CN103066602B

CN103066602B - Microgrid mixed type mixed type power filter harmonic current prediction method based on back-direction optimum linear prediction theory

Info

Publication number: CN103066602B
Application number: CN201210587102.9A
Authority: CN
Inventors: 李圣清
Original assignee: Hunan University of Technology
Current assignee: Hunan University of Technology
Priority date: 2012-12-31
Filing date: 2012-12-31
Publication date: 2015-04-15
Anticipated expiration: 2032-12-31
Also published as: CN103066602A

Abstract

The invention provides a microgrid mixed type mixed type power filter harmonic current prediction method based on a back-direction optimum linear prediction theory. The microgrid mixed type mixed type power filter harmonic current prediction method based on the back-direction optimum linear prediction theory is successfully used on microgrid mixed type mixed type power filter harmonic current prediction. The method comprises the following steps: (1) figuring out a back-direction optimum linear prediction error, (2) figuring out a back-direction optimum linear prediction coefficient canonical equation and a prediction error power equation, (3) figuring out a back-direction optimum linear prediction error and a step updating equation of the coefficient, and (4) an optimum harmonic current predicted value is obtained according to the prediction error value and the step updating equation. Simulation and experimental results show that the method can accurately and rapidly predict value of x(k-m) according to the values of x(k-m+1), -x(k), analyze and evaluate the values, and provide feasible basis for a harmonic current wave control method after a period of time.

Description

Based on the micro-capacitance sensor hybrid power filter harmonic current prediction method of backward optimum linear prediction theory

Technical field

The present invention relates to a kind of micro-capacitance sensor electric-power filter harmonic current prediction method, particularly a kind of micro-capacitance sensor hybrid power filter harmonic current prediction method based on backward optimum linear prediction theory.

Background technology

Permitted eurypalynous distributed power generation power supply in micro-capacitance sensor and be limited by natural conditions, run uncertain strong, have intermittence, complexity, diversity, unsteadiness feature, its quality of power supply feature and conventional electric power system have very big-difference, bring larger difficulty to harmonic current managing.Harmonic current followed the tracks of and predicts, then according to predicted value, harmonic current being compensated, solution harmonic compensation latency issue being served and acts on energetically.Consider from predicted time sequential angles, harmonic prediction method is divided into forward prediction and the large type of back forecast two, and forward prediction is basis value prediction value.Correspondingly, by value prediction value, is just called back forecast.

A kind of Model Predictive Control strategy of " research based on the APF of model algorithm predictive control strategy " selected works in " power electronic technology " of o. 11th in 2011, export according to Active Power Filter-APF (APF) reality and carry out feedback compensation and rolling optimization with the error of prediction outlet chamber, overcome the uncertainty of system, improve the harmonic current compensation characteristic of APF.Weak point is that system-computed is comparatively complicated, is unfavorable for practical operation.In " protecting electrical power system and the control " of the 22nd phase in 2009, " P studies based on the parallel connection type Hybrid active power filter of gray prediction theory " one literary composition proposes a kind of controlling without latency prediction based on gray model; and be successfully applied in the control of parallel connection type hybrid power filter, make it have the performance of good harmonics restraint and reactive power compensation.But system robustness and stability are not too strong.In " the Power System and its Automation journal " of the 1st phase in 2011, " three-phase four-wire active power filter new type nerve PREDICTIVE CONTROL " literary composition proposes the RBF neural PREDICTIVE CONTROL scheme of a kind of Three phase parallel connection type APF.Set up the Mathematical Modeling of three-phase four-wire system parallel connection type APF and the discretization model of predictive current control, design neural predictive controller, there is preferably property and dynamic characteristic real-time.But algorithm calculates more complicated, and the stability of a system is not high, is subject to the impact of ambient parameters.

Summary of the invention

The object of this invention is to provide a kind of micro-capacitance sensor hybrid power filter harmonic current prediction method based on backward optimum linear prediction theory.The method according to the harmonic current of present moment, can dope the harmonic current of previous instant, for its deployment analysis and evaluation studies and formulate follow-up harmonic current control method and provide foundation.Emulation and experimental result show, the method has precision of prediction height and the feature such as compensation effect is good.Its principle and basic step are:

The first step, asks backward optimum linear prediction error: if harmonic current signal ? the sampled value in moment is respectively , by known wherein value is predicted value, then its linear predictor is:

Corresponding linear prediction error is

Second step, asks backward optimum linear prediction coefficient regular equation and predicated error power equation: make linear prediction error minimum being called optimum prediction coefficient, for asking its error minimum value, making it right partial derivative be zero can to obtain

In formula , thus obtain the orthogonal equation of backward linear prediction

In formula . the regular equation that must meet

In formula , can obtain minimum posteriori prediction errors power equation is

3rd step, asks the rank renewal equation of backward optimum linear prediction error and coefficient: definition posteriori prediction errors with priori prediction errors coefficient correlation be

For optimum prediction coefficient, can obtain according to forward linear prediction method

The rank renewal equation that can be obtained predicated error and coefficient by Levinson-Durbin algorithm is

In formula be called reflection coefficient, in linear prediction, play important effect.

1 rank recursion is had

2 rank recursion are had

4th step, according to prediction error value and rank renewal equation, draws preferred harmonic current forecasting value.

In sum, the backward optimum prediction coefficient that this model provides, minimum posteriori prediction errors power, and the upgrading in time of backward optimum prediction coefficient and posteriori prediction errors power, thus ensure that pinpoint accuracy and the fast tracking capability of the method prediction electric harmonic.

