CN111884217A - Single-machine infinite electric power system optimization control method based on T-S model - Google Patents

Single-machine infinite electric power system optimization control method based on T-S model Download PDF

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CN111884217A
CN111884217A CN202010751817.8A CN202010751817A CN111884217A CN 111884217 A CN111884217 A CN 111884217A CN 202010751817 A CN202010751817 A CN 202010751817A CN 111884217 A CN111884217 A CN 111884217A
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CN111884217B (en
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陈华昊
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Haikou Power Supply Bureau of Hainan Power Grid Co Ltd
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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
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]

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Abstract

The invention provides a single-machine infinite electric power system optimization control method based on a T-S model, which comprises the following steps: s1, establishing a single-machine infinite electric power system model; s2, carrying out local linearization processing on the single-machine infinite electric power system model to obtain a fuzzy state model of the single-machine infinite electric power system; s3, converting the fuzzy state model of the single-machine infinite electric power system into a global fuzzy state model of the single-machine infinite electric power system according to fuzzy rules; s4, constructing a fuzzy event trigger controller, and defining a trigger form and conditions; and S5, establishing an optimized performance index in a zero initial state, and performing single-machine infinite electric power system optimized control by adopting a fuzzy event trigger controller.

Description

Single-machine infinite electric power system optimization control method based on T-S model
Technical Field
The invention relates to the technical field of power system stability control, in particular to a single-machine infinite power system optimization control method based on a T-S model.
Background
During the period of more than 40 years of Chinese reform, the consumption of energy sources such as coal, petroleum and the like in China is unprecedentedly huge, and the consumption and the release of a large amount of coal and petroleum cause the problems of serious energy shortage and environmental pollution, so that the trend of energy development at present depends on the subjects of cleanness and greenness whether distributed clean energy sources at the power generation end or electric vehicles at the power utilization end. Also because of this characteristic of electrical energy, the nation has vigorously developed electrical grids that enable the delivery of electrical energy to thousands of households.
In south China, due to the fact that residents use household appliances such as air conditioners and electric fans in a large scale in summer, the power consumption is increased rapidly; in addition, the electric automobile industry develops rapidly and the sales volume of new energy automobiles in China is up to 120 thousands of automobiles in 2019, wherein the sales volume of new energy passenger automobiles is 106.0 thousands of automobiles, and the sales volume is increased by 0.7% on year-by-year basis; the sales volume of the pure electric passenger vehicle is 78.8 thousands, the sales volume is increased by 5.9 percent on a same scale, the charging of the electric vehicle has strong uncertainty, and if the pure electric passenger vehicle happens to be charged in a large-scale centralized manner, the power consumption of a load end is increased rapidly, so that the stability of an electric power system is influenced. If the treatment is not good, the suddenly increased electric load can cause the grid to be unstable and cause large-scale power failure. Therefore, the stability of the power grid is guaranteed to be very important for national economy and people's life. Because of the stand-alone infinity electric power system model for the electric power system model after simplifying helps the research of electric power system stability, avoided considering unnecessary influence factor, consequently adopt stand-alone infinity electric power system to carry out this patent research, this patent is mainly to the research that stand-alone infinity electric power system stability research and stand-alone infinity electric power system and event trigger technique fuse mutually to reach the effect that makes electric power system's stability and intelligent improvement.
Disclosure of Invention
The invention aims to provide a T-S model-based single infinite electric power system optimization control method, which is used for solving the problems of modeling and controller design of a single infinite electric power system, realizing the high-performance event trigger control target of the single infinite electric power system and meeting the reliable and stable operation of the single infinite electric power system.
The invention is realized by the following technical scheme: a single machine infinite electric power system optimization control method based on a T-S model comprises the following steps:
s1, establishing a single-machine infinite electric power system model;
s2, carrying out local linearization processing on the single-machine infinite electric power system model to obtain a fuzzy state model of the single-machine infinite electric power system;
s3, converting the fuzzy state model of the single-machine infinite electric power system into a global fuzzy state model of the single-machine infinite electric power system according to fuzzy rules;
s4, constructing a fuzzy event trigger controller, and defining a trigger form and conditions;
and S5, establishing an optimized performance index in a zero initial state, and performing single-machine infinite electric power system optimized control by adopting a fuzzy event trigger controller.
