An optimization method for the topology structure of an onshore wind power DC collection system and related devices

Abstract

An optimization method for the topology of an onshore wind power DC collection system and related devices, including grouping the wind turbines based on the topology of the series-parallel type wind power DC collection system; based on the grouped input of known conditions: the failure rates, fault repair times, and cost data of the cables, wind turbines, and DC / DC devices in the wind power DC collection system; obtaining a scheme for re-grouping the wind turbines according to the constraint conditions; evaluating the reliability of the wind power DC collection system under different wind turbine grouping schemes to obtain the system reliability index EENS; based on the system reliability index EENS, using the meshless ray optimization algorithm to optimize the wind turbine grouping scheme with the lowest life cycle cost of the wind power DC collection system. The present invention quantifies the reliability of the wind power collection system with the cost of power shortage loss, comprehensively considers the reliability and economy of the system, and optimizes the wind turbine grouping scheme with the lowest total cost, which can provide a reference for the structure and optimization of the wind power DC collection system.

Description

Technical Field

[0001] The present invention belongs to the field of wind power collection systems, and relates to a method for optimizing the topological structure of an onshore wind power DC collection system and related devices. Background Art

[0002] Large-scale wind power is collected, transmitted, and incorporated into the AC system through DC, effectively leveraging the technical advantages of DC power transmission. This can better achieve the efficient access of new energy and stable power supply to the load side during the fluctuation of new energy. As a key link in wind power collection and transmission, the reliability and economy of the topological structure of the collection system are the basis for the good operation of the wind farm. Therefore, the evaluation and optimization of the topological structure of the wind power collection system are of great significance.

[0003] The grid network structure in large-scale wind power bases in western China is weak, and there is an urgent need for a DC grid with stability advantages for the collection and long-distance transmission of wind power. As a key link in wind power collection and transmission, the wind power collection system has a large number of electrical equipment and complex connection methods between various equipment. At present, the structure of the wind power collection system is mostly designed by staff based on on-site experience, and the quality of system design cannot be guaranteed. There is still a large room for improvement in the optimized design of the topological structure of the collection system. The topological structure of the wind power DC collection system is oriented to the all-DC power generation system of wind power. There is currently no corresponding actual project, and the design idea of the wind power AC collection system is difficult to be used for the DC collection system, and few literatures provide optimized design ideas for onshore wind power DC collection systems. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing the topological structure of an onshore wind power DC collection system and related devices to solve the problems that the quality of system design cannot be guaranteed due to the lack of optimized design and the design idea of the wind power AC collection system is difficult to be used for the DC collection system.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for optimizing the topological structure of an onshore wind power DC collection system includes

[0007] Grouping the wind turbines based on the topological structure of the wind turbine series-parallel type wind power DC collection system;

[0008] Based on the grouped input of known conditions: the failure rate, fault repair time, and cost data of cables, wind turbines, and DC / DC devices in the wind power DC collection system;

[0009] Obtaining a scheme for re-grouping the wind turbines according to the DC transmission voltage constraint, the wind turbine quantity constraint, the voltage collection capacity constraint, the collection line voltage drop constraint, and the current-carrying capacity constraint of power electronic devices;

[0010] Evaluate the reliability of a wind power DC collection system under different fan grouping schemes to obtain the system reliability index EENS;

[0011] Based on the system reliability index EENS, use the meshless ray optimization algorithm to optimize the fan grouping scheme of the wind power DC collection system with the lowest life cycle cost.

[0012] Furthermore, the DC transmission voltage constraint formula is:

[0013]

[0014] In the formula: U ac is the AC voltage; U dc is the DC output voltage of the wind farm; Δ% is the allowable voltage deviation;

[0015] The fan quantity constraint formula is:

[0016]

[0017] In the formula: g is the number of fan groups; p is the number of parallel branches in each group; s is the number of series-connected fans in each branch; N is the number of fans in the wind farm;

[0018] The voltage collection capacity constraint formula is:

[0019] g = f(U dc , P g )

[0020]

[0021] In the formula: The f function represents the relationship between the collection voltage level and the collection capacity of each group of fans in the wind power DC collection system;

[0022] The voltage drop constraint formula of the collection line is:

[0023]

[0024] In the formula: P g is the capacity of each group of fans; L is the farthest distance from the fans in the group to the collection bus; ξ is the calculation coefficient; S is the cross-sectional area of the conductor;

[0025] The current-carrying capacity constraint formula of the power electronics is:

[0026]

[0027] In the formula: I SM,in represents the input current of each sub-module of the DC / DC; I IGBT is the rated current of I GBT ; I SM,out represents the output current of each sub-module of the DC / DC; ID is the rated current of the diode.

