CN117318063A - Converter active support type tide optimization control method based on MOGWO - Google Patents

Converter active support type tide optimization control method based on MOGWO Download PDF

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CN117318063A
CN117318063A CN202311257380.2A CN202311257380A CN117318063A CN 117318063 A CN117318063 A CN 117318063A CN 202311257380 A CN202311257380 A CN 202311257380A CN 117318063 A CN117318063 A CN 117318063A
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direct current
power
converter
power flow
control method
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张远实
钱文妍
胡秦然
章飞
李扬
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Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Automation & Control Theory (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a converter active supporting type tide optimization control method based on MOGWO, which comprises the steps of firstly determining the expected fault types of a wind power plant, and setting corresponding reference power variation of a converter station according to different fault types; and then establishing a sensitivity matrix, establishing an optimization target, establishing a MOGWO algorithm model, and finally adjusting system parameters under different types of faults of the offshore wind farm by utilizing the established model so as to achieve the optimal optimization target. The invention provides a line power flow control method of an active support flexible direct current system of an inverter, which is based on system-level control of a direct current power grid, utilizes self-adaptive voltage droop control to provide additional degrees of freedom and is used for direct current power flow control, and greatly reduces input cost and operation loss of power flow control.

Description

Converter active support type tide optimization control method based on MOGWO
Technical Field
The invention belongs to the technical field of flexible direct current transmission, and mainly relates to an active supporting type power flow optimization control method of an inverter based on MOGWO.
Background
Offshore wind power generation is one of the mainstream renewable energy technologies, and is rapidly evolving in many countries in europe, north america and asia. There are two possible techniques to integrate offshore wind farms into existing ac systems, namely high voltage ac power grids and high voltage direct current (VSC-HVDC) power grids based on voltage source converters. However, the advantages of independent control of active and reactive power, subsea connection and power flow redirection capabilities make HVDC grids, i.e. multi-terminal HVDC (MTDC) systems, the preferred solution for integrating offshore wind farms.
According to the current research results, in order to realize the direct current power flow control, a direct current power flow controller based on power electronic equipment must be introduced to increase the degree of freedom of control. At present, attention to direct current power flow control is mainly focused on improving the topology of a direct current power flow controller. However, the high investment and operating costs of dc power flow controllers remain one of the main technical challenges limiting the development of the field of dc system control. Therefore, if we can improve the degree of freedom of the direct current power flow control from the perspective of the direct current power grid system level control, the investment cost and the operation loss are obviously reduced, and great economic benefit is brought. Such system level improvement will become an important research direction in the future dc system operation control field, and is expected to solve the technical challenges facing at present, and promote more efficient and sustainable development of the dc power system.
Disclosure of Invention
Aiming at the problem of direct current line power flow out-of-limit caused by serious faults of a wind power plant in the prior art, the invention provides an active supporting type power flow optimization control method of a converter based on MOGWO, which comprises the steps of firstly determining the expected fault types of the wind power plant and setting corresponding reference power variation of the converter station according to different fault types; and then establishing a sensitivity matrix, establishing an optimization target, establishing a MOGWO algorithm model, and finally adjusting system parameters under different types of faults of the offshore wind farm by utilizing the established model so as to achieve the optimal optimization target. The invention provides a line power flow control method of an active support flexible direct current system of an inverter, which is based on system-level control of a direct current power grid, utilizes self-adaptive voltage droop control to provide additional degrees of freedom and is used for direct current power flow control, and greatly reduces input cost and operation loss of power flow control.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: an active supporting type tide optimization control method of an inverter based on MOGWO comprises the following steps:
s1: determining the expected fault type of the wind power plant, and setting corresponding reference power variation of the converter station according to different fault types; the fault category comprises a large-amplitude power fluctuation fault caused by renewable energy sources, a constant active power control converter station fault, a sagging control converter station fault interruption and a direct current system disconnection fault;
s2: establishing a sensitivity matrix, wherein the sensitivity matrix S is expressed as:
S＝ΔP l /P l_rated
wherein P is l_rated The power matrix is a direct current line rated power matrix; ΔP l For the line tide transformation quantity, Y L Is a node admittance matrix; v is the DC node voltage; deltaV is the voltage change vector of the direct current node; LN is an outlier matrix; a is an incidence matrix;
s3: setting an optimization target, wherein the optimization target is that the power flow fraction of the direct current line after an accident, the total deviation of the direct current voltage and the generating cost of a conventional unit are minimum;
s4: establishing a MOGWO algorithm model, and determining the size of a search area in an initial search stage according to constraint condition boundaries; randomly generating a group of gray wolf individuals, wherein each individual represents a potential system optimization parameter, simultaneously determining the fitness value of each individual, setting the parameter of an algorithm, and iterating the maximum iteration times and the group size until the termination condition is met;
s5: and (3) adjusting system parameters under different types of faults of the offshore wind farm by using the model established in the step (S4) so as to achieve the optimal optimization target.