Preferably, in step S1, the established model of the single infinite electric power system with uncertainty is:
Figure BDA0002610252740000021
in the formula, the running angle of the generator rotor is shown; omega is the relative rotating speed of the generator; e'qIs a generator q-axis transient potential; omega0Is the initial angular velocity of the generator; h is the rotational inertia of the generator rotor; pmMechanical power output by the prime mover; vsInfinite bus voltage; x'dzIs the sum of transient reactances of the generators; d is a damping coefficient of the generator; t'doThe time constant of the excitation winding when the stator winding is closed; x'dIs a generator shaft transient reactance; t isdoIs the time constant of the exciting winding when the exciting winding is closed; vfFor the field winding voltage, defined as a control variable, w1(t)、w2(t) is the amount of interference, xdEqual reactance for the generator shaft.
Preferably, the step S2 includes locally linearizing the single infinite electric power system model by a sector method, where the linearized single infinite electric power system model includes:
Figure BDA0002610252740000031
wherein x is1(t)＝，x2(t)＝ω，x3(t)＝E′q，u(t)＝Vf，x(t)＝[x1(t) x2(t) x3(t)]T，w(t)＝[0 w1(t) w2(t)]T，
Figure BDA0002610252740000032
Figure BDA0002610252740000033
Figure BDA0002610252740000034
a1、a2Are each z1(t) maximum and minimum values, expressed as:
Figure BDA0002610252740000035
preferably, in step S3, the fuzzy rule includes an If-Then modeling rule, and the If-Then modeling rule includes a first rule and a second rule, where the first rule is: when z is1(t)、z2(t) is 1, obtaining a first fuzzy state equation:
Figure BDA0002610252740000036
the second rule is: when z is1(t)、z2(t) is-1, obtaining a second fuzzy state equation:
Figure BDA0002610252740000037
wherein
Figure BDA0002610252740000038
Membership function:
Figure BDA0002610252740000039
preferably, in step S3, a global fuzzy state model is obtained according to the first fuzzy state equation and the second fuzzy state equation:
Figure BDA0002610252740000041
Figure BDA0002610252740000042
preferably, in step S4The fuzzy event trigger controller is designed by adopting a parallel distribution compensation technology, and the following control rules are set: if x1(t) is F1And x isg(t) is F2Then, then
Figure BDA0002610252740000043
Wherein KhFor controlling the static gain feedback matrix in 1 x 3 dimensions corresponding to the fuzzy rule, tkIndicating the moment of triggering, F1、F2Is a function of membership.
Preferably, the fuzzy event trigger controller is triggered in the form of:
e(t)＝x(tk)-x(t)
where e (t) is the event-triggered travel of the system, k represents the number of event triggers, tkIndicating the moment of trigger.
Preferably, the trigger condition of the fuzzy event trigger controller is:
Figure BDA0002610252740000044
where p is a set dimensionless number for adjusting the trigger condition, HeAnd HxIs a preset matrix.
Preferably, the optimized performance index in the zero initial state includes:
Figure BDA0002610252740000045
where γ is a constant and is a suppression index reference value of the external disturbance.
Preferably, in step S5, the fuzzy event trigger controller performs real-time control of the single infinite power system to gradually stabilize the closed-loop system, and obtains a set of positive definite symmetric matrix solutions P to satisfy the following linear matrix inequality:
Figure BDA0002610252740000051
wherein Q ═ P-1，Hh＝KhQ，l＝1,2，h＝1,2，K1、K2P is a 3-dimensional symmetric positive definite matrix corresponding to the 1-dimensional 3-dimensional control gain matrix of the corresponding fuzzy rule.
Compared with the prior art, the invention has the following beneficial effects:
the single infinite power system optimization control method based on the T-S model provided by the invention starts from two aspects of fuzzy modeling and controller design of the single infinite power system to carry out overall design, can realize the high-function event trigger optimization control target of the single infinite power system, meets the high functionality and high reliability of the power system in operation, has strong feasibility, completely meets the high functional requirement of real-time event trigger control of the single infinite power system, and realizes the improvement of the robustness and high functionality of the single infinite power system.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a method for optimizing and controlling a single infinite power system based on a T-S model according to the present invention;
FIG. 2 is a sector diagram of cos (x1(t)) of the present invention;
FIG. 3 is a state response of the stand-alone infinity power system of the present invention;
FIG. 4 is a control input for the stand-alone infinity power system of the present invention;
FIG. 5 is an optimized output response of the stand-alone infinite power system of the present invention;
fig. 6 is an event trigger signal of the stand-alone infinite power system of the present invention.
