Power distribution network voltage sag evaluation method, system and device and storage medium

Abstract

The invention relates to the technical field of voltage sag assessment, and discloses a power distribution network voltage sag assessment method, system and device and a storage medium, and the method comprises the steps: calculating an initial terminal voltage sag amplitude according to an initial reactive power control coefficient corresponding to a fault of a photovoltaic field station in a photovoltaic output scene, and building an initial field station equivalent model; accessing the initial station equivalent model to the power distribution network for fault simulation to obtain a reactive power control coefficient and a unit working condition number, and performing iterative updating on the reactive power control coefficient and the unit working condition number according to a preset algorithm until a final station equivalent model is obtained; and establishing a voltage sag severity degree set according to the node voltage sag amplitude corresponding to the final station equivalent model, and performing weighted summation on the voltage sag severity degree set according to the scene weight to obtain a voltage sag evaluation index. According to the method, the voltage sag severity degree under the condition that the high-proportion photovoltaic field station is connected to the power distribution network can be accurately evaluated, so that the safety and the reliability of power grid operation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage sag assessment, and in particular to a method, system, device and storage medium for assessing voltage sag in a distribution network. Background Art

[0002] The trend of high-proportion distributed new energy sites being connected to the distribution network is becoming increasingly obvious, which has promoted the optimization of the energy industry structure and achieved low-carbon, environmentally friendly and ecologically harmonious development. However, the access of large-scale distributed new energy sites will affect the stable structure of the distribution network. Taking photovoltaic power sources as an example, after being connected to the grid, they show dynamic characteristics different from traditional power sources. The impact of their low penetration characteristics and off-grid characteristics on the active distribution network has attracted widespread attention.

[0003] Voltage sag is the most frequent and most serious power quality problem, which causes great economic losses to sensitive users. As more and more new energy sources are connected to the distribution network, the voltage sag problem has been affected to a certain extent. Photovoltaic stations with low voltage ride-through capabilities will compensate for reactive power during faults, which alleviates the voltage sag to a certain extent. However, severe voltage sag may cause the photovoltaic power station to be continuously disconnected from the grid, further deepening the severity of the voltage sag in the distribution network.

[0004] For the voltage sag assessment of high-proportion distributed renewable energy sites connected to the distribution network, most of the existing fault simulation-based assessment methods ignore the fault characteristics of the photovoltaic power generation system during the grid fault and only simplify it as a traditional power source. However, the equivalent modeling of the existing single photovoltaic power source is difficult to characterize the output characteristics of the photovoltaic site under various uncertain factors, as well as the mutual correlation between different photovoltaic power sources in the site. Therefore, it is difficult to accurately characterize the impact of different working conditions of large-scale photovoltaic sites on the severity of voltage sag, resulting in inaccurate voltage sag assessment results. In order to achieve accurate assessment of voltage sag when large-scale renewable energy sites are connected to the distribution network, a distribution network voltage sag assessment method that considers the working conditions of the internal units of the photovoltaic site is urgently needed. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a distribution network voltage sag assessment method, system, device and storage medium, which can achieve the technical effect of accurately assessing the severity of voltage sag when a high proportion of photovoltaic stations are connected to the distribution network by accurately characterizing the interaction between voltage sag and the operating conditions of the units inside the photovoltaic station.

[0006] In a first aspect, the present invention provides a method for evaluating a voltage sag in a distribution network, the method comprising: According to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, the initial terminal voltage sag amplitude of the photovoltaic unit is calculated, and according to the initial terminal voltage sag amplitude, the initial station equivalent model based on the number of unit operating conditions is established; The initial station equivalent model is connected to the distribution network for fault simulation to obtain the reactive power control coefficient and the corresponding number of unit operating conditions, and the reactive power control coefficient and the number of unit operating conditions are iteratively updated according to the preset algorithm until the final station equivalent model is obtained; According to the node voltage sag amplitude corresponding to the final station equivalent model, a voltage sag severity set is established, and the voltage sag severity set is weighted and summed according to the scenario weight to obtain the voltage sag evaluation index.

[0007] Furthermore, the steps of calculating the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station in each photovoltaic output scenario, and establishing the initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude include: According to the photovoltaic output scenario and the corresponding fault set, the photovoltaic units of the photovoltaic station are analyzed for off-grid disconnection, and the photovoltaic off-grid probability is obtained; Using the Monte Carlo method, an initial simulation scheme based on photovoltaic output scenarios, fault information and initial reactive power control coefficients is established, and the initial simulation scheme is simulated to obtain the initial node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario; The initial grid connection point in the initial node voltage sag amplitude is taken as the balancing node, and the power flow calculation of the photovoltaic station is performed according to the initial reactive power control coefficient to obtain the initial terminal voltage sag amplitude of the photovoltaic unit; The initial terminal voltage sag amplitude is compared with the voltage threshold, and the number of initial unit operating conditions under different operating conditions is obtained according to the comparison result and the photovoltaic grid-off probability; According to the initial reactive power control coefficient and the corresponding number of initial unit operating conditions, an initial station equivalent model of the photovoltaic station is established.

