CN115528685A

CN115528685A - Security stability control method, apparatus, device, storage medium, and program product

Info

Publication number: CN115528685A
Application number: CN202211354770.7A
Authority: CN
Inventors: 叶志英; 张伟; 宋学清; 邓远发; 旷奎
Original assignee: Southern Power Grid Digital Grid Research Institute Co Ltd
Current assignee: Southern Power Grid Digital Grid Research Institute Co Ltd
Priority date: 2022-11-01
Filing date: 2022-11-01
Publication date: 2022-12-27

Abstract

The present application relates to a security stability control method, apparatus, device, storage medium and program product, the method comprising: firstly, under the condition that the electric power system is unstable, a planning function and a planning constraint condition are obtained, secondly, the planning function is solved by taking the minimum value of the planning function as a target based on the planning constraint condition, and finally, a target resource is determined in the electric power system based on the solving result of the planning function and is cut off to eliminate the unstable state of the electric power system.

Description

Security stability control method, apparatus, device, storage medium, and program product

Technical Field

The present application relates to the field of power system technologies, and in particular, to a safety and stability control method, apparatus, device, storage medium, and program product.

Background

With the development of electric power systems in China, more and more new energy power generation technologies appear, however, in recent years, large-range chain off-grid accidents of fans occur in wind power bases such as northwest, northwest and the like, a warning clock is sounded in a new energy extensive development mode, higher requirements are provided for the operation control level of a power grid, and meanwhile, the stability characteristics of the power grid are changed profoundly due to large-scale new energy grid-connected operation.

Therefore, the problem that the safety and stability of the power grid cannot be guaranteed in the power system needs to be solved urgently.

Disclosure of Invention

In view of the above, it is necessary to provide a security stability control method, apparatus, device, storage medium and program product for solving the above technical problems.

In a first aspect, the present application provides a safety and stability control method. The method comprises the following steps:

under the condition that an electric power system is unstable, obtaining a planning function and a planning constraint condition, wherein the planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set and the active power output of each load in the electric power system, the planning constraint condition is constructed according to a load shedding proportion and an electric power system control vector, and the electric power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector; based on the planning constraint condition, solving the planning function by taking the minimum value of the planning function as a target; and determining target resources in the power system based on the solution result of the planning function, and cutting the target resources to eliminate the instability state of the power system, wherein the target resources comprise a generator set and/or a load.

In one embodiment, based on the planning constraint condition, solving the planning function with the minimum value of the planning function as a target includes: acquiring parameter information of an OMIB equivalent machine corresponding to the power system, wherein the parameter information comprises an inertia time constant, a rotor angular speed at a saddle point in a first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment; calculating the minimum emergency control quantity of the OMIB equivalent machine according to the parameter information; and determining a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

In one embodiment, the minimum emergency control quantity of the OMIB equivalent machine is calculated according to the parameter information, and the method comprises the following steps: performing Taylor expansion on the equivalent rotor angle of the difference between the electromagnetic torque and the mechanical torque to obtain a first expansion formula; performing quadratic curve fitting processing based on the first expansion to obtain a target function parameter; and calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

In one embodiment, the minimum emergency control quantity is calculated based on the objective function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state, and the minimum emergency control quantity comprises the following steps: acquiring a control quantity calculation formula group based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state; solving the control quantity calculation formula group to obtain the minimum emergency control quantity; wherein, the control quantity calculation formula group includes:

where Δ P is the minimum emergency control amount, δ _DSP Is the equivalent rotor angle at the saddle point in the initial swing state, a, b and c are target function parameters,

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, D is the deceleration area parameter increased by the first swing after the cutting machine, delta _cg Is the equivalent rotor angle of the starting moment, M _O Is the time constant of inertia, ω _O,DSP The rotor angular velocity at the saddle point in the initial swing state.

In one embodiment, the minimum emergency control quantity includes a plurality of control quantity sets, and the determining the solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity includes: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target controlled quantity set from each controlled quantity set according to the quality information corresponding to each controlled quantity set and the planning constraint condition, and taking the target controlled quantity set as a solving result of a planning function.

In one embodiment, before determining the target resource in the power system based on the solution to the planning function, the method further comprises: carrying out simulation processing based on the solution result of the planning function to obtain a simulation result; determining whether the simulation result meets a preset precision condition; correspondingly, determining a target resource in the power system based on the solution to the planning function includes: and if the accuracy condition is met, determining the target resource in the power system based on the solution result of the planning function.

In one embodiment, the method further comprises: and if the precision condition is not met, iteratively executing the step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until the simulation result corresponding to the solution result of the planning function meets the precision condition.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w For the weight coefficient of the removed wind power generator set, gi is the state variable of the ith wind power generator, P _wi Is the current active power output of the ith wind power generator, c _g Weight factor for the excised photovoltaic generator set, g _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s Weight factor, g, for the removed synchronous generator set _r Is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight factor for the load removed, l _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li For actual load shedding proportion, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

epsilon is a given small positive number, which is the control vector for the load.

In a second aspect, the present application further provides a safety and stability control apparatus. The device includes:

the system comprises an acquisition module, a planning module and a planning constraint condition, wherein the acquisition module is used for acquiring a planning function and a planning constraint condition under the condition that an electric power system is unstable, the planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set and the active power output of each load in the electric power system, the planning constraint condition is constructed according to a load shedding proportion and an electric power system control vector, and the electric power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector;

the solving module is used for solving the planning function by taking the minimum value of the planning function as a target based on the planning constraint condition;

and the cutting module is used for determining target resources in the power system based on the solution result of the planning function and cutting the target resources to eliminate the instability state of the power system, wherein the target resources comprise the generator set and/or the load.

In one embodiment, the solving module comprises a first solving unit, a second solving unit and a third solving unit.

The first solving unit is used for acquiring parameter information of an OMIB (open multimedia interface) equivalent machine corresponding to the power system, wherein the parameter information comprises an inertia time constant, a rotor angular speed at a saddle point in a first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment.

The second solving unit is used for calculating the minimum emergency control quantity of the value machines such as the OMIB and the like according to the parameter information.

And the third solving unit is used for determining a solving result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

In one embodiment, the second solving unit includes a first solving subunit, a second solving subunit and a third solving subunit.

And the first solving subunit is used for performing Taylor expansion on the equivalent rotor angle of the difference between the electromagnetic torque and the mechanical torque to obtain a first expansion.

And the second solving subunit is used for performing quadratic curve fitting processing based on the first expansion to obtain the target function parameters.

And the third solving subunit is used for calculating the minimum emergency control quantity based on the objective function parameter, the inertia time constant, the rotor angular speed at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

In one embodiment, the third solving subunit is specifically configured to: acquiring a control quantity calculation formula set based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state; solving the control quantity calculation formula group to obtain the minimum emergency control quantity;

wherein, the control quantity calculation formula group includes:

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, and D is the deceleration area parameter increased by the first swing after the cutting machineQuantity, delta _cg Is the equivalent rotor angle of the starting moment, M _O Is the inertia time constant, ω _O,DSP The rotor angular velocity at the saddle point in the initial swing state.

In one embodiment, the third solving unit is specifically configured to: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target control quantity set from each control quantity set according to the quality information corresponding to each control quantity set and the planning constraint conditions, and taking the target control quantity set as a solving result of the planning function.

In one embodiment, the apparatus further includes a simulation module, a determination module, and an execution module, and before determining the target resource in the power system based on the solution to the planning function, each module further performs the following steps:

the simulation module is used for carrying out simulation processing based on the solution result of the planning function to obtain a simulation result.

The determining module is used for determining whether the simulation result meets a preset precision condition.

Correspondingly, the cutting-out module is specifically configured to execute the step of determining the target resource in the power system based on the solution result of the planning function if the accuracy condition is satisfied.

In one embodiment, the execution module is configured to iteratively execute a step of solving the planning function based on the planning constraint condition and with a minimum value of the planning function as a target until a simulation result corresponding to a solution result of the planning function satisfies the precision condition if the precision condition is not satisfied.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor for the excised photovoltaic generator set, g _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active power output of the jth photovoltaic generator, c _s For the weight factor of the synchronous generator set to be cut off, gr is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight factor for the load removed, l _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li In proportion to the actual cutting load, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

In a third aspect, an embodiment of the present application provides an apparatus, on which a computer program is stored, where the computer program is executed by a processor to implement the steps in any one of the above first aspects.

In a fourth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps in any one of the above-mentioned first aspects.

In a fifth aspect, the present application provides a program product, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of any one of the first aspect.

The safety stability control method, the device, the equipment, the storage medium and the program product are characterized in that firstly, under the condition that an electric power system is unstable, a planning function and a planning constraint condition are obtained, wherein the planning function is constructed according to the active power of each wind generating set, the active power of each photovoltaic generating set, the active power of each synchronous generating set and the active power of each load in the electric power system, the planning constraint condition is constructed according to a load shedding proportion and an electric power system control vector, the electric power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector, secondly, the minimum value of the planning function is taken as a target based on the planning constraint condition, the planning function is solved, finally, a target resource is determined in the electric power system based on the solving result of the planning function, and is cut off to eliminate the unstable state of the electric power system, and the target resource comprises the generating set and/or the load.

Drawings

FIG. 1 is a schematic flow chart of a safety and stability control method in one embodiment;

FIG. 2 is a flow diagram of a method for solving a planning function, according to one embodiment;

FIG. 3 is a flow diagram illustrating a method for calculating minimum critical control values in one embodiment;

FIG. 4 is a flow chart illustrating a method for calculating a minimum critical control value in another embodiment;

FIG. 5 is a diagram of the margin of the trajectory of the equivalent nose pendulum cutter in one embodiment;

FIG. 6 is a flow diagram illustrating a method for determining solution results in one embodiment;

FIG. 7 is a flow diagram illustrating a simulation process in one embodiment;

FIG. 8 is a schematic flow chart diagram illustrating a safety and stability control method according to one embodiment;

FIG. 9 is a block diagram showing the construction of a safety and stability control apparatus according to an embodiment;

fig. 10 is a block diagram showing the construction of a safety and stability control apparatus according to another embodiment;

FIG. 11 is an internal block diagram of a computer device that is a server in one embodiment;

fig. 12 is an internal structural diagram of a computer device as a terminal in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

With the development of power systems in China, more and more new energy power generation technologies such as wind power generation, photovoltaic power generation, gas power generation and the like appear, but the new energy power generation such as wind power generation, photovoltaic power generation and the like has the characteristics of randomness, volatility and intermittency, and in addition, technical bottlenecks in prediction, scheduling and control of the new energy power generation technologies make independent power generation characteristics and source network coordination of the two new energy resources still have larger differences compared with a conventional power supply.

Through continuous rapid increase of new energy installation for many years, the installed capacity of partial new energy in the three northeast provinces of China reaches more than 50% of the load level, and the installed capacity of wind power in Jiangsu provinces is rapidly developed in the coastal region of the middle east. However, in recent years, in the northwest, the north-opened and other wind power bases, large-range linkage off-grid accidents of the fan occur for many times, the alarm clock is sounded in a new energy extensive development mode, and higher requirements are provided for the operation control level of the power grid. The large-scale new energy grid-connected operation changes the stability characteristics of the power grid deeply.

Therefore, the influence mechanism of new energy grid connection on the system stability characteristic is researched, the power grid stability characteristic under the multi-source condition is known and mastered, and the technical development of energy storage elements and the like is fully utilized, so that the safety and stability control method suitable for the multi-source environment is provided, on one hand, the stable operation level of the power system can be improved, and the safety of a large power grid is ensured; on the other hand, the method is also an inevitable path for breaking the power grid safety constraint to limit the new energy development, and has important practical significance for the new energy development and the safe operation of the power grid in China.

According to the security and stability control method provided by the embodiment of the application, the execution main body can be a computer device, and the computer device can be a server or a terminal.

In one embodiment, as shown in fig. 1, there is provided a safety and stability control method including the steps of:

step 101, acquiring a planning function and a planning constraint condition under the condition that the power system is unstable.

Optionally, the power system may be in a large-area grid disconnection state, a generator set may have a collective fault, and the power system may have limited resource allocation.

The planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set and the active power output of each load in the power system, the planning constraint condition is constructed according to the load shedding proportion and the power system control vector, and the power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector.

Illustratively, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor for the excised photovoltaic generator set, g _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s Weight factor, g, for the removed synchronous generator set _r Is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight coefficient for load shedding,/ _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li In proportion to the actual cutting load, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

In particular, g _i The state variable is the state variable of whether the ith wind power generator is cut off, if the state variable is cut off, the value of the state variable is 1, and if the state variable is not cut off, the value of the state variable is 0; g _j The state variable of the j th photovoltaic generator is cut off, if the state variable is cut off, the value of the state variable is 1, and if the state variable is not cut off, the value of the state variable is 0; g is a radical of formula _r The state variable for the cutting or not of the r-th synchronous generator has a value of 1 if cut and 0 if not cut.

And 102, solving the planning function by taking the minimum value of the planning function as a target based on the planning constraint condition.

It can be understood that the planning function is a set of data values, and when the system is unstable, the planning function is solved with the objective of minimum value, and the minimum value can determine that the power system can recover to the stable state from the unstable state.

And 103, determining target resources in the power system based on the solution result of the planning function, and cutting the target resources to eliminate the instability state of the power system.

Wherein the target resource comprises a generator set and/or a load, and the cutting refers to an emergency control means for the generator set and the load.

The safety stability control method comprises the steps of firstly, under the condition that an electric power system is unstable, obtaining a planning function and a planning constraint condition, wherein the planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set and the active power output of each load in the electric power system, the planning constraint condition is constructed according to a load shedding proportion and an electric power system control vector, the electric power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector, secondly, on the basis of the planning constraint condition, the planning function is solved with the minimum value of the planning function as a target, finally, a target resource is determined in the electric power system on the basis of the solving result of the planning function, the target resource is cut to eliminate the unstable state of the electric power system, and comprises the generating sets and/or the loads, and the safety stability control when the electric power system is unstable can be realized through the method.

As described above, in the solving process, the planning function needs to be solved based on the planning constraint condition with the minimum value of the planning function as the target, as shown in fig. 2, an embodiment of the present application provides a method for solving the planning function, which includes the following steps:

step 201, obtaining parameter information of value machines such as OMIB corresponding to the power system.

The parameter information comprises an inertia time constant, the rotor angular speed at the saddle point in the first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment.

And 202, calculating the minimum emergency control quantity of the value machines such as the OMIB according to the parameter information.

In a possible implementation manner, a control quantity calculation formula group is obtained through the parameter information, and the control quantity calculation formula group is solved to obtain the minimum emergency control quantity Δ P. Wherein the control quantity calculation formula group includes:

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, D is the deceleration area parameter increased by the first swing after the cutting machine, delta _cg Is the equivalent rotor angle of the starting moment, M _O Is a target inertia time constant, ω _O,DSP The rotor angular velocity at the saddle point in the initial swing state.

And step 203, determining a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

Wherein the planning constraints include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, γ _li For actual load shedding proportion, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

As described above, in the process of solving the planning function, the minimum emergency control quantity of the value machine such as the OMIB needs to be calculated according to the parameter information, as shown in fig. 3, an embodiment of the present application provides a method for calculating the minimum emergency control quantity, including the following steps:

step 301, performing taylor expansion on the difference between the electromagnetic torque and the mechanical torque about the equivalent rotor angle to obtain a first expansion.

In a possible implementation, the electromagnetic torque P is determined from the power system analysis _e,O May be used with respect to _O Is represented by a high order Taylor expansion, where δ _O Is an OMIB equivalent rotor angle; similarly, the difference between the electromagnetic torque and the mechanical torque can also be used with respect to δ _O Wherein a, b, and c are objective function parameters. Thus, the information about δ can be utilized _O Approximate representation of a quadratic function

The difference between the electromagnetic torque and the mechanical torque is a first expansion:

and 302, performing quadratic curve fitting processing based on the first expansion to obtain target function parameters.

Wherein the objective function parameters refer to a, b, and c in the first expansion.

In one possible implementation, a, b, and c in the first expansion pass through δ _DSP The previous partial data are obtained using a quadratic curve fit, where δ _DSP The equivalent rotor angle at the saddle point of the first swing state.

And 303, calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular speed at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

Further, in the process of calculating the minimum emergency control quantity, the minimum emergency control quantity needs to be calculated based on the objective function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state, and the equivalent rotor angle at the saddle point in the first swing state, as shown in fig. 4, an embodiment of the present application provides another method for calculating the minimum emergency control quantity, including:

step 401, a control quantity calculation formula set is obtained based on the objective function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

Firstly, a generator set in the power system is dynamically divided into a critical cluster S and a complementary cluster A relative to a system inertia center. Assuming that the generators 1,2,. K belong to the cluster S, the generators k +1, k +2,. N belong to the cluster A. Under the COI coordinate system, equivalent rotor angles and equivalent rotor angular velocities of two inertia centers are respectively defined as follows:

and

wherein, delta _S And delta _A Respectively equal angles of the cluster S and the cluster A; omega _S And ω _A Respectively equal angular velocities of the cluster S and the cluster A; m _S And M _A Respectively equal inertia time constants of the cluster S and the cluster A; delta. For the preparation of a coating _i And ω _i The rotor angle and angular speed of the ith generator under the COI coordinate are respectively, i =1,2,. Ang., n; m is a group of _i I =1, 2.., n, which is the inertia time constant of the ith generator.

Further, an OMIB equivalent rotor angle delta is defined by making the OMIB equivalent _O Angular speed omega of rotor _O And time constant of inertia M _O Comprises the following steps:

wherein M is _T Is the inertial time constant, delta, of the system generator set _S 、δ _A 、ω _S 、ω _A 、M _S And M _A As mentioned above, it should be noted that the defined parameters are not described in detail later.

Then, the equation of motion of the rotor of the OMIB equivalent machine can be written as follows:

wherein, P _m,O And P _e,O Defined as OMIB equivalent mechanical moment and electromagnetic moment, P _m,O And P _e,O The calculation formula is:

wherein, M _T Is the inertia time constant, P, of the system generator set _mi Is the mechanical moment of the i-th generator, P _mj Is the mechanical moment of the jth generator, P _ei Electromagnetic torque, P, of the ith generator _ej The electromagnetic torque of the jth generator.

If the system is unstable, the energy-type transient stability margin of the OMIB equivalent machine can be defined according to the EEAC theory as follows:

wherein A is _Dec And A _Inc The maximum deceleration area and the maximum acceleration area of the OMIB equivalent machine head swing are respectively shown as the following calculation formulas:

and

wherein, delta _O,0 And ω _O,0 Respectively an equivalent angle and an angular velocity delta of the starting moment of the OMIB equivalent machine head swing _O,s And ω _O,s Respectively equal angle and angular velocity delta of fault clearing moment or OMIB equivalent machine head swing stable balance point _O,DSP Is the isoangle, omega, at the saddle point of OMIB isodyne head swing state _O,DSP The angular velocity at the saddle point in the initial swing state.

When the system is unstable, the stability margin of the system is negative, and when the system is stable, the stability margin of the system is positive, so that the critical state of the system is that the stability margin is zero, namely the temporary stability margin is zero:

if the system is unstable in transient state, the emergency control is performed on the system, in this embodiment, the emergency control mode is an emergency shutdown, and the minimum emergency control cost is that the system is in a critical stable state or a critical unstable state after the emergency control, that is, the stability margin of the system is zero, as shown in equation (4-9).

In a possible implementation manner, assuming that the instability mode is unchanged after emergency control and the removed generators are excluded, that is, the cluster S and the generators included in the cluster a are identical, the difference between the equivalent electromagnetic torque and the mechanical torque after emergency control is implemented, which may be referred to as power difference for short, can be approximately represented by data before emergency control is implemented and the equivalent emergency control quantity, and the mathematical expression is as follows:

wherein the content of the first and second substances,

and

the equivalent electromagnetic torque and the mechanical torque after the machine is cut are respectively, and delta P is equivalent emergency control quantity.

Setting the minimum equivalent emergency control quantity to be delta P, after emergency control, the system is in a critical state, and the first swing state saddle point is

Equivalent machine track margins before and after emergency control can be obtained through the formulas (4-8), (4-9) and (4-10).

The equivalence machine trajectory margins are shown in FIG. 5, where the x-axis is the equivalent rotor angle, the y-axis is the difference between the electromagnetic and mechanical moments, and δ _O Of the OMIB equivalent rotor angle, delta _c l is the equivalent rotor angle at the moment of ablation, delta _cg Is the equivalent rotor angle of the starting moment, delta _DSP At the saddle point of the first swing state _DSP Is the equivalent rotor angle at the saddle point of the head swing state after the cutter cutting, and the delta P is the minimum equivalentAnd (5) emergency control quantity. In addition, a, b, c, d, e, f, g, h and i are A ₁ 、A ₂ And A ₃ Each end point formed by three patterns, A ₁ The area of acceleration before emergency control can be directly calculated by the formula (4-8); a. The ₂ The maximum deceleration area before emergency control can be directly calculated by the formula (4-6); a. The ₃ In order to implement the increased deceleration area of the first pendulum after emergency control, the calculation formula is as follows:

substitution of formula (4-8) for formula (4-11) gives:

from the foregoing, it can be appreciated that an equivalent rotor angle δ for OMIB can be utilized _O Approximate representation of a quadratic function

The difference between the electromagnetic torque and the mechanical torque, and as a first expansion:

and wherein the parameters a, b and c of the objective function pass through δ _DSP Previous partial data was obtained using quadratic curve fitting.

Further, the minimum equivalent emergency control quantity obtained from the equations (4-10) and (4-13) is calculated as:

meanwhile, the formula of the track margin can be determined as follows:

the combined vertical type (4-13), the formula (4-14) and the formula (4-15) can obtain the deceleration area increased by the head pendulum after the emergency control is implemented as follows:

wherein D is a deceleration area parameter increased by the first pendulum after the cutting machine, and the calculation formula of D is as follows:

substitution of formulae (4-7), formulae (4-8) and formulae (4-16) for formulae (4-9) can be mentioned

The system of unary cubic equations of (a) is:

wherein the control amount calculation formula group includes formulae (4-14), (4-17), and (4-18):

where Δ P is the minimum emergency control amount, δ _DSP Is the equivalent rotor angle at the saddle point of the first swing state, a, b and c are target function parameters,

And step 402, solving the control quantity calculation formula group to obtain the minimum emergency control quantity.

In a possible implementation mode, the equation (4-18) is solved by using a Newton iteration method, and the equivalent angle corresponding to the DSP point of the first pendulum after the operation of the cutting machine is implemented can be obtained

Will be solved

The equation (4-14) can be substituted to obtain the equivalent machine minimum emergency control quantity delta P.

As described above, the minimum emergency control quantity includes a plurality of control quantity sets, and in the process of solving the planning function, it is necessary to determine a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity, as shown in fig. 6, an embodiment of the present application provides a method for determining a solution result, including the following steps:

and 601, determining participation factors and risk cost coefficients corresponding to the control quantities of the various types.

The participation factors and the risk cost coefficients refer to operation constraint conditions of each generator set and the load.

And step 602, determining quality information corresponding to each controlled variable set according to the participation factor and the risk cost coefficient corresponding to each type of controlled variable.

Step 603, determining a target controlled quantity set from each controlled quantity set according to the quality information corresponding to each controlled quantity set and the planning constraint conditions, and taking the target controlled quantity set as a solving result of the planning function.

In addition, before determining the target resource in the power system based on the solution result of the planning function, a simulation step needs to be performed, as shown in fig. 7, an embodiment of the present application provides a method for simulation processing, including the following steps:

and 701, performing simulation processing based on the solution result of the planning function to obtain a simulation result.

The simulation processing refers to cutting off each generator set and load according to the solving result of the planning function.

Step 702, determining whether the simulation result meets a preset precision condition.

Wherein the accuracy condition refers to that the power system is restored from instability to be stable after the emergency control.

And 703, if the precision condition is met, determining the target resource in the power system based on the solution result of the planning function.

Wherein, the target resource refers to each generator set and load needing to be cut off.

Further, if the accuracy condition is not satisfied, an iterative processing step is performed, which is provided in an embodiment of the present application and includes: and if the precision condition is not met, iteratively executing the step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until the simulation result corresponding to the solution result of the planning function meets the precision condition.

In one embodiment, as shown in fig. 8, there is provided a safety stability control method including the steps of:

step 801, acquiring a planning function and a planning constraint condition under the condition that the power system is unstable.

And step 802, acquiring parameter information of value machines such as OMIB corresponding to the power system.

Step 803, performing Taylor expansion on the difference between the electromagnetic torque and the mechanical torque about an equivalent rotor angle to obtain a first expansion; and performing quadratic curve fitting processing based on the first expansion to obtain target function parameters.

And step 804, acquiring a control quantity calculation formula set based on the target function parameter, the inertia time constant, the rotor angular speed at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

Wherein, the control quantity calculation formula group includes:

where Δ P is a minimum emergency control amount, δ _DSP Is the equivalent rotor angle at the saddle point in the initial swing state, a, b and c are target function parameters,

And 805, solving the control quantity calculation formula group to obtain the minimum emergency control quantity.

And 806, determining participation factors and risk cost coefficients corresponding to the control quantities of the types.

And 807, determining quality information corresponding to each controlled variable set according to the participation factor and the risk cost coefficient corresponding to each type of controlled variable.

And 808, determining a target controlled quantity set from each controlled quantity set according to the quality information corresponding to each controlled quantity set and the planning constraint condition, and taking the target controlled quantity set as a solving result of a planning function.

And step 809, performing simulation processing based on the solution result of the planning function to obtain a simulation result.

And step 810, determining whether the simulation result meets a preset precision condition.

And 811, if the precision condition is met, executing a step of determining target resources in the power system based on the solution result of the planning function, and cutting the target resources to eliminate the instability state of the power system, wherein the target resources comprise a generator set and/or a load.

And 812, if the precision condition is not met, iteratively executing a step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until a simulation result corresponding to the solution result of the planning function meets the precision condition.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a safety and stability control device for realizing the safety and stability control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so specific limitations in one or more embodiments of the safety and stability control device provided below can be referred to the limitations of the safety and stability control method in the foregoing, and details are not described herein again.

In one embodiment, as shown in fig. 9, there is provided a safety stability control device 900 comprising: an acquisition module 901, a solving module 902, and an ablation module 903, wherein:

the obtaining module 901 is configured to obtain a planning function and a planning constraint condition when the power system is unstable, where the planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set, and the active power output of each load in the power system, the planning constraint condition is constructed according to a load shedding proportion and a power system control vector, and the power system control vector includes a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector, and a load control vector.

And a solving module 902, configured to solve the planning function based on the planning constraint condition and with a goal of minimum value of the planning function.

And the removing module 903 is configured to determine a target resource in the power system based on a solution result of the planning function, and remove the target resource to eliminate a destabilization state of the power system, where the target resource includes a generator set and/or a load.

In one embodiment, the solving module 902 includes a first solving unit, a second solving unit, and a third solving unit.

And the third solving subunit is used for calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular speed at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

wherein the control quantity calculation formula group includes:

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, D is the deceleration area parameter increased by the first swing after the cutting machine, delta _cg Is the equivalent rotor angle of the starting moment, M _O Is the inertia time constant, ω _O,DSP Is a headAngular rotor velocity at the saddle point in the swing state.

In one embodiment, the third solving unit is specifically configured to: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target controlled quantity set from each controlled quantity set according to the quality information corresponding to each controlled quantity set and the planning constraint condition, and taking the target controlled quantity set as a solving result of a planning function.

In one embodiment, as shown in fig. 10, another safety and stability control apparatus 1000 provided in an embodiment of the present application is shown, where the safety and stability control apparatus 1000 includes, in addition to each module included in the safety and stability control apparatus 900, a simulation module 904, a determination module 905, and an execution module 906, and before determining a target resource in an electric power system based on a solution result of a planning function, each module further executes the following steps:

the simulation module 904 is configured to perform simulation processing based on a solution result of the planning function to obtain a simulation result.

The determining module 905 is configured to determine whether the simulation result meets a preset precision condition.

Correspondingly, the removing module 903 is specifically configured to execute a step of determining a target resource in the power system based on a solution result of the planning function if the accuracy condition is satisfied.

In one embodiment, the executing module 906 is configured to iteratively execute a step of solving the planning function based on the planning constraint condition and with a minimum value of the planning function as a target until a simulation result corresponding to a solution result of the planning function satisfies the accuracy condition if the accuracy condition is not satisfied.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor for the excised photovoltaic generator set, g _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s Weight factor, g, for the removed synchronous generator set _r Is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight coefficient for load shedding,/ _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li For actual load shedding proportion, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

The modules in the safety and stability control device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 11. The computer device comprises a processor, a memory, an Input/Output (I/O) interface and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used for storing data. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a security and stability control method.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 12. The computer apparatus includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device. The processor, the memory and the input/output interface are connected by a system bus, and the communication interface, the display unit and the input device are connected by the input/output interface to the system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a security and stability control method. The display unit of the computer device is used for forming a visual picture and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the configurations shown in fig. 11 or fig. 12 are only block diagrams of portions of configurations relevant to the present application, and do not constitute a limitation on the computer apparatus to which the present application is applied, and a particular computer apparatus may include more or less components than those shown in the figures, or may combine certain components, or have a different arrangement of components.

In one embodiment, there is provided an apparatus comprising a memory and a processor, the memory having a computer program stored therein, the processor when executing the computer program implementing the steps of:

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring parameter information of an OMIB equivalent machine corresponding to the power system, wherein the parameter information comprises an inertia time constant, a rotor angular velocity at an saddle point in a first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment; calculating the minimum emergency control quantity of the OMIB equivalent machine according to the parameter information; and determining a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

In one embodiment, the processor when executing the computer program further performs the steps of: carrying out Taylor expansion on the difference between the electromagnetic torque and the mechanical torque about an equivalent rotor angle to obtain a first expansion form; performing quadratic curve fitting processing based on the first expansion to obtain target function parameters; and calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a control quantity calculation formula group based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state; solving the control quantity calculation formula group to obtain the minimum emergency control quantity; wherein, the control quantity calculation formula group includes:

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, D is the deceleration area parameter increased by the first swing after the cutting machine, delta _cg Equivalent rotor angle, M, at the start time _O Is the time constant of inertia, ω _O,DSP The rotor angular velocity at the saddle point in the initial swing state.

In one embodiment, the minimum emergency control quantity comprises a plurality of sets of control quantities, and the processor when executing the computer program further performs the steps of: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target control quantity set from each control quantity set according to the quality information corresponding to each control quantity set and the planning constraint conditions, and taking the target control quantity set as a solving result of the planning function.

In one embodiment, the processor, when executing the computer program, further performs the steps of: carrying out simulation processing based on the solution result of the planning function to obtain a simulation result; determining whether the simulation result meets a preset precision condition; and if the accuracy condition is met, determining the target resource in the power system based on the solution result of the planning function.

In one embodiment, the processor, when executing the computer program, further performs the steps of: and if the precision condition is not met, iteratively executing the step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until the simulation result corresponding to the solution result of the planning function meets the precision condition.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor for the excised photovoltaic generator set, g _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s Weight factor, g, for the removed synchronous generator set _r Is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight factor for the load removed, l _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li For actual load shedding proportion, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

In one embodiment, a storage medium is provided, on which a computer program is stored, which computer program, when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring parameter information of an OMIB equivalent machine corresponding to the power system, wherein the parameter information comprises an inertia time constant, a rotor angular speed at a saddle point in a first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment; calculating the minimum emergency control quantity of the OMIB equivalent machine according to the parameter information; and determining a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

In one embodiment, the computer program when executed by the processor further performs the steps of: performing Taylor expansion on the equivalent rotor angle of the difference between the electromagnetic torque and the mechanical torque to obtain a first expansion formula; performing quadratic curve fitting processing based on the first expansion to obtain a target function parameter; and calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular speed at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a control quantity calculation formula set based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state; solving the control quantity calculation formula group to obtain the minimum emergency control quantity; wherein the control quantity calculation formula group includes:

where Δ P is a minimum emergency control amount, δ _DSP Is the equivalent rotor angle at the saddle point of the first swing state, a, b and c are target function parameters,

In one embodiment, the minimum emergency control quantity comprises a plurality of sets of control quantities, and the computer program, when executed by the processor, further performs the steps of: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target controlled quantity set from each controlled quantity set according to the quality information corresponding to each controlled quantity set and the planning constraint condition, and taking the target controlled quantity set as a solving result of a planning function.

In one embodiment, the computer program when executed by the processor further performs the steps of: carrying out simulation processing based on the solution result of the planning function to obtain a simulation result; determining whether the simulation result meets a preset precision condition; and if the accuracy condition is met, determining the target resource in the power system based on the solution result of the planning function.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the precision condition is not met, iteratively executing the step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until the simulation result corresponding to the solution result of the planning function meets the precision condition.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor, g, for the excised photovoltaic generator set _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s For the weight factor of the removed synchronous generator set, gr is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight factor for the load removed, l _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li In proportion to the actual cutting load, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

In one embodiment, a program product is provided, comprising a computer program which, when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of: carrying out Taylor expansion on the difference between the electromagnetic torque and the mechanical torque about an equivalent rotor angle to obtain a first expansion form; performing quadratic curve fitting processing based on the first expansion to obtain target function parameters; and calculating the minimum emergency control quantity based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state.

wherein, Δ P _{In order to minimize the amount of emergency control,} δ _DSP is the equivalent rotor angle at the saddle point of the first swing state, a, b and c are target function parameters,

is equivalent at saddle point of head swing state after cutting machineRotor angle, D is the deceleration area parameter, delta, increased by the head pendulum after the cutter _cg Equivalent rotor angle, M, at the start time _O Is the inertia time constant, ω _O,DSP The rotor angular velocity at the saddle point in the first swing state.

In one embodiment, the minimum emergency control quantity comprises a plurality of sets of control quantities, and the computer program, when executed by the processor, further performs the steps of: determining participation factors and risk cost coefficients corresponding to the control quantities of various types; determining quality information corresponding to each control quantity set according to the participation factor and the risk cost coefficient corresponding to each type of control quantity; and determining a target control quantity set from each control quantity set according to the quality information corresponding to each control quantity set and the planning constraint conditions, and taking the target control quantity set as a solving result of the planning function.

In one embodiment, the computer program when executed by the processor further performs the steps of: and if the precision condition is not met, iteratively executing a step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until a simulation result corresponding to the solution result of the planning function meets the precision condition.

In one embodiment, the planning function includes:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w Weight coefficient of wind-power generator set, g, for removal _i Is the state variable, P, of the ith wind turbine generator _wi Is the current active power output of the ith wind power generator, c _g Weight factor, g, for the excised photovoltaic generator set _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active output of the jth photovoltaic generator, c _s Weight factor, g, for the synchronous generator set removed _r Is the state variable of the r-th synchronous generator, P _sr Is the current active power output of the r-th synchronous generator, c _l Weight coefficient for load shedding,/ _k Is the state variable of the kth load, P _lk Is the current active power of the kth load, gamma _li In proportion to the actual cutting load, gamma _lmaxi Eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector for the synchronous generator,

is the control vector of the load, epsilon is a given small positive number.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A safety stability control method, characterized in that the method comprises:

under the condition that an electric power system is unstable, obtaining a planning function and a planning constraint condition, wherein the planning function is constructed according to the active power output of each wind generating set, the active power output of each photovoltaic generating set, the active power output of each synchronous generating set and the active power output of each load in the electric power system, the planning constraint condition is constructed according to a load shedding proportion and an electric power system control vector, and the electric power system control vector comprises a wind generating set control vector, a photovoltaic generating set control vector, a synchronous generating set control vector and a load control vector;

based on the planning constraint condition, solving the planning function by taking the minimum value of the planning function as a target;

and determining target resources in the power system based on the solution result of the planning function, and cutting off the target resources to eliminate the instability state of the power system, wherein the target resources comprise a generator set and/or a load.

2. The method according to claim 1, wherein solving the planning function based on the planning constraint condition with a minimum value of the planning function as a target comprises:

acquiring parameter information of an OMIB equivalent machine corresponding to the power system, wherein the parameter information comprises an inertia time constant, a rotor angular speed at a saddle point in a first swing state, an equivalent rotor angle at the saddle point in the first swing state, a mechanical moment and an electromagnetic moment;

calculating the minimum emergency control quantity of the OMIB equivalent machine according to the parameter information;

and determining a solution result of the planning function based on the planning constraint condition and the minimum emergency control quantity.

3. The method of claim 2, wherein said calculating a minimum amount of emergency control for the OMIB equivalent machine based on the parameter information comprises:

performing Taylor expansion on an equivalent rotor angle on the difference between the electromagnetic torque and the mechanical torque to obtain a first expansion formula;

performing quadratic curve fitting processing based on the first expansion to obtain a target function parameter;

and calculating the minimum emergency control quantity based on the objective function parameter, the inertia time constant, the rotor angular speed at the saddle point in the head swing state and the equivalent rotor angle at the saddle point in the head swing state.

4. The method of claim 3, wherein said calculating the minimum critical control quantity based on the objective function parameters, the inertial time constant, the rotor angular velocity at the lead-swing saddle point, and the equivalent rotor angle at the lead-swing saddle point comprises:

acquiring a control quantity calculation formula group based on the target function parameter, the inertia time constant, the rotor angular velocity at the saddle point in the first swing state and the equivalent rotor angle at the saddle point in the first swing state;

solving the control quantity calculation formula group to obtain the minimum emergency control quantity;

wherein the control amount calculation formula group includes:

wherein Δ P is the minimum emergency control amount, δ _DSP Is the equivalent rotor angle at the saddle point in the initial swing state, a, b and c are the objective function parameters,

is the equivalent rotor angle at the saddle point of the first swing state after the cutting machine, D is the deceleration area parameter increased by the first swing after the cutting machine, delta _cg Is the equivalent rotor angle of the starting moment, M _O Is said inertia time constant, ω _O,DSP The rotor angular velocity at the saddle point in the initial swing state.

5. The method of claim 2, wherein the minimum emergency control quantity comprises a plurality of control quantity sets, and wherein determining the solution to the planning function based on the planning constraints and the minimum emergency control quantity comprises:

determining participation factors and risk cost coefficients corresponding to the control quantities of various types;

determining quality information corresponding to each control quantity set according to participation factors and risk cost coefficients corresponding to each type of control quantity;

and determining a target control quantity set from each control quantity set according to the quality information corresponding to each control quantity set and the planning constraint condition, and taking the target control quantity set as a solving result of the planning function.

6. The method of any of claims 1 to 5, wherein prior to determining a target resource in the power system based on the solution to the planning function, the method further comprises:

carrying out simulation processing based on the solution result of the planning function to obtain a simulation result;

determining whether the simulation result meets a preset precision condition;

correspondingly, the determining a target resource in the power system based on the solution to the planning function includes:

and if the accuracy condition is met, executing a step of determining the target resource in the power system based on a solution result of the planning function.

7. The method of claim 6, further comprising:

and if the precision condition is not met, iteratively executing a step of solving the planning function based on the planning constraint condition by taking the minimum value of the planning function as a target until a simulation result corresponding to the solution result of the planning function meets the precision condition.

8. The method according to any of claims 1 to 5, wherein the planning function comprises:

the constraint conditions include:

s.t1γ _li ≤γ _lmaxi (k＝1,.......N)

wherein, c _w For the weight coefficient of the wind turbine generator set removed, g _i For the ith state variable, P, of the wind turbine _wi Is the current active power output of the ith wind power generator, c _g For cutting off the photovoltaic generator setWeight coefficient of (g) _j Is the state variable of the jth photovoltaic generator, P _gj Is the current active power output, c, of the jth photovoltaic generator _s For the removed weight coefficient of the synchronous generator set, g _r For the state variable of the r-th said synchronous generator, P _sr Is the current active output of the r-th synchronous generator, c _l Weight coefficient of the load to be cut off, l _k Is the state variable of the kth of said load, P _lk For the current active power, γ, of the kth of said loads _li For actually cutting off the proportion of the load, gamma _lmaxi The load proportion can be cut off to the maximum, eta is the temporary stability margin of the system under the implementation of emergency control measures,

is a control vector of the wind power generator,

is a control vector of the photovoltaic generator,

is a control vector of the synchronous generator,

for the control vector of the load, ε is a given small positive number.

9. A safety and stability control apparatus, characterized in that the apparatus comprises:

and the cutting module is used for determining target resources in the power system based on the solution result of the planning function and cutting the target resources to eliminate the instability state of the power system, wherein the target resources comprise a generator set and/or a load.

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.

11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.

12. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 8 when executed by a processor.