A wind farm multi-scenario reactive voltage control method

Abstract

The application discloses a kind of wind farm multi-scenario reactive voltage control methods, it is related to wind farm reactive power optimization scheduling field;The method comprises: obtaining the node information data of target wind farm;Determine the reactive voltage prediction model of wind farm;Based on wind speed fluctuation evaluation index, the scene division is carried out to target wind farm, and scene information is obtained;Multi-objective optimization model is constructed;Optimization model includes: objective function and constraint condition;Objective function is determined based on scene information and control index;Control index includes: voltage control index and active loss control index;Using crayfish optimization algorithm, the objective function is solved according to constraint condition, and reactive voltage control optimization scheme is obtained, to adjust the fan reactive power output of target wind farm.The application can realize wind farm reactive power optimization scheduling and adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of reactive power optimization dispatch of wind farms, in particular to a wind farm multi-scenario reactive power voltage control method. BACKGROUND

[0002] With the proposal of the "carbon peak" and "carbon neutralization" targets, the development of wind power technology is becoming increasingly important. Due to the continuous increase of wind power generation capacity, more uncertainty factors are brought to the stable operation of the power grid, so it is necessary to develop appropriate reactive power voltage control strategies to improve the reliability and stability of the power system. In order to realize the reactive power voltage control of wind power integration, the wind farm needs to provide certain reactive power support capability. The devices in the wind farm that can provide reactive power support capability mainly include static var compensator (SVC), static var generator (SVG), static synchronous compensator (STATCOM), doubly fed induction generator (DFIG), etc., and the operation and maintenance cost of SVC, SVG, STATCOM and other devices is high, and the response speed is slow, so the DFIG with higher flexibility and adaptability becomes the key research object of reactive power voltage control.

[0003] Optimal Reactive Power Dispatch (ORPD) of wind farms is a multi-constrained and multi-objective optimization problem, while Model Predictive Control (MPC) as an advanced control method has the characteristics of prediction, ability to handle constraint conditions and online solution of optimization problems, and has feedback correction effect on errors caused by uncertain factors, so in the research of ORPD of wind farms based on DFIG, MPC is widely applied, and in the definition of the objective function, the voltage stability and economic benefits of the power system are considered comprehensively to reduce the grid voltage fluctuation and active power loss as the control target, and multi-objective optimization is realized, but there are still the following shortcomings:

[0004] 1) The weight coefficients of each target in the optimization process are relatively single, and lack of analysis of generated scenarios and more reasonable weight coefficient adjustment scheme when facing the variable wind speed conditions of wind farms.

[0005] 2) In the process of combining MPC with wind farm ORPD, the source of grid-connected voltage fluctuation is lack of analysis and processing, and the optimization dispatch scheme is not flexible enough when considering wind power uncertainty. SUMMARY

[0006] The purpose of the present application is to provide a wind farm multi-scenario reactive voltage control method, which can realize wind farm reactive optimization scheduling and adjustment.

[0007] To achieve the above purpose, the present application provides the following solutions:

[0008] The present application provides a wind farm multi-scenario reactive voltage control method, which is applied to a power system; the wind farm multi-scenario reactive voltage control method comprises:

[0009] Obtain node information data of a target wind farm; the node information data comprises a node voltage amplitude and a node voltage phase angle;

[0010] Determine a wind farm reactive voltage prediction model; the wind farm reactive voltage prediction model is obtained based on the node information data after determining a reactive-voltage sensitivity matrix; the wind farm reactive voltage prediction model is used to predict the node voltage based on a reactive power output change;

[0011] Based on a wind speed fluctuation evaluation index, perform scene division on the target wind farm to obtain scene information; the scene information is used to represent the wind speed change of the target wind farm;

[0012] Construct a multi-objective optimization model; the optimization model comprises an objective function and a constraint condition; the objective function is determined based on the scene information and a control index; the control index comprises a voltage control index and an active loss control index; the voltage control index comprises a voltage deviation prediction value; the voltage deviation prediction value is determined based on the predicted node voltage obtained by the wind farm reactive voltage prediction model;

[0013] Use a crayfish optimization algorithm to solve the objective function according to the constraint condition to obtain a reactive voltage control optimization scheme; the reactive voltage control optimization scheme is used to adjust the reactive power output of the wind turbine of the target wind farm.

[0014] Optionally, determining the wind farm reactive voltage prediction model specifically comprises:

[0015] Use Newton-Raphson method to determine a power flow equation according to the node information data;

[0016] Linearize the power flow equation to obtain a reactive-voltage sensitivity matrix;

[0017] Determine the reactive-voltage sensitivity matrix as the wind farm reactive voltage prediction model.

