CN102969713B - Low-frequency oscillation mode time-frequency analyzing method of power system - Google Patents
Low-frequency oscillation mode time-frequency analyzing method of power system Download PDFInfo
- Publication number
- CN102969713B CN102969713B CN201210526221.3A CN201210526221A CN102969713B CN 102969713 B CN102969713 B CN 102969713B CN 201210526221 A CN201210526221 A CN 201210526221A CN 102969713 B CN102969713 B CN 102969713B
- Authority
- CN
- China
- Prior art keywords
- atom
- frequency
- oscillation mode
- low
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a low-frequency oscillation mode time-frequency analyzing method of a power system. The low-frequency oscillation mode time-frequency analyzing method comprises the following steps of: researching an atom matching trace method based on improved PSO (Particle Swarm Optimization), which is applicable to low-frequency oscillation signal analysis; and analyzing a time-frequency variation property of a low-frequency oscillation mode of the power system by using the atom matching trace method based on the improved PSO. Therefore, an atom for reflecting properties of an oscillation mode most can be selected by comparing an optimal adaptive value of the atom, so that the selection process of the atom is converted into the optimization problem of solving a function by using a particle swarm optimization algorithm. Finally, the PSO can be repeatedly operated and improved for multiple times, so as to obtain a plurality of groups of atom parameters, and a calculation result is fit by using a least square method. The low-frequency oscillation mode time-frequency analyzing method has the advantage of self-adaptively obtaining a modulus parameter of an oscillation mode, has better time-frequency resolution capability, and can be used for effectively identifying non-linear and non-stable signals. Furthermore, the effect time of each mode can be accurately reflected, and the reference foundation can be provided for mastering the low-frequency oscillation transmission property.
Description
Technical field
The present invention relates to the power transmission and distribution project technical field in electric power system, be specifically related to a kind of low-frequency oscillation of electric power system pattern Time-Frequency Analysis Method.
Background technology
Along with the development of power industry, electric network composition is complicated, and equipment is various.Adopt modern fast, under the condition of high limited value multiple excitation system, low-frequency oscillation often occurs on the interconnection of long distance, heavy load, and the safe and stable operation of electrical network is caused to important impact.Low frequency oscillation mode is analyzed, can be instructed power system stability controller parameter to adjust, and can carry out effective early warning to system safety territory.Therefore, research low frequency oscillation mode parameters analysis method has important practical significance.And the poor effect that the oscillating signal that existing low frequency oscillation mode analytical method is disturbed nonlinear, Noise is processed, and isolated the contact between the time-domain and frequency-domain of pattern analysis, be worth further improving.
Summary of the invention
Technical problem to be solved by this invention is: for above-mentioned the deficiencies in the prior art, provide a kind of low-frequency oscillation of electric power system pattern Time-Frequency Analysis Method.
Technical scheme provided by the invention is: a kind of low-frequency oscillation of electric power system pattern Time-Frequency Analysis Method, is characterized in: the method step is as follows:
Step 1) obtain low-frequency oscillation of electric power system signal, mains frequency, voltage or current value when this signal is vibration;
Step 2) each oscillation mode comprising for this signal, adopts respectively the damped sinusoidal quantity model that is applicable to this oscillation mode as the atom in former word bank;
Step 3) with the parameter of above-mentioned each atom, comprise that amplitude, frequency, damping coefficient, phase place, time started and end time are respectively as the parameter group of particle to be optimized in particle cluster algorithm, inner product value is adaptive value function;
Step 4) adopt again the atom matching pursuit algorithm based on improving particle cluster algorithm to be optimized search to atom match tracing process, obtain array atomic parameter, be respectively the modal parameter of each oscillation mode;
Step 5) repeating step 4), stop afterwards for 19 times, the modal parameter of each oscillation mode has been exported the Different Results of 20 times;
Step 6) adopt least square method respectively the result of calculation of 20 of the modal parameter of each oscillation mode times to be carried out to matching, finally obtain the modal parameter group of each oscillation mode optimum, comprise amplitude, frequency, damping coefficient, phase place, time started and end time, thereby complete the time frequency analysis process of the whole low frequency oscillation mode of this signal.
