Phase modifier optimal configuration method for multi-infeed direct current system

Abstract

The invention provides a multi-infeed direct current system-oriented phase modifier capacity optimization configuration method. The method comprises the following steps of: constructing a system comprehensive voltage recovery capability index model, a system voltage rise improvement total amount model and a full life cycle cost model; constructing a multi-objective optimization model based on the system voltage rise improvement total amount model, the system comprehensive voltage recovery capability index model and the full life cycle cost model; and solving the multi-objective optimization model by adopting a particle swarm optimization algorithm, and determining the access number of the screening node phase modifier of the direct current system. According to the method provided by the invention, the transient overvoltage in the multi-infeed direct current system can be effectively suppressed, and the voltage recovery speed and the operation stability of the power system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimal configuration of power system dynamic reactive power compensation, and particularly relates to a phase modifier optimal configuration method for a multi-infeed DC system. BACKGROUND

[0002] With the large-scale access of new energy and the development of high-voltage direct current transmission projects, the power system is facing new challenges. In a high-proportion new energy DC sending-end system, when the DC system fails to commutate or is blocked, a large amount of reactive power exchange will occur at the converter station, causing serious transient overvoltage problems, and in severe cases, it will lead to a large-scale near-zone new energy station cascading trip. Therefore, it is necessary to equip the new energy sending system with reactive power compensation devices for accurate and rapid compensation or removal. The synchronous phase modifier can effectively improve the voltage stability of the power system by providing reactive power support, reduce the risk of commutation failure, and shorten the voltage recovery time after the fault. In recent years, new large-capacity phase modifiers have been put into use, and the effect of power improvement is significant. In the multi-DC high-proportion new energy area, distributed phase modifiers will be put into production to greatly improve the new energy sending level.

[0003] However, due to the high cost and long construction period of the phase modifier, the rationality of the number, capacity and access location of the phase modifier is a key problem in project implementation. Existing researches mainly focus on single-scene fixed-point configuration or experience-based site selection optimization, and lack of voltage dynamic behavior characteristic evaluation methods for multi-infeed DC systems. Some researches introduce optimization algorithms for selection, but are limited to static analysis and do not integrate dynamic characteristics and economic constraints. In addition, there is no unified process framework and evaluation standard for the key link of phase modifier configuration, which is difficult to meet the scientificity, economy and feasibility requirements of large-scale new energy access to phase modifier configuration. SUMMARY

[0004] In order to overcome the above technical defects, the present application provides a phase modifier capacity optimal configuration method for a multi-infeed DC system. In order to achieve the above purpose, the present application is implemented according to the following technical solution:

[0005] The present application provides a phase modifier capacity optimal configuration method for a multi-infeed DC system, comprising:

[0006] obtaining an initial node voltage change curve of each node in the system without access to the phase modifier, a first node voltage change curve when accessing a phase modifier with different capacities, and a node voltage response of the system node in the disturbance process;

[0007] determining a phase modifier access node candidate set based on the initial node voltage change curve and the first node voltage change curve;

[0008] constructing a system comprehensive voltage recovery capability index model based on the phase modifier access node candidate set and the node voltage response.

[0009] constructing a system voltage rise improvement total model based on the set of STATCOM access node candidates, the initial node voltage change curve and the primary node voltage change curve;

[0010] obtaining operation and maintenance cost of each STATCOM;

[0011] constructing a full life cycle cost model based on the set of STATCOM access node candidates and the operation and maintenance cost of each STATCOM;

[0012] constructing a multi-objective optimization model based on the system voltage rise improvement total model, the system comprehensive voltage recovery capability index model and the full life cycle cost model;

[0013] solving the multi-objective optimization model by using a particle swarm optimization algorithm to determine the access quantity of the selected node STATCOM of the DC system.

[0014] Optionally, the step of obtaining the initial node voltage change curve of each node in the system when not accessing the STATCOM and the primary node voltage change curve when accessing STATCOMs of different capacities and the node voltage response of the system node in the disturbance process comprises:

[0015] obtaining system topology and operating characteristics;

[0016] constructing a disturbance set of typical fault scenarios based on the system topology and operating characteristics;

[0017] performing dynamic simulation on the disturbance set to obtain the initial node voltage change curve of each node in the system when not accessing the STATCOM;

[0018] accessing STATCOMs of different capacities in each node of the system;

[0019] performing dynamic simulation on the typical fault scenarios to obtain the primary node voltage change curve of each node in the system when accessing STATCOMs of different capacities and the node voltage response of the system node in the disturbance process.

