CN108306284B - Online load modeling method based on local intelligent measurement - Google Patents

Online load modeling method based on local intelligent measurement Download PDF

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CN108306284B
CN108306284B CN201810010291.0A CN201810010291A CN108306284B CN 108306284 B CN108306284 B CN 108306284B CN 201810010291 A CN201810010291 A CN 201810010291A CN 108306284 B CN108306284 B CN 108306284B
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induction motor
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CN108306284A (en
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汤奕
朱亮亮
王�琦
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Southeast University
Liyang Research Institute of Southeast University
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Liyang Research Institute of Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • 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
    • 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

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Abstract

The invention discloses an online load modeling method based on local intelligent measurement, which comprises the following steps: step 1, user clustering is carried out based on daily load data acquired by an intelligent meter reading system, a part of typical users are selected from each class of users for invasive load monitoring, and load monitoring equipment is an intelligent socket; step 2, carrying out online measurement and identification based on the real-time load information acquired by the intelligent socket to obtain a load model, and replacing other users in the class with the load model of the typical user in each class to obtain a comprehensive load model of all users in real time; step 3, respectively establishing a polymerization model of the static load of a bottom layer user and the load of the induction motor; and 4, considering the influence of the power distribution network, gradually equating the static load and induction motor load aggregation model from the low-voltage side to the high-voltage side, and finally obtaining a comprehensive load model under the 220/110kV bus of the power distribution network. The method can improve the accuracy of the load model and reflect the actual characteristics of the load.

Description

Online load modeling method based on local intelligent measurement
Technical Field
The invention belongs to the technical field of power system operation control, and particularly relates to an online load modeling method based on local intelligent measurement.
Background
The accuracy of the load model has very important influence on the design, calculation and safety and stability analysis of the power system. With the continuous expansion of the system scale and the application of new equipment in new technology, greater challenges are brought to load modeling work. For a long time, researchers at home and abroad carry out a large amount of power load modeling work, two load modeling methods, namely a statistical synthesis method and a total measurement and identification method, are generally formed, and the methods achieve certain effects and have respective limitations. The basic idea of the statistical synthesis method is to count the load composition of various types of users, determine the specific gravity of static load and induction motor load, and synthesize to obtain a total load model, and the statistical synthesis method has the defects of time and labor consumption and poor model time variability. The basic idea of the total measuring and distinguishing method is that a load group is regarded as a whole, the parameters of a load model are integrally distinguished according to field collected measuring data, the measuring work of the total measuring and distinguishing method is complex, the accuracy of the model is difficult to guarantee, and the like.
The load model obtained by the traditional load modeling method is generally connected with a 220kV or 110kV bus. Generally, the lower the voltage level, the more well defined the load component near the end, while the more samples are taken, the more accurate the load identification. In the past, due to the limitation of technical means, users cannot go deep into the bottom layer of the power distribution network to carry out load actual measurement and modeling work. However, with the construction of a large-scale smart grid, the rapid development of information technologies such as calculation, communication, sensing and the like makes the online monitoring of the power load information possible. The load online monitoring equipment can collect the load information of the users of the power system in real time and carry out online measurement and identification, provides real-time and accurate load information for the power department, and can carry out real-time load modeling research work by utilizing the measurement information.
Disclosure of Invention
The invention aims to provide an online load modeling method based on local intelligent measurement, which is characterized in that a load model of all elements on the bottom layer voltage level of a power distribution network is subjected to hierarchical aggregation, and is equivalent upwards step by step to finally obtain a 220kV/110kV bus comprehensive load model; compared with the traditional load modeling method, the method can improve the accuracy of the load model and reflect the actual characteristics of the load.
In order to achieve the above purpose, the solution of the invention is:
an online load modeling method based on local intelligent measurement comprises the following steps:
step 1, user clustering is carried out based on daily load data acquired by an intelligent meter reading system, a part of typical users are selected from each class of users for invasive load monitoring, and load monitoring equipment is an intelligent socket;
step 2, carrying out online measurement and identification based on the real-time load information acquired by the intelligent socket to obtain a load model, and replacing other users in the class with the load model of the typical user in each class to obtain a comprehensive load model of all users in real time;
step 3, respectively establishing a polymerization model of the static load of a bottom layer user and the load of the induction motor;
and 4, considering the influence of the power distribution network, gradually equating the static load and induction motor load aggregation model from the low-voltage side to the high-voltage side, and finally obtaining a comprehensive load model under the 220/110kV bus of the power distribution network.
