CN115986770A - Multi-dimensional transient safety-related cascading failure emergency control method and device - Google Patents
Multi-dimensional transient safety-related cascading failure emergency control method and device Download PDFInfo
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Abstract
The invention provides a cascading failure emergency control method and device considering multi-dimensional transient safety. The method comprises the following steps: the method comprises the steps of establishing a dynamic simulation model of the power system by obtaining electrical parameters of the power system and an expected propagation path of the power system when a cascading failure occurs, calculating states of the power system in different failure stages, establishing a constraint model considering multi-dimensional transient safety, generating a state space and an action space of the power system according to the dynamic model and the constraint model, forming a state-action combination, and finally, efficiently and accurately obtaining an emergency control result of the cascading failure by calculating and comparing system costs corresponding to the state-action combination, and meanwhile guaranteeing transient safety and steady-state safety of the power system when an emergency control strategy process is executed.
Description
Technical Field
The invention relates to the technical field of power system dispatching automation, in particular to a cascading failure emergency control method and device considering multi-dimensional transient safety.
Background
In an electric power system, a cascading failure is usually an event caused by a simple fault, which causes a series of component shutdown events due to power flow transfer, system instability and the like, and may eventually cause a large-scale power failure event. In recent years, with the improvement of grid-connected capacity of renewable energy sources in a power system, stability margin of the system is gradually insufficient, and the risk of cascading failure is increased. Therefore, during the operation of the system, it is necessary to make an emergency control strategy for cascading failures in consideration of the operation safety requirements of the system at each stage of the expected high-risk cascading failure path, and take control measures such as cutting machine and load shedding to reduce the risk of accidents.
At present, most of emergency control strategies study overload-leading cascading failures, and mainly consider that the system power flow transfer causes the power flow overload of a line, so that the outage probability of the line is changed, the fault evolution mechanism of the line failure is caused, and the voltage of the system and the power flow safety of the line are ensured after emergency control measures are executed. However, overload-dominated cascading failures consider only steady-state safety and ignore transient safety, which may face severe transient safety issues in the implementation of emergency control schemes. Because the transient analysis model of the power system is essentially characterized by adopting a complex high-dimensional differential algebraic equation system and is complex to solve, the power system cascading failure emergency control research considering the transient safety is rare.
Disclosure of Invention
The embodiment of the invention provides a power cascading failure emergency control method and device considering multi-dimensional transient safety, and aims to solve the problem that in the prior art, a transient analysis model of a power system is complex to solve.
In a first aspect, an embodiment of the present invention provides a cascading failure emergency control method considering multi-dimensional transient safety, including:
acquiring electrical parameters of each element in a power system and an expected propagation path when the power system has a cascading failure;
constructing a dynamic model of the power system, and carrying out safety simulation on the power system;
constructing a constraint model considering multi-dimensional transient security;
generating a state space of the power system from the dynamic model based on the electrical parameters and the anticipated propagation paths;
generating an action space of the power system according to the constraint model;
and constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system.
As an embodiment of the present application, the building a dynamic model of the power system and performing safety simulation on the power system includes:
establishing a motion equation simulation model of a generator rotor of the power system:
wherein M is i Representing the time constant of inertia, ω i Representing angular speed of rotor movement, D, of the i-th generator i Denotes the damping coefficient, P m Representing the mechanical power of the generator, P m,i The machine of the ith generator is representedMechanical power, P g Representing the electromagnetic power of the generator, P g,i Then represents the electromagnetic power of the i-th generator, r G Representing the total number of generators;
establishing a power angle equation simulation model of a generator of the power system:
wherein, delta i Represents the power angle of the ith generator, f 0 Represents a system rated frequency;
establishing an excitation winding equation simulation model of a generator of the power system:
wherein, E q ′ ,i Representing the transient potential of the ith generator, X d,i Denotes d-axis reactance, T, of the ith generator d ′ 0,i Representing the d-axis transient time constant, X, of the ith generator d ′ ,i Representing d-axis transient reactance, V, of the i-th generator i Representing the voltage at the corresponding node of the i-th generator, E fd,i Represents the excitation potential of the ith generator;
establishing a speed regulator-prime mover equation simulation model of a generator of the power system:
wherein, mu Gi Denotes the regulation factor of the ith generator, s denotes the Laplace operator, F HPi Represents the equivalent high-pressure cylinder power coefficient, T, of the ith generator Ri Represents a reheat time constant, f, of the ith generator i Representing the frequency of the corresponding node of the ith generator;
and according to the motion equation simulation model of the generator rotor, the power angle equation simulation model of the generator, the excitation winding equation simulation model of the generator and the speed regulator-prime mover equation simulation model of the generator, constructing a dynamic model of the power system and carrying out safety simulation on the power system.
As another embodiment of the present application, the constructing a constraint model considering multi-dimensional transient security includes:
establishing a node voltage transient safety constraint model in the constraint model:
V s min ≤V s k ≤V s max ,s＝1,2,...,N bus ,k＝1,2,...,m
wherein, V s k Voltage amplitude, V, of the kth node of the desired propagation path s min Is the minimum value of the voltage at the s-th node, V s max Is the maximum value of the voltage at the s-th node, N bus Representing the total number of corresponding nodes and load nodes of the generator, and m representing the number of phases of the expected propagation path;
establishing a node frequency transient safety constraint model in the constraint model:
wherein, roCoF s k Representing the frequency change rate of the system frequency safety index of the s-th node in the k-th stage, roCoF max A safety threshold value corresponding to the frequency change rate of the system frequency safety index,
frequency lowest point of system frequency safety index in the kth stage represented by the s node, f nadir,min A safety threshold corresponding to a frequency minimum representing the system frequency safety index, based on a predetermined threshold value>
Represents the system frequency of the s-th node in the k-th stageQuasi-steady-state frequency value of rate safety index, f ∞,min A safety threshold value corresponding to a quasi-steady-state frequency value representing the system frequency safety index;
establishing a generator power angle transient state safety constraint model in the constraint model:
wherein, the first and the second end of the pipe are connected with each other,
for the transient power angle, delta, of the ith generator in the kth phase min Is the minimum safety threshold, delta, of the power angle of the ith generator max A maximum safety threshold value for the power angle of the i-th generator>
The inertia center of the power angle of all the generators in the power system in the k stage is obtained.