The step of the above-mentioned atom matching pursuit algorithm based on improvement particle cluster algorithm of mentioning is as follows:
Step 1) produce at random initial position and the speed of atom;
Step 2) set population scale, simplex Local Search probability P is set
1, variation probability P
2, maximum evolutionary generation I
max;
Step 3) calculate the adaptive value of atom, when reaching I
maxduring inferior iteration, to optimum atom P
bestrandom number R between configuration [0,1]
1if, R
1be less than P
1, with P
bestfor forming initial simplex, summit carries out Local Search; Otherwise, go to step 4);
Step 4) in each iteration, relatively the current adaptive value of atom and the optimal adaptation value of the process of atom own, if current adaptive value is better, upgrade optimal adaptation value;
Step 5) compare the optimal adaptation value of all atoms in population, if there is the P of being better than
bestadaptive value, upgraded substituting, as new global extremum point, search for again;
Step 6) by position and the speed of individual extreme value and global extremum information correction atom;
Step 7) population Distribution Entropy and threshold value are compared, to the random number R between i atom configuration [0,1]
2if, R
2be less than P
2, carry out mutation operation; Otherwise, go to step 8);
Step 8) repeating step 3) to step 7), until stopping criterion for iteration.
Particle cluster algorithm (particle swarm optimization, PSO) be prior art, the present invention improves on the basis of existing particle cluster algorithm, add simplex search and mutation operation, and the PSO algorithm application after improving is optimized in atom match tracing, by comparing the optimal adaptation value of atom, choose the atom that can reflect oscillation mode characteristic, thereby realize, the process of choosing of atom is converted into the particle swarm optimization algorithm function optimization problem of utilizing.Finally, rerun and repeatedly improve PSO, obtain many group atomic parameters, adopt least square fitting result of calculation, obtain one group of optimum modal parameter group.Each atom is corresponding to an oscillation mode, thereby completes the time frequency analysis process of the whole low frequency oscillation mode of a signal.
Compared with prior art, advantage is in the present invention: this method possesses the advantage of obtaining adaptively oscillation mode modal parameter, and has good time-frequency resolution capability, effectively non-linear the and non-stationary signal of identification; This method possesses timi requirement function, can accurately reflect the action time of each pattern, for grasping the propagation characteristic of low-frequency oscillation, provides reference frame.
Accompanying drawing explanation
Fig. 1 is the test signal oscillogram that the embodiment of the present invention is obtained.
Fig. 2 is 2 effective model atom MA schematic diagrames that the present invention picks out.
Wherein: a associative mode 1, b associative mode 2.
Embodiment
The present invention is a kind of low-frequency oscillation of electric power system pattern Time-Frequency Analysis Method, and its step is as follows:
Step 1) obtain low-frequency oscillation of electric power system signal, mains frequency/voltage or current value when this signal is vibration;
Step 2) each oscillation mode comprising for this signal, adopts respectively the damped sinusoidal quantity model that is applicable to this oscillation mode as the atom in former word bank;
Step 3) with the parameter of above-mentioned each atom, comprise that amplitude, frequency, damping coefficient, phase place, time started and end time are respectively as the parameter group of particle to be optimized in particle cluster algorithm, inner product value is adaptive value function;
Step 4) adopt again the atom matching pursuit algorithm based on improving particle cluster algorithm to be optimized search to atom match tracing process, obtain array atomic parameter, be respectively the modal parameter of each oscillation mode;
Step 5) repeating step 4), stop afterwards for 19 times, the modal parameter of each oscillation mode has been exported the Different Results of 20 times;
Step 6) adopt least square method respectively the result of calculation of 20 of the modal parameter of each oscillation mode times to be carried out to matching, finally obtain the modal parameter group of each oscillation mode optimum, comprise amplitude, frequency, damping coefficient, phase place, time started and end time, thereby complete the time frequency analysis process of the whole low frequency oscillation mode of this signal.
The step of the above-mentioned atom matching pursuit algorithm based on improvement particle cluster algorithm of mentioning is as follows:
Step 1) produce at random initial position and the speed of atom;
Step 2) set population scale, simplex Local Search probability P is set
1, variation probability P
2, maximum evolutionary generation I
max;
Step 3) calculate the adaptive value of atom, when reaching I
maxduring inferior iteration, to optimum atom P
bestrandom number R between configuration [0,1]
1if, R
1be less than P
1, with P
bestfor forming initial simplex, summit carries out Local Search; Otherwise, go to step 4);
Step 4) in each iteration, relatively the current adaptive value of atom and the optimal adaptation value of the process of atom own, if current adaptive value is better, upgrade optimal adaptation value;
Step 5) compare the optimal adaptation value of all atoms in population, if there is the P of being better than
bestadaptive value, upgraded substituting, as new global extremum point, search for again;
Step 6) by position and the speed of individual extreme value and global extremum information correction atom;
Step 7) population Distribution Entropy and threshold value are compared, to the random number R between i atom configuration [0,1]
2if, R
2be less than P
2, carry out mutation operation; Otherwise, go to step 8);
Step 8) repeating step 3) to step 7), until stopping criterion for iteration.
With specific embodiment, describe below.
Obtain low-frequency oscillation of electric power system signal, this signal comprises two oscillation modes, the frequency of pattern 1 and pattern 2 is respectively 0.35Hz (0～15s) and 0.52Hz (15～30s), attenuation coefficient is respectively-0.35 (0～15s) and-0.08 (15～30s), this signal waveform is referring to Fig. 1, and design parameter is in Table 1.With the atom matching pursuit algorithm based on improving PSO, atom match tracing process is optimized to search, the initialization atom number of PSO gets 40, and maximum iteration time is 40 times, inertia weight factor w=0.7298, accelerator coefficient c
1=c
2=1.4962, choose the atom that can reflect oscillation mode characteristic, 2 pattern atom MA of extraction are shown in Fig. 2.Loop after 20 times, adopt the identification result after least square fitting to see the following form 2.From following table 2, by resulting 2 mode frequencies of the inventive method, be respectively 0.3488Hz and 0.5166Hz, attenuation coefficient is respectively-0.3479 and-0.0810, also has amplitude, phase place and the action time etc. of pattern, all and ideal value, namely with table 1 in parameter very approaching.Therefore the inventive method is frequency, attenuation coefficient, time started and the termination time etc. of recognition mode effectively.
Table 1
Table 2
Claims (1)
1. a low-frequency oscillation of electric power system pattern Time-Frequency Analysis Method, is characterized in that: the method step is as follows:
Step 1) obtain low-frequency oscillation of electric power system signal, mains frequency, voltage or current value when this signal is vibration;
Step 2) each oscillation mode comprising for this signal, adopts respectively the damped sinusoidal quantity model that is applicable to this oscillation mode as the atom in former word bank;
Step 3) with the parameter of above-mentioned each atom, comprise that amplitude, frequency, damping coefficient, phase place, time started and end time are respectively as the parameter group of particle to be optimized in particle cluster algorithm, inner product value is adaptive value function;
Step 4) adopt again the atom matching pursuit algorithm based on improving particle cluster algorithm to be optimized search to atom match tracing process, obtain array atomic parameter, be respectively the modal parameter of each oscillation mode;
Step 5) repeating step 4), stop afterwards for 19 times;
Step 6) adopt least square method respectively the result of calculation of 20 of the modal parameter of each oscillation mode times to be carried out to matching, finally obtain the modal parameter group of each oscillation mode the best, comprise amplitude, frequency, damping coefficient, phase place, time started and end time, thereby complete the time frequency analysis process of the whole low frequency oscillation mode of this signal;
Wherein, the step of the above-mentioned atom matching pursuit algorithm based on improvement particle cluster algorithm is as follows:
Step 1, the initial position that produces at random atom and speed;
Step 2, setting population scale, arrange simplex Local Search probability P
1, variation probability P
2, maximum evolutionary generation I
max;
The adaptive value of step 3, calculating atom, when reaching I
maxduring inferior iteration, to optimum atom P
bestrandom number R between configuration [0,1]
1if, R
1be less than P
1, with P
bestfor forming initial simplex, summit carries out Local Search; Otherwise, go to step 4;
In step 4, each iteration, relatively the current adaptive value of atom and the optimal adaptation value of the process of atom own, if current adaptive value is better, upgrade optimal adaptation value;
The optimal adaptation value of all atoms in step 5, comparison population, if there is the P of being better than
bestadaptive value, upgraded substituting, as new global extremum point, search for again;
Step 6, by position and the speed of individual extreme value and global extremum information correction atom;
Step 7, population Distribution Entropy and threshold value are compared, to the random number R between i atom configuration [0,1]
2if, R
2be less than P
2, carry out mutation operation; Otherwise, go to step 8;
Step 8, repeating step 3 are to step 7, until stopping criterion for iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210526221.3A CN102969713B (en) | 2012-12-10 | 2012-12-10 | Low-frequency oscillation mode time-frequency analyzing method of power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210526221.3A CN102969713B (en) | 2012-12-10 | 2012-12-10 | Low-frequency oscillation mode time-frequency analyzing method of power system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102969713A CN102969713A (en) | 2013-03-13 |
| CN102969713B true CN102969713B (en) | 2014-12-10 |
Family
ID=47799674