[0018] Optionally, the expression of the reactive-voltage sensitivity matrix is:

[0019]

[0020] wherein S(k) is the reactive power-voltage sensitivity matrix at k time; ΔU is the increment matrix of node voltage amplitude; ΔQ is the reactive power increment matrix injected by the node to the system; U(k) is the node voltage state matrix at k time; J is the Jacobian matrix; P is the active power data of the node in the power system; Q is the reactive power data of the node in the power system; U is the node voltage amplitude; θ is the node voltage phase angle; T is the transpose operator symbol of the matrix; L is the symbol of the matrix; M is the symbol of the matrix; N is the symbol of the matrix; H is the symbol of the matrix.

[0021] Optionally, the expression of the wind speed fluctuation evaluation index is:

[0022]

[0023] wherein is the wind speed fluctuation evaluation index; m is the number of wind turbines in the target wind farm; w i (k) is the wind speed information of the i th wind turbine at k time; w i (k+1) is the wind speed information of the i th wind turbine at k+1 time; w i (k+N1) is the wind speed information of the i th wind turbine at k+N1 time; N1 is the total number of time in the time sequence; i is the serial number.

[0024] Optionally, the expression of the target function is:

[0025] minF(k)=λ(k)f vol +[1-λ(k)]f Ploss ;

[0026]

[0027] wherein F(k) is the target function; λ(k) is the weight coefficient; f vol is the voltage control index; f Ploss is the active power loss control index; λ1 is the weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval 0-s1; λ2 is the weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval s1-s2; λ l is the weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval s l-1 -s l ; is the wind speed fluctuation evaluation index; s1 is the critical value of the division interval corresponding to the first scenario in the scenario information; s2 is the critical value of the division interval corresponding to the second scenario in the scenario information; sl-1 is a critical value of a division interval corresponding to the lth scene in the scene information; and l is a total number of scenes contained in the scene information. l-1 is a critical value of a division interval corresponding to the lth scene in the scene information; and l is a total number of scenes contained in the scene information. l is a critical value of a division interval corresponding to the lth scene in the scene information; and l is a total number of scenes contained in the scene information.

[0028] Optionally, the constraint conditions comprise a power flow equation constraint, a node voltage fluctuation range constraint, a wind turbine reactive power instruction range constraint and a power factor constraint.

[0029] Optionally, an expression of the power flow equation constraint is:

[0030]

[0031] An expression of the node voltage fluctuation range constraint is:

[0032]

[0033] An expression of the wind turbine reactive power instruction range constraint is:

[0034] Q min ≤Q ref ≤Q max ;

[0035] An expression of the power factor constraint is:

[0036] cosφ min ≤cosφ≤cosφ max ;

[0037] wherein, P i is active power injected at the ith node; Q i is reactive power injected at the ith node; B ij is mutual admittance between the ith node and the jth node; G ij is mutual conductance between the ith node and the jth node; θ ij is a difference between node voltage phase angles between the ith node and the jth node; V i is a node voltage amplitude of the ith node; V j is a node voltage amplitude of the jth node; i and j are serial numbers; is a lower limit of the node voltage amplitude; is an upper limit of the node voltage amplitude; N2 is a total number of nodes; Q ref is a wind turbine reactive power instruction; Q min is a lower limit of the wind turbine input reactive power instruction; Q max is an upper limit of the wind turbine input reactive power instruction; cosφ is a power factor of a doubly-fed induction motor in the wind farm, cosφ minThe lower limit of power factor cosφ max The upper limit of power factor cosφ

[0038] Optionally, the crayfish optimization algorithm is used to solve the target function according to the constraint condition, and an optimal reactive voltage control scheme is obtained, and the method specifically comprises the following steps:

[0039] An initial parameter and an initial position of a crayfish population are determined; the crayfish population is composed of multiple crayfish individuals; and the initial parameter comprises an environmental temperature, a set iteration number and node information data;

[0040] An adaptability value at a current iteration number is determined based on the node information data at the current iteration number and the position at the current iteration number and the target function;

[0041] Whether an iteration stop condition is met is determined; the iteration stop condition is that the current iteration number reaches the set iteration number;

[0042] If the iteration stop condition is met, an optimal position at the current iteration number is output;

[0043] If the iteration stop condition is not met, the following steps are performed:

[0044] The crayfish population is controlled to enter an exploration state according to the environmental temperature; the exploration state comprises a foraging stage, a summering stage or a competition stage;

[0045] When the environmental temperature exceeds 30 DEG C and rand is less than or equal to 0.5, the summering stage is entered; rand is a set random number, and the value range is [0, 1];