[0028] Furthermore, the steps for evaluating the reliability of the collection system by the non-sequential Monte Carlo simulation method are as follows:

[0029] Step S1: Input the number of samplings, the annual load data of the wind farm, the topology of the wind power DC collection system, and the corresponding outage rate model;

[0030] Step S2: Simulate the annual output sequence of the wind power collection system, and use the outage rate sampling of the collection system to correct the annual output sequence of the wind power collection system;

[0031] Step S3: Use the expected energy not supplied (EENS) of the system as the system reliability index;

[0032] Step S4: Calculate the coefficient of variation η of the reliability index. When the coefficient of variation is less than the set coefficient of variation or the number of samplings reaches the maximum number of samplings, output the system reliability index EENS.

[0033] Furthermore, the calculation formula for the outage rate of the collection system is:

[0034]

[0035] In the formula: Q g is the outage rate of the wind power DC collection system; Q wt is the outage rate of the DC type fan; Q DC / DC is the outage rate of the DC / DC; k wt,max is the maximum number of fan failures allowed for each branch; Q brk is the outage rate of the DC circuit breaker; Q cable is the outage rate of the DC cable; i s is the number of failed fans in the series branch; j p is the number of outage branches; j g is the number of outage groups.

[0036] Furthermore, the calculation formula for the expected energy not supplied (EENS) of the system is:

[0037]

[0038] In the formula: EENS ins is the expected energy not supplied of the system at the ins-th sampling;

[0039] The calculation formula for the coefficient of variation is:

[0040]

[0041] In the formula: F(X) is the reliability index; N s is the number of samplings.

[0042] Furthermore, the steps of the meshless ray optimization algorithm are as follows:

[0043] Set the initial point X(0) = (x(0), y(0)), the initial direction P(0) = (p(0), q(0)), the step size λ, and the iteration number k = 0;

[0044] Calculate the next iteration point X(k + 1), and calculate the objective function corresponding to the point X(k + 1). The objective function is the total life cycle cost of the wind power DC collection system, that is, the velocity v(k) at this point;

[0045] Calculate the velocities v x (k+1) and v y (k+1) ; Compare the velocities of the trial points in each dimension with the velocity of the point X(k + 1), determine whether to reflect or refract, and calculate the direction P(k + 2) of the next iteration point;

[0046] Repeat the above steps until the termination iteration condition is met, and output the fan grouping scheme with the minimum objective function.

[0047] Furthermore, the minimum objective function means the lowest total life cycle cost. The calculation formula for the total life cycle of the collection system is:

[0048]

[0049] In the formula: C invest is the investment cost of the wind power collection system; C mt is the maintenance cost of the wind power collection system; C loss is the loss cost of the wind power DC collection system; A lost is the power shortage loss cost; r is the annual interest rate; T is the average life of the system;

[0050] The termination iteration condition is:

[0051] N FLRO = 1000

[0052] v (k+2) -v (k+1) ≤ -0.01

[0053] The calculation formula for the power shortage loss cost is:

[0054] A lost = mEENS

[0055] In the formula: m is the energy price.

[0056] Furthermore, an onshore wind power DC collection system topology optimization system includes:

[0057] The first grouping module is used to group the wind turbines based on the topology of the series-parallel type wind power DC collection system.

[0058] The condition input module is used to input known conditions based on grouping: the failure rates, failure repair times, and cost data of the cables, wind turbines, and DC / DC devices in the wind power DC collection system.

[0059] The second grouping module is used to obtain a scheme for re-grouping the wind turbines according to the DC transmission voltage constraint, the wind turbine quantity constraint, the voltage collection capacity constraint, the voltage drop constraint of the collection line, and the current-carrying capacity constraint of the power electronic device.

[0060] The evaluation module is used to evaluate the reliability of the wind power DC collection system under different wind turbine grouping schemes and obtain the system reliability index EENS.

