LU500534B1 - Online load model parameter correction method based on aggregation-identification two-tier architecture - Google Patents
Online load model parameter correction method based on aggregation-identification two-tier architecture Download PDFInfo
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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
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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
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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- G05B13/044—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance not using a perturbation signal
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- H—ELECTRICITY
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- 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
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
An online load model parameter correction method based on an aggregation-identification two-tier architecture, comprising following steps of: (1) acquiring historical measurement data of a bus to be identified through offline statistics of a load composition of the bus; (2) performing load model aggregation by a capacity weighting method considering an induction motor load rate and a critical slip based on statistical and historical measurement data, to establish an initial aggregation model of loads; (3) setting an identification range of dominant parameters and fixing non-dominant parameters based on online measurement data and the initial aggregation model, and identifying the dominant parameters by a Particle Swarm Optimization (PSO) algorithm to obtain a composite load model; (4) establishing initial aggregation and composite load models of loads at all voltage levels through online application at different voltage levels of the method of the invention, and calculating an accuracy index of each composite load model.
Description
DESCRIPTION 17900556
TECHNICAL FIELD The present invention relates to the field of online load modeling of a power system, in particular to an online load model parameter correction method based on an aggregation-identification two-tier architecture.
BACKGROUND OF THE PRESENT INVENTION Power load characteristics have important influence on power flow calculation, transient stability analysis and voltage stability of a power system. As a terminal of the power system, the load makes it difficult to guarantee the accuracy of load models due to its diversity, time-varying and regional characteristics. In addition, large-scale distributed new energy generation access to load areas in recent years has posed new challenges to load modeling. In the planning design and dispatching operation of the power system, an adopted load model with a big deviation will directly affect the operation scheme for dispatchers, thus causing the waste of resources and even endangering the stable operation of the system. It is of great practical significance to establish a load model that can accurately reflect the dynamic characteristics of loads in real time.
With the development of non-invasive load monitoring technology, the base load can be decomposed to provide the base load composition and real-time electricity consumption information for load model aggregation. At present, power system monitoring systems such as supervisory control and data acquisition system (SCADA), wide area measurement system (WAMS) and fault recording and monitoring system (FRMS) are increasingly improved, providing detailed operational data support for the establishment of load models at different voltage levels. Accordingly, the load modeling methods based on measurement data have been developed rapidly. HU500534
SUMMARY OF THE PRESENT INVENTION To overcome the deficiencies of the prior art, the present invention provides a method for establishing an accurate load model online, which combines a load aggregation method with a identification method through the comprehensive utilization of off-line statistical data and measurement data. The method of the present invention overcomes the defects of lack of timeliness and low accuracy of traditional single load modeling methods and provides a more accurate power load model for online simulation analysis and operation dispatching of power systems.
In order to achieve the above objectives of the present invention, a technical solution employed in the present application is as follows. An online load model parameter correction method based on an aggregation- identification two-tier architecture is provided, including: Step 1: acquiring information on lines connected to a bus to be identified and on load outgoing lines based on a topology of a power system, acquiring all load composition types of the bus to be identified through offline research and statistics, while acquiring historical measurement data of the bus to be identified in a typical time period, wherein the data comprise the voltage U of the bus to be identified, the active power P and the reactive power Q transmitted by the lines and transformers connected to the bus; Step 2: aggregating, based on the load composition types of the bus to be identified and the historical measurement data of the bus to be identified in the typical time period, load clusters of the same bus to be identified by a capacity weighting method considering the influence of an induction motor load rate and a critical slip, to obtain an initial aggregation model of equivalent loads; Step 3: identifying dominant parameters of the initial aggregation model of equivalent loads to obtain a composite load model of the bus to be identified; and
Step 4: stratifying the power system by voltage levels through online HU500534 application at different voltage levels of the online load model parameter correction method based on the aggregation-identification two-tier architecture, establishing initial aggregation models of equivalent loads at all voltage levels from the bottom up, so as to obtain composite load models at all voltage levels, and providing an accuracy index of the composite load model at each voltage level.
Further, it includes following steps of processing measured PMU data of the power system based on the information on the lines and transformers connected to the bus to be identified, to obtain the active power and reactive power of the loads of the bus to be identified; setting an identification range of dominant parameters and fixing non- dominant parameters based on parameters such as the active power and the reactive power of the loads and the initial aggregation model of equivalent loads, wherein the dominant parameters include: induction motor proportion Km, motor initial slip So, active constant impedance proportion Zp, active constant current proportion Ip, reactive constant impedance proportion Zq and reactive constant current proportion /q in a static ZIP model; and identifying the dominant parameters by a Particle Swarm Optimization (PSO) algorithm to obtain the composite load model of the bus to be identified.
