CN115663912A - Control method and system of power system, electronic equipment and storage medium - Google Patents
Control method and system of power system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a control method and a control system of a power system, electronic equipment and a storage medium, belonging to the field of power system control, wherein the control method comprises the following steps: acquiring current power flow data of a power system; determining a current minimum damping ratio of the power system and a current oscillation frequency corresponding to the current minimum damping ratio based on the prediction model and according to the current load flow data; if the current minimum damping ratio is smaller than the damping ratio threshold value and the current oscillation frequency is larger than the frequency threshold value, determining a constraint condition according to the upper limit of active power, the lower limit of active power, the current tide data, the sensitivity of each generator to the damping ratio, the current minimum damping ratio and the damping ratio threshold value of each generator; based on the constraint conditions, a correction control model is established by taking the minimum active power adjustment quantity of each generator as a target, the solution is carried out, the active power adjustment quantity of each generator is determined, the active power of the corresponding generator is adjusted, and the stability of the power system is improved.
Description
Technical Field
The present invention relates to the field of power system control, and in particular, to a method and a system for controlling a power system based on sub-synchronous oscillation analysis, an electronic device, and a storage medium.
Background
With the development of a power grid and the synchronization of new equipment and new energy, the problem of subsynchronous oscillation of a power system is more and more, the induction mechanism is more and more complex, and the traditional analysis method is difficult to meet the requirement of analyzing new problems. SSO (sub-synchronous oscillation) can cause harmonic pollution and affect the quality of electric energy, can cause damage to new energy equipment and large-area grid disconnection of new energy, seriously affects grid connection and consumption of the new energy, and even can cause fatigue of a thermal power generating unit shafting to cause the generator shafting to break, further induce a regional power grid chain accident, form a huge threat to the safety of the unit and the power equipment in a power grid and the stable operation of the whole power system, and have high importance.
Therefore, whether subsynchronous oscillation risks exist in the operation of the power system needs to be judged correctly, and a proper correction method needs to be found based on the power generation scheduling requirement.
Disclosure of Invention
The invention aims to provide a control method, a control system, an electronic device and a storage medium of a power system, which can improve the stability of the power system.
In order to achieve the purpose, the invention provides the following scheme:
a method of controlling a power system, comprising:
acquiring current power flow data of a power system; the current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node;
determining a current minimum damping ratio of the power system and a current oscillation frequency corresponding to the current minimum damping ratio according to the current load flow data based on a prediction model; the prediction model is obtained by adopting a training sample set to train a width learning network in advance; the training sample set comprises a plurality of training samples; each training sample comprises historical load flow data of the power system and historical minimum damping ratio and historical oscillation frequency corresponding to the historical load flow data;
judging whether the current minimum damping ratio is smaller than a damping ratio threshold value or not and whether the current oscillation frequency is larger than a frequency threshold value or not;
if the current minimum damping ratio is smaller than a damping ratio threshold value and the current oscillation frequency is larger than a frequency threshold value, determining a constraint condition according to an active power upper limit, an active power lower limit, the current tide data, the sensitivity of each generator to the damping ratio, the current minimum damping ratio and the damping ratio threshold value;
based on the constraint conditions, establishing a correction control model by taking the minimum active power adjustment quantity of each generator as a target;
solving the correction control model to determine the active power adjustment quantity of each generator;
and adjusting the active power of the corresponding generator according to the active power adjustment quantity of each generator.
Optionally, the control method of the power system further includes:
and taking the current power flow data and the current minimum damping ratio and the current oscillation frequency corresponding to the current power flow data as training samples, adding the training samples into a training sample set of the width learning network, and training the width learning network.
Optionally, the constraint conditions of the correction control model include a generator power constraint, a minimum damping ratio constraint, and a damping ratio variation constraint.