In the step 1, the specific content of user clustering based on daily load data acquired by the intelligent meter reading system is as follows: randomly selecting K users according to the daily load data of the given N users, wherein each user represents an initial clustering center of a user group, and distributing the rest other users to the clustering centers closest to the users according to the Euclidean distance of power consumption to form K user groups; and recalculating the clustering center of each user group, namely averaging the electricity consumption of all the users in the user group, allocating all the users to the clustering center with the closest distance again, and repeating the process continuously according to the process until the clustering center of each user group does not change or the clustering criterion function reaches the convergence condition.
In the step 2, the smart socket is composed of five parts, namely a filtering sampling module, a data processing module, a communication module, an execution module and a power module, wherein the filtering sampling module is used for collecting the current loaded voltage, current and frequency parameters of the smart socket and converting high-voltage and high-current signals into low-voltage signals for the data processing module to analyze; the data processing module integrally stages the operation of the whole system from a hardware framework, and the following software functions are completed inside the data processing module: parameter calculation, data communication, advanced protection and instruction execution; the communication module is one of the channels for interaction between the intelligent socket and the control server; the execution module is responsible for executing a control instruction of the data processing module, feeding back an execution result and simultaneously supporting two modes of loop on-off and infrared adjustment; the power module converts 220V alternating current into direct current of 5V and 3.3V for system operation.
In the step 3: the static load model takes the polynomial form recommended by the IEEE Task Force as follows:
Figure RE-GDA0001660750440000021
in the formula, a, b and c are active power coefficients, alpha, beta and gamma are reactive power coefficients, U is the actual voltage of the load, and U is the actual voltage of the load0Is the rated voltage of the load, P0、Q0Active power and reactive power of the load under rated voltage respectively, and P, Q actual active power and reactive power consumed by the load respectively;
the method for establishing the static load aggregation model comprises the following steps: weighting the active power and the reactive power of the load according to the constant impedance, the constant current and the constant power component respectively according to coefficients, and the following formula is as follows:
Figure RE-GDA0001660750440000031
Figure RE-GDA0001660750440000032
Figure RE-GDA0001660750440000033
in the formula, P01,P02,…,P0nRated active power, Q, for a single static load01,Q02,…,Q0nRated reactive power, P, for a single static load0SAnd Q0SRated active and reactive power, a, respectively, for a collective static load1,a2,…,an；b1,b2,…,bnActive power coefficient, alpha, for a single static load1,α2,…, αn；β1,β2,…,βnReactive power coefficient, a, of a single static load, respectivelyS、bS、cSActive power coefficient, alpha, for aggregating static loadsS、βS、γSThe reactive power coefficient of the static load is aggregated.
In the step 3: the induction motor load adopts a three-order electromechanical transient model as follows:
Figure RE-GDA0001660750440000034
in the formula, T0′＝(Xr+Xm)/ω0Rr，X＝Xs+Xm，X′＝Xs+XmXr/(Xm+Xr)，T0'is the transient open-circuit time constant, X is the open-circuit reactance of the rotor, X' is the short-circuit reactance when the rotor is not moving,
Figure RE-GDA0001660750440000035
in order to be a transient electromotive force,
Figure RE-GDA0001660750440000036
is the voltage of the motor and is,
Figure RE-GDA0001660750440000037
is the motor current, omega is the motor speed, omega0For rated speed of the motor, TEFor electromagnetic torque, TMIs mechanical torque, H is inertia time constant, RrIs rotor resistance, XrIs rotor reactance, RsIs stator resistance, XsIs a stator reactance, XmIs an excitation reactance;
the rated capacity of the aggregated induction motor load is the sum of the rated capacities of the single induction motor loads, namely:
Figure RE-GDA0001660750440000041
in the formula, k is the number of induction motor loads; the equivalent circuit parameters of the load of the aggregated induction motor are weighted averages of admittances of all branches in the equivalent circuit, namely:
Figure RE-GDA0001660750440000042
in the formula, the proportionality coefficient rhoi＝SNi/SNM，ZiImpedance of the electrical branch, Z, for a single induction motor loadMFor the electrical branch impedance of the aggregate induction motor load, for the stator branch ZM＝Rs+jXsTo excitation branch ZM＝jXmTo the rotor branch ZM＝Rr/s+jXr(ii) a The inertial time constant of the aggregate induction motor load is:
Figure RE-GDA0001660750440000043
in the formula, HiIs the inertia time constant of the load of a single induction motor, HMIs the time constant of inertia of the aggregate induction motor load.
