CN113919210B - PI parameter optimization method and device of VSC-HVDC system - Google Patents

PI parameter optimization method and device of VSC-HVDC system Download PDF

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CN113919210B
CN113919210B CN202111108071.XA CN202111108071A CN113919210B CN 113919210 B CN113919210 B CN 113919210B CN 202111108071 A CN202111108071 A CN 202111108071A CN 113919210 B CN113919210 B CN 113919210B
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balance
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parameters
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CN113919210A (en
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朱鑫要
李群
孙蓉
李文博
徐柯
李铮
解兵
王大江
姚伟
周泓宇
林思齐
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Huazhong University of Science and Technology
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Huazhong University of Science and Technology
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The application relates to the technical field of power system control, and discloses a PI parameter optimization method and device of a VSC-HVDC system. And then sequentially determining candidate parameters, target balance candidate parameters, index terms and generation rates for any PI parameter, and finally updating the candidate parameters according to the target balance candidate parameters, the index terms and the generation rates to determine optimal parameters of any PI parameter and finish optimization of all PI parameters. According to the application, different balance candidate parameters can be distributed through different selection probabilities, so that PI parameter optimization efficiency is improved, and the stability of the VSC-HVDC system is obviously improved.

Description

PI parameter optimization method and device of VSC-HVDC system
Technical Field
The application relates to the technical field of power system control, in particular to a PI parameter optimization method and device of a VSC-HVDC system.
Background
With the reduction of global fossil fuel reserves, energy structure reform is pushed to surge, new energy power generation layout is being accelerated at present, and targets of carbon neutralization are striven for quality assurance and quality assurance. Wind power generation is one of new energy power generation modes which pay attention at present, plays an important role in the reform of energy structures, and has great potential. Due to the extremely abundant resources and relatively mature technology of wind power generation, the global installed capacity of wind power is continuously increased in recent years, and the trend of developing from land to offshore is gradually developed.
With the large-scale development and utilization of offshore wind power, the technical bottleneck problems of system synchronism, power transmission stability, power transmission efficiency and the like of alternating current power transmission cannot fully meet the demands. In order to solve the problem of remote grid connection of large-scale offshore wind power, a VSC-HVDC (voltage source converter based high voltage direct current, flexible high-voltage direct current transmission) system is widely applied, the transmission capacity of the VSC-HVDC technology is not limited by distance, good dynamic reactive support can be provided for a wind power plant, and isolation between the wind power plant and an alternating current power grid can be realized. The VSC-HVDC system controller consists of two parts, namely rectifier control and inverter control, and because the VSC-HVDC system has special advantages for the application of offshore wind power, the research of the control technology of the VSC-HVDC system has important significance for the wind power grid connection field.
The high uncertainty of offshore wind power makes the design of optimal control of VSC-HVDC systems a very tricky and vital task. In the VSC-HVDC system, the traditional PI (proportional-integral) controller has simple structure, high reliability and good stability.
However, when the design of the PI controller includes many coupled PI gains, if PI parameters are not properly adjusted, the control performance of the PI controller is often greatly reduced, and even the stability of the VSC-HVDC system is crashed in some cases.
Disclosure of Invention
The application discloses a PI parameter optimization method and device of a VSC-HVDC system, which are used for solving the technical problems that when the design of a PI controller comprises a plurality of coupled PI gains in the prior art, if the PI parameter is not properly adjusted, the control performance of the PI controller is often greatly reduced, and even the stability of the VSC-HVDC system is crashed under certain conditions.
The first aspect of the application discloses a PI parameter optimization method of a VSC-HVDC system, which comprises the following steps:
Acquiring system operation parameters of a VSC-HVDC system, wherein the system operation parameters comprise rectifier regulation reactive power error, DC bus voltage error, inverter regulation reactive power error, active power error, rotor q-axis voltage, rotor d-axis voltage, operation time, outer loop proportional gain, outer loop integral gain, inner loop proportional gain, inner loop integral gain, DC voltage, AC grid voltage and reactive power;
Generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions;
determining PI parameters according to the VSC-HVDC system model;
for any PI parameter, acquiring a plurality of candidate parameters of the any PI parameter according to a preset first random vector;
Determining a balanced candidate set according to the candidate parameters;
Determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model and the balance candidate set;
determining a target balance candidate parameter according to the balance candidate probability;
Acquiring the current iteration times, and determining an iteration function according to the current iteration times and a preset first searching capability value, wherein the first searching capability value is a constant value for managing searching capability;
Determining an initial value of the iterative function according to the iterative function, a preset second random vector and a preset second searching capability value, wherein the second searching capability value is a constant value for controlling global searching capability;
determining an index term according to the iteration function and the initial value of the iteration function;
Acquiring a power generation rate control parameter, and determining a generation rate initial value according to the power generation rate control parameter, the target balance candidate parameter, the second random vector and the candidate parameter;
Determining a generation rate according to the generation rate initial value and the index term;
And updating the candidate parameters according to the target balance candidate parameters, the index term and the generation rate, and determining the optimal parameters of any PI parameter.
