CN112039051A - Real-time modeling method for accessing double-fed wind driven generator into substation bus load - Google Patents

Real-time modeling method for accessing double-fed wind driven generator into substation bus load Download PDF

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CN112039051A
CN112039051A CN202010619324.9A CN202010619324A CN112039051A CN 112039051 A CN112039051 A CN 112039051A CN 202010619324 A CN202010619324 A CN 202010619324A CN 112039051 A CN112039051 A CN 112039051A
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load
model
bus
induction motor
power
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马瑞
燕彤彤
蒋铁铮
刘文飞
智勇
郝如海
陈仕斌
祁莹
史玉杰
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Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
Changsha University of Science and Technology
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Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
Changsha University of Science and Technology
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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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention discloses a real-time modeling method for accessing a double-fed wind driven generator into a substation bus load, which relates to the field of power systems and comprises the following steps: firstly, acquiring historical bus data, and preprocessing daily load data of an SCADA (supervisory control and data acquisition) system by adopting a CEEMD-LSTM model; clustering several types of bus daily load typical curves on SCADA system data by using an SCK-SHAPE model; then respectively establishing load models of a static ZIP, an induction motor and a DFIG wind driven generator; determining the load proportion of the model by introducing the wind power penetration rate and the dynamic and static load ratio; then determining a composition structure of a load at a bus node of the transformer substation by using two operation states of the DFIG; and finally, identifying the bus load parameters by using the WAMS system data. The method can be applied to real-time load modeling and parameter identification of the power system, and provides a basis for subsequent scheduling and control of the power system.

Description

Real-time modeling method for accessing double-fed wind driven generator into substation bus load
Technical Field
The invention relates to the field of power systems, in particular to a real-time modeling method for a bus load of a transformer substation accessed by a doubly-fed wind driven generator.
Background
The load is one of important components in the power system, and has a great influence on the analysis and simulation calculation of the static, dynamic and transient characteristics and stability of the power system. However, the widely used load model is still relatively simplified and rough, the excessive roughness of the load model becomes a key factor for restricting the analysis and simulation calculation precision of the power system, and the establishment of the dynamic load model which accords with the reality and can accurately reflect the actual important characteristics has very important practical significance.
In recent years, due to energy shortage and increasingly prominent environmental pollution problems, the development and utilization of wind power as pollution-free clean energy are greatly promoted and developed, and the grid connection of high-proportion wind power generation makes the grid structure become more complex and the types of power loads become more diversified. Wind power output has random volatility and intermittence, and the load has time-varying property, and the interaction of the wind power output and the load aggravates the time-varying property of a generalized load node, so that the traditional load model and the research method can influence the safe operation of a power grid, and the requirements of operation scheduling personnel cannot be met. With the continuous development of information Acquisition technology, the power grid is configured with various Data detection systems according to the importance degree of equipment and the target and need of operation Control, and mainly includes a Data Acquisition and monitoring System (abbreviated as SCADA System), a Wide Area Measurement System (abbreviated as WAMS System)/vector Measurement unit (abbreviated as PMU), which are gradually popularized and developed in the power System, and a large amount of Measurement Data is gathered into a scheduling System of the power grid, so that a large Data support is provided for further load modeling and parameter identification research in a complex power grid form.
Disclosure of Invention
The invention aims to solve the technical problem of providing a real-time modeling method for the bus load of the transformer substation accessed by the doubly-fed wind generator, and providing a foundation for the safety and stability analysis and control of a power grid.
In order to achieve the purpose, the invention adopts the following technical scheme: a real-time modeling method for a bus load of a transformer substation accessed by a doubly-fed wind generator comprises the following steps:
(1) and acquiring historical data of an SCADA system and a WAMS system of the load node, wherein the data comprises the amplitude and the phase angle of bus voltage, the amplitude and the phase angle of current, active power, reactive power and bus frequency f.
(2) And preprocessing the daily load data of the SCADA system collected on the plurality of buses by adopting a CEEMD-LSTM model. And processing the load data missing values on the plurality of buses.
(3) And clustering daily load curves on the plurality of buses by using the SCK-SHAPE model and daily load data acquired by the SCADA system to obtain typical daily load curves of several types of buses.