Detailed Description
In order to better understand the technical content of the invention, specific embodiments are provided below, and the invention is further described with reference to the accompanying drawings.
Referring to fig. 1, the present embodiment is a method for controlling an infinite power system based on a T-S model, which is applied to control the infinite power system, and now an event triggering technology has been developed into a mature technology and has been widely applied in many fields, and the use of the event triggering technology makes the application system have an obvious effect in both improving the work efficiency and reducing the work task load, so that the present embodiment establishes a global fuzzy state model of the infinite power system by linearizing a mathematical model of the infinite power system, and constructs a fuzzy event trigger controller, and after defining an event triggering form and a triggering condition and establishing an optimization performance index in a zero initial state, can perform event triggering optimization control on the infinite power system, and can achieve a high-function event triggering control target of the infinite power system, the method for optimizing and controlling the single-machine infinite electric power system based on the T-S model has the advantages that the high safety and the high reliability of the electric power system during operation are met, the control capability of the control method in the aspect of the information sampling technology is improved, the times of the system for acquiring unnecessary information are greatly reduced, the pressure of a system information transmission channel is effectively relieved, the robustness and the high functionality of the single-machine infinite electric power system are improved, and the method for optimizing and controlling the single-machine infinite electric power system based on the T-S model comprises the following steps:
s1, establishing a single-machine infinite electric power system model, wherein the established single-machine infinite electric power system model containing uncertainty is as follows:
Figure BDA0002610252740000061
in the formula, the running angle of the generator rotor is shown; omega is the relative rotating speed of the generator; e'qIs a generator q-axis transient potential; omega0Is the initial angular velocity of the generator; h is the rotational inertia of the generator rotor; pmMechanical power output by the prime mover; vsInfinite bus voltage; x'dzIs the sum of transient reactances of the generators; d is hairA motor damping coefficient; t'doThe time constant of the excitation winding when the stator winding is closed; x'dIs a generator shaft transient reactance; t isdoIs the time constant of the exciting winding when the exciting winding is closed; vfFor the field winding voltage, defined as a control variable, w1(t)、w2(t) is the amount of interference, xdEqual reactance for the generator shaft.
S2, local linearization is carried out on the single-machine infinite electric power system model by adopting a sector method, and the linearized single-machine infinite electric power system fuzzy state model is as follows:
Figure BDA0002610252740000071
wherein x is1(t)＝，x2(t)＝ω，x3(t)＝E′q，u(t)＝Vf，x(t)＝[x1(t) x2(t) x3(t)]T，w(t)＝[0 w1(t) w2(t)]T，
Figure BDA0002610252740000072
Figure BDA0002610252740000073
Figure BDA0002610252740000074
a1、a2Are each z1(t) maximum and minimum values. The expression is as follows:
Figure BDA0002610252740000075
as can be seen from FIG. 2, sector b1,b2]Is formed by two lines b1x1And b2x1Composition, slope respectively
Figure BDA0002610252740000076
Finally, cos (x) can be converted by the sector method1(t)) linearizing.
S3, converting the fuzzy state model of the single infinite power system into a global fuzzy state model of the single infinite power system according to fuzzy rules, wherein the fuzzy rules comprise If-Then modeling rules, the If-Then modeling rules comprise a first rule and a second rule, and the first rule is as follows: when z is1(t)、z2(t) is 1, obtaining a first fuzzy state equation:
Figure BDA0002610252740000077
the second rule is: when z is1(t)、z2(t) is-1, obtaining a second fuzzy state equation:
Figure BDA0002610252740000078
wherein
Figure BDA0002610252740000079
Membership function:
Figure BDA0002610252740000081
obtaining a global fuzzy state model according to the first fuzzy state equation and the second fuzzy state equation:
Figure BDA0002610252740000082
s4, designing a fuzzy event trigger controller by adopting a parallel distribution compensation technology, and setting the following control rules: if x1(t) is F1And x isg(t) is F2Then, then
Figure BDA0002610252740000083
Wherein KhFor controlling the static gain feedback matrix in 1 x 3 dimensions corresponding to the fuzzy rule, tkIndicating the moment of triggering, F1、F2Is subject toAnd defining the trigger form of the fuzzy event trigger controller as follows:
e(t)＝x(tk)-x(t)
where e (t) is the event-triggered travel of the system, k represents the number of event triggers, tkIndicating the moment of trigger.