Optionally, the limiting conditions include a control cost weight factor, an outer loop proportional gain limit, an outer loop integral gain limit, an inner loop proportional gain limit, an inner loop integral gain limit, a direct current voltage limit, an alternating current grid voltage limit, a reactive power limit, a rotor q-axis voltage limit, and a rotor d-axis voltage limit.
Optionally, the obtaining, according to a preset first random vector, a plurality of candidate parameters of the arbitrary PI parameter includes:
and acquiring a plurality of candidate parameters of any PI parameter according to the preset PI parameter upper bound, the preset PI parameter lower bound and the first random vector.
Optionally, the determining a balanced candidate set according to the plurality of candidate parameters includes:
determining 4 balance candidate parameters according to the candidate parameters;
determining an arithmetic average balance candidate parameter according to the 4 balance candidate parameters, wherein the arithmetic average balance candidate parameter is an arithmetic average value of the 4 balance candidate parameters;
And determining a balance candidate set according to the 4 balance candidate parameters and the arithmetic average balance candidate parameter.
Optionally, the determining, according to the VSC-HVDC system model and the balanced candidate set, a balanced candidate probability corresponding to each balanced candidate parameter in the balanced candidate set includes:
And determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model, the balance candidate set, the preset minimum selection probability and the preset maximum selection probability.
Optionally, the determining an iteration function according to the current iteration number and a preset first searching capability value includes:
and determining an iteration function according to the current iteration number, the preset maximum iteration number and the preset first searching capability value.
Optionally, the acquiring the power generation rate control parameter includes:
And determining the power generation rate control parameter according to the preset first random value, the preset second random value and the preset generation probability.
Optionally, the updating the candidate parameter according to the target balance candidate parameter, the exponential term and the generation rate, and determining the optimal parameter of any PI parameter includes:
and updating the candidate parameters according to the target balance candidate parameters, the index term, the generation rate and the preset control volume, and determining the optimal parameters of any PI parameters.
Optionally, the first random vector and the second random vector are random vectors in [0,1] interval.
The second aspect of the present application discloses a PI parameter optimization device of a VSC-HVDC system, where the PI parameter optimization device of the VSC-HVDC system is applied to the PI parameter optimization method of the VSC-HVDC system disclosed in the first aspect of the present application, and the PI parameter optimization device of the VSC-HVDC system includes:
The system operation parameter acquisition module is used for acquiring system operation parameters of the VSC-HVDC system, wherein the system operation parameters comprise rectifier regulation reactive power error, direct current bus voltage error, inverter regulation reactive power error, active power error, rotor q-axis voltage, rotor d-axis voltage, operation time, outer ring proportional gain, outer ring integral gain, inner ring proportional gain, inner ring integral gain, direct current voltage, alternating current grid voltage and reactive power;
The system model generation module is used for generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions;
The PI parameter acquisition module is used for determining PI parameters according to the VSC-HVDC system model;
The candidate parameter acquisition module is used for acquiring a plurality of candidate parameters of any PI parameter according to a preset first random vector aiming at the any PI parameter;
A balanced candidate set determining module, configured to determine a balanced candidate set according to the plurality of candidate parameters;
the balance candidate probability determining module is used for determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model and the balance candidate set;
the target balance candidate parameter acquisition module is used for determining target balance candidate parameters according to the balance candidate probability;
the iteration function determining module is used for obtaining the current iteration times and determining an iteration function according to the current iteration times and a preset first search capacity value, wherein the first search capacity value is a constant value for managing the search capacity;
the iteration function initial value determining module is used for determining an iteration function initial value according to the iteration function, a preset second random vector and a preset second search capability value, wherein the second search capability value is a constant value for controlling global search capability;
the index term acquisition module is used for determining an index term according to the iteration function and the initial value of the iteration function;
the generation rate initial value acquisition module is used for acquiring a generation rate control parameter and determining a generation rate initial value according to the generation rate control parameter, the target balance candidate parameter, the second random vector and the candidate parameter;
the generation rate acquisition module is used for determining the generation rate according to the generation rate initial value and the index term;
And the parameter optimization module is used for updating the candidate parameters according to the target balance candidate parameters, the index term and the generation rate, and determining the optimal parameters of any PI parameter.