As an improvement of the invention, the renewable energy source causes large power fluctuation, and the reference power fluctuation amount delta P is set dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
the constant active power control converter station fails, and the reference power fluctuation quantity delta P is set dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
the converter in droop control converter station is interrupted by fault, the converter for converting the faulty droop control converter into active power control, the droop control coefficient is set to 0, and the reference power fluctuation amount delta P is set dc,i * The value of (2) is the negative number of the original reference power value;
and two virtual direct current power supplies are additionally arranged at two ends of a broken line of the direct current system, and the value of the virtual direct current power supplies is determined by the line power before the broken line.
As another improvement of the present invention, the dc node voltage change vector Δv in the step S2 is specifically:
wherein J is a Jacobian matrix; r is a sagging coefficient matrix of the converter station;and DeltaV * The reference power and the reference voltage change amounts of the converter station respectively.
As a further improvement of the present invention, the optimization objective of the step S3 is:
wherein g 1 The constraint equation set is an alternating current-direct current connection point; g 2 A system of direct current node equations representing constant active power control and a system of direct current node equations independent of the alternating current system; g 3 An alternating current node equation which is irrelevant to the direct current system node; h is inequality constraint, including AC system limit, converter operation limit, DC voltage limit and DC line capacity limit; θ s And V s Column vectors respectively representing the phase and voltage amplitude of the ac node; v (V) dc Is the voltage of the direct current node; p (P) s (V dc ) Is a direct current tide equation; p (P) s (θ s ,V s ) Is an alternating current tide equation; f (f) pl 、f V And f cost And respectively representing the power flow fraction of the direct current line after the accident, the total deviation of the direct current voltage and the power generation cost optimization target of the conventional unit.
As a further improvement of the present invention, the dc line power flow fraction f pl The method comprises the following steps:
wherein n is the total amount of the direct current circuit; p (P) l,i The actual active power flow of the ith direct current line after the emergency; p (P) l_rated,i Is the rated power of the ith direct current line.
As a further improvement of the invention, in the iteration of step S4, the following steps are performed in each generation:
selecting a leader wolf from the current population through non-dominant ranking, and updating the position of each follower wolf according to the position and the objective function value of the leader wolf, wherein the method specifically comprises the following steps:
wherein isNew position of gray wolf i, +.>The current position r is a random coefficient, and d is an adjustment vector of the position of the follower wolves according to the leader wolves;
according to the distance and the fitness value between the gray wolves, carrying out cooperation and competition, and updating solutions in the population; selecting and maintaining a set of non-dominant solutions using non-dominant ranking and crowding distance techniques;
and (3) checking whether a termination condition is met, returning to a non-dominant solution set, selecting an optimal compromise solution based on a TOPSIS method, and setting the optimal compromise solution as a post-emergency converter station parameter.
As a further improvement of the present invention, the parameter setting in the step S5 is specifically: according to the calculated line power flow, if the power flow is out of limit, firstly calculating the reference voltage change amount of a power flow out-of-limit line related converter station to control the out-of-limit power flow, and then optimizing the parameters of other converter stations by using the established MOGWO model; otherwise, optimizing all the controlled parameters of the converter station under the self-adaptive droop control by using the established MOGWO model.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a flexible direct current system line power flow control method for active support of an inverter, which is based on system-level control of a direct current power grid, utilizes self-adaptive voltage droop control to provide additional degrees of freedom and is used for direct current power flow control, and greatly reduces input cost and operation loss of power flow control; the invention establishes a direct current power flow control method based on multi-target optimal power flow, aims at various faults possibly occurring in a flexible direct current system, and eliminates the problem that the operating point of an alternating current-direct current hybrid system is out of limit after the faults by configuring the operating parameters of a VSC (voltage source converter) converter of the flexible direct current system under adaptive droop control through the optimal power flow.
