Inverse time limit over-current protection boundary evaluation method and system for distributed photovoltaic access

Abstract

The invention discloses an inverse time limit over-current protection boundary evaluation method and system for distributed photovoltaic access. The system comprises a model construction module, a model target optimization solving module and a protection boundary evaluation module. Constructing a photovoltaic maximum penetration capacity optimization model of the distributed photovoltaic access power grid; solving the photovoltaic maximum penetration capacity optimization model, and optimizing and solving the maximum photovoltaic penetration capacity of each to-be-evaluated power grid meeting inverse time limit over-current protection by using a particle swarm optimization algorithm; and carrying out inverse time limit over-current protection applicable boundary evaluation on the to-be-evaluated power grid. According to the invention, the capability of the traditional inverse time limit over-current protection criterion in the aspect of consumption of distributed new energy by the power distribution / micro-grid is evaluated.

Description

Technical Field

[0001] The present invention relates to the field of relay protection for AC distribution / microgrids with a high proportion of new energy access, and in particular to a method and system for evaluating applicable boundaries of inverse time overcurrent protection for distribution / microgrids with distributed photovoltaic access. Background Art

[0002] For AC distribution / microgrids with distributed renewable energy access, the short-circuit current supplied by renewable energy will cause the magnitude and direction of the short-circuit current to change when the distribution / microgrid fails, thereby affecting the applicability of inverse time overcurrent protection.

[0003] The inverse time characteristic of current protection refers to the characteristic that the action time can change with the magnitude of the fault current, so it has the ability to adaptively respond to the severity of the fault. Inverse time overcurrent protection is widely used in line protection of medium and low voltage distribution networks.

[0004] With the access of distributed renewable energy to distribution networks and microgrids, distribution / microgrids show new characteristics of being multi-faceted and wide-ranging. Compared with traditional distribution networks, distribution / microgrids containing renewable energy sources, lines and loads have certain independent operation capabilities, but the short-circuit current supplied by renewable energy will change the magnitude and direction of the short-circuit current when the distribution / microgrid fails, thereby affecting the applicability of inverse time overcurrent protection. In view of this situation, it is necessary to design an evaluation method for the applicable boundary of inverse time overcurrent protection of distribution / microgrids suitable for the access of distributed renewable energy. The distribution network is a large, centralized power system. It adopts a centralized control strategy, and the main control center centrally manages the entire system. The microgrid is a small, distributed power system that uses a distributed control strategy, and each component is collaboratively controlled through a communication protocol.

[0005] The distribution network should evaluate the maximum feasible domain of droop-inertia support coefficient under different operating conditions and report it to the system operator before participating in primary frequency regulation. According to the operating state constraints of the distribution network, the maximum droop feasible domain that it can provide can be calculated, where the droop coefficient should be dynamically adjusted according to the reserve power. In the model-based evaluation method, the droop and inertia support coefficients are generally expressed as analytical functions of the state of charge and illumination of the distributed photovoltaic storage, respectively, and scholars evaluated the boundaries of each feasible domain under different states. Scholars used a cubic Kalman filter to evaluate the boundaries of the inertia feasible domain based on the rotor speed of the distributed photovoltaic storage. However, the above method only constructs the feasible domain of each coefficient independently. In fact, droop and inertia control in the distribution network are both achieved by adjusting the active power output, so they interact with each other. If the analysis of the interaction mechanism between droop and inertia support coefficients is ignored, it will be difficult for the distribution network to construct an accurate feasible domain and achieve optimal primary frequency regulation support.

[0006] In power systems with high renewable energy penetration, when a large number of distribution networks provide support based on the evaluation results, the frequency dynamics will change, resulting in different frequency changes under the same load fluctuations, which in turn affect the capacity of each distribution network. In this case, in order to obtain the maximum coefficient boundary, it is very important to consider the coupling relationship between the primary frequency response capabilities of the distribution networks. In theory, the droop-inertia feasible domain of the distribution network can be analytically solved at the system operator end through physical models and centralized optimization methods. MVKazemi et al. calculated the droop-inertia support coefficient feasible domain of the photovoltaic storage in the system based on frequency stability, illumination and distributed photovoltaic storage status using a parameter space method. Some scholars constructed the droop-inertia feasible domain of each distribution network using the minimum frequency value and frequency change rate, and solved the optimal coefficient solution based on the rotor stability of the distributed photovoltaic storage. However, the centralized evaluation in the above method requires the system operator to obtain the state measurement data of all distributed photovoltaic storage in the grid, which will lead to a heavy communication burden, and the real-time distributed photovoltaic storage status data is difficult to upload from the distribution network.

