A new energy station oscillation suppression method and system

Abstract

The application provides a new energy station oscillation suppression method and system, comprising: when an oscillation component is greater than an oscillation component threshold, determining a wind turbine generator set grid-connected electrical distance fuzzy subset and a wind turbine generator set operating power relative value fuzzy subset based on acquired parameter information; adopting a fuzzy logic strategy to determine a power instruction distribution coefficient based on the wind turbine generator set grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset; and lowering the wind turbine generator set operating power based on the power instruction distribution coefficient. The application adopts a fuzzy logic strategy to determine a power instruction distribution coefficient based on a wind turbine generator set grid-connected electrical distance fuzzy subset and an operating power relative value fuzzy subset, and lowers the wind turbine generator set operating power, thereby minimizing the amount of abandoned wind and light, and improving the safety and stability margin of a new energy grid-connected system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy power generation grid connection technology, and particularly relates to a new energy station oscillation suppression method and system. BACKGROUND

[0002] China's new energy development is rapid, and the installed capacity ranks first in the world. However, in the "three north" region of China, with the increase of new energy installed capacity, the synchronous characteristics dominated by traditional generators are gradually weakened, and the stability characteristics of the power system have changed profoundly. The wideband oscillation problem of new energy station grid-connected system occurs frequently, which seriously affects the safe and stable operation of the power grid.

[0003] The electrical distance of the wind turbine from the station grid-connected point is represented as impedance, mainly including the impedance of the box transformer, the medium voltage collection line and the main transformer. The wind turbine located at the end of the feeder has a relatively longer electrical distance from the grid-connected point, and has a greater impact on oscillation stability.

[0004] For new energy grid-connected wideband oscillation, the current actual system mainly realizes oscillation suppression by monitoring and stability control device to remove new energy stations. However, large-scale removal of new energy will have a great impact on the instantaneous power balance of the power grid, and will also damage new energy generation and station power equipment. SUMMARY

[0005] In order to solve the problem that the existing oscillation suppression large-scale removal of new energy will have a great impact on the instantaneous power balance of the power grid, the present application considers a new energy station oscillation suppression method, which comprises:

[0006] When the oscillation component is greater than the oscillation component threshold, the wind turbine grid-connected electrical distance fuzzy subset and the wind turbine operating power relative value fuzzy subset are determined based on the obtained parameter information;

[0007] The fuzzy logic strategy is used to determine the down-regulation power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset;

[0008] The wind turbine operating power is down-regulated based on the down-regulation power instruction distribution coefficient.

[0009] Preferably, the wind turbine grid-connected electrical distance fuzzy subset is determined based on the obtained parameter information, comprising:

[0010] The active power, cable parameters and length information in the parameter information are combined to obtain the equivalent grid-connected impedance of different wind turbines by an equivalent grid-connected impedance calculation formula;

[0011] The equivalent grid-connected impedance of different wind turbines is normalized to obtain the distance value of different wind turbines;

[0012] obtaining a grid-connected electrical distance fuzzy subset based on the distance value of the different wind turbine and a distance threshold value.

[0013] Preferably, the obtaining the grid-connected electrical distance fuzzy subset based on the distance value of the different wind turbine and a distance threshold value comprises:

[0014] when the distance value of the wind turbine is less than a first distance threshold value, constructing a small grid-connected electrical distance fuzzy subset;

[0015] when the distance value of the wind turbine is greater than the first distance threshold value and less than a second distance threshold value, constructing a medium grid-connected electrical distance fuzzy subset;

[0016] when the distance value of the wind turbine is greater than the second distance threshold value, constructing a large grid-connected electrical distance fuzzy subset;

[0017] wherein the first distance threshold value is less than the second distance threshold value.

[0018] Preferably, the determining the wind turbine operation power relative value fuzzy subset based on the parameter information comprises:

[0019] combining the wind turbine actual power value in the parameter information and the average power value of the wind turbine to calculate the wind turbine operation power value by using an operation power calculation formula;

[0020] determining the wind turbine operation power relative value fuzzy subset based on the wind turbine operation power value and an operation power threshold value.

[0021] Preferably, the determining the wind turbine operation power relative value fuzzy subset based on the wind turbine operation power value and an operation power threshold value comprises:

[0022] when the wind turbine operation power value is less than a first operation power threshold value, constructing a small operation power relative value fuzzy subset;

[0023] when the wind turbine operation power value is greater than the first operation power threshold value and less than a second operation power threshold value, constructing a medium operation power relative value fuzzy subset;

[0024] when the wind turbine operation power value is greater than the second distance threshold value, constructing a large operation power relative value fuzzy subset;

[0025] wherein the first operation power threshold value is less than the second operation power threshold value.

[0026] Preferably, the determining the power down instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset by using the fuzzy logic strategy comprises:

[0027] When the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a small operating power relative value fuzzy subset or a medium operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the small power reduction command allocation coefficient fuzzy subset.

[0028] When the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset or a medium grid-connected electrical distance fuzzy subset, and the operating power relative value fuzzy subset is a large operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the large power reduction command allocation coefficient fuzzy subset;

[0029] When the grid-connected electrical distance fuzzy subset is a medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a small operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the small power reduction command allocation coefficient fuzzy subset.

[0030] When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the medium operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the medium power reduction command allocation coefficient fuzzy subset;

[0031] When the grid-connected electrical distance fuzzy subset is a large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a small operating power relative value fuzzy subset or a medium operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the medium power reduction command allocation coefficient fuzzy subset;

[0032] When the grid-connected electrical distance fuzzy subset is a large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a large operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the large power reduction command allocation coefficient fuzzy subset.

[0033] Preferably, the step of reducing the operating power of the wind turbine based on the power reduction command allocation coefficient includes:

[0034] The change in the operating power of the wind turbine is determined by combining the power reduction command allocation coefficient and the total power reduction command of the wind turbine with the power change calculation formula.

[0035] The operating power of the wind turbine is reduced by combining the change in the operating power of the wind turbine with the power reduction calculation formula.

