Wind power system reactive power optimization method considering alternating solution of dfig output range

Abstract

The application discloses a wind power system reactive power optimization method considering DFIG output range alternation solving, comprising the following steps: 1, establishing a doubly-fed induction generator (DFIG) internal power flow model, calculating active power output upper limit P DFIG,max according to DFIG reactive power set value Q DFIG,set ; obtaining DFIG reactive power output Q DFIG range based on DFIG internal constraints; 2, establishing a DFIG active power output lower limit model and calculating, obtaining P DFIG range; 3, integrating the DFIG model into a power grid, obtaining a wind power system DFIG model, calculating post-grid-connection P DFIG range; 4, calculating post-grid-connection Q DFIG range, and iteratively calculating post-grid-connection DFIG output range; 5, establishing a wind power system reactive power optimization model of DFIG output range alternation solving; and 6, solving the reactive power optimization model by using a PSO-GWO algorithm. The application can effectively consider the influence of various factors on the DFIG output range, and perform reactive power optimization on the power grid, on the basis of quantifying the DFIG operation range, reduce power grid voltage fluctuation and network loss, and improve the stability of power grid operation.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a reactive power optimization method that takes into account the alternating solution of the active / reactive power output range of DFIG. Background Technology

[0002] With the scarcity of fossil fuels and the pressures of environmental pollution and the greenhouse effect, my country's wind power capacity has increased rapidly. In 2020, the national installed wind power capacity reached 281.53 million kW, accounting for 12.79% of the total installed capacity, and wind power generation accounted for 30.09% of the total renewable energy generation, making it an indispensable form of power generation in the power grid. Doubly-fed induction generators (DFIGs) are widely used due to their low cost.

[0003] The volatility and randomness introduced by wind power generation can affect grid stability and reduce power quality, with reactive power and voltage issues from wind farms being among the most prominent problems. Furthermore, with the continuous increase in wind power capacity, to ensure grid load balance and maintain the low pollution levels of wind power, the only option is to force some thermal power units to operate at reduced capacity or even shut down. From the perspective of grid safety, wind turbines need to participate in active power dispatch and frequency regulation. Wind turbines can not only maintain the reactive power balance of the grid, but wind farms can also control voltage. When grid disturbances occur, wind farms need to inject reactive power into the grid as quickly as possible to reduce the impact of faults on the grid and improve system stability. Therefore, it is necessary to determine the adjustable range of active and reactive power of the DFIG (Dual Active Power Injection Group) to ensure the safe operation of the grid. When the system has sufficient reactive power, reactive power optimization of the grid including wind power systems is necessary to reduce voltage fluctuations caused by wind power grid connection.

[0004] In existing research, when addressing reactive power optimization problems, the active power output of a DFIG is typically set to range from 0 to its maximum output in the model constraints, while the reactive power output is generally set to the range obtained under Maximum Power Point Tracking (MPPT) mode. However, similar to synchronous motors, DFIGs also have a lower limit on active power output. Their output range should be from this lower limit to the maximum output at the current wind speed; therefore, their active power output cannot be considered a parameter that changes continuously from 0. The magnitude of DFIG reactive power is related to active power; therefore, changes in DFIG active power output will affect the range of their reactive power. Furthermore, the calculation of DFIG active power requires a reactive power setpoint to solve for the active power; therefore, the reactive power output of the DFIG will also affect its active power. Summary of the Invention

[0005] The present invention aims to address the shortcomings of the existing technology by proposing a reactive power optimization method for wind power systems that takes into account the alternating solution of the DFIG output range. This method aims to consider the mutual influence between DFIG output powers, accurately calculate the active / reactive power output range of the DFIG, thereby achieving better reactive power optimization results and improving the stability of power grid operation.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] The present invention provides a reactive power optimization method for wind power systems that considers alternating solutions based on the DFIG output range, characterized by the following steps:

[0008] S1. Establish the internal power flow model of the doubly-fed induction motor (DFIG) under maximum power point tracking (MPPT) mode, and based on the reactive power setpoint Q of the DFIG... DFIG,set Calculate the upper limit of active power output;

[0009] Based on the internal constraints of the doubly-fed induction generator (DFIG), the upper limit of reactive power output Q of the DFIG under MPPT mode is calculated using the upper limit of active power output. DFIG,max And the lower limit of no effort output Q DFIG,min Thus, the reactive power output Q of DFIG is obtained. DFIG scope;

[0010] S2. Establish a model for the lower limit of the active power output of the doubly fed induction motor (DFIG) and calculate the lower limit of the active power output. Based on the upper and lower limits of the active power output, the range of the active power output of the DFIG can be obtained.