[0007] In contrast, the evaluation on the distribution network side shows significant characteristics such as fast response, high reliability and light communication burden. Priyatosh Mahish et al. used the aggregated light and power reserves, as well as the frequency of each distribution network at the common coupling point, as allocation indicators to iteratively evaluate the droop and inertia feasible domain of each distribution network based on system dynamics. However, the droop-inertia support factor of the power system in some literature is set as a constant, which cannot reflect the changes in the frequency regulation capability of the system. In fact, the droop-inertia support factor of the power system should be aggregated from multiple distribution networks in real time instead of being set to a constant value. Therefore, it is very important to propose an iterative evaluation scheme coordinated between the distribution network and the system operator, which should take into account the changes in the primary frequency regulation response capability of the distribution network in the system.

[0008] Although the above-mentioned coordinated iterative methods do not require centralized modeling and evaluation, the distribution network still needs to obtain static state parameters. In this way, if the distribution network model is inaccurate or incomplete, the feasible domain of the evaluation is definitely unreliable. In addition, the long-term solution and optimization based on large-scale physical models make these methods unsuitable for online applications and tracking illumination changes. To address these issues, data-driven methods have attracted widespread attention because they can use historical data to construct an equivalent primary frequency response model of the distribution network. Scholars proposed an ANN model to fit the nonlinear dynamics of the primary frequency response of the distribution network and evaluate the active power of the distribution network under different operating conditions. Other scholars proposed a deep reinforcement learning method to construct a primary frequency response model of the distribution network and evaluate the droop-inertia feasible domain. Some scholars also proposed an improved fuzzy method to evaluate the feasible domain of the droop coefficient of the distribution network according to illumination changes. However, these methods require a large amount of sampling data and a complex training process, and lack physical interpretability. Due to the lack of historical samples of extreme scenarios, the accuracy of the maximum boundary of the feasible domain of the distribution network cannot be guaranteed.

[0009] Due to the advantages of global linearization, Koopman operator theory (KOT) has been applied to fit nonlinear dynamic models. Based on KOT, nonlinear dynamic algebraic models in low-dimensional state space can be converted to global linear forms in infinite-dimensional state space without loss of accuracy, and the linear Koopman operator matrix can be estimated by data-driven training. In practical applications, increasing the dimensionality to an appropriate number is sufficient to transform the nonlinear model into a high-precision linear mathematical model. The global linear property enables the establishment of accurate equivalent primary frequency modulation assessment models over a wide range without the need for historical samples to cover extreme scenarios. Summary of the invention

[0010] Aiming at the problem that the reliable identification of phase-to-phase short-circuit faults of inverse time overcurrent protection after distributed renewable energy access to AC distribution / microgrid is affected by the capacity of renewable energy access, the present invention aims to propose a boundary assessment method and system for inverse time overcurrent protection of distributed photovoltaic access, and utilizes a particle swarm algorithm to perform target optimization solution of the photovoltaic maximum access capacity optimization model of the power grid with distributed photovoltaic access, so as to realize the applicable boundary assessment method for inverse time overcurrent protection of the power grid to be evaluated.

[0011] In order to achieve the above-mentioned object of the invention, the present invention proposes the following technical solutions:

[0012] In a first aspect, the present invention proposes a method for evaluating the applicable boundary of inverse time overcurrent protection of a distributed photovoltaic access power grid, comprising:

[0013] The current setting value of each protection of each line of the power grid to be evaluated based on the inverse time overcurrent protection setting principle is calculated, and the inverse time overcurrent protection action time curve of each protection of each line is calculated. The maximum distributed photovoltaic access capacity of the power grid including the distribution network / microgrid is taken as the objective function, and the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time limit difference between the upper and lower protection actions are taken as constraints to construct the photovoltaic maximum access capacity optimization model of the power grid with distributed new energy access;

[0014] Solving the photovoltaic maximum access capacity optimization model, including: using a particle swarm algorithm to update the photovoltaic capacity combination of particles, obtaining the current optimal photovoltaic capacity combination, optimizing the maximum photovoltaic access capacity of each grid to be evaluated that meets the inverse time overcurrent protection, iteratively solving the optimal photovoltaic access capacity of each protection on each line of each access point of all grids, calculating the sum of all optimal photovoltaic access capacities to obtain the maximum photovoltaic access capacity, and outputting the maximum photovoltaic access capacity as the distributed photovoltaic maximum access capacity of the grid to be evaluated as the applicable boundary of the current protection;

[0015] An assessment of the applicable boundaries of the inverse time overcurrent protection of the power grid to be evaluated is performed, and the evaluation criteria include: if the photovoltaic access capacity of the current line in the power grid to be evaluated is less than the maximum access capacity of the distributed photovoltaic, each current protection sensitively and selectively identifies the fault and starts protection; if the photovoltaic access capacity of the current line in the power grid to be evaluated is greater than the maximum access capacity of the distributed photovoltaic, each current protection malfunctions.