[0008] Furthermore, the step of connecting the initial station equivalent model to the distribution network for fault simulation to obtain the reactive power control coefficient and the corresponding number of unit operating conditions includes: The initial station equivalent model is connected to the distribution network for fault simulation to obtain the node voltage sag amplitude; According to the first grid connection point voltage in the node voltage sag amplitude, a first reactive power control coefficient is obtained through a preset coefficient identification model; The first unit terminal voltage sag amplitude is obtained according to the first grid connection point voltage and the first reactive power control coefficient, and the number of first unit operating conditions under different operating conditions is determined according to the first unit terminal voltage sag amplitude.

[0009] Furthermore, the coefficient identification model is composed of a neural network model and a parameter calculation model, the input data of the neural network model is the voltage sag amplitude at the grid connection point, the output data of the neural network model is the voltage sag amplitude at the machine end, the input data of the parameter calculation model are the voltage sag amplitude at the grid connection point and the voltage sag amplitude at the machine end, and the output data of the parameter calculation model is the reactive power control coefficient.

[0010] Furthermore, the step of iteratively updating the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained includes: Establishing a station equivalent model of the photovoltaic station according to the first reactive power control coefficient and the number of first unit operating conditions; Connecting the station equivalent model to the distribution network for fault simulation, obtaining the voltage of the second grid connection point, and determining whether the difference between the second grid connection point and the first grid connection point is greater than a difference threshold; If it is greater than, then the second reactive power control coefficient is obtained according to the second grid connection point voltage and the coefficient identification model, and the second reactive power control coefficient is used as the updated reactive power control coefficient; otherwise, the first reactive power control coefficient is used as the updated reactive power control coefficient; According to the updated reactive power control coefficient, the number of operating conditions of the second unit is obtained, and according to the updated reactive power control coefficient and the number of operating conditions of the second unit, the station equivalent model is updated to obtain an updated station equivalent model; Determine whether the number of operating conditions of the second unit is consistent with the number of operating conditions of the first unit. If they are consistent, the updated station equivalent model is used as the final station equivalent model. If they are inconsistent, the updated station equivalent model is connected to the distribution network for fault simulation iteration until the final station equivalent model is obtained.

[0011] Furthermore, the step of establishing a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and weighted summing the voltage sag severity set according to the scenario weight to obtain the voltage sag evaluation index includes: According to the node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario in the final station equivalent model, a voltage sag severity set is established; According to the entropy weight method and the probability of photovoltaic output scenarios, the scene weights of each photovoltaic output scenario are obtained; The voltage sag severity set is weighted and summed according to the scenario weights to obtain the voltage sag assessment index.

[0012] Furthermore, the step of taking the initial grid connection point in the initial node voltage sag amplitude as the balancing node and performing power flow calculation on the photovoltaic station according to the initial reactive power control coefficient to obtain the initial terminal voltage sag amplitude of the photovoltaic unit includes: According to the reactive power priority control strategy and the initial reactive power control coefficient of the photovoltaic station during the power grid fault, a current reference value is obtained; The initial grid-connected point in the initial node voltage sag amplitude is taken as the balancing node. According to the initial reactive power control coefficient and the current reference value, the power flow calculation is performed on the photovoltaic station to obtain the initial terminal voltage sag amplitude of the photovoltaic unit.

[0013] In a second aspect, the present invention provides a distribution network voltage sag assessment system, the system comprising: The initial model building module is used to calculate the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, and to establish the initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude; The model iteration and update module is used to connect the initial station equivalent model to the distribution network for fault simulation, obtain the reactive power control coefficient and the corresponding number of unit operating conditions, and iteratively update the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained; The evaluation index calculation module is used to establish a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and to perform weighted summation on the voltage sag severity set according to the scenario weight to obtain the voltage sag evaluation index.

[0014] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0016] The present invention provides a distribution network voltage sag assessment method, system, device and storage medium. Through simulation iterative calculation, the present invention can accurately depict the interactive influence relationship between voltage sag and the internal unit operating conditions of photovoltaic stations, and can accurately assess the severity of voltage sag when a high proportion of photovoltaic stations are connected to the distribution network, thereby improving the weak links of grid voltage, improving the quality of grid power supply, providing accurate data support, and further improving the safety and reliability of grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of a flow chart of a method for evaluating a voltage sag in a distribution network according to an embodiment of the present invention; Figure 2 is a schematic diagram of the division of the uncertain region of low voltage ride-through of a photovoltaic unit in an embodiment of the present invention; Figure 3 is a structural schematic diagram of a distribution network voltage sag assessment system according to an embodiment of the present invention; Figure 4 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 The first embodiment of the present invention provides a method for evaluating voltage sag in a distribution network, which includes steps S10 to S30: Step S10, calculating the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station in each photovoltaic output scenario, and establishing an initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude; Step S20, connecting the initial station equivalent model to the distribution network for fault simulation, obtaining the reactive power control coefficient and the corresponding number of unit operating conditions, and iteratively updating the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained; Step S30, establishing a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and performing weighted summation on the voltage sag severity set according to the scenario weight to obtain a voltage sag evaluation index.