Optionally, the generator power constraint is:
the minimum damping ratio constraint is: xi 0 +Δξ≥ξ limit ；
The damping ratio variation is constrained as follows:
wherein the content of the first and second substances,
for the lower limit of the active power of the generator i,
is the upper active power limit, PG, of generator i i For the active power of generator i, Δ PG, in the current tidal current data i For the active power adjustment of the generator i, xi 0 Is the current minimum damping ratio, delta xi is the variation of the current minimum damping ratio, xi limit Is a damping ratio threshold value, n is the total number of generators, C i The sensitivity of the generator i to the damping ratio.
Optionally, the constraint condition of the correction control model further includes a system power flow constraint.
Optionally, the system flow constraint is:
wherein, Δ P j Is the active increment, P, of node j in the power system Gi Active power, P, of generator i in an electric power system Lk Active power, P, for a load k in an electric power system j Injecting active power, Δ Q, for node j in a power system j For reactive increments, Q, of node j in the power system Gi For reactive power of generator i, Q, in an electric power system Lk Being reactive to load k in an electric power systemPower, Q j Reactive power is injected for node j in the power system.
In order to achieve the above purpose, the invention also provides the following scheme:
a control system for an electrical power system, comprising:
the data acquisition unit is used for acquiring current power flow data of the power system; the current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node;
the damping prediction unit is connected with the data acquisition unit and used for determining the current minimum damping ratio of the power system and the current oscillation frequency corresponding to the current minimum damping ratio based on a prediction model according to the current power flow data; the prediction model is obtained by training a width learning network by adopting a training sample set in advance; the training sample set comprises a plurality of training samples; each training sample comprises historical load flow data of the power system, a historical minimum damping ratio corresponding to the historical load flow data and historical oscillation frequency;
the judging unit is connected with the damping predicting unit and is used for judging whether the current minimum damping ratio is smaller than a damping ratio threshold value or not and whether the current oscillation frequency is larger than a frequency threshold value or not;
a constraint determining unit, connected to the determining unit, configured to determine a constraint condition according to an upper active power limit, a lower active power limit, the current power flow data, a sensitivity of each generator to a damping ratio, the current minimum damping ratio, and the damping ratio threshold of each generator when the current minimum damping ratio is smaller than the damping ratio threshold and the current oscillation frequency is greater than the frequency threshold;
the control model establishing unit is connected with the constraint determining unit and used for establishing a correction control model by taking the minimum active power adjustment quantity of each generator as a target based on the constraint condition;
the solving unit is connected with the control model establishing unit and used for solving the correction control model and determining the active power adjustment quantity of each generator;
and the adjusting unit is connected with the solving unit and is used for adjusting the active power of the corresponding generator according to the active power adjustment quantity of each generator.
In order to achieve the above purpose, the invention also provides the following scheme:
an electronic device includes a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to execute the control method of the power system.
In order to achieve the purpose, the invention also provides the following scheme:
a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the control method of the power system described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the current power flow data of the power system, a width learning network is adopted to predict a current minimum damping ratio and a corresponding current oscillation frequency, the training time of the width learning network is short, the stability is high, the calculation efficiency and the accuracy of the minimum damping ratio and the oscillation frequency are improved, when the current minimum damping ratio is smaller than a damping ratio threshold value and the current oscillation frequency is larger than a frequency threshold value, a correction control model is established based on constraint conditions and with the aim of minimum active power regulating quantity of each generator, the correction control model is solved to determine the active power regulating quantity of each generator, the active power of the corresponding generator is further regulated, and the stability of the power system is improved and is enabled to be always in a safe and stable power grid operation mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method of controlling a power system according to the present invention;
FIG. 2 is a schematic diagram of a web-based interactive system;
fig. 3 is a schematic block diagram of a control system of the power system according to the present invention.
Description of the symbols:
the device comprises a data acquisition unit-1, a damping prediction unit-2, a judgment unit-3, a constraint determination unit-4, a control model establishment unit-5, a solving unit-6 and an adjustment unit-7.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide a control method, a control system, electronic equipment and a storage medium of an electric power system.