[0016] In some embodiments, the expression of the photovoltaic maximum access capacity optimization model is as follows:

[0017] ①The objective function of each line i is expressed as follows:

[0018] maxS isum =S pvi1 +S pvi2 +...+S pvin

[0019] In the formula, S pvn represents the nth photovoltaic access capacity corresponding to each line i in the power grid;

[0020] ②The constraints are expressed as follows:

[0021]

[0022] In the formula, I op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. m represents the number of lines other than the end line of each feeder. I PVi'.up It represents the reverse fault current value flowing through the nearest protection upstream of each photovoltaic access point i', t i” Indicates the action time limit of each protection i" when a fault occurs at the head end of the line, t i”.up Indicates the action time limit of the superior protection of each protection i" when a fault occurs at the head end of the line.

[0023] In some embodiments, the step of optimizing and solving the maximum photovoltaic access capacity of each grid to be evaluated that satisfies the inverse time overcurrent protection by using a particle swarm algorithm further comprises:

[0024] Update the photovoltaic capacity combination of particles, for both and t i”.up -t i” The particles with ≥Δt are updated to the optimal photovoltaic access capacity on each line of the current photovoltaic access point and the optimal photovoltaic access capacity on each line of each distributed photovoltaic access point in the current power grid until the total number of particles in the particle swarm is reached, and the particle swarm optimization is completed. op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, It represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. Δt represents the minimum time difference between the upper and lower level inverse time overcurrent protection.

[0025] In some embodiments, the particle swarm algorithm further includes calculating the position of the photovoltaic capacity combination of particle j with the iteration update number k+1 The speed of the combination with the change of photovoltaic capacity As shown below:

[0026]

[0027] In the formula, k represents the current number of iterations, K max represents the maximum number of iterations, j represents the number of particles, ω represents the inertia weight, ω max ,ω min They represent the maximum and minimum values ​​of the inertia weight, c1 and c2 represent normal numbers that measure the importance of self-cognition factors and social influence factors, r1 and r2 represent random numbers uniformly distributed in the region [0,1]. and They represent the position of the photovoltaic capacity combination of particle j at the current iteration number k and the change speed of the photovoltaic capacity combination, Pbest j represents the optimal photovoltaic capacity combination currently found by particle j, and Gbest represents the optimal photovoltaic capacity combination found by all particles so far.

[0028] In a second aspect, the present invention proposes a distributed renewable energy access grid inverse time overcurrent protection applicable boundary assessment system, including a model building module, a model target optimization solution module and a protection boundary assessment module, wherein:

[0029] The model building module is used to calculate the current setting value of each protection of each line of the power grid to be evaluated based on the inverse time overcurrent protection setting principle, calculate the inverse time overcurrent protection action time curve of each protection of each line, take the maximum distributed photovoltaic access capacity of the power grid including the distribution network / microgrid as the objective function, and take the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time difference between the upper and lower protection actions as constraints to build the photovoltaic maximum access capacity optimization model of the distribution / microgrid with distributed new energy access;

[0030] The model target optimization solution module solves the photovoltaic maximum access capacity optimization model, including: using the particle swarm algorithm to update the photovoltaic capacity combination of particles to obtain the current optimal photovoltaic capacity combination, so as to optimize the maximum photovoltaic access capacity of each grid to be evaluated that meets the inverse time overcurrent protection, iteratively solve the optimal photovoltaic access capacity of each protection on each line of each access point of all grids, calculate the sum of all optimal photovoltaic access capacities to obtain the maximum photovoltaic access capacity, and output the maximum photovoltaic access capacity as the distributed photovoltaic maximum access capacity of the grid to be evaluated as the applicable boundary of the current protection;

[0031] The protection boundary assessment module is used to evaluate the applicable boundaries of the inverse time overcurrent protection of the power grid to be evaluated. The assessment criteria include: if the photovoltaic access capacity of the current line in the power grid to be evaluated is less than the maximum access capacity of the distributed photovoltaic, each current protection sensitively and selectively identifies the fault and starts protection; if the photovoltaic access capacity of the current line in the power grid to be evaluated is greater than the maximum access capacity of the distributed photovoltaic, each current protection malfunctions.

[0032] In some embodiments, the expression of the photovoltaic maximum access capacity optimization model is as follows:

[0033] ①The objective function of each line i is expressed as follows:

[0034] maxS isum =S pvi1 +S pvi2 +...+S pvin

[0035] In the formula, S pvn represents the nth photovoltaic access capacity corresponding to each line i in the power grid;

[0036] ②The constraints are expressed as follows:

[0037]

[0038] In the formula, I op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. m represents the number of lines other than the end line of each feeder. I PVi'.up It represents the reverse fault current value flowing through the nearest protection upstream of each photovoltaic access point i', t i” Indicates the action time limit of each protection i" when a fault occurs at the head end of the line, t i”.up Indicates the action time limit of the superior protection of each protection i" when a fault occurs at the head end of the line.