As another embodiment of the present application, the generating a state space of the power system according to the dynamic model calculation based on the electrical parameter and the expected propagation path includes:
calculating the state of each element in the power system according to the dynamic model based on the electrical parameters;
calculating the states of the elements in the power system at different stages in a mode of calculating the states of the elements in the power system based on the expected propagation path;
according to
Generating a state space of the power system;
wherein s is k For the state space of the power system in the k-th phase,
indicating the ith generator in the kth stageActive power output increment>
Represents the active power output reduction of the i-th generator in the k-th stage, based on the comparison result>
The active load shedding amount, which is greater or less than the active load shedding amount of the ith load node in the kth stage>
Indicating the reactive power contribution increase of the ith generator in the kth stage,
represents the amount of reduction of the reactive power output of the i-th generator in the k-th phase, based on the comparison result>
Representing the reactive load shedding amount, r, of the ith load node in the kth stage L Representing the total number of load nodes.
As another embodiment of the present application, the generating an action space of the power system according to the constraint model includes:
discretizing the states of the elements at different stages according to the constraint model to form corresponding actions of the elements at different stages;
according to
Generating an action space corresponding to a state space of the power system;
wherein, a k Shows the operation of each element of the power system at the k-th stage, A k Representing the motion space of the power system in the k-th phase,
indicates the active power take-up action of the i-th generator in the k-th stage>
Indicating an active power take-off action in the kth stage for the ith generator>
Indicates that the ith load node has an active load shedding action, based on the status of the load node at the kth stage>
Indicates the reactive power increasing action of the i-th generator in the k-th stage>
Indicates that the i-th generator has a reactive power reducing action in the k-th phase>
The method shows the reactive load shedding action of the ith load node in the kth stage.
As another embodiment of the present application, the constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system includes:
selecting a first state of the power system in the state space, and matching the first state with the action in the action space corresponding to the first state to obtain a set formed by state-action combinations;
calculating the system income corresponding to the state-action combination in the set;
selecting an optimal action corresponding to the first state according to the system income corresponding to the state-action combination in the set;
calculating the system cost of the optimal action corresponding to the first state, and detecting whether the system cost reaches a training target;
if the training target is reached, outputting the optimal action corresponding to the first state to obtain a cascading failure emergency control result corresponding to the power system in the first state;
and if the training target is not reached, skipping to the step of selecting the optimal action corresponding to the first state according to the system income corresponding to the state-action combination in the set and executing the subsequent steps.
As another embodiment of the present application, the calculating the system benefit corresponding to the state-action combination in the set includes:
according to
Calculating the reward value corresponding to the state-action combination;
wherein(s) k ,a k ) Represents the corresponding state-action combination, r, of the power system in the k stage k (s k ,a k ) Representing the reward value, p, corresponding to said state-action combination n Representing the probability of the predicted propagation path entering the nth stage, alpha representing the adjustment cost of the generator, beta representing the adjustment cost of the load node, gamma representing a penalty factor, N X Indicating that the power system performs action a k Last state s k ' a number of constraints not satisfied in the constraint model;
according to Q(s) k ,a k )＝(1-λ)Q(s k ,a k )+λ[r k (s k ,a k )+ηmaxQ(s k′ ,a k′ )]Calculating the system income corresponding to the state-action combination;
wherein, Q(s) k ,a k ) Representing the system benefits corresponding to said state-action combinations, a k′ Is in a state s k′ Where λ represents a learning parameter, and η represents a conversion coefficient.
As another embodiment of the present application, the selecting an optimal action corresponding to the first state according to a system benefit corresponding to a state-action combination in the set includes:
generating an action sequence corresponding to the maximum value of the system profit based on the system profit corresponding to the state-action combination;
sampling the motion sequence to form a low difference motion sequence:
Φ＝[x 1 ,x 2 ,x 3 ,......,x t ]
where Φ represents a low difference sequence consisting of a plurality of actions, x i Is the ith sample value of the low difference sequence, and t represents the action number obtained by sampling;
according to
Selecting an optimal action in the sequence of low disparity actions;
wherein p is xk Indicating selection of sample value x in the low disparity motion sequence k Represents a parameter of said greedy strategy.
As another embodiment of the present application, the calculating a system cost of an optimal action corresponding to the first state, and detecting whether the system cost reaches a training target includes:
according to
Calculating the system cost of the optimal action corresponding to the first state;
wherein, H represents the system cost corresponding to the first state-action combination;
according to
Detecting whether the system cost reaches a training target;
where minH represents the minimum value of the system cost corresponding to the first state-action combination.
In a second aspect, an embodiment of the present invention provides a cascading failure emergency control device considering multi-dimensional transient safety, including:
the data acquisition module is used for acquiring the electrical parameters of each element in the power system and an expected propagation path when the power system has cascading failure;
the safety simulation module is used for constructing a dynamic model of the power system and carrying out safety simulation on the power system;
the constraint module is used for constructing a constraint model considering multi-dimensional transient security;
a state space generation module for generating a state space of the power system from the dynamic model based on the electrical parameters and the expected propagation paths;
the action space generating module is used for generating an action space of the power system according to the constraint model;
and the optimization module is used for constructing a state-action combination according to the state space and the action space, calculating and comparing system costs corresponding to the state-action combination, and obtaining a cascading failure emergency control result of the power system.
The embodiment of the invention provides a cascading failure emergency control method and device considering multi-dimensional transient state safety.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions 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 creative efforts.