For better understanding of the solution of the present invention, first, the technical terms involved in the present invention are introduced:
subsynchronous oscillation: an abnormal situation occurs when the generator set is disturbed at an operating (balancing) point in a special operating state in which significant energy exchange takes place between the power system and the generator set at one or more frequencies below the system synchronization frequency.
And (3) load flow calculation: under a certain operation mode and a certain wiring mode, the power system calculates the voltage, the current magnitude and the current direction and the power distribution situation of the power system from the power supply to the load.
The width learning algorithm: a neural network structure independent of a depth structure has the advantages of excellent operation speed and a simple structure compared with deep learning, and is suitable for real-time analysis.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Example one
As shown in fig. 1, the control method of the power system provided by the present embodiment includes:
s1: and acquiring current power flow data of the power system. The current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node.
S2: and determining the current minimum damping ratio of the power system and the current oscillation frequency corresponding to the current minimum damping ratio based on a prediction model according to the current power flow data. The prediction model is obtained by training a width learning network by adopting a training sample set in advance. The set of training samples includes a plurality of training samples. Each training sample comprises historical power flow data of the power system and historical minimum damping ratios and historical oscillation frequencies corresponding to the historical power flow data.
S3: and judging whether the current minimum damping ratio is smaller than a damping ratio threshold value or not and whether the current oscillation frequency is larger than a frequency threshold value or not.
S4: and if the current minimum damping ratio is smaller than a damping ratio threshold value and the current oscillation frequency is larger than a frequency threshold value, determining a constraint condition according to an active power upper limit, an active power lower limit, the current tide data, the sensitivity of each generator to the damping ratio, the current minimum damping ratio and the damping ratio threshold value.
In the present embodiment, the constraint conditions of the correction control model include a generator power constraint, a minimum damping ratio constraint, and a damping ratio variation constraint. Namely, the constraint conditions of the correction control model are as follows:
wherein the content of the first and second substances,
the lower limit of the active power of the generator i,
is the upper active power limit, PG, of the generator i i For the active power of generator i, Δ PG, in the current power flow data i Adjustment of the active power of generator i, ξ 0 Is the current minimum damping ratio, and delta xi is the variation of the current minimum damping ratio, xi limit Is a damping ratio threshold value, ξ limit =0.03,n is the total number of generators, C i The sensitivity of the generator i to the damping ratio.
In particular, using the formula
The sensitivity of the generator i to the damping ratio is determined. Wherein, Δ ξ i For the active power adjustment of generator i, Δ ξ i Adjusting delta xi for active power of generator i i And then, the amount of change in the minimum damping ratio.
The sensitivity of the generator to the damping ratio is determined by the specific position of the process: after the current minimum damping ratio is obtained, a micro perturbation is carried out on a control variable (the active power of the generator), the perturbed data (the load flow data) are input into a prediction model, the prediction model calculates the corresponding minimum damping ratio, and then the sensitivity of the control variable (the active power of the generator) to the damping ratio is obtained.
Further, the constraint conditions of the correction control model also include system power flow constraints. In this embodiment, the system power flow constraint is the node power equation:
wherein, Δ P j Is the active increment, P, of node j in the power system Gi Active power, P, of generator i in an electric power system Lk Active power, P, for a load k in an electric power system j Injecting active power, Δ Q, for node j in a power system j For reactive increments, Q, of node j in the power system Gi For the reactive power of generator i, Q, in an electric power system Lk For reactive power of load k, Q, in an electric power system j Reactive power is injected for node j in the power system.
S5: and establishing a correction control model by taking the minimum active power adjustment quantity of each generator as a target based on the constraint condition. The calibration control model is aimed at
In this embodiment, the matlab optimization tool box is used to program the generators with the minimum active power adjustment amount as the target based on the constraint conditions, and the generators are presented in the optimization tool box and become the correction control model.
