CN116581759A - Power transmission section active correction control method, system and equipment - Google Patents
Power transmission section active correction control method, system and equipment Download PDFInfo
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Abstract
The invention discloses a method, a system and equipment for controlling active correction of a power transmission section, and relates to the field of power systems; training an artificial intelligent model by utilizing a data set formed by the power of the generator and the transmission power of the line, and determining a power sensitivity calculation model; determining a key transmission section of a power system, setting an expected fault set according to an N-1 principle, and determining a section line power transmission limit; determining power sensitivity according to a section line power transmission limit and a power sensitivity calculation model; and constructing an objective function and constraint conditions, and optimizing and correcting the generator active power by combining the power sensitivity. The invention can eliminate the heavy-load line, balance the section line current and avoid the line overload on the premise of ensuring the section total current control effect.
Description
Technical Field
The invention relates to the field of static safety and stability online evaluation and optimization of power systems, in particular to a method, a system and equipment for controlling active correction of a power transmission section.
Background
Along with the high-speed healthy development of the national economy of China, the electricity consumption demand of each industry is remarkably improved, and the electric power system is developing towards large-scale, large-capacity, ultra-high voltage and long-distance power transmission. Related files of assisting 'carbon peak and carbon neutralization' are released by national power grid companies and southern power grid companies, new energy mainly comprising wind energy and solar energy is greatly developed, a novel power system mainly comprising the new energy is promoted to be built, and a green power system is constructed. In recent years, intelligent power grids, new energy grid-connected alternating current-direct current series-parallel connection and other power grid construction are greatly developed in China, and the conditions of interconnection and intercommunication of regional power grids, access of new energy elements to the power grids and the like cause the trend of complex power grid structures, and the large-scale power grids can improve the operation efficiency of a power system, but the structure of the system becomes more and more complex, so that a plurality of potential safety hazards are brought to the system. There are a large number of parallel transmission channels in the grid, and cascading overload tripping of the transmission line is easy to occur in the parallel transmission channels with the same power supply area or load area as the overload line. The major power failure accident of the power system mostly follows the same evolution mode, namely, the cross-regional power transmission section breaks down, so that the power flow is transferred in a large range, the cascading overload trip is caused, and finally, the large-area power failure is caused. This is well established by a series of blackout incidents such as the 2019 uk "8.9" blackout, 2021 us dezhou "2.15" blackout. Therefore, the safety protection of the power transmission section becomes the core and the key point of the novel system protection research.
The transmission section is an important concept in the safety and stability analysis of the power system, and generally refers to a group of transmission lines with tight connection among power grid areas and the same tide direction. The N-1 static safety constraint is mainly expressed in that when any line in the power transmission section is disconnected, other lines are not overloaded. When overload occurs on a line in the power system and the line is cut off, internal cascading failure of a power transmission section can be caused by power flow redistribution, and system safety risks are increased. In order to ensure safe and stable operation of the power transmission section, the power transmission section should be corrected safely in time, and overload of the line is eliminated. The active power flow correction control of the power transmission section mainly prevents overload of a power transmission line and a tie line group by adjusting the active power output of a generator, and is an important prevention control means in actual power grid operation.
The sensitivity index can be used for identifying key factors influencing the profile power flow distribution and providing very valuable power flow control guidance information for the dispatcher, so that the active safety correction problem can be solved by adopting a method based on sensitivity analysis. The method generally takes the minimum regulating quantity or the power generation cost output by the generator as an objective function, then calculates the sensitivity relation between the line power and the generator on the premise of meeting the specified constraint condition, and determines the reassigned control variable according to the constraint condition of the branch if the control variable is out of range in the process of adjusting the active output of each generator until the out of limit is eliminated.