Fig. 1 is a flowchart of an implementation of a cascading failure emergency control method according to an embodiment of the present invention;
FIG. 2 is a flow chart for calculating and comparing the system cost of a state-action combination provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a cascading failure emergency control device provided by an embodiment of the invention;
fig. 4 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
To make the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 is a flowchart of an implementation of the cascading failure emergency control method considering multi-dimensional transient safety according to an embodiment of the present invention, which is detailed as follows:
in step 101, electrical parameters of each element in the power system and an expected propagation path when a cascading failure occurs in the power system are obtained.
Optionally, the obtaining of the electrical parameters of each element in the power system in this step may include obtaining parameters of a synchronous generator set of the power system, parameters of a load of the power system, and reliability parameters of each element of the power system.
Optionally, the obtaining of the expected propagation path when the cascading failure occurs in the power system in the step of the embodiment may include obtaining an expected cascading failure propagation stage of the power system and a line failure set corresponding to different propagation stages.
And 102, constructing a dynamic model of the power system, and carrying out safety simulation on the power system.
Optionally, in this step, a dynamic model of the power system may be constructed by establishing a motion equation simulation model of a generator rotor of the power system, a power angle equation simulation model of the generator, an excitation winding equation simulation model of the generator, and a speed regulator-prime mover equation simulation model of the generator, so as to perform safety simulation on the power system.
Optionally, the equation of motion simulation model formula of the generator rotor of the power system established in the embodiment of the step is as follows:
wherein, M i Representing the time constant of inertia, ω i Representing angular speed of rotor movement, D, of the i-th generator i Denotes the damping coefficient, P m Representing the mechanical power of the generator, P m,i Then represents the mechanical power of the ith generator, P g Representing the electromagnetic power of the generator, P g,i Then represents the electromagnetic power of the i-th generator, r G Representing the total number of generators.
Optionally, the equation of the power angle simulation model of the generator of the power system established in the embodiment of the step is as follows:
wherein, delta i Represents the power angle of the ith generator, f 0 Representing the nominal frequency of the system.
Optionally, the formula of the simulation model of the excitation winding equation of the generator of the power system established in the embodiment of the step is as follows:
wherein E is q ′ ,i Representing the transient potential of the ith generator, X d,i Representing d-axis reactance of the i-th generator，T d ′ 0,i Representing the d-axis transient time constant, X, of the ith generator d ′ ,i Representing d-axis transient reactance, V, of the i-th generator i Representing the voltage at the corresponding node of the i-th generator, E fd,i Representing the excitation potential of the ith generator.
Optionally, in this step, a formula of a speed regulator-prime mover equation simulation model of a generator of the power system is established as follows:
wherein, mu Gi Denotes the regulation factor of the ith generator, s denotes the Laplace operator, F HPi Represents the equivalent high-pressure cylinder power coefficient, T, of the ith generator Ri Represents a reheat time constant, f, of the ith generator i Representing the frequency of the corresponding node of the ith generator.
Optionally, may be according to f i ＝ω i 2 pi calculates the frequency of the corresponding node of the ith generator
And 103, constructing a constraint model considering the multi-dimensional transient security.
Optionally, the constraint model considering the multidimensional transient security constructed in the embodiment of the step includes an equality constraint model and an inequality constraint model, where the equality constraint includes a power flow equation constraint model and a line outage probability; the inequality constraint model comprises a generator output constraint model, a generator adjustment quantity constraint model, a load shedding quantity constraint model, a line power constraint model and a multi-dimensional transient constraint model.
Optionally, the power flow equation constraint model in this step includes m development stages in total, and the power flow equation constraint of each development stage corresponds to an expected propagation stage when a cascading failure occurs in the power system, that is:
wherein, P Gi The active generator output of the ith generator in the initial state of the power system,
for the active output increase of the i-th generator in the n-th phase>
Active power output reduction, P, of the ith generator in the nth stage Li Is the active load of the ith load node in the initial state of the power system, G ij For the real part of the corresponding element in the ith row and jth column of the node admittance matrix, be->
Representing the real part of the voltage of the ith load node in the kth stage, B ij For the imaginary part, f, of the corresponding element in the ith row and jth column of the node admittance matrix i k Representing the imaginary voltage part, N, of the ith load node in the k-th phase bus Representing the total number of power system generator corresponding nodes and load nodes, and m representing the number of phases of the intended propagation path. />
Wherein Q is Gi The reactive power output of the generator of the ith generator in the initial state of the power system,
for the reactive power increase of the i-th generator in the n-th stage>
For the reactive power reduction, Q, of the ith generator in the nth stage Li The reactive load of the ith load node in the initial state of the power system.
Optionally, in the overload-dominated cascading failure, the probability of line disconnection in the k-th stage is determined by the power flowing through the line in the previous stage, and the line fault probability constraint model constructed in this step may be according to:
wherein p is k As the probability of occurrence of the k-th stage fault,
represents the power flowing through the line disconnected in phase k-1>
The corresponding nominal power, which represents the open line in the k-th phase in the k-1 th phase, is greater or less than>
And the corresponding limit power of the line disconnected in the k stage in the k-1 stage is shown.
Optionally, the step of constructing the generator output constraint model may be according to:
wherein, the first and the second end of the pipe are connected with each other,
is the lower limit of the active power output of the generator>
For the upper limit of the active power output of the generator>
For the lower limit of the idle output of the generator>
Is the upper limit of the reactive power output of the generator.
Optionally, the generator adjustment quantity model constructed in this step may be according to:
wherein the content of the first and second substances,
is the upper limit of the active power increment of the generator>
For the upper limit of the reactive power increment of the generator>
For the upper limit of the active power reduction of the generator>
Is the upper limit of the reactive power reduction of the generator.
Optionally, the construction of the load shedding amount constraint model in this step may be according to:
wherein r is L Is the total number of load nodes in the line.
Optionally, constructing the line power constraint model in this step may be according to:
P l min ≤P l k ≤P l max ,l＝1,2,...,N line ,k＝1,2,...,m
wherein, P l min Is the minimum value of the active power flow on the l line, P l k For the active power flow of the l line in the k phase, P l max Is the maximum value of the active power flow on the l line, N line Is the number of power system lines.
