CN106684914A - Adaptive PI control method for improving electric power spring pressure regulating performance - Google Patents
Adaptive PI control method for improving electric power spring pressure regulating performance Download PDFInfo
- Publication number
- CN106684914A CN106684914A CN201710015388.6A CN201710015388A CN106684914A CN 106684914 A CN106684914 A CN 106684914A CN 201710015388 A CN201710015388 A CN 201710015388A CN 106684914 A CN106684914 A CN 106684914A
- Authority
- CN
- China
- Prior art keywords
- adaptive
- electric power
- power spring
- fuzzy
- particle
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 22
- 230000001105 regulatory effect Effects 0.000 title claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 29
- 238000009434 installation Methods 0.000 claims abstract description 5
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 230000008859 change Effects 0.000 claims description 7
- 238000006116 polymerization reaction Methods 0.000 claims description 6
- 238000011002 quantification Methods 0.000 claims description 6
- 230000006872 improvement Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000000205 computational method Methods 0.000 claims description 2
- 230000001276 controlling effect Effects 0.000 claims description 2
- 230000033228 biological regulation Effects 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 abstract description 2
- 230000010355 oscillation Effects 0.000 abstract description 2
- 238000010248 power generation Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000001172 regenerating effect Effects 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000005483 Hooke's law Effects 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000003139 buffering effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- H02J3/382—
-
- 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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
Landscapes
- Feedback Control In General (AREA)
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
Abstract
The present invention discloses an adaptive PI control method for improving an electric power spring pressure regulating performance. The method comprises the following steps: (1) calculating the initial values Kp0 and Ki0 of an adaptive PI controller parameters Kp and Ki according to the new energy generating capacity prediction and the capacity configuration of key load or non-key load in an electric power spring installation environment; (2) obtaining the voltage measurement model and a voltage reference value at the moment t in real time, and calculating the errors and an error changing rate therebetween; and (3) taking the errors and an error changing rate as input, performing real-time regulation of the initial parameters of the adaptive PI controller, and adaptively setting the parameters Kp and Ki of the PI controller. The adaptive PI control method for improving the electric power spring pressure regulating performance employs an adaptive PI controller based on the combination of the improved particle swarm optimization and the misty control algorithm to improve the voltage regulation capacity of the electric power spring and allow the electric power spring to effectively respond to the changing of the non-key load so as to improve the stability of the electric power spring and inhibit the appearance of the voltage oscillation.
Description
Technical field
It is steady with itself the present invention relates to operation and control of electric power system, more particularly to a kind of raising electric power spring pressure regulating performance
Qualitatively self-tuning PI control method,.
Background technology
Distributed power generation refers to the generator unit for being connected to power distribution network based on regenerative resource, is to utilize regenerative resource
One of important form of generating.A large amount of distributed power generations access active power distribution network, can effectively reduce and power transmission network power is conveyed
Demand, improve power supply reliability simultaneously reduce grid power loss.However, because wind energy, solar energy have stronger randomness,
Causing the power output of distributed wind energy/photovoltaic generation has strong fluctuation so that the access of a large amount of distributed power generations is right
Voltage of active power distribution network etc. brings tremendous influence, in some instances it may even be possible to cause the stable operation subject to severe risks of damage of power system.With
The continuous improvement of distributed power generation permeability, this influence is also more serious, or even causes the electrical equipment of Partial key to be sent out
Raw failure.
At present, in terms of voltage ripple of power network is solved the problems, such as, main technical scheme has two kinds：Reactive power compensation technology and storage
Can technology.Reactive-load compensation (such as STATCOM) is the technology being most widely used, but the side that the technology is installed concentratedly using Large Copacity
Formula, it is impossible to effectively meet the application scenarios of distributed power generation access；Energy storage technology (such as battery) is also effectively to solve voltage wave
One of method of dynamic problem, but the universal capacity of battery is smaller at present, causes that installation number is larger, cost of investment is high and useless
Influence of the Battery disposal to environment is also extremely serious.
Shu Yue (Ron) Hui professors of Hong Kong University and its problem group membership, electric power bullet is proposed in September, 2012 first
The concept of spring (Electric Spring, ES), its core concept is by the Hooke's law analogy of mechanical spring to power system
In, similar expression formula is found, so as to realize buffering the fluctuation of renewable energy power generation, play a part of stabilization power network principal voltage.