With the development of artificial intelligence technology, more and more novel algorithms are proposed and applied to many fields of power systems, and machine learning algorithms represented by support vector machines and neural networks are widely applied at present. Machine learning refers to learning from massive data by utilizing an algorithm, finding out rules in the rules, and predicting a development trend of a period of time in the future through the data. The vector measurement unit (PMU) can acquire the active power and the reactive power of the load, and the electric quantity such as the active power and the reactive power output by the generator, thereby providing reliable data support for the safety and stability of a remote control power system. The machine learning algorithm can obtain potential rules and characteristics of power grid operation after data are mined and processed, and the power grid operation is dynamically monitored, so that the machine learning algorithm can be used for calculating sensitivity, and active safety correction of a power transmission section is realized.
However, the above-mentioned technique cannot overcome the problems that the existing control method needs to additionally select other parameters for correction and is easy to have limited adjustment, and cannot realize that the sensitivity is calculated more simply and rapidly when the sensitivity is used for realizing the control of the power flow of the section branch.
Disclosure of Invention
The invention aims to provide a method, a system and equipment for controlling active correction of a power transmission section, which can eliminate a heavy-load line, balance the power flow of the section line and avoid overload of the line on the premise of ensuring the total power flow control effect of the section.
In order to achieve the above object, the present invention provides the following solutions:
a power transmission section active correction control method comprises the following steps:
acquiring operation data of the power system in various operation modes; the operation data includes: amplitude and phase angle of the voltage of the PQ node, reactive power and phase angle of the PV node, active power and reactive power of the load node, and voltage and phase angle of the balance node;
carrying out power flow calculation on the operation data, and determining line transmission power between each branch of the power system in different operation modes;
training an artificial intelligent model by utilizing a data set formed by the power of the generator and the transmission power of the line, and determining a power sensitivity calculation model;
acquiring real-time operation data of the power system, determining a key power transmission section of the power system based on a method of consistent power flow characteristics of a power transmission channel, setting an expected fault set according to an N-1 principle, and determining a section line power transmission limit;
determining power sensitivity according to a section line power transmission limit and a power sensitivity calculation model;
and taking the minimum total amount of the regulated active power or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition, and carrying out optimization and correction on the active power of the generator by combining the power sensitivity.
Optionally, the calculating the power flow of the operation data to determine the line transmission power between each branch of the power system in different operation modes specifically includes:
and carrying out load flow calculation on the operation data by adopting a load flow calculation program in Python.
Optionally, the artificial intelligence model includes: BP neural network algorithm.
Optionally, when the adjusted total amount of active force is the minimum as the objective function, the objective function is:
the constraint conditions are as follows:
V i,min ≤V i ≤V i,max i∈S G ；
P Gi,min ≤P Gi ≤P Gi,max i∈S G ；
P Lj,min ≤P Lj ≤P Lj,max i∈S L ；
P ij,min ≤P ij ≤P ij,max i∈S L ；
wherein f (x) is an objective function, n is the total number of system generators, V i For the amplitude of the i-th node voltage, V j Is the amplitude of the voltage of the j-th node, Y ij The element amplitude values of the node admittance matrix; delta ij ＝δ i -δ j -α ij ，δ i The phase angle of the voltage of the ith node; delta j The phase angle of the voltage of the j node; alpha ij The element phase angle of the node admittance matrix; s is S N P is a system node set Gi For the active power of the generator, V i,max An upper limit for the magnitude of the i-th node voltage; v (V) i,min Is the lower limit of the amplitude of the i-th node voltage, P Gi,min ，P Gi,max Respectively the upper limit and the lower limit of the active power of the generator, P Gi For the active power of the generator, P Lj,min ，P Lj,max Respectively upper and lower limits of active power of load nodes, P Lj For the active power of the load node, P ij For line power, P ij,min ，P ij,max Respectively the upper limit and the lower limit of the line power, P l 0 ，P l Respectively correcting the active power of the lines in the front section and the rear section, P lmax For the maximum power flow allowed by the section during normal operation of the system S m Is a section power transmission line set,for the active power increase of the adjustable generator, < >>For the active reduction of the adjustable generator, P Di As active power of load, Q Gi Is the active power of the generator, Q Di Is the reactive power of the load, S G Is a generator set, S L For line collection, C 1 、C 2 、C 3 ...C n For power sensitivity, ΔP 1 、△P 2 、△P 3 ..△P n Is the variation of the generator power.
