CN113054648B - Direct-current micro-grid droop coefficient optimization method and system based on improved whale algorithm - Google Patents
Direct-current micro-grid droop coefficient optimization method and system based on improved whale algorithm Download PDFInfo
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Abstract
The application provides a direct current micro-grid droop coefficient optimization method based on an improved whale algorithm, which comprises the following steps of: s1, calculating the active deviation of each micro source according to the current and voltage parameters of each micro source, determining an adaptability function, and creating an initial population according to a plurality of active deviations; s2, calculating an fitness function value, and selecting three stages of searching foraging, contracting and surrounding and spirally updating positions according to a probability factor p and a coefficient A; and S3, updating the individual position, and repeating the step 2 until the requirement is met or the iteration upper limit is reached, so as to obtain a new droop coefficient. The application optimizes the sagging coefficient through an improved whale algorithm, can accurately monitor the power unbalance state in the system, reasonably distributes the micro-source output, effectively compensates the busbar voltage deviation, better performs sagging control of the direct-current micro-grid, realizes stable and reliable operation of the independent direct-current micro-grid, and has important effect and significance in the control of the direct-current micro-grid.
Description
Technical Field
The application relates to the technical field of sagging control, in particular to a direct-current micro-grid sagging coefficient optimization method and system based on an improved whale algorithm.
Background
With the deep research, the direct current equipment is continuously developed and updated, the defects of an alternating current micro-grid exist, and the direct current micro-grid technology is greatly popularized, wherein the defects of more energy conversion links, large line loss and complex control are gradually revealed. The advantages of the direct current micro grid are:
(1) The system is relatively stable. The frequency and the phase of the AC micro-grid are required to be ensured to be within a specified range to maintain reliable operation, and the DC micro-grid only needs to stabilize the DC bus voltage, so that each unit of the control system is easier to coordinate;
(2) The loss is reduced. The DC micro-grid has few converters, does not need to consider reactive factors and use a reactive compensation device, can save electric energy and effectively reduce system loss;
(3) The use is flexible. The distributed power supply is flexibly connected with the micro-grid, and can be flexibly cut off when faults occur, so that the integral operation of the system is not influenced;
(4) Is safer. The DC line has small corona loss and radio interference, and generates little electromagnetic radiation.
In general, as a new energy utilization means, a direct current micro grid has received widespread attention worldwide. However, compared with the ac microgrid, the dc microgrid technology is still in an exploration stage, and the theory and technology are not perfect. And the research on the control strategy is the core of the research on the direct current micro-grid, and has important research significance.
The direct current micro grid system generally adopts droop control, the droop coefficient in the traditional droop control is constant, the dynamic response is slow when the droop coefficient is small, the frequency and the voltage can deviate from the reference values far when the droop coefficient is large, the stability of the micro grid is difficult to ensure, and the stability of the direct current voltage is deviated due to the self attribute of the droop control. In 2016, the australian scholars mirjallii and Lewis were inspired by observing the foraging behavior of whales at the head of the seat, and a new intelligent algorithm, whale Optimization Algorithm (WOA), was proposed. The algorithm mainly realizes the solution of the optimization problem by simulating the predation behavior of whales, has the advantages of simple principle, unique search mechanism and the like, and is widely applied to the fields of micro-grid coordinated control optimization and the like. However, in the research process, many problems still exist, and similar to other population intelligent algorithms based on population, the WOA algorithm also has the defects of low convergence speed and low solving accuracy.
The document "hybrid direct current transmission system control parameter optimization method based on improved whale algorithm" (electric power construction, volume 40, fourth period 2019, month Liu Jipiao, shang Xiaochen, yang Jia, sun Guojiang, wei Zhinong) describes that the hybrid direct current transmission system control parameter optimization problem is solved by adopting the whale algorithm, but also describes that the method has real-time problem and the time required for optimizing is long.
Disclosure of Invention
The technical problem to be solved by the application is to provide the direct current micro-grid droop coefficient optimization method with high instantaneity, high convergence speed and high solving precision.