(4) Establishing a load model
1) Static ZIP load model
The static ZIP load model adopts a polynomial model and belongs to an input and output model, so that the static ZIP load model shown as the formula (1) is established;
Figure RE-GDA0002688881080000021
in the formula: p is the active power of the static load, Q is the reactive power of the static load, U is the voltage, U0Is an initial value of voltage, P0As an initial value of active power, Q0Is an initial value of reactive power, p1、p2、p3Is the proportion of constant impedance, constant current and constant power in the active load q1、q2、q3The proportion of constant impedance, constant current and constant power in the reactive load is shown;
in the formula (1), p1、p2、p3、q1、q2、q3The parameters to be identified need to satisfy the constraint condition shown in the formula (2);
Figure RE-GDA0002688881080000022
2) induction motor load model
In order to ensure the identification accuracy of the bus load model parameters, the induction motor model should consider the mechanical transient and electromagnetic transient processes of the motor at the same time, so the induction motor model is described by a third-order model, and the dynamic behavior of the induction motor is often expressed by a differential equation as shown in formula (3);
Figure RE-GDA0002688881080000023
in the formula: e'dAnd e'qD-axis and q-axis components of transient potential, T, of the wind turbined0'is the time constant of the rotor loop when the stator loop is open, x is the sum of the leakage reactance of the stator winding and the excitation reactance, x' is the transient reactance of the stator winding when the rotor loop is short-circuited, idAnd iqD-axis and q-axis components of stator current, s is slip, omega is rotor speed, TJIs the equivalent mechanical inertia time constant of the generator, MmAnd MeRespectively the mechanical load torque and the electromagnetic torque of the doubly-fed induction machine.
The output equation of the induction motor is:
Figure RE-GDA0002688881080000031
the steady state constraints are:
Figure RE-GDA0002688881080000032
the electromagnetic transient part of the induction motor is identified by the parameter r1，x′，Td0', the parameter to be identified of the electromechanical transient part is TJ，α，β，Me，s。
3) Mathematical model of DFIG
The doubly-fed induction machine can be regarded as an asynchronous generator in nature, and is similar to a model of a common induction motor, and the main difference is a bidirectional converter for providing rotor excitation voltage and a machine side and grid side control device thereof. The accuracy and the efficiency of simulation calculation of the power system are comprehensively considered, and a DFIG third-order state equation can be constructed by referring to an induction motor electromagnetic equation as shown in the following formula.
Figure RE-GDA0002688881080000033
Wherein, Vdr、VqrRepresenting the d-axis and q-axis components of the rotor voltage, respectively.
Equivalent description of rotor excitation voltage:
Figure RE-GDA0002688881080000034
in steady state, the transient potential of the fan is constant, so that the left side of the equation of state is equal to zero, and V can be obtaineddr0、Vqr0The parameters λ and η can be solved from μ by the above formula, as shown in the following formula:
Figure RE-GDA0002688881080000041
in summary, the mathematical model of DFIG is similar to induction motors, but with two more parameters μ, to be identified than induction motors.
(5) And determining the load proportion of the ZIP model, the induction motor model and the DFIG model.
The relational expression of the output power and the wind speed of the DFIG doubly-fed wind generator, namely a wind speed-power model, is as follows:
Figure RE-GDA0002688881080000042
wherein v is the wind speed; v. ofcTo cut into the wind speed; v. ofrRated wind speed; v. ofsCutting out the wind speed; p is a radical ofw(v) For variable power between cut-in wind speed and rated wind speed, i.e.
Figure RE-GDA0002688881080000043
Cp.eqIs a constant power coefficient, rho is the air density, A is the area swept by the wind wheel; p is a radical ofrIs the rated power.
The wind power penetration rate k is introducedpwDynamic-static load ratio kpm. The wind power penetration rate is the proportion of the total wind power generation capacity to the total installed capacity of the area where the system is located; the dynamic-static load ratio represents the active power ratio of the dynamic part (namely the induction motor) of the load under the steady state condition, and the active power ratio of the static part of the load is kpzip＝1-kpm。
Figure RE-GDA0002688881080000044
Wherein, PzipActive power, P, for static ZIP loads at bus bar nodesmIs the active power of the induction motor load at the bus bar node.
(6) And determining the composition structure of the load at the bus node of the transformer substation.
The load of the high-proportion renewable energy source accessed to the bus of the transformer substation consists of a static ZIP, an induction motor and a DFIG doubly-fed wind driven generatorAre connected in parallel. When the rotating speed of the doubly-fed wind generator is in the sub-synchronous rotating speed (namely the rotating speed is lower than the synchronous rotating speed), the unit absorbs power from the power grid to carry out excitation, and at the moment, P iswIf the load is less than 0, the DFIG wind power generation system can be compared with the induction motor load, and the mathematical expression of the load model can be represented by the mathematical model of the induction motor.