Electric power spring has overturned the conventional electric power system running pattern that power load demand determines generated energy so that institute is loaded in power network
The electric energy of consumption can change with the change of renewable energy power generation amount, it is ensured that the voltage stabilization of critical loads.But
In current achievement in research, the PI controller parameters used in the control of electric power spring are fixed value, and its regulation performance needs
In further optimization, and do not consider that non-key load occurs the situation of suddenly change.
It would therefore be highly desirable to solve the above problems.
The content of the invention
Goal of the invention：In order to overcome the shortcoming of prior art, electric power spring pressure regulating performance is improved the invention provides one kind
With the self-tuning PI control method of self stability.
Technical scheme：To realize the purpose of the present invention, adopt the following technical scheme that：One kind improves electric power spring pressure regulating performance
Self-tuning PI control method, comprise the following steps：
Step 1：The prediction of generation of electricity by new energy amount and critical load or non-key negative in electric power spring installation environment
The capacity configuration of lotus, calculates adaptive PI controller parameter Kp、KiInitial value Kp0, Ki0；
Step 2：The voltage measurement model V of t is obtained in real times(t) and voltage reference value Vs_ref, and by equation below
Calculate error e (t) between the two and error rate Δ e (t)：
Wherein, VsT () is t electrical system bus voltage, Vs_refIt is system reference voltage.
Step 3：Using error e (t) and error rate Δ e (t) as input, to the initial parameter of adaptive PI controller
Carry out real-time adjustment, the parameter K of self-adaptive sites PI controllersp、Ki。
Further, in step 1, the computational methods calculate self adaptation to improve PSO algorithms based on PSO algorithms are improved
PI controller parameters Kp、KiInitial value Kp0, Ki0Detailed process be：
(1) position of each particle and speed in random initializtion population, wherein, each particle represents a kind of possible PI controls
Initial parameter processed, due to x=[Kp0,Ki0], therefore search space dimension is 2；
(2) fitness of each primary, initialization global optimum g are calculatedbestWith individual optimal value pbest；
(3) whether evaluation algorithm meets the condition of convergence, and step (7) is jumped to if meeting, and otherwise performs step (4)；
(4) the speed v and position x of each particle are updated, the fitness of particle is calculated, global optimum g is updatedbestWith it is individual
Body optimal value pbest；
(5) particle evolution degree e, particle degree of polymerization a are calculated respectivelyjWith inertial factor w；
(6) iterations ItIncrease once, and return to step (3)；
(7) K is exportedp0、Ki0Optimizing result.
Particle evolution degree e, particle degree of polymerization a in the improvement PSO algorithmsjComputing formula with inertial factor w is as follows：
W=f (e, aj)=w0-0.5e+0.1aj
Wherein, PsizeRepresent population；Pgbest(T)And Pgbest(T-1)The T times iteration global optimum and T- are represented respectively
1 global optimum of iteration；Pi(T)Represent the T times adaptive value of i-th particle of iteration；w0It is the initial value of w, takes w0=
0.9；
The speed of each particle and the more new formula of position are as follows：
Wherein, r1And r2It is equally distributed random number, c between 0 to 11And c2Respectively self summarize the factor and to optimal
Individual Studying factors；
Secondly, in step 3, the self-adaptive sites method is FUZZY ALGORITHMS FOR CONTROL, according to the error e (t) being input into and by mistake
Difference rate of change Δ e (t), with reference to fuzzy rule and obscure oneself, the fuzzy quantity of control parameter variable quantity is obtained using reasoning algorithm
Output, then the exact value of PI control parameter variable quantities is obtained by ambiguity solution algorithm, and actual PI control parameter values are calculated, its meter
Calculate formula as follows：
Wherein, Δ KpWith Δ KiIt is the PI control parameter variable quantities of FUZZY ALGORITHMS FOR CONTROL output.
The error e of the voltage of the FUZZY ALGORITHMS FOR CONTROL, fuzzy set domain is { -30, -20, -10,0,10,20,30 },
Fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }；Error rate Δ e, fuzzy set domain for -1500, -1000, -
500,0,500,1000,1500 }, fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }；E and Δ e are subordinate to using triangle
Function；Output quantity Δ KpFuzzy subset is taken as { NB, NM, NS, ZE, PS, PM, PB }, and domain quantification gradation is { -0.3Kp0,-
0.2Kp0, -0.1Kp0, 0,0.1Kp0, 0.2Kp0, 0.3Kp0}；Output quantity Δ KiFuzzy subset be taken as { NM, NS, ZE, PS, PM },
Domain quantification gradation is { -0.2Ki0, 0.1Ki0, 0,0.1Ki0, 0.2Ki0}。
The reasoning algorithm uses mamdani algorithms；Ambiguity solution algorithm uses mom algorithms.