The application solves the technical problems by the following technical means:
a direct current micro-grid droop coefficient optimization method based on an improved whale algorithm comprises the following steps:
s1, reading current and voltage parameters of each micro source under a direct current micro-grid, setting whale number according to the micro source number, setting iteration times, calculating active deviation of each micro source according to the current and voltage parameters, determining a fitness function, and creating an initial population according to a plurality of active deviations;
s2, calculating an adaptability function value, and entering a new updating period when the adaptability function value is judged to be larger than the system requirement and the iteration number is smaller than a set value: selecting three stages of searching foraging, contracting and surrounding and spiral updating positions according to the probability factor p and the coefficient A, entering the spiral updating position when the probability factor is larger than or equal to a set value, otherwise, entering the stages of searching foraging and contracting and surrounding;
and S3, updating the individual position, and repeating the step 2 until the requirement is met or the iteration upper limit is reached, so as to obtain a new droop coefficient.
The method provided by the application is suitable for the droop control process of the direct-current micro-grid, the droop coefficient is optimized through an improved whale algorithm, the power unbalance state in the system can be accurately monitored, the micro-source output is reasonably distributed, the busbar voltage deviation is effectively compensated, the droop control of the direct-current micro-grid is better carried out, the stable and reliable operation of the independent direct-current micro-grid is realized, and the method has important effect and significance in the control of the direct-current micro-grid; at the set optimization time point of the droop coefficient, the micro-grid takes the active power deviation of the line unbalance information in real-time operation as input, and controls different active power deviation degrees with different intensities when optimizing each time, so that the optimization force with large deviation degree is large, the optimization force with small deviation is small, and the droop coefficient can be optimized at the fastest speed.
Further, in the step S2, the search foraging is that whale individuals perform random search according to the mutual positions, and the mathematical model is expressed as follows:
D＝|C·X rand (t)-X(t)|
wherein X is rand (t) is the number of whale individuals randomly selected from the current whale population, and X (t) is the current whale individual location;
X(t+1)＝X rand (t)-A·|C·X rand (t)-X(t)|
wherein A and C are coefficient vectors defined as
A＝2a·r 1 -a
C＝2·r 2
R in the formula 1 And r 2 Is [0, 1]]The random number, a, between is called the control parameter, decreases linearly from 2 to 0 with increasing iteration number t, i.e
Wherein Max_iter is the maximum iteration number; when the A is more than or equal to 1, whales enter a searching and feeding stage, and whale individuals can randomly search according to the respective phase positions.
Further, the shrink wrap in the searching step 2 is specifically: when |a| <1, the whale individual will approach toward the whale at the current location that is optimal, the mathematical model can be expressed as:
D＝|C·X best (t)-X(t)|
X(t+1)＝X best (t)-A·|C·X best (t)-X(t)|
wherein X is best (t) is the best positioned whale individual in the current whale population, A.|C.X best (t) -X (t) | is the bounding step size, the larger the A| the larger the step size of whale wander.
Further, in the stage of updating the position by the spiral, the initial point of the spiral update is the current position of the whale individual, the target end point is the current position of the best whale individual, and the mathematical model can be expressed as follows:
D′＝|X best (t)-X(t)|
wherein D' represents the distance between the current individual whale and the whale at the optimal position;
X(t+1)＝D′·e bl ·cos(2πl)+X best (t)
where b is a constant coefficient and l is a random number between [ -1,1 ].
Further, the selection of three stages of searching foraging, contracting surrounding and spiral updating position is determined by the values of a probability factor p and a coefficient |A|; p <0.5, entering a search foraging stage and a contraction bounding stage, the mathematical model of contraction bounding and foraging can be expressed as:
further, the position update formula when the whale searches for food is as follows:
X(t+1)＝X rand (t)-w·A·D
wherein w is a nonlinear time-varying adaptive weight factor defined as follows:
where k is the adjustment coefficient.
Further, the position update formula when the whale is contracted and surrounded is as follows:
X(t+1)＝X best (t)-A·D+λ
wherein λ is a differential variation perturbation factor, and is defined as follows:
λ＝F·(X best (t)-X(t))
wherein F is a variation scale factor.
Further, the position update formula when the whale spiral is updated is as follows:
X(t+1)＝w·D′·(b·l)·cos(2πl)+X best (t)
correspondingly, the application also provides a direct-current micro-grid droop coefficient optimization system based on an improved whale algorithm, which comprises a plurality of micro-grid island, droop controllers and WOA modules which are connected in parallel on a direct-current bus, wherein the droop controllers acquire the current power of the direct-current bus, calculate the current droop coefficient and send the current droop coefficient to the WOA modules, the WOA modules execute the method, the optimized droop coefficient is obtained and sent to the droop controllers, and the droop controllers control the power output of the micro-grid island according to the optimized droop coefficient.