S6: and solving the correction control model to determine the active power adjustment quantity of each generator. In addition, the invention can also determine the voltage adjustment amount of the PV node in the power system and adjust the voltage of the PV node.
S7: and adjusting the active power of the corresponding generator according to the active power adjustment quantity of each generator.
And if the current minimum damping ratio is greater than or equal to the damping ratio threshold value and the current oscillation frequency is less than or equal to the frequency threshold value, determining that the power system has no subsynchronous oscillation risk and does not need to adjust the active power of the generator.
Further, the control method of the power system of the present invention further includes:
s8: and adding the current tidal current data and the current minimum damping ratio and the current oscillation frequency corresponding to the current tidal current data as training samples into a training sample set of the width learning network, and training the width learning network. The method predicts the minimum damping ratio and the oscillation frequency of the power system in real time, updates the training sample set of the width learning network in real time, and improves the stability and the precision of a prediction model.
The method can prevent the subsynchronous oscillation phenomenon possibly occurring in the operation process of the evaluated power system, can predict the operation state of the power grid of the evaluated power system, can obtain the prediction data (the minimum damping ratio and the oscillation frequency) capable of analyzing the subsynchronous oscillation risk of the power system under the condition that the operation mode of the current topological structure of the power grid of the power system is not changed, can automatically calculate the active power adjustment quantity of the generator by adopting the prediction data, enables the analysis and the correction decision of the subsynchronous oscillation risk of the power grid to be simple and easy, shortens the working flow and the making time, lightens the working intensity and the pressure of operators, and comprehensively improves the voltage quality and the safety stability of the operation of the power grid.
Further, the process of establishing the prediction model comprises:
and acquiring a historical tidal stream data report and a historical unit parameter data report of the power system. The unit parameter data report includes transient reactance of the generator, sub-transient reactance, transient time constant, sub-transient time constant, rated power of the generator, power factor of the motor and the like.
And analyzing the historical tide data report by adopting a data interface to determine historical tide data. The obtained data is arranged into a fixed data format, so that the subsequent reading operation is facilitated. Besides, while historical tidal current data is analyzed, node equivalence processing is also carried out on the data (nodes which can be ignored are equivalent according to a required network structure), and the nodes which can be ignored are equivalent to admittance or load.
And carrying out load flow calculation according to the historical load flow data to obtain a load flow calculation result. The power flow calculation result comprises the voltage, the current magnitude and direction and the power distribution situation of the power system from the power supply to the load. The voltage, current and power of each node are obtained.
And determining a historical minimum damping ratio and a historical oscillation frequency corresponding to the historical load flow data according to the load flow calculation result and the historical unit parameter data report. In this embodiment, the differential algebraic equation of the dynamic characteristics of the power system is linearized using the lyapunov linearization theory to obtain a corresponding state matrix. And evaluating the subsynchronous oscillation stability analysis by utilizing the information such as the characteristic value calculated by the state matrix.
Specifically, the model of the entire power system can be mathematically unified into a differential algebraic equation of the general form:
0＝g(x,y)；
where x represents the state variable, y represents the output variable, and f (x, y) and g (x, y) are generally non-linear. A stable operation point, also called a balance point, needs to be found, and the above formula is linearized according to the Lyapunov theorem to obtain the following formula:
where d Δ x/dt is the linearized state vector, Δ x is the linearization control vector, Δ y is the linearized output vector,
all are various linear coefficient matrixes, the values of which are not fixed, and when the stable operation point changes, the stable operation point changes.
After the formula is simplified, the formula can be changed into the following standard form, which is more favorable for analysis:
in the formula (I), the compound is shown in the specification,
the matrix A ∈ R x×x And is often referred to as a state matrix or coefficient matrix.
