CN110854884A - Wind power collection region subsynchronous oscillation risk online assessment and early warning method - Google Patents

Wind power collection region subsynchronous oscillation risk online assessment and early warning method Download PDF

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CN110854884A
CN110854884A CN201911246786.4A CN201911246786A CN110854884A CN 110854884 A CN110854884 A CN 110854884A CN 201911246786 A CN201911246786 A CN 201911246786A CN 110854884 A CN110854884 A CN 110854884A
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邢华栋
张叔禹
尹柏清
张伟
尹凯
王琪
翟寅
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Inner Mongolia Electric Power Research Institute of Inner Mongolia Power Group 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Abstract

The invention discloses an online evaluation and early warning method for a wind power collection area subsynchronous oscillation risk, which belongs to the technical field of power and electrical engineering, and comprises the following steps: establishing a respective sub-state space model for each wind turbine group from a wind power plant side, establishing a sub-state space model of the subsystem by taking a synchronous generator, a power transmission network and a load from a power grid side, and establishing a closed-loop state space model by combining the sub-state space models of the wind power plant side and the power grid side; establishing a real-time closed loop state space model of the wind power grid-connected system based on real-time operation information (wind speed, number of grid-connected fans and control strategy) of a wind power plant and real-time operation information (network topology and power flow) of a power grid; establishing a prediction closed-loop state space model of the wind power grid-connected system based on the wind speed prediction data and the load prediction data; and calculating the characteristic information of the real-time and predicted closed-loop space model, evaluating the risk of subsynchronous oscillation of the wind power grid-connected system according to the characteristic value result and making an oscillation early warning.

Description

Wind power collection region subsynchronous oscillation risk online assessment and early warning method
Technical Field
The invention belongs to the technical field of new energy power systems, and particularly relates to an online evaluation and early warning method for a sub-synchronous oscillation risk of a wind power collection area.
Background
Wind energy is a new energy source with large-scale development value, and is abundant in China. In areas such as Xinjiang, Gansu and inner Mongolia in China, a plurality of large-scale wind power plants are built, and the dynamic stability of a large-scale wind power collection area sending end system is of great importance. Subsynchronous oscillation is one of the most critical problems in the dynamic stability of a power system, in recent years, subsynchronous oscillation of the power system caused by wind power access frequently occurs in a power grid of China, particularly subsynchronous continuous power oscillation frequently occurs in a large-scale wind power field in a Xinjiang power grid of China since 2015, and subsynchronous oscillation caused by wind power grid connection threatens new energy consumption of the power grid of China, and is a major technical challenge facing the development of the power grid of China.
At present, the main analysis methods of the subsynchronous oscillation problem of the wind power grid-connected system comprise a time domain simulation method, an impedance analysis method, a mode analysis method and the like. Due to the practical limitation of engineering, on one hand, a wind turbine generator manufacturer protects intellectual property rights and does not disclose a control structure inside the wind turbine generator, and on the other hand, a power grid company protects the operation safety and the real-time operation information and grid structure parameters of a power grid are also kept secret. The asymmetry of the information is an important technical bottleneck for realizing the online evaluation of the subsynchronous oscillation risk of the wind power grid-connected system.
The time domain simulation method is characterized in that an electromagnetic transient model of power system elements including a wind power plant, a synchronous generator, a power transmission network, a load and the like is established, time domain simulation expansion analysis is performed, and system stability is judged according to response characteristics of state quantities such as power, voltage, current and the like of the elements after fault disturbance. The time domain simulation method cannot effectively solve the problem of asymmetric information because a complete system model needs to be established, and has the problems of low speed and no timeliness in time domain simulation aiming at a large-scale system because a large number of wind generation sets are contained in a wind power plant, so that the time domain simulation method is difficult to apply in engineering.
The impedance analysis method can obtain the impedance-frequency characteristic of the element by measuring the port characteristic of the element without depending on a mathematical model of the system element, construct an impedance model of the interconnection system, and analyze the stability of the interconnection system according to the nyquist stability criterion. However, for a large wind power collection area including a plurality of wind power clusters, an impedance analysis method needs to be applied to establish a multi-input multi-output impedance model, which is difficult in stability evaluation, and the impedance frequency characteristics cannot be measured on line in real time, and cannot meet the requirement of stability on-line evaluation.
Therefore, the online evaluation and early warning method for the sub-synchronous oscillation risk in the wind power collection region is urgently needed by considering the problem of information asymmetry of the wind power plant and the power grid, meeting the requirement of online evaluation of the sub-synchronous oscillation risk, having timeliness and accuracy.