[0011] S3. After integrating the internal power flow model and the lower limit active power output model of the doubly-fed induction generator (DFIG) into the power grid, the grid-connected power flow model and the lower limit active power output model of the wind power system are obtained, and the upper limit of active power output P after grid connection is calculated. DFIG,max And the lower limit of active power output P after grid connection DFIG,min The active power output range of the DFIG after grid connection is obtained;

[0012] S4. Calculate the reactive power output range of the DFIG after grid connection using the active power output range of the DFIG after grid connection, and then iteratively calculate the output range of the DFIG after grid connection.

[0013] S4.1 Set the iteration precision to ε, define the current iteration number as k, and initialize k=1;

[0014] Define the upper limit of DFIG reactive power output in the (k-1)th iteration as . and initialize =Q DFIG,set ;

[0015] Define the upper limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,max ;

[0016] S4.2, Based on the upper limit of DFIG active power output in the (k-1)th iteration. Calculate the upper limit of DFIG reactive power output in the k-th iteration of the wind power system. ;

[0017] The upper limit of DFIG reactive power output in the kth iteration. The reactive power setpoint Q replaces the DFIG grid-connected power flow model and the active power output lower limit model. DFIG,set Then, calculate the upper limit of the DFIG active power output of the wind power system in the (k+1)th iteration. and the lower limit of meritorious service Thus, the active power output range of the wind power system in the (k+1)th iteration is obtained;

[0018] S4.3 Utilizing the upper limit of DFIG active power output in the (k+1)th iteration Calculate the upper limit of DFIG reactive power output of the wind power system in the (k+1)th iteration. and the lower limit of no effort Thus, the reactive power output range of the DFIG of the wind power system after the (k+1)th iteration is obtained;

[0019] S4.4 Calculate the upper limit of the DFIG active power output of the wind power system in the (k+2)th iteration according to the process in step S4.2. ;

[0020] S4.5 Calculate the upper limit difference of DFIG active power output of the wind power system using equation (1). ;

[0021] (1)

[0022] S4.6, if If the value is greater than ε, then k+1 is assigned to k, and the process returns to step S4.2 for sequential execution. Otherwise, it means that the active power output range and reactive power output range of the DFIG in the wind power system in the (k+1)th iteration are obtained, and the reactive power output range of the DFIG is recorded as the first reactive power output range.

[0023] S4.7, Define the lower limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,min ;

[0024] The reactive power output range of the DFIG within the wind power system in the (k+1)th iteration is obtained according to the process of S4.2-S4.6, and is denoted as the second reactive power output range.

[0025] S4.8. Take the union of the first reactive power output range and the second reactive power output range as the reactive power output range of the DFIG in the wind power system.

[0026] S5. Establish a reactive power optimization model for a wind power system with alternating solutions for the DFIG output range, including: the operating constraints of the wind power system, and the objective function considering the active power loss of the wind power system.

[0027] The objective function is the function that minimizes the active power loss of the power grid. The operating constraints include: constraint equations for node power balance, constraint equations for the active and reactive power output range of DFIG, constraint equations for node voltage amplitude and phase angle, constraint equations for transformer turns ratio, and constraint equations for parallel capacitor capacity.

[0028] S6. Solve the reactive power optimization model of the wind power system using the Particle Swarm Optimization-Grey Wolf (PSO-GWO) algorithm, and output the optimal node voltage and active power loss.

[0029] The characteristic of the reactive power optimization method for wind power systems that takes into account the alternating solution of DFIG output range described in this invention is that step S1 includes:

[0030] S1.1 Constructing the internal power flow model of DFIG under MPPT mode using equation (2):

[0031] (2)

[0032] In equation (2): ΔQ s The stator reactive power balance equation is given by ΔP. m and ΔQ m The active and reactive power balance equations for the excitation circuit are given by ΔP. g and ΔQ g The active and reactive power balance equations for the converter are given, ΔT is the balance torque equation, and Q is the value of Q. DFIG,set Q is the reactive power setpoint for DFIG. sm Q is the reactive power flowing from the stator to the excitation. sg P represents the reactive power flowing from the stator to the converter. ms and Q ms P represents the active and reactive power flowing to the stator during excitation. mr and Q mr Q represents the active and reactive power flowing from the magnetizer to the rotor. mm P is the reactive power in the excitation circuit. rm P is the active power flowing from the rotor to the excitation. gs and Qgs Q represents the active and reactive power flowing from the converter to the stator. g,set P is the reactive power setpoint of the grid-side converter. wt For wind turbine power capture, P em Let be the electromagnetic power of the DFIG, and s be the slip.

