodiment," "some embodiments," "preferred embodiment," "example," "specific example," or "some examples," etc., indicates that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions, and modifications can be made herein without departing from the scope of protection of the patent application.

Claims

1. A method of reliability assessment of an offshore wind farm power collection system, c h a r a c t e r i s e d i n that Includes the following steps: S1: Establish a model of the offshore wind farm power collection system and a model of the network reconfiguration strategy after the failure of the offshore wind farm power collection system; S2: Construct a reliability assessment model, which includes a minimum objective function and constraints on the objective function; The objective function minimizes the power generation loss caused by the fault by minimizing the sum of the fault impact variables and fault duration variables of all wind turbine nodes under all fault scenarios, thus obtaining an accurate reliability index for network reconfiguration after considering the fault. S3: Solve the reliability assessment model to obtain the reliability assessment results of the offshore wind farm power collection system.

2. The method of claim 1, wherein, In step S2, the objective function to be minimized is expressed by the following formula: wherein, denotes the set of all submarine cable lines, is the set containing all wind farm nodes, denotes the fault impact variable representing the impact range of the fault outage event, is the fault duration variable representing the impact range of the fault outage event.

3. The method of claim 1, wherein, The constraints include reliability index constraints, steady-state power flow constraints under normal operation and fault conditions, and fault impact constraints. The reliability index constraints are used to describe the reliability of the power collection system, the steady-state power flow constraints under normal operation and fault conditions are used to describe the operating state of the power collection system, and the fault impact constraints are used to describe the fault impact of the power collection system.

4. The method as described in claim 3, characterized in that, The reliability index constraints include wind turbine outage frequency constraints, wind turbine outage duration constraints, and expected untransmitted power constraints. The wind turbine outage frequency constraints and wind turbine outage duration constraints are used to characterize the reliability of the wind turbine, and the expected untransmitted power constraints are used to characterize the reliability of the power collection system.

5. The method as described in claim 4, characterized in that, The wind turbine power outage frequency constraint is expressed by the following formula: The constraint on the power outage duration of the wind turbine is expressed by the following formula: The expected untransmitted power constraint is expressed by the following formula: in, Indicates a fan. This represents the collection of all submarine cable lines. It is a set that includes all wind turbine nodes. For cables The probability of the above failure occurring, To connect the cable via switch operation The time required to isolate a fault on the line locally. To make the cable The time required to repair the fault after it has been isolated on the line. The fault impact variable represents the scope of the impact of a power outage event. The fault duration variable represents the extent of the impact of a power outage event. This refers to the average annual effective utilization hours of the wind turbine. For wind turbine Rated capacity.

6. The method as described in claim 3, characterized in that, The steady-state power flow constraints under normal operation and fault conditions include: active power balance constraints in the collector system, constraints on the relationship between active power in the feeder and active power in the cable connected to the feeder, coupling constraints between active power flow in the cable and cable connection state, upper and lower limit constraints on active power flowing through the cable in the collector system, and upper limit constraints on active power flowing through the feeder in the collector system.

7. The method as described in claim 3, characterized in that, The fault impact constraints include cable state constraints in the fault scenario, influence relationship constraints between the faulty cable and the wind turbine node belonging to the same feeder as the faulty cable, on-site dispatch logic constraints when a fault occurs, coupling constraints between the wind turbine's power generation and the fault persistence variable, system radial operation constraints, value range constraints of wind turbine-feeder dependent variables and cable-feeder dependent variables, and quantity constraints of each wind turbine or each cable and feeder.

8. A planning method for an offshore wind farm power collection system, characterized in that, Includes the following steps: A1: Establish a power collection system network planning model based on the power collection system network of offshore wind farms; A2: Establish a power collection system planning model based on the power collection system network planning model and the reliability assessment model as described in any one of claims 1-7. The power collection system planning model includes topological constraints, which are used to describe the coupling relationship between decision variables in the planning model and variables in the reliability assessment model. A3: Solve the current collector system planning model to obtain the planning results with assessed reliability.

9. The method as described in claim 8, characterized in that, The topological constraints are expressed by the following formula: in, Represents the Big M method, Indicates the relationship between the feeder and the normal operating condition. The connection status of directly connected cables. Indication and feeder Directly connected cables, Indicates cable With feeder Subordinate relationship Indicates cable With feeder Subordinate relationship Indicates cable With feeder Subordinate relationship Describe the fan With feeder Subordinate relationship Indicates feeder Directly connected cables The feeder dependent variable, Indicates the relationship between the feeder and the normal operating condition. Directly connected cables The connection status, This represents the collection of all submarine cable lines. This is the set of all feeders.

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