Disclosure of Invention
Aiming at the problems, the invention provides an online evaluation and early warning method for the risk of subsynchronous oscillation in a wind power collection region, which solves the problem of information asymmetry, realizes the rapid and accurate online evaluation and early warning of the risk of subsynchronous oscillation and comprises the following steps:
step 1: respectively establishing respective sub-state space models by taking a wind turbine group and a power system accessed by the wind turbine group as interconnection subsystems, and forming a closed-loop feedback model of a wind turbine grid-connected system;
step 2: updating each sub-state space model based on the real-time operation information of the wind power plant and the real-time operation information of the power grid, and establishing a real-time closed-loop feedback model;
and step 3: updating each sub-state space model based on wind speed prediction data and load prediction data, and establishing a prediction closed loop feedback model of the wind power grid-connected system;
and 4, step 4: and calculating the characteristic information of the real-time and prediction closed-loop feedback model, evaluating the risk of subsynchronous oscillation of the wind power grid-connected system according to the characteristic value result and making an oscillation early warning.
The step 1 comprises the following steps: the closed-loop feedback model of the wind power grid-connected system is formed by interconnecting a wind power group and a sub-state space model of a power system to which the wind power group is connected;
the sub-state space model of the wind turbine group can be a double-fed wind turbine group and a direct-driven wind turbine group, and the wind turbine group model comprises a wind turbine shafting dynamic state, a generator stator and rotor winding dynamic state, a converter control dynamic state, a phase-locked loop dynamic state, a filter dynamic state and a direct current capacitor dynamic state;
the power system sub-state space model accessed by the wind turbine group comprises a turbo generator model, a load and a power transmission line, wherein the turbo generator model specifically comprises multi-mass block shafting dynamics, excitation control dynamics and turbo generator stator and rotor dynamics, the load model adopts a constant impedance form, and the power transmission line model considers electromagnetic transient;
and the sub-state space models of the wind turbine groups and the sub-state space model of the power system accessed by the wind turbine groups are feedback interconnection structures, and finally, the state space model of the closed-loop system is established.
The step 2 comprises the following steps: collecting real-time operation information of the wind power plant at the wind power plant side, wherein the real-time operation information comprises wind speed, the number of grid-connected fans and a control strategy, and updating a sub-state space model of the wind power plant group; on the power grid side, collecting real-time operation information of the power grid, including network topology and power flow, and updating a sub-state space model of a power system accessed by a wind turbine group; establishing a real-time closed-loop feedback model according to a plurality of interconnected sub-state space models;
the step 3 comprises the following steps: acquiring wind speed prediction information of a wind power plant at the wind power plant side; on the side of a power grid, acquiring load prediction information and a current day scheduling plan, and determining prediction information of current on the current day; based on the prediction information, establishing a time-varying sub-state space model of the wind turbine group, and establishing a time-varying sub-state space model of a power system to which the wind turbine group is connected; establishing a prediction closed-loop feedback model according to a plurality of interconnected sub-state space models;
the step 4 comprises the following steps: calculating a characteristic value of a closed loop state matrix by adopting a mode analysis method based on a real-time closed loop feedback model, screening a weak damping subsynchronous oscillation mode, and evaluating the risk of subsynchronous oscillation in real time; based on a prediction closed loop feedback model, a pattern analysis method is adopted to calculate a characteristic value sequence of a time sequence closed loop state matrix, a weak damping subsynchronous oscillation pattern and a corresponding time point are screened, a subsynchronous oscillation risk possibly existing in the current operation process is evaluated, and an oscillation early warning is given.
The invention has the beneficial effects that:
(1) the online evaluation and early warning method for the sub-synchronous oscillation risk of the wind power collection region provided by the invention solves the problem of information asymmetry of a wind power plant and a power grid, and is convenient for engineering application;
(2) the online evaluation and early warning method for the risk of the subsynchronous oscillation of the wind power collection region can evaluate the risk of the subsynchronous oscillation of the system in real time, and early warning the risk of the subsynchronous oscillation which possibly occurs in the same day, so as to provide guidance for the stable operation of the system.
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FIG. 1 is a schematic diagram of an implementation process of a wind power collection region subsynchronous oscillation risk online evaluation and early warning method
Fig. 2 is a schematic diagram of a closed-loop state space model construction method of a wind power grid-connected system.