[0020] Optionally, the step of determining the set of STATCOM access node candidates based on the initial node voltage change curve and the primary node voltage change curve comprises:

[0021] determining an initial transient voltage rise severity index of each node based on the initial node voltage change curve;

[0022] determining a primary transient voltage rise severity index of each node based on the primary node voltage change curve;

[0023] Determine a candidate set of phase modulation machine access nodes based on the initial transient voltage rise severity index and the first transient voltage rise severity index.

[0024] Optionally, the determining of the candidate set of phase modulation machine access nodes based on the initial transient voltage rise severity index and the first transient voltage rise severity index comprises:

[0025] Determine a change in transient voltage rise severity index of each node based on the initial transient voltage rise severity index and the first transient voltage rise severity index.

[0026] Obtain a change in reactive power injection of the node corresponding to the change in transient voltage rise severity index.

[0027] Determine a phase modulation machine reactive power compensation sensitivity coefficient of each node based on the change in transient voltage rise severity index and the change in reactive power injection.

[0028] Determine a candidate set of phase modulation machine access nodes based on the first transient voltage rise severity index and the phase modulation machine reactive power compensation sensitivity coefficient.

[0029] Optionally, the determining of the candidate set of phase modulation machine access nodes based on the first transient voltage rise severity index and the phase modulation machine reactive power compensation sensitivity coefficient comprises:

[0030] Standardize the first transient voltage rise severity index to obtain a standardized first transient voltage rise severity index.

[0031] Standardize the phase modulation machine reactive power compensation sensitivity coefficient to obtain a standardized phase modulation machine reactive power compensation sensitivity coefficient.

[0032] Construct a node comprehensive scoring model based on the standardized first transient voltage rise severity index and the standardized phase modulation machine reactive power compensation sensitivity coefficient.

[0033] Determine a candidate set of phase modulation machine access nodes based on the node comprehensive scoring model.

[0034] Optionally, the determining of the candidate set of phase modulation machine access nodes based on the node comprehensive scoring model comprises:

[0035] Determine a scoring result of all nodes of the system based on the node comprehensive scoring model.

[0036] Sort the scoring results of all nodes of the system in descending order, and select a first number of high-score nodes as the candidate set of phase modulation machine access nodes.

[0037] Optionally, based on the phase modifier access node candidate set, the initial node voltage change curve and the primary node voltage change curve, a system voltage rise improvement total amount model is constructed, including:

[0038] Based on the phase modifier access node candidate set, the initial transient voltage rise severity index and the primary transient voltage rise severity index, a system voltage rise improvement total amount model is constructed.

[0039] Optionally, the system comprehensive voltage recovery capability index model is constructed based on the phase modifier access node candidate set and the node voltage response, including:

[0040] Based on the node voltage response, a system node voltage recovery speed index is determined.

[0041] Based on the system node voltage recovery speed index and the phase modifier access node candidate set, a system comprehensive voltage recovery capability index model is constructed.

[0042] Optionally, the constraint conditions of the multi-objective optimization model include the following:

[0043] Power balance constraint, state variable constraint, control variable constraint.

[0044] The present application has the following beneficial effects:

[0045] The method provided by the present application can effectively suppress transient overvoltage in a multi-infeed DC system, and improve the voltage recovery speed and operation stability of the power system.