Accompanying drawing explanation

To linear prediction filter after Fig. 1

To harmonic prediction control method after Fig. 2

Fig. 3 prediction and actual harmonic wave forms

Fig. 4 predicated error curve

Fig. 5 is table 1 system PPF parameter

Fig. 6 is table 2 motor load individual harmonic current

Fig. 7 is table 3 power capacitor load individual harmonic current

Embodiment

the prediction principle of harmonic current:

The backward optimum linear prediction principle of harmonic current and basic step are:

Corresponding linear prediction error is

Backward linear prediction filter operation principle as shown in Figure 1.

In formula , thus obtain the orthogonal equation of backward linear prediction

In formula . the regular equation that must meet

In formula , can obtain minimum posteriori prediction errors power equation is

1 rank recursion is had

2 rank recursion are had

the forecast Control Algorithm of harmonic current:

Based on backward optimum linear prediction theory micro-capacitance sensor hybrid power filter harmonic prediction control method as shown in Figure 2.It is made up of Harmonic currents detection, prediction, control and compensation module.

Detection module adopts Fourier algorithm, calculates moment micro-capacitance sensor harmonic current, and send into prediction module.Prediction module adopts backward harmonic current prediction method, according to the harmonic current of moment load and historical empirical data, calculate the harmonic current in moment.Then extract from the harmonic current data equal intervals in load past equivalence, sets up backward linear prediction model, according to these historical datas, adopts the method to dope value.And carry out analysis and synthesis, for recent harmonic current assessment and formulate after a period of time harmonic current control method foundation is provided.This algorithm is every the individual sampling period performs 1 time, and completes the dynamic linear adjustment of filter prediction coefficents, and before guaranteeing the arrival of next sampling period, backward filter predictive coefficient has calculated.Its advantage is the self-adaptative adjustment computing of predictive coefficient of having had ample time, and guarantees whole process implementation optimum linear prediction, makes posteriori prediction errors power minimum.

Can hybrid power filter reach the harmonic compensation effect of expection according to its operation principle, except aforesaid harmonic detecting, prediction algorithm speed is fast, precision is high and system circuit design rationally except, also depend on the performance of controller to a great extent.This control module adopts Adaptive Fuzzy Control algorithm, calculates subsequent time pulse-width signal, realizes the control action to hybrid power filter main circuit.In order to improve control precision, filtering system part known according to filtering system is unknown, the completely unknown three kinds of situations of filtering system design predictive controller.The first situation is fairly simple, is restrained directly draw by Fuzzy Predictive Control; The second situation directly approaches controller with fuzzy logic system; The third situation carries out Automatic adjusument based on generalized error estimated value to controller parameter, then by ensureing to reach control object in sequence converges to the small neighbourhood of initial point.

simulation and experiment interpretation of result

In order to check the validity of proposed harmonic current prediction method, Matlab software is adopted to emulate it.Major parameter is: AC series equivalent impedance is 0.02 Ω, and parallel equivalent impedance is 460 Ω, and reactor inductance value is 1.8 mH, and DC bus capacitor is 300 μ F, and voltage is 550 V, and triangular carrier frequency is 3000 Hz.

Suddenly changing in 0.25s moment load makes the amplitude of harmonic current become original 2 times, and now prediction and actual current waveform are as shown in Figure 3.As can be seen from the figure harmonic prediction value lags behind actual value, and about after half primitive period, predicted value has followed the tracks of rapidly actual value.This Forecasting Methodology has good dynamic response characteristic as can be seen here.Fig. 4 gives the error curve diagram of harmonic prediction value and actual value, and as can be seen from the figure only when load changing, prediction exists certain error, and all the other time period predicated errors are minimum, and institute's extracting method tracking error is less herein in checking further.

In order to verify further the present invention put forward the correctness of forecast Control Algorithm, test, the component parameter of experiment is respectively: Fluke F435 model stress_responsive genes instrument; The capacity of APF is 0.5kVA; Switching frequency is 5kHz; Direct current capacitor capacity is 1100 μ F; Direct voltage is 300V; The parameter of PPF is in table 1.Respectively induction electric unit load harmonic source and power capacitor bank harmonic source are tested.

(1) motor load situation

Adopt Fluke F435 model stress_responsive genes instrument to test a certain times when motor load harmonic source, each harmonic value measured is as shown in table 2.Micro-capacitance sensor hybrid power filter based on backward optimum linear prediction theory is predicted this times when motor load harmonic source, and each harmonic value doped is still as shown in table 1.The predicated error of each harmonic has been calculated, most high level error 1.83%, minimum error 0% in table.As can be seen here, proposed harmonic current prediction method has higher precision.

(2) power capacitor loading condition

Said method is adopted to test power capacitor load harmonic source and test, that measure as shown in table 3 with each harmonic value that is that dope.Most high level error 1.54%, minimum error 0%, again demonstrates proposed harmonic current prediction method and has higher precision for perception is same with capacitive load.

The present invention proposes the micro-capacitance sensor hybrid power filter harmonic current prediction method based on backward optimum linear prediction theory, and be used successfully in micro-capacitance sensor hybrid power filter harmonic prediction.Be deduced best back forecast coefficient regular equation, draw the rank renewal equation of predicated error, and then draw the minimum predicated error power of single order, second order recursion.Emulation and experimental result show, the method energy according to value predict quickly and accurately value, provides practicable foundation for carrying out assessment and analysis to these values and formulating rear a period of time humorous current wave control method.

Claims

1. based on the micro-capacitance sensor hybrid power filter harmonic current of backward optimum linear prediction theory

Forecasting Methodology, is characterized in that it comprises the following steps:

Corresponding linear prediction error is

In formula , thus obtain the orthogonal equation of backward linear prediction

In formula , the regular equation that must meet

In formula , can obtain minimum posteriori prediction errors power equation is

in formula be called reflection coefficient, in linear prediction, play important effect;

1 rank recursion is had

2 rank recursion are had