Optionally, the candidate parameter obtaining module includes:
and the candidate parameter acquisition unit is used for acquiring a plurality of candidate parameters of any PI parameter according to the preset PI parameter upper bound, the preset PI parameter lower bound and the first random vector.
Optionally, the balancing candidate set determining module includes:
A balance candidate parameter obtaining unit, configured to determine 4 balance candidate parameters according to the plurality of candidate parameters;
An arithmetic average unit, configured to determine an arithmetic average balance candidate parameter according to the 4 balance candidate parameters, where the arithmetic average balance candidate parameter is an arithmetic average value of the 4 balance candidate parameters;
and the balance candidate set acquisition unit is used for determining a balance candidate set according to the 4 balance candidate parameters and the arithmetic average balance candidate parameter.
Optionally, the balancing candidate probability determining module includes:
And the balance candidate probability acquisition unit is used for determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model, the balance candidate set, the preset minimum selection probability and the preset maximum selection probability.
Optionally, the iterative function determining module includes:
and the iteration function acquisition unit is used for determining an iteration function according to the current iteration times, the preset maximum iteration times and the preset first searching capacity value.
Optionally, the generating rate initial value obtaining module includes:
The power generation rate control parameter acquisition unit is used for determining the power generation rate control parameter according to a preset first random value, a preset second random value and a preset generation probability.
Optionally, the parameter optimization module includes:
and the optimal parameter acquisition unit is used for updating the candidate parameters according to the target balance candidate parameters, the index term, the generation rate and the preset control volume, and determining the optimal parameters of any PI parameters.
The application relates to the technical field of power system control, and discloses a PI parameter optimization method and device of a VSC-HVDC system. And then sequentially determining candidate parameters, target balance candidate parameters, index terms and generation rates for any PI parameter, and finally updating the candidate parameters according to the target balance candidate parameters, the index terms and the generation rates to determine optimal parameters of any PI parameter and finish optimization of all PI parameters. According to the application, different balance candidate parameters can be distributed through different selection probabilities, so that PI parameter optimization efficiency is improved, and the stability of the VSC-HVDC system is obviously improved.
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In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic workflow diagram of a PI parameter optimization method of a VSC-HVDC system according to an embodiment of the present application;
Fig. 2 is a schematic structural diagram of a PI parameter optimization system of a VSC-HVDC system according to an embodiment of the present application.
Detailed Description
In order to solve the technical problem that in the prior art, when the design of a PI controller comprises a plurality of coupled PI gains, if PI parameters are not properly adjusted, the control performance of the PI controller is often greatly reduced, and even the stability of a VSC-HVDC system is crashed under certain conditions, the application discloses a PI parameter optimization method and device of the VSC-HVDC system through the following two embodiments.
The first embodiment of the application discloses a PI parameter optimization method of a VSC-HVDC system, which is shown in a working flow diagram in FIG. 1 and comprises the following steps:
Step S101, acquiring system operation parameters of a VSC-HVDC system, wherein the system operation parameters comprise rectifier regulation reactive power error, DC bus voltage error, inverter regulation reactive power error, active power error, rotor q-axis voltage, rotor d-axis voltage, operation time, outer loop proportional gain, outer loop integral gain, inner loop proportional gain, inner loop integral gain, DC voltage, AC grid voltage and reactive power.
And step S102, generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions.
Further, the limiting conditions include a control cost weight coefficient, an outer loop proportional gain limit, an outer loop integral gain limit, an inner loop proportional gain limit, an inner loop integral gain limit, a direct current voltage limit, an alternating current grid voltage limit, a reactive power limit, a rotor q-axis voltage limit, and a rotor d-axis voltage limit.