In the step 4: considering the influence of a distribution network, when the static load and induction motor load aggregate model is gradually equalized from a low-voltage side to a high-voltage side, the method comprises the following steps:
Figure RE-GDA0001660750440000044
in the formula, ZDTo distribution network impedance, YSFor low-pressure side static load equivalent admittance, lambda1，λ2，λ3All variables are related to the equivalent admittance of the low-voltage side static load and the impedance of the power distribution network;
when the static load aggregation model is equalized from the low-pressure side to the high-pressure side, the high-pressure side static load equivalent admittance is as follows:
Figure RE-GDA0001660750440000045
in the formula, PH、QHRespectively active and reactive power, U, flowing into the high-side busHIs the high side bus voltage;
when the induction motor load aggregation model is equalized from a low-voltage side to a high-voltage side, the method for calculating the load equivalent parameters of the high-voltage side induction motor comprises the following steps:
Figure RE-GDA0001660750440000051
in formula (II) T'0H＝(XrH+XmH)/ω0HRrH，T′0HIs the time constant of transient open circuit of high-voltage side induction motor0HFor high-side induction motor rated speed, THE、TMHElectromagnetic torque, mechanical torque, H, of the high-side induction motorHIs the inertia time constant, R, of the high-side induction motorrH、XrHRespectively, the rotor resistance and the rotor reactance R of the high-voltage side induction motorsH、XsHRespectively, the stator resistance, the stator reactance, X of the high-voltage side induction motormHIs a high side induction motor field reactance.
After adopting the scheme, compared with the prior art, the invention has the beneficial effects that:
(1) the comprehensive load model obtained by the modeling method is closer to the actual load of the power distribution network, and the simulation precision of the power system can be improved;
(2) the modeling method can realize dynamic update of the load model and reflect the real-time running state of the load of the power system.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a daily power curve for a typical powered device;
FIG. 3 is a diagram of a 3-machine 9-node simple distribution network algorithm;
FIG. 4 is a graph of a 110kV node voltage simulation;
fig. 5 is a simulation graph of active power of a 110kV node.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
As shown in fig. 1, the present invention provides an online load modeling method based on local intelligent measurement, which includes the following steps:
step 1, user clustering is carried out based on daily load data acquired by an intelligent meter reading system, a part of typical users are selected from each class of users for invasive load monitoring, and load monitoring equipment is an intelligent socket;
step 2, carrying out online measurement and identification based on the real-time load information acquired by the intelligent socket to obtain a load model, and replacing other users in the class with the load model of the typical user in each class to obtain a comprehensive load model of all users in real time;
step 3, respectively establishing a polymerization model of the static load of a bottom layer user and the load of the induction motor;
and 4, considering the influence of the power distribution network, gradually equating the static load and induction motor load aggregation model from the low-voltage side to the high-voltage side, and finally obtaining a comprehensive load model under the 220/110kV bus of the power distribution network.
In the step 1, the user clustering method based on the daily load data of the users comprises the following steps: and randomly selecting K users according to the given daily load data of the N users, wherein each user represents an initial clustering center of a user group. And distributing the rest other users to the nearest clustering centers according to the Euclidean distance of the power consumption, and forming K user groups. And recalculating the clustering center of each user group, namely averaging the electricity consumption of all the users in the user group, allocating all the users to the clustering center with the closest distance again, and repeating the process continuously according to the process until the clustering center of each user group does not change or the clustering criterion function reaches the convergence condition.
In the step 2, the intrusive load identification tool is an intelligent socket and mainly comprises a filtering sampling module, a data processing module, a communication module, an execution module and a power module. The filtering sampling module collects the current loaded voltage, current and frequency parameters of the intelligent socket, and converts high-voltage and high-current signals into low-voltage signals for analysis by the data processing module. The data processing module is the core of the intelligent socket, the operation of the whole system is planned on a hardware framework, and the following software functions are completed inside the data processing module: parameter calculation, data communication, high-level protection, instruction execution. The communication module is one of the interactive channels of smart jack and control server, can support wiFi, Zigbee, and the loRa mode is optional. The execution module is responsible for executing the control instruction of the data processing module, feeding back the execution result and simultaneously supporting two modes of loop on-off and infrared regulation. The power module is an energy source of the whole intelligent socket module and converts 220V alternating current into direct current of 5V and 3.3V for system operation.