Further, the WOA module comprises an active deviation calculation unit, an fitness function calculation unit and a clock updating unit; the output end of the sagging controller is in communication connection with the active deviation calculating unit, the active deviation calculating unit is in communication connection with the fitness function calculating unit, the fitness function calculating unit is in communication connection with the clock updating unit, and the clock updating unit is in communication connection with the sagging controller.
The application has the advantages that:
the application is suitable for the sagging control process of the direct-current micro-grid, optimizes the sagging coefficient through an improved whale algorithm, can accurately monitor the power unbalance state in the system, reasonably distributes micro-source output, effectively compensates bus voltage deviation, better performs sagging control of the direct-current micro-grid, realizes stable and reliable operation of the independent direct-current micro-grid, and has important effect and significance in the control of the direct-current micro-grid; at the set optimization time point of the droop coefficient, the micro-grid takes the active power deviation of the line unbalance information in real-time operation as input, and controls different active power deviation degrees with different intensities when optimizing each time, so that the optimization force with large deviation degree is large, the optimization force with small deviation is small, and the droop coefficient can be optimized at the fastest speed.
Drawings
FIG. 1 is a flow chart of a method for optimizing droop coefficients of a DC micro-grid based on an improved whale algorithm in an embodiment of the application;
FIG. 2 is a circuit diagram of an application scenario of an optimization method in an embodiment of the application;
FIG. 3 is a graph showing dynamic sagging for an example of application of the optimization method according to the embodiment of the present application;
FIG. 4 is a graph showing the voltage deviation of the bus bar in the conventional method.
FIG. 5 is a graph showing the voltage deviation of the bus bar according to the method of the embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described in the following in conjunction with the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a method for optimizing droop coefficients of a direct-current micro-grid based on an improved whale algorithm, wherein a main circuit diagram of the direct-current micro-grid is shown in fig. 2, and active droop coefficients in two parallel micro-source units are optimized in the embodiment. In the droop control process, when the micro-grid island operates, the droop control module inputs the active power deviation of the circuit to the optimizing WOA module in real time through the connecting circuit along with the occurrence of the power unbalance condition of the system, namely, the droop control module optimizes by adopting the method to obtain an optimized droop coefficient, and updates the original droop coefficient to improve the power distribution under the impedance unbalance.
As shown in fig. 1, the method comprises the steps of:
step 1, reading parameters such as a fan, a photovoltaic system, a composite energy storage system and the like, setting the number of whales and the iteration times, calculating a corresponding fitness function fitness, creating an initial population, and completing a first cycle;
the fitness function is the mean square error (Mean Squared Error, MSE) of the active power deviation, and the specific formula is:
wherein P is 1 Indicating the magnitude of the active power output by the first micro-source, P ref The method is used for indicating the rated distributed active power of the micro-sources of the direct current micro-grid system, and G is used for indicating the number of the micro-sources.
Step 2, when judging that the fitness function value is larger than the system requirement and the iteration number is smaller than the set value, entering a new updating period: the WOA algorithm is a bionic intelligent optimization algorithm of seersucker net foraging behavior unique to whale of a model seat, and other individuals in the population approach to the optimal individual on the assumption that the current optimal individual is a prey. The WOA algorithm is mainly divided into three phases: search for food, shrink wrap and spiral update location. Selecting three stages of searching foraging, contracting and surrounding and spiral updating positions according to the probability factor p and the coefficient A, entering the spiral updating position when the probability factor is more than or equal to 0.5, otherwise, entering the stages of searching foraging and contracting and surrounding;
the search foraging stage is a process of randomly searching food by whales, whale individuals randomly search according to the positions of each other, and corresponds to the global exploration stage of an algorithm, and a mathematical model can be expressed as follows:
D＝|C·X rand (t)-X(t)|
wherein X is rand (t) is the number of whale individuals randomly selected from the current whale population and X (t) is the current whale individual location.
X(t+1)＝X rand (t)-A·|C·X rand (t)-X(t)|
Wherein A and C are coefficient vectors defined as
A＝2a·r 1 -a
C＝2·r 2
R in the formula 1 And r 2 Is [0, 1]]The random number, a, between is called the control parameter, decreases linearly from 2 to 0 with increasing iteration number t, i.e
Where max_iter is the maximum number of iterations. When the A is more than or equal to 1, whales enter a searching and feeding stage, and whale individuals can randomly search according to the respective phase positions.