Detailed Description
The invention provides a wind power collection region subsynchronous oscillation risk online assessment and early warning method, which comprises the following steps as shown in figure 1;
step 1: respectively establishing respective sub-state space models by taking a wind turbine group and a power system accessed by the wind turbine group as interconnection subsystems, and forming a closed-loop feedback model of a wind turbine grid-connected system;
based on the idea of element interconnection modeling, a closed-loop state space model of the whole system is constructed under an x-y synchronous rotating coordinate system, as shown in FIG. 2. Firstly, respectively establishing respective sub-state space models for each wind turbine group and each turbonator. Under the x-y synchronous rotation coordinate system, the sub-state space model of each wind turbine group can be characterized as follows:
wherein, XwkIs the state variable, V, of the kth wind turbine groupwkFor the grid-connected point voltage of the kth wind turbine group, IwkIs the output current of the kth wind turbine group, Awk、Bwk、CwkRespectively is a state matrix, an input matrix and an output matrix of the kth wind turbine group sub-state space model.
Under the x-y synchronous rotating coordinate system, the sub-state space model of each turbonator can be characterized as follows:
Figure BDA0002307671670000052
wherein, XgkIs the state variable of the kth turbogenerator, VgkFor the grid-connected point voltage of the kth turbonator, IgkIs the output current of the kth turbogenerator, Agk、Bgk、CgkRespectively a state matrix, an input matrix and an output matrix of the kth steam turbine generator sub-state space model.
The load adopts a constant impedance model, an impedance matrix of the alternating current transmission network is corrected, the electromagnetic transient state of the transmission line is considered, and the state variables of the transmission network comprise line current and node voltage. Under an x-y synchronous rotating coordinate system, the sub-state space model of the power transmission network can be characterized as follows:
Figure BDA0002307671670000061
wherein the content of the first and second substances,for the grid-connected point voltage state variables of all the wind turbine groups,
Figure BDA0002307671670000063
for the grid-connected point voltage state variables of all the turbonators,
Figure BDA0002307671670000064
for the state variables of the output currents of all the wind turbines,
Figure BDA0002307671670000065
for the state variable, V, of the output current of all turbonatorsrAnd I is the state variable of the voltage of the rest nodes of the power transmission network, and I is the current state variable of the power transmission network line.
The joint type (2) and (3) can construct a sub-state space model of the power grid side, as shown in formula (4), including a turbonator, a power transmission network and a load. According to the operation data of the power grid, including network topology, tide data, shafting structure and parameters of a turbonator and electrical parameters of the turbonator, the initialization of a sub-state space model on the power grid side can be realized, and a state matrix, an input matrix and an output matrix are formed:
Figure BDA0002307671670000071
the sub-state space model of the wind turbine group shown in the formula (1) is used for realizing the initialization of the sub-state space model of the wind turbine group and forming a state matrix, an input matrix and an output matrix according to the running wind speed of the wind turbine group, a control strategy and the number of grid-connected units. Because the large-scale wind power collection region comprises a plurality of wind power groups, a sub-state space model of each wind power group is respectively established.
The sub-state space models of the multiple wind turbines and the sub-state space model on the power grid side are feedback interconnection structures, as shown in fig. 1. Finally constructing a closed-loop state space matrix according to the input and output characteristics of the sub-state space models shown in the formulas (1) and (4):
Figure BDA0002307671670000072
wherein, X is all state variables of the system, and A is a closed-loop state matrix.
Step 2: updating each sub-state space model based on the real-time operation information of the wind power plant and the real-time operation information of the power grid, and establishing a real-time closed-loop feedback model;
collecting real-time operation information of a wind turbine group at a wind power plant side, wherein the real-time operation information comprises wind speed, the number of grid-connected fans and a control strategy, and updating a sub-state space model of the wind turbine group, namely an equation (1); on the power grid side, collecting real-time operation information of the power grid, including network topology and power flow, and updating a sub-state space model of a power system accessed by a wind turbine group, namely an equation (4); and establishing a real-time closed-loop feedback model according to the plurality of interconnected sub-state space models, namely an equation (5).
And step 3: updating each sub-state space model based on wind speed prediction data and load prediction data, and establishing a prediction closed loop feedback model of the wind power grid-connected system;
acquiring wind speed prediction information of a wind power plant at the wind power plant side; on the side of a power grid, acquiring load prediction information and a current day scheduling plan, and determining prediction information of current on the current day; based on the prediction information, establishing a time-varying sub-state space model of the wind turbine group:
Figure BDA0002307671670000081
wherein h represents the prediction time point, i.e. Awk(h)、Bwk(h)、Cwk(h) The state matrix, the input matrix and the output matrix at the h moment are obtained based on the prediction information.
Establishing a time-varying sub-state space model of a power system accessed by a wind turbine group;
Figure BDA0002307671670000091
wherein A isij(h)、Buw(h)、Cuw(h) The state matrix elements, the input matrix elements and the output matrix elements at the h moment are obtained based on the prediction information.