Beneficial effect：Compared with prior art, parameter is permanent during the present invention replaces electric power spring using adaptive PI controller
Fixed conventional PI control device, lifts the voltage regulation capability of electric power spring, and it is non-key to enable that electric power spring is successfully managed
The various change for occurring is loaded, so as to lift the self stability of electric power spring, suppresses the appearance of voltage oscillation；Employ one kind
Based on the adaptive PI controller that particle cluster algorithm is combined with FUZZY ALGORITHMS FOR CONTROL is improved, existing conventional electric power spring is solved
Controller parameter relies on experience value and invariable weak point, makes full use of and improves particle cluster algorithm and fuzzy control
Advantage so that electric power spring is capable of the operation of stability and high efficiency.
Brief description of the drawings
Fig. 1 is the Control system architecture block diagram of conventional electric power spring；
Fig. 2 is control structure sketch of the invention；
Fig. 3 is the flow chart of FUZZY ALGORITHMS FOR CONTROL in the present invention.
Specific embodiment
Technical scheme is described further below in conjunction with the accompanying drawings.
Embodiment：
Fig. 1 show traditional electric power spring control system structured flowchart, and the present invention provides a kind of for improving electric power bullet
The self-tuning PI control method of spring pressure regulating performance and self stability, using adaptive PI controller instead of parameter in electric power spring
Constant conventional PI control device, specifically includes following steps：
Step 1：Generation of electricity by new energy amount prediction in electric power spring installation environment, critical load/non-key load
Capacity configuration, adaptive PI controller parameter K is calculated based on PSO algorithms are improvedp、KiInitial value Kp0, Ki0, detailed process is such as
Under：
(1) position of each particulate and speed in random initializtion population, wherein each particulate represent a kind of possible PI controls
Initial parameter processed.Due to x=[Kp0,Ki0], therefore search space dimension is 2；
(2) fitness of each primary, initialization global optimum g are calculatedbestWith individual optimal value pbest；
(3) whether evaluation algorithm meets the condition of convergence, and (7) are jumped to if meeting, and otherwise performs (4)；
(4) speed and the position of each particulate are updated, the fitness of particle is calculated, global optimum g is updatedbestAnd individuality
Optimal value pbest；
(5) particle evolution degree e, particle degree of polymerization a are calculated respectivelyjWith inertial factor w；
(6) iterations ItIncrease once, and return to (3) execution；
(7) K is exportedp0、Ki0Optimizing result.
Particle evolution degree e, particle degree of polymerization a in the improvement PSO algorithmsjComputing formula with inertial factor w is as follows：
W=f (e, aj)=w0-0.5e+0.1aj
In above formula, PsizeRepresent population；Pgbest(T)And Pgbest(T-1)The T times iteration global optimum and are represented respectively
The T-1 global optimum of iteration；Pi(T)Represent the T times adaptive value of i-th particle of iteration；w0It is the initial value of w, this hair
It is bright to take w0=0.9.
The speed and location updating formula of each particulate are as follows：
In above formula, r1And r2It is equally distributed random number, c between 0 to 11And c2Respectively self summarize the factor and to most
Excellent individual Studying factors.
Step 2：The voltage measurement model V of t is obtained in real times(t) and voltage reference value Vs_ref, and calculate between the two
Error e (t) and error rate Δ e (t)：
In above-mentioned formula, VsT () is t electrical system bus voltage, Vs_refIt is system reference voltage.
Step 3：Using error e (t) and error rate Δ e (t) as input, using FUZZY ALGORITHMS FOR CONTROL to adaptive PI
Controlling its initial parameter carries out real-time adjustment, the parameter K of self-adaptive sites PI controllersp、Ki。
FUZZY ALGORITHMS FOR CONTROL implements process：The FUZZY ALGORITHMS FOR CONTROL becomes according to the error e (t) and error of input
Rate Δ e (t), with reference to fuzzy rule and obscure oneself, using reasoning algorithm obtain control parameter variable quantity fuzzy quantity export,
In the exact value by ambiguity solution algorithm acquisition PI control parameter variable quantities, and actual PI control parameter values are calculated, it calculates public
Formula is as follows：
In above-mentioned formula, Δ KpWith Δ KiIt is the PI control parameter variable quantities of FUZZY ALGORITHMS FOR CONTROL output.