[0039] In some embodiments, the step of optimizing and solving the maximum photovoltaic access capacity of each grid to be evaluated that satisfies the inverse time overcurrent protection by using a particle swarm algorithm further comprises:

[0040] Update the photovoltaic capacity combination of particles, for both and t i”.up -t i” The particles with ≥Δt are updated to the optimal photovoltaic access capacity on each line of the current photovoltaic access point and the optimal photovoltaic access capacity on each line of each distributed photovoltaic access point in the current power grid until the total number of particles in the particle swarm is reached, and the particle swarm optimization is completed. op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, It represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. Δt represents the minimum time difference between the upper and lower level inverse time overcurrent protection.

[0041] In some embodiments, the particle swarm algorithm further includes calculating the position of the photovoltaic capacity combination of particle j with the iteration update number k+1 The speed of the combination with the change of photovoltaic capacity As shown below:

[0042]

[0043] In the formula, k represents the current number of iterations, K max represents the maximum number of iterations, j represents the number of particles, ω represents the inertia weight, ω max ,ω min They represent the maximum and minimum values ​​of the inertia weight, c1 and c2 represent normal numbers that measure the importance of self-cognition factors and social influence factors, r1 and r2 represent random numbers uniformly distributed in the region [0,1]. and They represent the position of the photovoltaic capacity combination of particle j at the current iteration number k and the change speed of the photovoltaic capacity combination, Pbest j represents the optimal photovoltaic capacity combination currently found by particle j, and Gbest represents the optimal photovoltaic capacity combination found by all particles so far.

[0044] In the third aspect, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for evaluating applicable boundaries of inverse time overcurrent protection for grid access of distributed renewable energy sources according to any one of claims 1 to 4.

[0045] In a fourth aspect, the present invention proposes a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for evaluating applicable boundaries of inverse time overcurrent protection for a power grid with access to distributed renewable energy sources according to any one of claims 1 to 4.

[0046] Compared with the prior art, the present invention can achieve the following beneficial technical effects:

[0047] 1) The present invention realizes the evaluation of the ability of the traditional inverse time overcurrent protection criterion in accommodating distributed renewable energy in distribution / microgrids. The method is simple in principle and easy to implement. It provides a calculation method for the maximum capacity of distributed renewable energy that can be connected to distribution / microgrids in actual projects, so as to guide the installation of distributed renewable energy in actual projects and ensure the safe operation of power grids.

[0048] 2) A photovoltaic maximum access capacity optimization model is constructed, and the particle swarm algorithm is used to update the photovoltaic capacity combination of particles to obtain the calculation result of the optimal photovoltaic access capacity. Based on this, the maximum access capacity of distributed photovoltaics in the power grid to be evaluated is obtained as the applicable boundary of inverse time overcurrent protection, and a more effective evaluation strategy is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is an example diagram of the AC microgrid topology structure connected to traditional distributed photovoltaics;

[0050] Figure 2 It is an overall flow chart of the inverse time overcurrent protection boundary evaluation method for distributed photovoltaic access of the present invention;

[0051] Figure 3 This is a module diagram of the inverse time overcurrent protection boundary assessment system for distributed photovoltaic access of the present invention;

[0052] Figure 4 This is the flow chart of the particle swarm algorithm with inertia weight;

[0053] Figure 5 The following are example diagrams of the change of photovoltaic access capacity with the number of iterations. (a) is an example of the change of a single distributed photovoltaic access capacity with the number of iterations; (b) is an example of the change of distributed photovoltaic access capacity with the number of iterations. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0055] like Figure 1 As shown in the figure, taking the traditional distributed photovoltaic access AC microgrid topology as an example, the distributed photovoltaic access AC microgrid topology example includes PV1 and PV2 as photovoltaic power sources and Z S is the system impedance, PCC1 and PCC2 are two common connection points, substation outgoing line switch CB1, section switch FSW1, section switch FSW2, substation outgoing line switch CB2, section switch FSW3 are the protection of the lines at the exits of AB section, BC section, CD section, AE section and EF section respectively, PV1 and PV2 both adopt dual-loop control strategy, negative sequence current suppression control strategy and low voltage ride-through control strategy considering fault current limiting, and the current limit is 1.2 times the rated current. For load LOAD1( Figure 1 L1) and LOAD2( Figure 1 Assuming that the load is three-phase balanced, the single-phase load equivalent impedance is Z LOAD1 =Z LOAD2 .

[0056] The commonly used action characteristic equation of the inverse time overcurrent relay is shown in the following formula:

[0057]

[0058] In the formula, I op is the starting current of the relay, I is the current flowing into the relay, T p is the time setting coefficient, and t is the action time. op When the current is greater than I op , the relay starts.

[0059] Combination Figure 1 For the grid structure, the line setting starts with the protection FSW2 which is farthest from the power source. The expression of the setting result is as follows:

[0060]

[0061] In the formula, I op is the current setting value, K rel is the reliability coefficient, K ss is the self-starting coefficient, K re is the return coefficient of the current relay, I L.max It is the maximum load current for normal operation.