Optionally, the multidimensional transient constraint model in the embodiment of the step includes a node voltage transient safety constraint model, a node frequency transient safety constraint model, and a generator bus transient safety constraint model.
Optionally, the node voltage transient safety constraint model in the multidimensional transient constraint model established in this step may be according to the following constraint ranges:
V i min ≤V i k ≤V i max ,i＝1,2,...,N bus ,k＝1,2,...,m
wherein, V i k The voltage amplitude, V, of the kth node in the predicted propagation path i min Is the minimum value of the voltage at the i-th node, V i max Is the maximum value of the voltage on the ith node.
Optionally, the node frequency transient security constraint model in the multidimensional transient constraint model established in this step may be according to the following constraint ranges:
wherein, roCoF i k Representing the frequency change rate of the system frequency safety index, roCoF, of the ith node in the kth stage max A safety threshold value corresponding to the frequency change rate of the system frequency safety index,
frequency lowest point of system frequency safety index in the kth stage represented by the ith node, f nadir,min A safety threshold corresponding to a frequency minimum representing a system frequency safety indicator, based on a predetermined threshold value>
Quasi-steady-state frequency value f representing system frequency safety index of ith node in kth stage ∞,min And the safety threshold value corresponds to the quasi-steady-state frequency value of the system frequency safety index.
Optionally, the power-angle transient safety constraint model of the generator in the multidimensional transient constraint model established in this step may be according to the following constraint ranges:
wherein the content of the first and second substances,
for the transient power angle, delta, of the ith generator in the kth phase min Is the minimum safety threshold, delta, of the power angle of the ith generator max Is the maximum safe threshold value for the power angle of the i-th generator>
Is the center of inertia of the power angle at the kth stage for all generators in the power system. />
Optionally, in this step, the states of the elements in the power system may be calculated according to the constructed dynamic model of the power system based on the obtained electrical parameters of the elements in the power system.
Further, in this step, the states of the elements in the power system corresponding to different propagation stages of the expected propagation path may be obtained according to a method of calculating the states of the elements in the power system.
Further, can be based on
Generating power system state spaces of the expected propagation paths in different propagation stages;
wherein s is k Is the state space of the power system in the k-th phase.
And 105, generating an action space of the power system according to the constraint model.
Optionally, in this step, states of the elements in the power system at different stages may be discretized according to a constraint range in the constraint model to form corresponding actions of the elements at different stages. When the dispersion is performed according to the constraint range, the number of the discretely formed actions can be selected according to specific actual requirements, and the greater the number of the discrete actions, the higher the accuracy of the obtained element actions is, and the more calculation cost is consumed.
Optionally, in this step of the embodiment, the active output increase of the generator at the kth stage is discretized according to a constraint range of a constraint model, and an active output increase action corresponding to the generator at the kth stage is formed, that is:
wherein the content of the first and second substances,
showing the active power output increasing action of the ith generator in the kth stage.
Optionally, in this step of the embodiment, the active output reduction amount at the kth stage of the generator is discretized according to the constraint range of the constraint model, and an active output reduction action corresponding to the generator at the kth stage is formed, that is:
wherein the content of the first and second substances,
indicating the active power reduction action of the ith generator in the kth stage.
Optionally, in this step of the embodiment, the reactive power output increment of the generator at the kth stage is discretized according to the constraint range of the constraint model, and a reactive power output increment action corresponding to the generator at the kth stage is formed, that is:
wherein, the first and the second end of the pipe are connected with each other,
indicating the reactive power output increasing action of the ith generator in the kth stage.
Optionally, in this step of the embodiment, the reactive power output reduction amount at the kth stage of the generator is discretized according to the constraint range of the constraint model, so as to form a reactive power output reduction action corresponding to the generator at the kth stage, that is:
wherein the content of the first and second substances,
indicating the reactive power reduction action of the ith generator in the kth stage.
Optionally, in this step of the embodiment, the active load shedding amount of the load at the kth stage is discretized according to the constraint range of the constraint model, and an active load shedding action corresponding to the load at the kth stage is formed, that is:
wherein the content of the first and second substances,
and the real load shedding action of the ith load node in the kth stage is shown.
Optionally, in this step of the embodiment, the reactive load shedding amount of the load at the kth stage is dispersed according to the constraint range of the constraint model, so as to form a reactive load shedding action corresponding to the load at the kth stage, that is:
wherein the content of the first and second substances,
the method shows the reactive load shedding action of the ith load node in the kth stage.
Further, the present step is according to
Generating an action space corresponding to the state space of each element of the power system;
wherein, a k Shows the operation of each element in the power system at the k-th stage, A k Representing the motion space of the power system in the k-th phase.
And 106, constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system.
Optionally, the step 106 of constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system may include the steps shown in fig. 2.
Optionally, in this step, according to the propagation stage of the power system when the cascading failure occurs, the state of the power system at this propagation stage is obtained according to the state space, and the state is taken as the first state.
Furthermore, the corresponding motion space is determined according to the first state, and the first state and the discrete motion in the motion space are matched and combined to obtain a set formed by state-motion combination.
Optionally, in this step, the bonus value corresponding to the state-action combination may be obtained according to the following calculation method:
wherein(s) k ,a k ) Represents the corresponding state-action combination, r, of the power system in the k-th stage k (s k ,a k ) Indicating the reward value, p, corresponding to a state-action combination n The probability of the predicted propagation path entering the nth stage is represented, alpha represents the adjustment cost of the generator, beta represents the adjustment cost of the load node, gamma represents a penalty factor, and N represents the adjustment cost of the load node X Indicating that the electric power system is performing action a k Rear state s k ' the number of constraints not satisfied in the constraint model.