[0036] Preferably, the power change calculation formula is as follows:

[0037]

[0038] In the above formula, ΔP jPj is the power needed to be reduced for the jth wind turbine, j is the jth wind turbine, μ j Pj is the active power distribution coefficient of the jth wind turbine, N W N is the number of wind turbines of the new energy station, ΔP wf ΔP is the total reduction instruction of the new energy station.

[0039] Preferably, the power reduction calculation formula is as follows:

[0040] P d, j=P j -ΔP j

[0041] In the above formula, P d,j Pj is the active power set value of the jth wind turbine, P j Pj is the operating power value of the jth wind turbine.

[0042] In another aspect, the application also provides a new energy station oscillation suppression system, comprising:

[0043] The subset determination module is configured to determine the wind turbine grid-connected electrical distance fuzzy subset and the wind turbine operating power relative value fuzzy subset based on the obtained parameter information when the monitored oscillation component is greater than the oscillation component threshold value;

[0044] The coefficient determination module is configured to determine the reduction power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset by using a fuzzy logic strategy;

[0045] The power reduction module is configured to reduce the wind turbine operating power based on the reduction power instruction distribution coefficient.

[0046] Preferably, the subset determination module comprises:

[0047] The first numerical value obtaining submodule is configured to obtain the equivalent grid-connected impedance of different wind turbines by combining the active power, cable parameters and length information in the parameter information with the equivalent grid-connected impedance calculation formula;

[0048] The second numerical value obtaining submodule is configured to obtain the distance values of different wind turbines by normalizing the equivalent grid-connected impedances of different wind turbines;

[0049] The first subset obtaining submodule is configured to obtain the grid-connected electrical distance fuzzy subset based on the distance values of different wind turbines and the distance threshold value;

[0050] The third numerical value obtaining submodule is configured to obtain the wind turbine operating power value by combining the wind turbine actual power value and the average power value of the wind turbine in the parameter information with the operating power calculation formula;

[0051] The second subset obtaining submodule is configured to determine a wind turbine operating power relative value fuzzy subset based on the wind turbine operating power value and an operating power threshold value.

[0052] Preferably, the first subset obtaining submodule is specifically configured to:

[0053] construct a small grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is less than a first distance threshold value;

[0054] construct a medium grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is greater than the first distance threshold value and less than a second distance threshold value;

[0055] construct a large grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is greater than the second distance threshold value;

[0056] wherein the first distance threshold value is less than the second distance threshold value.

[0057] Preferably, the second subset obtaining submodule is specifically configured to:

[0058] construct a small operating power relative value fuzzy subset when the wind turbine operating power value is less than a first operating power threshold value;

[0059] construct a medium operating power relative value fuzzy subset when the wind turbine operating power value is greater than the first operating power threshold value and less than a second operating power threshold value;

[0060] construct a large operating power relative value fuzzy subset when the wind turbine operating power value is greater than the second distance threshold value;

[0061] wherein the first operating power threshold value is less than the second operating power threshold value.

[0062] Preferably, the coefficient determining module is specifically configured to:

[0063] obtain a down-regulation power instruction distribution coefficient from a small down-regulation power instruction distribution coefficient fuzzy subset when the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a small operating power relative value fuzzy subset or a medium operating power relative value fuzzy subset;

[0064] obtain a down-regulation power instruction distribution coefficient from a large down-regulation power instruction distribution coefficient fuzzy subset when the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset or a medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a large operating power relative value fuzzy subset;

[0065] When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the running power relative value fuzzy subset is the small running power relative value fuzzy subset, a down-regulation power instruction distribution coefficient is obtained from the small down-regulation power instruction distribution coefficient fuzzy subset;

[0066] When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the running power relative value fuzzy subset is the medium running power relative value fuzzy subset, a down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0067] When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the running power relative value fuzzy subset is the small running power relative value fuzzy subset or the medium running power relative value fuzzy subset, a down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0068] When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the running power relative value fuzzy subset is the large running power relative value fuzzy subset, a down-regulation power instruction distribution coefficient is obtained from the large down-regulation power instruction distribution coefficient fuzzy subset.

[0069] Preferably, the power down-regulation module comprises:

[0070] The variation amount determination sub-module is configured to combine the down-regulation power instruction distribution coefficient of the wind turbine and the total down-regulation power instruction to calculate the wind turbine running power variation amount according to a power variation calculation formula;

[0071] The running power down-regulation sub-module is configured to combine the wind turbine running power variation amount and a power down-regulation calculation formula to down-regulate the wind turbine running power.

[0072] Compared with the prior art, the present application has the following beneficial effects:

[0073] The present application provides a new energy station oscillation suppression method and system, comprising: when an oscillation component is greater than an oscillation component threshold, determining a wind turbine grid-connected electrical distance fuzzy subset and a wind turbine running power relative value fuzzy subset based on acquired parameter information; using a fuzzy logic strategy to determine a down-regulation power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the running power relative value fuzzy subset; and down-regulating the wind turbine running power based on the down-regulation power instruction distribution coefficient. The present application uses a fuzzy logic strategy to determine a down-regulation power instruction distribution coefficient based on a wind turbine grid-connected electrical distance fuzzy subset and a running power relative value fuzzy subset, and down-regulates the wind turbine running power, thereby minimizing the amount of curtailed wind power and light, and improving the safety and stability margin of the new energy grid-connected system. BRIEF DESCRIPTION OF DRAWINGS

[0074] Figure 1 A flow chart of main steps of a new energy station oscillation suppression method of the present application;

[0075] Figure 2 A fuzzy subset distribution graph of a new energy unit grid-connected electrical distance relative value domain of an embodiment of the present application;

[0076] Figure 3 A fuzzy subset distribution graph of a wind turbine operating power relative value domain of an embodiment of the present application;

[0077] Figure 4 A new energy station fuzzy logic strategy graph of an embodiment of the present application;

[0078] Figure 5 A fuzzy subset distribution graph of a power command distribution coefficient down-regulation domain of an embodiment of the present application;

[0079] Figure 6 A new energy station oscillation suppression fuzzy control strategy block diagram of an embodiment of the present application;

[0080] Figure 7 A wind farm system graph of an embodiment of the present application;