Therefore, the load composition structure of the high-proportion renewable energy source accessed to the bus of the transformer substation can be the output power p of the DFIG doubly-fed wind driven generatorwAnd (6) determining. When P is presentwWhen the current is less than 0, the bus load structure consists of a static ZIP load and an induction motor load; when P is presentwWhen the voltage is more than 0, the bus load structure consists of a static ZIP load and a DFIG doubly-fed wind generator.
(7) And (4) according to the bus typical daily load curve obtained by clustering, using the data acquired by the static ZIP load model, the dynamic three-order induction motor model, the DFIG model and the WAMS system given in the step (4), identifying load parameters of the high-proportion renewable energy accessed to the substation bus under the same time scale with the bus typical daily load curve, and obtaining all parameter change trends under the bus typical daily load curve.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of CEEMD-LSTM model bus load data missing value processing;
FIG. 3 is a diagram of a load model with a static ZIP and an induction motor;
FIG. 4 is a diagram of a load model architecture for a wind turbine including static ZIP and DFIG;
fig. 5 shows an equivalent circuit of an induction motor represented by a transient electromotive force and a reactance.
Detailed Description
The invention comprises the following steps:
(1) acquiring historical data of an SCADA system and a WAMS system of a load node, wherein the data comprises the amplitude and phase angle of bus voltage, the amplitude and phase angle of current, active power, reactive power and bus frequency f;
(2) and preprocessing the daily load data of the SCADA system collected on the plurality of buses by adopting a CEEMD-LSTM model. And processing the load data missing values on the plurality of buses.
Taking a daily load data as an example, the preprocessing steps are as follows:
1) decomposing the bus load sequence x (t) by using a complementary empirical mode decomposition algorithm (CEEMD algorithm) to obtain n intrinsic mode function components IMF1(t)、IMF2(t)、…、IMFn(t) and a residual component r (t). Wherein, the n natural mode function components and the residual components are vectors with m columns in a row.
2) Respectively establishing n +1 long-short term memory neural network (LSTM) models for n inherent mode function components and residual components obtained after CEEMD decomposition processing in the step 1).
3) Carrying out model training on the n +1 LSTM models established in the step 2) according to the training process thereof, and setting parameters of the LSTM models: the number of hidden layers, the learning rate and the training times. Obtaining the predicted value IMF of each inherent modal function component and residual error1′(t)、IMF2′(t)、…、IMFn′(t)、r′(t)。
4) And 3) reconstructing the result of the step 3) to obtain a final prediction result of the bus load data missing value.
(3) And clustering daily load curves on the plurality of buses by using the SCK-SHAPE model and daily load data acquired by the SCADA system to obtain typical daily load curves of several types of buses.
The method comprises the following specific steps of a SCK-SHAPE-based bus load curve clustering model:
1) input bus load dataset
2) And (3) processing the missing value by adopting the data preprocessing method in the step (2).
3) Load data standardization processing: the bus load data is transformed to-1, 1 using the Z-SCORE method, i.e., the data is normalized using the following equation.
Figure RE-GDA0002688881080000061
Wherein x ismean＝mean(x1,x2,...,xn) Is the mean of the load, and σ is the standard deviation of the load.
4) And calculating the profile coefficient of the bus load data set according to the following formula, determining the optimal clustering number k, and initializing the center of each cluster.
Figure RE-GDA0002688881080000062
Figure RE-GDA0002688881080000063
Figure RE-GDA0002688881080000064
Where a (o) is the distance of the data sample o to all other points in the cluster to which it belongs; b (o) is the average distance of data sample o to all points in a cluster nearest to it; SC (o) is the profile coefficient of the data sample o, whose value range is [ -1,1 [)]The closer to 1, the better the clustering degree of the cluster where o is located, and the more reasonable the clustering degree. Data sample o ∈ Ci(1≤i≤k)，CiA cluster with the number of the clustering results being k, o' is CiAll samples except o, dist (o, o ') is the distance from o to o'.
5) The SBD value of each data in the load data set to the respective cluster center is calculated according to equation (15) and classified into the one with the smallest value.
Figure RE-GDA0002688881080000065
Wherein X, Y is two time series, R0Representing two time series that are completely similar and do not need relative displacement between them, and the corresponding SBD coefficient value.