The seersucker net predation behavior of whale individuals together includes two mechanisms, contraction wrap and spiral update positions, corresponding to the local development stage of the algorithm. When |a| <1, the whale individual will approach toward the whale at the current location that is optimal. The mathematical model thereof can be expressed as:
D＝|C·X best (t)-X(t)|
X(t+1)＝X best (t)-A·|C·X best (t)-X(t)|
wherein X is best (t) is the best positioned whale individual in the current whale population, A.|C.X best (t) -X (t) | is the bounding step size, the larger the A| the larger the step size of whale wander.
During the spiral update position phase, whales, while approaching the optimal whale individual, will walk and feed in a spiral fashion, searching for the optimal solution that may exist from whale individual to optimal individual. The initial point of the spiral update is the current whale individual's location and the target end point is the current best whale individual's location. The mathematical model thereof can be expressed as:
D′＝|X best (t)-X(t)|
wherein D' represents the distance between the current individual whale and the whale at the optimal position.
X(t+1)＝D′·e bl ·cos(2πl)+X best (t)
Where b is a constant coefficient and l is a random number between [ -1,1 ].
The selection of three stages of searching foraging, contracting and enclosing and spiral updating position is determined by the values of a probability factor p and a coefficient |A|; p <0.5, entering a search foraging phase and a shrink wrapping phase, the mathematical model of which can be expressed as:
the position updating formula when the whale searches for food is as follows:
X(t+1)＝X rand (t)-w·A·D
wherein w is a nonlinear time-varying adaptive weight factor defined as follows:
where k is the adjustment coefficient.
The position updating formula when the whale is contracted and surrounded is as follows:
X(t+1)＝X best (t)-A·D+λ
wherein λ is a differential variation perturbation factor, and is defined as follows:
λ＝F·(X best (t)-X(t))
wherein F is a variation scale factor.
The position updating formula during the screw updating of whale is as follows:
X(t+1)＝w·D′·(b·l)·cos(2πl)+X best (t)
in this embodiment, the fitness function is the mean square error of the active power deviation.
And 3, updating the individual position, and repeating the step 2 until the requirement is met or the iteration upper limit is reached, so as to obtain a new droop coefficient.
The method for optimizing the direct-current micro-grid droop system for improving the whale algorithm is suitable for optimizing the droop coefficient through the improved whale algorithm in the direct-current micro-grid droop control process, can accurately monitor the power imbalance state in the system, reasonably distributes micro-source output, effectively compensates bus voltage deviation, better performs droop control of the direct-current micro-grid, realizes stable and reliable operation of the independent direct-current micro-grid, and has important effect and significance in direct-current micro-grid control; at the set optimization time point of the droop coefficient, the micro-grid takes the active power deviation of the line unbalance information in real-time operation as input, and controls different active power deviation degrees with different intensities when optimizing each time, so that the optimization force with large deviation degree is large, the optimization force with small deviation is small, and the droop coefficient can be optimized at the fastest speed.
Based on the method, the application also provides a direct current micro grid droop system optimization for improving whale algorithm, as shown in figure 2, which comprises a plurality of micro grid island, droop controllers and WOA modules connected in parallel on a direct current bus. The droop controller comprises a power input end, an active instantaneous power calculation unit, a P-V variable slope droop coefficient calculation unit, a voltage outer loop unit, a current inner loop unit and an SVPWM (space vector pulse modulation unit); the WOA module comprises an active deviation calculation unit, an fitness function calculation unit and a clock updating unit; the input end of the sagging control receives the current and voltage signals of the bus, then the active instantaneous power calculating unit calculates the instantaneous power, and the active instantaneous power calculating unit sends the instantaneous power to the P-V variable slope sagging coefficient calculating unit to calculate the current sagging coefficient, namely whale. And then the P-V variable slope droop coefficient calculation unit sends the droop system to the active deviation unit to perform active power deviation calculation, the active deviation unit sends the active deviation to the fitness function unit, the fitness function unit obtains the current optimal solution and sends the current optimal solution to the time engineering update unit to record the iteration times, the time engineering update unit sends the current optimal solution to the P-V variable slope droop coefficient calculation unit, at the moment, an iteration is completed, and the process from the P-V variable slope droop coefficient calculation unit to the P-V variable slope droop coefficient calculation unit is repeated until the optimized droop coefficient is obtained. And according to the optimized sagging system, the controller performs compensation according to the current voltage and current, and finally outputs a control pulse signal from the SVPWM unit. And controlling the power output of the micro-grid island.
