CN109449983A - Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy - Google Patents
Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy Download PDFInfo
- Publication number
- CN109449983A CN109449983A CN201811502668.0A CN201811502668A CN109449983A CN 109449983 A CN109449983 A CN 109449983A CN 201811502668 A CN201811502668 A CN 201811502668A CN 109449983 A CN109449983 A CN 109449983A
- Authority
- CN
- China
- Prior art keywords
- cloud
- controller
- control
- interconnected network
- parameter
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 239000000428 dust Substances 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000009977 dual effect Effects 0.000 description 4
- 230000005611 electricity Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000011217 control strategy Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
Classifications
-
- H02J3/386—
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Feedback Control In General (AREA)
Abstract
Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy, belong to electric system O&M technical field, including cloud controller and PI controller, the cloud controller includes input cloud, establishes cloud control rule, cloud reasoning and export inverse cloud, for realizing the on-line tuning to PI parameter, the increment of P and I is generated；The PI controller parameter is adjustable, to the output valve of cloud controller carry out it is whole and, for realizing the adjustment to controlled device.The present invention combines cloud model with routine PI controller, use cloud PI control method, by the fast quick-recovery of frequency to system allowed band, the exchange power of adjustment interconnection is zero rapidly, maintain the constant of interregional exchange power, the stability and accuracy for significantly improving the control of controlled system LOAD FREQUENCY, ensure the safe and reliable operation of interconnected network.
Description
Technical field
The invention belongs to electric system O&M technical field, it is negative to especially relate to the electric system based on cloud PI controller
Lotus control method for frequency.
Background technique
The frequency of maintenance power grid is being the necessary condition of system reliability service in stationary value.In recent years, by shortage of resources and
Pressure brought by environmental degradation increasingly increases, and fossil energy is also gradually substituted by some pollution-free, sustainable energy, rule
The wind-electricity integration of modelling shows big advantage on environment and social level.But between the active output of wind-powered electricity generation has
The characteristics of having a rest property, fluctuation and uncontrollability.With the continuous rising of wind power integration power grid ratio, a high proportion of wind-powered electricity generation is received
It can be impacted to the frequency stabilization of system, therefore, the LOAD FREQUENCY emergent control plan after research large-scale wind power access power grid
Slightly, make the fast quick-recovery of frequency to system allowed band by active balance control, to the safe and reliable operation for ensureing interconnected network
There is important meaning.
In traditional LOAD FREQUENCY control strategy, conventional PI controller is higher one kind of applying frequency in parameter tuning
Controller, because it has the characteristics that simple structure, strong robustness and controls more preferably for linear time invariant system, but conventional
PI controller can not revise online parameter, and interconnected network is a kind of parameter and structure constantly in the nonlinear control of change
System processed, therefore its own contains certain ambiguity, in addition blower power output, system failure etc. all have randomness, so that often
The control of rule PI is unable to satisfy the requirement of system, therefore there is an urgent need for a kind of novel technical solutions in the prior art to solve this
One problem.
Summary of the invention
The technical problems to be solved by the present invention are: providing a kind of interconnected network LOAD FREQUENCY cloud containing extensive new energy
PI control method combines cloud model with routine PI controller, using cloud PI control method, by the fast quick-recovery of frequency to system
Allowed band, the exchange power for adjusting interconnection rapidly is zero, maintains the constant of interregional exchange power, significantly improves controlled system
The stability and accuracy of LOAD FREQUENCY of uniting control, ensure the safe and reliable operation of interconnected network.
Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy, it is characterized in that: include the following steps, and
Following steps sequentially carry out,
Step 1: the design of the parameter self-tuning PI controller based on cloud model
Including cloud controller and PI controller, the cloud controller includes input cloud, establishes cloud control rule, cloud reasoning
And inverse cloud is exported, for realizing the on-line tuning to PI parameter, generate the increment of P and I；The PI controller parameter is adjustable
Section, to the output valve of cloud controller carry out it is whole and, for realizing the adjustment to controlled device；
Step 2: the design of cloud PI controller control rule
Cloud control rule is established, rule is written in cloud controller by linguistic variable quantization and is completed to control target
Adjustment, the linguistic variable state vocabulary of input is respectively honest PB, just small PS, zero ZE, bears small NS, bears big NB, each Linguistic Value
Maximum membership degree be " 1 ", system frequency is controlled.
Cloud model in the step 1 is,
Wherein U is the quantitative domain determined with exact value, and C is the qualitative expression in quantitative domain U, quantitative value
X ∈ U is a Stochastic implementation of qualitativing concept C, and x is the random number with steady tendency to degree of certainty μ (x) ∈ [0,1] of C.
The control algolithm of cloud model is in the step 1
(1) particular value (x is inputted0,y0) and cloud control the qualitativing concept A in regular former piece1(Ex,Enx,Hex)、 A2(Ey,
Eny, Hey) and cloud control consequent in qualitativing concept B (Ez, Enz, Hez), generating a desired value is Enx, variance
It is the normal random number Enx'=NORMRND (Enx, Hex) of Hex；
(2) generating a desired value is Eny, and variance is the normal random number Eny'=NORMRND (Eny, Hey) of Hey；
(3) it calculates cloud and controls the particular value (x inputted in regular former piece0,y0) degree of certainty
(4) generating a desired value is Enz, and variance is the normal random number Enz'=NORMRND (Enz, Hez) of Hez；
(5) work as x0When < Ex,Work as x0>=Ex,
Wherein, Z0For the output valve in the case where degree of certainty is μ in cloud control consequent；
(6) cloud consequent set water dust drop (z is exported0,μ)。
PI controller is to the whole and algorithm of output valve progress of cloud controller in the step 1
Wherein: KP0、KI0The respectively parameter value of current time PI controller, Δ KP、ΔKIRespectively pass through cloud controller
The setting valve of latter two parameter.
Through the above design, the present invention can be brought the following benefits: the interconnected network containing extensive new energy
LOAD FREQUENCY cloud PI control method, by designed control rule, three numerical characteristics of cloud model and routine PI controller
It combines, the fast quick-recovery of frequency to system allowed band is adjusted into rapidly the exchange power of interconnection using cloud PI control method
It is zero, maintains the constant of interregional exchange power, significantly improve the stability and accuracy of the control of controlled system LOAD FREQUENCY, protect
Hinder the safe and reliable operation of interconnected network.After extensive new energy is linked into interconnected network, and frequency is caused to fluctuate on a large scale, cloud
PI controller will be continually adjusted to PI parameter most preferably according to the response condition of real system, until obtaining ideal control
Effect processed finally plays the stable effect of safeguards system.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is further illustrated:
Fig. 1 is the interconnected network LOAD FREQUENCY cloud PI Controlling model schematic diagram of the invention containing extensive new energy.
Fig. 2 is interconnected network dual area LOAD FREQUENCY analogue system signal of the embodiment of the present invention containing extensive new energy
Figure.
When Fig. 3 is interconnected network dual area LOAD FREQUENCY wind power swing of the embodiment of the present invention containing extensive new energy
Frequency response schematic diagram.
When Fig. 4 is interconnected network dual area LOAD FREQUENCY wind power swing of the embodiment of the present invention containing extensive new energy
District control deviation responds schematic diagram.
When Fig. 5 is interconnected network dual area LOAD FREQUENCY wind power swing of the embodiment of the present invention containing extensive new energy
Dominant eigenvalues respond schematic diagram.