[0020] In the present invention, for high-proportion distributed new energy stations connected to the distribution network, the randomness of photovoltaic output makes the amplitude of temporary drop of the distribution network busbar also uncertain. Therefore, under different output conditions, the working conditions of the units in the photovoltaic station caused by the same short-circuit fault may be different. According to the amplitude of the temporary drop of the terminal voltage of the photovoltaic unit, the unit working conditions can be divided into three states, namely normal working state, low voltage ride-through state and off-grid state. Among them, due to the ambiguity of the low voltage ride-through of the photovoltaic inverter, there is uncertainty in the off-grid of the photovoltaic unit, which will cause the equivalent model during the photovoltaic station fault to change. Therefore, it is necessary to consider the uncertainty of photovoltaic output, the uncertainty of faults and the uncertainty of photovoltaic off-grid, and analyze the working conditions of the units inside the photovoltaic station, so as to accurately characterize the output characteristics of the photovoltaic station, thereby accurately evaluating the voltage drop of the distribution network.

[0021] In a preferred embodiment, the uncertainty of power grid output, the uncertainty of fault probability and the uncertainty of power off-grid are first analyzed to establish a multiple uncertainty model of photovoltaic unit operating conditions. Since the reactive control coefficient of the photovoltaic inverter is different under different photovoltaic output scenarios and different distribution network faults, the reactive control parameters are also uncertain, which in turn affects the flow calculation inside the photovoltaic station and causes changes in the unit operating conditions. Therefore, in this embodiment, the impact of control parameter uncertainty on the unit operating conditions is considered. On the basis of the multiple uncertainty model of photovoltaic unit operating conditions, an equivalent model of photovoltaic stations considering multiple uncertainties of operating conditions is established. The specific steps include: According to the photovoltaic output scenario and the corresponding fault set, the photovoltaic units of the photovoltaic station are analyzed for off-grid disconnection to obtain the photovoltaic off-grid probability; Using the Monte Carlo method, an initial simulation scheme based on photovoltaic output scenarios, fault information and initial reactive power control coefficients is established, and the initial simulation scheme is simulated to obtain the initial node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario; Taking the initial grid-connected point in the initial node voltage sag amplitude as the balancing node, the power flow calculation of the photovoltaic station is performed according to the initial reactive power control coefficient to obtain the initial terminal voltage sag amplitude of the photovoltaic unit; The initial terminal voltage sag amplitude is compared with the voltage threshold, and the number of initial unit operating conditions under different operating conditions is obtained according to the comparison result and the photovoltaic grid-off probability; According to the initial reactive power control coefficient and the corresponding number of initial unit operating conditions, an initial station equivalent model of the photovoltaic station is established.

[0022] In this embodiment, the photovoltaic output scenario is divided into n scenarios (S1, S2, S3, ..., Sn), and the probability density function f(P pv ) calculates the probability of each scenario, and the calculation formula of its probability density function is: Where P pv is the output power of the solar cell, P pv,max is the maximum output power of the solar cell, is the Beta distribution function, and are the parameters related to the Beta distribution function.

[0023] Then, the probability of a specific photovoltaic output range is calculated based on the probability density function. Specifically, the photovoltaic output range [X 1 ,X 2 ], indicating the photovoltaic power output power in the interval; secondly, according to f(P pv ) Calculate the photovoltaic output in the range [X1 ,X 2 ], where the probability density function is integrated to obtain: Then calculate the probability within the interval: Where f(*) represents the probability density function, P represents the probability of photovoltaic output scenario, and X represents the photovoltaic output power.

[0024] The Monte Carlo method is used to randomly sample faults in each scenario to obtain the fault set F S1,i ,F S2,i ,F S3,i ,…,F Sn,i , where i=1,2,3,…,m, and m represents the number of faults in the fault set. The off-grid analysis of the internal units of the photovoltaic station is carried out for the fault sets under different scenarios. Combined with the uncertainty of photovoltaic low voltage ride-through, the data-driven method is used to fit the photovoltaic off-grid probability. The specific fitting steps include: According to the influence of the sag event characteristics on the photovoltaic grid-trip characteristics, the low voltage ride-through uncertainty area is divided into three areas, namely A, B and C. min and t max They represent the minimum and maximum values ​​of the voltage sag duration t, U pv,min and U pv,max They represent the minimum and maximum values ​​of the voltage sag amplitude U, respectively. Figure 2 As shown in Figure 2, the probability of grid disconnection in area A depends on the voltage sag amplitude U and the voltage sag duration t, which can be expressed as ; The probability of disconnection in area B depends on the voltage sag amplitude U, which is expressed as ; The probability of disconnection in area C depends on the duration of voltage sag t, which can be expressed as ; a, b, c, d represent the variable influencing parameters, which are obtained through data-driven fitting.