Defining the trigger condition of the fuzzy event trigger controller as follows:
Figure BDA0002610252740000084
where p is a set dimensionless number for adjusting the trigger condition, HeAnd HxIs a preset matrix.
S5, establishing an optimized performance index in a zero initial state:
Figure BDA0002610252740000085
and triggering a controller according to the fuzzy event, carrying out real-time control on a single infinite power system to gradually stabilize a closed-loop system, and solving a group of positive definite symmetric matrix solutions P to ensure that the following linear matrix inequality is established:
Figure BDA0002610252740000091
wherein Q ═ P-1，Hh＝KhQ，l＝1,2，h＝1,2，K1、K2P is a 3-dimensional symmetric positive definite matrix corresponding to the 1-dimensional 3-dimensional control gain matrix of the corresponding fuzzy rule.
The following experiments were conducted with a single machine infinite power system, where the main technical performance indicators and equipment parameters were selected as follows: d is 0.15, H is 12.9, Vs＝1，Td0＝6.45，T′d0＝1.2，xd＝0.83，x′d＝0.105，x′d∑＝0.16，ω0314.154, the corresponding parameters in step S2 are:
Figure BDA0002610252740000092
Figure BDA0002610252740000093
Figure BDA0002610252740000094
a 1 x 3 dimensional control static gain feedback matrix corresponding to a fuzzy rule in the fuzzy time trigger controller:
K1＝[-28.0562 -127.2194 -77.7566]
K2＝[-28.6512 -126.5596 -77.3654]
a positive definite symmetric matrix obtained according to the parameters and the data:
Figure BDA0002610252740000095
setting the initial condition of a single-machine infinite power system as x0＝[0.21 0.62 0.33]TFIG. 3 is a system state response graph of a stand-alone infinity power system operating with a stable closed loop system, where x1(t)、x2(t)、x3(t) are respectively the generator rotor running angle, the generator relative rotating speed and the generator q-axis transient potential, and x is shown in figure 3 after the system is interfered2(t)、x3(t) substantially stabilizes after 1s of system operation with a faster recovery capability and x1(t) no obvious phenomenon of free increase indicates that the anti-interference performance is certain; FIG. 4 is a control input curve diagram of a single infinite power system during stable operation of a closed loop system, and it can be seen from FIG. 4 that the control input completely approaches to balance in about 1s, which can prove that the system has strong robustness; FIG. 5 is the optimized output response of the single infinite power system, and it can be seen from FIG. 5 that the output response of the system is greatly reduced after 1s, and then the system is basically recovered to normal after 2-3 s; FIG. 6 is a state curve of a single-machine infinite system under event trigger control, and the fact that the system is in fact can be seen from FIG. 6When the actual value exceeds the preset value, the system can automatically adjust and effectively control.
The data and the curve chart prove that the method improves the control capability of the single infinite electric power system in the aspect of the information sampling technology, greatly reduces the times of acquiring unnecessary information by the system, effectively relieves the pressure of a system information transmission channel, and improves the robustness and the high functionality of the single infinite electric power system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A single machine infinite electric power system optimization control method based on a T-S model is characterized by comprising the following steps:
s1, establishing a single-machine infinite electric power system model;
s2, carrying out local linearization processing on the single-machine infinite electric power system model to obtain a fuzzy state model of the single-machine infinite electric power system;
s3, converting the fuzzy state model of the single-machine infinite electric power system into a global fuzzy state model of the single-machine infinite electric power system according to fuzzy rules;
s4, constructing a fuzzy event trigger controller, and defining a trigger form and conditions;
and S5, establishing an optimized performance index in a zero initial state, and performing single-machine infinite electric power system optimized control by adopting a fuzzy event trigger controller.
