Short-circuit current optimization control method based on new energy station control parameter adjustment

Abstract

The invention discloses a short-circuit current optimization control method based on new energy field station control parameter adjustment, which is applied to the field of relay protection of a power system with high-proportion new energy access, takes a limiting current as an optimization target, and changes a current adjustment factor for multiple times to obtain an optimal target value of a short-circuit current optimization target. The optimal target value is that the current of each power transmission branch is within the numerical range of the limiting current; judging whether the optimal target value meets a preset constraint condition or not, and limiting the optimal target value in a feasible region; the constraint condition is that the maximum short-circuit current when each power transmission branch is short-circuited is lower than that when the power transmission branch is short-circuited last time. The short-circuit current optimization of each power transmission branch is realized, and the problem that the short-circuit current of the new energy power system exceeds the standard can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system relay protection with a high proportion of renewable energy access, and specifically to a grid short-circuit current control method based on renewable energy access. Background Technology

[0002] Short-circuit current optimization control is a conventional dispatching and operation method for addressing excessive short-circuit currents in power systems. While reducing short-circuit current levels, it weakens the grid structure, leading to problems such as decreased local grid power supply reliability, uneven power flow distribution, and difficulties in scheduling main transformer outages, making it difficult to meet the requirements of short-circuit current control. For short-circuit currents approaching or exceeding switch breaking capacity, measures such as adjusting grid operation planning, upgrading switchgear and other equipment, and adjusting dispatching and operation modes can be taken to reduce short-circuit currents. However, operation planning adjustments and equipment upgrades involve a series of problems, including long construction periods and high project investments.

[0003] Large-scale grid-connected renewable energy and other power electronic equipment possess flexible controllability. Adjusting the parameters of grid-connected renewable energy inverters can alter the short-circuit current level of the power system, providing a new technical means for adjustable and controllable short-circuit current. Existing short-circuit current optimization control technologies have not considered the flexible control capabilities of renewable energy, thus failing to fully utilize these capabilities. Therefore, designing a short-circuit current control optimization strategy based on the control parameters of renewable energy power plants, fully considering the flexible adjustment capabilities of renewable energy and other power electronic equipment, is the technical problem addressed by this invention. Summary of the Invention

[0004] To address the problem of excessive short-circuit current in power grids due to large-scale renewable energy integration, this invention proposes a short-circuit current optimization control method based on the adjustment of control parameters of renewable energy power plants. By optimizing the reactive current coefficient and current limit value of the grid-connected renewable energy inverter, the short-circuit current level of the renewable energy power system is reduced, thus solving the problem of excessive short-circuit current in renewable energy power systems to a certain extent.

[0005] The technical solution of the present invention includes:

[0006] This invention proposes a short-circuit current optimization control method based on the adjustment of control parameters for new energy power plants, comprising the following steps:

[0007] Obtain topology information and current adjustment factors for the distribution network system. The topology information includes the impedance of each transmission branch, the location of three-phase short-circuit faults, power supply capacity, system load, and the capacity of photovoltaic power plants connected to the grid. The current adjustment factors include reactive current coefficient and current limiting value.

[0008] The short-circuit current when a short circuit occurs in each transmission branch is calculated based on the topology information and the current adjustment factor.

[0009] The maximum short-circuit current is determined based on the short-circuit current when a short circuit occurs in each transmission branch.

[0010] The limiting current of the entire photovoltaic power distribution network system is obtained based on the maximum short-circuit current.

[0011] Using the limiting current as the optimization target, the current adjustment factor is changed multiple times to obtain the optimal target value of the short-circuit current optimization target. The optimal target value is the current of each transmission branch within the range of the limiting current.

[0012] If the optimal target value is determined to meet the preset constraint conditions, the optimal target value is limited to the feasible region; the constraint condition is that the maximum short-circuit current when a short circuit occurs in each transmission branch is lower than that when a short circuit occurs in the previous short circuit.