In this embodiment, the parameters are listed by the network-machine interactive system, and the network-machine interactive system is shown in fig. 2. According to the mathematical equation of each part and the schematic diagram of the machine-network interaction system, the overall model of the power system can be established by utilizing the topological relation of the network and combining the grid coordinate conversion formula, so that the full-state equation of the power system is obtained, and the following formula is shown in the specification:
in the formula (I), the compound is shown in the specification,
X s set of state variables, X, representing shafting in a system G Set of state variables, X, representing the electromagnetic part of the generator in the system E Set of state variables, X, representing the excitation control system in the system P Set of state variables, X, representing motors and speed regulators in the system O Set of state variables representing other generators in the system, X D Set of state variables, X, representing the DC network in the system A Represents the set of state variables of the ac network in the system, and T represents the transpose operation.
And after the data quantity meeting the prediction requirement is obtained, training the width learning network by taking historical tidal current data as input and historical minimum damping ratio and historical oscillation frequency corresponding to the historical tidal current data as labels. And after actual prediction, that is, in step S8, adding the current tidal current data and the current minimum damping ratio and the current oscillation frequency corresponding to the current tidal current data as training samples into a training sample set of the width learning network, that is, adding a data expansion data set in real time.
The invention is actually a preventive adjustment measure for subsynchronous oscillation of the power system, and the method is characterized in that real-time operation data of the current power system are calculated, whether subsynchronous oscillation risks exist in the power system in the current power grid topological structure operation state is analyzed, if the subsynchronous oscillation risks exist in the power system, the control variable or constraint condition of the power system is optimized, the active output, the reactive output and the PV node voltage of the unit of the power system are coordinately controlled, the subsynchronous oscillation of the power system is effectively prevented, and the stability of the power system is further improved.
In order to better understand the solution of the present invention, the following takes an improved 3-machine 9-node power system as an example (in which one generator is replaced by an IEEE first standard model to simulate a sub-synchronous oscillation environment), and further explains the effect of the control method of the power system of the present invention.
The control method of the invention is adopted to carry out real-time subsynchronous oscillation analysis and correction control on the power system. The power system comprises 9 nodes, 9 lines, 3 generators and 3 transformers.
The prediction results of the improved electric power system label set of the 3 machines and 9 nodes are shown in table 1, and the training time of the prediction model is 0.6s. In the estimation of the minimum damping ratio, MAPE (Mean Absolute Percent Error) is within 4%, and the established prediction model can be proved to have a good effect in the estimation aspect of the minimum damping ratio. The smaller the RMSE (Root Mean square Error) index is, the more the stability of the prediction model can be reflected.
TABLE 1
| Predicting variables | MAPE(％) | RMSE(x10^-3) |
| Minimum damping ratio: xi min | 0.072 | 0.875 |
| Oscillation frequency: f. of | 0.051 | 0.645 |
The actual prediction results of the minimum damping ratio and the oscillation frequency of the improved 3-machine 9-node power system are shown in table 2, the oscillation frequency is 27.72Hz, and when P is shown in table 2 G2 (active power of the second generator) is 195.6MW, P G3 (active power of the third generator) at 72.75MW, the resonant frequency of the system of the electrical part is 28.4375Hz, close to the complementary oscillation frequency, with the risk of subsynchronous oscillations. By changing the power values of the two generators, the result of the predicted minimum damping ratio and the corresponding frequency can be obtained, the minimum damping ratio is increased, the corresponding frequency is 19.1555Hz, the system is relatively stable, and the risk of subsynchronous oscillation does not exist.
TABLE 2
| P G2 (MW) | P G3 (MW) | Minimum damping ratio | Corresponding frequency (Hz) |
| 195.6 | 72.75 | -0.832 | 28.4375 |
| 189.08 | 74.8 | -0.795 | 19.1555 |
In conclusion, the minimum damping ratio and the corresponding frequency of the power system can be controlled by reasonably adjusting the active power output of the generator set, so that the power system does not generate subsynchronous oscillation and always keeps a stable operation mode.