Further, it includes following steps of performing scene simulation based on the parameters of the composite load model to obtain simulated data P’, Q' and U” and calculating errors between the data and corresponding online measurement data P Q and U, the accuracy index of the model is: Np _p) N AN N ETA fé) iles te) wherein, a relative error is adopted instead of an absolute error for the root- mean-square errors of all measurements in the present invention, so as to avoid the situation that a large absolute error overwhelms the errors of other measurements due to the difference in dimension and amplitude. HU500534 Beneficial Effects
1. The method of the present invention solves the problem of on-line load modeling of complex power systems in the context of the current smart grid, and fully integrates multi-source data such as statistics and measurements to further improve the accuracy of online modeling of load models. Meanwhile, compared with the traditional single load modeling methods, the method of the present invention combines load aggregation with dominant parameter identification, which not only reduces the dimension for parameter identification and greatly shortens the identification time, but also retains the response characteristics of non-dominant parameters to a certain extent, thus providing a more time-sensitive and accurate load model for real-time simulation of power systems.
2. The method provided in the present invention also provides technical support for online modeling of power system load models. Based on the idea of two-layer modeling, load aggregation results are taken as the initial values for dominant parameter identification, and parameters for a load model are generated in two steps, which can realize the fast online correction of the load model on the big data platform of the power system.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flowchart according to the present invention.
DETAILED DESCRIPTION OF THE PRESENT INVENTION The present invention will be described in detail with reference to the accompanying drawings.
As shown in FIG. 1, the present invention provides an online load model parameter correction method based on an aggregation-identification two-tier architecture, including following steps 1 to 4.
In the step 1, information on lines connected to a bus to be identified and on load outgoing lines is acquired based on a topology of a power system, all HU500534 load composition types of the bus to be identified are acquired through offline research and statistics, while historical measurement data of the bus to be identified in a typical time period are acquired, wherein the data include the voltage U of the bus to be identified, the active power P and the reactive power Q transmitted by the lines and transformers connected to the bus.
In the step 2, load clusters of the same bus to be identified are aggregated by a capacity weighting method considering the influence of an induction motor load rate and a critical slip based on the offline research and statistics and the historical measurement data, to obtain an initial aggregation model of equivalent loads.
For a load node at a low voltage levels, load model aggregation is performed by a capacity weighting and aggregation method based on the offline research and statistics and the historical measurement data, to obtain an initial aggregation model of loads at a parent node. The mathematical expression of the aggregated parameters is as follows: Ko, Mage = ès ra LE, “LE, wherein, M is a set of parameters to be identified of an induction motor, including stator winding equivalent resistance Rs, stator winding equivalent reactance Xs, excitation reactance Xm, rotor winding equivalent resistance R, rotor winding equivalent reactance Xr and induction motor inertia time constant Ti, Magg is a parameter of the induction motor after aggregation, Mi is a parameter of the /" induction motor before aggregation, and oi is the proportion of the capacity Siof the i induction motor to its capacity Sago after aggregation. Ser is a motor critical slip obtained by the following formula:
R LU500534 § =r woe Le) In the step 3, measured PMU data is called from an actual power grid data platform, and then processed based on the information on the lines and transformers connected to the bus to be identified to obtain the active power and reactive power of the loads of the bus to be identified. An identification range of dominant parameters is set and other non-dominant parameters are fixed based on the active power and the reactive power of the loads and the initial aggregation model of equivalent loads, and the dominant parameters are identified by a Particle Swarm Optimization (PSO) algorithm to obtain a composite load model of the bus to be identified.
Key parameters obtained based on parameter sensitivity analysis that can reflect load characteristics are taken as the dominant parameters of the model to be identified in the present invention, including induction motor proportion Km, motor initial slip Se, active constant impedance proportion Zp, active constant current proportion Ip, reactive constant impedance proportion Za and reactive constant current proportion /q in a static ZIP model. The Particle Swarm Optimization (PSO) simulates the foraging process of bird flocks, and constantly adjusts its searching behavior to gradually approach the best fitness through inter-flock communication, continuous learning, and accumulated experience. An iterative formula of the velocity and position of a particle is as follows: a = ov’, + Crand, (Prosi —x ) +C,rand, (Gresr.a — Xi) x} = xt + vi! wherein, superscript k and subscript d represent the velocity of the particle in the d'” dimension after the ki" iteration, respectively. C1 and Cz represent the trust degree of the particle to itself and to swarm, respectively, and w is a HU500534 velocity weight coefficient. Presti is a best fitness position of the ith particle from the initial position to the current position, and Geesti is a best position searched in the whole swarm.