[0062] The starting current is set to I according to formula (2) op1 , the corresponding action time is t1; when the first section of line CD is short-circuited, the short-circuit current I LCD.max Under the action of, the time limit action characteristic curve of protection FSW2 can be obtained, so as to determine the time setting coefficient Td at the protection installation location. FSW2 ; Set the protection FSW1, and set its starting current to I op2 ; When the line CD is short-circuited at the beginning, in order to ensure the selectivity of the network, when the short-circuit current is I LCD.max To ensure selectivity, the action time limit of FSW1 must be higher than that of FSW2 by a time step Δt, that is, t b =t c +Δt. Based on this, the time limit action characteristics of protection FSW1 can be obtained, and the time setting coefficient Td can be determined FSW1 The determination method of the action characteristic curves of other protections is the same as that of the above protections.

[0063] Therefore, according to the inverse time overcurrent protection principle, the present invention proposes an inverse time overcurrent protection applicable boundary evaluation method with the maximum access capacity of new energy as the target, and the current protection sensitivity under new energy access, the reverse fault without false operation, and the time difference between the upper and lower protection actions as the constraint conditions. When the new energy access capacity is less than the calculated maximum access capacity of new energy, the inverse time overcurrent protection can meet the requirements and be reliably applicable, while when the new energy access capacity is greater than the calculated maximum access capacity of new energy, the inverse time overcurrent protection cannot be reliably applicable.

[0064] like Figure 2 As shown, the method and system for evaluating the applicable boundary of the inverse time overcurrent protection of the power grid connected to the distributed new energy of the present invention include the following steps:

[0065] Step 1: According to the multiple lines i based on the inverse time overcurrent protection setting principle contained in the operating topology of the distributed photovoltaic access power grid (including distribution network / microgrid), the current setting value I of each protection of each line is calculated based on formula (2): opi1 , I opi2 ,...,i=1,2,3...i...;

[0066] Step 2: According to the inverse time overcurrent protection setting principle, calculate the inverse time overcurrent protection action time curve t of each protection of each line i i1 , t i2 , …;

[0067] Step 3: Taking the maximum distributed photovoltaic access capacity of the power grid including the distribution network / microgrid as the objective function, and taking the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time difference between the upper and lower protection actions as constraints, the photovoltaic maximum access capacity optimization model of the distribution / microgrid with distributed new energy access is constructed to analyze the applicable boundary of the inverse time overcurrent protection. The expression of the photovoltaic maximum access capacity optimization model is as follows:

[0068] ①The objective function of each line i is expressed as follows:

[0069] max S isum =S pvi1 +S pvi2 +...+S pvin (3)

[0070] In the formula, S pvn Represents the nth photovoltaic access capacity corresponding to each line i in the power grid (including distribution network / microgrid);

[0071] ②The constraints are expressed as follows:

[0072]

[0073] In the formula, I op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. m represents the number of lines other than the end line of each feeder. I PVi'.up It represents the reverse fault current value flowing through the nearest upstream protection of each photovoltaic access point i'. Studies have shown that after the distributed photovoltaic access system, no matter the fault occurs upstream or downstream of the photovoltaic, the action time difference between the upper and lower protection levels will gradually increase with the increase of the fault distance. i” Indicates the action time limit of each protection i" when a fault occurs at the head end of the line, ti”.up It indicates the action time limit of the upper level protection of each protection i" when a fault occurs at the head end of the line. Δt indicates the minimum time difference between the upper and lower level protections of the inverse time overcurrent. S pv_max It represents the maximum capacity of a single photovoltaic (for the system level, a set maximum value). The objective function means to find the maximum photovoltaic capacity that the entire system can accommodate. S pvn Indicates the maximum value of each photovoltaic actually connected;

[0074] Step 4: Optimize the photovoltaic maximum access capacity optimization model including the objective function and constraint condition, such as Figure 2 As shown in FIG. 1 , the particle swarm algorithm is iteratively used to obtain the maximum photovoltaic access capacity of each grid to be evaluated after optimization and solution that meets the inverse time overcurrent protection requirements. The specific optimization process includes the following steps:

[0075] Step 4.1, initialize the particle swarm information, for example: the calculated I opi1 , I opi2 ,…t i1 , t i2 , ... are the values ​​required by the constraint condition; initialize the particle swarm information, specifically, the particle swarm information includes the number of particles in the particle swarm PsoSize, the particle dimension, i.e., the number of distributed photovoltaics connected to the distribution network PsoDim, and the maximum number of iterations of the algorithm K max And the photovoltaic capacity combination x and photovoltaic capacity change combination v of each particle, set the number of iterations k, and start the iteration process;

[0076]

[0077] In the formula, S and ΔS represent the capacity and capacity change of photovoltaic, respectively, and the subscripts represent the corresponding distributed photovoltaic;