[0046] In addition to the purposes, features and advantages described above, the present application has other purposes, features and advantages. The present application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and the illustrative embodiments of the present application and their description serve the purpose of explaining the present application. In the drawings:

[0048] Figure 1 is a flowchart of a phase modifier capacity optimization configuration method for a multi-infeed DC system provided by an embodiment of the present application;

[0049] Figure 2 is a fault response curve diagram of FA region provided by an embodiment of the present application, Figure 2 (a) is a diagram of changes in various electrical quantities when the phase modifier is put into FA region, Figure 2 (a1) is a waveform diagram of rectifier side reactive power compensation and rectifier station consumption reactive power; Figure 2 (a2) is a waveform diagram of infeed sending end AC system and unbalanced reactive power.Figure 2 (a3) is the sending end converter bus voltage waveform diagram; Figure 2 (a4) is the DC voltage and DC current waveform diagram of the rectifier output; Figure 2 (b) is the electrical quantity change diagram when the phase modifier is not put into the FA area, Figure 2 (b1) is the waveform diagram of the reactive power compensation at the rectifier side and the reactive power consumed by the rectifier station; Figure 2 (b2) is the waveform diagram of the unbalanced reactive power fed into the sending end AC system; Figure 2 (b3) is the sending end converter bus voltage waveform diagram; Figure 2 (b4) is the DC voltage and DC current waveform diagram of the rectifier output;

[0050] Figure 3 is the FB area fault response curve diagram provided by the simulation of the present application, Figure 3 (a) is the electrical quantity change diagram when the phase modifier is put into the FA area, Figure 3 (a1) is the waveform diagram of the reactive power compensation at the rectifier side and the reactive power consumed by the rectifier station; Figure 3 (a2) is the waveform diagram of the unbalanced reactive power fed into the sending end AC system; Figure 3 (a3) is the sending end converter bus voltage waveform diagram; Figure 3 (a4) is the DC voltage and DC current waveform diagram of the rectifier output; Figure 3 (b) is the electrical quantity change diagram when the phase modifier is not put into the FA area, Figure 3 (b1) is the waveform diagram of the reactive power compensation at the rectifier side and the reactive power consumed by the rectifier station; Figure 3 (b2) is the waveform diagram of the unbalanced reactive power fed into the sending end AC system; Figure 3 (b3) is the sending end converter bus voltage waveform diagram; Figure 1 (b4) is the DC voltage and DC current waveform diagram of the rectifier output. DETAILED DESCRIPTION

[0051] The embodiments of the present application will be described in detail below with reference to the accompanying drawings, but the present application can be implemented in various different ways limited and covered by the claims.

[0052] Therefore, in order to solve the above problems, as Figure 2 shown, the present application provides a phase modifier capacity optimization configuration method for a multi-feed DC system, comprising:

[0053] Step S101: obtaining the initial node voltage change curve of each node in the system when the phase modifier is not connected, the first node voltage change curve when the phase modifier of different capacity is connected, and the node voltage response of the system node in the disturbance process;

[0054] For the problem of transient overvoltage that may occur in the multi-infeed DC system under the operating conditions of the target power grid, disturbance analysis under typical fault conditions is carried out to identify the key nodes in the system where the pressure rise risk is prominent. First, for the problem of transient overvoltage that may occur in the multi-infeed DC system under the operating conditions of the target power grid, disturbance analysis under typical fault conditions is carried out to identify the key nodes in the system where the pressure rise risk is prominent. However, before obtaining the node voltage, the system topology and operating characteristics need to be obtained first, and then according to the system and operating characteristics, the disturbance set of the typical fault scenario is constructed, and the typical fault scenario includes multiple DC blocking, single-phase grounding of AC line, and three-phase short circuit.

[0055] After constructing the typical fault scenario, the power system time domain simulation tool is used to perform dynamic simulation on the disturbance set to obtain the initial node voltage change curve of each node in the system without connecting the phase modifier where i represents the node number, j represents the fault type number, and l represents the fault location number. Then, 50Mvar, 100Mvar, 150Mvar, etc. of different capacity of phase modifier are connected to each node of the system, and dynamic simulation is performed again, so that the first node voltage change curve of each node in the system when connecting different capacity of phase modifier is obtained x is the capacity of the connected phase modifier, and the voltage response V of the system node in the disturbance process i (t).