Drawings
Fig. 1 is a step flow chart of an active support type tide optimization control method of an inverter based on MOGWO.
Detailed Description
The present invention is further illustrated in the following drawings and detailed description, which are to be understood as being merely illustrative of the invention and not limiting the scope of the invention.
Example 1
An active supporting type tide optimization control method of an inverter based on MOGWO, as shown in figure 1, comprises the following steps:
step S1: setting the expected fault types of the wind power plant and the reference power change types of the converter stations under different types; the wind farm expected failure categories include:
first, the reference power fluctuation amount delta P is set due to large power fluctuation caused by renewable energy sources dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
second, constant active power controlled converter station fault, set reference power fluctuation amount delta P dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
third, in the event of a droop control station fault interruption, a converter for converting a faulty droop control converter into active power control, setting its droop control coefficient to 0 and setting the reference power fluctuation amount Δp dc,i * The value of (2) is the negative number of the original reference power value;
and in the fourth class, two virtual direct current power supplies are additionally arranged at two ends of a broken line of the direct current system, and the value of the virtual direct current power supplies is determined by the line power before the broken line.
Step S2: calculating the change condition of the power flow before and after the emergency and establishing a sensitivity matrix; the calculating of the sensitivity moment matrix comprises the following specific steps:
step S21: the generalized DC voltage sag control equation is expressed as:
P i -P i * +R i (V i -V i * )＝0
wherein P is i And P i * The actual value and the set reference value of the active power injected by the ith converter station are respectively, wherein i epsilon {1, …, n }, n representing the number of DC nodes; v (V) i And V is equal to i * The actual voltage and the voltage set reference value respectively; r is R i The sagging coefficient of the converter station is MW/kV, and the unit is MW/kV, and the sagging coefficient can be obtained through the following formula
K in i Is the unit sag constant; p (P) i r And V is equal to r Is rated power and rated voltage. For sag control converter stations, R thereof i For non-zero, R for constant active power controlled converter stations i Zero. Furthermore, if the DC node is not connected to a converter station, R i And P i * Are all set to zero;
step S22: establishing an incidence matrix A, wherein if x routes and n nodes exist in the MTDC system, the matrix A is called an incidence matrix; a comprises three types of elements of-1, 0 or 1: -1 and 1 represent the p-th line current in-out node q;0 indicates that the p-th line is independent of node q;
step S23: establishing an off-point matrix LN, wherein LN contains 0 or 1 type elements, and 1 represents a p-th line current in-out node q;0 indicates that the p-th line is independent of node q;
step S24: the sensitivity matrix S is established, firstly, the line flow variation caused by line emergencies is calculated, and the following formula is adopted
Wherein DeltaP l For the line tide transformation quantity, Y L Is a node admittance matrix; v is the DC node voltage; deltaV is the voltage change vector of the direct current node; given by the formula:
j is a Jacobian matrix; r is a sagging coefficient matrix of the converter station;and DeltaV * The reference power and the reference voltage change amounts of the converter station respectively. The sensitivity matrix S can be expressed as:
S＝ΔP l /P l_rated
wherein P is l_rated Is a direct current line rated power matrix.
Step S3: setting up an optimization objective, which can be expressed as
Wherein g 1 The constraint equation set is an alternating current-direct current connection point; g 2 A system of direct current node equations representing constant active power control, and a system of direct current node equations independent of the alternating current system; g 3 An alternating current node equation which is irrelevant to the direct current system node; h is inequality constraint, including AC system limit, converter operation limit, DC voltage limit and DC line capacity limit; θ s And V s Column vectors respectively representing the phase and voltage amplitude of the ac node; v (V) dc Is the voltage of the direct current node; p (P) s (V dc ) Is a direct current tide equation; p (P) s (θ s ,V s ) Is an alternating current tide equation; f (f) pl 、f V And f cos t represents the current fraction of the direct current line after an accident, the total deviation of direct current voltage and the optimization target of the generating cost of the conventional unit, and the current fraction of the direct current line can be calculated according to the following equation:
wherein n is the total amount of the direct current circuit; p (P) l,i The actual active power flow of the ith direct current line after the emergency; p (P) l_rated,i Is the ith direct current lineIs set to the rated power of (3).