6) And outputting a clustering result.
(4) Establishing a load model
1) Static ZIP load model
The static ZIP load model adopts a polynomial model and belongs to an input and output model, so that the static ZIP load model shown as the formula (1) is established;
Figure RE-GDA0002688881080000071
in the formula: p is the active power of the static load, Q is the reactive power of the static load, U is the voltage, U0Is an initial value of voltage, P0As an initial value of active power, Q0Is an initial value of reactive power, p1、p2、p3Is the proportion of constant impedance, constant current and constant power in the active load q1、q2、q3The proportion of constant impedance, constant current and constant power in the reactive load is shown;
in the above formula, p1、p2、p3、q1、q2、q3The parameters to be identified need to satisfy the constraint condition of the following formula;
Figure RE-GDA0002688881080000072
2) induction motor load model
The induction motor model should consider the mechanical transient and electromagnetic transient processes of the motor at the same time, so the induction motor model is described by a three-order model, and the dynamic behavior of the induction motor is often expressed by a differential equation as shown in the following formula;
Figure RE-GDA0002688881080000073
in the formula: e'dAnd e'qD-axis and q-axis components of transient potential, T, of the wind turbined0'is the time constant of the rotor loop when the stator loop is open, x is the sum of the leakage reactance of the stator winding and the excitation reactance, x' is the transient reactance of the stator winding when the rotor loop is short-circuited, idAnd iqD-axis and q-axis components of stator current, s is slip, omega is rotor speed, TJIs the equivalent mechanical inertia time constant of the generator, MmAnd MeRespectively the mechanical load torque and the electromagnetic torque of the doubly-fed induction machine.
The output equation of the induction motor is:
Figure RE-GDA0002688881080000081
mechanical torque of induction motor:
Mm＝k[α+(1-α)(1-s)β] (16)
induction motor electromagnetic torque:
Figure RE-GDA0002688881080000082
wherein, UNRated voltage for the terminal (can be replaced by initial value voltage in normal mode); scrIs the critical slip of the induction motor; memaxIs the induction motor maximum electromagnetic torque (per unit).
The electromechanical transient part of equation (3) can be simplified by equations (16) and (17) to the following equation:
Figure RE-GDA0002688881080000083
the electromagnetic transient part of the induction motor is identified by the parameter r1，x′，Td0', the parameter to be identified of the electromechanical transient part is TJ，α，β，Memax，scr。
3) Mathematical model of DFIG
The doubly-fed induction machine can be regarded as an asynchronous generator in nature, and is similar to a model of a common induction motor, and the main difference is a bidirectional converter for providing rotor excitation voltage and a machine side and grid side control device thereof. The accuracy and the efficiency of simulation calculation of the power system are comprehensively considered, and a DFIG third-order state equation can be constructed by referring to an induction motor electromagnetic equation as shown in the following formula.
Figure RE-GDA0002688881080000084
Wherein, Vdr、VqrRepresenting the d-axis and q-axis components of the rotor voltage, respectively.
Equivalent description of rotor excitation voltage:
Figure RE-GDA0002688881080000091
in steady state, the transient potential of the fan is constant, so that the left side of the equation of state is equal to zero, and V can be obtaineddr0、Vqr0The parameters λ and η can be solved from μ by the above formula, as shown in the following formula:
Figure RE-GDA0002688881080000092
the mathematical model of the DFIG is similar to that of the induction motor, and the parameters to be identified are the same as the induction motor, but two more parameters mu to be identified are needed than the induction motor.
(5) The ratios of the ZIP, induction motor, DFIG models are determined.
The relational expression of the output power and the wind speed of the DFIG doubly-fed wind generator, namely a wind speed-power model, is as follows:
Figure RE-GDA0002688881080000093
wherein v is the wind speed; v. ofcTo cut into the wind speed; v. ofrRated wind speed; v. ofsCutting out the wind speed;pw(v) for variable power between cut-in wind speed and rated wind speed, i.e.
Figure RE-GDA0002688881080000094
Cp.eqIs a constant power coefficient, rho is the air density, A is the area swept by the wind wheel; p is a radical ofrIs the rated power.
The wind power penetration rate k is introducedpwDynamic-static load ratio kpm. The wind power penetration rate is the proportion of the total wind power generation capacity to the total installed capacity of the area where the system is located; the dynamic-static load ratio represents the active power ratio of the dynamic part (namely the induction motor) of the load under the steady state condition, and the active power ratio of the static part of the load is kpzip＝1-kpm。
Figure RE-GDA0002688881080000095
Wherein, PzipActive power, P, for static ZIP loads at bus bar nodesmIs the active power of the induction motor load at the bus bar node.