A power transmission section active correction control system comprising:
the operation data acquisition module is used for acquiring operation data of the power system in various operation modes; the operation data includes: amplitude and phase angle of the voltage of the PQ node, reactive power and phase angle of the PV node, active power and reactive power of the load node, and voltage and phase angle of the balance node;
the power flow calculation module is used for carrying out power flow calculation on the operation data and determining the line transmission power between each branch of the power system in different operation modes;
the power sensitivity calculation model determining module is used for training an artificial intelligent model by utilizing a data set formed by the power of the generator and the transmission power of the line to determine a power sensitivity calculation model;
the power sensitivity determining module is used for acquiring real-time operation data of the power system, determining a key transmission section of the power system based on a method of the power flow consistency characteristic of the transmission channel, setting an expected fault set according to the N-1 principle, and determining the power transmission limit of a section line; determining power sensitivity according to a section line power transmission limit and a power sensitivity calculation model;
and the optimization correction module is used for optimizing and correcting the generator power by taking the minimum total active output or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition and combining the power sensitivity.
A transmission section active power correction control apparatus comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method.
Optionally, the memory is a computer readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a power transmission section active correction control method, a system and equipment, which are used for constructing power sensitivity between system line power and generator active output and determining a generator most suitable for correcting section active by using the power sensitivity. Aiming at the active safety correction of the power transmission section, presetting the active overload condition of the internal circuit of the section, using the minimum total active adjustment quantity of the generator or the minimum power generation cost as an objective function, restraining the active power flow of the overload circuit of the power transmission section in the constraint condition, reflecting the specific constraint condition for eliminating the section out-of-limit, estimating the power approximation sensitivity according to an artificial intelligent model, establishing an optimal active modulation model of the generator, determining an active output correction scheme of the generator set, and finally realizing the elimination of the active overload of the section circuit. The invention solves the problems that the existing control method needs to additionally select other parameters for correction and is easy to have limited adjustment, and can calculate the sensitivity more simply, conveniently and rapidly when the sensitivity is utilized to realize the control of the power flow of the section branch. The invention eliminates the heavy load line, balances the section line current and avoids the line overload on the premise of ensuring the section total current control effect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed 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 other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for controlling active correction of a power transmission section according to the present invention;
fig. 2 is a schematic diagram of active correction control of a power transmission section under the safety constraint of the power system N-1 according to the present invention;
FIG. 3 is a graph of the performance analysis of the BP neural network algorithm according to the present invention;
FIG. 4 is a cross-sectional view of a system architecture topology and cutset of the IEEE-39 embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method, a system and equipment for controlling active correction of a power transmission section, which can eliminate a heavy-load line, balance the power flow of the section line and avoid overload of the line on the premise of ensuring the total power flow control effect of the section.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for controlling active correction of a power transmission section provided by the invention comprises the following steps:
s101, acquiring operation data of an electric power system in various operation modes; the operation data includes: the magnitude and phase angle of the PQ node voltage, the reactive power and phase angle of the PV node, the active power and reactive power of the load node, the voltage and phase angle of the balancing node.
S102, carrying out load flow calculation on the operation data, and determining line transmission power between each branch of the power system in different operation modes; and carrying out load flow calculation on the operation data by adopting a load flow calculation program in Python.
The power flow calculation refers to calculating the voltage, the current and the direction of the power system from a power source to a load and the distribution condition of the power under a certain operation mode and a certain wiring mode of the power system. The power flow constraint refers to a power flow which takes static stability as a constraint under the constraint condition of the power flow.
S103, training an artificial intelligent model by utilizing a data set formed by the active power of the generator and the transmission power of the line, and determining a power sensitivity calculation model; the artificial intelligence model includes, but is not limited to: BP neural network algorithm.