In the step 3: the static load model takes the polynomial form recommended by the IEEE Task Force as follows:
Figure RE-GDA0001660750440000061
in the formula, a, b and c are active power coefficients, alpha, beta and gamma are reactive power coefficients, U is the actual voltage of the load, and U is the actual voltage of the load0Is the rated voltage of the load, P0、Q0Respectively, the real power and the reactive power of the load at the rated voltage, and P, Q, respectively, the actual real power and the reactive power consumed by the load.
The method for establishing the static load aggregation model comprises the following steps: weighting the active power and the reactive power of the load according to the constant impedance, the constant current and the constant power component respectively according to coefficients, and the following formula is as follows:
Figure RE-GDA0001660750440000062
Figure RE-GDA0001660750440000071
Figure RE-GDA0001660750440000072
in the formula, P01,P02,…,P0nRated active power, Q, for a single static load01,Q02,…,Q0nRated reactive power, P, for a single static load0SAnd Q0SRespectively for aggregate static loadRated active and reactive power, a1,a2,…,an；b1,b2,…,bnActive power coefficient, alpha, for a single static load1,α2,…, αn；β1,β2,…,βnReactive power coefficient, a, of a single static load, respectivelyS、bS、cSActive power coefficient, alpha, for aggregating static loadsS、βS、γSThe reactive power coefficient of the static load is aggregated.
The induction motor load adopts a three-order electromechanical transient model as follows:
Figure RE-GDA0001660750440000073
in the formula, T0′＝(Xr+Xm)/ω0Rr，X＝Xs+Xm，X′＝Xs+XmXr/(Xm+Xr)，T0'is the transient open-circuit time constant, X is the open-circuit reactance of the rotor, X' is the short-circuit reactance when the rotor is not moving,
Figure RE-GDA0001660750440000074
in order to be a transient electromotive force,
Figure RE-GDA0001660750440000075
is the voltage of the motor and is,
Figure RE-GDA0001660750440000076
is the motor current, omega is the motor speed, omega0For rated speed of the motor, TEFor electromagnetic torque, TMIs mechanical torque, H is inertia time constant, RrIs rotor resistance, XrIs rotor reactance, RsIs stator resistance, XsIs a stator reactance, XmIs the excitation reactance.
The rated capacity of the aggregated induction motor load is the sum of the rated capacities of the single induction motor loads, namely:
Figure RE-GDA0001660750440000077
where k is the number of induction motor loads. The equivalent circuit parameters of the load of the aggregated induction motor are weighted averages of admittances of all branches in the equivalent circuit, namely:
Figure RE-GDA0001660750440000078
in the formula, the proportionality coefficient rhoi＝SNi/SNM，ZiImpedance of the electrical branch, Z, for a single induction motor loadMFor the electrical branch impedance of the aggregate induction motor load, for the stator branch ZM＝Rs+jXsTo excitation branch ZM＝jXmTo the rotor branch ZM＝Rr/s+jXr. The inertial time constant of the aggregate induction motor load is:
Figure RE-GDA0001660750440000081
in the formula, HiIs the inertia time constant of the load of a single induction motor, HMIs the time constant of inertia of the aggregate induction motor load.
In the step 4: considering the influence of a distribution network, when the static load and induction motor load aggregate model is gradually equalized from a low-voltage side to a high-voltage side, the method comprises the following steps:
Figure RE-GDA0001660750440000082
in the formula, ZDTo distribution network impedance, YSFor low-pressure side static load equivalent admittance, lambda1，λ2，λ3Are variables related to the equivalent admittance of the low-voltage side static load and the impedance of the distribution network.
When the static load aggregation model is equalized from the low-pressure side to the high-pressure side, the high-pressure side static load equivalent admittance is as follows:
Figure RE-GDA0001660750440000083
in the formula, PH、QHRespectively active and reactive power, U, flowing into the high-side busHIs the high side bus voltage.