The error e of the voltage of the FUZZY ALGORITHMS FOR CONTROL, fuzzy set domain is { -30, -20, -10,0,10,20,30 },
Fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }；Error rate Δ e, fuzzy set domain for -1500, -1000, -
500,0,500,1000,1500 }, fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }.E and Δ e are subordinate to using triangle
Function.Output quantity Δ KpFuzzy subset is taken as { NB, NM, NS, ZE, PS, PM, PB }, and domain quantification gradation is { -0.3Kp0,-
0.2Kp0, -0.1Kp0, 0,0.1Kp0, 0.2Kp0, 0.3Kp0}；Output quantity Δ KiFuzzy subset be taken as { NM, NS, ZE, PS, PM },
Domain quantification gradation is { -0.2Ki0, 0.1Ki0, 0,0.1Ki0, 0.2Ki0}.Reasoning algorithm synthesis uses mamdani algorithms, Xie Mo
Paste algorithm uses mom algorithms.
Fig. 2 show the electric power spring structure sketch of the adaptive PI control system proposed using the inventive method, passes through
PSO algorithms are improved, the initial parameter value to adaptive PI controller is optimized, improve the regulating effect of electric power spring itself,
By improving the governing speed of electric power spring, reduce the difference between system busbar voltage and rated voltage；Based on fuzzy control
Algorithm, real-time adjustment is realized according to the situation that actually enters to PI control parameters, further improves the pressure regulating performance of electric power spring, and
Ensure the stability of electric power spring operation, it is to avoid due to busbar voltage unstability situation caused by non-key load variations.
Fig. 3 show the FUZZY ALGORITHMS FOR CONTROL flow chart that the inventive method is used.The mould that table 1 is used by fuzzy control
Paste control rule：
Table 1
Those skilled in the art of the present technique it is understood that unless otherwise defined, all terms used herein (including skill
Art term and scientific terminology) have with art of the present invention in those of ordinary skill general understanding identical meaning.Also
It should be understood that those terms defined in such as general dictionary should be understood that with the context of prior art in
The consistent meaning of meaning, and unless defined as here, will not be explained with idealization or excessively formal implication.
Above-described specific embodiment, has been carried out further to the purpose of the present invention, technical scheme and beneficial effect
Describe in detail, should be understood that and the foregoing is only specific embodiment of the invention, be not limited to this hair
Bright, all any modification, equivalent substitution and improvements within the spirit and principles in the present invention, done etc. should be included in the present invention
Protection domain within.
Claims (6)
1. it is a kind of improve electric power spring pressure regulating performance self-tuning PI control method, it is characterised in that comprise the following steps：
Step 1：The prediction of generation of electricity by new energy amount and critical load or non-key load in electric power spring installation environment
Capacity configuration, calculates adaptive PI controller parameter Kp、KiInitial value Kp0, Ki0；
Step 2：The voltage measurement model V of t is obtained in real times(t) and voltage reference value Vs_ref, and calculated by equation below
Error e (t) between the two and error rate Δ e (t)：
Wherein, VsT () is t electrical system bus voltage, Vs_refIt is system reference voltage；
Step 3：Using error e (t) and error rate Δ e (t) as input, the initial parameter to adaptive PI controller is carried out
Real-time adjustment, the parameter K of self-adaptive sites PI controllersp、Ki。
2. self-tuning PI control method according to claim 1, it is characterised in that：In step 1, the computational methods are to change
Enter PSO algorithms, adaptive PI controller parameter K is calculated based on PSO algorithms are improvedp、KiInitial value Kp0, Ki0Detailed process
For：
(1) position of each particle and speed in random initializtion population, wherein, each particle represents a kind of possible PI controls just
Beginning parameter, due to x=[Kp0,Ki0], therefore search space dimension is 2；
(2) fitness of each primary, initialization global optimum g are calculatedbestWith individual optimal value pbest；
(3) whether evaluation algorithm meets the condition of convergence, and step (7) is jumped to if meeting, and otherwise performs step (4)；
(4) the speed v and position x of each particle are updated, the fitness of particle is calculated, global optimum g is updatedbestWith individuality most
Figure of merit pbest；
(5) particle evolution degree e, particle degree of polymerization a are calculated respectivelyjWith inertial factor w；
(6) iterations ItIncrease once, and return to step (3)；
(7) K is exportedp0、Ki0Optimizing result；
Particle evolution degree e, particle degree of polymerization a in the improvement PSO algorithmsjComputing formula with inertial factor w is as follows：
W=f (e, aj)=w0-0.5e+0.1aj
Wherein, PsizeRepresent population；Pgbest(T)And Pgbest(T-1)The T times iteration global optimum is represented respectively and is changed for the T-1 times
The global optimum in generation；Pi(T)Represent the T times adaptive value of i-th particle of iteration；w0It is the initial value of w, takes w0=0.9；
The speed of each particle and the more new formula of position are as follows：
Wherein, r1And r2It is equally distributed random number, c between 0 to 11And c2Respectively self summarize the factor and to optimum individual
Studying factors.