Step S4, a MOGWO algorithm model is established, and the MOGWO optimization establishment step comprises the following steps:
determining the size of a search area in an initial search stage according to the constraint condition boundary;
a population of wolf individuals is randomly generated, each individual representing a potential system optimization parameter, while fitness values for each individual are determined. Setting parameters of an algorithm, maximum iteration times and population size.
The following steps are performed in each generation:
one leader wolf is selected from the current population by non-dominant ranking, and the position of each follower wolf is updated according to the position of the leader wolf and the objective function value.
This can be accomplished by the following formula:
wherein isNew position of gray wolf i, +.>Is the current position, r is a random coefficient, and d is the adjustment vector of the follower wolf according to the position of the leader wolf.
And carrying out cooperation and competition according to the distance and the fitness value between the wolves. Updating solutions in the population according to the nature of the multi-objective optimization problem; techniques such as non-dominant ranking and crowdedness distance are employed to select and maintain a set of non-dominant solutions.
A check is made as to whether the termination condition is met and a non-dominant solution set is returned, which solutions represent a set of potentially optimal solutions to the multi-objective optimization problem. Based on the TOPSIS method, the best compromise solution is selected and set as the post-emergency converter station parameter.
S5, adjusting system parameters under different types of faults of the offshore wind farm by using the established model so as to achieve an optimal optimization target;
the parameter setting flow comprises the following steps: according to the calculated line power flow, if the power flow is out of limit, firstly calculating the reference voltage change amount of a power flow out-of-limit line related converter station to control the out-of-limit power flow, and then optimizing the parameters of other converter stations by using the established MOGWO model; otherwise, optimizing all the controlled parameters of the converter station under the self-adaptive droop control by using the established MOGWO model.
In summary, the invention provides a line current control method of an active support flexible direct current system of an inverter, which is based on system-level control of a direct current power grid, utilizes self-adaptive voltage droop control to provide additional degrees of freedom and is used for direct current control, and greatly reduces input cost and operation loss of current control. A direct current power flow control method based on multi-objective optimal power flow is established, and the problem that the operating point of an alternating current-direct current hybrid system is out of limit after the fault is solved by configuring the operating parameters of a VSC (voltage source converter) of the flexible direct current system under the self-adaptive droop control through the optimal power flow aiming at various faults possibly occurring in the flexible direct current system.
It should be noted that the foregoing merely illustrates the technical idea of the present invention and is not intended to limit the scope of the present invention, and that a person skilled in the art may make several improvements and modifications without departing from the principles of the present invention, which fall within the scope of the claims of the present invention.

Claims (7)

1. An active supporting type tide optimization control method of an inverter based on MOGWO is characterized by comprising the following steps:
s1: determining the expected fault type of the wind power plant, and setting corresponding reference power variation of the converter station according to different fault types; the fault category comprises a large-amplitude power fluctuation fault caused by renewable energy sources, a constant active power control converter station fault, a sagging control converter station fault interruption and a direct current system disconnection fault;
s2: establishing a sensitivity matrix, wherein the sensitivity matrix S is expressed as:
S＝ΔP l /P l_rated
wherein P is l_rated The power matrix is a direct current line rated power matrix; ΔP l For the line tide transformation quantity, Y L Is a node admittance matrix; v is the DC node voltage; deltaV is the voltage change vector of the direct current node; LN is an outlier matrix; a is an incidence matrix;
s3: setting an optimization target, wherein the optimization target is that the power flow fraction of the direct current line after an accident, the total deviation of the direct current voltage and the generating cost of a conventional unit are minimum;
s4: establishing a MOGWO algorithm model, and determining the size of a search area in an initial search stage according to constraint condition boundaries; randomly generating a group of gray wolf individuals, wherein each individual represents a potential system optimization parameter, simultaneously determining the fitness value of each individual, setting the parameter of an algorithm, and iterating the maximum iteration times and the group size until the termination condition is met;
s5: and (3) adjusting system parameters under different types of faults of the offshore wind farm by using the model established in the step (S4) so as to achieve the optimal optimization target.