When the induction motor load aggregation model is equalized from a low-voltage side to a high-voltage side, the method for calculating the load equivalent parameters of the high-voltage side induction motor comprises the following steps:
Figure RE-GDA0001660750440000084
in formula (II) T'0H＝(XrH+XmH)/ω0HRrH，T′0HIs the time constant of transient open circuit of high-voltage side induction motor0H
For high-side induction motor rated speed, THE、TMHElectromagnetic torque, mechanical torque, H, of the high-side induction motorHIs the inertia time constant, R, of the high-side induction motorrH、XrHRespectively, the rotor resistance and the rotor reactance R of the high-voltage side induction motorsH、XsHRespectively, the stator resistance, the stator reactance, X of the high-voltage side induction motormHIs a high side induction motor field reactance.
The present invention will be described in further detail with reference to examples, but the present invention is not limited to the examples given.
Daily load data of 200 users in a certain city for three consecutive days is selected as a research sample, the data sampling interval is 1 hour, and the data scale is 200 x 3 x 24 ═ 14400. The clustering method is utilized to perform user clustering, when the clustering number is 3, the clustering effect is optimal, wherein the numbers of users belonging to the first class, the second class and the third class are 91, 86 and 23 respectively. And selecting part of typical users from each class of users to carry out invasive load monitoring, collecting electrical information of different electrical equipment in real time through an intelligent socket, uploading the electrical information to a control server, carrying out sample training and characteristic matching with each type of preset load electrical information in the server, and identifying the load type. And selecting typical load model parameters of different types of loads according to the load types obtained by identification. Fig. 2 shows the daily power curves of several types of typical electric devices collected by the smart socket, and the sampling frequency is 1 Hz.
A circuit as shown in figure 3 is built in PSCAD/EMTDC, and a simple power distribution network is connected below a WSCC9 node system node 6. In fig. 3, the integrated load at the 220V bus represents the load aggregation model of the three types of users. The load under 10kV bus represents large industrial load, and the reactance of 110kV/10kV transformer takes XT10.03p.u., X is taken for 10kV/220V transformer reactanceT20.02p.u. The aggregate model parameters of the three typical residential users are shown in table 1 as Load 1, Load 2 and Load 3, and the aggregate model parameter of the industrial Load is shown as Load 4.
TABLE 1 load parameters
Rs Xs Xm Rr Xr A B H
Load
1 0.023 0.126 3.39 0.0136 0.126 0.85 0 1.07
Load 2 0.032 0.096 2.69 0.032 0.096 1 0 0.50
Load 3 0.083 0.095 2.1 0.046 0.095 1 0 0.47
Load 4 0.018 0.117 3.6 0.009 0.117 1 0 1.40
Z％ I％ P％ ηs RD XD kM SN
Load 1 0.33 0.32 0.35 0.85 0.002 0.042 0.35 15
Load 2 0.20 0.50 0.30 0.85 0.001 0.04 0.2 12
Load 3 0.20 0.55 0.25 0.85 0.001 0.04 0.45 8
Load 4 0.10 0.85 0.05 0.85 0.003 0.04 0.75 25
The parameters in the table include upper and lower lines, the first line is the basic motor load parameter, and the second line, Z%, I% and P% represent the constant impedance, constant current and constant power load proportion in the static load, etasFor static load power factor, RD、XDIs the distribution line impedance, kMAs motor ratio, SNIs the rated capacity (MW) of the load.
Comparing the model accuracy of the method provided by the invention with that of the traditional integral identification method:
(1) by adopting the bottom-up load aggregation and gradual equivalence method provided by the invention, the 110kV bus comprehensive load is subjected to refined modeling, and the concrete process is as follows: 1) converting Load 1, Load 2 and Load 3 Load models to 220V buses through distribution lines respectively; 2) establishing a polymerization model of three types of loads under a 220V bus; 3) converting a polymerization model under a 220V bus to a 10kV bus through a 10kV/220V transformer; 4) converting the Load 4 Load model to a 10kV bus through a distribution line; 5) establishing a load polymerization model under a 10kV bus; 6) converting a polymerization model under a 10kV bus to a 110kV bus through a 110kV/10kV transformer, and obtaining a comprehensive load model under the 110kV bus;
(2) and only collecting the electric quantity of the 110kV bus, and integrally identifying the load model of the power distribution network under the 110kV bus by adopting a particle swarm algorithm.