[0061] The optimization module is used to optimize the wind turbine grouping scheme with the lowest total life cycle cost for the wind power DC collection system based on the system reliability index EENS by using the meshless ray optimization algorithm.

[0062] Furthermore, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for optimizing the topology of an onshore wind power DC collection system.

[0063] Furthermore, a computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of a method for optimizing the topology of an onshore wind power DC collection system.

[0064] Compared with the prior art, the present invention has the following technical effects:

[0065] Provided is a method and system for optimizing the topology of an onshore wind power DC collection system. First, based on the topology of the series-parallel type wind power DC collection system, the wind turbines are grouped. Then, a reasonable wind turbine grouping scheme is obtained according to relevant technical constraint conditions, and the reliability of different wind turbine grouping schemes in this topology is evaluated. Finally, considering the power shortage loss cost brought by the system economy and reliability issues, an optimization model for the topology of the wind power DC collection system is constructed with the lowest total cost as the goal, and the optimal wind turbine grouping scheme is obtained. The present invention quantifies the reliability of the wind power collection system with the power shortage loss cost, overall considers the reliability and economy of the system, and optimizes the wind turbine grouping scheme with the lowest total cost, which can provide a reference for the structure and optimization of the wind power DC collection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flowchart of the present invention.

[0067] Figure 2 This is the topology of the wind power DC collection system based on the series-parallel connection of wind turbines in the embodiments of the present invention;

[0068] Figure 3 This is the topology structure of the wind power DC collection system with the lowest life cycle cost of the present invention; Specific implementation manners

[0069] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners.

[0070] The technical solution of the present invention is: to provide a method and system for optimizing the topology structure of an onshore wind power DC collection system. First, based on the topology structure of the series-parallel connection of wind turbines in the wind power DC collection system, the wind turbines are grouped. Then, according to relevant technical constraints, a reasonable wind turbine grouping scheme is obtained, and the reliability of different wind turbine grouping schemes in this topology structure is evaluated. Finally, considering the power shortage loss cost brought by system economy and reliability problems, with the goal of the lowest total cost, an optimization model of the topology structure of the wind power DC collection system is constructed, and the optimal wind turbine grouping scheme is calculated.

[0071] The present invention adopts the following technical solutions:

[0072] A method and system for optimizing the topology structure of an onshore wind power DC collection system, including the following steps:

[0073] Step S1: Group the wind turbines based on the topology structure of the series-parallel connection of wind turbines in the wind power DC collection system;

[0074] Step S2: Input the failure rate, failure repair time, and cost data of equipment such as cables, wind turbines, and DCDC in the wind power DC collection system;

[0075] Step S3: Obtain a reasonable wind turbine grouping scheme according to relevant technical constraints; the relevant technical constraints include DC transmission voltage constraints, wind turbine quantity constraints, voltage collection capacity constraints, collection line voltage drop constraints, and current-carrying capacity constraints of power electronic devices;

[0076] Step S4: Use the non-sequential Monte Carlo simulation method to evaluate the reliability of the wind power DC collection system under different wind turbine grouping schemes;

[0077] Step S5: Use the meshless ray optimization algorithm to optimize and select the wind turbine grouping scheme of the wind power DC collection system with the lowest life cycle cost.

[0078] Further, in step S2, the cost data includes investment cost, maintenance cost, and loss cost data of the wind power collection system;

[0079] Further, in step S3, the DC transmission voltage constraint formula is:

[0080]

[0081] Where: U ac is the AC voltage; U dc is the DC output voltage of the wind farm; Δ% is the allowable voltage deviation.

[0082] The constraint formula for the number of wind turbines is:

[0083]

[0084] Where: g is the number of groups of wind turbines, p is the number of parallel branches in each group, s is the number of wind turbines connected in series in each branch; N is the number of wind turbines in the wind farm.

[0085] The constraint formula for voltage collection capacity is:

[0086] g = f(U dc , P g )

[0087]

[0088] Where: The f function represents the relationship between the voltage level for collection and the collection capacity of each group of wind turbines in the wind power DC collection system;

[0089] The constraint formula for the voltage drop of the collection line is:

[0090]

[0091] Where: P g is the capacity of each group of wind turbines; L is the farthest distance from the wind turbines in the group to the collection bus; ξ is the calculation coefficient; S is the cross-sectional area of the conductor.