[0013] In some implementations, taking the current limitation as the optimization target and changing the current adjustment factor multiple times to obtain the optimal target value of the short-circuit current optimization target, further includes constructing particles for the particle swarm algorithm with the initial values ​​of the reactive current coefficient and the current limit value of the power station; setting the particle boundaries as the upper limit of the maximum value and the lower limit of the minimum value of the reactive current coefficient, and the upper limit of the maximum value and the lower limit of the minimum value of the current limit value.

[0014] In some implementations, determining whether the optimal target value satisfies the preset constraints further includes, when the target function is satisfied, using the constraints to determine whether the maximum short-circuit current value when a short circuit occurs in the transmission branch of each branch is smaller than the maximum short-circuit current value when a short circuit occurs in the previous iteration; when the constraints are not satisfied, the reactive current coefficient and current limit value are changed; when the constraints are satisfied, the process ends.

[0015] In some implementations, the step of repeatedly changing the current adjustment factor further includes, based on the particle swarm optimization algorithm, generating a set of reactive current coefficients and current limit values ​​for each iteration of the algorithm with particles of different reactive current coefficients and current limit values, using the minimum short-circuit current value in the set as the optimal position experienced in this iteration, using the minimum value among the previous optimal positions as the optimal solution for the optimal positions experienced by all particles in previous iterations, and combining the learning factor to randomly generate change values ​​for the reactive current coefficients and current limit values ​​to ensure that the reactive current coefficients and current limit values ​​are within the range after the change.

[0016] In some implementations, the reactive current coefficient ranges from [1.5, 3], and the current limiting value ranges from [0.7, 1.7].

[0017] Compared with existing technologies, the present invention can achieve the following beneficial technical effects:

[0018] 1) Adopting an innovative design sequence of first defining the objective function and then the constraints, this optimized modeling approach ensures that the core objective is clearly defined first, avoiding premature constraints and improving the clarity, efficiency, and logic of the model;

[0019] 2) Collect all the above-mentioned relevant parameters to determine the expected short-circuit current at a specific fault point, use it as a limiting current, generate an objective function, which helps to serve as a reference for selecting protection devices, thereby ensuring the safe and reliable operation of the system;

[0020] 3) When calculating short-circuit current, the reactive current coefficient and current limit value obtained after particle swarm iteration can reduce the maximum possible short-circuit current in the system when used in new energy power plants. At the same time, it can increase the system voltage, reduce the loss of the line caused by the short-circuit current after the system fault, and protect the new energy photovoltaic power plant, thus having certain economic benefits. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process of a short-circuit current optimization control method for a new energy power station according to the present invention;

[0022] Figure 2 This is an example diagram of the topology of a traditional photovoltaic system with a high proportion of renewable energy sources integrated into the grid.

[0023] Figure 3 This is a schematic diagram showing the reactive current coefficient and current limiting value of the photovoltaic power station that best meet the target after particle swarm iteration.

[0024] Figure 4 This is a roadmap for implementing a short-circuit current optimization control method for a new energy power station according to the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0026] Figure 1 The overall process of the short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations according to the present invention includes the following steps:

[0027] S1. Obtain topology information and current adjustment factors for the distribution network system. The topology information includes the impedance of each transmission branch, the location of the three-phase short-circuit fault, the power supply capacity, the system load, and the capacity of the photovoltaic power station connected to the grid. The current adjustment factors include the reactive current coefficient and the current limit value.

[0028] S2. Calculate the short-circuit current when a short circuit occurs in each transmission branch based on the topology information and the current adjustment factor.

[0029] S3. Determine the maximum short-circuit current based on the short-circuit current when a short circuit occurs in each transmission branch.

[0030] S4. Obtain the limiting current of the entire photovoltaic distribution network system based on the maximum short-circuit current;

[0031] S5. Using the limiting current as the optimization target, the current adjustment factor is changed multiple times to obtain the optimal target value of the short-circuit current optimization target. The optimal target value is the current of each transmission branch within the range of the limiting current.

[0032] S6. If the optimal target value is determined to meet the preset constraint conditions, the optimal target value is limited to the feasible region; the constraint condition is that the maximum short-circuit current when a short circuit occurs in each transmission branch is lower than the previous short circuit.

