CN108711866B - Reactive voltage control system for new energy power station - Google Patents
Reactive voltage control system for new energy power station Download PDFInfo
- Publication number
- CN108711866B CN108711866B CN201810316010.4A CN201810316010A CN108711866B CN 108711866 B CN108711866 B CN 108711866B CN 201810316010 A CN201810316010 A CN 201810316010A CN 108711866 B CN108711866 B CN 108711866B
- Authority
- CN
- China
- Prior art keywords
- power
- voltage
- control system
- model
- new energy
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 238000005070 sampling Methods 0.000 claims abstract description 7
- 238000003064 k means clustering Methods 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000000877 morphologic effect Effects 0.000 claims description 6
- 239000003990 capacitor Substances 0.000 claims description 5
- 230000005284 excitation Effects 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 description 14
- 230000005540 biological transmission Effects 0.000 description 7
- 230000033228 biological regulation Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241001123248 Arma Species 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000013076 uncertainty analysis Methods 0.000 description 1
Images
Classifications
-
- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
-
- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
Abstract
The invention discloses a control system for reactive voltage of a new energy power station, which is characterized in that: the control system comprises a prediction model, a typical scene set model and an MPC optimization control model; and meanwhile, predicting a bus voltage value and a transformer substation load by using an autoregressive moving average method, generating a discretized scene for the predicted value through Latin hypercube sampling, acquiring a typical scene set by using a K-means clustering algorithm, establishing an optimal control model with an objective function being the minimum value of the internal network loss and the voltage offset in a future time window, and solving by using a second-order cone programming method. By adopting the method, the reactive power output can be reasonably arranged, the voltage stability is maintained, and the regional network loss is reduced.
Description
Technical Field
The invention relates to reactive power optimization of a power grid area, in particular to a control system for reactive voltage of a new energy power station.
Background
With the large-scale access of wind power plants and photovoltaic power stations, new problems are brought to the safe and reliable operation of the power grid. Because the new energy power station has the characteristics of intermittence and randomness, the problem of how to effectively cope with the fluctuation of the voltage of the grid-connected point of the power station is increasingly prominent because the new energy power station is greatly influenced by uncertain environmental factors such as wind speed, illumination and the like. Secondly, the aim of reactive power optimization of the traditional power grid is to balance locally, reactive power is not allowed to be sent to the transmission line between the power plant and the power grid, reactive power flow is reduced as much as possible, and therefore the problem of how to coordinate reactive power compensation of the two is also increasingly important.
Compared with the traditional voltage regulation measures of regulating main transformer tap-off joints, switching capacitors and the like, the new energy power station is provided with the static reactive power compensator or the static reactive power generator for coping with the fluctuation of the grid-connected point voltage, and the two devices can continuously and smoothly output reactive power. In addition, the generator set of the new energy power station also has a certain reactive power regulation measure. At present, a double-fed induction wind turbine generator is mainly adopted as a domestic wind turbine generator, and reactive power can be emitted or absorbed within a certain range by the double-fed induction wind turbine generator. The intelligent inverter in the photovoltaic power station can still keep 0.9 power factor grid connection under the condition of rated active output.
Therefore, how to coordinate and control the reactive compensation measures in the power grid and the power station, ensure the voltage stability of the grid-connected point, and reduce the transmission loss at the same time is a key for the voltage reactive control of the new energy power station.
Disclosure of Invention
The invention aims to solve the technical problem of providing a control system for reactive voltage of a new energy power station, which ensures voltage stability of grid-connected points and reduces transmission loss.
In order to achieve the above purpose, the technical scheme of the invention is that a control system for reactive voltage of a new energy power station is characterized in that: the control system comprises a prediction model, a typical scene set model and an MPC optimization control model; and meanwhile, predicting a bus voltage value and a transformer substation load by using an autoregressive moving average method, generating a discretized scene for the predicted value through Latin hypercube sampling, acquiring a typical scene set by using a K-means clustering algorithm, establishing an optimal control model with an objective function being the minimum value of the internal network loss and the voltage offset in a future time window, and solving by using a second-order cone programming method.
The prediction model of the control system comprises a wind power/photovoltaic power prediction part and a load/bus voltage prediction part, wherein the wind power/photovoltaic power prediction error is fitted by adopting beta distribution, and the load/bus voltage is predicted by adopting an autoregressive moving average method.
A typical scene set model of the control system firstly generates a discretized scene by pulling Ding Chao cubic samples of predicted values of new energy output, bus voltage and transformer substation load in an electric island, and then acquires a typical scene set by using a K-means clustering algorithm.