Specifically, the VSC-HVDC system model requires that the objective function be minimized within the constraints, thereby achieving optimal control of the VSC-HVDC system over offshore wind grid connection. The VSC-HVDC system model is specifically generated by the following formula:
Wherein F (x) refers to an objective function, It is meant that the rectifier adjusts the reactive power error,Refers to the voltage error of the direct current bus,It means that the inverter adjusts the reactive power error,The active power error is denoted by u qi, the rotor Q-axis voltage is denoted by u di, the rotor d-axis voltage is denoted by u i1 and ω i2 are control cost weight coefficients, ω i1＝ωi2 =1/16, t is denoted by run time, (K pQi,KpVdc,KpP2) the outer loop proportional gain, (K iQi,KiVdc,KiP2) the outer loop integral gain, (K pIdi,KpIqi) the inner loop proportional gain, (K iIdi,KiIqi) the inner loop integral gain, V dci is the direct current voltage, u si is the alternating current grid voltage, Q si is reactive power, i=1 is the rectifier, and i=2 is the inverter.
Specifically, the control cost weighting coefficients ω i1 and ω i2 are used to weight the control costs for q-axis and d-axis voltages, the outer loop proportional gain limit and the outer loop integral gain limit are [0,25] and [0,600], the inner loop proportional gain limit and the inner loop integral gain limit are [0,5] and [0,250], the DC voltage limit is [0.98,1.02] p.u., the AC grid voltage limit is [0.2,1.0] p.u., the reactive power limit is [ -1.0,1.0] p.u., the rotor q-axis voltage limit is represented as |u qi | ltoreq 80kV, and the rotor d-axis voltage limit is represented as |u di | ltoreq 60kV.
And step S103, determining PI parameters according to the VSC-HVDC system model.
In some embodiments of the application, the PI parameters contemplated by the present application include KpQ1、KiQ1、KpQ2、KiQ2,KpVdc、KiVdc、KpP2、KiP2、KpId1、KiId1、KpId2、KiId2、KpIq1、KiIq1、KpIq2 and K iIq2.
The balance optimizer (equilibrium optimizer, EO) is a newly proposed intelligent optimization algorithm, and the source inspiration is a simple mixed control volume dynamic balance phenomenon. Compared with a common Particle Swarm Optimization (PSO) algorithm, a gray wolf algorithm (grey wolf optimizer, GWO) and a salep swarm algorithm (SALP SWARM algorithm, SSA), the EO algorithm has the characteristics of strong optimizing capability and high convergence rate, and the actual performance of the algorithm is more excellent. The subsequent steps of the embodiment provide an enhanced balance optimizer (enhanced equilibrium optimizer, EEO), which is to further design an adaptive allocation candidate solution mechanism based on the EO algorithm, that is, allocate different balance candidates according to the fitness function thereof and through different selection probabilities, thereby further improving the optimization efficiency.
Step S104, for any PI parameter, a plurality of candidate parameters of the any PI parameter are obtained according to a preset first random vector.
Further, the obtaining, according to a preset first random vector, a plurality of candidate parameters of the arbitrary PI parameter includes:
and acquiring a plurality of candidate parameters of any PI parameter according to the preset PI parameter upper bound, the preset PI parameter lower bound and the first random vector.
Further, the first random vector is a random vector in the [0,1] interval.
Specifically, the initial concentration of EEOs is constructed based on the number of particles and the optimization space for each particle, and is initialized uniformly and randomly within the solution space, and a plurality of candidate parameters for any PI parameter are obtained by the following formula:
Wherein, The method is characterized in that the method comprises the steps of referring to the ith candidate parameter of the ith PI parameter to be optimized, C max and C min respectively represent an upper PI parameter boundary and a lower PI parameter boundary, n refers to the number of candidate parameters, and the number of candidate parameters is determined according to actual needs.
Step S105, determining a balanced candidate set according to the plurality of candidate parameters.
Further, the determining a balanced candidate set according to the plurality of candidate parameters includes:
And determining 4 balance candidate parameters according to the candidate parameters.
And determining an arithmetic average balance candidate parameter according to the 4 balance candidate parameters, wherein the arithmetic average balance candidate parameter is an arithmetic average value of the 4 balance candidate parameters.
And determining a balance candidate set according to the 4 balance candidate parameters and the arithmetic average balance candidate parameter.
Specifically, the equilibrium state of the EEO is the final convergence state of the present embodiment. At the beginning of the optimization process, only the balance candidate parameters are determined to provide the search pattern for the particles. Based on different types of optimization problems, these balanced candidate parameters are the best candidate parameter identified throughout the optimization process plus another candidate parameter, the concentration of which is the arithmetic average of the four candidate parameters. These five candidate parameters are designated as balanced candidate parameters and are used to construct a balanced candidate set.
Determining a balance candidate set according to the following formula;
Wherein E p refers to the balanced candidate set, Respectively represent four balanced candidate parameters, namely four optimal candidate parameters found by the current iteration,Representing an arithmetic mean balance candidate parameter.