[0033] S1.2, Based on the reactive power setpoint Q of the doubly fed induction motor DFIG DFIG,set Calculate the upper limit of active power output P of DFIG. DFIG,max ;

[0034] S1.3, Constructing stator current constraints using equation (3):

[0035] (3)

[0036] In equation (3): U s For stator voltage, I s For stator current, I s,max For the maximum stator current, r s The radius of the reactive power output range on the stator side;

[0037] S1.4. Using equation (4) to construct rotor current constraints:

[0038] (4)

[0039] In equation (4): X s X m These are the stator-side reactance and the magnetizing reactance, respectively. r For rotor current, I r,max For the maximum rotor current, r r The radius of the reactive power output range on the rotor side;

[0040] S1.5. Using equation (6), construct the operating range considering slip power:

[0041] (5)

[0042] In equation (5): P DFIG For the meritorious contribution of DFIG, and P sm =P DFIG / (1-s);

[0043] S1.6. Construct the capacity constraint of the grid-side converter using equation (7):

[0044] (6)

[0045] In equation (6): Q DFIG For DFIG's unproductive efforts, S GSC,NThis refers to the rated capacity of the grid-side converter.

[0046] S1.7. Using equation (7), the reactive power operating range of the doubly fed induction motor (DFIG) in MPPT tracking mode is obtained:

[0047] (7)

[0048] In equation (7): Q DFIG,max Q represents the upper limit of reactive power output of DFIG. DFIG,min This represents the lower limit of reactive power output of DFIG.

[0049] Step S2 includes:

[0050] S2.1. Using equation (8), establish the lower limit model of the active power output of the doubly fed induction motor (DFIG):

[0051] (8)

[0052] In equation (8): S RSC,N U is the rated capacity of the rotor-side converter RSC. r U is the rotor voltage. m θ is the excitation voltage. rm R is the phase difference between the rotor and the excitation. r and X r Let the rotor resistance and rotor reactance be denoted by , and we have:

[0053] (9)

[0054] Equation (9) is the capacity constraint of the rotor-side converter RSC;

[0055] S2.2. Based on the active power output lower limit model of the doubly fed induction motor (DFIG), calculate the active power output lower limit of the DFIG, and thus the active power output range of the DFIG is formed by the active power output upper limit and the active power output lower limit.

[0056] S3 includes:

[0057] S3.1. Using equations (10) and (11), establish the upper limit model of DFIG active power output and the lower limit model of DFIG active power output of the wind power system, respectively.

[0058] (10)

[0059] (11)

[0060] In equations (10) and (11): ΔP sys ΔQ sys These represent the active and reactive power imbalances at the grid nodes, Δθ. sys, ΔU sys J represents the phase angle and voltage correction for the power grid nodes. sys J is a matrix representing the derivative of the wind power system nodes with respect to the wind power system node parameters. sys,DFIG J is the matrix representing the derivative of the wind power system nodes with respect to the DFIG node parameters. DFIG,sys J is the matrix representing the derivative of the DFIG nodes with respect to the nodal parameters of the wind power system. DFIG The matrix representing the derivative of the DFIG nodes with respect to their parameters;

[0061] S3.2 Calculate the upper limit of active power output P of the DFIG of the wind power system. DFIG,max And DFIG's contribution lower limit P DFIG,min .

[0062] The node voltage after the influence of wind power system flow is calculated using equation (12), and the stator node voltage U of the DFIG of the wind power system is obtained using equation (13). s :

[0063] (12)

[0064] (13).

[0065] S6 includes:

[0066] S6.1 Initialize gray wolf population parameters: Set the maximum number of iterations to N, the current number of iterations to m, initialize m=1, the position of each gray wolf is W, and the position of each gray wolf is the parameter in the objective function of step S5;

[0067] S6.2 Calculate the fitness value of each gray wolf individual in the m-th generation gray wolf population, and set the first three optimal fitness values ​​as α of the optimal gray wolf individual in the m-th generation gray wolf population. m β m , λ m The corresponding position is recorded as , , ;

[0068] S6.3. Using equation (14), determine the i-th gray wolf individual and the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m distance , , Then, using equation (15), update the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m Position after guidance , , ;

[0069] (14)

[0070] (15)

[0071] In equation (14), C1, C2, and C3 are oscillation factors and are random numbers between [0,1].

[0072] In equation (15), A1, A2, and A3 are convergence factors;

[0073] S6.4. Using equation (16), the position of the updated i-th gray wolf individual in the m-th generation gray wolf population is obtained as follows: ;

[0074] (16)

[0075] S6.4. Use equation (17) to update the movement speed of the i-th gray wolf in the m-th generation gray wolf population. and location This allows us to obtain the movement speed of individual gray wolves in the (m+1)th generation gray wolf population. and location ;

[0076] (17)

[0077] In equation (17), c1, c2, and c3 are learning factors, and rand3 is a random number between [0,1].

[0078] S6.5. Calculate the position of each individual gray wolf in the (m+1)th generation gray wolf population according to steps S6.2-S6.4 and construct the (m+1)th generation gray wolf position vector W. m+1

[0079] S6.5 After assigning m+1 to m, determine whether m>N holds true. If true, output the direction vector W of the optimal gray wolf individual in the m-th generation. m If the result is the optimal solution for the objective function, then proceed to step S6.2 sequentially.

[0080] S6.6. Based on the optimal solution of the objective function, calculate the optimal node voltage and active power loss.