[0081] Figure 8 A wind farm grid-connected point output power simulation waveform of an embodiment of the present application;

[0082] Figure 9 A wind farm grid-connected point output current simulation waveform of an embodiment of the present application;

[0083] Figure 10 A 3-feeder wind turbine power distribution value down-regulation graph of an embodiment of the present application;

[0084] Figure 11 A first and last end wind turbine output power simulation result graph of a 3-feeder of an embodiment of the present application;

[0085] Figure 12 A wind farm grid-connected point output power simulation waveform graph of an embodiment of the present application;

[0086] Figure 13 A wind farm grid-connected point output current simulation waveform of an embodiment of the present application;

[0087] Figure 14 A 3-feeder wind turbine power distribution value down-regulation graph of an embodiment of the present application;

[0088] Figure 15 A first and last end wind turbine output power simulation result graph of a 3-feeder of an embodiment of the present application;

[0089] Figure 16A main structure diagram of a new energy station oscillation suppression system. DETAILED DESCRIPTION

[0090] The application provides a new energy station oscillation suppression method and system based on fuzzy logic, which can realize wideband oscillation suppression of the new energy station and reduce wind and light power abandonment of the new energy station.

[0091] The specific embodiments of the application will be further described in detail below with reference to the drawings.

[0092] Embodiment 1

[0093] The application provides a comprehensive energy system operation strategy verification method, as shown in the figure, which comprises the following steps: Figure 1

[0094] Step S101: when the oscillation component is greater than the oscillation component threshold, determining the wind turbine generator set grid-connected electrical distance fuzzy subset and the wind turbine generator set operation power relative value fuzzy subset based on the obtained parameter information

[0095] Step S102: determining a power instruction distribution coefficient based on the wind turbine generator set grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset by using a fuzzy logic strategy.

[0096] Step S103: reducing the wind turbine generator set operation power based on the power instruction distribution coefficient.

[0097] The application is described in detail by taking a wind power station as an example in this embodiment.

[0098] Step S101 specifically comprises the following steps:

[0099] Step S101a: obtaining the equivalent grid-connected impedance of different wind turbine generator sets by combining the active power, cable parameters and length information in the parameter information with an equivalent grid-connected impedance calculation formula. The calculation method of the relative value of the grid-connected electrical distance of each wind turbine generator set is as follows: first, the equivalent grid-connected impedance of each wind turbine generator set is calculated, as shown in the following formula:

[0100]

[0101] In the formula, Z eq,j is the equivalent grid-connected impedance of the jth generator set from the starting point of the grid connection point of a certain collection line; P i is the active power of the ith wind turbine generator set from the starting point of the grid connection point of a certain collection line; Z1 is the collection line impedance between the first wind turbine generator set and the adjacent generator set close to the grid connection point from the starting point of the grid connection point of a certain collection line; Z2 is the collection line impedance between the second wind turbine generator set and the adjacent generator set close to the grid connection point from the starting point of the grid connection point of a certain collection line; Z j ​Zij is the impedance of the gathering line from the grid-connected point to the jth wind turbine; i is the ith wind turbine; and j is the jth wind turbine.

[0102] In the embodiment, P i Pij is the active power of the ith wind turbine on a gathering line, each wind turbine should upload the output active power value, if not, the value is measured by the field power meter.

[0103] Z j Z is the distance impedance, which is calculated according to the cable parameters and length.

[0104] For example, the cable model is 0.0005H / km, the distance is 1km, and the impedance is 0.0005H, and the impedance value at the power frequency is calculated.

[0105] Step S101b: normalizing the equivalent grid-connected impedance of different wind turbines to obtain distance values of different wind turbines. The equivalent impedance of each wind turbine is normalized and calculated as follows:

[0106]

[0107] In the above formula, d j is the distance value of the jth wind turbine; Z eq,j is the equivalent grid-connected impedance of the jth wind turbine on a gathering line from the grid-connected point; Z eq,2 is the equivalent grid-connected impedance of the second wind turbine on a gathering line from the grid-connected point; Z eq,NL is the equivalent grid-connected impedance of the N L th wind turbine on a gathering line from the grid-connected point; N L is the number of wind turbines connected to the gathering line; Z eq,1 is the equivalent grid-connected impedance of the first wind turbine on a gathering line from the grid-connected point.

[0108] Step S101c: obtaining a grid-connected electrical distance fuzzy subset based on the distance values of different wind turbines and distance thresholds.

[0109] Step S101c specifically includes:

[0110] Step S101c1: when the distance value of a wind turbine is less than a first distance threshold, a small grid-connected electrical distance fuzzy subset is constructed;

[0111] Step S101c2: when the distance value of the wind turbine is greater than the first distance threshold and the distance value is less than a second distance threshold, a medium grid-connected electrical distance fuzzy subset is constructed;

[0112] Step S101c3: constructing a large grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is greater than the second distance threshold value;

[0113] wherein the first distance threshold value is less than the second distance threshold value.

[0114] In combination with the accompanying drawings Figure 2 , the specific process of step S101c is as follows:

[0115] The fuzzy subset distribution of the design wind turbine grid-connected electrical distance relative value d domain is shown in FIG. 1. Figure 2 Five fuzzy subsets are set: near (N), relatively near (SN), medium (M), relatively far (SL), and far (L).

[0116] The wind turbine grid-connected electrical distance of each radial collection line access is calculated according to the above method.

[0117] Step S101d: combining the wind turbine actual power value in the parameter information and the wind turbine average power value to calculate the wind turbine operating power value by using the operating power calculation formula. The wind turbine operating power relative value is the difference between the actual power value of the unit and the average power value of all units in the feeder, as shown in the following formula:

[0118]

[0119] In the above formula, p j is the jth wind turbine operating power relative value; P j is the active power of the jth wind turbine from the grid-connected point of the collection line; P N is the rated power of the jth wind turbine; and N L is the number of wind turbines connected to the collection line.

[0120] In this embodiment, it is assumed that the rated capacity of all units in the feeder is P N , and if they are not the same, the power unit value can be calculated separately for each unit.

[0121] The wind turbine operating power relative value of each radial collection line access is calculated according to the above method.