[0033] Figure 2 This diagram illustrates a traditional photovoltaic (PV) system with a high proportion of renewable energy integration. The system topology includes a synchronous voltage source ES1, transmission branches AB, BC, CD, AE, and EF, and nodes A, B, C, D, E, and F on these transmission branches. Load L1 is connected at node D, and load L2 is connected at node F. Near-end circuit breakers (sectionalizing switches) FSW1, FSW2, and FSW3, and far-end circuit breakers (substation outgoing switches) CB1 and CB2 are used to monitor and disconnect short-circuit currents. Three-phase short-circuit faults f1, f2, f3, f4, and f5 occur at points B, C, D, E, and F. The PV power station includes PV1 and PV2, with connection points C and E.

[0034] Figure 3 This invention illustrates a technical roadmap for a short-circuit current optimization control method based on the adjustment of control parameters at renewable energy power plants, combined with the aforementioned topology of a renewable photovoltaic system with a high proportion of renewable energy integration. Figure 2 An example structure of a new energy photovoltaic system is described in detail below:

[0035] Step 1: Initialize the transmission branches to be optimized and obtain the location of the three-phase short-circuit fault. This step further includes obtaining the network topology information of the target photovoltaic power station to be optimized. This network topology information includes at least the impedance of each transmission branch, reactive current coefficient, current limiting value, power supply capacity (synchronous voltage source voltage level), system load, and photovoltaic power station grid connection capacity. Based on the impedance of each transmission branch, the synchronous voltage source voltage level, the system load, and the photovoltaic power station grid connection capacity, determine the overcurrent situation and identify the location of the three-phase short-circuit fault.

[0036] Step 2: Set the range of reactive current coefficient and current limit value for the photovoltaic power station; specifically, set the reactive current coefficient range to [1.5, 3] and the current limit value range to [0.7, 1.7]; Figure 2 In a photovoltaic power system with a high proportion of renewable energy integration, photovoltaic power plants PV1 and PV2 are connected at points C and E, respectively. The reactive current coefficients of photovoltaic power plants PV1 and PV2 are expressed as k. Lvrt1 k Lvrt2 The current limiting values ​​are expressed as k. OL1 k OL2 ;

[0037] Step 3: Substitute the initial values ​​of reactive current coefficient and current limit value into the impedance matrix to calculate the current when a short circuit occurs in each branch; further, it includes obtaining the short circuit current value of each transmission branch through the transmission branch impedance, reactive current coefficient and current limit value, extracting the maximum short circuit current on the transmission branch where the three-phase short circuit fault point is located, which is the maximum allowable current of the transmission branch, and setting it as the limiting current. This method uses the limiting current as the optimization target.

[0038] Specifically, the initial values ​​of reactive current coefficient and current limit value are substituted into the transmission branch impedance matrix algorithm to calculate the short-circuit current value of each transmission branch AB, BC, CD, AE, EF when a short circuit occurs.

[0039] by Figure 2 For example, assuming a three-phase short-circuit fault occurs on the branch where point D is located, and another photovoltaic power station PV2 is connected to point E, if the connected capacity of PV2 is too large, the system will experience overcurrent. Based on this, it is inferred that the maximum short-circuit current occurs at FSW3. The range of short-circuit current variation on line CD where FSW3 is located is set to not exceed the maximum short-circuit current of 31.5kA, thus preemptively constraining all transmission branches that may experience excessive short-circuit current. The short-circuit current on each transmission branch is expressed as: I 1_CB1_3 (k Lvrt k OL ), I 3_FSW2_3 (k Lvrt k OL ), I 4_CB2_3 (k Lvrt k OL ), I 5_FSW3_3 (k Lvrt k OL ), using I s_x_3 This generalizes to the current flowing through the circuit breaker when a three-phase short circuit occurs at point s (_3 represents a three-phase short circuit); where the subscript x represents the circuit breaker (e.g., CB1, CB2) or sectionalizing switch (e.g., FSW2, FSW3) on the branch through which the current flows, and k Lvrt k represents the reactive current coefficient. OL Represents the current limiting value, I max This represents the maximum allowable current, with a value of 31.5kA. This value is determined based on the maximum allowable current of the circuit breaker.