(6) And determining the composition structure of the load at the bus node of the transformer substation.
The load of the high-proportion renewable energy source accessed to the substation bus is formed by connecting a static ZIP, an induction motor and a DFIG doubly-fed wind driven generator in parallel. When the rotating speed of the doubly-fed wind generator is in the sub-synchronous rotating speed (namely the rotating speed is lower than the synchronous rotating speed), the unit absorbs power from the power grid to carry out excitation, and at the moment, P iswIf the load is less than 0, the DFIG wind power generation system can be compared with the induction motor load, and the mathematical expression of the load model can be represented by the mathematical model of the induction motor.
Therefore, the load composition structure of the high-proportion renewable energy source accessed to the bus of the transformer substation can be the output power p of the DFIG doubly-fed wind driven generatorwAnd (6) determining. When P is presentw< 0 or kpwWhen the current value is less than 0, the bus load structure consists of a static ZIP load and an induction motor load, and the DFIG is equivalent to the induction motor load and is the same as a common induction motor; when P is presentw> 0 or kpwWhen the voltage is more than 0, the bus load structure is composed of a static ZIP load and a DFIG doubly-fed wind generator, and the induction motor is contained in the DFIG.
(7) And (4) according to the bus typical daily load curve obtained by clustering, using the data acquired by the static ZIP load model, the dynamic three-order induction motor model, the DFIG model and the WAMS system given in the step (4), identifying load parameters of the high-proportion renewable energy accessed to the substation bus under the same time scale with the bus typical daily load curve, and obtaining all parameter change trends under the bus typical daily load curve.
The process of various load model parameter identification is described below.
1) Static ZIP load model parameter identification
The model of formula (1) is used, where U is the input signal, P, Q are the output signals, P1、p2、p3、q1、q2、 q3Is the parameter to be identified.
Is provided with a Ui、Pi、QiIs the ith input and output signal. Taking the identification of the parameters of the active model as an example, the identification criterion is the following nonlinear programming equation
Figure RE-GDA0002688881080000101
Constraint conditions
Figure RE-GDA0002688881080000102
The optimal solution can be solved by nonlinear programming. Order to
Figure RE-GDA0002688881080000111
The optimal solution of the nonlinear programming by the Cohen-Tuck theorem should satisfy the following requirements
Figure RE-GDA0002688881080000112
The parameter p to be identified can be obtained according to the formula1、p2、p3The optimal solution of (1). Similarly, the parameter q to be identified of the reactive power can be obtained1、q2、q3。
2) Three-order induction motor parameter identification
Considering that the electromagnetic transient process is significantly shorter than the electromechanical transient, the parameter T is first utilizedd0'，x'，r1Calculating x, s from the typical values and the steady state conditions of0And a load factor k in the electromechanical transient equation, and then identifying parameters of the electromagnetic transient part and the electromechanical transient part.
The first expression of equation (5) is obtained from the equivalent circuit shown in fig. 4:
Figure RE-GDA0002688881080000113
written in matrix form as
Figure RE-GDA0002688881080000114
Can calculate ed0′，eq0′。
The derivative to the left of the equal sign of equation (3) at steady state is equal to zero, i.e.
Figure RE-GDA0002688881080000115
And
Figure RE-GDA0002688881080000116
can be pushed to
Figure RE-GDA0002688881080000117
Knowing I at steady stated0、Iq0Independent parameter T'd0X' and the already determined ed0′，eq0', the non-independent parameters x, s can be calculated by the above formula0。
Mechanical torque of induction motor:
Mm＝k[α+(1-α)(1-s)β]
induction motor electromagnetic torque:
Figure RE-GDA0002688881080000121
from the third equation of motion of the rotor in equation (3), at steady state
Figure RE-GDA0002688881080000122
Namely, it is
Figure RE-GDA0002688881080000123
The load factor k can be calculated.