Specific embodiment
Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy, as shown in Figure 1, including
One, the parameter self-tuning PI controller design based on cloud model,
The principle of cloud model is as follows: setting U is the quantitative domain determined with exact value, and C is in quantitative domain U
Qualitative expression, if quantitative value x ∈ U is a Stochastic implementation of qualitativing concept C, x is to degree of certainty μ (x) ∈ [0,1] of C
Random number with steady tendency,
Cloud model portrays the overall permanence of qualitativing concept with three numerical characteristics, and three numerical characteristics it is expected Ex respectively
(Expected value), entropy En (Entropy) and super entropy He (Hyper entropy).
The composition of double condition list Rule Generators includes two-dimentional former piece cloud generator and one-dimensional consequent cloud generator, as follows:
Input: particular value (x0,y0) and regular former piece in qualitativing concept A1(Ex,Enx,Hex)、A2(Ey,Eny,Hey)
And the qualitativing concept B (Ez, Enz, Hez) in consequent.
Output: consequent set U2In water dust drop (z0,μ)。
Its algorithm steps are as follows:
(1) generating a desired value is Enx, and variance is the normal random number Enx'=NORMRND (Enx, Hex) of Hex；
(2) generating a desired value is Eny, and variance is the normal random number Eny'=NORMRND (Eny, Hey) of Hey；
(3) it calculates cloud and controls the particular value (x inputted in regular former piece0,y0) degree of certainty
(4) generating a desired value is Enz, and variance is the normal random number Enz'=NORMRND (Enz, Hez) of Hez；
(5) work as x0When < Ex,Work as x0>=Ex,
Wherein, Z0For the output valve in the case where degree of certainty is μ in cloud control consequent；
(6) water dust drop (z is exported0,μ)。
Cloud PI controller suitable for LOAD FREQUENCY control mainly includes cloud controller and Parameter adjustable PI controller two
Point, the former realizes the on-line tuning to PI parameter, generates the increment of P and I, is the basis for completing entire control process.Its medium cloud
Controller is divided into input cloud again, establishes cloud control rule, cloud reasoning and export inverse cloud Four processes.And the latter passes through to preceding
The output valve of person carry out it is whole and, realize the adjustment to controlled device.
Wherein: KP0、KI0The respectively parameter value of current time PI controller, Δ KP、ΔKIRespectively pass through cloud controller
The setting valve of latter two parameter；
Two, the design of cloud PI controller control rule
The design of cloud control rule is the working centre of entire cloud PI controller, and the essence for controlling rule is exactly operator
The experience of member's accumulation, is integrated into conditional statement one by one, is written into controller the adjustment completed to control target.
The vocabulary of description input language variable states, this selected works must be set before accurate input quantity quantization to corresponding cloud model domain
Five linguistic variable values, respectively honest (Positive Big, PB), just small (Positive Small, PS), zero are taken
(Zero, ZE), small (Negative Small, NS), negative big (Negative Big, NB) are born.The maximum membership degree of each Linguistic Value
It is " 1 ", system frequency is controlled.
Δ K has been obtained according to the selection principle of control rulePWith Δ KIControl rule sets are as shown in Table 1 and Table 2.
1 Δ K of tablePControl rule
2 Δ K of tableIControl rule
Method of the invention is by by designed control rule, three numerical characteristics of cloud model and routine PI
Initial parameter is set in controller together, after extensive new energy is linked into interconnected network, and frequency is caused to fluctuate on a large scale.
Cloud PI controller will be continually adjusted to PI parameter most preferably according to the response condition of real system, until obtaining ideal
Control effect finally plays the stable effect of safeguards system.
As shown in Fig. 2, G1~G6 is generator, the two-region containing wind-powered electricity generation built by Maltab/Simulink software in figure
Domain LOAD FREQUENCY control system.Wherein there are 3 automatic-generation-control units in each control area, and system frequency is set as
50Hz, Tie line Power deviation, district control deviation ACE are zero.In the wind power curve addition system of generation,
Access way are as follows: region one is inscribed into wind-powered electricity generation, is temporarily not processed in region two.Then to the control rule of cloud PI controller into
Row design: the departure e of signal and change of error amount ec domain are taken as [- 1.5,1.5] and [- 1,2] respectively, will output P with
The domain of the setting valve of I is taken as [- 1.7,1.3], obtains the domain range of controller input and output variable, corresponding
The membership clouds characteristic parameter of five qualitativing concepts is as shown in table 3.It is controlled by simulation analysis comparison routine PI control strategy and cloud PI
The effect for making strategy obtains the dynamic response such as the region one of Fig. 3 and the control of two LOAD FREQUENCY of region.