[0025] By inputting the sag event set R as the training input and the photovoltaic grid-off situation set T corresponding to the sag event set as the training output, the variable influencing parameter feature training is performed to determine the parameters a, b, c, and d.

[0026] Monte Carlo sampling is used to determine the photovoltaic output scenario and the corresponding fault information, and the initial reactive power control coefficient under the corresponding scenario is generated, so as to establish an initial simulation scheme based on the photovoltaic output scenario, fault information and initial reactive power control coefficient. Among them, the reactive power control coefficient is expressed in the form of a set: In the formula, (Kq1 ,K q2 ) ni represents the reactive power control coefficient combination under the ith fault in the nth PV output scenario, K q1 and K q2 Both are reactive power control coefficients.

[0027] After the initial simulation scheme is obtained, the initial node voltage sag amplitude set under the i-th fault in the n-th photovoltaic output scenario is obtained by performing fault simulation on the initial simulation scheme. The node in this embodiment refers to the grid node. Then, according to the voltage sag amplitude of the grid node, the initial terminal voltage sag amplitude of the photovoltaic unit in the photovoltaic station in each scenario is calculated. The specific calculation steps include: According to the reactive power priority control strategy and the initial reactive power control coefficient of the photovoltaic station during the power grid fault, a current reference value is obtained; The initial grid-connected point in the initial node voltage sag amplitude is taken as the balancing node. According to the initial reactive power control coefficient and the current reference value, the power flow calculation is performed on the photovoltaic station to obtain the initial terminal voltage sag amplitude of the photovoltaic unit.

[0028] In this embodiment, during a grid fault, the photovoltaic power plant adopts a reactive power priority control strategy, and the photovoltaic output current quickly tracks the upper current reference value. The current reference value is shown in the following formula: In the formula, I qref and I dref are the reference values ​​of the q-axis and d-axis components on the AC side of the inverter, I dref0 is the initial value of the d-axis current reference value, I N is the rated current, I max is the maximum allowable current value of the photovoltaic inverter during the fault period, preferably 1.2 times of I N , U x is the voltage drop amplitude at the terminal of the PV generator during the fault period, U th,lvrt is the voltage threshold for the unit to enter low voltage ride through, U th,off K is the voltage threshold for the unit to enter the off-grid state. q1 K is the reactive power control coefficient when the voltage is between [0.2, 0.9] q2 k is the reactive power compensation coefficient when the voltage is between [0,0.2], u Indicates the voltage drop degree.

[0029] The photovoltaic output power expression during the grid fault is obtained by combining the output current and the photovoltaic unit output during the photovoltaic fault. The grid connection point of the photovoltaic station is used as the balance node, and the power flow equation during the internal fault of the photovoltaic station is listed. Among them, the photovoltaic unit output expression is: The output current reference value expression during the fault period and the photovoltaic unit output expression are combined to obtain the photovoltaic unit output expression during the fault period: Where P x , Q x is the expression of active and reactive output of photovoltaic units, K P is the inverter active output parameter, P x,fault , Q x,fault are respectively the active output and reactive output of the photovoltaic unit during the fault period, P x,ref is the active output reference value, which depends on the sunlight at the time of the fault, that is, g(U x )、h(U x ) represent the active and reactive power of the photovoltaic unit with respect to U x expression.

[0030] Based on the output expression of the photovoltaic unit during the fault period, the Jacobian matrix during the fault period is derived, and the power flow calculation is performed using the Newton-Raphson method to obtain the voltage sag amplitude at the terminal of each photovoltaic unit. The specific power flow calculation steps can refer to the conventional calculation steps and will not be repeated here.

[0031] After obtaining the terminal voltage sag amplitude of the photovoltaic unit, the unit is divided into three operating conditions according to the comparison relationship between the terminal voltage sag amplitude and the above-mentioned photovoltaic low voltage protection action threshold, among which U ni,pvx It represents the voltage drop amplitude of the generator terminal under the ith fault of the nth photovoltaic output scenario. When the photovoltaic unit is in normal operation, When the PV system is in low voltage ride-through state, When the PV system is disconnected from the grid.