[0122] Step S101e: determining the wind turbine operating power relative value fuzzy subset based on the wind turbine operating power value and the operating power threshold value.

[0123] Step S101e specifically includes:

[0124] Step S101e1: constructing a small operating power relative value fuzzy subset when the wind turbine operating power value is less than the first operating power threshold value;

[0125] Step S101e2: constructing a middle operation power relative value fuzzy subset when the wind turbine operation power value is greater than the first operation power threshold value and less than the second operation power threshold value;

[0126] Step S101e3: constructing a large operation power relative value fuzzy subset when the wind turbine operation power value is greater than the second distance threshold value;

[0127] Wherein, the first operation power threshold value is less than the second operation power threshold value.

[0128] In combination with the accompanying drawings Figure 3 , the specific process of step S101e is as follows:

[0129] The fuzzy subset distribution of the domain of the wind turbine operation power relative value p is shown in the accompanying Figure 3 Five fuzzy subsets are set: small (S), relatively small (LS), middle (M), relatively large (LB), and large (B).

[0130] In this embodiment, only the domain of the power relative value between [-0.05pu, 0.05pu] is given, and in actual application, the historical operation of the wind farm can be designed according to the historical operation of the wind farm, and for the units with power relative values outside the domain, they can be processed according to small (S) or large (B).

[0131] In this embodiment, the distance fuzzy subset and the operation power fuzzy subset are set, the wind turbines with large operation power are preferentially operated, and the wind turbines with long electrical distance are preferentially connected, thereby minimizing the amount of abandoned wind and light and improving the safety and stability margin of the new energy grid-connected system.

[0132] Step S102 specifically includes:

[0133] Step S102a: obtaining a down-regulation power instruction distribution coefficient from the small down-regulation power instruction distribution coefficient fuzzy subset when the grid-connected electrical distance fuzzy subset is the small grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is the small operation power relative value fuzzy subset or the middle operation power relative value fuzzy subset;

[0134] Step S102b: obtaining a down-regulation power instruction distribution coefficient from the large down-regulation power instruction distribution coefficient fuzzy subset when the grid-connected electrical distance fuzzy subset is the small grid-connected electrical distance fuzzy subset or the middle grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is the large operation power relative value fuzzy subset;

[0135] Step S102c: obtaining a down-regulation power instruction distribution coefficient from the small down-regulation power instruction distribution coefficient fuzzy subset when the grid-connected electrical distance fuzzy subset is the middle grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is the small operation power relative value fuzzy subset;

[0136] Step S102d: When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the medium operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0137] Step S102e: When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset or the medium operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0138] Step S102f: When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the large operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the large down-regulation power instruction distribution coefficient fuzzy subset.

[0139] In combination with Figure 4 , the specific process of step S102 is as follows:

[0140] The wind farm comprises N W wind turbine generators, and the relative value d of the electrical distance of each wind turbine generator connected to the grid point and the relative value p of the operating active power are taken as input quantities, and the down-regulation power instruction distribution coefficient μ is taken as an output quantity. The unit of the electrical distance d connected to the grid point is per unit, the unit of the operating power relative value p is per unit, and the unit of the down-regulation power instruction distribution coefficient μ is per unit.

[0141] In combination with Figure 5 , the fuzzy subset distribution of the argument domain of the down-regulation power instruction distribution coefficient μ is designed as shown in Figure 5 Five fuzzy subsets are set, i.e., small (SD), relatively small (ND), medium (MD), relatively large (HD), and large (BD). The argument domain of the down-regulation power instruction distribution coefficient μ is taken as [0, 1].

[0142] The fuzzy control strategy rule of the oscillation suppression of the new energy station is designed as shown in Table 1. The down-regulation power distribution coefficient of the wind turbine generator close to the gathering bus and having small active power is small, and the down-regulation power distribution coefficient of the wind turbine generator far from the gathering bus and having large active power is large.

[0143] Table 1: Fuzzy control strategy rule table of wind farm oscillation suppression

[0144]

[0145]

[0146] The embodiment sets different down-regulation power instruction distribution coefficients for wind turbines with different operating powers and distances, thereby minimizing the amount of abandoned wind and light.

[0147] Step S103 specifically includes:

[0148] Step S103a: combining the down-regulation power instruction distribution coefficient of the wind turbine and the total down-regulation power instruction to calculate the wind turbine operating power change amount by using a power change calculation formula. Figure 6 , a new energy station wideband oscillation suppression strategy is established as shown in Figure 6 When the grid interconnection point of the station monitors the oscillation component, the power down-regulation control is triggered, the total down-regulation power instruction of the station is output to the fuzzy logic controller, the fuzzy controller down-regulates the operating power of each unit, and outputs the down-regulated power instruction. The power instruction of each unit is calculated according to the following formula:

[0149]

[0150] In the above formula, ΔP j is the power that the jth wind turbine needs to down-regulate, j is the jth wind turbine, μ j is the active power distribution coefficient of the jth wind turbine, N W is the number of wind turbines in the new energy station, and ΔP wf is the total down-regulation instruction of the new energy station.

[0151] Step S103b: down-regulating the operating power of the wind turbine by combining the wind turbine operating power change amount with a power down-regulation calculation formula. The down-regulated operating power of the wind turbine is calculated by combining the wind turbine operating power change amount with the following formula:

[0152] P d,j = P j - ΔP j

[0153] In the above formula, P d,j is the active power set value of the jth wind turbine, P j is the operating power value of the jth wind turbine, and ΔP j is the power that the jth wind turbine needs to down-regulate.

[0154] The embodiment determines the down-regulation power instruction distribution coefficient and down-regulates the operating power of the wind turbine, thereby minimizing the amount of abandoned wind and light and improving the safety and stability margin of the new energy grid-connected system.

[0155] Embodiment 2:

[0156] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0157] The wind farm system is constructed as shown in Figure 7 The detailed circuit simulation model of the wind farm system is established in MATLAB / Simulink, including 33 3MW direct-drive wind turbines, 11 wind turbines connected to each feeder, and a total of 3 feeders, and the distance between two adjacent wind turbines is 500m. Two cases are designed for simulation analysis, and in the two cases, the step of the wind farm power reduction instruction is set to 0.05pu.