S104, acquiring real-time operation data of the power system, determining a key transmission section of the power system based on a method of consistent power flow characteristics of a transmission channel, setting an expected fault set according to an N-1 principle, and determining a section line power transmission limit; the N-1 principle is that any line in the group of connecting lines is disconnected below the total active limit value of the given connecting line, and the flow of the rest lines is not overloaded.
S105, determining the power sensitivity according to the section line power transmission limit and the power sensitivity calculation model.
S106, taking the minimum total amount of the regulated active power or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition, and carrying out optimization and correction on the active power of the generator by combining the power sensitivity.
1) Assuming k sets of data in the sample, the load power and generator power are taken as input variables x, and the system line active power is taken as output variable y.
x＝[PL i QL i PG i QG i ]。
y＝[P ij ]。
Wherein: PL (PL) i ，QL i Active power and reactive power for each load; PG i ，QG i Active power and reactive power exist for each generator; p (P) ij For the line between nodes i, jActive power.
2) Sensitivity C of generator x active power to damping ratio x By the variation Δp of the active power x And the variation delta zeta of the corresponding damping ratio x And (5) calculating to obtain the product.
Wherein: ΔP x The active power adjustment quantity corresponding to the generator x; n is the total number of system generators; ΔP x Is sized to meet the upper and lower limit constraints of the generator.
3) And (5) a power sensitivity coefficient equation corresponding to each generator of the system.
ΔP ij ＝C 1 ΔP 1 +C 2 ΔP 2 +…+C n ΔP n 。
Wherein: ΔP ij C is the variation of the active power of the line 1 、C 2 、C 3 ...C n For power sensitivity, ΔP 1 、△P2、△P 3 ..△P n Is the variation of the generator power.
4) When the minimum amount of active power output is adjusted as an objective function, the objective function is:
constraints include equality constraints and inequality constraints.
Equation constraint: and (5) a tide equation of each node in the network.
Inequality constraints are network physical and operational constraints, including:
1. node voltage limits, the value of which is related to voltage level, type of node, area or node normal or emergency conditions.
V i,min ≤V i ≤V i,max i∈S G 。
2. And the unit output limit comprises an active output limit and a reactive output limit.
P Gi,min ≤P Gi ≤P Gi,max i∈S G 。
P Lj,min ≤P Lj ≤P Lj,max i∈S L 。
3. Line flow constraints.
P ij,min ≤P ij ≤P ij,max i∈S L 。
4. And (5) constraint of the power transmission section.
Wherein f (x) is an objective function, n is the total number of system generators, V i For the amplitude of the i-th node voltage, V j Is the amplitude of the voltage of the j-th node, Y ij The element amplitude values of the node admittance matrix; delta ij ＝δ i -δ j -α ij ，δ i The phase angle of the voltage of the ith node; delta j The phase angle of the voltage of the j node; alpha ij The element phase angle of the node admittance matrix; s is S N P is a system node set Gi For the active power of the generator, V i,max An upper limit for the magnitude of the i-th node voltage; v (V) i,min Is the lower limit of the amplitude of the i-th node voltage, P Gi,min ，P Gi,max Respectively the upper limit and the lower limit of the active power of the generator, P Gi For the active power of the generator, P Lj,min ，P Lj,max Respectively upper and lower limits of active power of load nodes, P Lj For the active power of the load node, P ij For line power, P ij,min ，P ij,max Respectively the upper limit and the lower limit of the line power, P l 0 ，P l Respectively correcting the active power of the lines in the front section and the rear section, P lmax For allowing passage of the section during normal operation of the systemMaximum tide, S m Is a section power transmission line set,for the active power increase of the adjustable generator, < >>For the active reduction of the adjustable generator, P Di As active power of load, Q Gi Is the active power of the generator, Q Di Is the reactive power of the load, S G Is a generator set, S L For line collection, C 1 、C 2 、C 3 ...C n For power sensitivity, ΔP 1 、△P2、△P 3 ..△P n Is the variation of the generator power.