And step S106, according to the VSC-HVDC system model and the balance candidate set, determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set.
Further, the determining, according to the VSC-HVDC system model and the balanced candidate set, a balanced candidate probability corresponding to each balanced candidate parameter in the balanced candidate set, includes:
And determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model, the balance candidate set, the preset minimum selection probability and the preset maximum selection probability.
Specifically, unlike EO, the balancing candidates of EEOs are not randomly selected from the balancing library, but rather the different selection probabilities for all balancing candidates, i.e., the smaller the adaptation value, the higher the selection probability. It should be noted that the fitness values of all balance candidates are normalized values. And determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set through the following formula:
Wherein, p j represents the balance candidate probability corresponding to the jth balance candidate parameter, p min and p max represent the minimum selection probability and the maximum selection probability respectively, which are determined in advance according to the actual application scene, and F i and F j represent the objective function values corresponding to the ith balance candidate parameter and the jth balance candidate parameter respectively.
Step S107, determining target balance candidate parameters according to the balance candidate probability.
Specifically, a target balance candidate parameter is selected from a balance candidate set according to the magnitude of the balance candidate probability.
Step S108, obtaining the current iteration times, and determining an iteration function according to the current iteration times and a preset first search capability value, wherein the first search capability value is a constant value for managing the search capability.
Further, the determining an iteration function according to the current iteration number and a preset first searching capability value includes:
and determining an iteration function according to the current iteration number, the preset maximum iteration number and the preset first searching capability value.
Specifically, the iterative function is determined by the following formula:
Wherein t is an iteration function, and along with the increase of the iteration number, iter and iter max respectively represent the current iteration number and the maximum iteration number, the maximum iteration number is determined in advance according to the actual application scene, and a 1 is a first search capability value.
Step S109, determining an initial value of the iterative function according to the iterative function, a preset second random vector and a preset second search capability value, where the second search capability value is a constant value for controlling the global search capability.
Further, the second random vector is a random vector in the [0,1] interval.
Specifically, the initial value of the iterative function is determined by the following formula:
Wherein t 0 refers to the initial value of the iterative function, Refers to a second random vector, a 2 refers to a second search capability value, which is a constant value that controls global search capability, the second search capability value being inversely proportional to global search performance,Determining the direction of global search and local search, sign () refers to a sign function, e refers to the base of natural logarithm, and the second random vectorA random vector for the interval 0, 1.
Step S110, determining an index term according to the iteration function and the initial value of the iteration function.
Specifically, the exponential term is determined by the following formula:
Wherein, Refers to exponential terms that contribute to the primary concentration update mechanism, which is important to balance global and local searches.
Step S111, acquiring a power generation rate control parameter, and determining a generation rate initial value according to the power generation rate control parameter, the target balance candidate parameter, the second random vector, and the candidate parameter.
Further, the obtaining the power generation rate control parameter includes:
And determining the power generation rate control parameter according to the preset first random value, the preset second random value and the preset generation probability.
Specifically, the power generation rate control parameter is obtained by the following formula:
Wherein, The power generation rate control parameters, r 1 and r 2, are respectively a first random value and a second random value, are determined in advance according to an actual application scene, and GP represents the generation probability and is determined in advance according to the actual application scene.
The initial value of the generation rate is determined by the following formula:
Wherein, It is referred to as an initial value of the generation rate,Refers to the target balance candidate parameter(s),The ith candidate parameter of the d PI parameter to be optimized of the current iteration, the second random vector is a random vector of the [0,1] interval.
And step S112, determining the generation rate according to the generation rate initial value and the index term.
Specifically, the rate of generationIs one of the keys to provide accurate solutions by improving the development phase. The generation rate is determined by the following formula:
and step S113, updating the candidate parameters according to the target balance candidate parameters, the index term and the generation rate, and determining the optimal parameters of any PI parameters.
Further, the updating the candidate parameter according to the target balance candidate parameter, the exponential term and the generation rate, and determining the optimal parameter of any PI parameter includes:
and updating the candidate parameters according to the target balance candidate parameters, the index term, the generation rate and the preset control volume, and determining the optimal parameters of any PI parameters.
Specifically, the candidate parameters are updated by the following formula:
wherein V represents control volume, and is determined in advance according to actual application scenes.