[0078] Calculate the position of the photovoltaic capacity combination of particle j with the iteration update number k+1 The speed of the combination with the change of photovoltaic capacity As shown below:

[0079]

[0080] In the formula, k represents the current number of iterations, K max represents the maximum number of iterations, j represents the number of particles, ω represents the inertia weight, ω max ,ω min They represent the maximum and minimum values ​​of the inertia weight, c1 and c2 represent normal numbers that measure the importance of self-cognition factors and social influence factors, r1 and r2 represent random numbers uniformly distributed in the region [0,1]. and They represent the position of the photovoltaic capacity combination of particle j at the current iteration number k and the change speed of the photovoltaic capacity combination, Pbest j represents the optimal photovoltaic capacity combination currently found by particle j, and Gbest represents the optimal photovoltaic capacity combination found by all particles so far;

[0081] Step 4.2, set the current iteration number k = 1, and set the particle initial value j = 1;

[0082] Step 4.3: During the iteration process, update the photovoltaic capacity combination of particle j. and t i”.up -t i” The particles with ≥Δt are updated to the individual optimum (the optimal energy access capacity of each protection on each line of the current photovoltaic access point) and the global optimum (the optimal energy access capacity of each protection on each line of each distributed photovoltaic access point in the current power grid). Specifically, it is determined whether the number of particles j that have completed the inspection reaches the total number of particles PsoSize of the particle swarm. If so, the next step is executed; if not, j=j+1 is set, and then the individual optimum and the global optimum are returned to continue to be updated;

[0083] Step 4.4: Check whether the number of iterations k has reached the maximum number of iterations K max : If it is reached, the current optimal photovoltaic capacity combination is obtained; if it is not reached, set k = k + 1 and j = 1, and then return to continue the iteration; until k = Kmax, the iteration ends, in which the sum of all photovoltaic access capacities is taken as the maximum photovoltaic access capacity, that is, the applicable boundary of current protection;

[0084] Step 4.5, output the maximum photovoltaic access capacity obtained in step 4.4 as the maximum access capacity of distributed photovoltaics in the power grid system to be evaluated;

[0085] Step 5: Evaluate the applicable boundary of the inverse time overcurrent protection of the power grid to be evaluated: When the photovoltaic capacity is less than the maximum distributed photovoltaic access capacity of the power grid to be evaluated as allowed by calculation, the current protection can sensitively and selectively identify the fault and start protection; however, when the access photovoltaic capacity is greater than the maximum distributed photovoltaic access capacity of the power grid to be evaluated as allowed by calculation, the current protection will malfunction.

[0086] like Figure 3 As shown, a block diagram of a distributed renewable energy access grid inverse time overcurrent protection applicable boundary evaluation system for executing a distributed renewable energy access grid inverse time overcurrent protection applicable boundary evaluation method of the present invention is shown, the system includes a model building module 100, a model target optimization solution module 200 and a protection boundary evaluation module 300, wherein:

[0087] The model building module 100 is used to calculate the current setting value of each protection of each line of the power grid to be evaluated based on the inverse time overcurrent protection setting principle, calculate the inverse time overcurrent protection action time curve of each protection of each line, take the maximum distributed photovoltaic access capacity of the power grid including the distribution network / microgrid as the objective function, and take the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time limit difference between the upper and lower protection actions as constraints to build a photovoltaic maximum access capacity optimization model for the distribution / microgrid with distributed new energy access;

[0088] The model target optimization solution module 200 is used to solve the photovoltaic maximum access capacity optimization model, including: using a particle swarm algorithm to update the photovoltaic capacity combination of particles to obtain the current optimal photovoltaic capacity combination, so as to optimize and solve the maximum photovoltaic access capacity of each grid to be evaluated that meets the inverse time overcurrent protection, iteratively solve the optimal photovoltaic access capacity of each protection on each line of each access point of all grids, calculate the sum of all optimal photovoltaic access capacities to obtain the maximum photovoltaic access capacity, and output the maximum photovoltaic access capacity as the distributed photovoltaic maximum access capacity of the grid to be evaluated as the applicable boundary of the current protection;

[0089] The protection boundary assessment module 300 is used to evaluate the applicable boundary of the inverse time overcurrent protection of the power grid to be evaluated. The evaluation criteria include: if the photovoltaic access capacity of the current line in the power grid to be evaluated is less than the maximum access capacity of the distributed photovoltaic, each current protection sensitively and selectively identifies the fault and starts protection; if the photovoltaic access capacity of the current line in the power grid to be evaluated is greater than the maximum access capacity of the distributed photovoltaic, then each current protection fails.