[0040] Step 4: Based on the particle swarm optimization algorithm, begin to find the optimal solution to the objective. The specific process is described below:

[0041] Step 4.1: The current iteration number q is 1, and the particle swarm optimization process begins iteratively.

[0042] Step 4.2: Determine whether the transmission branch current of each branch is within the current limit, that is, whether the current condition meets the requirement that the short-circuit current of the transmission branch containing the photovoltaic power station is less than the maximum allowable current I. max If this constraint is not met, the reactive current coefficient and current limit value are repeatedly changed until the short-circuit current of the transmission branch containing the photovoltaic power station satisfies the aforementioned objective function; if it is met, proceed to step 4.3.

[0043] Step 4.3: Set the constraint condition to determine whether the maximum short-circuit current when each branch is short-circuited is smaller than the previous one; set the objective function of the short-circuit current optimization strategy as min(max(F))=max[I 1_CB1_3 I 3_FSW2_3 I 4_CB2_3 I 5_FSW3_3 The objective function of this strategy represents an optimization model constrained by the condition that the short-circuit current of each transmission branch does not exceed the limit under any fault state, so that the maximum short-circuit current that the system may have during a three-phase short-circuit fault (i.e. the most severe fault) is minimized; the reactive current coefficient and current limit value of each photovoltaic station when the short-circuit current level is the lowest are obtained as the selected reactive current coefficient and current limit value.

[0044] Specifically, the constraints are constantly changing within the set range of current limit value and reactive current coefficient. In multiple iterations, the system searches for whether the maximum short-circuit current when each branch is short-circuited is smaller than the previous one. The reactive current coefficient and current limit value obtained when the target optimal short-circuit current is obtained are the optimal solutions.

[0045] Assume the initial value of the reactive current coefficients for PV1 and PV2 in the photovoltaic power station is k. Lvrt1_1 k Lvrt2_1 The initial value of the current limiting value is k. OL1_1 k OL2_1 The maximum short-circuit current of the system is calculated. Considering the relatively large short-circuit current levels at the synchronous power source outlet and the downstream outlet of the new energy source when a three-phase short-circuit fault occurs, the fault current at these points may be larger than that at other points, meaning the maximum short-circuit current scenario is possible. Therefore, the maximum short-circuit currents B, E, D, and F that may occur in the system when a three-phase short-circuit fault occurs at the synchronous power source outlet and the downstream outlet of the new energy source are calculated, and the current flowing through I during a three-phase short-circuit fault is determined. 1_CB1_3 I 4_CB2_3 I3_FSW2_3 I 5_FSW3_3 The short-circuit current;

[0046] At this moment, I 1_CB1_3 I 4_CB2_3 I 3_FSW2_3 I 5_FSW3_3 The maximum value in the objective function is min(max(F1)) = max[I 1_CB1_3 I 4_CB2_3 I 3_FSW2_3 I 5_FSW3_3 The optimal solution in I 1_CB1_3 I 4_CB2_3 I 3_FSW2_3 I 5_FSW3_3 The maximum value I max When the short-circuit current is less than 31.5kA, the optimal solution F of the objective function of the short-circuit current optimization strategy obtained in the first iteration is used. q Moving on to the next step, in the first iteration, the index iteration number q is 1.