[0081] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the reactive power optimization method of the wind power system, and the processor is configured to execute the program stored in the memory.

[0082] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the method for optimizing reactive power in a wind power system.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] This invention first establishes an analytical expression for the initial value of the DFIG under rotor overspeed active power reserve mode. Based on this, an RSC capacity constraint is added to establish an algorithm for solving the lower limit of DFIG active power. Considering both stator and rotor current constraints and grid-side converter capacity constraints, the influence of DFIG active power output on reactive power output is also considered. The desired active power output is substituted into the DFIG reactive power operating range calculation formula, thereby obtaining a more accurate Q. DFIG Scope: This paper proposes the DFIG output iterative algorithm and establishes a reactive power optimization model based on it. This algorithm can effectively reduce the active power loss on the grid side and has advantages in ensuring the stability and security of power system operation. Attached Figure Description

[0085] Figure 1 Here is a structural diagram of a DFIG in the prior art;

[0086] Figure 2 For the present invention P DFIG With Q DFIG Flowchart of the steps of simultaneous iteration. Detailed Implementation

[0087] In this embodiment, a reactive power optimization method for a wind power system that takes into account the alternating solution of the DFIG output range includes the following steps:

[0088] S1, using Figure 1 An internal power flow model of a doubly-fed induction generator (DFIG) under maximum power point tracking (MPPT) mode is established, and the reactive power setpoint Q of the DFIG is used as the basis for this model. DFIG,set Calculate the upper limit of active power output P DFIG,max ;

[0089] Based on the internal constraints of the doubly-fed induction motor (DFIG), the upper limit of active power output P is utilized. DFIG,max Calculate the upper limit of reactive power output Q of DFIG under MPPT mode. DFIG,max And the lower limit of no effort output Q DFIG,min Thus, the reactive power output range of DFIG is obtained;

[0090] S1.1 Constructing the internal power flow model of DFIG under MPPT mode using equation (1):

[0091] (1)

[0092] In equation (1): ΔQ s The stator reactive power balance equation is given by ΔP. m and ΔQ m The active and reactive power balance equations for the excitation circuit are given by ΔP. g and ΔQ g The active and reactive power balance equations for the converter are given, ΔT is the balance torque equation, and Q is the value of Q. DFIG,set Q is the reactive power setpoint for DFIG. sm Q is the reactive power flowing from the stator to the excitation. sg P represents the reactive power flowing from the stator to the converter. ms and Q ms P represents the active and reactive power flowing to the stator during excitation. mr and Q mr Q represents the active and reactive power flowing from the magnetizer to the rotor. mm P is the reactive power in the excitation circuit. rm P is the active power flowing from the rotor to the excitation. gs and Q gs Q represents the active and reactive power flowing from the converter to the stator. g,set P is the reactive power setpoint of the grid-side converter. wt For wind turbine power capture, P em Let be the electromagnetic power of the DFIG, and s be the slip.

[0093] S1.2, Based on the reactive power setpoint Q of the doubly fed induction motor DFIG DFIG,set Calculate the upper limit of active power output P of DFIG. DFIG,max ;

[0094] S1.3, Constructing stator current constraints using equation (2):

[0095] (2)

[0096] In equation (2): U s For stator voltage, I s For stator current, I s,max For the maximum stator current, r s The radius of the reactive power output range on the stator side;

[0097] S1.4. Construct rotor current constraints using equation (3):

[0098] (3)

[0099] In equation (3): X s X m These are the stator-side reactance and the magnetizing reactance, respectively. r For rotor current, I r,max For the maximum rotor current, rr The radius of the reactive power output range on the rotor side;

[0100] S1.5, Using equation (4), construct the operating range considering slip power:

[0101] (4)

[0102] In equation (4): P DFIG For the meritorious contribution of DFIG, and P sm =P DFIG / (1-s);

[0103] S1.6. Construct the capacity constraint of the grid-side converter using equation (5):

[0104] (5)

[0105] In equation (5): Q DFIG For DFIG's unproductive efforts, S GSC,N This refers to the rated capacity of the grid-side converter.

[0106] S1.7. Using equation (6), the reactive power operating range of the doubly fed induction motor (DFIG) in MPPT tracking mode is obtained:

[0107] (6)

[0108] In equation (6): Q DFIG,max Q represents the upper limit of reactive power output of DFIG. DFIG,min This represents the lower limit of reactive power output of DFIG.

[0109] S2. Establish the lower limit model of the active power output of the doubly-fed induction generator (DFIG) and calculate the lower limit P of the active power output. DFIG,min Thus, based on the upper and lower limits of active power output, the active power output range of DFIG can be obtained;

[0110] S2.1. Using equation (7), establish the lower limit model of the active power output of the doubly fed induction motor (DFIG):

[0111] (7)

[0112] In equation (7): S RSC,N U is the rated capacity of the rotor-side converter RSC. r U is the rotor voltage. m θ is the excitation voltage. rm R is the phase difference between the rotor and the excitation. r and X r Let the rotor resistance and rotor reactance be denoted by , and we have:

[0113] (8)

[0114] Equation (8) is the capacity constraint of the rotor-side converter RSC;

[0115] S2.2. Based on the active power output lower limit model of the doubly fed induction motor (DFIG), calculate the active power output lower limit of the DFIG, and thus the active power output range of the DFIG is formed by the active power output upper limit and the active power output lower limit.