[0056] Step S102: determining a phase modifier access node candidate set based on the initial node voltage change curve and the first node voltage change curve;

[0057] After determining the initial node voltage, the initial transient overvoltage severity index of each node is determined according to the initial node voltage (Transient Overvoltage Severity), which is used to quantify the voltage out-of-limit risk of the node under fault disturbance. The determination process of the index is as follows:

[0058]

[0059] wherein, is the transient overvoltage severity of node i under fault j and fault location l, V cr,k is the voltage threshold value, V e is the system rated voltage, T k is the pressure rise duration in the kth disturbance sample, and n is the total number of disturbance condition samples. B m is whether the wind power grid connection point m causes transient overvoltage risk under DC disturbance, and the expression is as follows:

[0060]

[0061] wherein, represents the voltage maximum value of the grid-connected point m after the disturbance.

[0062] The greater the value, the more serious the voltage limit degree and duration, which is the main indicator for measuring the risk of voltage rise.

[0063] After obtaining the voltage change curve of the primary node, the primary transient voltage rise severity index of each node is determined The calculation process is as follows:

[0064]

[0065] After the initial transient voltage rise severity index and the primary transient voltage rise severity index are calculated, the difference between the two can be obtained to obtain the transient voltage rise severity index change amount ΔTOS of each node i,j,l Since the transient voltage rise severity index change amount of each node is calculated by connecting different capacity phase-modulating machines to each node of the system, the reactive power injection change amount ΔQ corresponding to the transient voltage rise severity index change amount is also obtained at this time sc,i , ΔQ sc,i It can be understood that the reactive power injection change amount of node i is determined according to the transient voltage rise severity index change amount and the reactive power injection change amount, and the phase-modulating machine reactive power compensation sensitivity coefficient SO of each node of the system i The calculation process is as follows:

[0066]

[0067] In the formula, ΔQ sc,i is the reactive power injection change amount of node i, ΔTOS i,j,l is the change of TOS sc,i before and after the installation of the phase-modulating machine with capacity ΔQ i,j,l at node i. The coefficient measures the effect of the phase-modulating machine on the voltage limit improvement of the node, and the greater the SO i , the stronger the voltage rise suppression effect of the unit capacity.

[0068] To realize the optimal selection of nodes, the application introduces a normalization and weighted scoring mechanism to perform dimensionless processing and unified evaluation on the primary transient voltage rise severity index and the phase-modulating machine reactive power compensation sensitivity coefficient SO i of each node of the system, that is, to perform standardization processing on the primary transient voltage rise severity index to obtain the standardized primary transient voltage rise severity index At the same time, the phase-modulating machine reactive power compensation sensitivity coefficient is standardized to obtain the standardized phase-modulating machine reactive power compensation sensitivity coefficient The formula is as follows

[0069]

[0070] In the formula, TOS max , TOS min , SO max , SO min are the maximum and minimum values of TOS and SO i in all nodes, respectively.

[0071] Since the larger the value of TOS indicates the worse the performance, the application reverses the direction of TOS

[0072]

[0073] In the formula, TOS is an index after reversing the index of the first transient voltage rise severity index after standardization.

[0074] According to TOS and the standardized sensitivity coefficient of the SVC reactive power compensation , a node comprehensive scoring model is constructed as follows:

[0075]

[0076] wherein, Score i is the final score of node i, and a ∈ (0, 1) represents the weight of the voltage rise risk factor.

[0077] Further, according to the node comprehensive scoring model, the candidate set Ω c of the SVC access nodes is determined, that is, according to the node comprehensive scoring model, the scoring results of all nodes of the system can be determined, and then the scoring results of all nodes of the system are sorted in descending order, and the first quantity of high-score nodes are selected as the candidate set of the SVC access nodes, and the first quantity can be determined according to the target access quantity, and the set will be used as the input basis for subsequent capacity optimization configuration.

[0078] Step S103: based on the candidate set of the SVC access nodes and the first node voltage change curve, a system comprehensive voltage recovery capability index model is constructed.

[0079] After the determination of the candidate set Ω c of the SVC access nodes is completed, in order to further evaluate the influence of different access positions and configuration capacities on the dynamic response performance of the system, the voltage recovery capability modeling after disturbance is further carried out, and the support effect of the SVC on the transient voltage recovery of the system under different access conditions is quantified.

[0080] In the above access to different capacity of the phase modifier, the node voltage response V i (t) of each node in the system when accessing different capacity of the phase modifier has been collected, which can also be the curve of the voltage of each node changing with time, used to investigate the time required for it to reach a stable value after disturbance, and the rising speed and amplitude.