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210526221.3A Expired - Fee Related CN102969713B (en) | 2012-12-10 | 2012-12-10 | Low-frequency oscillation mode time-frequency analyzing method of power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102969713B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646146B (en) * | 2013-12-20 | 2017-03-29 | 武汉大学 | Based on the SVC controller method for designing for improving Atomic Decomposition parameter identification |
| CN104820164A (en) * | 2015-04-14 | 2015-08-05 | 华南理工大学 | Method for positioning low-frequency oscillation disturbance source of electric power system |
| CN105572473B (en) * | 2015-12-18 | 2018-06-12 | 中国航天科工集团八五一一研究所 | High-resolution linear Time-Frequency Analysis Method |
| CN106338651B (en) * | 2016-08-31 | 2018-09-14 | 长沙理工大学 | Particle filter analysis method applied to low-frequency oscillation of electric power system pattern-recognition |
| CN108562794A (en) * | 2018-01-12 | 2018-09-21 | 国网冀北电力有限公司秦皇岛供电公司 | Duration power quality disturbances method and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2302754A1 (en) * | 2008-09-27 | 2011-03-30 | State Grid Corporation of China | A low frequency oscillation integrated analysis method and its system |
| CN102305891A (en) * | 2011-07-04 | 2012-01-04 | 武汉大学 | On-line monitoring method of low-frequency oscillation of power system |
-
2012
- 2012-12-10 CN CN201210526221.3A patent/CN102969713B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2302754A1 (en) * | 2008-09-27 | 2011-03-30 | State Grid Corporation of China | A low frequency oscillation integrated analysis method and its system |
| CN102305891A (en) * | 2011-07-04 | 2012-01-04 | 武汉大学 | On-line monitoring method of low-frequency oscillation of power system |
Non-Patent Citations (4)
| Title |
|---|
| 基于原子稀疏分解的低频振荡模态参数辨识方法;李勋等;《电工技术学报》;20120930;第27卷(第9期);第124-131页 * |
| 李勋等.基于原子稀疏分解的低频振荡模态参数辨识方法.《电工技术学报》.2012,第27卷(第9期),第124-131页. * |
| 李勋等.采用原子分解能量熵的低频振荡主导模式检测方法.《中国电机工程学报》.2012,第32卷(第1期),第131-137页. * |
| 采用原子分解能量熵的低频振荡主导模式检测方法;李勋等;《中国电机工程学报》;20120105;第32卷(第1期);第131-137页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102969713A (en) | 2013-03-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102969713B (en) | Low-frequency oscillation mode time-frequency analyzing method of power system | |
| CN103208808B (en) | Power system sub-synchronous oscillation mode identification method | |
| Kocewiak | Harmonics in large offshore wind farms | |
| CN103177188B (en) | The power system load dynamic characteristic sorting technique that a kind of feature based maps | |
| CN104459397A (en) | Power quality disturbance recognizing method with self-adaptation multi-resolution generalized S conversion adopted | |
| CN107832777A (en) | A kind of electrical energy power quality disturbance recognition methods using the quick S-transformation feature extraction of time domain data compression multiresolution | |
| Alam et al. | Enhancement of power system damping employing TCSC with genetic algorithm based controller design | |
| CN103646146B (en) | Based on the SVC controller method for designing for improving Atomic Decomposition parameter identification | |
| RU2013148040A (en) | STABILITY OPTIMIZATION SYSTEM POWER SYSTEM | |
| CN110007141B (en) | Resonance point detection method based on voltage and current harmonic similarity | |
| CN105067966A (en) | Low-voltage AC arc fault detection method based on characteristic mode component energy analysis | |
| CN104569747A (en) | System and method for checking insulativity of power-off cable | |
| CN106646205A (en) | Random big-disturbance signal removing algorithm for analyzing circuit breaker fault through sound and vibration combination | |
| Ding et al. | Sound absorption of porous substrates covered by foliage: experimental results and numerical predictions | |
| CN101714354B (en) | Method for generating time-frequency molecules by polymerization of time-frequency atoms | |
| CN107036709B (en) | A kind of substation's noise matching separation method | |
| CN102393491B (en) | Automatic measurement equipment and automatic measurement method for electromagnetic radiation disturbance | |
| CN202888764U (en) | Vortex induced vibration damper | |
| CN103972912A (en) | Frequency domain analysis method employing wind power system frequency response | |
| CN105388390B (en) | Weak transient zero-sequence current fault signature extracting method based on particle group optimizing | |
| CN105046057B (en) | LSSVM fluctuating wind speed prediction technique based on Morlet Wavelet Kernel | |
| CN102122824A (en) | Method and device for identifying low-frequency oscillation mode parameters of power system | |
| CN103776901B (en) | Based on the sticky cartridge clip Rotating fields ageing state recognition methods of vibratory response information | |
| CN108680782B (en) | Voltage flicker parameter detection method based on extreme point symmetric mode decomposition | |
| CN107765259A (en) | A kind of transmission line of electricity laser ranging Signal denoising algorithm that threshold value is improved based on Lifting Wavelet |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20141210 Termination date: 20151210 |
|
| EXPY | Termination of patent right or utility model |