Establishing a prediction closed-loop feedback model according to a plurality of interconnected sub-state space models shown in the formulas (6) and (7);
Figure BDA0002307671670000092
where a (h) is a closed-loop state matrix at time h obtained based on the prediction information.
And 4, step 4: and calculating the characteristic information of the real-time and prediction closed-loop feedback model, evaluating the risk of subsynchronous oscillation of the wind power grid-connected system according to the characteristic value result and making an oscillation early warning.
Based on a real-time closed-loop feedback model, a mode analysis method is adopted to calculate the characteristic value of a closed-loop state matrix and screen a weakly damped subsynchronous oscillation mode lambdaiEvaluating the risk of subsynchronous oscillation in real time; calculating a characteristic value sequence lambda of a time sequence closed loop state matrix by adopting a mode analysis method based on a prediction closed loop feedback modeli(h) And screening a sub-synchronous oscillation mode with weak damping and a corresponding time point, evaluating a possible sub-synchronous oscillation risk in the current operation process, and making an oscillation early warning.
The embodiment is only a specific embodiment of the invention, but the scope of the invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the invention disclosed herein are intended to be covered by the scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A wind power collection region subsynchronous oscillation risk online assessment and early warning method is characterized by comprising the following steps:
step 1: respectively establishing respective sub-state space models by taking a wind turbine group and a power system accessed by the wind turbine group as interconnection subsystems, and forming a closed-loop feedback model of a wind turbine grid-connected system;
step 2: updating each sub-state space model based on the real-time operation information of the wind power plant and the real-time operation information of the power grid, and establishing a real-time closed-loop feedback model;
and step 3: updating each sub-state space model based on wind speed prediction data and load prediction data, and establishing a prediction closed loop feedback model of the wind power grid-connected system;
and 4, step 4: and calculating the characteristic information of the real-time and prediction closed-loop feedback model, evaluating the risk of subsynchronous oscillation of the wind power grid-connected system according to the characteristic value result and making an oscillation early warning.
2. The method of claim 1, wherein the step 1 comprises: the closed-loop feedback model of the wind power grid-connected system is formed by interconnecting a wind power group and a sub-state space model of a power system to which the wind power group is connected;
the sub-state space model of the wind turbine group can be a double-fed wind turbine group and a direct-driven wind turbine group, and the wind turbine group model comprises a wind turbine shafting dynamic state, a generator stator and rotor winding dynamic state, a converter control dynamic state, a phase-locked loop dynamic state, a filter dynamic state and a direct current capacitor dynamic state;
the power system sub-state space model accessed by the wind turbine group comprises a turbo generator model, a load and a power transmission line, wherein the turbo generator model specifically comprises multi-mass block shafting dynamics, excitation control dynamics and turbo generator stator and rotor dynamics, the load model adopts a constant impedance form, and the power transmission line model considers electromagnetic transient;
and the sub-state space models of the wind turbine groups and the sub-state space model of the power system accessed by the wind turbine groups are feedback interconnection structures, and finally, the state space model of the closed-loop system is established.
3. The method of claim 1, wherein the step 2 comprises: collecting real-time operation information of the wind power plant at the wind power plant side, wherein the real-time operation information comprises wind speed, the number of grid-connected fans and a control strategy, and updating a sub-state space model of the wind power plant group; on the power grid side, collecting real-time operation information of the power grid, including network topology and power flow, and updating a sub-state space model of a power system accessed by a wind turbine group; and establishing a real-time closed-loop feedback model according to the plurality of interconnected sub-state space models.
4. The method of claim 1, wherein the step 3 comprises: acquiring wind speed prediction information of a wind power plant at the wind power plant side; on the side of a power grid, acquiring load prediction information and a current day scheduling plan, and determining prediction information of current on the current day; based on the prediction information, establishing a time-varying sub-state space model of the wind turbine group, and establishing a time-varying sub-state space model of a power system to which the wind turbine group is connected; and establishing a prediction closed-loop feedback model according to the plurality of interconnected sub-state space models.
5. The method of claim 1, wherein the step 4 comprises: calculating a characteristic value of a closed loop state matrix by adopting a mode analysis method based on a real-time closed loop feedback model, screening a weak damping subsynchronous oscillation mode, and evaluating the risk of subsynchronous oscillation in real time; based on a prediction closed loop feedback model, a pattern analysis method is adopted to calculate a characteristic value sequence of a time sequence closed loop state matrix, a weak damping subsynchronous oscillation pattern and a corresponding time point are screened, a subsynchronous oscillation risk possibly existing in the current operation process is evaluated, and an oscillation early warning is given.
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