[0032] Then, according to the probability of photovoltaic off-grid, the number of unit operating conditions corresponding to each operating condition is calculated: Where M on,ni 、M lvrt,ni and M off,ni They represent the number of unit operating conditions under normal operation, low voltage ride-through and off-grid conditions, respectively. th,lvrt is the voltage threshold for the unit to enter low voltage ride through, U th,off is the voltage threshold for the unit to enter the off-grid state, m on,ni Indicates the number of units in normal working condition, m lvrt,ni Indicates the number of units whose voltage is lower than the low voltage ride-through threshold, m off,ni Indicates the number of units whose voltage is lower than the low voltage protection threshold, Poff Represents the probability of PV off-grid.

[0033] Finally, according to the number of unit operating conditions under the three operating conditions corresponding to each reactive control coefficient combination in the reactive control coefficient set, the unit operating condition set M is established. ni,pv , and establish the initial equivalent model Z of the photovoltaic station based on the set of unit operating conditions corresponding to the reactive power control coefficient pv0 , where M ni,pv ={M on,ni ,M lvrt,ni ,M off,ni}.

[0034] In the above steps, this embodiment establishes an initial equivalent model of the photovoltaic station based on the multiple uncertainties of the photovoltaic unit operating conditions. In the actual operation of the distribution network, the value of the reactive control coefficient depends on the voltage at the photovoltaic station grid connection point. At the same time, as a variable for the calculation of the internal power flow of the photovoltaic station, it affects the calculation results, thereby affecting the division of the operating conditions of the units inside the photovoltaic station, and further affecting the evaluation results of the voltage sag of the distribution network, that is, the two are mutually influential. At the same time, for the off-grid units, photovoltaic off-grid will aggravate the severity of the voltage sag, and more serious voltage sags may cause other photovoltaic chains inside the station to be off-grid, and the two are also interactive. Based on this, the present invention considers the interactive influence of voltage sag and the operating conditions of the internal units of the photovoltaic station on the basis of the multiple uncertainty model of the unit operating conditions, establishes an interactive influence model of the photovoltaic station and the distribution network, and updates the internal unit operating conditions of the photovoltaic station with different scenarios and different faults through simulation iterative calculation.

[0035] First, the initial station equivalent model is connected to the distribution network for fault simulation to obtain a node voltage sag amplitude set, and the grid connection point voltage of the photovoltaic station is selected from the node voltage sag amplitude set, and the reactive power control coefficient and the corresponding number of unit operating conditions are determined according to the grid connection point voltage. The specific steps include: The initial station equivalent model is connected to the distribution network for fault simulation to obtain the node voltage sag amplitude; According to the first grid connection point voltage in the node voltage sag amplitude, a first reactive power control coefficient is obtained through a preset coefficient identification model; The first unit terminal voltage sag amplitude is obtained according to the first grid connection point voltage and the first reactive power control coefficient, and the number of first unit operating conditions under different operating conditions is determined according to the first unit terminal voltage sag amplitude.

[0036] In this embodiment, the initial station equivalent model is connected to the distribution network for fault simulation, and the voltage of the grid connection point of the photovoltaic station is selected from the node voltage sag amplitude of the simulated grid node as the first grid connection point voltage U ni,pv1 , input the first grid-connected point voltage into the coefficient identification model to obtain the first reactive power control coefficient (Kq1 ,K q2 ) 1 Here, the coefficient identification model is a pre-established model for generating reactive control coefficients. The coefficient identification model is constructed based on the neural network model and the parameter calculation model. The neural network model adopts a fully connected neural network, and its input data is the voltage sag amplitude of the grid connection point, and its output data is the voltage sag amplitude of the machine end. Its training data set is obtained by connecting the internal detailed model of the photovoltaic station to the distribution network for fault simulation. The training data set includes the voltage sag amplitude of the photovoltaic station grid connection point, the reactive control coefficient set, and the voltage sag amplitude of the internal unit of the station. The parameter calculation model is spliced ​​behind the neural network model. The parameter calculation model is constructed based on the reverse deduction of the calculation step of the machine end voltage sag amplitude in the above steps, that is, the reactive control coefficient is obtained by reversely deducing the node voltage sag amplitude and the machine end voltage sag amplitude. At this point, the input data and output data of the neural network model are used as the input data of the parameter calculation model to obtain the predicted value of the reactive control coefficient. The predicted value is calculated with the true value through the loss function and used for the parameter update of the fully connected neural network. Finally, a trained coefficient identification model is obtained. According to the coefficient identification model, only the grid connection point voltage needs to be input to obtain an accurate reactive control coefficient set: Where U pv is the grid connection point voltage of the photovoltaic station, (K q1 ,K q2 ) i represents the i-th type of reactive power control coefficient combination, U i The voltage boundary value corresponding to each type of reactive power control coefficient combination.