[0092] The constraint formula for the current-carrying capacity of power electronics is:

[0093]

[0094] Where: I SM,in represents the input current of each sub-module of the DC / DC; I IGBT is the rated current of the IGBT; I SM,out represents the output current of each sub-module of the DC / DC; I D is the rated current of the diode.

[0095] Furthermore, in step S4, the steps for evaluating the reliability of the collection system by the non-sequential Monte Carlo simulation method are:

[0096] Step S41: Input the number of sampling times, the annual load data of the wind farm, the topological structure of the wind power DC collection system, and the corresponding outage rate model;

[0097] Step S42: Simulate the annual output sequence of the wind power collection system, and use the outage rate sampling of the collection system to correct the annual output sequence of the wind power collection system;

[0098] Step S43: Use the system annual expected energy not supplied (EENS) as the system reliability index;

[0099] Step S44: Calculate the coefficient of variation η of the reliability index. When the coefficient of variation is less than the set coefficient of variation or the number of sampling times reaches the maximum number of sampling times, output the system reliability index EENS.

[0100] Further, in Step S41, the calculation formula for the outage rate of the collection system is:

[0101]

[0102] In the formula: Q g is the outage rate of the wind power DC collection system; Q wt is the outage rate of the DC type fan; Q DC / DC is the outage rate of the DC / DC; k wt,max is the maximum number of fan failures allowed for each branch; Q brk is the outage rate of the DC circuit breaker; Q cable is the outage rate of the DC cable; i s is the number of failed fans in the series branch; j p is the number of outage branches; j g is the number of outage groups.

[0103] The outage rate of a single device is expressed as

[0104]

[0105] In the formula, and are the average fault repair time and average failure rate of the components in the device, respectively, and can be expressed as

[0106]

[0107]

[0108] In the formula, λ iγ is the failure rate of the i γ th component; MTTR iγ is the fault repair time of the i γ th component.

[0109] In a system composed of α parallel devices, the outage rate of the system is

[0110]

[0111] In the formula, Qiα is the outage rate of the i-th device. α In a system composed of β devices connected in series, the failure of one device will cause the system to shut down. Therefore, the outage rate of the system is

[0112] In a system composed of β devices connected in series, the failure of one device will cause the system to shut down. Therefore, the outage rate of the system is

[0113]

[0114] where Q iβ is the outage rate of the i-th device. β is the outage rate of the i-th device.

[0115] Furthermore, for the system reliability index described in step S43, the calculation formula for the expected value of annual energy shortage EENS of the system is:

[0116]

[0117] where: EENS ins is the expected value of annual energy shortage of the system at the i-th sampling. ns is the expected value of annual energy shortage of the system at the i-th sampling.

[0118] Furthermore, the calculation formula for the coefficient of variation of the reliability index described in step S44 is:

[0119]

[0120] where: F(X) is the reliability index; N s is the number of samplings.

[0121] Furthermore, in step S5, the steps of the meshless ray optimization algorithm are:

[0122] Step S51: Set the initial point X (0) =(x (0) , y (0) ), the initial direction P (0) =(p (0) , q (0) ), the step size λ, and the iteration number k = 0;

[0123] Step S52: Calculate the next iteration point X (k+1) , and calculate the objective function corresponding to the point X (k+1) . The objective function is the total life cycle cost of the wind power DC collection system, that is, the speed v (k) at this point;

[0124] Step S53: Calculate the speeds v x (k+1) and v y (k+1) of each trial point; Compare each dimension trial point with the point X (k+1)Based on the speed, determine whether to reflect or refract, and calculate the direction of the next iteration point P (k+2) ;

[0125] Step S54: Repeat steps S52 - S53 until the termination iteration condition is met, and output the fan grouping scheme with the minimum objective function.

[0126] Furthermore, in step S54, the minimum objective function means the lowest life - cycle cost. The calculation formula for the life - cycle cost of the collection system is:

[0127]

[0128] In the formula: C invest is the investment cost of the wind power collection system; C mt is the maintenance cost of the wind power collection system; C loss is the loss cost of the wind power DC collection system; A lost is the power shortage loss cost; r is the annual interest rate; T is the average life of the system.