[0090] Specifically, in the example, the number of iterations is set to 100 times, and the number of particles is 20, that is, 20 particle phasors are initialized immediately, and the particle dimension is 2, that is, distributed photovoltaics are installed at positions C and E. Among them, after each iteration generates particles, the current protection constant is adjusted for the corresponding particles, and then the current protection sensitivity of the inverse time overcurrent protection, the reverse fault does not misoperate, and the time limit difference constraint conditions of the upper and lower protection actions are determined. With the increase in the number of iterations, the maximum total access capacity of distributed photovoltaics increases due to the optimization of the particle swarm algorithm, and reaches stability around the 70th iteration. The change of the access capacity of distributed photovoltaics with inverse time overcurrent protection with the number of iterations is obtained. The maximum total access capacity is 6.55MW. At this time, the access capacity of PV1 and PV2 single photovoltaics is 3.185MW and 3.367MW, as shown in Figure 5 As shown in (a)(b).

[0091] In addition, based on similar inventive concepts, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0092] In addition, based on similar inventive concepts, an embodiment of the present invention further provides a non-transitory computer-readable storage medium on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications made without departing from the principle of the present invention should also be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for evaluating the boundary of inverse time overcurrent protection for distributed photovoltaic access, characterized in that: include: The current setting value of each protection of each line of the power grid to be evaluated based on the inverse time overcurrent protection setting principle is calculated, and the inverse time overcurrent protection action time curve of each protection of each line is calculated. The maximum access capacity of distributed photovoltaic is taken as the objective function, and the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time limit difference between the upper and lower protection actions are taken as constraints to construct the photovoltaic maximum access capacity optimization model; Solving the photovoltaic maximum access capacity optimization model, including: using a particle swarm algorithm to update the photovoltaic capacity combination of particles, obtaining the current optimal photovoltaic capacity combination, optimizing the maximum photovoltaic access capacity of each grid to be evaluated that meets the inverse time overcurrent protection, iteratively solving the optimal photovoltaic access capacity on each access point of all grids on each line, calculating the sum of all optimal photovoltaic access capacities to obtain the maximum photovoltaic access capacity, and outputting the maximum photovoltaic access capacity as the distributed photovoltaic maximum access capacity of the grid to be evaluated as the applicable boundary of the current protection; An assessment of the applicable boundaries of the inverse time overcurrent protection of the power grid to be evaluated is performed, and the evaluation criteria include: if the photovoltaic access capacity of the current line in the power grid to be evaluated is less than the maximum access capacity of the distributed photovoltaic, each current protection sensitively and selectively identifies the fault and starts protection; if the photovoltaic access capacity of the current line in the power grid to be evaluated is greater than the maximum access capacity of the distributed photovoltaic, the current protection malfunctions.

2. According to claim 1, a distributed photovoltaic access inverse time overcurrent protection boundary assessment method is characterized in that: The expression of the photovoltaic maximum access capacity optimization model is as follows: ①The objective function of each line i is expressed as follows: maxS isum =S pvi1 +S pvi2 +...+S pvin In the formula, S pvn represents the nth photovoltaic access capacity corresponding to each line i in the power grid; ②The constraints are expressed as follows: In the formula, I op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. m represents the number of lines other than the end line of each feeder. I PVi'.up It represents the reverse fault current value flowing through the nearest protection upstream of each photovoltaic access point i', t i” Indicates the action time limit of each protection i" when a fault occurs at the head end of the line, t i”.up Indicates the action time limit of the superior protection of each protection i" when a fault occurs at the head end of the line.

3. The method for evaluating the boundary of inverse time overcurrent protection for distributed photovoltaic access according to claim 1 is characterized in that: The step of optimizing and solving the maximum photovoltaic access capacity of each grid to be evaluated that satisfies the inverse time overcurrent protection by using the particle swarm algorithm further includes: Update the photovoltaic capacity combination of particles, for both and t i”.up -t i” The particles with ≥Δt are updated to the optimal photovoltaic access capacity on each line of the current photovoltaic access point and the optimal photovoltaic access capacity on each line of each distributed photovoltaic access point in the current power grid until the total number of particles in the particle swarm is reached, and the particle swarm optimization is completed. op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, It represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. Δt represents the minimum time difference between the upper and lower level inverse time overcurrent protection.

4. The method for evaluating the boundary of inverse time overcurrent protection for distributed photovoltaic access according to claim 1 is characterized in that: The particle swarm algorithm further includes calculating the position of the photovoltaic capacity combination of particle j with the iterative update number k+1 The speed of the combination with the change of photovoltaic capacity As shown below: In the formula, k represents the current number of iterations, K max represents the maximum number of iterations, j represents the number of particles, ω represents the inertia weight, ω max ,ω min They represent the maximum and minimum values ​​of the inertia weight, c1 and c2 represent normal numbers that measure the importance of self-cognition factors and social influence factors, r1 and r2 represent random numbers uniformly distributed in the region [0,1]. and They represent the position of the photovoltaic capacity combination of particle j at the current iteration number k and the change speed of the photovoltaic capacity combination, Pbest j represents the optimal photovoltaic capacity combination currently found by particle j, and Gbest represents the optimal photovoltaic capacity combination found by all particles so far.