[0037] When the first grid-connected point voltage U ni,pv1 After that, it is input into the trained coefficient identification model to output the corresponding first reactive power control coefficient (K q1 ,K q2 ) 1 , the same as the steps of establishing the initial station equivalent model mentioned above, according to the first grid-connected point voltage and the first reactive power control coefficient, the first terminal voltage sag amplitude U of the photovoltaic unit is calculated. ni,pvx1 , according to the first terminal voltage sag amplitude U ni,pvx1 Determine the working conditions of the photovoltaic units, obtain the number of working conditions of the first unit of the photovoltaic station, and thus establish the working condition set M of the first unit ni,pv1 ={M on1,ni ,M lvrt1,ni ,M off1,ni}, then establish the equivalent model Z of the photovoltaic station based on the first unit operating condition set pv1 .

[0038] In this embodiment, the station equivalent model is obtained by performing fault simulation on the initial station equivalent model, and then the station equivalent model is connected to the distribution network to simulate the same fault, and the final station equivalent model is obtained through simulation iteration. The specific iterative update steps include: Establishing a station equivalent model of the photovoltaic station according to the first reactive power control coefficient and the number of first unit operating conditions; Connecting the station equivalent model to the distribution network for fault simulation, obtaining the voltage of the second grid connection point, and determining whether the difference between the second grid connection point and the first grid connection point is greater than a difference threshold; If it is greater than, then the second reactive power control coefficient is obtained according to the second grid connection point voltage and the coefficient identification model, and the second reactive power control coefficient is used as the updated reactive power control coefficient; otherwise, the first reactive power control coefficient is used as the updated reactive power control coefficient; According to the updated reactive power control coefficient, the number of operating conditions of the second unit is obtained, and according to the updated reactive power control coefficient and the number of operating conditions of the second unit, the station equivalent model is updated to obtain an updated station equivalent model; Determine whether the number of operating conditions of the second unit is consistent with the number of operating conditions of the first unit. If they are consistent, the updated station equivalent model is used as the final station equivalent model. If they are inconsistent, the updated station equivalent model is connected to the distribution network for fault simulation iteration until the final station equivalent model is obtained.

[0039] In this embodiment, the station equivalent model is connected to the distribution network to simulate the same fault, and the second grid connection point voltage U ni,pv2 , according to the difference of the voltage boundary values ​​corresponding to each type of reactive control coefficient combination, the difference threshold is determined, and according to the comparison relationship between the difference between the second grid connection point voltage and the first grid connection point voltage and the difference threshold, it is judged whether the first reactive control coefficient needs to be updated: like , , then the reactive power control coefficient is updated, that is, the second grid-connected point voltage is input into the coefficient identification model to obtain the second reactive power control coefficient (K q1 ,K q2 ) 2 ;like , the reactive power control coefficient is not updated, and the first reactive power control coefficient (K q1 ,K q2 ) 1 .

[0040] For the purpose of unified description, the reactive power control coefficient after coefficient update is called the updated reactive power control coefficient. According to the updated reactive power control coefficient, the corresponding second unit operating condition quantity M is calculated by the above steps. ni,pv2 ={M on2,ni ,M lvrt2,ni ,Moff2,ni}, and based on the updated reactive power control coefficient and the number of second unit operating conditions, the station equivalent model Z pv1 Update and obtain the updated station equivalent model Z pv2 .

[0041] Then determine whether the number of units in the second operating condition is equal to the number of units in the first operating condition, that is, whether the number of units under different operating conditions is consistent. If they are consistent, the updated station equivalent model Z pv2 As the final station equivalent model, if the number of any unit is inconsistent, the updated station equivalent model Z pv2 Connect to the distribution network to continue fault simulation, and perform iterative calculations according to the above steps until the number of unit operating conditions obtained from the two fault simulations is completely consistent, that is, the final station equivalent model is obtained.

[0042] Then, according to the node voltage sag amplitude corresponding to the final station equivalent model, a voltage sag severity set is established, and the voltage sag severity set is weighted and summed according to the scenario weight to obtain the voltage sag evaluation index. The specific steps include: According to the node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario in the final station equivalent model, a voltage sag severity set is established; According to the entropy weight method and the probability of photovoltaic output scenarios, the scene weights of each photovoltaic output scenario are obtained; The voltage sag severity set is weighted and summed according to the scenario weights to obtain the voltage sag assessment index.

[0043] In this embodiment, firstly, the voltage sag amplitude of the corresponding grid node is obtained according to the final station equivalent model, and the voltage sag severity set S is established. sag,n : Where U j,n represents the expected voltage sag value of each node under the nth photovoltaic output scenario, U i,j represents the voltage sag amplitude of the jth node under the ith fault, and m represents the total number of faults.

[0044] Then, according to the probability of the photovoltaic output scene, the scene weight is set by the entropy weight method. Specifically, according to the probability of the photovoltaic output scene P i , construct a weight evaluation matrix, and normalize all probability indicators by Min-Max to obtain C i Indicators, and get the evaluation matrix: Where P iis the probability of the ith photovoltaic output scenario, c i is the normalized C corresponding to the nth feature variable i index.