[0129] The investment cost C invest of the wind power collection system includes the cost C wt of the DC - type fan, the energy storage cost C E , the cost C DC / DC of the DC / DC converter, the cost C brk of the DC circuit breaker, and the cost C cable of the DC cable. Its calculation formula is:

[0130] C invest = C wt + C E + C DC / DC + C brk + C cable

[0131] The cost model of the DC cable is:

[0132] C cable = 0.6405(A dc + B dc P cable )L cable

[0133] In the formula: P cable is the cable transmission power; L cable is the cable length; U cable is the inter - pole voltage of the cable; I cable is the cable transmission current. A dc , B dc are the cable cost coefficients.

[0134] The cost model of the DC / DC converter is:

[0135] C DCDC = C IGBT + C D + C trans + C other

[0136] Where: C IGBT is the cost of IGBT in DC / DC; C D is the cost of diodes; C trans is the cost of high-frequency transformers; C other is other costs, including other components, controllers, labor costs, etc.

[0137] The maintenance cost C of the wind power collection system mt includes expenses such as labor, materials, and finances.

[0138] The loss cost C of the wind power DC collection system loss The calculation formula is:

[0139] C loss = 8760m∑P loss

[0140] Where: m is the energy price; ∑P loss is the loss of the wind power DC collection system. It is mainly composed of the loss P wt,loss of the wind turbine and the loss P co,loss in the collection network. The loss of the wind turbine includes the loss P ge,loss of the generator and the loss P vsc,loss of the converter in the wind turbine. The loss in the wind farm collection network is further divided into the power loss P DC / DC,loss of the DC / DC converter, the transmission line loss P cable,loss and the curtailment loss P v,loss .

[0141] The converter in the wind turbine is composed of the fully controlled device IGBT, and its power loss calculation formula is:

[0142] P vsc,loss = P sw,vsc + P c,vsc

[0143] Where: P sw,vsc is the switching loss of the converter; P c,vsc is the conduction loss of the converter.

[0144] The calculation formula for the switching loss of the converter is:

[0145]

[0146] Where: f s is the switching frequency of the converter; E von,I , Eon,I are the turn-on loss and turn-off loss of the IGBT in the converter; E off,D is the turn-off loss of the diode in the converter; U v,ref is the rated voltage of the converter; I v,ref is the rated current of the converter; I wt is the current at the outlet of a single wind turbine.

[0147] The calculation formula for the conduction loss of the converter is:

[0148] P c,vsc = 6N vsc (P vcond,I + P vcond,D )

[0149] In the formula: N vsc is the number of IGBTs in the converter; P vcond,I , P vcond,D are the conduction losses of the IGBT and diode in the converter respectively.

[0150] The calculation formula for the power loss of the DC / DC converter is:

[0151] P DC / DC,loss = P sw,DC / DC + P c,DC / DC

[0152] In the formula: P sw,DC / DC is the switching loss of the DC / DC; P c,DC / DC is the conduction loss of the DC / DC.

[0153] The calculation formula for the switching loss of the DC / DC is:

[0154] P sw,DC / DC = N DC / DC,I f s (E on,I + E off,I ) + N DC / DC,D f s E off,D

[0155] In the formula: N DC / DC,D and N DC / DC,I are the numbers of diodes and IGBTs in the DC / DC converter; E on,I , E off,I are the turn-on loss and turn-off loss of the IGBT in the DC / DC converter respectively; E off,D is the turn-off loss of the diode in the DC / DC converter.

[0156] The calculation formula for the conduction loss of the DC / DC is:

[0157] P c,DC / DC = N DC / DC,I (VCE,0 I SM,in +I SM,in 2 r CE )+N DC / DC,D (V D-fwd,0 I SM,out +I SM,out 2 r fwd )

[0158] Where: V CE,0 and V D-fwd,0 are the conduction state voltages of the IGBT and the diode, respectively; I SM,in is the input current of a single sub-module; I SM,out is the output current of a single sub-module; r CE and r fwd are the conduction state resistances of the IGBT and the diode, respectively.

[0159] The wind power curtailment of a wind farm can be expressed as

[0160]

[0161] Where: i wt is the number of wind turbines; Pi wt (R min ,v) is the power of the wind turbine at the wind speed corrected by the wind power correlation; Pi wt (v) is the power of the wind turbine at the normal wind speed.