The breadth learning algorithm is established in a planar network form, original input is used as mapping characteristics to be transmitted and placed in characteristic nodes, and the structure is widely expanded in enhanced nodes. Specifically, the width learning algorithm is remolded in an incremental mode without integral retraining from the beginning, and the width learning algorithm adopts singular value decomposition to reduce the model, so that the final structure is simplified.
Example two
In order to implement a corresponding method of the above-described embodiments to achieve corresponding functions and technical effects, a control system of an electric power system is provided below.
As shown in fig. 3, the control system of the power system provided in the present embodiment includes: the device comprises a data acquisition unit 1, a damping prediction unit 2, a judgment unit 3, a constraint determination unit 4, a control model establishment unit 5, a solving unit 6 and an adjustment unit 7.
The data acquisition unit 1 is used for acquiring current power flow data of the power system. The current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node.
The damping prediction unit 2 is connected with the data acquisition unit 1, and the damping prediction unit 2 is used for determining the current minimum damping ratio of the power system and the current oscillation frequency corresponding to the current minimum damping ratio according to the current power flow data based on a prediction model. The prediction model is obtained by training a width learning network by adopting a training sample set in advance. The training sample set comprises a plurality of training samples; each training sample comprises historical power flow data of the power system and historical minimum damping ratio and historical oscillation frequency corresponding to the historical power flow data.
The judging unit 3 is connected to the damping predicting unit 2, and the judging unit 3 is configured to judge whether the current minimum damping ratio is smaller than a damping ratio threshold, and whether the current oscillation frequency is greater than a frequency threshold.
And the constraint determining unit 4 is connected with the judging unit 3, and the constraint determining unit 4 is used for determining constraint conditions according to the upper limit of active power, the lower limit of active power, the current tide data, the sensitivity of each generator to the damping ratio, the current minimum damping ratio and the damping ratio threshold value of each generator when the current minimum damping ratio is smaller than the damping ratio threshold value and the current oscillation frequency is greater than the frequency threshold value.
The control model establishing unit 5 is connected to the constraint determining unit 4, and the control model establishing unit 5 is configured to establish a correction control model based on the constraint condition and with a target of minimum active power adjustment amount of each generator.
The solving unit 6 is connected with the control model establishing unit 5, and the solving unit 6 is used for solving the correction control model and determining the active power adjustment quantity of each generator.
The adjusting unit 7 is connected to the solving unit 6, and the adjusting unit 7 is configured to adjust the active power of the corresponding generator according to the active power adjustment amount of each generator.
EXAMPLE III
The embodiment provides an electronic device, which includes a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the control method of the power system in the first embodiment.
Optionally, the electronic device may be a server.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the control method of the power system according to the first embodiment.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A control method of an electric power system, characterized by comprising:
acquiring current power flow data of a power system; the current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node;
determining a current minimum damping ratio of the power system and a current oscillation frequency corresponding to the current minimum damping ratio according to the current power flow data based on a prediction model; the prediction model is obtained by training a width learning network by adopting a training sample set in advance; the training sample set comprises a plurality of training samples; each training sample comprises historical load flow data of the power system and historical minimum damping ratio and historical oscillation frequency corresponding to the historical load flow data;
judging whether the current minimum damping ratio is smaller than a damping ratio threshold value or not and whether the current oscillation frequency is larger than a frequency threshold value or not;
if the current minimum damping ratio is smaller than a damping ratio threshold value and the current oscillation frequency is larger than a frequency threshold value, determining a constraint condition according to an active power upper limit, an active power lower limit, the current tide data, the sensitivity of each generator to the damping ratio, the current minimum damping ratio and the damping ratio threshold value;
based on the constraint conditions, establishing a correction control model by taking the minimum active power adjustment quantity of each generator as a target;
solving the correction control model to determine the active power adjustment quantity of each generator;
and adjusting the active power of the corresponding generator according to the active power adjustment quantity of each generator.