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples: the method for controlling the active correction of the power transmission section under the N-1 safety constraint based on data driving is shown by referring to FIG. 2, and comprises the following steps:
step one, acquiring operation data of a system in various operation modes, wherein the operation data comprise active power, reactive power, voltage amplitude and voltage phase angle of each node of the system:
and step two, calculating real-time data by using a tide analysis program, so as to obtain the transmission power of each line of the system in different operation modes, and forming a data set.
And thirdly, forming the acquired data into an artificial intelligent model data set for training. And training the BP neural network model by using the calculated transmission power data of each line of the system to obtain the relation between the input and the output in the data set, and adjusting the parameters of the model to ensure that the model evaluation achieves a better effect, and is shown in figure 3.
And step four, determining a power transmission section of the system, setting an expected accident set and inputting the transmission limit of the section.
Step five, under the determined operation mode, judging whether any line in the section is overloaded after any line is disconnected; if the line is overloaded, a calibration control procedure is started.
Step six, starting a correction control program, calculating power sensitivity by adopting an artificial intelligence algorithm, optimizing and adjusting the active power change quantity of the generator, wherein an objective function is that the total quantity of the adjusted active power is minimum, constraint conditions comprise section active constraint, eliminating line overload and simultaneously ensuring that no new overload exists.
And step seven, checking the corrected calculation result file, outputting a calculation result report, wherein the result can ensure that the power system to be evaluated is in a safe and stable operation mode, and the power grid operation mode is the safe and stable operation limit of the power system to be evaluated.
Taking IEEE-39 node system as an example, the system is subjected to real-time low-frequency oscillation operation analysis and optimal correction control. The analysis steps are as follows:
s1, the system is provided with 39 buses, 10 generators and 46 transmission lines.
S2, acquiring operation data such as output power, load power output and the like of a generator of the system.
S3, enabling the system load to randomly fluctuate between 70% and 130%, enabling the generator to output active power and reactive power to fluctuate by 30%, guaranteeing the power balance of the system, carrying out load flow calculation by utilizing a load flow program, generating a large amount of original data, and forming a data set.
And S4, training the BP neural network prediction model by using a large amount of generated original data, and adjusting parameters of the model to enable the model evaluation to achieve a good effect, so that the relation between the output of the generator and the power of the line is obtained, and the power sensitivity can be estimated more accurately.
S5, under a certain operation mode, judging the condition of line power after any line in the section is disconnected, and if the line is overloaded, starting a correction control program.
S6, establishing a correction control optimization model, perturbing the active output of the system-controllable generator, calculating the power sensitivity, and optimizing the active change of the generator.
And S7, checking the corrected calculation result file, and outputting a calculation result report, wherein the result can ensure that the power system to be evaluated is in a safe and stable operation mode, and the power grid operation mode is the safe and stable operation limit of the power system to be evaluated.
The following is an example: in the operation of the IEEE-39 node system, the method has obvious active safety and correction control effects on the calculated power system.
The patent adopts BP neural network to construct power sensitivity calculation model. The accuracy of the prediction results was expressed using MAPE and RMSE as errors for the fitting, and the prediction cases are shown in Table 1 and FIG. 4 below.
TABLE 1
The system topology and cut-set cross-section are shown in figure 4 of the drawings, and branches 25-02, 17-18, 14-04, 11-06 are selected as cross-sections. The given initial mode of operation does not satisfy the N-1 check of static active safety. In the active safety correction of the power transmission section considering the N-1 principle, the purpose is to quickly take active correction measures to reduce the flow of an overload branch after identifying that a new overload possibly occurs in a branch formed in the power transmission section, prevent the occurrence of interlocking overload tripping, and obtain the sensitivity value of each generator set to the branch through an artificial intelligent model. The damping ratio sensitivity results of the generators estimated through the BP neural network model are shown in table 2, wherein the G1 unit is set as a balance unit of the system and does not participate in active modulation, and the sensitivity is 0.