[0046] In the summering stage, the crayfish population moves to a set cave, a position of the crayfish population entering the cave to summer is obtained, and the position is updated as a real-time position of the current crayfish population;

[0047] When the environmental temperature exceeds 30 DEG C and rand is greater than 0.5, the competition stage is entered;

[0048] In the competition stage, the crayfish population appears a phenomenon of competing for the cave, competition position information of the crayfish population is obtained, and the competition position information is updated as the real-time position of the current crayfish population;

[0049] When the environmental temperature does not exceed 30 DEG C, the foraging stage is entered;

[0050] In the foraging stage, whether a food amount is less than a set value is determined; the food amount is determined according to the adaptability value at the current iteration number; and the set value is determined according to a food factor;

[0051] If yes, the crayfish population moves to the food and eats, and the position of the eating place is updated as the real-time position of the current crayfish population;

[0052] If no, the crayfish population tears the food and alternately eats, and the position corresponding to the alternately eating is updated as the real-time position of the current crayfish population;

[0053] The real-time position of the current crayfish population that does not satisfy the iteration stop condition is taken as the position in the next iteration number, and the process returns to "determining the fitness value in the current iteration number based on the target function according to the node information data in the current iteration number and the position in the current iteration number";

[0054] Based on the comparison of all the fitness values and the historical optimal fitness value, the optimal position corresponding to the fitness value within the set range is determined as the reactive voltage control optimization scheme.

[0055] According to the specific embodiments provided in the present application, the present application discloses the following technical effects:

[0056] The present application provides a wind farm multi-scenario reactive voltage control method, which combines multi-scenario division with MPC, establishes a wind farm reactive voltage prediction model and a multi-objective optimization model to realize feedback correction; according to the wind speed change of the wind farm, multi-scenario division is performed, and the classification basis of each scenario is given; in each control period, according to the current scenario to which the wind farm belongs, the weights of each item in the objective function of the MPC controller are adaptively adjusted; the multi-objective optimization model is solved by using the crayfish optimization algorithm (Crayfish Optimization Algorithm, COA). The present application adaptively adjusts the weight coefficients under different generated scenarios, which can improve the flexibility and dynamic optimization capability of the wind farm ORPD. Therefore, the present application can realize the reactive power optimization scheduling and adjustment of the wind farm. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0058] Figure 1 The flowchart of the wind farm multi-scenario reactive voltage control method in the embodiments of the present application;

[0059] Figure 2 The COA optimization flowchart provided by the embodiments of the present application contains MPC;

[0060] Figure 3 The overall control strategy structure diagram provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0061] 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. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0062] The above purposes, features and advantages of the present application can be more obvious and easy to understand. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0063] The embodiment of the present application provides a wind farm multi-scenario reactive voltage control method, which is applied to a power system. As shown in the figure, Figure 1 The wind farm multi-scenario reactive voltage control method comprises the following steps.

[0064] Step 100: Obtain node information data of a target wind farm. The node information data comprises a node voltage amplitude and a node voltage phase angle.

[0065] Step 200: Determine a wind farm reactive voltage prediction model. The wind farm reactive voltage prediction model is obtained after determining a reactive-voltage sensitivity matrix based on the node information data; the wind farm reactive voltage prediction model is used for predicting the node voltage based on a reactive power output change.

[0066] Step 300: Based on a wind speed fluctuation evaluation index, the target wind farm is divided into scenes to obtain scene information. The scene information is used to represent the wind speed change of the target wind farm.

[0067] Specifically, the expression of the wind speed fluctuation evaluation index is as follows:

[0068]

[0069] wherein, is the wind speed fluctuation evaluation index; m is the number of wind turbines in the target wind farm; w i (k) is the wind speed information of the i th wind turbine at the k th moment; w i (k+1) is the wind speed information of the i th wind turbine at the k+1 th moment; w i (k+N1) is the wind speed information of the i th wind turbine at the k+N1 th moment; N1 is the total number of moments in the time sequence; i is a serial number.

[0070] Step 400: Constructing a multi-objective optimization model. The optimization model includes: an objective function and constraint conditions; the objective function is determined based on the scenario information and control indicators; the control indicators include: a voltage control indicator and an active loss control indicator; the voltage control indicator includes: a voltage deviation prediction value; the voltage deviation prediction value is determined based on the predicted node voltage obtained from the wind farm reactive voltage prediction model.