[0158] (1) Case one: all wind turbines have equal initial power

[0159] The active power of all wind turbines in the wind farm is set to 0.9pu, and the time-domain simulation results of the power and current waveforms at the grid connection point of the wind farm are shown in Figure 8 and Figure 9 It can be seen that at the 6th second, the oscillation suppression control is started, and after the wind farm power is reduced by 0.05pu, the oscillation is eliminated. Figure 10 The power reduction instruction values of each wind turbine on the three feeders are given, and it can be seen that since all wind turbines have an initial power of 0.9pu, the only factor affecting the power reduction instruction distribution is the connection position of the wind turbine, the power reduction instruction of the wind turbine connected to the head end is smaller, and the power reduction instruction of the wind turbine connected to the tail end is larger. Figure 11 The simulation results of the output power of the wind turbines at the head and tail ends of the three feeders are given, and it can be seen that the power reduction of the wind turbines at the head ends of the three feeders is equal, about 0.01pu, and the power reduction of the wind turbines at the tail ends of the three feeders is equal, about 0.09pu.

[0160] (2) Case two: the power distribution of wind turbines on the three feeders is different

[0161] The power of the wind turbines connected to the feeder 1 from the head end to the tail end is set to 0.95pu, 0.94pu, …, 0.85pu, the power of the wind turbines connected to the feeder 2 from the head end to the tail end is set to 0.85pu, 0.86pu, …, 0.95pu, and the power of the wind turbines connected to the feeder 3 is set to 0.9pu, so that the power of the wind turbines at the same connection position in the three collection feeders is not the same except for the wind turbine at the middle position (the 6th) of the feeder.

[0162] The time-domain simulation results of the power and current waveforms at the grid connection point of the wind farm are shown in Figure 12 and 13 It can be seen that at the 6th second, the oscillation suppression control is started, and after the wind farm power is reduced by 0.05pu, the oscillation is eliminated. Figure 14The power reduction command values ​​for each wind turbine on the three feeders are given. It can be seen that since the power of the wind turbines connected to feeder 1 decreases from high to low at the beginning and end, the power distribution is consistent with the target of fuzzy control. Therefore, the power reduction command for the turbines in feeder 1 is...

[0163] Equal. The power output of the wind turbines connected to feeder 2 increases from low to high at the beginning and end of the line. The power reduction commands for the four turbines at the beginning are all relatively small, while the power reduction commands for the four turbines at the end are all relatively large. The power output of the turbines on feeder 3 is 0.9 pu, the same as in Case 1. Figure 15 Simulation results of the output power of the wind turbines at the beginning and end of the three feeders are presented, and compared with... Figure 14 The results shown are consistent.

[0164] Example 3:

[0165] This invention also provides a system for verifying the operation strategy of an integrated energy system, such as... Figure 16 As shown, it includes:

[0166] The subset determination module is used to determine the fuzzy subset of the grid-connected electrical distance of the wind turbine and the fuzzy subset of the relative value of the wind turbine's operating power based on the acquired parameter information when the detected oscillation component is greater than the oscillation component threshold.

[0167] The coefficient determination module is used to determine the power reduction command allocation coefficient based on the fuzzy subset of the grid-connected electrical distance of the wind turbine and the fuzzy subset of the relative value of the operating power using a fuzzy logic strategy.

[0168] The power reduction module is used to reduce the operating power of the wind turbine based on the power reduction command allocation coefficient.

[0169] This embodiment uses a wind farm as an example to illustrate the invention in detail:

[0170] The subset determination module specifically includes:

[0171] The first numerical acquisition submodule is used to combine the active power, cable parameters, and length information from the parameter information with the equivalent grid-connected impedance calculation formula to obtain the equivalent grid-connected impedance of different wind turbine units. The calculation method for the relative values ​​of the grid-connected electrical distance of each wind turbine unit is as follows: First, calculate the equivalent grid-connected impedance of each wind turbine unit, as shown in the following formula:

[0172]

[0173] In the above formula, Z eq,j Let P be the equivalent grid-connected impedance of the j-th generating unit starting from the grid connection point on a certain collecting line; iZ1 represents the active power of the i-th wind turbine unit starting from the grid connection point of a certain collection line; Z2 represents the collection line impedance between the first wind turbine unit starting from the grid connection point of a certain collection line and a nearby turbine unit; Z3 represents the collection line impedance between the second wind turbine unit starting from the grid connection point of a certain collection line and a nearby turbine unit. j Let be the impedance of the collection line between the j-th wind turbine and a nearby turbine near the grid connection point, starting from the grid connection point; i is the i-th wind turbine; j is the j-th wind turbine.

[0174] In this embodiment, P i This represents the active power of the i-th generating unit starting from the grid connection point on a certain collection line. Each generating unit should upload its output active power value. If not, it can be obtained by measuring the power at the site.

[0175] Z j The pitch impedance is calculated based on the cable parameters and length.

[0176] For example, if the cable type is 0.0005H / km and the distance is 1km, then the impedance is 0.0005H. Then calculate the impedance value at the power frequency.

[0177] The second numerical acquisition submodule is used to normalize the equivalent grid-connected impedance of the different wind turbine units to obtain the distance values ​​for each wind turbine unit. The normalization calculation for the equivalent impedance of each wind turbine unit is shown in the following formula:

[0178]

[0179] In the above formula, d j Z represents the distance value of the j-th wind turbine; eq,j Z represents the equivalent grid-connected impedance of the j-th generating unit starting from the grid connection point on a certain collecting line; eq,2 The equivalent grid-connected impedance of the second wind turbine unit starting from the grid connection point of a certain collecting line; For a certain merging line, starting from the grid connection point, the Nth... L The equivalent grid-connected impedance of each wind turbine unit; N L Z represents the number of wind turbines connected to this collection line. eq,1 This is the equivalent grid-connected impedance of the first wind turbine unit starting from the grid connection point of a certain collecting line.

[0180] The first subset acquisition submodule is used to obtain a grid-connected electrical distance fuzzy subset based on the distance values ​​and distance thresholds of the different wind turbine units.