TABLE 2
And then, obtaining the active adjustment quantity of the generator according to the estimated sensitivity, and performing system optimization correction control. The process and results of the generator active modulation are shown in table 3.
The initial condition of the section power flow when the failure mode N-1 of the check is not satisfied and the power flow distribution after the section active correction control are shown in the following table.
The above examples illustrate: on the premise of a certain network structure, when the power flow of the power transmission section cannot meet N1 static safety constraint, the active output of the generator set can be adjusted in order to improve the power transmission capacity of the section. The method eliminates the heavy load line, balances the section line current and avoids line overload on the premise of ensuring the section total current control effect. The scheme can be established by adopting a scheme of generating a real-time control strategy by adopting data driving according to specific input data conditions.
Corresponding to the method, the invention also provides a power transmission section active correction control system, which comprises the following steps:
the operation data acquisition module is used for acquiring operation data of the power system in various operation modes; the operation data includes: the magnitude and phase angle of the PQ node voltage, the reactive power and phase angle of the PV node, the active power and reactive power of the load node, the voltage and phase angle of the balancing node.
And the power flow calculation module is used for carrying out power flow calculation on the operation data and determining the line transmission power between each branch of the power system under different operation modes.
And the power sensitivity calculation model determining module is used for training an artificial intelligent model by utilizing a data set formed by the generator active power and the line transmission power to determine a power sensitivity calculation model.
The power sensitivity determining module is used for acquiring real-time operation data of the power system, determining a key transmission section of the power system based on a method of the power flow consistency characteristic of the transmission channel, setting an expected fault set according to the N-1 principle, and determining the power transmission limit of a section line; and determining the power sensitivity according to the section line power transmission limit and the power sensitivity calculation model.
And the optimization correction module is used for optimizing and correcting the generator power by taking the minimum total active output or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition and combining the power sensitivity.
In order to execute the method corresponding to the embodiment to realize the corresponding functions and technical effects, the invention also provides a power transmission section active correction control device, which comprises: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method.
The memory is a computer-readable storage medium.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can prevent the line overload condition possibly occurring in the operation process of the power system to be evaluated, can perform stable optimization calculation on the power grid of the power system to be evaluated, can obtain power grid operation mode optimization data required by static safe and stable operation of the power system to be evaluated through a section active correction calculation program in any operation mode, can perform automatic calculation and verification by using the power grid operation mode optimization data, and can be directly used for guiding the establishment of the safe and stable power grid operation mode of the power system to be evaluated so as to ensure the safe, stable and reliable operation of the power grid. And constructing the safety protection of the power transmission section, and when overload occurs to the branch, observing the influence on the parallel power transmission section of the overload branch after tripping by the safety protection of the power transmission section, so that the interlocking tripping can be avoided.
2. On the premise that the real-time operation data of the current power grid are very reliable, the calculated power flow after the operation monitoring and control circuit is opened and closed can obviously improve the section conveying capacity limited by the N-1 static safety constraint, and has important significance for improving the power exchange capacity between areas and comprehensively improving the safety and stability of the operation of the power grid.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (7)
1. The power transmission section active correction control method is characterized by comprising the following steps of:
acquiring operation data of the power system in various operation modes; the operation data includes: amplitude and phase angle of the voltage of the PQ node, reactive power and phase angle of the PV node, active power and reactive power of the load node, and voltage and phase angle of the balance node;
carrying out power flow calculation on the operation data, and determining line transmission power between each branch of the power system in different operation modes;
training an artificial intelligent model by utilizing a data set formed by the power of the generator and the transmission power of the line, and determining a power sensitivity calculation model;
acquiring real-time operation data of the power system, determining a key power transmission section of the power system based on a method of consistent power flow characteristics of a power transmission channel, setting an expected fault set according to an N-1 principle, and determining a section line power transmission limit;
determining power sensitivity according to a section line power transmission limit and a power sensitivity calculation model;
and taking the minimum total amount of the regulated active power or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition, and carrying out optimization and correction on the active power of the generator by combining the power sensitivity.