[0162] The formula for calculating the cost of power shortage loss is:

[0163] A lost = mEENS

[0164] Where: m is the energy price.

[0165] Furthermore, the termination iteration condition described in step S54 is:

[0166] N FLRO = 1000

[0167] v (k+2) -v (k+1) ≤ -0.01.

[0168] Example:

[0169] See Figure 1 shown, a method and system for optimizing the topology of an onshore wind power DC collection system, including the following steps:

[0170] Step 1. Group the wind turbines based on the topology of the series-parallel wind power DC collection system. In the embodiment, the rated capacity of the wind farm is 50 MW, and the power of a single wind turbine is 2.5 MW. Refer to Figure 2 for the topology of the series-parallel wind power DC collection system of grouped wind turbines. Suppose the wind turbines are divided into g groups, with p branches in parallel in each group, and s wind turbines in series in each branch;

[0171] Step 2. Input the failure rates, failure repair times, and cost data of equipment such as cables, wind turbines, and DCDCs in the wind power DC collection system; Table 1 gives the reliability parameters of each equipment in the wind power DC collection system; Tables 2-4 give the investment cost parameters of the wind power DC collection system;

[0172] Table 1 Reliability parameters of the wind power DC collection system

[0173]

[0174] Table 2 Investment cost parameters of the wind power DC collection system

[0175]

[0176] Table 3 Cost parameters of medium-voltage DC circuit breakers

[0177]

[0178]

[0179] Table 4 Cost of high-frequency transformers

[0180]

[0181] The maintenance cost C of the wind power collection system mt includes expenses such as manpower, material resources, and financial resources. According to operation experience, the average annual reference maintenance cost per wind turbine in a wind farm is 70,000 yuan per unit. The annual maintenance costs of DC / DC and DC cables are given as percentages of their respective investment costs. Referring to low-voltage DC / DC, the percentage of its annual maintenance cost in its investment cost is 0.2%, and the percentage of the annual maintenance cost of DC transmission cables in their investment costs is calculated as 0.5%;

[0182] Table 5 gives the parameters related to calculating the loss cost of the wind power collection system.

[0183] Table 5 Parameters for calculating the cost of the collection system

[0184]

[0185]

[0186] Step 3. Obtain a reasonable fan grouping scheme according to relevant technical constraints; the relevant technical constraints include DC transmission voltage constraint, fan quantity constraint, voltage collection capacity constraint, voltage drop constraint of the collection line, and current-carrying capacity constraint of power electronic devices; Table 6 gives the reasonable fan grouping scheme.

[0187] Table 6 Reasonable fan grouping scheme

[0188]

[0189] Step 4. Use the non-sequential Monte Carlo simulation method to evaluate the reliability of the wind power DC collection system under different fan grouping schemes; Table 7 gives the EENS of the wind power DC collection system under different fan grouping schemes.

[0190] Table 7 EENS under different fan grouping schemes

[0191]

[0192] Step 5. Use the meshless ray optimization algorithm to optimize and select the fan grouping scheme of the wind power DC collection system with the lowest life cycle cost. Table 8 gives the total cost during the whole life cycle of the wind power DC collection system under different fan grouping schemes.

[0193] Table 8 Total cost during the whole life cycle under different fan grouping schemes

[0194]

[0195] Considering economy and reliability comprehensively, when the fan grouping scheme is (5, 2, 2), the total cost is the lowest, which is 422.4369 million yuan. The specific scheme is as follows: 20 fans are divided into 2 groups, each group has 4 parallel branches, and each branch has 5 fans connected in series. Use the meshless ray optimization algorithm to optimize and select the topology structure of the wind power DC collection system with the lowest life cycle cost as Figure 3 shown. The voltage within each group of the collection system in this scheme is ±35 kV, the rated output voltage U of the DC / DC is 14 kV, the farthest distance from the fan to the collection bus is 1.63 km, and the cross-sectional area of the collection line cable is 240 mm 2 , and the voltage drop of the collection line is 0.07%.

[0196] It can be seen from the above embodiments that a topology optimization method and system for an onshore wind power DC collection system of the present invention can effectively balance reliability and economy, optimize and select the topology structure of the collection system with the lowest life cycle cost, and provide a reference for the optimal design of the topology structure of the wind power DC collection system.