[0081] In order to quantitatively evaluate the voltage recovery performance, according to the node voltage response V i (t), the system node voltage recovery speed index V rec,i can be determined, which reflects the voltage recovery ability of node i under the support of the phase modifier, and the greater the index, the higher the average value of voltage per unit time and the faster the recovery. The determination process is as follows:

[0082]

[0083] In the formula, T r is a preset evaluation time window.

[0084] Considering the overall performance optimization of the system, the present application further constructs a system comprehensive voltage recovery ability index model V rec,i according to the system node voltage recovery speed index V c and the phase modifier access node candidate set Ω rec,sum , and the recovery ability of all candidate access nodes is weighted and superimposed, and is defined as follows:

[0085]

[0086] In the formula, V rec,sum is the overall voltage recovery ability; k i is the voltage recovery contribution factor of node i, which can be obtained by simulation to obtain its influence on the voltage recovery of the main node of the system; Q sci is the reactive power support capacity of the phase modifier configured for node i. The expression reflects the contribution degree of unit capacity to the recovery ability, which is used for subsequent target model design.

[0087] Step S104: based on the phase modifier access node candidate set, the initial node voltage change curve and the first node voltage change curve, a system voltage rise improvement total amount model is constructed;

[0088] In the foregoing step S102, after determining the transient voltage rise severity indexes of the nodes, the reactive power capacity of each node is adjusted to make the transient overvoltage suppression effect of the overall system optimal, that is, according to the candidate set of phase modifier access nodes, the initial node voltage change curve and the primary node voltage change curve, a system voltage rise improvement total amount model S is constructed, which represents the suppression effect of the phase modifier configuration of all nodes. The greater the index value, the more significant the overall improvement effect of the phase modifier arrangement on the voltage out-of-limit problem.

[0089] Since the initial node voltage change curve and the primary node voltage change curve are obtained, the corresponding initial transient voltage rise severity index and the primary transient voltage rise severity index are obtained, and then according to the initial transient voltage rise severity index and the primary transient voltage rise severity index, and the candidate set of phase modifier access nodes, a system voltage rise improvement total amount model S is constructed, as follows:

[0090]

[0091] Step S105: Obtain the operation and maintenance cost of each phase modifier;

[0092] To realize the optimization of the overall system, the goal of the phase modifier access point selection is to maximize the voltage stability of the system while minimizing the number and configuration cost of the phase modifiers. At this time, the operation and maintenance cost c o of each phase modifier is obtained first.

[0093] Step S106: Construct a full life cycle cost model based on the candidate set of phase modifier access nodes and the operation and maintenance cost of each phase modifier;

[0094] Suppose that N sc,i phase modifiers are configured at node i, the purchase cost of each phase modifier is c sc,i (unit: ten thousand yuan per unit), and the full life cycle cost model C sum is

[0095] C sum = C p +C op (11)

[0096]

[0097] Wherein, C p is the total purchase cost of all nodes in the entire system, C op is the total operation cost of all nodes in the system, the power consumed by the phase modifier in unit time is λ loss ×Q sc,i (unit: MWh), wherein λ loss is the power consumption coefficient of the phase modifier, and if the online time of the phase modifier per year is τsc (unit: hour), and the cost of each unit of electricity is c e (unit: ten thousand yuan / MWh), c o is the operation and maintenance cost of each SVC, and a sc,i is the operation and maintenance coefficient of the SVC, and N sc,i is used to represent the maintenance cost reduction effect due to the centralized configuration of multiple SVCs at the same node:

[0098] a sc,i = 1 - β × (N sc,i - 1) (13)

[0099] where β is the operation and maintenance cost reduction coefficient, usually taking a value between 0.05-0.1, reflecting the maintenance cost saving effect brought by centralized configuration.

[0100] Step S107: based on the system total voltage rise improvement model, the system comprehensive voltage recovery capability index model, and the full life cycle cost model, a multi-objective optimization model is constructed;

[0101] After the construction of the system total voltage rise improvement model, the system comprehensive voltage recovery capability index model, and the system comprehensive configuration cost model, to achieve the global optimal design of the SVC configuration, the present application further constructs a multi-objective optimization model, and introduces corresponding operation constraints and power grid engineering conditions, so that the obtained configuration scheme reaches the coordinated optimal under multiple targets such as voltage rise suppression, voltage recovery, investment control, etc.