[0045] According to the evaluation matrix C, calculate the information entropy H of the nth scene n , and calculate the information redundancy D n , and further obtain the weight of the nth scene : The evaluation index of the severity of voltage sag considering the uncertainty of PV output is calculated based on the obtained weights: The present embodiment provides a method for evaluating voltage sag in a distribution network. The present invention combines the obtained voltage sag severity set with the tolerance characteristics of sensitive users, evaluates the voltage sag risk by considering the possibility of voltage sag and the severity of user impact, and achieves accurate evaluation of the voltage sag risk in the distribution network that takes into account the impact of photovoltaic unit operating conditions under multiple uncertainties. Through the voltage sag evaluation index, the weak links of the power grid voltage are improved, which can improve the power supply quality of the power grid, reduce the social losses caused by voltage sag, and further improve the safety and reliability of power grid operation.

[0046] See also Figure 3 Based on the same inventive concept, a distribution network voltage sag assessment system proposed in a second embodiment of the present invention includes: The initial model building module 10 is used to calculate the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, and establish an initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude; The model iteration and update module 20 is used to connect the initial station equivalent model to the distribution network for fault simulation, obtain the reactive power control coefficient and the corresponding number of unit operating conditions, and iteratively update the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained; The evaluation index calculation module 30 is used to establish a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and perform weighted summation on the voltage sag severity set according to the scenario weight to obtain the voltage sag evaluation index.

[0047] The technical features and technical effects of the distribution network voltage sag assessment system proposed in the embodiment of the present invention are the same as the method proposed in the embodiment of the present invention, and will not be described in detail here. Each module in the above-mentioned distribution network voltage sag assessment system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0048] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0049] See also Figure 4 , an internal structure diagram of a computer device in an embodiment, the computer device may specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for evaluating a voltage sag in a distribution network is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0050] It can be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computing device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0051] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0052] In summary, an embodiment of the present invention proposes a distribution network voltage sag assessment method, system, device and storage medium. The method calculates the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, and establishes an initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude; the initial station equivalent model is connected to the distribution network for fault simulation to obtain the reactive control coefficient and the corresponding number of unit operating conditions, and the reactive control coefficient and the number of unit operating conditions are iteratively updated according to a preset algorithm until the final station equivalent model is obtained; according to the node voltage sag amplitude corresponding to the final station equivalent model, a voltage sag severity set is established, and the voltage sag severity set is weightedly summed according to the scenario weight to obtain a voltage sag assessment index. The present invention comprehensively considers the uncertainty of photovoltaic output, the uncertainty of faults and the uncertainty of photovoltaic off-grid to analyze the operating conditions of the internal units of the photovoltaic stations in the distribution network, and establishes a photovoltaic station equivalent model suitable for voltage sag analysis according to different operating conditions. Through simulation iterative calculation, the accurate characterization of the interactive influence of voltage sag and the operating conditions of the internal units of the photovoltaic station is achieved, and the severity of the voltage sag is accurately evaluated when a high proportion of photovoltaic stations are connected to the distribution network, thereby providing accurate data support for improving the weak links of the grid voltage and improving the power supply quality of the grid, and further improving the safety and reliability of the grid operation.

[0053] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0054] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for evaluating voltage sag in a distribution network, characterized in that: include: According to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, the initial terminal voltage sag amplitude of the photovoltaic unit is calculated, and according to the initial terminal voltage sag amplitude, the initial station equivalent model based on the number of unit operating conditions is established; The initial station equivalent model is connected to the distribution network for fault simulation to obtain the reactive power control coefficient and the corresponding number of unit operating conditions, and the reactive power control coefficient and the number of unit operating conditions are iteratively updated according to the preset algorithm until the final station equivalent model is obtained; According to the node voltage sag amplitude corresponding to the final station equivalent model, a voltage sag severity set is established, and the voltage sag severity set is weighted and summed according to the scenario weight to obtain the voltage sag evaluation index.

2. The method for evaluating voltage sag in distribution network according to claim 1, characterized in that: The steps of calculating the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station in each photovoltaic output scenario, and establishing the initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude include: According to the photovoltaic output scenario and the corresponding fault set, the photovoltaic units of the photovoltaic station are analyzed for off-grid disconnection, and the photovoltaic off-grid probability is obtained; Using the Monte Carlo method, an initial simulation scheme based on photovoltaic output scenarios, fault information and initial reactive power control coefficients is established, and the initial simulation scheme is simulated to obtain the initial node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario; The initial grid connection point in the initial node voltage sag amplitude is taken as the balancing node, and the power flow calculation of the photovoltaic station is performed according to the initial reactive power control coefficient to obtain the initial terminal voltage sag amplitude of the photovoltaic unit; The initial terminal voltage sag amplitude is compared with the voltage threshold, and the number of initial unit operating conditions under different operating conditions is obtained according to the comparison result and the photovoltaic grid-off probability; According to the initial reactive power control coefficient and the corresponding number of initial unit operating conditions, an initial station equivalent model of the photovoltaic station is established.