Then to Td0'，x'，r1And identifying parameters of the electromagnetic transient part, wherein the state equation to be identified is as follows:
Figure RE-GDA0002688881080000124
Figure RE-GDA0002688881080000125
wherein the parameter to be identified of the electromagnetic transient part is theta1＝[r1,x′,T′d0]。
The electromechanical transient part of equation (3) can be simplified by equations (20) and (21) as follows:
Figure RE-GDA0002688881080000126
i.e. the parameter to be identified for the electromechanical transient portion is theta2＝{TJ,α,β,Memax,scr}。
3) DFIG doubly-fed wind generator parameter identification
1. Collecting data samples [ V ] required by identifications P Q]
2. Given a parameter theta to be identified1＝[r1,x′,T′d0]、θ2＝{TJ,α,β,Memax,scr}、θ3＝{μ,}
3. And (3) according to the data sample, simulating the process of non-independent parameter identification in the steady state of the induction motor, and obtaining the initial values of the non-independent identification parameters and the state variables of the DFIG model.
4. Input model excitation Vs.kSolving for the rotor side voltage Vr.kSubstituting the state equation to obtain the transient potential e 'at the moment'd.k、e′q.kAnd slip sk。
5. From e'd.k、e′q.kCalculating the current i of the stator side of the DFIGs.kAnd the current i on the rotor sider.kFurther, the response power P 'of the model is calculated'k、Q′k。
6. And repeating the steps 4 and 5 until all model response powers P 'in the data sample are calculated'k、Q′k. SCADA data is acquired every 15min and WAMS data is acquired every 40ms, so k is 1, 2.
7. And calculating the mean square error of the measured value and the model response. If the given set condition is satisfied, outputting the model parameters, otherwise, repeating the steps 2 to 4.
The above embodiments are merely illustrative, and not restrictive, and various changes and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention, and therefore all equivalent technical solutions are intended to be included within the scope of the invention.

Claims (7)

1. A real-time modeling method for a bus load of a transformer substation accessed by a doubly-fed wind generator is characterized by comprising the following steps:
(1) acquiring historical data of an SCADA system and a WAMS system of a load node, wherein the data comprises the amplitude and phase angle of bus voltage, the amplitude and phase angle of current, active power, reactive power and bus frequency f;
(2) preprocessing daily load data of the SCADA system collected on the multiple buses by adopting a CEEMD-LSTM model;
(3) clustering daily load curves on a plurality of buses by using a SCK-SHAPE model and daily load data acquired by an SCADA system to obtain typical daily load curves of several types of buses;
(4) respectively establishing load models of a static ZIP, an induction motor and a DFIG doubly-fed wind generator;
(5) determining the load proportion of a ZIP model, an induction motor model and a DFIG model;
(6) determining a composition structure of a load at a bus node of a transformer substation;
(7) and identifying load parameters of a bus of a high-proportion renewable energy source access substation by using data acquired by the WAMS system under the same time scale with a typical daily load curve obtained by clustering, and obtaining all parameter change trends under the typical daily load curve.
2. The real-time modeling method for the bus load of the doubly-fed wind generator access substation according to claim 1, wherein in the step 2), a CEEMD-LSTM model is adopted to preprocess SCADA system daily load data acquired from a plurality of buses;
1) decomposing the bus load sequence x (t) by using a complementary empirical mode decomposition algorithm (CEEMD algorithm) to obtain n intrinsic mode function components IMF1(t)、IMF2(t)、…、IMFn(t) and a residual component r (t), wherein the n normal mode function components and the residual component are a row of m lines of vectors;
2) respectively establishing n +1 long-short term memory neural network (LSTM) models for n inherent mode function components and residual components obtained after CEEMD decomposition processing in the step 1);
3) for n +1 LSTs established in step 2)The M model carries out model training according to the training process, and the parameters of the LSTM model are set as follows: hiding the layer number, learning rate and training times to obtain the predicted value IMF of each inherent modal function component and residual error1′(t)、IMF2′(t)、…、IMFn′(t)、r′(t)；
4) And 3) reconstructing the result of the step 3) to obtain a final prediction result of the bus load data missing value.
3. The real-time modeling method for the bus load of the doubly-fed wind generator access substation according to claim 1, wherein the daily load data acquired by an SCK-SHAPE model and an SCADA system in the step 3) are utilized to cluster daily load curves on a plurality of buses to obtain typical daily load curves of several types of buses;
1) inputting a bus load data set;
2) the data preprocessing method according to claim 2, processing the deficiency value;
3) load data standardization processing: the Z-SCORE method is adopted, namely, the data is standardized by the following formula, and the bus load data is transformed to [ -1,1 ];
Figure RE-FDA0002688881070000021
4) calculating the profile coefficient of a bus load data set according to the following formula, determining the optimal clustering number k, and initializing the center of each cluster;
Figure RE-FDA0002688881070000022
Figure RE-FDA0002688881070000023
Figure RE-FDA0002688881070000024
5) calculating the SBD value of each data in the load data set to the respective clustering center according to the following formula, and classifying the SBD value into a class with the minimum value;
Figure RE-FDA0002688881070000025
6) and outputting a clustering result.