Further, this step may obtain the system benefit corresponding to the state-action combination in the set according to the following calculation method:
Q(s k ,a k )＝(1-λ)Q(s k ,a k )+λ[r k (s k ,a k )+ηmax Q(s k′ ,a k′ )]
wherein, Q(s) k ,a k ) Representing system benefits corresponding to state-action combinations, a k′ Is in a state s k′ The operation performed by the medium electric power system, λ represents a learning parameter, and η represents a conversion coefficient.
Optionally, based on the system benefit corresponding to the state-action combination, an action sequence corresponding to the maximum value of the system benefit is generated. In the step, the action corresponding to the maximum value of the system income can be selected according to the 1-epsilon principle so as to avoid trapping in local optimization when the optimal action is selected, and a to-be-selected action sequence is formed:
wherein, the first and the second end of the pipe are connected with each other,
and representing the action sequence corresponding to the maximum value of the system benefit, argmax represents a function for collecting the maximum value, epsilon represents a parameter of a 1-epsilon strategy, and epsilon is a positive number smaller than 1.
Further, the low difference motion sequence is constructed by sampling the motion sequence. In the embodiment of the step, a Sobol sequence selection method is used for sampling the action sequence to be selected so as to select the action in the same state, so that the uniformity of the action sampling result is avoided, and a low-difference action sequence phi = [ x ] is formed 1 ,x 2 ,x 3 ,......,x t ]；
Where Φ represents a low difference sequence consisting of a plurality of actions, x i Is the ith sample value of the low difference sequence, and t represents the action number obtained by sampling;
further, the step may be according to
Selecting an optimal action in a low variance action sequence;
wherein the content of the first and second substances,
indicating the selection of sample values x in a low disparity motion sequence k Is determined.
And 204, calculating the system cost of the optimal action corresponding to the first state, and detecting whether the system cost reaches a training target.
Optionally, the step can be according to
Calculating the system cost of the optimal action corresponding to the first state;
where H represents the system cost corresponding to the first state-action combination.
Further, can be based on
Detecting whether the system cost reaches a training target;
where minH represents the minimum value of the system cost corresponding to the first state-action combination.
Optionally, when the training target is set, the step may further include detecting whether the training times are reached.
Optionally, when the system cost reaches the training target, step 205 is executed, and when the system cost does not reach the training target, step 206 is executed.
And step 205, if the training target is reached, outputting the optimal action corresponding to the first state to obtain the cascading failure emergency control result corresponding to the power system in the first state.
Optionally, in this step, according to the first state of the power system in the propagation stage, the corresponding optimal action is output, that is, the generator output adjustment amount and the load shedding amount of each element of the power system are output, so as to obtain the emergency control result when the power system has a cascading failure.
And step 206, if the training target is not reached, jumping to the step 201 and executing the subsequent steps.
Optionally, when the training target is not reached, the step 201 is skipped to re-determine the states and corresponding actions of the elements in the power system, the system reward value is updated, the system cost is recalculated, and finally the optimal action corresponding to the power system state is determined until the training target is reached.
The embodiment of the invention carries out dynamic simulation on system elements of the power system when cascading failure occurs so as to calculate the states of the power system at different failure stages; by constructing a constraint model for considering multi-dimensional transient security, the transient security and the steady-state security of the power system in the process of executing an emergency control strategy are ensured; generating corresponding action spaces by combining actual requirements according to the state spaces of the power system at different fault stages to form a state-action combination, and selecting the optimal actions of the power system at different fault stages by calculating the system cost corresponding to the state-action combination to obtain the emergency control result of the cascading faults.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 3 shows a schematic structural diagram of a cascading failure emergency control device considering multi-dimensional transient safety according to an embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are shown, which is detailed as follows:
as shown in fig. 3, the cascading failure emergency control device 4 includes: the system comprises a data acquisition module 301, a safety simulation module 302, a constraint module 303, a state space generation module 304, an action space generation module 305 and an optimization module 306.
The data acquisition module 301 is used for acquiring electrical parameters of each element in the power system and an expected propagation path when the power system has a cascading failure;
the safety simulation module 302 is used for constructing a dynamic model of the power system and performing safety simulation on the power system;
a constraint module 303, configured to construct a constraint model taking multidimensional transient security into consideration;
a state space generation module 304, configured to calculate and generate a state space of the power system according to the dynamic model based on the electrical parameters and the expected propagation path;
an action space generation module 305 for generating an action space of the power system according to the constraint model;
and the optimization module 306 is configured to construct a state-action combination according to the state space and the action space, and calculate and compare system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system.
Optionally, the safety simulation module 302 constructs a dynamic model of the power system, and when performing safety simulation on the power system, may be configured to:
establishing a motion equation simulation model of a generator rotor of the power system:
wherein M is i Representing the time constant of inertia, ω i Representing angular speed of rotor movement, D, of the i-th generator i Representing the damping coefficient, P m Representing the mechanical power of the generator, P m,i Then represents the mechanical power of the ith generator, P g Representing the electromagnetic power of the generator, P g,i Then represents the electromagnetic power of the ith generator, r G Representing the total number of generators;
establishing a power angle equation simulation model of a generator of the power system:
wherein, delta i Represents the power angle of the ith generator, f 0 Represents a system rated frequency;
establishing an excitation winding equation simulation model of a generator of a power system:
wherein E is q ′ ,i Representing the transient potential, X, of the ith generator d,i Denotes d-axis reactance, T, of the i-th generator d ′ 0,i Representing the d-axis transient time constant, X, of the ith generator d ′ ,i Representing d-axis transient reactance, V, of the i-th generator i Representing the voltage at the corresponding node of the i-th generator, E fd,i Represents the excitation potential of the ith generator;
establishing a speed regulator-prime motor equation simulation model of a generator of the power system:
wherein, mu Gi Denotes the regulation factor of the ith generator, s denotes the Laplace operator, F HPi Represents the equivalent high-pressure cylinder power coefficient, T, of the ith generator Ri Represents a reheat time constant, f, of the ith generator i Representing the frequency of the corresponding node of the ith generator;
and constructing a dynamic model of the power system according to the motion equation simulation model of the generator rotor, the power angle equation simulation model of the generator, the excitation winding equation simulation model of the generator and the speed regulator-prime motor equation simulation model of the generator, and performing safety simulation on the power system.