And establishing an optimal control model with the minimum target function of the network loss value and the voltage offset value in a future time window by using the MPC optimal control model of the control system, and solving by adopting a second-order cone planning method.
In the wind power/photovoltaic power prediction, when the wind power predicted value is P t pred When the probability density function of the unit output active value x is:
where B (α, β) is the beta function, α and β are morphological parameters of the beta distribution, their values are the expected μ and variance σ of the beta distribution 2 The calculation formula is as follows:
the beta distribution form parameters alpha and beta are obtained, and then a probability density function of wind power prediction error distribution can be obtained;
the output power of the photovoltaic array in the optical power prediction also obeys the beta distribution, and the probability density formula is as follows:
wherein Γ is Gamma function, alpha and beta are morphological parameters of beta distribution, p max Maximum output power for the photovoltaic array; the wind power prediction distribution and the light power prediction distribution are equally divided from beta distribution, and the wind power prediction model and the light power prediction model are unified.
The generation of the typical scene set firstly carries out a large number of samples on random variables distributed according to a certain probability to obtain a scene data set reflecting the characteristics of the random variables, namely discretizing a continuous probability model, and then carrying out reduced clustering on the scenes to obtain the typical scene set so as to realize the purpose of representing the characteristics of the original scene by fewer scenes.
The control system aims at the objective function of the new energy power station voltage optimization control:
wherein ρ is s Is scene w s The corresponding probabilities; p (P) t (w s ) Is the electrical island at t pre Predicting active loss at time t in the period; v (V) t (w s ) Is the bus voltage at t pre A deviation value at time t in the prediction period; alpha and beta are weight coefficients. P (P) i.t (w s )、Q i.t (w s )、V i.t (w s ) Respectively t pre Existence of i node at t time in prediction periodPower, reactive power, voltage value; v (V) i.s Is the voltage set point of the i-node.
The control system incorporates reactive power that can be output by the DFIG into an optimal control, wherein the reactive power ranges:
wherein P is s An active value for the stator; u (U) s Is the stator side terminal voltage; x is X s Is stator side leakage reactance; x is X m Is an excitation reactance; i Imax For the maximum current value of the rotor converter.
The MPC optimization control model comprises discrete and continuous variables such as capacitor switching, gear adjustment, new energy output, SVC reactive compensation and the like, and adopts second-order cone relaxation planning to relax the saliency of a tide equation.
By adopting the method, the reactive power output can be reasonably arranged, the voltage stability is maintained, and the regional network loss is reduced.
Drawings
The invention will be described in further detail with reference to the drawings and the detailed description;
FIG. 1 is a grid-connected structure diagram of a new energy power station in a control system for reactive voltage of the new energy power station;
fig. 2 is a schematic diagram of a typical scenario set in a control system for reactive voltage of a new energy power station according to the present invention.
Detailed Description
The invention applies the model predictive control theory to the voltage reactive power control of the new energy power station, utilizes the ultra-short-term power predictive value of the new energy power station, simplifies the power grid structure model and realizes the optimal control of the voltage reactive power. At present, a wind power station in China is mainly connected to a power grid through 110kV and above buses, the structure of the wind power station is shown in figure 1, a wind turbine generator is connected to a 35kV bus in the station, and then the wind power station is connected to the power grid through a booster station. Medium-sized and above photovoltaic power stations need to be connected to a power grid through 10kV and above buses, and the structure of the medium-sized and above photovoltaic power stations is shown in figure 1. It is worth noting that the low-voltage class photovoltaic power station may not have a booster station, the in-station collecting bus is directly connected to the power grid, and the structure can be also included in the research scope of the invention, and only partial voltage regulation measures are absent.
According to the invention, the new energy power station and the power grid access side transformer substation are used as an area to carry out optimization control. Because of the wide reference of the AVC system in the power grid, the bus voltage fluctuation in the system is small, the high-voltage bus of the power grid side substation can be regarded as an infinite voltage source, and the bus voltage value can be predicted according to the historical value by utilizing an autoregressive moving average method (auto regression moving average, ARMA). The load output by the station is treated the same. The predicted value of the new energy power station can be obtained through an in-station power system.
The predicted value and the actual value of the system have certain deviation, firstly, the uncertainty analysis is performed, then, the simplified double-end power supply network model is combined to perform optimization with the minimum network loss as an objective function, and further, the voltage reactive power control is realized.