[0071] The expression of the objective function is:

[0072] minF(k)=λ(k)f vol +[1-λ(k)]f Ploss ;

[0073]

[0074] Wherein, F(k) is the objective function; λ(k) is the weight coefficient; f vol is the voltage control indicator; f Ploss is the active loss control indicator; λ1 is the weight coefficient corresponding to the wind speed fluctuation evaluation indicator in the interval 0-s1; λ2 is the weight coefficient corresponding to the wind speed fluctuation evaluation indicator in the interval s1-s2; λ l is the weight coefficient corresponding to the wind speed fluctuation evaluation indicator in the interval s l-1 -s l ; is the wind speed fluctuation evaluation indicator; s1 is the critical value of the division interval corresponding to the first scenario in the scenario information; s2 is the critical value of the division interval corresponding to the second scenario in the scenario information; s l-1 is the critical value of the division interval corresponding to the l-1th scenario in the scenario information; s l is the critical value of the division interval corresponding to the lth scenario in the scenario information; l is the total number of scenarios included in the scenario information.

[0075] The constraint conditions include: power flow equation constraints, node voltage fluctuation range constraints, wind turbine reactive power instruction range constraints, and power factor constraints.

[0076] The expression of the power flow equation constraint is:

[0077]

[0078] The node voltage fluctuation range constraint is given to prevent node voltage out-of-limit caused by unexpected conditions.

[0079] The expression of the node voltage fluctuation range constraint is:

[0080]

[0081] The expression of the wind turbine reactive power instruction range constraint is:

[0082] Q min ≤Q ref ≤Q max ;

[0083] The expression of the power factor constraint is:

[0084] cosφ min ≤cosφ≤cosφ max ;

[0085] Where, P i is the active power injected by the i-th node; Q i is the reactive power injected by the i-th node; B ij is the mutual admittance between the i-th node and the j-th node; G ij is the mutual conductance between the i-th node and the j-th node; θ ij is the difference between the phase angles of the node voltages between the i-th node and the j-th node; V i is the node voltage amplitude of the i-th node; V j is the node voltage amplitude of the j-th node; i and j are both serial numbers; is the lower limit of the node voltage amplitude; is the upper limit of the node voltage amplitude; N2 is the total number of nodes; Q ref is the wind turbine reactive power instruction; Q min is the lower limit of the wind turbine input reactive power instruction; Q max is the upper limit of the wind turbine input reactive power instruction; cosφ is the power factor of the doubly-fed induction motor in the wind farm, cosφ min is the lower limit of the power factor of the doubly-fed induction motor in the wind farm, cosφ max is the upper limit of the power factor of the doubly-fed induction motor in the wind farm.

[0086] Step 500: using the crayfish optimization algorithm, the objective function is solved according to the constraint condition, and the reactive voltage control optimization scheme is obtained; the reactive voltage control optimization scheme is used to adjust the reactive power output of the wind turbine of the target wind farm.

[0087] Wherein, the wind farm reactive voltage prediction model is determined, specifically including:

[0088] The Newton-Raphson method is used to determine the power flow equation according to the node information data; the power flow equation is linearized to obtain a reactive-voltage sensitivity matrix; the reactive-voltage sensitivity matrix is determined as the wind farm reactive voltage prediction model.

[0089] Specifically, the expression of the reactive-voltage sensitivity matrix is:

[0090]

[0091] Wherein, S(k) is the reactive power-voltage sensitivity matrix at k moment; ΔU is the increment matrix of node voltage amplitude; ΔQ is the reactive power increment matrix injected into the system by the node; U(k) is the node voltage state matrix at k moment; J is the Jacobian matrix; P is the active power data of the node in the power system; Q is the reactive power data of the node in the power system; U is the node voltage amplitude; θ is the node voltage phase angle; T is the transpose operator symbol of the matrix; L is the identity matrix; M is the diagonal matrix; N is the diagonal matrix; H is the diagonal matrix.

[0092] The crayfish optimization algorithm is used to solve the objective function according to the constraint condition, and the reactive power voltage control optimization scheme is obtained, which specifically includes:

[0093] The initial parameters and the initial position of the crayfish population are determined; the crayfish population is composed of multiple crayfish individuals; the initial parameters include: environmental temperature, set iteration number and node information data.

[0094] According to the node information data under the current iteration number and the position under the current iteration number, the fitness value under the current iteration number is determined based on the objective function.

[0095] It is judged whether the iteration stop condition is met or not; the iteration stop condition is that the current iteration number reaches the set iteration number.

[0096] If the iteration stop condition is met, the optimal position under the current iteration number is output.

[0097] If the iteration stop condition is not met, then:

[0098] According to the environmental temperature, the crayfish population is controlled to enter the exploration state; the exploration state includes: foraging stage, summering stage or competition stage.