[0181] The first subset obtains the submodule, specifically used for:

[0182] When the distance value of the wind turbine is less than the first distance threshold, a small grid-connected electrical distance fuzzy subset is constructed;

[0183] constructing a medium grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is greater than the first distance threshold value and less than the second distance threshold value;

[0184] constructing a large grid-connected electrical distance fuzzy subset when the distance value of the wind turbine is greater than the second distance threshold value;

[0185] wherein the first distance threshold value is less than the second distance threshold value.

[0186] The accompanying drawings are incorporated into and constitute a part of this specification and illustrate embodiments of the application. Figure 2 The first subset obtaining sub-module specifically processes as follows:

[0187] The fuzzy subset distribution of the argument of the relative value d of the grid-connected electrical distance of the wind turbine is shown in FIG. 1. Figure 2 Five fuzzy subsets are set, i.e., near (N), relatively near (SN), medium (M), relatively far (SL), and far (L).

[0188] The grid-connected electrical distance of the wind turbine connected by each of the radial collection lines is calculated according to the above method.

[0189] The third numerical value obtaining sub-module is configured to calculate the actual power value of the wind turbine and the average power value of the wind turbine in the parameter information to obtain the operating power value of the wind turbine by combining the operating power calculation formula. The relative value of the operating power of the wind turbine is the difference between the actual power value of the wind turbine and the average power value of all the wind turbines in the feeder, as shown in the following formula:

[0190]

[0191] In the above formula, p j is the relative value of the operating power of the jth wind turbine; P j is the active power of the jth wind turbine from the grid-connected point of the collection line; P N is the rated power of the jth wind turbine; and N L is the number of wind turbines connected to the collection line.

[0192] In this embodiment, it is assumed that the rated capacity of all the wind turbines in the feeder is P N If the rated capacities are not the same, the power unit value can be calculated for each wind turbine separately.

[0193] The relative value of the operating power of the wind turbine connected by each of the radial collection lines is calculated according to the above method.

[0194] The second subset obtaining sub-module is configured to determine the fuzzy subset of the relative value of the operating power of the wind turbine based on the operating power value of the wind turbine and the operating power threshold value.

[0195] The second subset obtaining sub-module specifically processes as follows:

[0196] When the operating power value of the wind turbine is less than the first operating power threshold, a fuzzy subset of the small relative operating power value is constructed.

[0197] When the operating power value of the wind turbine is greater than the first operating power threshold and the operating power value of the wind turbine is less than the second operating power threshold, a fuzzy subset of the relative operating power value is constructed.

[0198] When the operating power value of the wind turbine is greater than the second distance threshold, a fuzzy subset of the large relative operating power value is constructed.

[0199] Wherein, the first operating power threshold is less than the second operating power threshold.

[0200] Combined with appendix Figure 3 The specific process of obtaining the submodule from the second subset is as follows:

[0201] The fuzzy subset distribution of the universe of discourse for designing the relative operating power p of the wind turbine is shown in the appendix. Figure 3 As shown. Five fuzzy subsets are defined: small (S), smaller (LS), medium (M), larger (LB), and large (B).

[0202] In this embodiment, only the domain of discourse for relative power values ​​between [-0.05pu, 0.05pu] is given. In actual application, the design can be based on the historical operation of the wind farm. For units with relative power values ​​outside the domain of discourse, they can be treated as small (S) or large (B).

[0203] In this embodiment, a distance fuzzy subset and an operating power fuzzy subset are set to prioritize the operation of wind turbines with high power and those with long electrical connection distances, thereby minimizing wind and solar curtailment and improving the safety and stability margin of the new energy grid connection system.

[0204] The coefficient determination module is specifically used for:

[0205] When the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is a small operating power relative value fuzzy subset or a medium operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the small power reduction command allocation coefficient fuzzy subset.

[0206] When the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset or a medium grid-connected electrical distance fuzzy subset, and the operating power relative value fuzzy subset is a large operating power relative value fuzzy subset, the power reduction command allocation coefficient is obtained from the large power reduction command allocation coefficient fuzzy subset;

[0207] When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the small down-regulation power instruction distribution coefficient fuzzy subset;

[0208] When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the medium operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0209] When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset or the medium operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset;

[0210] When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the large operating power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the large down-regulation power instruction distribution coefficient fuzzy subset.

[0211] In combination with Figure 4 , the coefficient determination module has the following specific process:

[0212] The wind farm includes N W wind turbine generators, and the relative value d of the electrical distance of each wind turbine generator connected to the grid point and the relative value p of the operating active power are taken as input quantities, and the down-regulation power instruction distribution coefficient μ is taken as an output quantity. The unit of the electrical distance d of the grid point connected is a per-unit value, the unit of the operating power relative value p is a per-unit value, and the unit of the down-regulation power instruction distribution coefficient μ is a per-unit value.

[0213] In combination with Figure 5 , the fuzzy subset distribution of the argument domain of the down-regulation power instruction distribution coefficient μ is designed, as shown in Figure 5 Five fuzzy subsets are set: small (SD), small (ND), medium (MD), large (HD), and large (BD). The argument domain of the down-regulation power instruction distribution coefficient μ is [0, 1].

[0214] The fuzzy control strategy rule of the new energy station oscillation suppression is designed as shown in Table 1. The down-regulation power distribution coefficient of the wind turbine generator close to the gathering bus and having small active power is small, and the down-regulation power distribution coefficient of the wind turbine generator far from the gathering bus and having large active power is large.

[0215] Table 1: Fuzzy control strategy rule table of wind farm oscillation suppression

[0216]

[0217]

[0218] In this embodiment, a subset of power reduction command allocation coefficients is set to assign different power reduction command allocation coefficients to wind turbines with different operating power and distance, thereby minimizing the amount of wind and solar curtailment.