If the wind power prediction error has certain kurtosis and skewness, and the error generated by describing the wind power prediction error by normal distribution is larger, the beta distribution is adopted to fit the wind power prediction error. When the wind power predicted value is P t pred When the unit outputs the probability density function with the active value x as follows:
where B (α, β) is the beta function, α and β are morphological parameters of the beta distribution, their values are the expected μ and variance σ of the beta distribution 2 The calculation formula is as follows:
the beta distribution form parameters alpha and beta can be obtained through the two formulas, and then the probability density function of wind power prediction error distribution can be obtained.
The illumination intensity approximately obeys the beta distribution in the hour period, and the output power of the photovoltaic array also obeys the beta distribution by considering that the output power of the photovoltaic array is in linear relation with the area of the photovoltaic array and the photoelectric conversion efficiency, and the probability density formula is as follows:
wherein Γ is Gamma function, alpha and beta are morphological parameters of beta distribution, p max Maximum output power for the photovoltaic array.
The wind power prediction distribution and the light power prediction distribution are equally divided from the beta distribution, so that the wind power prediction model and the light power prediction model can be unified.
According to the invention, a high-voltage side bus in a transformer substation is connected with a new energy power station to be equivalent to an infinite power supply, although the bus voltage value fluctuates slightly in a short period, and the accuracy of optimal control is considered, and an autoregressive moving average model is adopted to predict the bus voltage value:
wherein U (t) i ) Is t i Predicted value of time, U (t i-k ) Is t i-k The historical measure of time, ε (t), is the prediction error at time t, p and q are the model autoregressive order and moving average order, respectively,
and θ is the coefficient to be solved.
The autoregressive moving average model requires that the time series must be smooth, so that a stationarity check of the historical values is required before prediction, and the autoregressive order, moving average order, and coefficients to be solved of the model are recalculated before each optimization procedure. The load values in the substation are also predicted according to the 6 autoregressive moving average model.
The power prediction model and the bus load prediction model of the new energy power station are obtained, probability density functions of the power prediction model and the bus load prediction model are continuous, and if the power prediction model and the bus load prediction model are directly used for voltage reactive power optimization control, calculation time and complexity are greatly increased, so that the power prediction model and the bus load prediction model are not suitable for field application. The adoption of the representative typical scene set capable of reflecting the probability distribution characteristics of the original data can effectively improve the calculation efficiency, reduce the calculation amount and ensure the optimization accuracy.
The generation of a typical scene set firstly carries out a large number of samples on random variables distributed according to a certain probability to obtain a scene data set reflecting the characteristics of the random variables, namely discretizing a continuous probability model, such as the dots in fig. 2, namely the dispersion of the original predicted value at each time point, and each dot corresponds to the probability that the occurrence is possible. However, the number of the scene sets obtained in this step is still huge, the number is more than 10000, the calculation is not facilitated, the scenes are required to be subjected to reduced clustering to obtain a typical scene set, the fact that fewer scenes are used for representing the features of the original scenes is achieved, and the black center dots in fig. 2 are obtained.
The Latin hypercube sampling (latin hypercube sampling, LHS) method is employed herein for each prediction interval t pred Sampling the new energy power predicted value and the voltage load predicted value for N times to obtain w i (i=1, 2, …, N) scenes, each scene corresponding to a probability ρ i (i=1, 2, …, N). This way the original input distribution can be characterized by a limited number of samples.
The K-means clustering algorithm is one of the unsupervised learning algorithms, and divides an original data set into K classes according to the characteristics of data, wherein each class at least comprises one data, and the data in the classes have similar characteristics and attributes. And carrying out cluster analysis on the N scenes obtained by sampling to obtain K classes, and obtaining K typical scene sets, wherein the calculation process is as follows:
1) Normalizing the predicted value in the scene according to the following formula, and mapping the value into a [0,1] interval;
2) Randomly selecting K initial clustering centers;
3) The Euclidean distance from each scene to the initial clustering center is calculated according to the following formula, and the scenes are divided into classes closest to each other;
4) Recalculating the arithmetic mean of the scenes in the classification of 2) as a new center of the class;
5) Repeating the step 2) until the clustering result is not changed.