[0099] When the environmental temperature exceeds 30 DEG C and rand is less than or equal to 0.5, the summering stage is entered; rand is a set random number, and the value range is [0, 1].

[0100] In the summering stage, the crayfish population moves to the set cave, the position of the crayfish population entering the cave for summering is obtained, and the position is updated as the real-time position of the current crayfish population.

[0101] When the environmental temperature exceeds 30 DEG C and rand is greater than 0.5, the competition stage is entered.

[0102] In the competition stage, the crayfish population appears the phenomenon of competing for the cave, and after the competition position information of the crayfish population is obtained, the competition position information is updated as the real-time position of the current crayfish population. ​​​​

[0103] When the ambient temperature is not more than 30 DEG C, the foraging stage is entered.

[0104] In the foraging stage, it is determined whether the food amount is less than a set value; the food amount is determined according to the fitness value under the current iteration number; and the set value is determined according to the food factor.

[0105] If yes, the crayfish population moves to the food and feeds, and the position of the feeding place is updated as the real-time position of the current crayfish population.

[0106] If no, the crayfish population tears the food and feeds alternately, and the position corresponding to the alternate feeding is updated as the real-time position of the current crayfish population.

[0107] The real-time position of the current crayfish population corresponding to the iteration stop condition not being met is taken as the position under the next iteration number, and the process returns to "determining the fitness value under the current iteration number based on the target function according to the node information data under the current iteration number and the position under the current iteration number".

[0108] Based on the comparison between all the fitness values and the historical optimal fitness value, the optimal position corresponding to the fitness value within the set range is determined as the reactive power voltage control optimization scheme.

[0109] The application can process the day-ahead wind speed prediction information of the wind farm, so as to determine the wind speed fluctuation in the current and future period of time, propose corresponding evaluation indexes, and give the value interval of the indexes as the basis for scene division. In each control period of MPC, scene division is performed, and then based on the scene division result, the weight coefficient of the MPC target function is adjusted adaptively, so as to improve the flexibility of reactive power dispatching and the dynamic optimization capability of the wind farm in the face of external uncertain factors.

[0110] On the basis of the traditional MPC, the application evaluates the wind speed fluctuation in the prediction time domain of the wind farm by using the day-ahead wind speed prediction information, and performs multi-scene division according to the evaluation result. In the control process, the weight coefficient of each sub-item of the target function is adjusted adaptively based on the current belonging scene, so as to realize multi-scene adaptive MPC, improve the flexibility and rationality of the OPRD of the wind farm, and optimize the effect of reactive power voltage control.

[0111] In actual application, the specific implementation steps of the application are as follows:

[0112] (1) A reactive power voltage prediction model of the wind farm is established based on a sensitivity matrix.

[0113] The nonlinear power flow equation is linearized at the stable operating point, and the sensitivity matrix between the node power and voltage is obtained as the prediction model for reactive power voltage control. Generally, the Newton-Raphson (N-R) method is used for power flow calculation, and the polar coordinate form is expressed as:

[0114]

[0115]

[0116] where ΔP(U, θ) is replaced by ΔP, which represents the active power increment matrix injected by the node into the system; ΔQ(U, θ) is replaced by ΔQ, which represents the reactive power increment matrix injected by the node into the system; U is the node voltage amplitude; θ is the node voltage phase angle; P is the active power data of the node in the power system; Q is the reactive power data of the node in the power system; T is the transpose operator symbol of the matrix; ΔU is the active power increment matrix of the node voltage amplitude; Δθ is the reactive power increment matrix of the node voltage phase angle; U(k) is the node voltage state matrix at time k; J is the Jacobian matrix. L is the replacement symbol; M is the replacement symbol; N is the replacement symbol; H is the replacement symbol.

[0117] The Jacobian matrix is inverted, and then multiplied by the left side of the power flow calculation formula to obtain:

[0118]

[0119] According to the second row elements of the left matrix, the relationship between the node voltage amplitude and the active and reactive power is obtained:

[0120] ΔU = [(L - MH -1 N) -1 MH -1 · ΔP(U, θ)

[0121] -(L - MH -1 N) -1 · ΔQ(U, θ)]· U(k)

[0122] Since wind power is mostly connected in a radial manner, the collection line is long, the voltage level is high, and the transmission line X / R value is large. In order to reduce the calculation amount and speed up the response speed of the prediction model, the influence of active power change on voltage is ignored, and the sensitivity matrix between reactive power and voltage is obtained:

[0123]

[0124] The reactive power-voltage sensitivity matrix is used as the prediction model, and the future node voltage can be predicted based on the reactive power output change amount:

[0125]

[0126] In the formula: U(k+i+1|k) is the node voltage prediction value at k+i+1 time at k time, U(k+i|k) is the node voltage prediction value at k+i time at k time, ΔU(k+i|k) is the node voltage increment prediction value from k+i time to its next time, S(k+i) is the reactive power-voltage sensitivity matrix at k+i time, ΔQ(k+i|k) is the future k+i time wind power reactive power output adjustment value set at k time, which is taken as the control variable to be optimized. i is the serial number.