[0116] S3. After incorporating the internal power flow model and the lower limit model of active power output of the doubly fed induction generator (DFIG) into the power grid, the grid-connected power flow model and the lower limit model of active power output of the wind power system are obtained, and the active power output range of the DFIG after grid connection is calculated.

[0117] S3.1. Using equations (9) and (10), establish the upper limit model of DFIG active power output and the lower limit model of DFIG active power output of the wind power system, respectively.

[0118] (9)

[0119] (10)

[0120] In equations (9) and (10): ΔP sys ΔQ sys These represent the active and reactive power imbalances at the grid nodes, Δθ. sys , ΔU sys J represents the phase angle and voltage correction for the power grid nodes. sys J is a matrix representing the derivative of the wind power system nodes with respect to the wind power system node parameters. sys,DFIG J is the matrix representing the derivative of the wind power system nodes with respect to the DFIG node parameters. DFIG,sys J is the matrix representing the derivative of the DFIG nodes with respect to the nodal parameters of the wind power system. DFIG The matrix representing the derivative of the DFIG nodes with respect to their parameters;

[0121] S3.2 Calculate the upper limit of active power output P of the DFIG of the wind power system. DFIG,max And DFIG's contribution lower limit P DFIG,min ;

[0122] S3.3 Calculate the node voltage after the influence of wind power system flow using equation (10), and obtain the stator node voltage U of the DFIG of the wind power system using equation (11). s :

[0123] (11)

[0124] (12)

[0125] S4. Using the active power output range of the DFIG after grid connection, calculate the reactive power output range of the DFIG after grid connection. The output range of the DFIG after grid connection is obtained through iterative calculation. The iterative process is as follows: Figure 2 ;

[0126] S4.1 Set the iteration precision to ε, define the current iteration number as k, and initialize k=1;

[0127] Define the upper limit of DFIG reactive power output in the (k-1)th iteration as . and initialize =Q DFIG,set ;

[0128] Define the upper limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,max ;

[0129] S4.2, Based on the upper limit of DFIG active power output in the (k-1)th iteration. Calculate the upper limit of DFIG reactive power output in the k-th iteration of the wind power system. ;

[0130] Using equation (13), the upper limit of the reactive power output of the DFIG in the kth iteration is... The reactive power setpoint Q replaces the DFIG grid-connected power flow model and the active power output lower limit model. DFIG,set Then, calculate the upper limit of the DFIG active power output of the wind power system in the (k+1)th iteration. and the lower limit of meritorious service Thus, the active power output range of the wind power system in the (k+1)th iteration is obtained;

[0131] (13)

[0132] In equation (13), The stator reactive power balance equations after the k-th iteration are: This represents the reactive power flowing from the stator to the excitation after the k-th iteration. This represents the reactive power flowing from the stator to the converter after the kth iteration.

[0133] S4.3 Utilizing the upper limit of DFIG active power output in the (k+1)th iteration Calculate the upper limit of DFIG reactive power output of the wind power system in the (k+1)th iteration. and the lower limit of no effort Thus, the reactive power output range of the DFIG of the wind power system after the (k+1)th iteration is obtained;

[0134] S4.4 Calculate the upper limit of the DFIG active power output of the wind power system in the (k+2)th iteration according to the process in step S4.2. ;

[0135] S4.5 Calculate the upper limit difference of DFIG active power output of the wind power system using equation (1). ;

[0136] (14)

[0137] S4.6, If ΔP DFIG If the value is greater than ε, then k+1 is assigned to k, and the process returns to step S4.2 for sequential execution. Otherwise, it means that the active power output range and reactive power output range of the DFIG in the wind power system in the (k+1)th iteration are obtained, and the reactive power output range of the DFIG is recorded as the first reactive power output range.

[0138] S4.7, Define the lower limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,min ;

[0139] The lower limit of active power output after grid connection is determined according to the process in S4.2-S4.6. The reactive power output range of the DFIG within the wind power system is obtained by processing, and is denoted as the second reactive power output range for the (k+1)th iteration.

[0140] S4.8. Take the union of the first reactive power output range and the second reactive power output range as the reactive power output range of the DFIG in the wind power system.

[0141] S5. Establish a reactive power optimization model for a wind power system with alternating solutions for the DFIG output range, including: the operating constraints of the wind power system, and the objective function considering the active power loss of the wind power system.