Optionally, when the constraint module 303 constructs a constraint model taking into account multidimensional transient security, it may be configured to:
establishing a node voltage transient safety constraint model in a constraint model:
V s min ≤V s k ≤V s max ,s＝1,2,...,N bus ,k＝1,2,...,m
wherein, V s k The voltage amplitude, V, of the kth node of the expected propagation path s min Is the minimum value of the voltage at the s-th node, V s max Is the maximum value of the voltage at the s-th node, N bus Representing the total number of nodes corresponding to the generator and load nodes, and m representing the number of stages of the expected propagation path;
establishing a node frequency transient safety constraint model in a constraint model:
wherein the content of the first and second substances,
representing the frequency change rate of the system frequency safety index of the s-th node in the k-th stage, roCoF max A safety threshold value corresponding to the rate of change of the frequency representing a safety indicator of the system frequency->
Frequency nadir, f, representing the system frequency safety index of the s-th node at the k-th stage nadir,min A safety threshold value corresponding to the lowest frequency point representing a system frequency safety indicator, <' > or>
Quasi-steady-state frequency value f of system frequency safety index of s-th node in k-th stage ∞,min A safety threshold value corresponding to a quasi-steady-state frequency value representing a system frequency safety index;
establishing a generator power angle transient state safety constraint model in a constraint model:
wherein, the first and the second end of the pipe are connected with each other,
for the transient power angle, delta, of the ith generator in the k-th phase min Is the minimum safety threshold, delta, of the power angle of the ith generator max Is the maximum safe threshold value for the power angle of the i-th generator>
Is the center of inertia of the power angle at the kth stage for all generators in the power system.
Optionally, when the state space generating module 304 generates the state space of the power system according to the dynamic model based on the electrical parameters and the expected propagation path, it may be configured to:
calculating the state of each element in the power system according to the dynamic model based on the electrical parameters;
calculating the states of the elements in the power system in different stages by calculating the states of the elements in the power system based on the expected propagation path;
according to
Generating a state space of the power system;
wherein s is k For the state space of the power system in the k-th phase,
indicates the active output increase of the i-th generator in the k-th stage>
Represents the active power output reduction of the i-th generator in the k-th stage, based on the comparison result>
The active load shedding amount, which is greater or less than the active load shedding amount of the ith load node in the kth stage>
Indicating the reactive power increase of the ith generator in the kth stage,
represents the amount of reduction of the reactive power output of the i-th generator in the k-th phase, based on the comparison result>
Representing the reactive load shedding amount, r, of the ith load node in the kth stage L Representing the total number of load nodes.
Optionally, when the action space generating module 305 generates the action space of the power system according to the constraint model, it may be configured to:
discretizing the states of the elements at different stages according to the constraint model to form corresponding actions of the elements at different stages;
according to
Generating an action space corresponding to a state space of the power system;
wherein, a k Shows the operation of each element of the power system at the k-th stage, A k Representing the motion space of the power system in the k-th phase,
indicates the active power take-up action of the i-th generator in the k-th stage>
Indicates the active power take-off action of the i-th generator in the k-th stage, based on the comparison result>
Indicates that the ith load node has an active load shedding action, based on the status of the load node at the kth stage>
Indicating a reactive power increasing action of the i-th generator in the k-th phase>
Indicates that the i-th generator has a reactive power reducing action in the k-th phase>
And the reactive load shedding action of the ith load node in the kth stage is shown.
Optionally, the optimization module 306 constructs a state-action combination according to the state space and the action space, and calculates and compares system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system, where the system costs are used to:
selecting a first state of the power system in a state space, and matching the first state with the action in an action space corresponding to the first state to obtain a set formed by state-action combinations;
calculating the system income corresponding to the state-action combination in the set;
selecting an optimal action corresponding to the first state according to the system income corresponding to the state-action combination in the set;
calculating the system cost of the optimal action corresponding to the first state, and detecting whether the system cost reaches a training target;
if the training target is reached, outputting the optimal action corresponding to the first state to obtain the corresponding cascading failure emergency control result of the power system in the first state;
and if the training target is not reached, skipping to the step of selecting a first state of the power system in the state space, matching the first state with the action in the action space corresponding to the first state to obtain a set formed by the state-action combination, and executing subsequent steps.
Optionally, when the optimization module 306 calculates the system benefit corresponding to the state-action combination in the set, the method may include:
according to
Calculating the reward value corresponding to the state-action combination;
wherein(s) k ,a k ) Represents the corresponding state-action combination, r, of the power system at the kth stage k (s k ,a k ) Indicating the prize value, p, corresponding to the state-action combination n The probability of the predicted propagation path entering the nth stage is represented, alpha represents the adjustment cost of the generator, beta represents the adjustment cost of the load node, gamma represents a penalty factor, and N represents the adjustment cost of the load node X Indicating that the power system is performing action a k Rear state s k ' the number of constraints not satisfied in the constraint model;
according to Q(s) k ,a k )＝(1-λ)Q(s k ,a k )+λ[r k (s k ,a k )+ηmaxQ(s k ′,a k ′)]Calculating the system income corresponding to the state-action combination;
wherein, Q(s) k ,a k ) Representing system benefits corresponding to state-action combinations, a k Is in a state s k ' the action performed by the electric power system, λ denotes a learning parameter, and η denotes a conversion coefficient.
Optionally, the optimization module 306 may be configured to, when selecting the optimal action corresponding to the first state according to the system benefit corresponding to the state-action combination in the set:
generating an action sequence corresponding to the maximum value of the system profit based on the system profit corresponding to the state-action combination;
sampling the motion sequence to form a low difference motion sequence:
Φ＝[x 1 ,x 2 ,x 3 ,......,x t ]
where Φ represents a low difference sequence consisting of a plurality of actions, x i Is the ith sample value of the low difference sequence, and t represents the action number obtained by sampling;
according to
Selecting an optimal action in a low variance action sequence;
wherein p is xk Indicating the selection of sample values x in a low disparity motion sequence k The random probability of (e), represents a parameter of the greedy strategy.