2. The method for controlling active power correction of a power transmission section according to claim 1, wherein the step of calculating the power flow of the operation data to determine the line transmission power between each branch of the power system in different operation modes specifically comprises:
and carrying out load flow calculation on the operation data by adopting a load flow calculation program in Python.
3. The method for controlling active power transmission section correction according to claim 1, wherein the artificial intelligence model comprises: BP neural network algorithm.
4. The power transmission section active correction control method according to claim 1, wherein when the adjusted total amount of active output force is taken as an objective function, the objective function is:
the constraint conditions are as follows:
V i,min ≤V i ≤V i,max i∈S G ；
P Gi,min ≤P Gi ≤P Gi,max i∈S G ；
P Lj,min ≤P Lj ≤P Lj,max i∈S L ；
P ij,min ≤P ij ≤P ij,max i∈S L ；
wherein f (x) is an objective function, n is the total number of system generators, V i For the amplitude of the i-th node voltage, V j Is the amplitude of the voltage of the j-th node, Y ij The element amplitude values of the node admittance matrix; delta ij ＝δ i -δ j -α ij ，δ i The phase angle of the voltage of the ith node; delta j The phase angle of the voltage of the j node; alpha ij The element phase angle of the node admittance matrix; s is S N P is a system node set Gi For the active power of the generator, V i,max An upper limit for the magnitude of the i-th node voltage; v (V) i,min Is the lower limit of the amplitude of the i-th node voltage, P Gi,min ，P Gi,max Respectively the upper limit and the lower limit of the active power of the generator, P Gi For the active power of the generator, P Lj,min ，P Lj,max Respectively upper and lower limits of active power of load nodes, P Lj For the active power of the load node, P ij For line power, P ij,min ，P ij,max Respectively the upper limit and the lower limit of the line power, P l 0 ，P l Respectively correcting the active power of the lines in the front section and the rear section, P lmax For the maximum power flow allowed by the section during normal operation of the system S m Is a section power transmission line set,for the active power increase of the adjustable generator, < >>For adjustable power generationActive reduction of machine, P Di As active power of load, Q Gi Is the active power of the generator, Q Di Is the reactive power of the load, S G Is a generator set, S L For line collection, C 1 、C 2 、C 3 ...C n For power sensitivity, ΔP 1 、△P2、△P 3 ..△P n Is the variation of the generator power.
5. An active correction control system for a power transmission section, comprising:
the operation data acquisition module is used for acquiring operation data of the power system in various operation modes; the operation data includes: amplitude and phase angle of the voltage of the PQ node, reactive power and phase angle of the PV node, active power and reactive power of the load node, and voltage and phase angle of the balance node;
the power flow calculation module is used for carrying out power flow calculation on the operation data and determining the line transmission power between each branch of the power system in different operation modes;
the power sensitivity calculation model determining module is used for training an artificial intelligent model by utilizing a data set formed by the power of the generator and the transmission power of the line to determine a power sensitivity calculation model;
the power sensitivity determining module is used for acquiring real-time operation data of the power system, determining a key transmission section of the power system based on a method of the power flow consistency characteristic of the transmission channel, setting an expected fault set according to the N-1 principle, and determining the power transmission limit of a section line; determining power sensitivity according to a section line power transmission limit and a power sensitivity calculation model;
and the optimization correction module is used for optimizing and correcting the generator power by taking the minimum total active output or the minimum power generation cost as an objective function, taking the upper limit and the lower limit of the active power and the reactive power of the generator and the upper limit and the lower limit of the node voltage, taking the section active constraint as a constraint condition and combining the power sensitivity.
6. An active power transmission section correction control apparatus, characterized by comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of any one of claims 1-4.
7. The transmission section active power correction control apparatus according to claim 6, wherein the memory is a computer-readable storage medium.
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