The parameters of the comprehensive load model obtained by the two methods are shown in table 2.
TABLE 2 comprehensive load model parameters obtained by two load modeling methods
Rs Xs Xm Rr Xr H A B SM
Bottom-up modeling method 0.034 0.152 3.166 0.020 0.113 1.05 0.96 0 20.4
Integral identification method 0.027 0.116 3.302 0.019 0.116 1.25 1 0 22.1
kM P0 a b c Q0 α β γ
Bottom-up modeling method 0.40 25.15 0.22 0.52 0.26 16.45 0.22 0.52 0.26
Integral identification method 0.43 24.22 0.24 0.47 0.29 16.01 0.25 0.46 0.29
And 2s, a short-time earth fault is set at the node No. 5, and fig. 4 and 5 show voltage and active power curves of a 110kV node of the power distribution network in a load model obtained by adopting different load modeling methods. The result shows that compared with the traditional comprehensive load model obtained through integral identification, the load model obtained through bottom-to-top fine modeling and aggregation is closer to the actual power distribution network model.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.

Claims (6)

1. An online load modeling method based on local intelligent measurement is characterized by comprising the following steps:
step 1, user clustering is carried out based on daily load data acquired by an intelligent meter reading system, a part of typical users are selected from each class of users for invasive load monitoring, and load monitoring equipment is an intelligent socket;
step 2, carrying out online measurement and identification based on the real-time load information acquired by the intelligent socket to obtain a load model, and replacing other users in the class with the load model of the typical user in each class to obtain a comprehensive load model of all users in real time;
step 3, respectively establishing a polymerization model of the static load of a bottom layer user and the load of the induction motor;
and 4, considering the influence of the power distribution network, gradually equating the static load and induction motor load aggregation model from the low-voltage side to the high-voltage side, and finally obtaining a comprehensive load model under the 220/110kV bus of the power distribution network.
2. The online load modeling method based on local intelligence measurement as claimed in claim 1, characterized in that: in the step 1, the specific content of user clustering based on daily load data acquired by the intelligent meter reading system is as follows: randomly selecting K users according to the daily load data of the given N users, wherein each user represents an initial clustering center of a user group, and distributing the rest other users to the clustering centers closest to the users according to the Euclidean distance of power consumption to form K user groups; and recalculating the clustering center of each user group, namely averaging the electricity consumption of all the users in the user group, allocating all the users to the clustering center with the closest distance again, and repeating the process continuously according to the process until the clustering center of each user group does not change or the clustering criterion function reaches the convergence condition.
3. The online load modeling method based on local intelligence measurement as claimed in claim 1, characterized in that: in the step 2, the intelligent socket consists of a filtering sampling module, a data processing module, a communication module, an execution module and a power module, wherein the filtering sampling module is used for collecting the current loaded voltage, current and frequency parameters of the intelligent socket and converting high-voltage and high-current signals into low-voltage signals for the data processing module to analyze; the data processing module integrally stages the operation of the whole system from a hardware framework, and the following software functions are completed inside the data processing module: parameter calculation, data communication, advanced protection and instruction execution; the communication module is one of the channels for interaction between the intelligent socket and the control server; the execution module is responsible for executing a control instruction of the data processing module, feeding back an execution result and simultaneously supporting two modes of loop on-off and infrared adjustment; the power module converts 220V alternating current into direct current of 5V and 3.3V for system operation.
4. The online load modeling method based on local intelligence measurement as claimed in claim 1, characterized in that: in the step 3: the static load model takes the polynomial form recommended by the IEEE Task Force as follows:
Figure FDA0002891753370000021
in the formula, a, b and c are active power coefficients, alpha, beta and gamma are reactive power coefficients, U is the actual voltage of the load, and U is the actual voltage of the load0Is the rated voltage of the load, P0、Q0Active power and reactive power of the load under rated voltage respectively, and P, Q actual active power and reactive power consumed by the load respectively;
the method for establishing the static load aggregation model comprises the following steps: weighting the active power and the reactive power of the load according to the constant impedance, the constant current and the constant power component respectively according to coefficients, and the following formula is as follows:
Figure FDA0002891753370000022
Figure FDA0002891753370000023
Figure FDA0002891753370000024
in the formula, P01,P02,…,P0nRated active power, Q, for a single static load01,Q02,…,Q0nRated reactive power, P, for a single static load0SAnd Q0SRated active and reactive power, a, respectively, for a collective static load1,a2,…,an；b1,b2,…,bnActive power coefficient, alpha, for a single static load1,α2,…,αn；β1,β2,…,βnReactive power coefficient, a, of a single static load, respectivelyS、bS、cSActive power coefficient, alpha, for aggregating static loadsS、βS、γSThe reactive power coefficient of the static load is aggregated.