[0102] The SVC optimization configuration problem set by the present application contains the following three types of optimization targets:

[0103] (1) Maximum S of voltage rise suppression efficiency

[0104] (2) Maximum V rec,sum of voltage recovery capability

[0105] (3) Minimum C sum of full life cycle cost

[0106] Since the above three targets have a mutual restraint relationship, such as increasing the voltage rise suppression and voltage recovery capability usually requires increasing the SVC capacity and the number of points, and then increasing the cost, therefore the present application adopts the weighting method to construct a single target multi-factor weighted combination optimization problem: the output is the number of access of the SVC at the screening node in the system.

[0107]

[0108] where J is the comprehensive objective function, S base is the maximum total voltage rise improvement obtained by multiple iterations; C is the total value of optimal recovery ability in all possible configuration schemes; C is the highest cost acceptable for the project; w1, w2, and w3 are weighting factors of respective targets, satisfying w1+w2+w3=1, and are set according to the focus of system operation. base C is the highest cost acceptable for the project; w1, w2, and w3 are weighting factors of respective targets, satisfying w1+w2+w3=1, and are set according to the focus of system operation.

[0109] In order to show the saturation effect of the dynamic reactive power compensation device, the installation of the phase modifier capacity constraint is converted into a penalty function added to the objective function, and the penalty factor is used to make the phase modifier configuration optimal, and the variable constraint condition of the dynamic reactive power compensation:

[0110] Power balance constraint:

[0111]

[0112] wherein, G ij , B ij , and θ ij respectively represent the conductance, susceptance and phase angle difference between branch i and branch j; P Gi is the active output of generator node i, P di is the active load of load node i; Q Gi is the reactive output of generator node i; Q di is the reactive load of load node i; U i is the voltage of node i.

[0113] State variable constraint:

[0114]

[0115] Control variable constraint:

[0116]

[0117] wherein, are the lower limit and upper limit of active power, reactive power and voltage, respectively; is the upper and lower limit of the installation of reactive power compensation capacity; N G is the number of generator nodes; N is the number of system nodes; n represents the number of candidate nodes of synchronous phase modifier dynamic reactive power compensation; installation location δ i .

[0118] Step S108: adopting a particle swarm optimization algorithm, solving the multi-objective optimization model to determine the number of access of the DC system screening node phase modifier.

[0119] In order to solve the aforementioned multi-objective constraint optimization problem, the particle swarm optimization algorithm (Particle Swarm Optimization, PSO) is used for solving.

[0120] Each particle represents a camera configuration scheme, and its position vector X contains two dimensions of each candidate node:

[0121] X = [Q] sc1 Q sc2 ,...,Q scn N sc1 N sc2 ,...,N scn (18)

[0122] Among them, Q sci Configure capacity for the camera at node i; N sci Let Q be the number of synchronous condensers at the i-th node; if the node is not selected to connect to a synchronous condenser, then Q... sci =0,N sci =0.

[0123] In each iteration, the particle's position and velocity are updated according to the standard PSO formula:

[0124] Speed ​​updates:

[0125]

[0126] Location update:

[0127]

[0128] in, It is the velocity of particle i in generation t; p is the position of particle i in generation t; bi The historical best position of particle i; g bi ω is the global optimal particle position; c1 and c2 are acceleration learning factors; r1 and r2 are random numbers in the range [0,1].

[0129] To satisfy the integer attribute of the constraint variable, this application specifies N. sci The process employs integer encoding and rounding, and prunes particles that exceed the variable boundaries.

[0130] The fitness function is the multi-objective function J constructed in step four. After each update, the camera configuration scheme corresponding to the particle position is adjusted as follows:

[0131] (1) Input the scheme into the system simulation platform and evaluate the corresponding voltage rise suppression performance S and voltage recovery performance V. rec,sum ;

[0132] (2) Call the equipment investment function and the operating loss model to calculate the life cycle cost C. sum ;

[0133] (3) Substitute the above index into the comprehensive objective function J as the fitness value of the particle;

[0134] (4) If the particle violates the constraint condition, the fitness value is set to a large penalty value to realize the penalty function method constraint processing.