[0197] In another embodiment of the present invention, a topology optimization system for an onshore wind power DC collection system is provided, which can be used to implement the above-mentioned topology optimization method for an onshore wind power DC collection system. Specifically, the system includes:

[0198] A first grouping module, configured to group the wind turbines based on the topology of the series-parallel type wind power DC collection system;

[0199] A condition input module, configured to input known conditions based on the grouping: the failure rates, failure repair times, and cost data of the cables, wind turbines, and DC / DC devices in the wind power DC collection system;

[0200] A second grouping module, configured to obtain a scheme for re-grouping the wind turbines according to the DC transmission voltage constraint, the wind turbine quantity constraint, the voltage collection capacity constraint, the collection line voltage drop constraint, and the current-carrying capacity constraint of the power electronic device;

[0201] An evaluation module, configured to evaluate the reliability of the wind power DC collection system under different wind turbine grouping schemes, and obtain the system reliability index EENS;

[0202] An optimization module, configured to optimize the wind turbine grouping scheme with the lowest life-cycle cost of the wind power DC collection system based on the system reliability index EENS by using the meshless ray optimization algorithm.

[0203] The division of modules in the embodiments of the present invention is illustrative. It is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present invention, each functional module may be integrated in a processor, may exist separately physically, or two or more modules may be integrated in one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0204] In another embodiment of the present invention, a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of a method for optimizing the topology structure of an onshore wind power DC collection system.

[0205] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for optimizing the topology structure of an onshore wind power DC collection system in the above embodiments.

[0206] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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.

[0207] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0208] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0209] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still, the specific implementation manners of the present invention can be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the topology structure of an onshore wind power DC collection system, characterized in that including Based on the topology of the series - parallel wind power DC collection system, the wind turbines are grouped for the first time; Based on the known conditions of the grouped inputs: the failure rates, repair times, and cost data of the cables, wind turbines, and DC / DC devices in the wind power DC collection system; According to the DC transmission voltage constraint, wind turbine quantity constraint, voltage collection capacity constraint, collection line voltage drop constraint, and power electronic device current - carrying capacity constraint, a scheme for re - grouping the wind turbines is obtained to re - group the results of the first grouping; Evaluate the reliability of the wind power DC collection system under different wind turbine grouping schemes to obtain the system reliability index EENS; Based on the system reliability index EENS, use the meshless ray optimization algorithm to optimize the wind turbine grouping scheme with the lowest life - cycle cost of the wind power DC collection system; The steps for evaluating the reliability of the collection system by the non - sequential Monte Carlo simulation method are as follows: Step S1: Input the number of sampling times, the annual load data of the wind farm, the topology of the wind power DC collection system, and the corresponding outage rate model; Step S2: Simulate the annual output sequence of the wind power collection system, and use the outage rate sampling of the collection system to correct the annual output sequence of the wind power collection system; Step S3: Use the expected value of annual energy shortage EENS of the system as the system reliability index; Step S4: Calculate the coefficient of variation η of the reliability index. When the coefficient of variation is less than the set coefficient of variation or the number of sampling times reaches the maximum number of sampling times, output the system reliability index EENS; The calculation formula for the outage rate of the collection system is: Where: Q g is the outage rate of the wind power DC collection system; Q wt is the outage rate of the DC type fan; Q DC / DC is the outage rate of the DC / DC; k wt,max is the maximum number of fan failures allowed for each branch; Q brk is the outage rate of the DC circuit breaker; Q cable is the outage rate of the DC cable; i s is the number of failed fans in the series branch; j p is the number of outage branches; j g is the number of outage groups.

2. The topology structure optimization method of an onshore wind power DC collection system according to claim 1, wherein The DC transmission voltage constraint formula is: Where: U ac is the AC voltage; U dc is the DC output voltage of the wind farm; Δ% is the allowable voltage deviation; The wind turbine quantity constraint formula is: In the formula: g is the number of wind turbine groups; p is the number of parallel branches in each group; s is the number of series - connected wind turbines in each branch; N is the number of wind turbines in the wind farm; The voltage collection capacity constraint formula is: In the formula: the f function represents the relationship between the collection voltage level and the collection capacity of each group of wind turbines in the wind power DC collection system; The collection line voltage drop constraint formula is: Where: P g is the capacity of each group of fans; L is the maximum distance from the fans in the group to the busbar; ξ is the calculation coefficient; S is the cross-sectional area of the conductor; The power electronic current - carrying capacity constraint formula is: Where: I SM,in represents the input current of each sub-module of the DC / DC; I IGBT is the rated current of the IGBT; I SM,out represents the output current of each sub-module of the DC / DC; I D is the rated current of the diode.