4. The real-time modeling method for the bus load of the access substation of the doubly-fed wind generator according to claim 1 is characterized in that load models of a static ZIP, an induction motor and a DFIG doubly-fed wind generator are respectively established in the step 4);
1) static ZIP load model:
Figure RE-FDA0002688881070000031
in the formula: p is the active power of the static load, Q is the reactive power of the static load, U is the voltage, U0Is an initial value of voltage, P0As an initial value of active power, Q0Is an initial value of reactive power, p1、p2、p3Is the proportion of constant impedance, constant current and constant power in the active load q1、q2、q3The proportion of constant impedance, constant current and constant power in the reactive load is shown;
2) induction motor load model:
Figure RE-FDA0002688881070000032
in the formula: e'dAnd e'qD-axis and q-axis components of transient potential, T, of the wind turbined0' is the rotor loop time constant when the stator loop is open, and x is the stator windingThe sum of leakage reactance and excitation reactance, x' is the transient reactance of the stator winding in case of short circuit of the rotor circuit, idAnd iqD-axis and q-axis components of stator current, s is slip, omega is rotor speed, TJIs the equivalent mechanical inertia time constant of the generator, MmAnd MeRespectively the mechanical load torque and the electromagnetic torque of the doubly-fed induction motor;
3) mathematical model of DFIG:
the accuracy and the efficiency of simulation calculation of the power system are comprehensively considered, and a DFIG three-order state equation can be constructed by referring to an induction motor electromagnetic equation as shown in the following formula;
Figure RE-FDA0002688881070000033
wherein, Vdr、VqrD-axis and q-axis components representing rotor voltages, respectively;
equivalent description of rotor excitation voltage:
Figure RE-FDA0002688881070000041
wherein, Vds、VqsRepresenting the d-axis and q-axis components of the stator voltage, respectively.
5. The method for modeling the bus load of the doubly-fed wind generator access substation in real time according to claim 1, wherein the load proportion of a ZIP model, an induction motor model and a DFIG model is determined in the step 5);
the relational expression of the output power and the wind speed of the DFIG doubly-fed wind generator, namely a wind speed-power model, is as follows:
Figure RE-FDA0002688881070000042
wherein v is the wind speed; v. ofcTo cut into the wind speed; v. ofrRated wind speed; v. ofsCutting out the wind speed; p is a radical ofw(v) For variable power between cut-in wind speed and rated wind speed, i.e.
Figure RE-FDA0002688881070000043
Cp.eqIs a constant power coefficient, rho is the air density, A is the area swept by the wind wheel; p is a radical ofrIs rated power;
the wind power penetration rate k is introducedpwDynamic-static load ratio kpmThe wind power penetration rate is the proportion of the total wind power generation capacity to the total installed capacity of the area where the system is located; the dynamic-static load ratio represents the active power ratio of the dynamic part (namely the induction motor) of the load under the steady state condition, and the active power ratio of the static part of the load is kpzip＝1-kpm；
Figure RE-FDA0002688881070000044
Wherein, PzipActive power, P, for static ZIP loads at bus bar nodesmIs the active power of the induction motor load at the bus bar node.
6. The method for modeling the bus load of the doubly-fed wind generator access substation in real time according to claim 1, wherein the composition structure of the load at the node of the bus of the substation is determined in the step 6);
the load of the high-proportion renewable energy source accessed to the substation bus is formed by connecting the static ZIP, the induction motor and the DFIG doubly-fed wind driven generator in parallel, and because when the rotating speed of the doubly-fed wind driven generator is in the sub-synchronous rotating speed (namely the rotating speed is lower than the synchronous rotating speed), the unit absorbs power from the power grid to excite, at the moment, the P is connected with the power grid to absorb power to excitewIf less than 0, the DFIG wind power generation system can be compared with the induction motor load, the mathematical expression of the load model can be represented by the mathematical model of the induction motor, so that the load composition structure of the high-proportion renewable energy source accessed to the substation bus can be represented by the output power p of the DFIG doubly-fed wind power generatorwDetermine when P isw< 0 or kpwWhen the current value is less than 0, the bus load structure consists of a static ZIP load and an induction motor load, and the DFIG is equivalent to the induction motor load and is the same as a common induction motor; when P is presentw> 0 or kpwWhen the voltage is more than 0, the bus load structure is composed of a static ZIP load and a DFIG doubly-fed wind generator, and the induction motor is contained in the DFIG.