[0127] (2) The wind speed fluctuation evaluation index of the wind farm is given according to the day-ahead wind speed prediction information, and the scene is divided.

[0128] Considering the uncertainty and variability of environmental conditions, the current wind speed prediction information of the wind farm and the future wind speed prediction information in the prediction time domain are compared, the specific maximum wind speed fluctuation value of each wind turbine in the field is obtained, and then it is averaged as the evaluation index reflecting the wind speed fluctuation.

[0129]

[0130] If the wind farm has m wind turbines, w i (k) to w i (k+N) is the wind speed information of each wind turbine in the wind farm in the prediction time domain corresponding to k time, The wind speed fluctuation evaluation index of the whole wind farm, that is, the wind speed fluctuation evaluation index. Since the index value is continuously changing, several value intervals are given as the basis for scene division:

[0131] (0, s1], (s1, s2], …, (s l-1 , s l ].

[0132] In the formula: s1 to s l are the critical values of each interval of , each value interval corresponds to a scene, reflecting the current wind speed change of the wind farm. Before the start of the rolling optimization of each control period, the evaluation index value at this time is calculated, and then the current belonging scene is obtained according to the given value interval.

[0133] (3) The optimization model is established with the minimum voltage deviation and active power loss at multiple times in the prediction period as the control target, the weight ratio of the objective function under different scenes is adjusted adaptively, the crawfish optimization algorithm (COA) is used to solve the optimization model, the OPRD scheme of the wind farm is obtained, and the rolling optimization and feedback correction are carried out according to the idea of MPC.

[0134] Considering the voltage stability and power loss economy of power system operation, the objective function of each rolling optimization is composed of voltage control index and active loss control index. The voltage control index includes the voltage deviation prediction value of future N1 time through the prediction model:

[0135]

[0136] In the formula, V(k+i|k) is the voltage prediction value vector in the prediction time domain, V ref (k+i|k) is the voltage reference value vector in the prediction time domain.

[0137] The active loss control index of the objective function is expressed as:

[0138]

[0139] In the formula, M is the total number of nodes of the wind farm, G ij is the mutual conductance between the i-th node and the j-th node, V i is the node voltage amplitude of the i-th node, V j is the node voltage amplitude of the j-th node, θ ij is the difference between the node voltage phase angles of the i-th node and the j-th node.

[0140] Based on the scene division result of step (2), the weight coefficient of the MPC objective function is adaptively adjusted, that is, different weight allocation schemes are made for each scene interval, and then each control index is combined into the current objective function according to the determined scheme, as follows:

[0141]

[0142] minF(k)=λ(k)f vol +[1-λ(k)]f Ploss

[0143] In each control period, the weight coefficients of the two control indexes are adjusted in real time through the current belonging scene, so as to realize dynamic optimization of reactive power dispatching. When the wind speed fluctuates greatly, the large change of active power output will bring an unnegligible influence on grid-connected voltage, at this time, the weight of voltage control index in the objective function is increased; when the wind speed is relatively stable, in order to prevent overfitting of voltage and balance the demand of reducing active loss, the adjustment of voltage is appropriately weakened.

[0144] In addition, the multi-objective optimization model also needs to give related constraint conditions. The constraint conditions in the rolling optimization process mainly include power flow equation constraint, node voltage fluctuation range constraint, wind turbine reactive power instruction range constraint and power factor constraint.

[0145] The reactive power regulation capability of the DFIG is mainly restricted by factors such as active power output, unit capacity and converter current limit, but when the output reactive power of the DFIG is long-term at its physical limit, not only is it not conducive to the service life of the equipment, but also its economy is extremely poor, therefore, the power factor is used to additionally restrict the reactive power output of the fan.