[0135] In each generation, for each particle i, if the current fitness is better than its historical optimum p bi , update its historical optimum record:

[0136]

[0137] If the current particle fitness is better than the global optimum g b , update the global optimum:

[0138]

[0139] To improve the calculation efficiency and avoid unnecessary iterative calculation, set the convergence threshold ε = 10 -11 , if the change of the global optimal objective function is less than the set convergence threshold ε, stop early:

[0140] |J t -J t-1 |<ε (23)

[0141] Where J t represents the value of the comprehensive objective function of the global optimal particle at the tth iteration of the particle swarm optimization algorithm, that is, the number of access of the selected node of the direct current system filter.

[0142] Experimental simulation

[0143] In a large-scale multi-feed new energy direct current sending scene, the research and test of distributed filter configuration are carried out, and various information such as multiple binary table of overvoltage of each new energy station type node, index value of transient voltage rise severity of new energy station and node, maximum value of transient overvoltage suppression effect, and filter configuration screening node value are obtained. Among them, the main parameters of 50MVar small synchronous filter are:

[0144] Table 1 System parameters

[0145]

[0146] In the initial state, time domain simulation is carried out for different scenarios and different faults, and the transient voltage rise severity index value of the 35kV and above new energy power station and collection station node is calculated, the preliminary candidate configuration node of the phase modifier is selected, and according to the principle of reactive power configuration of the power system, the upper and lower limits of the dynamic reactive power compensation capacity of the station to be compensated are set to 800Mvar and 0Mvar under the requirement of ensuring the safe and stable operation of the power system, and the maximum number of 50Mvar small synchronous phase modifiers installed in each region is set to 16 for a total of 3 stations in FA, FB and FC regions.

[0147] The penalty factor ε is set to 10 -11 According to the investigation, the comprehensive cost parameter values of a single 50Mvar phase modifier are as follows: the installation cost of the phase modifier is set to c sc,i = 300,000 yuan per unit, the capital recovery rate is set to p sc = 0.35; the annual average electricity fee is c e = 0.06 yuan / MWh, the online time of the phase modifier is t sc = 7446 hours / year; the planning period is the service life of the phase modifier N year = 30; the power consumption coefficient of the phase modifier is l loss = 1.4%; the operation and maintenance cost of a single phase modifier is c op According to 3% of the cost, it is 900,000 yuan / Mvar. The position information of each particle is returned after each update iteration by PSO, the transient voltage level of each wind power grid under each kind of reactive power compensation capacity and different processing is recorded, TOS is calculated, the economic index of the phase modifier under different installation capacities of the three stations is calculated, and the optimal capacity model objective function value is obtained combined with the penalty factor.

[0148] To verify the effectiveness of the phase modifier optimization configuration method proposed in the application, Figure 3 and Figure 2 the transient voltage response comparison results of FA and FB converter stations under the conditions of phase modifier access and non-access are given.

[0149] Figure 2 It is shown that under the disturbance of three-phase short-circuit fault, the voltage drop amplitude of FA converter station is large. Figure 2 In (a), after configuring 300Mvar phase modifier, the voltage drop is reduced and the recovery speed is improved; Figure 3 In (b), without configuring the phase modifier, there is a significant reactive power gap after fault removal, and the DC system continuously absorbs reactive power in the transient stage, and the voltage is difficult to recover.

[0150] Figure 3 Reflects the transient voltage performance of FB node. Figure 3 In (a), after configuring 600Mvar phase modifier, the voltage waveform is stable; ​In the (b), when the phase modifier is not configured, the fault induced voltage fluctuates sharply and is lower than the critical stability voltage of the dynamic load, resulting in an increase in the slip of the induction motor, locked-rotor, and further voltage instability.