3 membership clouds characteristic parameter of table
When having accessed the stronger wind power of fluctuation in region one, cloud PI controller and routine PI that the present invention designs
Controller is compared, and the fluctuation of wind power can be preferably followed, and in time to system to feed back, makes system according in feedback
Appearance adjusts rapidly, maintains original equilibrium state or establishes new balance.It is embodied to believe to randomness strong interference
Number inhibition on have intuitive advantage.On the other hand, as shown in Fig. 4 and Fig. 5, which be can also ensure that when longer
Between in, district control deviation and Tie line Power all fluctuate in a lesser range, have system stronger steady
Qualitative and reliability.
Claims (4)
1. the interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy, it is characterized in that: include the following steps, and with
Lower step sequentially carries out,
Step 1: the design of the parameter self-tuning PI controller based on cloud model
Including cloud controller and PI controller, the cloud controller include input cloud, establish cloud control rule, cloud reasoning and
Inverse cloud is exported, for realizing the on-line tuning to PI parameter, generates the increment of P and I；The PI controller parameter is adjustable,
To the output valve of cloud controller carry out it is whole and, for realizing the adjustment to controlled device；
Step 2: the design of cloud PI controller control rule
Cloud control rule is established, rule is written to the tune completed in cloud controller to control target by linguistic variable quantization
Whole, the linguistic variable state vocabulary of input is respectively honest PB, just small PS, zero ZE, bears small NS, bears big NB, and each Linguistic Value is most
Big degree of membership is " 1 ", is controlled system frequency.
2. the interconnected network LOAD FREQUENCY cloud PI control method according to claim 1 containing extensive new energy, feature
Be: the cloud model in the step 1 is,
Wherein U is the quantitative domain determined with exact value, and C is the qualitative expression in quantitative domain U, quantitative value x ∈ U
It is a Stochastic implementation of qualitativing concept C, x is the random number with steady tendency to degree of certainty μ (x) ∈ [0,1] of C.
3. the interconnected network LOAD FREQUENCY cloud PI control method according to claim 1 containing extensive new energy, feature
Be: the control algolithm of cloud model is in the step 1
(1) particular value (x is inputted0,y0) and cloud control the qualitativing concept A in regular former piece1(Ex,Enx,Hex)、A2(Ey,Eny,
Hey) and cloud controls the qualitativing concept B (Ez, Enz, Hez) in consequent, and generating a desired value is Enx, and variance is Hex
Normal random number Enx'=NORMRND (Enx, Hex)；
(2) generating a desired value is Eny, and variance is the normal random number Eny'=NORMRND (Eny, Hey) of Hey；
(3) it calculates cloud and controls the particular value (x inputted in regular former piece0,y0) degree of certainty
(4) generating a desired value is Enz, and variance is the normal random number Enz'=NORMRND (Enz, Hez) of Hez；
(5) work as x0When < Ex,Work as x0>=Ex,Its
In, Z0For the output valve in the case where degree of certainty is μ in cloud control consequent；
(6) cloud consequent set water dust drop (z is exported0,μ)。
4. the interconnected network LOAD FREQUENCY cloud PI control method according to claim 1 containing extensive new energy, feature
Be: PI controller is to the whole and algorithm of output valve progress of cloud controller in the step 1
Wherein: KP0、KI0The respectively parameter value of current time PI controller, Δ KP、ΔKITwo respectively after cloud controller
The setting valve of a parameter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811502668.0A CN109449983A (en) | 2018-12-10 | 2018-12-10 | Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811502668.0A CN109449983A (en) | 2018-12-10 | 2018-12-10 | Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109449983A true CN109449983A (en) | 2019-03-08 |
Family
ID=65558496