2. The control method of the power system according to claim 1, characterized by further comprising:
and adding the current tidal current data and the current minimum damping ratio and the current oscillation frequency corresponding to the current tidal current data as training samples into a training sample set of the width learning network, and training the width learning network.
3. The control method of the power system according to claim 1, wherein the constraint conditions of the correction control model include a generator power constraint, a minimum damping ratio constraint, and a damping ratio variation constraint.
4. A method of controlling a power system according to claim 3, characterized in that the generator power constraint is:
the minimum damping ratio constraint is: xi 0 +Δξ≥ξ limit ；
The damping ratio variation is constrained as follows:
wherein the content of the first and second substances,
for the lower limit of the active power of the generator i,
is the upper active power limit, PG, of generator i i For the active power of generator i, Δ PG, in the current power flow data i For the active power adjustment of the generator i, xi 0 Is the current minimum damping ratio, and delta xi is the variation of the current minimum damping ratio, xi limit Is a damping ratio threshold value, n is the total number of generators, C i The sensitivity of the generator i to the damping ratio.
5. A control method of an electric power system according to claim 3, characterized in that the constraints of the correction control model further comprise system power flow constraints.
6. The method of controlling a power system according to claim 5, wherein the system power flow constraint is:
wherein, Δ P j Is the active increment, P, of node j in the power system Gi Active power, P, of generator i in an electric power system Lk Active power, P, for load k in an electric power system j Injecting active power, Δ Q, for node j in a power system j For reactive increments, Q, of node j in the power system Gi For the reactive power of generator i, Q, in an electric power system Lk For absence of load k in the power systemWork power, Q j Reactive power is injected for node j in the power system.
7. A control system of an electric power system, characterized by comprising:
the data acquisition unit is used for acquiring current power flow data of the power system; the current power flow data comprises active power of each generator, reactive power of each generator, active power of a load end, reactive power of the load end, active power of a power transmission line, reactive power of the power transmission line, voltage amplitude of each node and voltage phase angle of each node;
the damping prediction unit is connected with the data acquisition unit and used for determining the current minimum damping ratio of the power system and the current oscillation frequency corresponding to the current minimum damping ratio based on a prediction model according to the current power flow data; the prediction model is obtained by adopting a training sample set to train a width learning network in advance; the training sample set comprises a plurality of training samples; each training sample comprises historical load flow data of the power system, a historical minimum damping ratio corresponding to the historical load flow data and historical oscillation frequency;
the judging unit is connected with the damping predicting unit and used for judging whether the current minimum damping ratio is smaller than a damping ratio threshold value or not and whether the current oscillation frequency is larger than a frequency threshold value or not;
a constraint determining unit, connected to the determining unit, configured to determine a constraint condition according to an upper active power limit, a lower active power limit, the current power flow data, a sensitivity of each generator to a damping ratio, the current minimum damping ratio, and the damping ratio threshold of each generator when the current minimum damping ratio is smaller than the damping ratio threshold and the current oscillation frequency is greater than the frequency threshold;
the control model establishing unit is connected with the constraint determining unit and used for establishing a correction control model by taking the minimum active power adjustment quantity of each generator as a target based on the constraint condition;
the solving unit is connected with the control model establishing unit and used for solving the correction control model and determining the active power adjustment quantity of each generator;
and the adjusting unit is connected with the solving unit and is used for adjusting the active power of the corresponding generator according to the active power adjustment quantity of each generator.
8. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to execute the control method of the power system according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements a control method of an electric power system according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116316604A (en) * | 2023-04-07 | 2023-06-23 | 东北电力大学 | Active rescheduling damping lifting method based on local damping sensitivity |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116316604A (en) * | 2023-04-07 | 2023-06-23 | 东北电力大学 | Active rescheduling damping lifting method based on local damping sensitivity |
| CN116316604B (en) * | 2023-04-07 | 2024-04-19 | 东北电力大学 | Active rescheduling damping lifting method based on local damping sensitivity |
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