[0142] (15)

[0143] In equation (15): P loss The active power loss of the line is N, where N is the number of nodes and G is G. ij U is the electrical conductance between nodes i and j. i and θ i U represents the voltage magnitude and phase angle at node i, respectively. j and θ j These represent the voltage magnitude and phase angle at node j, respectively.

[0144] The objective function is to minimize the active power loss of the power grid. The operating constraints include: constraint equations for node power balance, constraint equations for the active and reactive power output range of DFIG, constraint equations for node voltage amplitude and phase angle, constraint equations for transformer turns ratio, and constraint equations for parallel capacitor capacity.

[0145] Power grid node balance constraints

[0146] (16)

[0147] In equation (16): ΔP i ΔQ i These represent the power imbalance at node i; P Gi Q Gi These represent the active and reactive power outputs of the thermal power unit at node i, respectively; P Li Q Li P represents the load power. i Q i Inject power into the node.

[0148] DFIG operating range constraints:

[0149] (17)

[0150] Node voltage magnitude and phase angle constraints:

[0151] (18)

[0152] Transformer turns ratio constraint:

[0153] (19)

[0154] In equation (19): T min T max These are the lower and upper limits of the adjustable transformer, respectively, and T is the tap of the adjustable transformer.

[0155] Parallel capacitor capacity constraints:

[0156] (20)

[0157] In equation (20): Q c,min Q c,max Q represents the lower and upper limits of the capacitance of the parallel capacitor, respectively. c This refers to the capacitance of the parallel capacitor.

[0158] S6. Solve the reactive power optimization model of the wind power system using the Particle Swarm Optimization-Grey Wolf (PSO-GWO) algorithm, and output the optimal node voltage and active power loss.

[0159] S6.1 Initialize gray wolf population parameters: Set the maximum number of iterations to N, the current number of iterations to m, initialize m=1, the position of each gray wolf is W, and the position of each gray wolf is the parameter in the objective function of step S5;

[0160] S6.2 Calculate the fitness value of each gray wolf individual in the m-th generation gray wolf population, and set the first three optimal fitness values ​​as α of the optimal gray wolf individual in the m-th generation gray wolf population. m β m , λ m The corresponding position is recorded as , , ;

[0161] S6.3. Using equation (14), determine the i-th gray wolf individual and the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m distance , , Then, using equation (15), update the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m Position after guidance , , ;

[0162] (14)

[0163] (15)

[0164] In equation (14), C1, C2, and C3 are oscillation factors and are random numbers between [0,1].

[0165] In equation (15), A1, A2, and A3 are convergence factors;

[0166] S6.4. Using equation (16), the position of the updated i-th gray wolf individual in the m-th generation gray wolf population is obtained as follows: ;

[0167] (16)

[0168] S6.4. Use equation (17) to update the movement speed of the i-th gray wolf in the m-th generation gray wolf population. and location This allows us to obtain the movement speed of individual gray wolves in the (m+1)th generation gray wolf population. and location ;

[0169] (17)

[0170] In equation (17), c1, c2, and c3 are learning factors, and rand3 is a random number between [0,1].

[0171] S6.5. Calculate the position of each individual gray wolf in the (m+1)th generation gray wolf population according to steps S6.2-S6.4 and construct the (m+1)th generation gray wolf position vector W. m+1 ;

[0172] S6.5 After assigning m+1 to m, determine whether m>N holds true. If true, output the direction vector W of the optimal gray wolf individual in the m-th generation. m If the result is the optimal solution for the objective function, then proceed to step S6.2 sequentially.

[0173] S6.6. Based on the optimal solution of the objective function, calculate the optimal node voltage and active power loss.

[0174] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor in executing the above-described reactive power optimization method for wind power systems. The processor is configured to execute the program stored in the memory.

[0175] In this embodiment, a computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the reactive power optimization method for the wind power system.