[0047] Step 4.4: According to the Particle Swarm Optimization (PSO) formula: (v(t) = v(t-1) + c1rand()[pbest(t-1) - p(t-1)] + c2rand()[gbest(t-1) - p(t-1)], that is, in multiple iterations of the PSO formula, the particle dimension in each iteration of the PSO algorithm is 10, i.e., [I CB1 I CB2 I FSW2 I FSW3 Ten sets are generated from ten different reactive current coefficients and current limiting values. pbest(t) represents the optimal position experienced in this iteration, i.e., the 10 sets of max[I] in this iteration. 1_CB1_3 I 4_CB2_3 I 3_FSW2_3 I 5_FSW3_3 The minimum value in [], gbest(t) represents the optimal solution of the optimal position experienced by all particles in previous iterations, that is, the minimum value among the previous iterations of pbest(t). The last two terms, c1 and c2, are learning factors, also known as acceleration constants, which represent the dynamic factors of absorbing their own experience and the experience of other individuals in the population, respectively. Randomly generate reactive current coefficient and current limit value change value v Lvrt1 v Lvrt2 v OL1 v OL2 After modification, the reactive current coefficients of PV1 and PV2 in the new energy power station were changed to k. Lvrt1 +v Lvrt1 k Lvrt2 +v Lvrt2 The current limit value is changed to k OL1 +v OL1k OL2 +v OL2 And ensuring that the reactive current coefficient is within the range of [1.5, 3] after the change, and the current limit value is within the range of [0.7, 1.7] after the change, and that the short-circuit current does not exceed 31.5kA at faults B, D, E, and F where the maximum short-circuit current may occur, the iteration number q is incremented by one to obtain the new objective function F. q+1 If the conditions are not met, repeat step 4.1.

[0048] After changing the reactive current coefficient and current limit value, find the optimal solution F of the objective function. q+1 The maximum short-circuit current exceeds the result F from the previous iteration. q At this point, the optimal reactive current coefficient and current limiting value still use the values ​​from the previous iteration, F. q+1= F q If the constraints are met and the number of iterations q exceeds 100, proceed to step 5 and end the process; if the number of iterations does not exceed 100, return to step 4.1.

[0049] Step 5, program ends.

[0050] In the above steps, the particle swarm optimization algorithm uses the initial values ​​of the reactive current coefficient and the current limit value of the power station as inputs; the particle includes the upper limit of the maximum value and the lower limit of the minimum value of the reactive current coefficient and the upper limit of the maximum value and the lower limit of the current limit value, with a dimension of 4, and is continuously changed within this range. Then, the upper limit of the number of iterations of the particle swarm optimization algorithm is set to 100.

[0051] Relevant parameters include:

[0052] (1) Suppose that N new energy power stations are connected to the power system, and the reactive current coefficients of new energy power stations 1, 2, ... N are k respectively. Lvrt1 k Lvrt2 ...k LvrtN and current limiting value k OL1 k OL2 ...k OLN Set the reactive current coefficient range to [k Lvrt_min , k Lvrt_max The current limiting value range is set to [k]. OL_min , k OL_max According to the standard GB / T19964-2012 "Technical Specifications for Photovoltaic Power Stations Connected to the Power System", k Lvrt_min The value can be 1.5, k Lvrt_max The value can be 3, k OL_min The value can be 0.7, k OL_max A value of 1.7 is acceptable.

[0053] (2) In power transmission branches of systems with a high proportion of new energy grid connection, the short-circuit level at the line outlet is relatively large when a three-phase short-circuit fault occurs at the downstream outlet of the new energy source, and the short-circuit level at the outlet location is relatively large when a three-phase short-circuit fault occurs at the outlet of the synchronous power supply line. Therefore, the maximum circuit current of the system connected to the new energy power station can be calculated based on the above conditions, which are I bef1_3 I bef2_3 ...I befW_3 Therefore, for new energy power systems, the maximum short-circuit current level I is used. bef1_3 I bef2_3 ...I befW_3 Minimize the maximum circuit current level I of the system under the condition that a three-phase short-circuit fault occurs at the synchronous power supply outlet and a three-phase short-circuit fault occurs at the downstream outlet of the new energy line, using this as the objective function. bef1_3 I bef2_3 ...I befW_3 Using the constraint of not exceeding the limit, the optimization model with the lowest short-circuit level is constructed as follows:

[0054] ① Objective function:

[0055] ;

[0056] ②Constraints on short-circuit current not exceeding the limit:

[0057] ;

[0058] (3) Based on the optimization model with Equation ① as the objective function and Equation ② as the constraint, the reactive current coefficient and current limit value of each new energy power station are obtained by calculating the minimum short-circuit current level using the particle swarm optimization algorithm within the constraints of the reactive current coefficient and current limit value. Specifically:

[0059] 1) Set the upper limit of the number of iterations for the particle swarm optimization algorithm to V, and set the initial values ​​of the reactive current coefficients for each new energy power station to k. Lvrt1_q k Lvrt2_q ...k LvrtN_q The current limiting values ​​are k OL1_q k OL2_q ...k OLN_q q is the iteration number, which is 1 in the first iteration;

[0060] 2) Calculate the initial value of the reactive current coefficient at the new energy power station as k. LvrtN_1 The initial value of the current limiting value is k. OLN_1 The maximum short-circuit current of the system at that time is calculated. Considering the relatively large short-circuit levels when three-phase short-circuit faults occur at the synchronous power source outlet and the downstream outlet of the new energy source, the maximum short-circuit current that may occur in the system when three-phase short-circuit faults occur at the synchronous power source outlet and the downstream outlet of the new energy source is I. bef1 Ibef2 ...I befW 。;

[0061] 3) At this time, I bef1_3 I bef2_3 ...I befW_3 The maximum value in is also the initial optimal solution in objective function ①, min(max(F1))=max[I bef1 I bef2 ,...I befW ], in I bef1_3 I bef2_3 ...I befW_3 When constraint ② is satisfied, the optimal solution F of the objective function obtained in the first iteration is... q Substitute this into the next step (in the first iteration, the index iteration number q is 1).

[0062] 4) At this point, the optimal solution F of the objective function q In the next iteration, the particle swarm optimization algorithm will calculate the reactive current coefficient k of each new energy power station. Lvrt1 k Lvrt2 ...k LvrtN and current limiting value k OL1 k OL2 ...k OLN The reactive current coefficient is set with a step size of an appropriate random number v. Lvrt1 v Lvrt2 …v LvrtN The current limiting value is set with a step size of an appropriate random number v. OL1 v OL2 ...v OLN The changes resulted in the reactive current coefficients of each new energy power station being k. Lvrt1 +v Lvrt1 k Lvrt2 +v Lvrt2 ...k LvrtN +v LvrtN The current limiting values ​​for each new energy field are k OL1 +v OL1 k OL2 +v OL2 ...k OLN +v OLN Ensure that after each parameter is changed, it remains within the range [k] Lvrt_min , k Lvrt_max ]、[k OL_min , k OL_max Under the condition that constraint ② is satisfied, the iteration count q is incremented by one to obtain the new objective function F. q+1 If constraint ② is not met, repeat step 4).

[0063] 5) If the optimal solution F of the objective function is found after the modification. q+1 The maximum short-circuit current exceeds the result F from the previous iteration. q At this point, the optimal reactive current coefficient and current limiting value still use the values ​​from the previous iteration, F. q+1= F q If the constraints are met and the number of iterations q exceeds V, proceed to step 6) to end the process; if the number of iterations does not exceed V, return to step 4).

[0064] 6) The algorithm ends.

[0065] The reasoning behind the implementation of this invention is as follows:

[0066] For high-proportion photovoltaic (PV) grid connections to 110kV distribution networks, if other voltage level distribution networks or transmission branches are used, the reference voltage value in the new energy PV power station needs to be modified to ensure that the power station control strategy can output stably. The active current equation and reactive current equation of the new energy PV system are shown in equation (1):

[0067] ; (1)

[0068] ;

[0069] ;

[0070] In the formula: I kd I kq These are the active and reactive currents generated after being regulated by control systems such as the Low Voltage Ride-Through Control Loop (LVRT), Phase-Locked Loop (PLL), and Pulse Width Modulation (PWM), respectively. dref I is the reference value for active current after a three-phase short-circuit fault occurs at a node in the power distribution system. N K is the rated current after being regulated by the photovoltaic maximum power point tracking strategy controller. OL U is a constraint value set to prevent overload of the current limiting control equipment in photovoltaic power plants. t For the grid connection voltage of new energy photovoltaic power plants, K Lvrt This represents the reactive current coefficient, which is fixed and typically taken as 1.5. The reactive current coefficient K... Lvrt and current limiting value K OL Under the premise that all are fixed, according to equation (1), the output of the new energy photovoltaic system is only related to the degree of voltage drop at the grid connection point.