3. The topology structure optimization method of an onshore wind power DC collection system according to claim 1, characterized in that The calculation formula for the expected value of annual energy shortage EENS of the system is: Where: EENS ins is the expected value of annual energy shortage of the system during the ns ith sampling; The calculation formula for the coefficient of variation is: Where: F(X) is the reliability index; N s is the number of samplings.

4. A method for optimizing the topology structure of an onshore wind power DC collection system according to claim 1, characterized in that, The steps of the meshless ray optimization algorithm are as follows: Set the initial point X(0)=(x(0),y(0)), the initial direction P(0)=(p(0),q(0)), the step size λ, and the iteration number k = 0; Calculate the next iteration point X(k + 1), and calculate the objective function corresponding to the point X(k + 1). The objective function is the life - cycle cost of the wind power DC collection system, that is, the speed v(k) at this point; Calculate the speeds vx(k + 1) and vy(k + 1) of each trial point; compare the speeds of each dimension trial point and the point X(k + 1) to determine whether to reflect or refract, and calculate the direction P(k + 2) of the next iteration point; Repeat the above steps until the termination iteration condition is met, and output the wind turbine grouping scheme with the minimum objective function.

5. The topology optimization method of an onshore wind power DC collection system according to claim 4, characterized in that The minimum objective function means the lowest life - cycle cost. The calculation formula for the life - cycle of the collection system is: Where: C invest is the investment cost of the wind power collection system; C mt is the maintenance cost of the wind power collection system; C loss is the loss cost of the wind power DC collection system; A lost is the power shortage loss cost; r is the annual interest rate; T is the average system life; The termination iteration condition is: The calculation formula for the power shortage loss cost is: In the formula: m is the energy price.

6. An onshore wind power DC collection system topology optimization system, characterized in that including: The first grouping module is used to group the wind turbines based on the topology of the series - parallel wind power DC collection system; A condition input module, configured to input known conditions based on grouping: failure rates, fault repair times, and cost data of cables, wind turbines, and DC / DC devices in a wind power DC collection system; A second grouping module, configured to obtain a scheme for regrouping wind turbines according to DC transmission voltage constraints, wind turbine quantity constraints, voltage collection capacity constraints, collection line voltage drop constraints, and power electronic device current-carrying capacity constraints; An evaluation module, configured to evaluate the reliability of the wind power DC collection system under different wind turbine grouping schemes, and obtain a system reliability index EENS; An optimization module, configured to optimize the wind turbine grouping scheme of the wind power DC collection system with the lowest life cycle cost based on the system reliability index EENS by using a meshless ray optimization algorithm; The steps for evaluating the reliability of the collection system by the non-sequential Monte Carlo simulation method are as follows: Step S1, input the number of sampling times, annual load data of the wind farm, the topology of the wind power DC collection system, and the corresponding outage rate model; Step S2, simulate the annual output sequence of the wind power collection system, and correct the annual output sequence of the wind power collection system by sampling the outage rate of the collection system; Step S3, use the expected value of annual energy shortage EENS of the system as the system reliability index; Step S4, calculate the coefficient of variation η of the reliability index. When the coefficient of variation is less than the set coefficient of variation or the number of sampling times reaches the maximum number of sampling times, output the system reliability index EENS; The calculation formula for the outage rate of the collection system is: Where: Q g is the outage rate of the wind power DC collection system; Q wt is the outage rate of the DC type fan; Q DC / DC is the outage rate of the DC / DC; k wt,max is the maximum number of fan faults allowed for each branch; Q brk is the outage rate of the DC breaker; Q cable is the outage rate of the DC cable; i s is the number of faulty fans in the series branch; j p is the number of outage branches; j g is the number of outage groups.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of an onshore wind power DC collection system topology optimization method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the steps of an onshore wind power DC collection system topology optimization method according to any one of claims 1 to 5 are implemented.

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