The invention aims to ensure that the bus voltage in an electric island formed by a new energy power station and a transformer substation is within a reasonable range on the basis of obtaining K typical scene sets, and simultaneously reduces the network loss of the electric island, and the specific objective function is as follows:
wherein ρ is s Is scene w s The corresponding probabilities; p (P) t (w s ) Is the electrical island at t pre Predicting active loss at time t in the period; v (V) t (w s ) Is the bus voltage at t pre A deviation value at time t in the prediction period; alpha and beta are weight coefficients. P (P) i.t (w s )、Q i.t (w s )、V i.t (w s ) Respectively t pre Predicting the active, reactive and voltage values of the inode at the moment t in the period; v (V) i.s Is the voltage set point of the i-node.
The optimization scope of the invention is a double-ended power supply network as shown in fig. 1, and the constraint condition of the equation and the constraint condition of the inequality are as follows:
P i.t ＝P i-1.t -P Li.t
Q i.t ＝Q i-1.t -Q Li.t
Q BC.t ≥0
τ i.t ≤τ imin
0≤ν i.t ≤ν imax
1≤σ i.t ≤σ imin
Q svc.i.min ≤Q svc.i.t ≤Q svc.i.max
equation 1-5 is the constraint of the equation of the tidal current equation of the network, and bus A is considered as an infinite power source, namely the balance node of the electric island, bus D is a PV node, and other buses are PQ nodes. Equation 6 is a node voltage magnitude constraint. Equation 7 is the constraint condition of the maximum transmission capacity of the transmission line. Equation 8 is a constraint condition of reactive power transmission of a power transmission line, and an electric company generally requires a new energy power station to achieve reactive power on-site balance, and does not allow reactive power to be sent. Equations 9-10 are constraints on the number of times the capacitor is allowed to act within the control period and the time interval between acts. Equations 11-12 are constraints on the number of times the transformer gear is allowed to act and the time interval between acts within the control period. Equation 13 is the maximum and minimum capacity constraint that SVC can output in new energy plants.
The invention brings the reactive power which can be output by the DFIG into the optimal control. Since the DFIG rotor outputs and absorbs less reactive power, it can be ignored, and the stator determining unit mainly determines the range of reactive power that it can output:
wherein P is s An active value for the stator; u (U) s Is the stator side terminal voltage; x is X s Is stator side leakage reactance; x is X m Is an excitation reactance; i Imax For the maximum current value of the rotor converter.
The photovoltaic power generation inverter also has a certain reactive power regulation capability. Considering that an inverter can operate at 1.1 times rated power, its reactive regulation range is as follows:
wherein S is INV Is the rated capacity of the inverter; p (P) PV Is that photovoltaic power generation has power.
The optimization model comprises discrete and continuous variables such as capacitor switching, gear adjustment, new energy output, SVC reactive compensation and the like, is a typical mixed integer non-convex nonlinear programming, and is difficult to directly solve an optimal solution. For this problem, a second order cone relaxation program (SOCP) may be used to relax the flow equation convexly, converting the optimization problem into a second order cone programming problem with integer variables. Meanwhile, the optimization model is a double-end power supply network, the number of nodes is small, and the accuracy of optimizing and the high efficiency of calculating can be effectively improved.
While the invention has been described above with reference to the accompanying drawings, it will be apparent that the invention is not limited to the embodiments described above, but is capable of being modified in various ways or applied to other applications without modification, and is within the scope of the invention.
Claims (4)
1. A control system for reactive voltage of a new energy power station is characterized in that: the new energy power station and the power grid access side transformer substation are used as an area to carry out optimization control integrally, and the control system comprises a prediction model, a typical scene set model and MPC optimization control; meanwhile, predicting a bus voltage value and a transformer substation load by using an autoregressive moving average method, generating a discretized scene for the predicted value through Latin hypercube sampling, acquiring a typical scene set by using a K-means clustering algorithm, establishing an optimal control model with an objective function being the minimum in a future time window internal network loss value and voltage offset value, and solving by using a second-order cone programming method;
the prediction model of the control system comprises a wind power/photovoltaic power prediction part and a load/bus voltage prediction part, wherein the wind power/photovoltaic power prediction error is fitted by adopting beta distribution, and the load/bus voltage is predicted by adopting an autoregressive moving average method;
the control system is characterized in that a typical scene set model of the control system firstly generates a discretized scene by pulling Ding Chao cubic samples of predicted values of new energy output, bus voltage and transformer substation load in an electric island, and then acquires a typical scene set by using a K-means clustering algorithm;
the MPC optimization control of the control system establishes an optimization control model with the minimum target function of the network loss value and the voltage offset value in a future time window, and solves the model by adopting a second order cone planning method; the MPC optimization control comprises capacitor switching constraint, transformer gear adjustment constraint and SVC reactive compensation constraint;
in the wind power/photovoltaic power prediction, when the wind power predicted value is P t pred When the probability density function of the unit output active value x is:
where B (α, β) is the beta function, α and β are morphological parameters of the beta distribution, their values are the expected μ and variance σ of the beta distribution 2 The calculation formula is as follows:
the beta distribution form parameters alpha and beta are obtained, and then a probability density function of wind power prediction error distribution can be obtained;
the output power of the photovoltaic array also obeys the beta distribution in the optical power prediction, and the probability density formula is as follows:
wherein Γ is Gamma function, alpha and beta are morphological parameters of beta distribution, p max For maximum output power of photovoltaic array, p solar The output power of the photovoltaic array; the wind power prediction distribution and the light power prediction distribution are equally divided from beta distribution, and the wind power prediction model and the light power prediction model are unified.