[0071] To address the issue that the output of a renewable photovoltaic system is only related to the voltage drop at the grid connection point, the problem of excessive short-circuit current caused by a high proportion of renewable energy being integrated into the system is prevented by adjusting the reactive current coefficient and current limiting value. The objective function is shown in the following equation:

[0072] (2)

[0073] in, The ultimate goal is to minimize the maximum short-circuit current of the entire distribution network system containing renewable photovoltaic power when a three-phase short circuit occurs in each branch. max [...] represents the short-circuit current value I when a three-phase short circuit occurs in each simulated branch (CB1, CB2, FSW2, FSW3). CB1 I CB2 The goal is to find the largest short-circuit current value, and ultimately minimize this value.

[0074] To comply with practical engineering requirements and national standards, the following constraints are set for the reactive current coefficient:

[0075] 1) Regarding the constraint conditions for the reactive current coefficient:

[0076] Because the output current of the renewable energy source needs to be controlled after a short circuit, the fault ride-through strategy shows that controlling the reactive current coefficient can adjust the output current. If the reactive current coefficient is too large, the renewable energy source may still output excessive reactive current after a fault, increasing the burden and loss of the system lines. If the reactive current coefficient is too low, the renewable energy source will have a weak regulatory effect on the system and will not be able to play its due role in current and voltage regulation after a short circuit.

[0077] (3)

[0078] In the formula, K PVi K represents the value of the reactive current coefficient in practice. PV.min and K PV.max The minimum and maximum values ​​that the reactive current coefficient can take are typically 1.5 and 3.

[0079] At the same time, it is necessary to adjust the current limiting value, i.e., K in equation (1). OL The following equation is satisfied:

[0080] (4)

[0081] In the formula, K OL.min This represents the value of the reactive current coefficient in practice. and K OL.max The minimum and maximum values ​​that the reactive current coefficient can take are typically 0.7 and 1.7.

[0082] 2) Regarding the current limiting constraint:

[0083] After the integration of new energy sources, the current value of the system lines will change. If the current exceeds a certain range, the circuit breaker will fail to open normally, and the excessive short-circuit current will also cause significant damage to the lines. In order to ensure that the impedance error of the impedance relay is within the allowable range, the minimum short-circuit current passing through the impedance relay when a short circuit occurs within the protection zone must not be less than the minimum accurate operating current, as shown below:

[0084] (5)

[0085] In the formula, I lim I is the current value that can appear in the circuit. lim.min To set the minimum allowable current value in the system, i.e., the short-circuit current value of the system before it is connected to the renewable energy power station, I lim.max This is used to set the maximum allowable current in the system.

[0086] 3) Constraints regarding short-circuit current not exceeding the limit

[0087] When a fault occurs at point f(f1-f5), the maximum short-circuit current may occur at location FSW3. The line current is set to not exceed 31.5kA. Simultaneously, since another photovoltaic PV2 is connected to point E, if the capacity of PV2 connected to the system is too large, the current it provides when a short circuit occurs at point D may also cause overcurrent in CB1 and FBS2. Therefore, the constraints should be as follows:

[0088] (6)

[0089] Among them, I 1_x_3 k represents the current flowing through the circuit breaker when a three-phase short circuit occurs at node 3, i.e., node D. lvrt I represents the reactive current coefficient used at this time. max The representative specifies that the maximum current of the line is 31.5kA.

[0090] The default initial branch connection to photovoltaic PV1 and PV2 at points C and E each has a photovoltaic capacity of 500MW, as shown in Table 1. These are the system parameters, and the current system parameters represent the branch short-circuit current values ​​when each node is short-circuited. Table 2 shows the initial branch short-circuit current analysis.