[0151] On the basis of iterative calculation, a configuration scheme of 300 MVar FA access, 600 MVar FB access, and 300 MVar FC access is formed, 6, 12, and 6 phase modifiers are configured respectively, a total of 24 phase modifiers are configured, and the optimal reactive power support capability under the minimum investment cost is realized. In the scheme, FB is preferentially configured with phase modifiers due to weak grid strength and concentrated load; when the configuration reaches the upper limit, FA is configured to bear auxiliary support function, and the overall voltage rise suppression and voltage recovery capability is effectively improved. Under the scheme, the value of the comprehensive objective function J is minimum, and the effectiveness of the optimization method is verified.

[0152] In summary, the method can effectively suppress the transient overvoltage in the multi-infeed DC system, and improve the voltage recovery speed and operation stability of the power system. The particle swarm optimization algorithm is used for solving, the economy and feasibility of the scheme are ensured, and the method has high practical value.

[0153] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for capacity optimization configuration of a phase modifier for a multi-infeed DC system, characterized in that, The method comprises the following steps: obtaining the initial node voltage variation curve of each node in the system when no phase modifier is connected, the primary node voltage variation curve when different capacity phase modifiers are connected, and the node voltage response of the system node during the disturbance process; determining the phase modifier connection node candidate set based on the initial node voltage variation curve and the primary node voltage variation curve; constructing the system comprehensive voltage recovery capability index model based on the phase modifier connection node candidate set and the node voltage response; constructing the system voltage rise improvement total amount model based on the phase modifier connection node candidate set, the initial node voltage variation curve and the primary node voltage variation curve; obtaining the operation and maintenance cost of each phase modifier; constructing the full life cycle cost model based on the phase modifier connection node candidate set and the operation and maintenance cost of each phase modifier; constructing the multi-objective optimization model based on the system voltage rise improvement total amount model, the system comprehensive voltage recovery capability index model and the full life cycle cost model; solving the multi-objective optimization model by using the particle swarm optimization algorithm to determine the connection number of the selected node phase modifier of the DC system.

2. The method of claim 1, wherein, The method further comprises the following steps before the step of obtaining the initial node voltage variation curve of each node in the system when no phase modifier is connected and the primary node voltage variation curve when different capacity phase modifiers are connected and the node voltage response of the system node during the disturbance process: obtaining the system topology structure and operating characteristics; constructing the disturbance set of the typical fault scenario based on the system topology structure and operating characteristics; obtaining the initial node voltage variation curve of each node in the system when no phase modifier is connected by performing dynamic simulation on the disturbance set; connecting different capacity phase modifiers to each node in the system; obtaining the primary node voltage variation curve of each node in the system when different capacity phase modifiers are connected and the node voltage response of the system node during the disturbance process by performing dynamic simulation on the typical fault scenario.

3. The method of claim 2, wherein, The method further comprises the following steps of determining the phase modifier connection node candidate set based on the initial node voltage variation curve and the primary node voltage variation curve: determining the initial transient voltage rise severity index of each node based on the initial node voltage variation curve; determining the primary transient voltage rise severity index of each node based on the primary node voltage variation curve; determining the phase modifier connection node candidate set based on the initial transient voltage rise severity index and the primary transient voltage rise severity index.

4. The method of claim 3, wherein, The method further comprises the following steps of determining the phase modifier connection node candidate set based on the initial transient voltage rise severity index and the primary transient voltage rise severity index: determining the transient voltage rise severity index variation of each node based on the initial transient voltage rise severity index and the primary transient voltage rise severity index; obtaining the reactive power injection variation of the node corresponding to the transient voltage rise severity index variation; determining the phase modifier reactive power compensation sensitivity coefficient of each node based on the transient voltage rise severity index variation and the reactive power injection variation; determining the phase modifier connection node candidate set based on the primary transient voltage rise severity index and the phase modifier reactive power compensation sensitivity coefficient.

5. The method of claim 4, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

6. The method of claim 5, wherein, The method comprises the following steps: The method comprises the following steps:

7. The method of claim 4, wherein the system voltage rise improvement total amount model is constructed based on the candidate set of phase modifier access nodes, the initial node voltage change curve and the first node voltage change curve, and the method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

8. The method of claim 1, wherein, The method comprises the following steps: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following:

9. The method of claim 1, wherein, The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise the following: The constraint conditions of the multi-objective optimization model comprise

Images