Optionally, the optimizing module 306 calculates a system cost of the optimal action corresponding to the first state, and detects whether the system cost reaches the training target, where the system cost may be used to:
according to
Calculating the system cost of the optimal action corresponding to the first state;
wherein, H represents the system cost corresponding to the first state-action combination;
according to
Detecting whether the system cost reaches a training target;
where minH represents the minimum value of the system cost corresponding to the first state-action combination.
According to the cascading failure emergency control device considering the multi-dimensional transient safety, the electrical parameters of the power system and the expected propagation path of the power system when cascading failure occurs are obtained, the dynamic safety simulation model of the power system and the constraint model considering the multi-dimensional transient safety are built, the state spaces and the corresponding action spaces of the power system in different failure stages are generated, a state-action combination is built, the optimal actions of the power system in different failure stages are selected by calculating the system cost corresponding to the state-action combination, the cascading failure emergency control result is quickly and accurately obtained, and meanwhile the transient safety and the steady-state safety of the power system when an emergency control strategy is adopted can be guaranteed.
Fig. 4 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 4, the terminal 400 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. The processor 401, when executing the computer program 403, implements the steps in each of the above embodiments of the cascading failure emergency control method, such as the steps 101 to 106 shown in fig. 1. Alternatively, the processor 401, when executing the computer program 403, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 301 to 306 shown in fig. 3.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401, to implement the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 403 in the terminal 400. For example, the computer program 403 may be divided into the modules/units 301 to 306 shown in fig. 3.
The terminal 400 may include, but is not limited to, a processor 401, a memory 402. Those skilled in the art will appreciate that fig. 4 is only an example of a terminal 400 and does not constitute a limitation of terminal 400, and may include more or less components than those shown, or combine certain components, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the terminal 400, such as a hard disk or a memory of the terminal 400. The memory 402 may also be an external storage device of the terminal 400, such as a plug-in hard disk provided on the terminal 400, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 402 may also include both internal and external memory units of the terminal 400. The memory 402 is used for storing computer programs and other programs and data required by the terminal. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program instructing related hardware, and the computer program may be stored in a computer readable storage medium, and when being executed by a processor, the computer program may implement the steps of the embodiments of the cascading failure emergency control method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A cascading failure emergency control method considering multidimensional transient safety is characterized by comprising the following steps:
acquiring electrical parameters of each element in a power system and an expected propagation path when the power system has a cascading failure;
constructing a dynamic model of the power system, and carrying out safety simulation on the power system;
constructing a constraint model considering multi-dimensional transient security;
generating a state space of the power system from the dynamic model based on the electrical parameters and the anticipated propagation paths;
generating an action space of the power system according to the constraint model;
and constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system.
2. The cascading failure emergency control method of claim 1, wherein the building of the dynamic model of the power system and the safety simulation of the power system comprise:
establishing a motion equation simulation model of a generator rotor of the power system:
wherein M is i Representing the time constant of inertia, ω i Representing angular speed of rotor movement, D, of the i-th generator i Representing the damping coefficient, P m Representing the mechanical power of the generator, P m,i Then represents the mechanical power of the ith generator, P g Representing the electromagnetic power of the generator, P g,i Then represents the electromagnetic power of the i-th generator, r G Representing the total number of generators;
establishing a power angle equation simulation model of a generator of the power system:
wherein, delta i Represents the power angle of the ith generator, f 0 Represents a system rated frequency;
establishing an excitation winding equation simulation model of a generator of the power system:
wherein, E q ′ ,i Representing the transient potential, X, of the ith generator d,i Denotes d-axis reactance, T, of the i-th generator d ′ 0,i Represents the d-axis transient time constant, X, of the ith generator d ′ ,i Representing d-axis transient reactance, V, of the i-th generator i Representing the voltage at the corresponding node of the i-th generator, E fd,i Represents the excitation potential of the ith generator;
establishing a speed regulator-prime mover equation simulation model of a generator of the power system:
wherein, mu Gi Denotes the regulation coefficient of the ith generator, s denotes the Laplacian, F HPi Represents the equivalent high-pressure cylinder power coefficient, T, of the ith generator Ri Represents a reheat time constant, f, of the ith generator i Representing the frequency of the corresponding node of the ith generator;
and according to the motion equation simulation model of the generator rotor, the power angle equation simulation model of the generator, the excitation winding equation simulation model of the generator and the speed regulator-prime mover equation simulation model of the generator, constructing a dynamic model of the power system and carrying out safety simulation on the power system.
3. The method of cascading failure emergency control of claim 2, wherein constructing a constraint model that accounts for multi-dimensional transient safety comprises:
establishing a node voltage transient safety constraint model in the constraint model:
wherein, V s k Voltage amplitude, V, of the kth stage of the predicted propagation path for the s-th node s min Is the minimum value of the voltage at the s-th node, V s max Is the maximum value of the voltage at the s-th node, N bus Representing the total number of corresponding nodes and load nodes of the generator, and m representing the number of phases of the expected propagation path;
establishing a node frequency transient safety constraint model in the constraint model:
wherein the content of the first and second substances,
representing the frequency change rate of the system frequency safety index of the s-th node in the k-th stage, roCoF max A safety threshold value corresponding to the rate of change of the frequency representing the system frequency safety criterion->
Frequency lowest point of system frequency safety index in the kth stage represented by the s node, f nadir,min A safety threshold value corresponding to a frequency minimum point representing the system frequency safety criterion, -a->
Quasi-steady-state frequency value f representing system frequency safety index of s-th node in k-th stage ∞,min A safety threshold value corresponding to a quasi-steady-state frequency value representing the system frequency safety index;
establishing a generator power angle transient state safety constraint model in the constraint model:
wherein the content of the first and second substances,
for the transient power angle, delta, of the ith generator in the k-th phase min Minimum safety threshold for power angle of i-th generator, delta max Is the maximum safe threshold value for the power angle of the i-th generator>
The inertia center of the power angle of all the generators in the power system in the k stage is obtained.