[0091] Table 1

[0092]

[0093] Table 2

[0094]

[0095] Based on the above method, it is found that when the reactive current coefficients of PV1 and PV2 are both 3 and the current limiting values ​​are both 3, the maximum short-circuit current that may occur in the system under any circumstances can be minimized. Table 3 shows the short-circuit current analysis of each branch after iteration.

[0096] Table 3

[0097]

[0098] As the scale of renewable energy sources connected to the distribution network gradually increases, the connection of traditional renewable energy sources may lead to a series of problems such as overcurrent during short circuits. Therefore, traditional renewable energy sources are no longer suitable for high-proportion connection to the system. This invention proposes a method to change the output current of renewable energy sources by modifying the reactive current coefficient and current limit value. This method can adjust the above parameters after identifying the short circuit point to prevent the system current from exceeding the limit. This method can ensure that the system's regulation capability is far superior to that of traditional renewable energy sources under the same connected capacity, and it is easy to implement. It is not limited by short-circuit current within a reasonable range of renewable energy connected capacity and has good applicability.

[0099] The above exemplary embodiments illustrate and describe the overall technical solution of the present invention. However, those skilled in the art should understand that the present invention is not limited to the above embodiments, and all modifications or substitutions to the present invention fall within the scope of protection of the present invention.

Claims

1. A short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations, characterized in that, Includes the following steps: Obtain topology information and current adjustment factors for the distribution network system. The topology information includes the impedance of each transmission branch, the location of three-phase short-circuit faults, power supply capacity, system load, and the capacity of photovoltaic power plants connected to the grid. The current adjustment factors include reactive current coefficient and current limiting value. The short-circuit current when a short circuit occurs in each transmission branch is calculated based on the topology information and the current adjustment factor. The maximum short-circuit current is determined based on the short-circuit current when a short circuit occurs in each transmission branch. The limiting current of the entire photovoltaic power distribution network system is obtained based on the maximum short-circuit current. Using the limiting current as the optimization target, the current adjustment factor is changed multiple times to obtain the optimal target value of the short-circuit current optimization target. The optimal target value is the current of each transmission branch within the range of the limiting current. If the optimal target value is determined to meet the preset constraint conditions, the optimal target value is limited to the feasible region; the constraint condition is that the maximum short-circuit current when a short circuit occurs in each transmission branch is lower than that when a short circuit occurs in the previous short circuit.

2. The short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations according to claim 1, characterized in that, Taking the current limitation as the optimization objective, the current adjustment factor is changed multiple times to obtain the optimal target value of the short-circuit current optimization objective. This further includes constructing the initial value of the reactive current coefficient and the initial value of the current limit value of the power station as the particles of the particle swarm algorithm. Set the particle boundaries as the upper limit of the maximum value and the lower limit of the minimum value of the reactive current coefficient, and the upper limit of the maximum value and the lower limit of the minimum value of the current limit.

3. The short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations according to claim 1, characterized in that, Determining whether the optimal target value satisfies the preset constraints further includes, when the target function is satisfied, using the constraints to determine whether the maximum short-circuit current value when a short circuit occurs in the transmission branch of each branch is smaller than the maximum short-circuit current value when a short circuit occurs in the previous iteration; when the constraints are not satisfied, the reactive current coefficient and current limit value are changed; when the constraints are satisfied, the process ends.

4. The short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations according to claim 1, characterized in that, The step of repeatedly changing the current adjustment factor also includes, based on the particle swarm optimization algorithm, generating a set of reactive current coefficients and current limit values ​​for each iteration of the algorithm with particles of different reactive current coefficients and current limit values ​​in each iteration, taking the minimum short-circuit current value in the set as the optimal position experienced in this iteration, taking the minimum value among the previous optimal positions as the optimal solution for the optimal positions experienced by all particles in previous iterations, and combining the learning factor to randomly generate change values ​​for reactive current coefficients and current limit values ​​to ensure that the reactive current coefficients and current limit values ​​are within the range after the change.

5. The short-circuit current optimization control method based on the adjustment of control parameters of new energy power stations according to claim 1, characterized in that, The reactive current coefficient ranges from [1.5, 3], and the current limiting value ranges from [0.7, 1.7].

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