[0219] The power down-regulation module specifically includes:

[0220] The change determination submodule is used to determine the change in the operating power of the wind turbine by combining the power reduction command allocation coefficient and the total power reduction command with the power change calculation formula. Figure 6 Establish such Figure 6 The broadband oscillation suppression strategy for new energy power plants, as shown, triggers power reduction control when oscillation components are detected at the grid connection point of the power plant. The total power reduction command for the power plant is output to the fuzzy logic controller, which then reduces the operating power of each unit and outputs the reduced power command. The power command for each unit is calculated using the following formula:

[0221]

[0222] In the above formula, ΔP j Let μ be the power that needs to be reduced for the j-th wind turbine, where j is the j-th wind turbine. j Let N be the active power allocation factor for the j-th wind turbine. W ΔP represents the number of wind turbines in a new energy power station. wf This is a general downward adjustment order for new energy power plants.

[0223] The operating power reduction submodule is used to reduce the operating power of the wind turbine by combining the change in the operating power of the wind turbine with a power reduction calculation formula. The reduced operating power of the wind turbine is calculated by combining the change in the operating power of the wind turbine with the following formula:

[0224] P d,j =P j -ΔP j

[0225] In the above formula, P d,j P is the active power setpoint for the j-th wind turbine. j Let ΔP be the operating power value of the j-th wind turbine unit; j Let j be the power that needs to be reduced for the j-th wind turbine.

[0226] In this embodiment, the power command allocation coefficient is determined and the operating power of the wind turbine is reduced, thereby minimizing the amount of wind and solar curtailment and improving the safety and stability margin of the new energy grid connection system.

[0227] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer readable program code.

[0228] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. 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 processing device 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, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0229] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0230] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0231] The foregoing is merely illustrative of the principles of this application and various modifications can be made by those skilled in the art without departing from the scope and spirit of the application. The above specification, examples and data provide a complete description of the manufacture and use of the composition of the application. The specification describes only one or preferred embodiments. However, working examples can be modified or varied and equivalents employed without departing from the scope and spirit of the application as disclosed in the claims.

Claims

1. A method for oscillation suppression in a new energy plant station, characterized in that, The method comprises the following steps: when the oscillation component is greater than the oscillation component threshold value, determining the wind turbine grid-connected electrical distance fuzzy subset and the wind turbine operating power relative value fuzzy subset based on the obtained parameter information; determining the power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset by using the fuzzy logic strategy; adjusting the wind turbine operating power based on the power instruction distribution coefficient; wherein the wind turbine operating power relative value is the difference between the actual power value of the wind turbine and the average power value of all wind turbines in the feeder.

2. The method of claim 1, wherein, The method for determining the wind turbine grid-connected electrical distance fuzzy subset based on the obtained parameter information comprises the following steps: combining the active power, cable parameters and length information in the parameter information to obtain the equivalent grid-connected impedance of different wind turbines by using the equivalent grid-connected impedance calculation formula; normalizing the equivalent grid-connected impedance of different wind turbines to obtain the distance value of different wind turbines; obtaining the grid-connected electrical distance fuzzy subset based on the distance value of different wind turbines and the distance threshold value; the equivalent grid-connected impedance of each wind turbine is shown in the following formula: In the above formula, Z eq,j is the equivalent grid-connected impedance of the jth unit from the grid-connected point of a certain collection line; P i is the active power of the ith wind turbine from the grid-connected point of a certain collection line; Z1 is the collection line impedance between the first wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point; Z2 is the collection line impedance between the second wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point; Z j is the collection line impedance between the jth wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point, which is calculated according to the cable parameters and length information; i is the ith wind turbine; and j is the jth wind turbine.

3. The method of claim 2, wherein, The method for obtaining the grid-connected electrical distance fuzzy subset based on the distance value of different wind turbines and the distance threshold value comprises the following steps: when the distance value of the wind turbine is less than the first distance threshold value, constructing the small grid-connected electrical distance fuzzy subset; when the distance value of the wind turbine is greater than the first distance threshold value and less than the second distance threshold value, constructing the medium grid-connected electrical distance fuzzy subset; when the distance value of the wind turbine is greater than the second distance threshold value, constructing the large grid-connected electrical distance fuzzy subset; wherein the first distance threshold value is less than the second distance threshold value.

4. The method of claim 3, wherein, The method for determining the wind turbine operating power relative value fuzzy subset based on the parameter information comprises the following steps: combining the actual power value of the wind turbine and the average power value of the wind turbine in the parameter information to obtain the wind turbine operating power relative value by using the operating power calculation formula; determining the wind turbine operating power relative value fuzzy subset based on the wind turbine operating power relative value and the operating power threshold value.

5. The method of claim 4, wherein, The method for determining the wind turbine operating power relative value fuzzy subset based on the wind turbine operating power relative value and the operating power threshold value comprises the following steps: when the wind turbine operating power relative value is less than the first operating power threshold value, constructing the small operating power relative value fuzzy subset; when the wind turbine operating power relative value is greater than the first operating power threshold value and less than the second operating power threshold value, constructing the medium operating power relative value fuzzy subset; when the wind turbine operating power relative value is greater than the second distance threshold value, constructing the large operating power relative value fuzzy subset; wherein the first operating power threshold value is less than the second operating power threshold value.

6. The method of claim 5, wherein, The method for determining the power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset by using the fuzzy logic strategy comprises the following steps: when the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a small operation power relative value fuzzy subset or a medium operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from a small down-regulation power instruction distribution coefficient fuzzy subset; when the grid-connected electrical distance fuzzy subset is a small grid-connected electrical distance fuzzy subset or a medium grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a large operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from a large down-regulation power instruction distribution coefficient fuzzy subset; when the grid-connected electrical distance fuzzy subset is a medium grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a small operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the small down-regulation power instruction distribution coefficient fuzzy subset; when the grid-connected electrical distance fuzzy subset is a medium grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a medium operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from a medium down-regulation power instruction distribution coefficient fuzzy subset; when the grid-connected electrical distance fuzzy subset is a large grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a small operation power relative value fuzzy subset or a medium operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the medium down-regulation power instruction distribution coefficient fuzzy subset; when the grid-connected electrical distance fuzzy subset is a large grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset is a large operation power relative value fuzzy subset, the down-regulation power instruction distribution coefficient is obtained from the large down-regulation power instruction distribution coefficient fuzzy subset.