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811502668.0A Pending CN109449983A (en) | 2018-12-10 | 2018-12-10 | Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109449983A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106532785A (en) * | 2016-12-28 | 2017-03-22 | 河海大学 | Load frequency control method of considering novel cloud model |
| CN108306340A (en) * | 2018-02-05 | 2018-07-20 | 河北工业大学 | Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy |
| CN108599215A (en) * | 2018-05-15 | 2018-09-28 | 杭州电子科技大学 | Regulate and control method based on the distribution network voltage of internet cloud platform and distributed energy storage |
-
2018
- 2018-12-10 CN CN201811502668.0A patent/CN109449983A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106532785A (en) * | 2016-12-28 | 2017-03-22 | 河海大学 | Load frequency control method of considering novel cloud model |
| CN108306340A (en) * | 2018-02-05 | 2018-07-20 | 河北工业大学 | Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy |
| CN108599215A (en) * | 2018-05-15 | 2018-09-28 | 杭州电子科技大学 | Regulate and control method based on the distribution network voltage of internet cloud platform and distributed energy storage |
Non-Patent Citations (1)
| Title |
|---|
| 张斌: "基于云模型理论的互联电力系统负荷频率控制策略研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106786807B (en) | A kind of wind power station active power control method based on Model Predictive Control | |
| Soued et al. | Optimal blade pitch control for enhancing the dynamic performance of wind power plants via metaheuristic optimisers | |
| Pandey et al. | Frequency regulation in hybrid power systems using particle swarm optimization and linear matrix inequalities based robust controller design | |
| Biegel et al. | Distributed low-complexity controller for wind power plant in derated operation | |
| CN105048499B (en) | Wind-electricity integration real-time scheduling method and system based on Model Predictive Control | |
| Jadhav et al. | Performance verification of PID controller in an interconnected power system using particle swarm optimization | |
| Xi et al. | A novel automatic generation control method based on the ecological population cooperative control for the islanded smart grid | |
| CN108306340A (en) | Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy | |
| CN105186511A (en) | Method for controlling participation of battery energy storage system in power grid secondary frequency modulation | |
| CN109802446A (en) | Wind bavin based on cloud model stores up hybrid power system electric voltage frequency sliding-mode control | |
| CN107346889B (en) | Load reduction optimization model construction method considering primary and secondary frequency modulation and minimum frequency deviation | |
| Bayat et al. | Power regulation and control of wind turbines: LMI‐based output feedback approach | |
| Dhillon et al. | Automatic generation control of interconnected power systems using elephant herding optimization | |
| CN109193750A (en) | Based on PIDD2The wind-powered electricity generation cluster of controller participates in AGC method | |
| Xu et al. | Short-term wind power forecasting based on Elman neural network with particle swarm optimization | |
| CN106532785A (en) | Load frequency control method of considering novel cloud model | |
| CN104362639A (en) | Power grid whole-grid reactive power optimization method based on improved differential evolution algorithm | |
| CN105207220B (en) | A kind of tapping voltage regulation and control method based on progressive learning | |
| Yao et al. | Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm | |
| Wang et al. | Wind power compound model-free adaptive predictive control based on full wind speed | |
| CN109449983A (en) | Interconnected network LOAD FREQUENCY cloud PI control method containing extensive new energy | |
| CN106952180B (en) | Method for establishing double-fed distributed wind power system low-order frequency response model | |
| CN110649604B (en) | Damping control method suitable for random fluctuation of new energy | |
| Manikandan | Stability analysis of cyber-physical micro grid load frequency control system with time-varying delay and non-linear load perturbations | |
| Li et al. | Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data‐Driven Control |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190308 |