2. The method as claimed in claim 1, wherein in step S1, the established model of the single infinite electric power system with uncertainty is:
Figure FDA0002610252730000011
in the formula, the running angle of the generator rotor is shown; omega is the relative rotating speed of the generator; e'qIs a generator q-axis transient potential; omega0Is the initial angular velocity of the generator; h is the rotational inertia of the generator rotor; pmMechanical power output by the prime mover; vsInfinite bus voltage; x'dzIs the sum of transient reactances of the generators; d is a damping coefficient of the generator; t'doThe time constant of the excitation winding when the stator winding is closed; x'dIs a generator shaft transient reactance; t isdoIs the time constant of the exciting winding when the exciting winding is closed; vfTo the field winding voltage, w1(t)、w2(t) is the amount of interference, xdEqual reactance for the generator shaft.
3. The method as claimed in claim 2, wherein the step S2 includes performing local linearization on the model of the single infinite electric power system by using a sector method, where the fuzzy state model of the linearized single infinite electric power system is:
Figure FDA0002610252730000021
wherein x is1(t)＝，x2(t)＝ω，x3(t)＝E′q，u(t)＝Vf，x(t)＝[x1(t) x2(t) x3(t)]T，w(t)＝[0 w1(t) w2(t)]T，
Figure FDA0002610252730000022
Figure FDA0002610252730000023
Figure FDA0002610252730000024
a1、a2Are each z1(t) maximum and minimum values, expressed as:
Figure FDA0002610252730000025
4. the method as claimed in claim 3, wherein in the step S3, the fuzzy rule includes an If-Then modeling rule, and the If-Then modeling rule includes a first rule and a second rule, where the first rule is: when z is1(t)、z2(t) is 1, obtaining a first fuzzy state equation:
Figure FDA0002610252730000026
the second rule is: when z is1(t)、z2(t) is-1, obtaining a second fuzzy state equation:
Figure FDA0002610252730000027
wherein
Figure FDA0002610252730000028
Membership function:
Figure FDA0002610252730000029
5. the method as claimed in claim 4, wherein in step S3, a global fuzzy state model is obtained according to the first fuzzy state equation and the second fuzzy state equation:
Figure FDA0002610252730000031
Figure FDA0002610252730000032
6. the method as claimed in claim 5, wherein in step S4, the fuzzy event-triggered controller is designed by using a parallel distribution compensation technique, and the following control rules are set: if x1(t) is F1And x isg(t) is F2Then, then
Figure FDA0002610252730000033
Figure FDA0002610252730000034
Wherein KhFor controlling the static gain feedback matrix in 1 x 3 dimensions corresponding to the fuzzy rule, tkIndicating the moment of triggering, F1、F2Is a function of membership.
7. The method for optimizing and controlling the stand-alone infinite electric power system based on the T-S model as claimed in claim 6, wherein the fuzzy event trigger controller is triggered in the form of:
e(t)＝x(tk)-x(t)
where e (t) is the event-triggered travel of the system, k represents the number of event triggers, tkIndicating the moment of trigger.
8. The method as claimed in claim 7, wherein the trigger conditions of the fuzzy event trigger controller are as follows:
Figure FDA0002610252730000035
where p is a set dimensionless number for adjusting the trigger condition, HeAnd HxIs a preset matrix.
9. The method as claimed in claim 8, wherein the optimization performance index in the zero initial state includes:
∫0 ∞[zT(t)z(t)-γ2wT(t)w(t)]dt<0
where γ is a constant and is a suppression index reference value of the external disturbance.
10. The method as claimed in claim 9, wherein in step S5, the fuzzy event trigger controller is used to perform real-time control of the single infinite power system, so as to gradually stabilize the closed-loop system, and obtain a set of positive symmetry matrix solutions P, so that the following linear matrix inequalities hold:
Figure FDA0002610252730000041
wherein Q ═ P-1，Hh＝KhQ，l＝1,2，h＝1,2，K1、K2P is a 3-dimensional symmetric positive definite matrix corresponding to the 1-dimensional 3-dimensional control gain matrix of the corresponding fuzzy rule.
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徐开军: "单机无穷大系统暂态稳定性仿真及分析", 《信息化研究》 *
钱婧怡: "单机无穷大电力系统的动态面控制", 《电器与自动化》 *
陈华昊: "基于 T- S 模糊模型的简单互联电力系统事件触发控制", 《中国水能及电气化》 *

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CN113435020A (en) * 2021-06-16 2021-09-24 南方电网能源发展研究院有限责任公司 Power system interference control method and device, computer equipment and storage medium
CN113642143A (en) * 2021-06-16 2021-11-12 南方电网能源发展研究院有限责任公司 Power system control method and device, computer equipment and storage medium

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