In particular, the VSC-HVDC system controller consists of two parts, namely rectifier control and inverter control. On the rectifier side, the outer loop control regulates the DC voltage V dc1 and the reactive power Q 1 to obtain the dq-axis current reference valueAndWhile the inner loop control is responsible for regulating the current described above, and then the final control inputs urq and urd of the rectifier are obtained via the compensation terms u 'q1 and u' d1. Similarly, on the inverter side, the outer loop control adjusts the active power P 2 and the reactive power Q 2 to obtain the dq-axis current reference valueAndWhile the inner loop control is responsible for regulating the current described above, and then the final control inputs uiq and uid of the inverter are obtained via the compensation terms u 'q2 and u' d2. After optimizing all PI parameters, the voltage output by the PI controller is fed back to the VSC-HVDC through PWM (pulse width modulation, voltage is subjected to pulse width modulation) for the next iteration. And finally, based on the objective function obtained after the iteration, adopting EEO to perform next iteration optimization on the PI parameter. Repeating the steps until the optimal PI parameter is obtained.
In some embodiments of the application, a VSC-HVDC system model of offshore wind power grid connection is firstly established; furthermore, a double-loop PI controller cooperative control framework is designed for a rectifier and an inverter of the VSC-HVDC system, active power and reactive power of a rectifier control part and reactive power and direct current voltage of an inverter control part are mainly optimally controlled, and EEO is adopted to optimize control parameters of the PI controller; and then, feeding the voltage output by the PI controller back to the VSC-HVDC through pulse width modulation PWM for the next iteration, and continuously iterating until convergence to obtain the optimal PI controller parameters. Compared with the original balance optimizer EO, the EEO provided by the application can distribute different balance candidate parameters according to the objective function of the EEO by different selection probabilities, so that PI parameter optimization efficiency is improved. According to the application, the EEO is used for effectively obtaining the high-quality PI control parameter, so that the problem that the PI parameter of the VSC-HVDC system is difficult to set under the condition of offshore wind power grid connection is solved.
According to the PI parameter optimization method of the VSC-HVDC system disclosed by the embodiment of the application, firstly, a VSC-HVDC system model is generated according to the system operation parameters and the limiting conditions. And then sequentially determining candidate parameters, target balance candidate parameters, index terms and generation rates for any PI parameter, and finally updating the candidate parameters according to the target balance candidate parameters, the index terms and the generation rates to determine optimal parameters of any PI parameter and finish optimization of all PI parameters. According to the application, different balance candidate parameters can be distributed through different selection probabilities, so that PI parameter optimization efficiency is improved, and the stability of the VSC-HVDC system is obviously improved.
The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.
The second embodiment of the application discloses a PI parameter optimization device of a VSC-HVDC system, which is applied to the PI parameter optimization method of the VSC-HVDC system disclosed in the first embodiment of the application, referring to a schematic structural diagram shown in fig. 2, the PI parameter optimization device of the VSC-HVDC system comprises:
The system operation parameter obtaining module 201 is configured to obtain system operation parameters of the VSC-HVDC system, where the system operation parameters include a rectifier adjustment reactive power error, a dc bus voltage error, an inverter adjustment reactive power error, an active power error, a rotor q-axis voltage, a rotor d-axis voltage, an operation time, an outer loop proportional gain, an outer loop integral gain, an inner loop proportional gain, an inner loop integral gain, a dc voltage, an ac grid voltage, and reactive power.
And the system model generating module 202 is used for generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions.
And the PI parameter acquisition module 203 is used for determining PI parameters according to the VSC-HVDC system model.
The candidate parameter obtaining module 204 is configured to obtain, for any PI parameter, multiple candidate parameters of the any PI parameter according to a preset first random vector.
Further, the candidate parameter obtaining module 204 includes:
and the candidate parameter acquisition unit is used for acquiring a plurality of candidate parameters of any PI parameter according to the preset PI parameter upper bound, the preset PI parameter lower bound and the first random vector.
A balanced candidate set determination module 205 is configured to determine a balanced candidate set according to the plurality of candidate parameters.
Further, the balanced candidate set determining module 205 includes:
And the balance candidate parameter acquisition unit is used for determining 4 balance candidate parameters according to the plurality of candidate parameters.
And the arithmetic average unit is used for determining an arithmetic average balance candidate parameter according to the 4 balance candidate parameters, wherein the arithmetic average balance candidate parameter is an arithmetic average value of the 4 balance candidate parameters.
And the balance candidate set acquisition unit is used for determining a balance candidate set according to the 4 balance candidate parameters and the arithmetic average balance candidate parameter.