4. The cascading failure emergency control method of claim 2, wherein generating the state space of the power system from the dynamic model based on the electrical parameters and the expected propagation paths comprises:
calculating the state of each element in the power system according to the dynamic model based on the electrical parameters;
calculating the states of the elements in the power system at different stages in a mode of calculating the states of the elements in the power system based on the expected propagation path;
according to
Generating a state space of the power system;
wherein s is k For the state space of the power system in the k-th phase,
represents the active output increase of the ith generator in the k stage>
Showing the active output of the ith generator in the kth stageDecrease amount->
The active load shedding amount, which is greater or less than the active load shedding amount of the ith load node in the kth stage>
Represents the reactive power output increment of the i-th generator in the k-th stage>
Represents the amount of reduction of the reactive power output of the i-th generator in the k-th phase, based on the comparison result>
Representing the reactive load shedding amount, r, of the ith load node in the kth stage L Representing the total number of load nodes.
5. The cascading failure emergency control method of claim 4, wherein the generating an action space of the power system according to the constraint model comprises:
discretizing the states of the elements at different stages according to the constraint model to form corresponding actions of the elements at different stages;
according to
Generating an action space corresponding to a state space of the power system;
wherein, a k Shows the operation of each element of the power system at the k-th stage, A k Representing the motion space of the power system in the k-th phase,
indicates the active power take-up action of the i-th generator in the k-th stage>
Indicates the active power take-off action of the i-th generator in the k-th stage, based on the comparison result>
Showing the active load shedding action of the ith load node in the kth stage,
indicates the reactive power increasing action of the i-th generator in the k-th stage>
Indicates that the i-th generator has a reactive power reducing action in the k-th phase>
The method shows the reactive load shedding action of the ith load node in the kth stage.
6. The cascading failure emergency control method according to claim 5, wherein the step of constructing a state-action combination according to the state space and the action space, and calculating and comparing system costs corresponding to the state-action combination to obtain a cascading failure emergency control result of the power system comprises:
selecting a first state of the power system in the state space, and matching the first state with the action in the action space corresponding to the first state to obtain a set formed by state-action combinations;
calculating the system income corresponding to the state-action combination in the set;
selecting an optimal action corresponding to the first state according to the system income corresponding to the state-action combination in the set;
calculating the system cost of the optimal action corresponding to the first state, and detecting whether the system cost reaches a training target or not;
if the training target is reached, outputting the optimal action corresponding to the first state to obtain a cascading failure emergency control result corresponding to the power system in the first state;
and if the training target is not reached, skipping to the step of selecting a first state of the power system in the state space, matching the first state with the action in the action space corresponding to the first state to obtain a set formed by state-action combination, and executing subsequent steps.
7. The method of claim 6, wherein the calculating the system gain corresponding to the state-action combination in the set comprises:
according to
Calculating the reward value corresponding to the state-action combination;
wherein(s) k ,a k ) Represents the corresponding state-action combination, r, of the power system in the k stage k (s k ,a k ) Representing the reward value, p, corresponding to said state-action combination n Representing the probability of the predicted propagation path entering the nth stage, alpha representing the adjustment cost of the generator, beta representing the adjustment cost of the load node, gamma representing a penalty factor, N X Indicating that the power system performs action a k Rear state s k′ A number of constraints not satisfied in the constraint model;
according to Q(s) k ,a k )＝(1-λ)Q(s k ,a k )+λ[r k (s k ,a k )+ηmaxQ(s k′ ,a k′ )]Calculating the system income corresponding to the state-action combination;
wherein, Q(s) k ,a k ) Representing the system benefits corresponding to said state-action combinations, a k′ Is in a state s k′ λ represents a learning parameter, and η represents a conversion coefficient.
8. The method of claim 7, wherein the selecting the optimal action corresponding to the first state according to the system gain corresponding to the state-action combination in the set comprises:
generating an action sequence corresponding to the maximum value of the system profit based on the system profit corresponding to the state-action combination;
sampling the motion sequence to form a low difference motion sequence:
Φ＝[x 1 ,x 2 ,x 3 ,......,x t ]
where Φ represents a low difference sequence consisting of a plurality of actions, x i Is the ith sample value of the low difference sequence, and t represents the action quantity obtained by sampling;
according to
Selecting an optimal action in the sequence of low disparity actions;
wherein the content of the first and second substances,
indicating selection of sample value x in the low disparity motion sequence k Represents a parameter of said greedy strategy.
9. The method of claim 8, wherein the calculating a system cost of an optimal action corresponding to the first state and detecting whether the system cost reaches a training target includes:
according to
Calculating the system cost of the optimal action corresponding to the first state;
wherein, H represents the system cost corresponding to the first state-action combination;
according to
Detecting whether the system cost reaches a training target;
where minH represents the minimum value of the system cost corresponding to the first state-action combination.
10. A cascading failure emergency control device with multi-dimensional transient safety taken into account, comprising:
the data acquisition module is used for acquiring the electrical parameters of each element in the power system and an expected propagation path when the power system has cascading failure;
the safety simulation module is used for constructing a dynamic model of the power system and carrying out safety simulation on the power system;
the constraint module is used for constructing a constraint model considering multi-dimensional transient security;
a state space generation module for generating a state space of the power system from the dynamic model based on the electrical parameters and the expected propagation paths;
the action space generating module is used for generating an action space of the power system according to the constraint model;
and the optimization module is used for constructing a state-action combination according to the state space and the action space, calculating and comparing system costs corresponding to the state-action combination, and obtaining a cascading failure emergency control result of the power system.
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