7. The real-time modeling method for the bus load of the doubly-fed wind generator access substation according to claim 1, wherein the load parameters of the bus of the substation accessed by the high-proportion renewable energy sources are identified by using the data acquired by the WAMS system in the step 7) under the same time scale with the typical daily load curve obtained by clustering, so that all parameter variation trends under the typical daily load curve are obtained;
1) static ZIP load model parameter identification
Taking the identification of the parameters of the active model as an example, the identification criterion is the following nonlinear programming equation
Figure RE-FDA0002688881070000051
Constraint conditions
Figure RE-FDA0002688881070000052
The optimal solution can be solved by nonlinear programming
Figure RE-FDA0002688881070000053
The optimal solution of the nonlinear programming by the Cohen-Tuck theorem should satisfy the following requirements
Figure RE-FDA0002688881070000061
The parameter p to be identified can be obtained according to the formula1、p2、p3The optimal solution of (a) can be used for obtaining the parameter q to be identified of the reactive power1、q2、q3；
2) Three-order induction motor parameter identification
Considering that the electromagnetic transient process is significantly shorter than the electromechanical transient, the parameter T is first utilizedd0'，x'，r1Calculating x, s from the typical values and the steady state conditions of0And the load rate k in the electromechanical transient equation, and then identifying the parameters of the electromagnetic transient part and the electromechanical transient part;
from the equivalent circuit of the induction motor, the following can be derived:
Figure RE-FDA0002688881070000062
written in matrix form as
Figure RE-FDA0002688881070000063
Can calculate ed0′，eq0′；
The equation of state of the induction motor in steady state being equal to zero, i.e.
Figure RE-FDA0002688881070000064
And
Figure RE-FDA0002688881070000065
can be pushed to
Figure RE-FDA0002688881070000066
Knowing I at steady stated0、Iq0Independent parameter T'd0X' and the already determined ed0′，eq0', the non-independent parameters x, s can be calculated by the above formula0；
Mechanical torque of induction motor:
Mm＝k[α+(1-α)(1-s)β]
induction motor electromagnetic torque:
Figure RE-FDA0002688881070000071
from the equation of motion of the rotor of the induction motor, at steady state
Figure RE-FDA0002688881070000072
Namely, it is
Figure RE-FDA0002688881070000073
Calculating the load rate k;
then to Td0'，x'，r1And identifying parameters of the electromagnetic transient part, wherein the state equation to be identified is as follows:
Figure RE-FDA0002688881070000074
wherein the parameter to be identified of the electromagnetic transient part is theta1＝[r1,x′,T′d0]；
The mechanical torque and the electromagnetic torque of the induction motor are substituted into a rotor motion equation, and the electromechanical transient part of the induction motor can be simplified into the following formula:
Figure RE-FDA0002688881070000075
i.e. the parameter to be identified for the electromechanical transient portion is theta2＝{TJ,α,β,Memax,scr}；
3) DFIG doubly-fed wind generator parameter identification
The process of parameter identification is as follows:
firstly, collecting data sample [ V ] required for identifications P Q]；
② setting a parameter theta to be identified1＝[r1,x′,T′d0]、θ2＝{TJ,α,β,Memax,scr}、θ3＝{μ,}；
Simulating the process of non-independent parameter identification of the induction motor in a steady state according to the data sample to obtain the initial values of the non-independent identification parameters and the state variables of the DFIG model;
input model excitation Vs.kSolving for the rotor side voltage Vr.kSubstituting the state equation to obtain the transient potential e 'at the moment'd.k、e′q.kAnd slip sk；
Is prepared from'd.k、e′q.kCalculating the current i of the stator side of the DFIGs.kAnd the current i on the rotor sider.kFurther, the response power P 'of the model is calculated'k、Q′k；
Sixthly, repeating the steps 4 and 5 until all model response powers P 'in the data sample are calculated'k、Q′kSCADA data is collected every 15min, WAMS data is collected every 40ms, so k is 1, 2.
And seventhly, calculating the mean square error of the measured value and the model response, outputting model parameters if the mean square error meets the given set conditions, and otherwise, repeating the steps 2 to 4.
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