7. The method of claim 6, wherein, The wind turbine operation power is down-regulated based on the down-regulation power instruction distribution coefficient, including: a wind turbine operation power change amount is determined by combining a down-regulation power instruction distribution coefficient of the wind turbine and a total power down-regulation instruction with a power change calculation formula; the wind turbine operation power is down-regulated by combining the wind turbine operation power change amount with a power down-regulation calculation formula.

8. The method of claim 7, wherein, The power change calculation formula is as shown in the following formula: In the above formula, ΔP j is the power required to be reduced for the jth wind turbine, j is the jth wind turbine, μ j is the active power distribution coefficient of the jth wind turbine, N W is the number of wind turbines of the new energy station, ΔP wf is the total reduction instruction of the new energy station.

9. The method of claim 8, wherein, The power down-regulation calculation formula is as shown in the following formula: P d,j = P j - ΔP j In the above formula, P d,j is the active power set value of the jth wind turbine, P j is the relative value of the operating power of the jth wind turbine.

10. A new energy plant oscillation suppression system for implementing the method according to claim 1, characterized in that, including: a subset determination module is configured to determine a wind turbine grid-connected electrical distance fuzzy subset and a wind turbine operation power relative value fuzzy subset based on acquired parameter information when it is monitored that an oscillation component is greater than an oscillation component threshold value; a coefficient determination module is configured to determine a down-regulation power instruction distribution coefficient based on the wind turbine grid-connected electrical distance fuzzy subset and the operation power relative value fuzzy subset by using a fuzzy logic strategy; a power down-regulation module is configured to down-regulate wind turbine operation power based on the down-regulation power instruction distribution coefficient.

11. The system of claim 10, wherein, The subset determination module includes: a first numerical value obtaining submodule is configured to obtain equivalent grid-connected impedances of different wind turbines by combining active power, cable parameters and length information in the parameter information with an equivalent grid-connected impedance calculation formula; a second numerical value obtaining submodule is configured to obtain distance values of the different wind turbines by normalizing the equivalent grid-connected impedances of the different wind turbines. The first subset obtaining module is configured to obtain a grid-connected electrical distance fuzzy subset based on the distance value of the different wind turbines and a distance threshold value; The third numerical value obtaining module is configured to obtain a wind turbine operating power relative value by combining the actual power value of the wind turbine in the parameter information and the average power value of the wind turbine with an operating power calculation formula; The second subset obtaining module is configured to determine a wind turbine operating power relative value fuzzy subset based on the wind turbine operating power relative value and an operating power threshold value; The equivalent grid-connected impedance of each wind turbine is shown in the following formula: In the above formula, Z eq,j is the equivalent grid-connected impedance of the jth unit from the grid-connected point of a certain collection line; P i is the active power of the ith wind turbine from the grid-connected point of a certain collection line; Z1 is the collection line impedance between the first wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point; Z2 is the collection line impedance between the second wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point; Z j is the collection line impedance between the jth wind turbine from the grid-connected point of a certain collection line and the adjacent unit close to the grid-connected point, which is calculated according to the cable parameters and length information; i is the ith wind turbine; and j is the jth wind turbine.

12. The system of claim 11, wherein, The first subset obtaining module is specifically configured to: When the distance value of the wind turbine is less than a first distance threshold value, a small grid-connected electrical distance fuzzy subset is constructed; When the distance value of the wind turbine is greater than the first distance threshold value and less than a second distance threshold value, a medium grid-connected electrical distance fuzzy subset is constructed; When the distance value of the wind turbine is greater than the second distance threshold value, a large grid-connected electrical distance fuzzy subset is constructed; The first distance threshold value is less than the second distance threshold value.

13. The system of claim 11, wherein, The second subset obtaining module is specifically configured to: When the wind turbine operating power relative value is less than a first operating power threshold value, a small operating power relative value fuzzy subset is constructed When the wind turbine operating power relative value is greater than the first operating power threshold value and less than a second operating power threshold value, a medium operating power relative value fuzzy subset is constructed; When the wind turbine operating power relative value is greater than the second distance threshold value, a large operating power relative value fuzzy subset is constructed; The first operating power threshold value is less than the second operating power threshold value.

14. The system of claim 13, wherein, The coefficient determining module is specifically configured to: When the grid-connected electrical distance fuzzy subset is the small grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset or the medium operating power relative value fuzzy subset, a small down-regulation power instruction distribution coefficient fuzzy subset is obtained to obtain the down-regulation power instruction distribution coefficient; When the grid-connected electrical distance fuzzy subset is the small grid-connected electrical distance fuzzy subset or the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the large operating power relative value fuzzy subset, a large down-regulation power instruction distribution coefficient fuzzy subset is obtained to obtain the down-regulation power instruction distribution coefficient; When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset, the small down-regulation power instruction distribution coefficient fuzzy subset is obtained to obtain the down-regulation power instruction distribution coefficient; When the grid-connected electrical distance fuzzy subset is the medium grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the medium operating power relative value fuzzy subset, a medium down-regulation power instruction distribution coefficient fuzzy subset is obtained to obtain the down-regulation power instruction distribution coefficient; When the grid-connected electrical distance fuzzy subset is the large grid-connected electrical distance fuzzy subset and the operating power relative value fuzzy subset is the small operating power relative value fuzzy subset or the medium operating power relative value fuzzy subset, the medium down-regulation power instruction distribution coefficient fuzzy subset is obtained to obtain the down-regulation power instruction distribution coefficient; When the large grid-connected electrical distance fuzzy subset is the grid-connected electrical distance fuzzy subset and the large operation power relative value fuzzy subset is the operation power relative value fuzzy subset, a down-regulation power instruction distribution coefficient is obtained from the large down-regulation power instruction distribution coefficient fuzzy subset.

15. The system of claim 14, wherein, The power down-regulation module comprises: A variation amount determination sub-module is configured to combine the down-regulation power instruction distribution coefficient of the wind turbine and the total down-regulation power instruction with a power variation calculation formula to determine a wind turbine operation power variation amount. An operation power down-regulation sub-module is configured to combine the wind turbine operation power variation amount with a power down-regulation calculation formula to down-regulate the wind turbine operation power.

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