5. A distributed photovoltaic access inverse time overcurrent protection applicable boundary assessment system, characterized in that: It includes model building module, model target optimization solution module and protection boundary assessment module, among which: The model building module is used to calculate the current setting value of each protection of each line of the power grid to be evaluated based on the inverse time overcurrent protection setting principle, calculate the inverse time overcurrent protection action time curve of each protection of each line, take the maximum distributed photovoltaic access capacity of the power grid including the distribution network / microgrid as the objective function, and take the single photovoltaic access capacity, current protection sensitivity, reverse protection without false operation and the time difference between the upper and lower protection actions as constraints to build the photovoltaic maximum access capacity optimization model of the distribution / microgrid with distributed new energy access; The model target optimization solution module is used to solve the photovoltaic maximum access capacity optimization model, including: using the particle swarm algorithm to update the photovoltaic capacity combination of particles, obtaining the current optimal photovoltaic capacity combination, optimizing the maximum photovoltaic access capacity of each grid to be evaluated that meets the inverse time overcurrent protection, iteratively solving the optimal photovoltaic access capacity on each line of each access point of all grids, calculating the sum of all optimal photovoltaic access capacities to obtain the maximum photovoltaic access capacity, and outputting the maximum photovoltaic access capacity as the distributed photovoltaic maximum access capacity of the grid to be evaluated as the applicable boundary of the current protection; The protection boundary assessment module is used to evaluate the applicable boundaries of the inverse time overcurrent protection of the power grid to be evaluated. The assessment criteria include: if the photovoltaic access capacity of the current line in the power grid to be evaluated is less than the maximum access capacity of the distributed photovoltaic, each current protection sensitively and selectively identifies the fault and starts protection; if the photovoltaic access capacity of the current line in the power grid to be evaluated is greater than the maximum access capacity of the distributed photovoltaic, each current protection malfunctions.

6. The method for evaluating the applicable boundary of inverse time overcurrent protection for distributed photovoltaic access according to claim 1 is characterized in that: The expression of the photovoltaic maximum access capacity optimization model is as follows: ①The objective function of each line i is expressed as follows: maxS isum =S pvi1 +S pvi2 +...+S pvin In the formula, S pvn represents the nth photovoltaic access capacity corresponding to each line i in the power grid; ②The constraints are expressed as follows: In the formula, I op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. m represents the number of lines other than the end line of each feeder. I PVi'.up It represents the reverse fault current value flowing through the nearest protection upstream of each photovoltaic access point i', t i” Indicates the action time limit of each protection i" when a fault occurs at the head end of the line, t i”.up Indicates the action time limit of the superior protection of each protection i" when a fault occurs at the head end of the line.

7. The method for evaluating the applicable boundary of inverse time overcurrent protection for distributed photovoltaic access according to claim 1 is characterized in that: The step of optimizing and solving the maximum photovoltaic access capacity of each grid to be evaluated that satisfies the inverse time overcurrent protection by using the particle swarm algorithm further includes: Update the photovoltaic capacity combination of particles, for both and t i”.up -t i” The particles with ≥Δt are updated to the optimal photovoltaic access capacity on each line of the current photovoltaic access point and the optimal photovoltaic access capacity on each line of each distributed photovoltaic access point in the current power grid until the total number of particles in the particle swarm is reached, and the particle swarm optimization is completed. op.i It represents the current protection threshold value of each line i based on the inverse time overcurrent protection setting principle, It represents the fault current flowing through each line i when the PV upstream of each line i and other feeders are out of operation and a two-phase short circuit fault occurs at the end of the next line of each line i. Δt represents the minimum time difference between the upper and lower level inverse time overcurrent protection.

8. The method for evaluating the boundary of inverse time overcurrent protection for distributed photovoltaic access according to claim 1 is characterized in that: The particle swarm algorithm further includes calculating the position of the photovoltaic capacity combination of particle j with the iterative update number k+1 The speed of the combination with the change of photovoltaic capacity As shown below: In the formula, k represents the current number of iterations, K max represents the maximum number of iterations, j represents the number of particles, ω represents the inertia weight, ω max ,ω min They represent the maximum and minimum values ​​of the inertia weight, c1 and c2 represent normal numbers that measure the importance of self-cognition factors and social influence factors, r1 and r2 represent random numbers uniformly distributed in the region [0,1]. and They represent the position of the photovoltaic capacity combination of particle j at the current iteration number k and the change speed of the photovoltaic capacity combination, Pbest j represents the optimal photovoltaic capacity combination currently found by particle j, and Gbest represents the optimal photovoltaic capacity combination found by all particles so far.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for evaluating applicable boundaries of inverse time overcurrent protection for distributed photovoltaic access according to any one of claims 1 to 4 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for evaluating applicable boundaries of inverse time overcurrent protection for distributed photovoltaic access according to any one of claims 1 to 4 is implemented.

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