2. A control system for reactive voltage of a new energy power station according to claim 1, characterized in that: the generation of the typical scene set firstly carries out a large number of samples on random variables distributed according to a certain probability to obtain a scene data set reflecting the characteristics of the random variables, namely discretizing a continuous probability model, and then carrying out reduced clustering on the scenes to obtain the typical scene set so as to realize the purpose of representing the characteristics of the original scene by fewer scenes.
3. A control system for reactive voltage of a new energy power station according to claim 1, characterized in that: the control system aims at the objective function of the new energy power station voltage optimization control:
wherein ρ is s Is scene w s The corresponding probabilities; p (P) t (w s ) Is the electrical island at t pre Predicting active loss at time t in the period; v (V) t (w s ) Is the bus voltage at t pre A deviation value at time t in the prediction period; a. b is a weight coefficient; k is the number of typical scene sets。
4. A control system for reactive voltage of a new energy power station according to claim 1, characterized in that: the control system incorporates reactive power that can be output by the DFIG into an optimal control, wherein the reactive power ranges:
wherein P is s An active value for the stator; u (U) s Is the stator side terminal voltage; x is X s Is stator side leakage reactance; x is X m Is an excitation reactance; i Imax For the maximum current value of the rotor converter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810316010.4A CN108711866B (en) | 2018-04-10 | 2018-04-10 | Reactive voltage control system for new energy power station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810316010.4A CN108711866B (en) | 2018-04-10 | 2018-04-10 | Reactive voltage control system for new energy power station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108711866A CN108711866A (en) | 2018-10-26 |
| CN108711866B true CN108711866B (en) | 2023-06-13 |
Family
ID=63866551
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810316010.4A Active CN108711866B (en) | 2018-04-10 | 2018-04-10 | Reactive voltage control system for new energy power station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108711866B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109274134B (en) * | 2018-11-08 | 2020-06-16 | 东南大学 | Active power distribution network robust active and reactive power coordination optimization method based on time sequence scene analysis |
| CN111082428B (en) * | 2018-12-20 | 2024-04-02 | 国网上海市电力公司 | New energy power station voltage control reference adjustment method based on big data analysis |
| CN110957735B (en) * | 2019-11-20 | 2023-05-16 | 广西电网有限责任公司 | Active power distribution network robust reactive power optimization method based on double time scales |
| CN112993979B (en) * | 2021-02-22 | 2023-02-17 | 广东电网有限责任公司韶关供电局 | Power distribution network reactive power optimization method and device, electronic equipment and storage medium |
| CN114374211B (en) * | 2021-12-10 | 2024-01-30 | 国网重庆市电力公司市区供电分公司 | Automatic voltage control method and device considering energy storage active plan |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106300417A (en) * | 2016-08-29 | 2017-01-04 | 上海交通大学 | Wind farm group reactive voltage optimal control method based on Model Predictive Control |
| CN106953359A (en) * | 2017-04-21 | 2017-07-14 | 中国农业大学 | A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8041465B2 (en) * | 2008-10-09 | 2011-10-18 | General Electric Company | Voltage control at windfarms |
-
2018
- 2018-04-10 CN CN201810316010.4A patent/CN108711866B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106300417A (en) * | 2016-08-29 | 2017-01-04 | 上海交通大学 | Wind farm group reactive voltage optimal control method based on Model Predictive Control |
| CN106953359A (en) * | 2017-04-21 | 2017-07-14 | 中国农业大学 | A kind of active reactive coordinating and optimizing control method of power distribution network containing distributed photovoltaic |
Non-Patent Citations (4)
| Title |
|---|
| " statistical analysis of wind power forecast error";Hans Bludszuweit 等;IEEE TRANSACTIONS ON POWER SYSTEMS;第23卷(第3期);第983-990页 * |
| "基于EMS的分布式地县电网AVC控制策略";苏志朋 等;电力系统保护与控制;第45卷(第6期);第137-140页 * |