3. self-tuning PI control method according to claim 1, it is characterised in that：In step 3, the self-adaptive sites side
Method is FUZZY ALGORITHMS FOR CONTROL, according to input error e (t) and error rate Δ e (t), with reference to fuzzy rule and obscure oneself,
The fuzzy quantity for obtaining control parameter variable quantity using reasoning algorithm is exported, then obtains the change of PI control parameters by ambiguity solution algorithm
The exact value of amount, and actual PI control parameter values are calculated, its computing formula is as follows：
Wherein, Δ KpWith Δ KiIt is the PI control parameter variable quantities of FUZZY ALGORITHMS FOR CONTROL output.
4. a kind of adaptive PI controlling party for improving electric power spring pressure regulating performance and self stability according to claim 3
Method, it is characterised in that：The error e of the voltage of the FUZZY ALGORITHMS FOR CONTROL, fuzzy set domain for -30, -20, -10,0,10,
20,30 }, fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }；Error rate Δ e, fuzzy set domain for -1500, -
1000, -500,0,500,1000,1500 }, fuzzy subset is { NB, NM, NS, ZE, PS, PM, PB }；E and Δ e use triangle
Shape membership function；Output quantity Δ KpFuzzy subset is taken as { NB, NM, NS, ZE, PS, PM, PB }, domain quantification gradation for-
0.3Kp0, -0.2Kp0, -0.1Kp0, 0,0.1Kp0, 0.2Kp0, 0.3Kp0}；Output quantity Δ KiFuzzy subset be taken as NM, NS, ZE,
PS, PM }, domain quantification gradation is { -0.2Ki0, 0.1Ki0, 0,0.1Ki0, 0.2Ki0}。
5. self-tuning PI control method according to claim 3, it is characterised in that：The reasoning algorithm uses mamdani
Algorithm.
6. self-tuning PI control method according to claim 3, it is characterised in that：The ambiguity solution algorithm is calculated using mom
Method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710015388.6A CN106684914B (en) | 2017-01-10 | 2017-01-10 | A kind of self-tuning PI control method improving electric power spring pressure regulating performance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710015388.6A CN106684914B (en) | 2017-01-10 | 2017-01-10 | A kind of self-tuning PI control method improving electric power spring pressure regulating performance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106684914A true CN106684914A (en) | 2017-05-17 |
| CN106684914B CN106684914B (en) | 2019-03-26 |
Family
ID=58849511
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710015388.6A Active CN106684914B (en) | 2017-01-10 | 2017-01-10 | A kind of self-tuning PI control method improving electric power spring pressure regulating performance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106684914B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110649648A (en) * | 2019-08-26 | 2020-01-03 | 南京理工大学 | Power spring control method based on variable-discourse-domain fuzzy PI self-adaptive control |
| CN115333103A (en) * | 2022-10-17 | 2022-11-11 | 国网浙江省电力有限公司宁波供电公司 | Power grid voltage control parameter self-adaptive setting method based on meteorological features |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105207193A (en) * | 2015-09-17 | 2015-12-30 | 东南大学 | Direct-current power spring topology and control method thereof |
| KR20160106335A (en) * | 2015-03-02 | 2016-09-12 | 군산대학교산학협력단 | Wind farm stabilization system |
| CN106257789A (en) * | 2016-08-09 | 2016-12-28 | 东南大学 | A kind of three-phase power spring topology and phase control method thereof |
-
2017
- 2017-01-10 CN CN201710015388.6A patent/CN106684914B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160106335A (en) * | 2015-03-02 | 2016-09-12 | 군산대학교산학협력단 | Wind farm stabilization system |
| CN105207193A (en) * | 2015-09-17 | 2015-12-30 | 东南大学 | Direct-current power spring topology and control method thereof |
| CN106257789A (en) * | 2016-08-09 | 2016-12-28 | 东南大学 | A kind of three-phase power spring topology and phase control method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 蔺小林等: "基于参数自适应模糊PI的三级倒立摆控制", 《控制工程》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110649648A (en) * | 2019-08-26 | 2020-01-03 | 南京理工大学 | Power spring control method based on variable-discourse-domain fuzzy PI self-adaptive control |