And the balanced candidate probability determining module 206 is configured to determine, according to the VSC-HVDC system model and the balanced candidate set, a balanced candidate probability corresponding to each balanced candidate parameter in the balanced candidate set.
Further, the balanced candidate probability determination module 206 includes:
And the balance candidate probability acquisition unit is used for determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model, the balance candidate set, the preset minimum selection probability and the preset maximum selection probability.
A target balance candidate parameter obtaining module 207, configured to determine a target balance candidate parameter according to the balance candidate probability.
The iteration function determining module 208 is configured to obtain a current iteration number, and determine an iteration function according to the current iteration number and a preset first search capability value, where the first search capability value is a constant value for managing search capability.
Further, the iterative function determining module 208 includes:
and the iteration function acquisition unit is used for determining an iteration function according to the current iteration times, the preset maximum iteration times and the preset first searching capacity value.
The iteration function initial value determining module 209 is configured to determine an iteration function initial value according to the iteration function, a preset second random vector, and a preset second search capability value, where the second search capability value is a constant value for controlling global search capability.
The exponential term obtaining module 210 is configured to determine an exponential term according to the iteration function and the initial value of the iteration function.
The generation rate initial value obtaining module 211 is configured to obtain a generation rate control parameter, and determine a generation rate initial value according to the generation rate control parameter, the target balance candidate parameter, the second random vector, and the candidate parameter.
Further, the generation rate initial value obtaining module 211 includes:
The power generation rate control parameter acquisition unit is used for determining the power generation rate control parameter according to a preset first random value, a preset second random value and a preset generation probability.
The generation rate obtaining module 212 is configured to determine a generation rate according to the generation rate initial value and the exponent term.
And the parameter optimization module 213 is configured to update the candidate parameters according to the target balance candidate parameter, the exponential term, and the generation rate, and determine an optimal parameter of the any PI parameter.
Further, the parameter optimization module 213 includes:
and the optimal parameter acquisition unit is used for updating the candidate parameters according to the target balance candidate parameters, the index term, the generation rate and the preset control volume, and determining the optimal parameters of any PI parameters.
The application has been described in detail in connection with the specific embodiments and exemplary examples thereof, but such description is not to be construed as limiting the application. It will be understood by those skilled in the art that various equivalent substitutions, modifications or improvements may be made to the technical solution of the present application and its embodiments without departing from the spirit and scope of the present application, and these fall within the scope of the present application. The scope of the application is defined by the appended claims.

Claims (8)

1. A PI parameter optimization method for a VSC-HVDC system, comprising:
Acquiring system operation parameters of a VSC-HVDC system, wherein the system operation parameters comprise rectifier regulation reactive power error, DC bus voltage error, inverter regulation reactive power error, active power error, rotor q-axis voltage, rotor d-axis voltage, operation time, outer loop proportional gain, outer loop integral gain, inner loop proportional gain, inner loop integral gain, DC voltage, AC grid voltage and reactive power;
Generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions;
determining PI parameters according to the VSC-HVDC system model;
for any PI parameter, acquiring a plurality of candidate parameters of the any PI parameter according to a preset first random vector;
Determining a balanced candidate set according to the candidate parameters;
Determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model and the balance candidate set;
determining a target balance candidate parameter according to the balance candidate probability;
Acquiring the current iteration times, and determining an iteration function according to the current iteration times and a preset first searching capability value, wherein the first searching capability value is a constant value for managing searching capability;
Determining an initial value of the iterative function according to the iterative function, a preset second random vector and a preset second searching capability value, wherein the second searching capability value is a constant value for controlling global searching capability;
determining an index term according to the iteration function and the initial value of the iteration function;
Acquiring a power generation rate control parameter, and determining a generation rate initial value according to the power generation rate control parameter, the target balance candidate parameter, the second random vector and the candidate parameter;
Determining a generation rate according to the generation rate initial value and the index term;
Updating the candidate parameters according to the target balance candidate parameters, the index term and the generation rate, and determining the optimal parameters of any PI parameter;
The determining, according to the VSC-HVDC system model and the balanced candidate set, a balanced candidate probability corresponding to each balanced candidate parameter in the balanced candidate set, includes:
Determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model, the balance candidate set, the preset minimum selection probability and the preset maximum selection probability:
Wherein, p j represents the balance candidate probability corresponding to the jth balance candidate parameter, p min and p max represent the minimum selection probability and the maximum selection probability respectively, which are determined in advance according to the actual application scene, and F i and F j represent the objective function values corresponding to the ith balance candidate parameter and the jth balance candidate parameter respectively;
The determining an iteration function according to the current iteration times and a preset first searching capability value comprises the following steps:
Determining an iteration function according to the current iteration number, the preset maximum iteration number and the preset first searching capability value;
the iterative function is determined by the following formula:
Wherein t is an iteration function, and along with the increase of the iteration number, iter and iter max respectively represent the current iteration number and the maximum iteration number, the maximum iteration number is determined in advance according to the actual application scene, and a 1 is a first search capability value.
2. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein said limiting conditions comprise a control cost weight factor, an outer loop proportional gain limit, an outer loop integral gain limit, an inner loop proportional gain limit, an inner loop integral gain limit, a direct current voltage limit, an alternating current grid voltage limit, a reactive power limit, a rotor q-axis voltage limit and a rotor d-axis voltage limit.
3. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein the obtaining a plurality of candidate parameters of any PI parameter according to a preset first random vector comprises:
and acquiring a plurality of candidate parameters of any PI parameter according to the preset PI parameter upper bound, the preset PI parameter lower bound and the first random vector.
4. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein said determining a balanced candidate set based on said plurality of candidate parameters comprises:
determining 4 balance candidate parameters according to the candidate parameters;
determining an arithmetic average balance candidate parameter according to the 4 balance candidate parameters, wherein the arithmetic average balance candidate parameter is an arithmetic average value of the 4 balance candidate parameters;
And determining a balance candidate set according to the 4 balance candidate parameters and the arithmetic average balance candidate parameter.
5. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein said obtaining power generation rate control parameters comprises:
And determining the power generation rate control parameter according to the preset first random value, the preset second random value and the preset generation probability.
6. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein said updating the candidate parameters according to the target balance candidate parameter, the exponential term and the generation rate, determining the optimal parameter of any PI parameter comprises:
and updating the candidate parameters according to the target balance candidate parameters, the index term, the generation rate and the preset control volume, and determining the optimal parameters of any PI parameters.
7. The PI parameter optimization method of a VSC-HVDC system according to claim 1, wherein the first and second random vectors are random vectors of the [0,1] interval.
8. PI parameter optimizing apparatus of a VSC-HVDC system, characterized in that the PI parameter optimizing apparatus of a VSC-HVDC system is applied to the PI parameter optimizing method of a VSC-HVDC system according to any one of claims 1 to 7, the PI parameter optimizing apparatus of a VSC-HVDC system comprising:
The system operation parameter acquisition module is used for acquiring system operation parameters of the VSC-HVDC system, wherein the system operation parameters comprise rectifier regulation reactive power error, direct current bus voltage error, inverter regulation reactive power error, active power error, rotor q-axis voltage, rotor d-axis voltage, operation time, outer ring proportional gain, outer ring integral gain, inner ring proportional gain, inner ring integral gain, direct current voltage, alternating current grid voltage and reactive power;
The system model generation module is used for generating a VSC-HVDC system model according to the system operation parameters and preset limiting conditions;
The PI parameter acquisition module is used for determining PI parameters according to the VSC-HVDC system model;
The candidate parameter acquisition module is used for acquiring a plurality of candidate parameters of any PI parameter according to a preset first random vector aiming at the any PI parameter;
A balanced candidate set determining module, configured to determine a balanced candidate set according to the plurality of candidate parameters;
the balance candidate probability determining module is used for determining the balance candidate probability corresponding to each balance candidate parameter in the balance candidate set according to the VSC-HVDC system model and the balance candidate set;
the target balance candidate parameter acquisition module is used for determining target balance candidate parameters according to the balance candidate probability;
the iteration function determining module is used for obtaining the current iteration times and determining an iteration function according to the current iteration times and a preset first search capacity value, wherein the first search capacity value is a constant value for managing the search capacity;
the iteration function initial value determining module is used for determining an iteration function initial value according to the iteration function, a preset second random vector and a preset second search capability value, wherein the second search capability value is a constant value for controlling global search capability;
the index term acquisition module is used for determining an index term according to the iteration function and the initial value of the iteration function;
the generation rate initial value acquisition module is used for acquiring a generation rate control parameter and determining a generation rate initial value according to the generation rate control parameter, the target balance candidate parameter, the second random vector and the candidate parameter;
the generation rate acquisition module is used for determining the generation rate according to the generation rate initial value and the index term;
And the parameter optimization module is used for updating the candidate parameters according to the target balance candidate parameters, the index term and the generation rate, and determining the optimal parameters of any PI parameter.
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