[0146] The COA is used to solve the multi-objective optimization model to obtain the optimal reactive power scheduling instruction in the prediction interval, that is, the reactive power voltage control optimization scheme. In the initialization link of the COA, the environmental temperature is randomly generated:

[0147] temp = 15 * rand + 20

[0148] temp represents the environmental temperature, and rand is a random number with a value range of [0, 1]. When the temperature exceeds 30 DEG C, the crayfish will seek a cave to avoid the heat. If rand<=0.5, it means that there is no other crayfish to compete for the cave at this time, so the crayfish will directly enter the cave, that is, enter the heat avoidance stage; if rand>0.5, the crayfish will compete with other crayfish for the cave, that is, enter the competition stage. When the temperature is suitable, the crayfish will enter the foraging stage. The food value, that is, the expression of the food size, is:

[0149]

[0150] C3 is the food factor, representing the maximum size of the food. If the above conditions are met, it means that the food volume is not large, at this time the crayfish will directly go to the food position and eat; if the above conditions are not met, it means that the food volume is too large, at this time the crayfish will first tear the food with the claws and then alternate feeding.

[0151] The COA updates the position information of the crayfish through the above process, and then substitutes it into the prediction model and the optimization model to calculate the fitness value, and constantly updates the optimal position until the upper limit of the iteration number is reached. The COA optimization flowchart containing MPC can be seen from Figure 2 The overall control strategy structure is shown in Figure 3 .

[0152] In order to improve the flexibility and voltage stability of the wind farm under uncertain factors, the wind speed fluctuation of the wind farm is analyzed based on the wind speed prediction information, the corresponding evaluation index is given, and the scene is divided according to the value range of the index. When facing the variable wind speed conditions of the wind farm, the analysis scheme of the generated scene is supplemented, and the analysis and processing of the source of voltage fluctuation of the wind farm are realized.

[0153] According to the scene division result, the weight coefficients of the target function are adaptively adjusted, while effectively ensuring the economy and stability of the wind farm operation, the flexibility and dynamic optimization capability of the reactive power and voltage control of the wind farm are enhanced, and the grid connection friendliness of high proportion of new energy access is improved.

[0154] The technical features of the above embodiments can be combined in any manner, and to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0155] The principles and implementation modes of the present application are described by applying specific examples herein, and the above embodiment descriptions are only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation modes and application ranges will have changes. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A wind farm multi-scenario reactive voltage control method, characterized in that, The wind farm multi-scenario reactive voltage control method is applied to a power system; The wind farm multi-scenario reactive voltage control method comprises: obtaining node information data of a target wind farm; the node information data comprises a node voltage amplitude and a node voltage phase angle; determining a wind farm reactive voltage prediction model; the wind farm reactive voltage prediction model is obtained based on the node information data and a reactive-voltage sensitivity matrix; the wind farm reactive voltage prediction model is used to predict a node voltage based on a reactive power output change; based on a wind speed fluctuation evaluation index, performing scene division on the target wind farm to obtain scene information; the scene information is used to represent a wind speed change of the target wind farm; constructing a multi-objective optimization model; the optimization model comprises an objective function and a constraint condition; the objective function is determined based on the scene information and a control index; the control index comprises a voltage control index and an active loss control index; the voltage control index comprises a voltage deviation prediction value; the voltage deviation prediction value is determined based on a predicted node voltage obtained by the wind farm reactive voltage prediction model; using a crayfish optimization algorithm to solve the objective function according to the constraint condition to obtain a reactive voltage control optimization scheme; the reactive voltage control optimization scheme is used to adjust a wind turbine reactive power output of the target wind farm; an expression of the wind speed fluctuation evaluation index is: wherein, is the wind speed fluctuation evaluation index; m is the number of wind turbines in the target wind farm; w i (k) is the wind speed information of the i th wind turbine at the k th time; w i (k+1) is the wind speed information of the i th wind turbine at the k+1 th time; w i (k+N1) is the wind speed information of the i th wind turbine at the k+N1 th time; N1 is the total number of time series; i is the serial number; an expression of the objective function is: minF(k) = λ(k)f vol + [1 - λ(k)]f Ploss ; wherein F(k) is an objective function; λ(k) is a weight coefficient; f vol is a voltage control index; f Ploss is an active loss control index; λ1 is a weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval 0-s1; λ2 is a weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval s1-s2; λ l is a weight coefficient corresponding to the wind speed fluctuation evaluation index in the interval s l-1 -s l ; is a wind speed fluctuation evaluation index; s1 is a critical value of a division interval corresponding to a first scene in scene information; s2 is a critical value of a division interval corresponding to a second scene in scene information; s l-1 is a critical value of a division interval corresponding to an (l-1)th scene in scene information; s l is a critical value of a division interval corresponding to an lth scene in scene information; and l is a total number of scenes contained in the scene information.

2. The wind farm multi-scenario reactive voltage control method of claim 1, wherein, determining a wind farm reactive voltage prediction model, specifically comprising: using a Newton-Raphson method to determine a power flow equation according to the node information data; performing linearization processing on the power flow equation to obtain a reactive-voltage sensitivity matrix; determining the reactive-voltage sensitivity matrix as the wind farm reactive voltage prediction model.