| CN110649648B (en) * | 2019-08-26 | 2022-09-06 | 南京理工大学 | Power spring control method based on variable-discourse-domain fuzzy PI self-adaptive control |
| CN115333103A (en) * | 2022-10-17 | 2022-11-11 | 国网浙江省电力有限公司宁波供电公司 | Power grid voltage control parameter self-adaptive setting method based on meteorological features |
| CN115333103B (en) * | 2022-10-17 | 2023-01-31 | 国网浙江省电力有限公司宁波供电公司 | Power grid voltage control parameter self-adaptive setting method based on meteorological features |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106684914B (en) | 2019-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Khooban et al. | A novel deep reinforcement learning controller based type-II fuzzy system: Frequency regulation in microgrids | |
| Khooban et al. | A new load frequency control strategy for micro-grids with considering electrical vehicles | |
| Lin et al. | Controls of hybrid energy storage systems in microgrids: Critical review, case study and future trends | |
| Wang et al. | Design and advanced control strategies of a hybrid energy storage system for the grid integration of wind power generations | |
| CN106099965B (en) | Exchange the control method for coordinating of COMPLEX MIXED energy-storage system under micro-grid connection state | |
| CN114362196B (en) | Multi-time-scale active power distribution network voltage control method | |
| CN104104107B (en) | The model predictive control method of wind power fluctuation is stabilized with hybrid energy-storing | |
| Guo et al. | Two‐stage optimal MPC for hybrid energy storage operation to enable smooth wind power integration | |
| CN110350538B (en) | Micro-grid coordination control method based on active demand side response | |
| CN111614110A (en) | Receiving-end power grid energy storage optimization configuration method based on improved multi-target particle swarm optimization | |
| Hassanzadeh et al. | Hierarchical optimal allocation of BESS using APT-FPSO based on stochastic programming model considering voltage sensitivity and eigenvalues analyses | |
| Ding et al. | Robust and cost-efficient coordinated primary frequency control of wind power and demand response based on their complementary regulation characteristics | |
| CN106684914A (en) | Adaptive PI control method for improving electric power spring pressure regulating performance | |
| CN112084680A (en) | Energy Internet optimization strategy method based on DQN algorithm | |
| Sabahi et al. | Deep Deterministic Policy Gradient Reinforcement Learning Based Adaptive PID Load Frequency Control of an AC Micro-Grid Apprentissage par renforcement du gradient de la politique déterministe profonde basé sur le contrôle adaptatif de la fréquence de charge PID d’un micro-réseau de courant alternatif | |
| Fu et al. | Nonsingular fast terminal control for the DFIG-based variable-speed hydro-unit | |
| Hongfei et al. | Optimal control virtual inertia of optical storage microgrid based on improved sailfish algorithm | |
| Mohamed et al. | Optimal parameters of interline power flow controller using particle swarm optimization | |
| Fang et al. | An improved virtual synchronous generator power control strategy considering time-varying characteristics of SOC | |
| He et al. | Distributed Optimal Power Scheduling for Microgrid System via Deep Reinforcement Learning with Privacy Preserving | |
| Miao et al. | Optimized energy-storage method based on deep-learning adaptive-dynamic programming | |
| He et al. | Optimal frequency control parameters design of wind farm considering wind turbine grouping | |
| Wang et al. | Entropy weight and grey relational analysis based virtual capacitance collaborative control for multiple energy storage units | |
| Zhou et al. | Probabilistic wind power penetration of power system using nonlinear predictor-corrector primal-dual interior-point method | |
| Xia et al. | Optimal allocation of modular SMES in microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221215 Address after: Floor 5, Building B4, No. 118, Software Avenue, Yuhuatai District, Nanjing, Jiangsu Province, 210000 Patentee after: JIANGSU QIFENG POWER TECHNOLOGY Co.,Ltd. Address before: 210024, No. 122, Ning Hai Road, Gulou District, Jiangsu, Nanjing Patentee before: NANJING NORMAL University |
|
| TR01 | Transfer of patent right |