5. The online load modeling method based on local intelligence measurement as claimed in claim 1, characterized in that: in the step 3: the induction motor load adopts a three-order electromechanical transient model as follows:
Figure FDA0002891753370000025
in formula (II) T'0＝(Xr+Xm)/ω0Rr，X＝Xs+Xm，X′＝Xs+XmXr/(Xm+Xr)，T′0Is a transient open-circuit time constant, X is open-circuit reactance of the rotor, X' is short-circuit reactance when the rotor is not in motion,
Figure FDA0002891753370000031
in order to be a transient electromotive force,
Figure FDA0002891753370000032
is the voltage of the motor and is,
Figure FDA0002891753370000033
is the motor current, omega is the motor speed, omega0For rated speed of the motor, TEFor electromagnetic torque, TMIs mechanical torque, H is inertia time constant, RrIs rotor resistance, XrIs rotor reactance, RsIs stator resistance, XsIs a stator reactance, XmIs an excitation reactance;
the rated capacity of the aggregated induction motor load is the sum of the rated capacities of the single induction motor loads, namely:
Figure FDA0002891753370000034
in the formula, k is the number of induction motor loads; the equivalent circuit parameters of the load of the aggregated induction motor are weighted averages of admittances of all branches in the equivalent circuit, namely:
Figure FDA0002891753370000035
in the formula, the proportionality coefficient rhoi＝SNi/SNM，ZiImpedance of the electrical branch, Z, for a single induction motor loadMFor the electrical branch impedance of the aggregate induction motor load, for the stator branch ZM＝Rs+jXsTo excitation branch ZM＝jXmTo the rotor branch ZM＝Rr/s+jXr(ii) a The inertial time constant of the aggregate induction motor load is:
Figure FDA0002891753370000036
in the formula, HiIs the inertia time constant of the load of a single induction motor, HMIs the time constant of inertia of the aggregate induction motor load.
6. The online load modeling method based on local intelligence measurement as claimed in claim 1, characterized in that: in the step 4: considering the influence of a distribution network, when the static load and induction motor load aggregate model is gradually equalized from a low-voltage side to a high-voltage side, the method comprises the following steps:
Figure FDA0002891753370000037
in the formula, ZDTo distribution network impedance, YSFor low-pressure side static load equivalent admittance, lambda1，λ2，λ3All variables are related to the equivalent admittance of the low-voltage side static load and the impedance of the power distribution network;
when the static load aggregation model is equalized from the low-pressure side to the high-pressure side, the high-pressure side static load equivalent admittance is as follows:
Figure FDA0002891753370000038
in the formula, PH、QHRespectively active and reactive power, U, flowing into the high-side busHIs the high side bus voltage;
when the induction motor load aggregation model is equalized from a low-voltage side to a high-voltage side, the method for calculating the load equivalent parameters of the high-voltage side induction motor comprises the following steps:
Figure FDA0002891753370000041
in formula (II) T'0Is a transient open-circuit time constant, RsIs stator resistance, XsIs a stator reactance, RrFor rotor electricityHindered, XrIs rotor reactance, XmFor exciting reactance, TEFor electromagnetic torque, TMIs mechanical torque, H is an inertia time constant, T'0H＝(XrH+XmH)/ω0HRrH，T′0HIs the time constant of transient open circuit of high-voltage side induction motor0HFor high-side induction motor rated speed, TEH、TMHElectromagnetic torque, mechanical torque, H, of the high-side induction motorHIs the inertia time constant, R, of the high-side induction motorrH、XrHRespectively, the rotor resistance and the rotor reactance R of the high-voltage side induction motorsH、XsHRespectively, the stator resistance, the stator reactance, X of the high-voltage side induction motormHIs a high side induction motor field reactance.
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