3. The wind farm multi-scenario reactive voltage control method of claim 2, wherein, an expression of the reactive-voltage sensitivity matrix is: Where S(k) is the reactive-voltage sensitivity matrix at time k; ΔU is the increment matrix of node voltage amplitude; ΔQ is the reactive power increment matrix injected into the system by the node; U(k) is the node voltage state matrix at time k; J is the Jacobian matrix; P is the active power data of the nodes in the power system; Q is the reactive power data of the nodes in the power system; U is the node voltage amplitude; θ is the node voltage phase angle; Τ is the matrix transpose operator; L is... The symbol of reference; M is The symbol for reference; N is The symbol for reference; H is The symbolic representation of .

4. The wind farm multi-scenario reactive voltage control method of claim 1, wherein, the constraint condition comprises a power flow equation constraint, a node voltage fluctuation range constraint, a wind turbine reactive instruction range constraint, and a power factor constraint.

5. The wind farm multi-scenario reactive voltage control method of claim 4, wherein, an expression of the power flow equation constraint is: an expression of the node voltage fluctuation range constraint is: an expression of the wind turbine reactive instruction range constraint is: Q min ≤Q ref ≤Q max ; an expression of the power factor constraint is: cosφ min ≤ cosφ ≤ cosφ max ; P i is the active power injected into the i-th node; Q i is the reactive power injected into the i-th node; B ij is the mutual admittance between the i-th node and the j-th node; G ij is the mutual conductance between the i-th node and the j-th node; θ ij is the phase angle difference between the i-th node and the j-th node; V i is the node voltage amplitude of the i-th node; V j is the node voltage amplitude of the j-th node; i and j are both serial numbers; V i min is the lower limit of the node voltage amplitude; V i max is the upper limit of the node voltage amplitude; N2 is the total number of nodes; Q ref is the wind turbine reactive power instruction; Q min is the lower limit of the wind turbine input reactive power instruction; Q max is the upper limit of the wind turbine input reactive power instruction; cosφ is the power factor of the doubly-fed induction motor in the wind farm, cosφ min is the lower limit of the power factor of the doubly-fed induction motor in the wind farm, cosφ max is the upper limit of the power factor of the doubly-fed induction motor in the wind farm.

6. The wind farm multi-scenario reactive voltage control method of claim 1, wherein, using a crayfish optimization algorithm to solve the objective function according to the constraint condition to obtain a reactive voltage control optimization scheme, specifically comprising: determining initial parameters and initial positions of a crayfish population; the crayfish population is composed of multiple crayfish individuals; the initial parameters comprise an environmental temperature, a set iteration number, and node information data; determining an adaptability value at a current iteration number based on the objective function according to node information data at the current iteration number and a position at the current iteration number; determining whether an iteration stop condition is met; the iteration stop condition is that the current iteration number reaches the set iteration number; if the iteration stop condition is met, outputting an optimal position at the current iteration number; if the iteration stop condition is not met, then: controlling the crayfish population to enter an exploration state according to the environmental temperature; the exploration state comprises a foraging stage, a summering stage, or a competition stage; When the environment temperature exceeds 30 DEG C and rand <= 0.5, entering the summering stage; rand is a set random number, and the value range is [0, 1]; In the summering stage, the crayfish population moves to a set cave, the position of the crayfish population entering the cave for summering is obtained, and the position is updated as the real-time position of the current crayfish population; When the environment temperature exceeds 30 DEG C and rand > 0.5, entering the competition stage; In the competition stage, the crayfish population appears the phenomenon of fighting for the cave, after the fighting position information of the crayfish population is obtained, the fighting position information is updated as the real-time position of the current crayfish population; When the environment temperature does not exceed 30 DEG C, entering the foraging stage; In the foraging stage, it is judged whether the food amount is less than a set value; the food amount is determined according to the fitness value under the current iteration number; the set value is determined according to a food factor; If yes, the crayfish population moves to the food and eats, and the position of eating is updated as the real-time position of the current crayfish population; If no, the crayfish population tears the food and alternately eats, and the position corresponding to the alternately eating is updated as the real-time position of the current crayfish population; The real-time position of the current crayfish population corresponding to not satisfying the iteration stop condition is taken as the position under the next iteration number, and the step of "determining the fitness value under the current iteration number based on the target function according to the node information data under the current iteration number and the position under the current iteration number" is returned; Based on comparison of all the fitness values and the historical optimal fitness value, the optimal position corresponding to the fitness value in a set range is determined as the reactive voltage control optimization scheme.

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