Claims

1. A reactive power optimization method for wind power systems that considers alternating solutions based on the output range of DFIG, characterized in that, Includes the following steps: S1. Establish the internal power flow model of the doubly-fed induction motor (DFIG) under maximum power point tracking (MPPT) mode, and based on the reactive power setpoint Q of the DFIG... DFIG,set Calculate the upper limit of active power output; Based on the internal constraints of the doubly-fed induction generator (DFIG), the upper limit of reactive power output Q of the DFIG under MPPT mode is calculated using the upper limit of active power output. DFIG,max And the lower limit of no effort output Q DFIG,min Thus, the reactive power output Q of DFIG is obtained. DFIG scope; S2. Establish a model for the lower limit of the active power output of the doubly fed induction motor (DFIG) and calculate the lower limit of the active power output. Based on the upper and lower limits of the active power output, the range of the active power output of the DFIG can be obtained. S3. After integrating the internal power flow model and the lower limit active power output model of the doubly-fed induction generator (DFIG) into the power grid, the grid-connected power flow model and the lower limit active power output model of the wind power system are obtained, and the upper limit of active power output P after grid connection is calculated. DFIG,max And the lower limit of active power output P after grid connection DFIG,min The active power output range of the DFIG after grid connection is obtained; S4. Calculate the reactive power output range of the DFIG after grid connection using the active power output range of the DFIG after grid connection, and then iteratively calculate the output range of the DFIG after grid connection. S4.1 Set the iteration precision to ε, define the current iteration number as k, and initialize k=1; Define the upper limit of DFIG reactive power output in the (k-1)th iteration as . and initialize =Q DFIG,set ; Define the upper limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,max ; S4.2, Based on the upper limit of DFIG active power output in the (k-1)th iteration. Calculate the upper limit of DFIG reactive power output in the k-th iteration of the wind power system. ; The upper limit of DFIG reactive power output in the kth iteration. The reactive power setpoint Q replaces the DFIG grid-connected power flow model and the active power output lower limit model. DFIG,set Then, calculate the upper limit of the DFIG active power output of the wind power system in the (k+1)th iteration. and the lower limit of meritorious service Thus, the active power output range of the wind power system in the (k+1)th iteration is obtained; S4.3 Utilizing the upper limit of DFIG active power output in the (k+1)th iteration Calculate the upper limit of DFIG reactive power output of the wind power system in the (k+1)th iteration. and the lower limit of no effort Thus, the reactive power output range of the DFIG of the wind power system after the (k+1)th iteration is obtained; S4.4 Calculate the upper limit of the DFIG active power output of the wind power system in the (k+2)th iteration according to the process in step S4.

2. ; S4.5 Calculate the upper limit difference of DFIG active power output of the wind power system using equation (1). ; (1) S4.6, if If the value is greater than ε, then k+1 is assigned to k, and the process returns to step S4.2 for sequential execution. Otherwise, it means that the active power output range and reactive power output range of the DFIG in the wind power system in the (k+1)th iteration are obtained, and the reactive power output range of the DFIG is recorded as the first reactive power output range. S4.7, Define the lower limit of the active power output of the DFIG in the (k-1)th iteration as . and initialize =P DFIG,min ; The reactive power output range of the DFIG within the wind power system in the (k+1)th iteration is obtained according to the process of S4.2-S4.6, and is denoted as the second reactive power output range. S4.

8. Take the union of the first reactive power output range and the second reactive power output range as the reactive power output range of the DFIG in the wind power system. S5. Establish a reactive power optimization model for a wind power system with alternating solutions for the DFIG output range, including: the operating constraints of the wind power system, and the objective function considering the active power loss of the wind power system. The objective function is the function that minimizes the active power loss of the power grid. The operating constraints include: constraint equations for node power balance, constraint equations for the active and reactive power output range of DFIG, constraint equations for node voltage amplitude and phase angle, constraint equations for transformer turns ratio, and constraint equations for parallel capacitor capacity. S6. Solve the reactive power optimization model of the wind power system using the Particle Swarm Optimization-Grey Wolf (PSO-GWO) algorithm, and output the optimal node voltage and active power loss.

2. The reactive power optimization method for wind power systems considering alternating solutions of DFIG output range as described in claim 1, characterized in that, Step S1 includes: S1.1 Constructing the internal power flow model of DFIG under MPPT mode using equation (2): (2) In equation (2): ΔQ s The stator reactive power balance equation is given by ΔP. m and ΔQ m The active and reactive power balance equations for the excitation circuit are given by ΔP. g and ΔQ g The active and reactive power balance equations for the converter are given, ΔT is the balance torque equation, and Q is the value of Q. DFIG,set Q is the reactive power setpoint for DFIG. sm Q is the reactive power flowing from the stator to the excitation. sg P represents the reactive power flowing from the stator to the converter. ms and Q ms P represents the active and reactive power flowing to the stator during excitation. mr and Q mr Q represents the active and reactive power flowing from the magnetizer to the rotor. mm P is the reactive power in the excitation circuit. rm P is the active power flowing from the rotor to the excitation. gs and Q gs Q represents the active and reactive power flowing from the converter to the stator. g,set P is the reactive power setpoint of the grid-side converter. wt For wind turbine power capture, P em Let be the electromagnetic power of the DFIG, and s be the slip. S1.2, Based on the reactive power setpoint Q of the doubly fed induction motor DFIG DFIG,set Calculate the upper limit of active power output P of DFIG. DFIG,max ; S1.3, Constructing stator current constraints using equation (3): (3) In equation (3): U s For stator voltage, I s For stator current, I s,max For the maximum stator current, r s The radius of the reactive power output range on the stator side; S1.

4. Using equation (4) to construct rotor current constraints: (4) In equation (4): X s X m These are the stator-side reactance and the magnetizing reactance, respectively. r For rotor current, I r,max For the maximum rotor current, r r The radius of the reactive power output range on the rotor side; S1.

5. Using equation (6), construct the operating range considering slip power: (5) In equation (5): P DFIG For the meritorious contribution of DFIG, and P sm =P DFIG / (1-s); S1.

6. Construct the capacity constraint of the grid-side converter using equation (7): (6) In equation (6): Q DFIG For DFIG's unproductive efforts, S GSC,N This refers to the rated capacity of the grid-side converter. S1.

7. Using equation (7), the reactive power operating range of the doubly fed induction motor (DFIG) in MPPT tracking mode is obtained: (7) In equation (7): Q DFIG,max Q represents the upper limit of reactive power output of DFIG. DFIG,min This represents the lower limit of reactive power output of DFIG.

3. The reactive power optimization method for wind power systems considering alternating solutions of DFIG output range according to claim 2, characterized in that, Step S2 includes: S2.

1. Using equation (8), establish the lower limit model of the active power output of the doubly fed induction motor (DFIG): (8) In equation (8): S RSC,N U is the rated capacity of the rotor-side converter RSC. r U is the rotor voltage. m θ is the excitation voltage. rm R is the phase difference between the rotor and the excitation. r and X r Let the rotor resistance and rotor reactance be denoted by , and we have: (9) Equation (9) is the capacity constraint of the rotor-side converter RSC; S2.

2. Based on the active power output lower limit model of the doubly fed induction motor (DFIG), calculate the active power output lower limit of the DFIG, and thus the active power output range of the DFIG is formed by the active power output upper limit and the active power output lower limit.

4. The reactive power optimization method for wind power systems considering alternating solutions of DFIG output range according to claim 3, characterized in that, S3 includes: S3.

1. Using equations (10) and (11), establish the upper limit model of DFIG active power output and the lower limit model of DFIG active power output of the wind power system, respectively. (10) (11) In equations (10) and (11): ΔP sys ΔQ sys These represent the active and reactive power imbalances at the grid nodes, Δθ. sys , ΔU sys J represents the phase angle and voltage correction for the power grid nodes. sys J is a matrix representing the derivative of the wind power system nodes with respect to the wind power system node parameters. sys,DFIG J is the matrix representing the derivative of the wind power system nodes with respect to the DFIG node parameters. DFIG,sys J is the matrix representing the derivative of the DFIG nodes with respect to the nodal parameters of the wind power system. DFIG The matrix representing the derivative of the DFIG nodes with respect to their parameters; S3.2 Calculate the upper limit of active power output P of the DFIG of the wind power system. DFIG,max And DFIG's contribution lower limit P DFIG,min .

5. The reactive power optimization method for wind power systems considering alternating solutions of DFIG output range according to claim 4, characterized in that, The node voltage after the influence of wind power system flow is calculated using equation (12), and the stator node voltage U of the DFIG of the wind power system is obtained using equation (13). s : (12) (13)。 6. The reactive power optimization method for wind power systems considering alternating solutions of DFIG output range according to claim 1, characterized in that, S6 includes: S6.1 Initialize gray wolf population parameters: Set the maximum number of iterations to N, the current number of iterations to m, initialize m=1, the position of each gray wolf is W, and the position of each gray wolf is the parameter in the objective function of step S5; S6.2 Calculate the fitness value of each gray wolf individual in the m-th generation gray wolf population, and set the first three optimal fitness values ​​as α of the optimal gray wolf individual in the m-th generation gray wolf population. m β m , λ m The corresponding position is recorded as , , ; S6.

3. Using equation (14), determine the i-th gray wolf individual and the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m distance , , Then, using equation (15), update the optimal gray wolf individual α in the m-th generation gray wolf population. m β m , λ m Position after guidance , , ; (14) (15) In equation (14), C1, C2, and C3 are oscillation factors and are random numbers between [0,1]. In equation (15), A1, A2, and A3 are convergence factors; S6.

4. Using equation (16), the position of the updated i-th gray wolf individual in the m-th generation gray wolf population is obtained as follows: ; (16) S6.

4. Use equation (17) to update the movement speed of the i-th gray wolf in the m-th generation gray wolf population. and location This allows us to obtain the movement speed of individual gray wolves in the (m+1)th generation gray wolf population. and location ; (17) In equation (17), c1, c2, and c3 are learning factors, and rand3 is a random number between [0,1]. S6.

5. Calculate the position of each individual gray wolf in the (m+1)th generation gray wolf population according to steps S6.2-S6.4 and construct the position vector W of the (m+1)th generation gray wolf population. m+1 S6.5 After assigning m+1 to m, determine whether m>N holds true. If true, output the direction vector W of the optimal gray wolf individual in the m-th generation. m If the result is the optimal solution for the objective function, then proceed to step S6.2 sequentially. S6.

6. Based on the optimal solution of the objective function, calculate the optimal node voltage and active power loss.

7. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the reactive power optimization method for the wind power system according to any one of claims 1-6, and the processor is configured to execute the program stored in the memory.

8. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the reactive power optimization method for the wind power system according to any one of claims 1-6.

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