3. The method for evaluating voltage sag in distribution network according to claim 2, characterized in that: The step of connecting the initial station equivalent model to the distribution network for fault simulation to obtain the reactive power control coefficient and the corresponding number of unit operating conditions includes: The initial station equivalent model is connected to the distribution network for fault simulation to obtain the node voltage sag amplitude; According to the first grid connection point voltage in the node voltage sag amplitude, a first reactive power control coefficient is obtained through a preset coefficient identification model; The first unit terminal voltage sag amplitude is obtained according to the first grid connection point voltage and the first reactive power control coefficient, and the number of first unit operating conditions under different operating conditions is determined according to the first unit terminal voltage sag amplitude.

4. The method for evaluating voltage sag in distribution network according to claim 3, characterized in that: The coefficient identification model is composed of a neural network model and a parameter calculation model, the input data of the neural network model is the voltage sag amplitude at the grid connection point, the output data of the neural network model is the voltage sag amplitude at the machine end, the input data of the parameter calculation model are the voltage sag amplitude at the grid connection point and the voltage sag amplitude at the machine end, and the output data of the parameter calculation model is the reactive power control coefficient.

5. The method for evaluating voltage sag in distribution network according to claim 3, characterized in that: The step of iteratively updating the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained includes: Establishing a station equivalent model of the photovoltaic station according to the first reactive power control coefficient and the number of first unit operating conditions; Connecting the station equivalent model to the distribution network for fault simulation, obtaining the voltage of the second grid connection point, and determining whether the difference between the second grid connection point and the first grid connection point is greater than a difference threshold; If it is greater than, then the second reactive power control coefficient is obtained according to the second grid connection point voltage and the coefficient identification model, and the second reactive power control coefficient is used as the updated reactive power control coefficient; otherwise, the first reactive power control coefficient is used as the updated reactive power control coefficient; According to the updated reactive power control coefficient, the number of operating conditions of the second unit is obtained, and according to the updated reactive power control coefficient and the number of operating conditions of the second unit, the station equivalent model is updated to obtain an updated station equivalent model; Determine whether the number of operating conditions of the second unit is consistent with the number of operating conditions of the first unit. If they are consistent, the updated station equivalent model is used as the final station equivalent model. If they are inconsistent, the updated station equivalent model is connected to the distribution network for fault simulation iteration until the final station equivalent model is obtained.

6. The method for evaluating voltage sag in distribution network according to claim 1, characterized in that: The step of establishing a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and performing weighted summation on the voltage sag severity set according to the scenario weight to obtain a voltage sag evaluation index comprises: According to the node voltage sag amplitude corresponding to each fault under each photovoltaic output scenario in the final station equivalent model, a voltage sag severity set is established; According to the entropy weight method and the probability of photovoltaic output scenarios, the scene weights of each photovoltaic output scenario are obtained; The voltage sag severity set is weighted and summed according to the scenario weights to obtain the voltage sag assessment index.

7. The method for evaluating voltage sag in distribution network according to claim 2, characterized in that: The step of taking the initial grid-connected point in the initial node voltage sag amplitude as the balancing node and performing power flow calculation on the photovoltaic station according to the initial reactive power control coefficient to obtain the initial terminal voltage sag amplitude of the photovoltaic unit comprises: According to the reactive power priority control strategy and the initial reactive power control coefficient of the photovoltaic station during the power grid fault, a current reference value is obtained; The initial grid-connected point in the initial node voltage sag amplitude is taken as the balancing node. According to the initial reactive power control coefficient and the current reference value, the power flow calculation is performed on the photovoltaic station to obtain the initial terminal voltage sag amplitude of the photovoltaic unit.

8. A distribution network voltage sag assessment system, characterized in that: include: The initial model building module is used to calculate the initial terminal voltage sag amplitude of the photovoltaic unit according to the initial reactive power control coefficient corresponding to each fault of the photovoltaic station under each photovoltaic output scenario, and to establish the initial station equivalent model based on the number of unit operating conditions according to the initial terminal voltage sag amplitude; The model iteration and update module is used to connect the initial station equivalent model to the distribution network for fault simulation, obtain the reactive power control coefficient and the corresponding number of unit operating conditions, and iteratively update the reactive power control coefficient and the number of unit operating conditions according to a preset algorithm until the final station equivalent model is obtained; The evaluation index calculation module is used to establish a voltage sag severity set according to the node voltage sag amplitude corresponding to the final station equivalent model, and to perform weighted summation on the voltage sag severity set according to the scenario weight to obtain the voltage sag evaluation index.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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