| "基于随机模型的光伏发电置信容量评估方法";梁双 等;电力系统自动化;第36卷(第13期);第32-34页 * |
| "综合利用 SVC 和风力发电机的风电场无功控制策略";赵利刚 等;电力系统保护与控制;第40卷(第2期);第45-47页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108711866A (en) | 2018-10-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108711866B (en) | Reactive voltage control system for new energy power station | |
| Xu et al. | Distributed subgradient-based coordination of multiple renewable generators in a microgrid | |
| CN108695857B (en) | Automatic voltage control method, device and system for wind power plant | |
| CN108306303A (en) | A kind of consideration load growth and new energy are contributed random voltage stability assessment method | |
| CN107732958B (en) | Independent micro-grid black start optimization method considering source-load cooperative recovery | |
| CN102684201A (en) | Voltage threshold probability-based reactive power optimizing method for grid containing wind power plant | |
| Yenealem et al. | Management of voltage profile and power loss minimization in a grid-connected microgrid system using fuzzy-based STATCOM controller | |
| Ramesh et al. | Optimization of solar hybrid power generation using conductance-fuzzy dual-mode control method | |
| Colak et al. | Intelligent techniques to connect renewable energy sources to the grid: A review | |
| Shobana et al. | Optimised coordinated control of hybrid AC/DC microgrids along PV-wind-battery: a hybrid based model | |
| Abdelwahab et al. | Experimental investigation and theoretical for the performance improvement of MPPT technique with PV systems connected to the grid | |
| Anh et al. | Advanced intelligent fuzzy control of standalone PV-wind-diesel hybrid system | |
| Li et al. | Dynamic reconfiguration of distribution network considering time-varying characteristics of DG | |
| Zhong et al. | Probabilistic optimal power flow calculation of AC/DC hybrid distribution network with photovoltaic power and electric vehicles | |
| CN114676921A (en) | Method for calculating wind power receptibility of system by considering source load storage coordination optimization | |
| CN108233373B (en) | Probability harmonic analysis method considering weather scene for distributed photovoltaic access power distribution network | |
| Kaysal et al. | Self-tuning fuzzy PID controller design and energy management in DC microgrid: Standalone and grid connected mode | |
| CN107968427B (en) | Coupling model construction method is unified in multiple-energy-source electric system based on equivalent source | |
| CN107968407B (en) | Device for improving quality of electric energy generated by new energy | |
| KRISHAN et al. | Reactive Power Control Strategies for Solar Inverters to Increase the Penetration Level of RE in Power Grid | |
| Otmane et al. | Maximum power point tracking using fuzzy logic based controllers compared to P&O technique in photovoltaic generator | |
| Zhang et al. | Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm | |
| Zhang et al. | Typical scenario reactive power optimization of active distribution network based on limit theory | |
| Haripriya et al. | Comparison between different MPPT controllers for solar PV fed DC grid connected applications | |
| CN113708412B (en) | Weak connection structure and method for micro-grid group and low-voltage power distribution network |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Xia Youbin Inventor after: Zhao Qian Inventor after: Xu Tao Inventor after: Huang Jin Inventor after: Xiao Ya Inventor after: Du Li Inventor after: Xia Ying Inventor after: Bai Tianyu Inventor after: Ling Xiaobin Inventor after: Yin Yuanya Inventor after: Yang Xiaojuan Inventor after: Su Zhipeng Inventor after: Chen Yanbin Inventor after: Song Mingmin Inventor after: Pan Wenhu Inventor after: Shen Xincun Inventor after: Yu Peng Inventor after: Feng Fei Inventor after: Zhou Qiyang Inventor after: Wang Peng Inventor before: Xia Youbin Inventor before: Zhao Qian Inventor before: Xu Tao Inventor before: Huang Jin Inventor before: Xiao Ya Inventor before: Du Li Inventor before: Xia Ying Inventor before: Bai Tianyu Inventor before: Su Zhipeng Inventor before: Song Mingmin Inventor before: Pan Wenhu Inventor before: Shen Xincun Inventor before: Yu Peng Inventor before: Feng Fei Inventor before: Zhou Qiyang Inventor before: Wang Peng |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |