CN117318041A - Controllable load intelligent dynamic regulation and control method for intelligent power grid - Google Patents
Controllable load intelligent dynamic regulation and control method for intelligent power grid Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
Abstract
The invention relates to a controllable load intelligent dynamic regulation and control method for a smart power grid, which aims to realize flexible and intelligent management of controllable loads and comprises the following steps: establishing a controllable load intelligent regulation dynamic model; establishing controllable load error regulation and control constraint; formulating a controllable load intelligent regulation dynamic performance index; compensating the transmission time delay of the load regulation information; and the self-adaptive fine and quick stable control of controllable load under the intelligent power grid is realized. The invention has the advantages that: the problem that the controllable load in the power system is poor in flexibility and low in precision in the regulation and control process is solved, the controllable load can be regulated and controlled in real time according to the change of the load demand of the power grid, the designed intelligent regulation and control dynamic method is utilized to compensate the influence of the transmission time delay of the load regulation and control information, the rapidity and the stability of the load regulation and control are further ensured, and the dynamic balance of the load regulation and control under the intelligent power grid is realized.
Description
Technical Field
The invention relates to the field of power load control, in particular to a controllable load intelligent dynamic regulation and control method for an intelligent power grid.
Background
The power system load scheduling refers to that in the running process of a power system, the system load is distributed in an equalizing way through reasonable scheduling and control according to the power demand and the supply condition, and the stable running of a power grid and the reliability of power supply are ensured. Load scheduling plays a vital role in power systems, which directly affects the efficiency of energy utilization, the balance of power supply and demand, and the economy and environmental sustainability of the grid. Traditional load scheduling mainly relies on a real-time manual intervention mode, such as manually regulating and controlling the operation parameters of a generator set to meet load demands. However, with the expansion of the scale of power systems and the continuous application of renewable energy and energy storage technologies, the continuous introduction of new energy and controllable loads increases the uncertainty and complexity of power systems, rendering traditional manual regulation unsatisfactory. In order to realize more efficient and reliable load scheduling, intelligent load regulation and control technology is widely focused and applied.
The load control has received a great deal of attention because it can ensure the balance of supply and demand of the power system, promote the integration and utilization of renewable energy, and improve the flexibility and toughness of the power system. Through regulating and controlling controllable load, the defect that thermal power generation is excessively relied on in the process of new energy consumption at the power generation side can be overcome, and the elasticity of a power grid is further improved. In the aspect of power load scheduling, load control has great exertion potential, and along with the rapid development of renewable energy sources and the increase of distributed energy sources, the load control can realize flexible scheduling and management of the distributed energy sources, promote the elasticity and adaptability of a power system and improve the stability and toughness of the power system. Today, how to realize accurate regulation of controllable loads under an intelligent power network has become a research core of power system load scheduling.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the controllable load intelligent dynamic regulation and control method for the intelligent power grid, which solves the problems of poor flexibility and low precision of controllable load in a regulation and control process in a power system, and compared with the traditional load dispatching, the method has smaller calculation complexity in the process of realizing load dispatching optimization, can regulate and control the controllable load in real time according to the load demand change of the power grid, and utilizes the designed controllable load intelligent dynamic regulation and control method to compensate the influence of the transmission time delay of load regulation and control information, ensure the rapidity and stability of load regulation and control, and realize the dynamic balance of load regulation and control under the intelligent power grid.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
the intelligent dynamic controllable load regulating and controlling method for intelligent power network includes introducing load error regulating and controlling constraint to realize given intelligent dynamic performance index, combining neural network system with limited covering primer and compensating the transmission time delay of load regulating and controlling information; by adjusting the parameters of the controller, the tracking error can be converged into any small residue, so that dynamic balance of load regulation and control under the intelligent power grid is realized;
the controllable load intelligent dynamic regulation and control method is realized based on the following steps:
s1, converting a state space equation of a regulating unit into a controllable load intelligent regulating model in a general form by adopting a mathematical equivalent conversion method, wherein the controllable load intelligent regulating model is used as a controlled object of a controllable load intelligent dynamic regulating method;
s2, introducing load error regulation and control constraint, namely adopting a performance function and an error conversion function to ensure a given intelligent regulation and control dynamic performance index;
s3, estimating an unknown function with time delay in a mathematical model of the controlled object by adopting a mode of combining a neural network and a finite coverage primer;
s4, designing a controllable load dynamic regulator, enabling the virtual control law to pass through a first-order low-pass filter, enabling the input of the neural network to be known, adopting the neural network to compensate time delay by combining the technology of finite coverage quotients, combining the performance function and the error conversion function in the step S2 to ensure given intelligent regulation dynamic performance indexes, and designing the controllable load intelligent dynamic regulation method provided with load regulation information transmission time delay compensation.
In step S1, the state space equation of the regulation unit is shown in formula (1):
in the formula (1), u H Representing load side voltage, unit V; i.e L Representing inductor current, unit a; u (U) de The direct current busbar voltage of the direct current micro-grid is represented by a unit V; l is an energy storage inductance, and the unit is mH; c (C) H The unit mF is a load side filter capacitor; r is internal resistance, unit omega; without loss of generality, considering external disturbance, the state space equation of the regulating and controlling unit can be rewritten into a mathematical model shown as a formula (2) in a mathematical transformation mode, and the mathematical model is used as a controlled object of the controllable load intelligent dynamic regulating and controlling method:
in the formula (2), the amino acid sequence of the compound, it is emphasized that the value is unknown during the controller design process; τ i ，/>And d i I=1, 2, respectively representing the generation of the sensor during the measurement of the load regulation informationIs of unknown time delay constant, has a time delay and is connected to the load side voltage u H A related unknown nonlinear smoothing function and external disturbances;
in the formula (2), u is the duty ratio of the MOS tube, namely, the control input signal;representing an output;
for a mathematical model of a controlled object of the controllable load intelligent dynamic regulation method, the following assumption is specified:
suppose 1: g i Not equal to 0, i=1, 2, is an unknown normal number, and satisfies g i ≥g imin ：
Suppose 2: desired tracking y r Is a smooth and bounded function;belonging to a tight set:
suppose 3: τ i I=1, 2 is an unknown normal number and satisfies 0.ltoreq.τ i ≤τ M Wherein τ M Denoted τ i Is the maximum value of (2):
suppose 4: unknown external disturbance d i I=1, 2, satisfy
To introduce step S2, a performance function and an error transfer function are used, and a tracking error e is defined as shown in equation (3):
e＝x 1 -y r (3)
for any moment when t is greater than or equal to 0, the performance function p (t) is a smooth and decreasing positive function, and the specific form is as shown in the formula (4):
in the formula (4), 0<σ<1 and lim t→∞ ＝p ∞ >0，p ∞ Allowed by stabilizationMaximum value of allowable regulation error; to convert equation (4) to an equivalent function, an error transfer function of the form is introduced as shown in equation (5):
e(t):＝p(t)Φ(S 1 ), (5)
in the formula (5), S 1 Is a conversion error converted by an error conversion function, Φ (S 1 ) Is a smooth and strictly monotonically increasing function whose inverse has the following properties:
and is also provided with
As can be seen from formula (7), ifThen equation (6) holds; taking further consideration of p (t) > 0 and formula (5), it is possible to obtain- σp (t) < p (t) Φ (S 1 ) =p (t) < 1 (when e (0) > 0) or-p (t) < Φ (S) 1 ) =p (t) < σp (t) (when e (0) < 0), i.e., formula (4) holds; thus, from the above analysis, it is only necessary to prove +.>The preparation method is finished; from phi (S) 1 ) The strictly monotonically increasing nature of (2) can give a conversion error S 1 The method comprises the following steps:
adopting the step S3 neural network to approach unknown smooth functions in mathematical models of controlled objects on lineWherein (1)>Representing a tight set, q being the dimension of the input vector; the neural network approximates the expression of the unknown function as shown in equation (9):
in the formula (9), the amino acid sequence of the compound,is an input vector to the neural network; />Is an optimal weight vector, and N is the number of nodes of the neuron, as shown in formula (10):
in the formula (10), the amino acid sequence of the compound,is a weight vector function; kappa (kappa) j (ζ i ) Is a radial basis function, as shown in equation (11):
κ j (ζ i )＝exp[-||ζ i -ζ j ||/2μ j 2 ],j＝1,...,N (11)
in the formula (11): zeta type toy j Is the center of the j-th basis function, mu j Is the width of the basis function; epsilon i Is the estimated error of the neural network;
suppose 5: epsilon i |≤υ i ，Wherein v i An upper bound value representing an estimation error;
annotation 1: it should be emphasized that when the unknown smoothing function has an unknown time delay constant τ i When, for example, f i (ζ i ,ζ i (t-τ i ) A direct approximation cannot be made using a neural network; however, due to the tight setCan be used to determine the unknown function using a neural network combined with a finite coverage primer>Performing approximation;
lemma 1: assume an input vector ζ i ＝(ζ i ,ζ i (t-τ i ) In line with time t, where τ i ∈[0,τ M ]Is an unknown time delay constant; then, for any given errorThere is a finite interval [0, τ ] independent of time t M ]In the form shown in the formula (12):
0＜t 1 ＜…＜t m ＜τ M (12)
at one time point in the formula (12), the formula (13) is satisfied:
the method can obtain:
combining lemma 1 and hypothesis 3, there are multiple time points τ 1/1 ,...,τ n/n ∈{t 1 ,...,t m -representing the unknown smooth function with time delay as shown in equation (15):
in formula (15):the estimation error representing the finite coverage theory, combined with the expression of the neural network approximation unknown function, equation (15) can be approximated as the one shown in equation (16):
in formula (16), delta i The sum of the upper bound value representing the neural network estimation error and the estimation error of the finite coverage primer; and the input vector is as shown in formula (17):
Θ i ＝(x 1 ,...,x i ,...,x 1 (t-t 1 ),...,x k (t-τ k/k ),...,x i (t-t m )) (17)。
step S4, designing a controllable load dynamic regulator, which comprises the following steps:
s41, combining a mathematical model of the controlled object, and converting the error S 1 The first derivative with respect to time t is shown in equation (18):
in formula (18):x 2d is a virtual control law;
from the formula (18), as shown in the formula (19):
in the formula (19), the amino acid sequence of the compound,
and (16) is combined with the formula (19)Unknown items of (a)Can be written as shown in formula (20):
and input vector theta 1 ＝(x 1 ,x 1 (t-t 1 ),...,x 1 (t-τ k/k ),...,x 1 (t-t m ),Ψ)；
With the help of the young's inequality, the following holds:
constructing a first candidate lyapunov function as shown in formula (23):
in the formula (23), the amino acid sequence of the compound,and->Is a positive parameter of the design, estimation error +.>And->Wherein (1)>Andrespectively express theta 1 And v 1 * Is a function of the estimated value of (2);
deriving a first candidate lyapunov function by combining the correlation calculation obtained in the formulas (19) - (22) to obtain:
the virtual control quantity and the self-adaptive law are designed as follows:
wherein k is 1 ，And->Are all positive parameters of the design;
substituting the designed virtual control quantity and the adaptive law into the derivative of the first candidate lyapunov function can be obtained as shown in formula (28):
a first order low pass filter as shown in equation (29) is introduced to calculate the derivative of the virtual control quantity:
in formula (29), τ and z 1 The time constant and the output of the filter, respectively;
will filter the error y 1 Is defined as shown in formula (30):
y 1 ＝z 1 -x 2d (30)
regulation and control error S 2 Is defined as shown in formula (31):
S 2 ＝x 2 -z 1 (31)
by filtering error y 1 And regulatory error S 2 The expression (32) can be obtained as follows:
x 2 -x 2d ＝S 2 +z 1 -x 2d ＝S 2 +y 1 (32)
consider the following inequality:
in combination with formula (32), it can be demonstrated that as shown in formula (38):
s42, regulating and controlling the error S according to the mathematical model of the controlled object 2 As shown in formula (39):
in formula (39): ,
obtainable by formula (39), as shown in formula (40):
in the formula (40), the amino acid sequence of the compound,
constructing a second candidate lyapunov function as shown in formula (41):
in the formula (41),and->Is a positive parameter of the design, estimation error +.>And->Wherein->Andrespectively is theta 2 And v 2 * Is a function of the estimated value of (2);
combining equation (40), deriving a second candidate lyapunov function is available as shown in equation (42):
combining formula (16), the unknown term in formula (42)Can be written as shown in formula (43): :
and input vector theta 2 The same as defined in formula (17);
in the same way as step S41, the derivative of the second candidate lyapunov function can be rewritten as shown in the formula (44) using the inequality of the formulas (21) - (22):
the design control law and the update law are as follows:
wherein k is 2 ，And->Is a positive parameter of the design;
the designed control law and update law are brought into the derivative of the second candidate lyapunov function as shown in equation (48):
in the same way as in step S41, according to the young' S inequality, the formula (48) can be written as:
compared with the prior art, the invention has the beneficial effects that:
1. the problems of poor flexibility and low precision of controllable loads in the power system in the regulation and control process are solved, and compared with the traditional load regulation and control method, the method has lower calculation complexity in the process of realizing load dispatching optimization;
2. establishing controllable load error regulation constraint to realize intelligent controllable load regulation dynamic performance index;
3. the controllable load can be regulated and controlled in real time according to the change of the load demand of the power grid, a neural network system is combined with a limited coverage theory, not only can an unknown smooth function be approximated, but also the influence of the transmission time delay of the load regulation and control information can be compensated, the rapidity and the stability of the load regulation and control are ensured, and finally the self-adaption fine rapid stable control of the controllable load under the intelligent power grid is completed, and the dynamic balance of the load regulation and control under the intelligent power grid is realized.
Drawings
FIG. 1 is a flow chart of a controllable load intelligent dynamic regulation method
FIG. 2 is a topological structure diagram of a regulatory unit.
Fig. 3 is a structural diagram of an experimental system.
Fig. 4 is a trace performance graph.
Fig. 5 is a tracking error map.
Detailed Description
The present invention will be described in detail below with reference to the drawings of the specification, but it should be noted that the practice of the present invention is not limited to the following embodiments.
1-5, a controllable load intelligent dynamic regulation method for a smart grid, which is shown in FIG. 1, is realized based on the following steps:
s101, adopting mathematical transformation to transform a state space equation of a regulating and controlling unit into a mathematical model in a general form, and taking the mathematical model as a controlled object of the controllable load intelligent dynamic regulating and controlling method;
s102, designing a controllable load dynamic regulator, enabling a virtual control law to pass through a first-order low-pass filter, enabling the input of a neural network to be known, adopting the neural network combined with a finite coverage primer technology to compensate time delay, and ensuring a given intelligent regulation dynamic performance index by introducing a load error regulation constraint, namely a performance function and an error conversion function, so as to design a controllable load intelligent dynamic regulation method provided with load regulation information transmission time delay compensation;
s103, controlling stability analysis and semi-physical simulation by using a control unit of the controllable load intelligent dynamic control method.
The state space equation of the control unit in step S101 is shown in formula (50):
in the formula (50), u H Representing load side voltage, unit V; i.e L Representing inductor current, unit a; u (U) de The direct current busbar voltage of the direct current micro-grid is represented by a unit V; l is a storageInductance and unit mH; c (C) H The unit mF is a load side filter capacitor; r is internal resistance, unit Ω. The topology structure of the regulating unit is shown in figure 2, wherein V 1 ，V 2 Representing a diode. Without loss of generality, considering external disturbance, the state space equation of the regulating and controlling unit can be rewritten into a mathematical model in the following form in a mathematical transformation mode, and the mathematical model is used as a controlled object of the controllable load intelligent dynamic regulating and controlling method:
in the middle of It is emphasized that the value is unknown during the controller design process; τ i ，/>And d i I=1, 2, respectively represent an unknown time delay constant generated during the measurement of the load regulation information by the sensor, a voltage u on the load side with a time delay H The related unknown nonlinear function is smooth and the external disturbance of the system;
in the formula (2), u is the duty ratio of the MOS tube, namely the control input signal of the system;representing an output of the system;
for a mathematical model of a controlled object of the controllable load intelligent dynamic regulation method, the following assumption is specified:
suppose 1: g i Not equal to 0, i=1, 2, is an unknown normal number, and satisfies g i ≥g imin 。
Suppose 2: desired tracking y r Is a smooth and bounded function;belonging to a tight set.
Suppose 3: τ i I=1, 2 is an unknown normal number and satisfies 0.ltoreq.τ i ≤τ M Wherein τ M Denoted τ i Is a maximum value of (a).
Suppose 4: unknown external disturbance d i I=1, 2, satisfy
The method for intelligently and dynamically regulating the controllable load has the stability and control aim that the voltage at the controllable load side in a regulating unit is stabilized near an expected voltage value under the condition of taking unknown time delay and external interference generated in the process of measuring load regulating information by a sensor into consideration by adopting a controllable load dynamic regulator, all signals in the closed loop system are consistent and finally bounded, and tracking errors can be converged into one residue.
Step S102, designing a controllable load dynamic regulator, which comprises the following steps:
s1021: combining the mathematical model of the controlled object, converting the error S 1 The first derivative with respect to time t is:
in the formula (52), the amino acid sequence of the compound,x 2d is a virtual control law.
From formula (52):
in the formula (53), the amino acid sequence of the compound,
combining formula (16), unknown term in formula (53)Can be written in the following form:
and input vector theta 1 ＝(x 1 ,x 1 (t-t 1 ),…,x 1 (t-τ k/k ),…,x 1 (t-t m ),Ψ)。
With the help of the young's inequality, the following holds:
the first candidate lyapunov function is constructed as follows:
wherein,and->Is a positive parameter of the design, estimation error +.>And->Wherein (1)>And->Respectively express theta 1 And v 1 * Is used for the estimation of the estimated value of (a).
Combining equations (53) - (56), deriving the first candidate lyapunov function yields:
the virtual control quantity and the self-adaptive law are designed as follows:
wherein k is 1 ，And->Are all positive parameters of the design.
Substituting the designed virtual control quantity and the adaptive law into the derivative of the first candidate Lyapunov function to obtain:
now, a first order low pass filter of the form below is introduced to calculate the derivative of the virtual control quantity:
wherein τ and z 1 The time constant and the output of the filter, respectively.
Will filter the error y 1 The definition is as follows: y is 1 ＝z 1 -x 2d (64)
Regulation and control error S 2 The definition is as follows:
S 2 ＝x 2 -z 1 (65)
by filtering error y 1 And regulatory error S 2 The method can obtain:
x 2 -x 2d ＝S 2 +z 1 -x 2d ＝S 2 +y 1 (66)
consider the following inequality:
in combination with formula (62), it can be demonstrated that:
s1022: regulating and controlling error S according to mathematical model of controlled object 2 The derivative of (2) is:
wherein,
from the above formula:
wherein,
constructing a second candidate lyapunov function as follows:
wherein,and->Is a positive parameter of the design, estimation error +.>And->Wherein->And->Respectively is theta 2 And v 2 * Is used for the estimation of the estimated value of (a).
Combining equation (74), deriving a second candidate lyapunov function yields:
combining formula (16), the unknown term in formula (76)Can be written in the following form:
and input vector theta 2 As defined in equation (17).
In step S1021, the derivative of the second candidate Lyapunov function can be rewritten as follows using the inequality of the formulae (55) - (56):
the design control law and the update law are as follows:
/>
wherein k is 2 ，And->Is a positive parameter of the design.
Bringing the designed control law and update law to the derivative of the second candidate lyapunov function yields:
in step S1021, according to the Young' S inequality, equation (82) may be written as:
step S103, controlling stability analysis and semi-physical simulation by using a control unit of a controllable load intelligent dynamic control method, wherein the steps are as follows:
firstly, the stability of the proposed controllable load intelligent dynamic regulation method will be analyzed and discussed.
Theorem 1: given the assumption of 1-5, the update laws of equations (80) and (81) of the present invention, the control law in equation (79), and for any given normal number q, if V (0) satisfies V (0). Ltoreq.q, all signals in the closed loop system are consistently bounded. By selecting a suitable design parameter k i ，And->i=1, 2, the tracking error can converge into one residue.
And (3) proving: for filtering error y 1 The derivation can be obtained:
wherein B is a continuous function.
Consider the following third candidate lyapunov function:
the derivation of three candidate lyapunov functions is available:
order theAnd in combination with equations (72), (83) and (86), the derivatives of which are: />
According to hypothesis 2, set:
is thatLast one tight set and B 0 > 0. Furthermore, the set:
is thatA tight set of the above. Due to the aggregate xi 1 ×Ξ 2 Also->A tight set of the above. Thus, B is in the tight set of Xis 1 ×Ξ 2 There is a maximum value M.
The following inequality is combined:
and orderAnd
thus, the derivative of V can be rewritten as:
order the
I.e. v=q,thus, for any time t.gtoreq.0, if V (0). Ltoreq.q, V (t). Ltoreq.q.
Solving the differential inequality (91) yields:
simultaneously means:
thus, all signals in a closed loop system, e.g. e, S i ， And y 1 The uniformity is finally bounded. Furthermore, by selecting an appropriate design parameter k i ，/>And->i=1, 2, and formula (94) can be arbitrarily small. Thus, all signals e, S in V (t) i ，/> And y 1 Can be arbitrarily small. It should be noted here that, for any instant t.gtoreq.0, the switching error S 1 The constraint of (c) ensures that the tracking error e will remain in the performance function p (t) defined by equation (8) at all times. The syndrome is known.
The validity of the proposed algorithm was verified using a semi-physical simulation platform, the structure of which is shown in fig. 3. In the establishment of the power electronic real-time simulation experiment platform, a second-order state space model of a regulating unit in a formula (50) is considered to be used as a controlled object, a programmed controlled object model and a controller model are built by Simulink in Matlab, codes are generated through a real-time simulator and a rapid control prototype respectively and downloaded into experiment equipment, a closed loop is formed with physical hardware, and real-time simulation and online verification experiments are carried out.
In the simulation experiment, the parameters of the regulation and control unit are set as：U de ＝1000V，L＝10mH，C H =1mf and r=0.1Ω. Setting the initialized design parameters of the controller: k (k) 1 ＝130，k 2 ＝150， a 1 ＝a 2 =3. The time constant of the first order low pass filter is τ=0 . 01. Select reference signal y r =450. Error transfer function->
Wherein, input vector in neural network systemAndwherein t is 1 ＝0.2，t 2 ＝0.4，t 3 =0.8. For the basis function psi 1 (Θ 1 ) 243 nodes are selected, centered at center ζ j J=1,..243, distributed one by one over the interval [ -3,3]×[-3,3]×[-3,3]×[-3,3]×[-3,3]And its width mu j =1; for the base function ψ 2 (Θ 2 ) 81 nodes are selected, the center of which is xi j J=1, &..81, distributed one by one over the interval [ -3,3]×[-3,3]×[-3,3]×[-3,3]On the width mu j ＝1。
Referring to fig. 4, a tracking performance diagram provided for the controllable load intelligent dynamic regulation method is provided, and the abscissa represents simulation time in seconds; the ordinate represents the voltage value in volts; the solid black line represents the desired signal, which has a value of 450V; the red dashed line represents the output voltage of the system. Referring to fig. 5, a tracking error diagram provided for the controllable load intelligent dynamic regulation method,the abscissa represents the simulation time in seconds; the ordinate represents the voltage value in volts; the black solid line represents the performance function p (t) = ± (1.2 e) -0.8t +0.1); the red dashed line represents the tracking error of the system. Experimental results show that the controllable load intelligent dynamic regulation and control method can realize effective tracking performance, and the tracking error is finally stabilized near 0.03V. It is noted that the tracking error curve is always included in the performance function p (t) = ± (1.2 e) during the simulation time -0.8t +0.1), namely the load regulation and control error is always contained in the controllable load error regulation and control constraint, thereby realizing the self-adaptive precise and rapid stable control of the controllable load under the intelligent power grid.
The method solves the problems of poor flexibility and low precision of controllable load in the power system in the regulation and control process, and has lower calculation complexity in the process of realizing load dispatching optimization compared with the traditional load regulation and control method; establishing controllable load error regulation constraint to realize intelligent controllable load regulation dynamic performance index; the controllable load can be regulated and controlled in real time according to the change of the load demand of the power grid, a neural network system is combined with a limited coverage theory, not only can an unknown smooth function be approximated, but also the influence of the transmission time delay of the load regulation and control information can be compensated, the rapidity and the stability of the load regulation and control are ensured, and finally the self-adaption fine rapid stable control of the controllable load under the intelligent power grid is completed, and the dynamic balance of the load regulation and control under the intelligent power grid is realized.
Claims (3)
1. The intelligent dynamic control method for the controllable load of the intelligent power grid is characterized in that given intelligent control dynamic performance indexes are realized by introducing load error control constraint, and a neural network system with limited coverage theorem is combined to compensate the transmission time delay of load control information; by adjusting the parameters of the controller, the tracking error can be converged into any small residue, so that dynamic balance of load regulation and control under the intelligent power grid is realized;
the controllable load intelligent dynamic regulation and control method is realized based on the following steps:
s1, converting a state space equation of a regulating unit into a controllable load intelligent regulating model in a general form by adopting a mathematical equivalent conversion method, wherein the controllable load intelligent regulating model is used as a controlled object of a controllable load intelligent dynamic regulating method;
s2, introducing load error regulation and control constraint, namely adopting a performance function and an error conversion function to ensure a given intelligent regulation and control dynamic performance index;
s3, estimating an unknown function with time delay in a mathematical model of the controlled object by adopting a mode of combining a neural network and a finite coverage primer;
s4, designing a controllable load dynamic regulator, enabling the virtual control law to pass through a first-order low-pass filter, enabling the input of the neural network to be known, adopting the neural network to compensate time delay by combining the technology of finite coverage quotients, combining the performance function and the error conversion function in the step S2 to ensure given intelligent regulation dynamic performance indexes, and designing the controllable load intelligent dynamic regulation method provided with load regulation information transmission time delay compensation.
2. The intelligent dynamic control method of controllable load for intelligent power grid according to claim 1, wherein in step S1, the state space equation of the control unit is shown in formula (1):
in the formula (1), u H Representing load side voltage, unit V; i.e L Representing inductor current, unit a; u (U) de The direct current busbar voltage of the direct current micro-grid is represented by a unit V; l is an energy storage inductance, and the unit is mH; c (C) H The unit mF is a load side filter capacitor; r is internal resistance, unit omega; without loss of generality, considering external disturbance, the state space equation of the regulating and controlling unit can be rewritten into a mathematical model shown as a formula (2) in a mathematical transformation mode, and the mathematical model is used as a controlled object of the controllable load intelligent dynamic regulating and controlling method:
in the formula (2), the amino acid sequence of the compound,it is emphasized that the value is unknown during the controller design process; τ i ，/>And d i I=1, 2, respectively represent an unknown time delay constant generated during the measurement of the load regulation information by the sensor, a voltage u on the load side with a time delay H A related unknown nonlinear smoothing function and external disturbances;
in the formula (2), u is the duty ratio of the MOS tube, namely, the control input signal;representing an output;
for the mathematical model of the controlled object of the controllable load intelligent dynamic regulation method, the following assumption is specified:
suppose 1: g i Not equal to 0, i=1, 2, is an unknown normal number, and satisfies g i ≥g imin ：
Suppose 2: desired tracking y r Is a smooth and bounded function;belonging to a tight set:
suppose 3: τ i I=1, 2 is an unknown normal number and satisfies 0.ltoreq.τ i ≤τ M Wherein τ M Denoted τ i Is the maximum value of (2):
suppose 4: unknown external disturbance d i I=1, 2, satisfy
To introduce the adoption of the performance function and the error transfer function described in step S2, the tracking error e is defined as shown in equation (3):
e＝x 1 -y r (3)
for any moment when t is greater than or equal to 0, the performance function p (t) is a smooth and decreasing positive function, and the specific form is as shown in the formula (4):
in the formula (4), 0 < sigma < 1 and lim t→∞ ＝p ∞ ＞0，p ∞ Is the maximum value of the allowable regulation error when stabilizing; to convert equation (4) to an equivalent function, an error transfer function of the form is introduced as shown in equation (5):
e(t):＝p(t)Φ(S 1 ), (5)
in the formula (5), S 1 Is a conversion error converted by an error conversion function, Φ (S 1 ) Is a smooth and strictly monotonically increasing function whose inverse has the following properties:
and is also provided with
As can be seen from formula (7), ifThen equation (6) holds; taking further consideration of p (t) > 0 and formula (5), it is possible to obtain- σp (t) < p (t) Φ (S 1 ) =p (t) < 1 (when e (0) > 0) or-p (t) < Φ (S) 1 ) =p (t) < σp (t) (when e (0) < 0), i.e., formula (4) holds; thus, from the above analysis, it is only necessary to prove +.>The preparation method is finished; from phi (S) 1 ) The strictly monotonically increasing nature of (2) can give a conversion error S 1 The method comprises the following steps:
adopting the neural network in the step S3 to approach the unknown smooth function f in the mathematical model of the controlled object on line i :Wherein (1)>Representing a tight set, q being the dimension of the input vector; the neural network approximates the expression of the unknown function as shown in equation (9):
in the formula (9), the amino acid sequence of the compound,is an input vector to the neural network; />Is an optimal weight vector, and N is the number of nodes of the neuron, as shown in formula (10):
in the formula (10), the amino acid sequence of the compound,is a weight vector function; kappa (kappa) j (ζ i ) Is a radial basis function, as shown in equation (11):
κ j (ζ i )＝exp[-||ζ i -ξ j ||/2μ j 2 ],j＝1,...,N (11)
in the formula (11): zeta type toy j Is the center of the j-th basis function, mu j Is the width of the basis function; epsilon i Is the estimated error of the neural network;
suppose 5: epsilon i |≤υ i ，Wherein v i An upper bound value representing an estimation error;
annotation 1: it should be emphasized that when the unknown smoothing function has an unknown time delay constant τ i When, for example, f i (ζ i ,ζ i (t-τ i ) A direct approximation cannot be made using a neural network; however, due to the tight setCan be used to determine the unknown function using a neural network combined with a finite coverage primer>Performing approximation;
lemma 1: assume an input vector ζ i ＝(ζ i ,ζ i (t-τ i ) In line with time t, where τ i ∈[0,τ M ]Is an unknown time delay constant; then, for any given errorThere is a finite interval [0, τ ] independent of time t M ]In the form shown in the formula (12):
0＜t 1 ＜…＜t m ＜τ M (12)
at one time point in the formula (12), the formula (13) is satisfied:
the method can obtain:
combining lemma 1 and hypothesis 3, there are multiple time points τ 1/1 ,...,τ n/n ∈{t 1 ,…,t m -representing the unknown smooth function with time delay as shown in equation (15):
in formula (15):the estimation error representing the finite coverage theory, combined with the expression of the neural network approximation unknown function, equation (15) can be approximated as the one shown in equation (16):
in formula (16), delta i The sum of the upper bound value representing the neural network estimation error and the estimation error of the finite coverage primer; and the input vector is as shown in formula (17):
Θ i ＝(x 1 ,...,x i ,...,x 1 (t-t 1 ),...,x k (t-τ k/k ),...,x i (t-t m )) (17)。
3. the intelligent dynamic control method for the controllable load of the smart grid according to claim 1, wherein step S4 designs the dynamic controller for the controllable load, and comprises the following steps:
s41, combining a mathematical model of the controlled object, and converting the error S 1 The first derivative with respect to time t is shown in equation (18):
in formula (18):x 2d is a virtual control law;
from the formula (18), as shown in the formula (19):
in the formula (19), the amino acid sequence of the compound,
combining formula (16), the unknown term in formula (19)Can be written as shown in formula (20):
and input vector theta 1 ＝(x 1 ,x 1 (t-t 1 ),…,x 1 (t-τ k/k ),…,x 1 (t-t m ),Ψ)；
With the help of the young's inequality, the following holds:
constructing a first candidate lyapunov function as shown in formula (23):
in the formula (23), the amino acid sequence of the compound,and->Is a positive parameter of the design, estimation error +.>And->Wherein (1)>And->Respectively express theta 1 And v 1 * Is a function of the estimated value of (2);
deriving a first candidate lyapunov function by combining the correlation calculation obtained in the formulas (19) - (22) to obtain:
the virtual control quantity and the self-adaptive law are designed as follows:
wherein k is 1 ，And->Are all positive parameters of the design;
substituting the designed virtual control quantity and the adaptive law into the derivative of the first candidate lyapunov function can be obtained as shown in formula (28):
a first order low pass filter as shown in equation (29) is introduced to calculate the derivative of the virtual control quantity:
in formula (29), τ and z 1 The time constant and the output of the filter, respectively;
will filter the error y 1 Is defined as shown in formula (30):
y 1 ＝z 1 -x 2d (30)
regulation and control error S 2 Is defined as shown in formula (31):
S 2 ＝x 2 -z 1 (31)
by filtering error y 1 And regulatory error S 2 The expression (32) can be obtained as follows:
x 2 -x 2d ＝S 2 +z 1 -x 2d ＝S 2 +y 1 (32)
consider the following inequality:
in combination with formula (32), it can be demonstrated that as shown in formula (38):
s42, regulating and controlling the error S according to the mathematical model of the controlled object 2 As shown in formula (39):
in formula (39): ,
obtainable by formula (39), as shown in formula (40):
in the formula (40), the amino acid sequence of the compound,
constructing a second candidate lyapunov function as shown in formula (41):
in the formula (41),and->Is a positive parameter of the design, estimation error +.>And->Wherein->And->Respectively is theta 2 And v 2 * Is a function of the estimated value of (2);
combining equation (40), deriving a second candidate lyapunov function is available as shown in equation (42):
combining formula (16), the unknown term in formula (42)Can be written as shown in formula (43): :
and input vector theta 2 The same as defined in formula (17);
in the same way as step S41, the derivative of the second candidate lyapunov function can be rewritten as shown in the formula (44) using the inequality of the formulas (21) - (22):
the design control law and the update law are as follows:
wherein k is 2 ，And->Is a positive parameter of the design;
the designed control law and update law are brought into the derivative of the second candidate lyapunov function as shown in equation (48):
in the same way as in step S41, according to the young' S inequality, the formula (48) can be written as:
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