A fitness function is adopted to determine the best positions of individual and swarm.
fitness in SR -PY +(0, 0) ;- In the step 4, the power system is stratified by voltage levels through online application at different voltage levels of the online load model parameter correction method based on the aggregation-identification two-tier architecture, initial aggregation models of equivalent loads at all voltage levels are established from the bottom up, so as to obtain composite load models at all voltage levels, and an accuracy index is provided for the composite load model at each voltage level. The accuracy index MAI of the model is configured to evaluate the accuracy of the equivalent load model, scene simulation is performed based on the parameters of the composite load model to obtain simulated data P, Q' and U' ; and errors between the data and corresponding online measurement data P, Q and U are calculated. The accuracy index of the model is: pa 1-4 Ls ; (92) ; 3 IANS P NEL © NEL U wherein, a relative error is adopted instead of an absolute error for the root-mean-square errors of all measurements in the present invention, So as to avoid the situation that a large absolute error overwhelms the errors of other measurements due to the difference in dimension and amplitude.
Claims (3)
1. An online load model parameter correction method based on an aggregation-identification two-tier architecture, comprising: step 1: acquiring information on lines connected to a bus to be identified and on load outgoing lines based on a topology of a power system, acquiring all load composition types of the bus to be identified through offline research and statistics, while acquiring historical measurement data of the bus to be identified in a typical time period, wherein the data comprise a voltage U of the bus to be identified, a active power P and a reactive power Q transmitted by the lines and transformers connected to the bus; step 2: aggregating, based on the load composition types of the bus to be identified and the historical measurement data of the bus to be identified in the typical time period, load clusters of the same bus to be identified by a capacity weighting method considering the influence of an induction motor load rate and a critical slip, to obtain an initial aggregation model of equivalent loads; step 3: identifying dominant parameters of the initial aggregation model of equivalent loads to obtain a composite load model of the bus to be identified; and step 4: stratifying the power system by voltage levels through online application at different voltage levels of the online load model parameter correction method based on the aggregation-identification two-tier architecture, establishing initial aggregation models of equivalent loads at all voltage levels from the bottom up, so as to obtain composite load models at all voltage levels, and providing an accuracy index for the composite load models at each voltage level.
2. The online load model parameter correction method based on an aggregation-identification two-tier architecture according to clam 1, is characterized in that the step 3 of identifying dominant parameters of the initial aggregation model of equivalent loads of the bus to be identified comprises following steps of:
processing measured PMU data of the power system based on the 500584 information on the lines and transformers connected to the bus to be identified, to obtain the active power and reactive power of the loads of the bus to be identified; setting an identification range of dominant parameters and fixing non- dominant parameters based on parameters such as the active power and the reactive power of the loads and the initial aggregation model of equivalent loads, wherein the dominant parameters comprise: induction motor proportion Km, motor initial slip So, active constant impedance proportion Zp, active constant current proportion Ip, reactive constant impedance proportion Zq and reactive constant current proportion /q in a static ZIP model; and identifying the dominant parameters by a Particle Swarm Optimization (PSO) algorithm to obtain the composite load model of the bus to be identified.
3. The online load model parameter correction method based on an aggregation-identification two-tier architecture according to clam 1, is characterized in that, in the step 4, the accuracy index of the composite load model are calculated by following steps of: performing scene simulation based on the parameters of the composite load model to obtain simulated data P', Q' and U” and calculating errors between the data and corresponding online measurement data P, Q and U, the accuracy index of the model is: Np _p) N AN N ETA way EEE) ES) [srr wherein, a relative error is adopted instead of an absolute error for the root- mean-square errors of all measurements in the present invention, so as to avoid the situation that a large absolute error overwhelms the errors of other measurements due to the difference in dimension and amplitude.
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| CN108306284B (en) * | 2018-01-05 | 2021-04-27 | 东南大学溧阳研究院 | Online load modeling method based on local intelligent measurement |
| CN109827310B (en) * | 2019-01-31 | 2020-12-11 | 河海大学 | Resident air conditioner load cluster die type establishing method |
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