2. The active supporting type power flow optimization control method for the converter based on the MOGWO as claimed in claim 1, wherein the active supporting type power flow optimization control method is characterized by comprising the following steps of: the renewable energy source causes large power fluctuation, and the reference power fluctuation amount delta P is set dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
the constant active power control converter station fails, and the reference power fluctuation quantity delta P is set dc,i * The change quantity of active power is referenced for the converter before and after the emergency;
the converter in droop control converter station is interrupted by fault, the converter for converting the faulty droop control converter into active power control, the droop control coefficient is set to 0 and the reference power fluctuation is setQuantity DeltaP dc,i * The value of (2) is the negative number of the original reference power value;
and two virtual direct current power supplies are additionally arranged at two ends of a broken line of the direct current system, and the value of the virtual direct current power supplies is determined by the line power before the broken line.
3. The active supporting type power flow optimization control method for the converter based on the MOGWO as claimed in claim 2, wherein the active supporting type power flow optimization control method is characterized by comprising the following steps of: the dc node voltage change vector Δv in step S2 is specifically:
wherein J is a Jacobian matrix; r is a sagging coefficient matrix of the converter station;and DeltaV * The reference power and the reference voltage change amounts of the converter station respectively.
4. A mogho-based active support type power flow optimization control method for a converter as claimed in claim 3, wherein: the optimization objective of the step S3 is:
wherein g 1 The constraint equation set is an alternating current-direct current connection point; g 2 A system of direct current node equations representing constant active power control and a system of direct current node equations independent of the alternating current system; g 3 An alternating current node equation which is irrelevant to the direct current system node; h is inequality constraint, including AC system limit, converter operation limit, DC voltage limit and DC line capacity limit; θ s And V s Column vectors respectively representing the phase and voltage amplitude of the ac node; v (V) dc Is the voltage of the direct current node; p (P) s (V dc ) Is a direct current tide equation; p (P) s (θ s ,V s ) Is an alternating current tide equation; f (f) pl 、f V And f cost And respectively representing the power flow fraction of the direct current line after the accident, the total deviation of the direct current voltage and the power generation cost optimization target of the conventional unit.
5. The active supporting type power flow optimization control method for the converter based on the MOGWO as claimed in claim 4, wherein the active supporting type power flow optimization control method is characterized in that: the current fraction f of the direct current line pl The method comprises the following steps:
wherein n is the total amount of the direct current circuit; p (P) l,i The actual active power flow of the ith direct current line after the emergency; p (P) l_rated,i Is the rated power of the ith direct current line.
6. The active supporting type power flow optimization control method for the converter based on the MOGWO as claimed in claim 1, wherein the active supporting type power flow optimization control method is characterized by comprising the following steps of: in the iteration of step S4, the following steps are performed in each generation:
selecting a leader wolf from the current population through non-dominant ranking, and updating the position of each follower wolf according to the position and the objective function value of the leader wolf, wherein the method specifically comprises the following steps:
wherein isNew position of gray wolf i, +.>Is the current position, r is a random coefficient, d is the follower wolfAccording to the adjustment vector of the position of the gray wolves of the collar and the sleeve;
according to the distance and the fitness value between the gray wolves, carrying out cooperation and competition, and updating solutions in the population; selecting and maintaining a set of non-dominant solutions using non-dominant ranking and crowding distance techniques;
and (3) checking whether a termination condition is met, returning to a non-dominant solution set, selecting an optimal compromise solution based on a TOPSIS method, and setting the optimal compromise solution as a post-emergency converter station parameter.
7. The active supporting type tide optimization control method for converter based on mogho as set forth in claim 5 or 6, wherein: the parameter setting in the step S5 specifically includes: according to the calculated line power flow, if the power flow is out of limit, firstly calculating the reference voltage change amount of a power flow out-of-limit line related converter station to control the out-of-limit power flow, and then optimizing the parameters of other converter stations by using the established MOGWO model; otherwise, optimizing all the controlled parameters of the converter station under the self-adaptive droop control by using the established MOGWO model.
CN202311257380.2A 2023-09-27 2023-09-27 Converter active support type tide optimization control method based on MOGWO Pending CN117318063A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117706943A (en) * 2024-02-06 2024-03-15 南京中鑫智电科技有限公司 Self-adaptive control method and system for converter transformer valve side sleeve end screen voltage divider

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117706943A (en) * 2024-02-06 2024-03-15 南京中鑫智电科技有限公司 Self-adaptive control method and system for converter transformer valve side sleeve end screen voltage divider
CN117706943B (en) * 2024-02-06 2024-04-16 南京中鑫智电科技有限公司 Self-adaptive control method and system for converter transformer valve side sleeve end screen voltage divider

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