In this embodiment, the active droop coefficients in the plurality of parallel micro-source units are optimized. In the droop control process, when the micro-grid island operates, the droop control module inputs the active power deviation of the circuit into the optimizing WOA module in real time through the connecting circuit along with the occurrence of the power unbalance condition of the system, namely, the method is adopted for optimizing, so as to obtain an optimized droop coefficient, and the original droop coefficient is updated to improve the power distribution under the impedance unbalance
In fig. 3, a straight line 0 is a conventional droop curve, and straight lines 1 and 2 are adaptive droop curves when load power in a system increases and decreases respectively after the method is adopted. The system runs along curve 0 while stabilizing, and the droop coefficient is unchanged. When the load power in the system is increased, power shortage occurs in the system, the bus voltage is reduced, at the moment, the sagging coefficient of the control system is reduced, so that the control system runs along the straight line 2, the micro source emits more power, the power shortage is complemented, and the voltage is increased to the rated voltage; when the load power in the system is reduced, surplus power occurs in the system, the bus voltage rises, and at the moment, the sagging coefficient of the control system is increased, so that the control system runs along the straight line 1, the power emitted by the micro source is reduced, the power in the system is balanced, and the bus voltage drops to the rated value.
Therefore, by a method based on the optimization of the sag factor of the direct current micro grid by improving the whale algorithm, the real-time distribution of the system power and the reduction of the bus voltage deviation until the stability can be realized.
As can be seen from comparison of fig. 4 and 5, the droop coefficient is optimized by adopting the improved whale algorithm in the direct-current micro-grid, so that the busbar voltage can be kept relatively stable under the conditions of micro-source and load fluctuation, and the busbar voltage can be controlled in a non-deviation manner and kept at about 600V; and with the traditional droop control, the busbar voltage cannot be kept near a given value, and the stable control of the direct-current micro-grid is affected. The parameters of the dc microgrid are designed as shown in table 1.
Table 1 system parameters
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. The direct-current micro-grid droop coefficient optimization method based on the improved whale algorithm is characterized by comprising the following steps of:
s1: reading current and voltage parameters of each micro source under a direct current micro-grid, setting whale number according to the micro source number, setting iteration times, calculating active deviation of each micro source according to the current and voltage parameters, determining an fitness function, and creating an initial population according to a plurality of active deviations;
s2: calculating an fitness function value, and entering a new updating period when the fitness function value is judged to be larger than the system requirement and the iteration number is smaller than a set value: selecting three stages of searching foraging, contracting and surrounding and spiral updating positions according to the probability factor p and the coefficient A, entering the spiral updating position when the probability factor is larger than or equal to a set value, otherwise, entering the stages of searching foraging and contracting and surrounding;
s3: updating the individual position, repeating the step 2 until the requirement is met or the iteration upper limit is reached, and obtaining a new sagging coefficient;
in the step S2, the search foraging is that whale individuals perform random search according to the mutual positions, and the mathematical model is expressed as follows:
D＝|C·X rand (t)-X(t)|
wherein X is rand (t) is the number of whale individuals randomly selected from the current whale population, and X (t) is the current whale individual location;
X(t+1)＝X rand (t)-A·|C·X rand (t)-X(t)|
wherein A and C are coefficient vectors defined as
A＝2a·r 1 -a
C＝2·r 2
R in the formula 1 And r 2 Is [0, 1]]The random number, a, between is called the control parameter, decreases linearly from 2 to 0 with increasing iteration number t, i.e
Wherein Max_iter is the maximum iteration number; when the A is more than or equal to 1, whales enter a searching and feeding stage, and whale individuals can randomly search according to the respective phase positions.
2. The method for optimizing the droop coefficient of the direct current micro-grid based on the improved whale algorithm according to claim 1, wherein the shrink wrap in the searching step 2 is specifically: when |a| <1, the whale individual will approach toward the whale at the current location that is optimal, the mathematical model can be expressed as:
D＝|C·X best (t)-X(t)|
X(t+1)＝X best (t)-A·|C·X best (t)-X(t)|
wherein X is best (t) is the best positioned whale individual in the current whale population, A.|C.X best (t) -X (t) | is the bounding step size, the larger the A| the larger the step size of whale wander.
3. The method for optimizing droop coefficient of direct current micro-grid based on improved whale algorithm according to claim 2, wherein in the stage of spiral update position, the initial point of spiral update is the position of the current whale individual, the target end point is the position of the current best whale individual, and the mathematical model can be expressed as:
D′＝|X best (t)-X(t)|
wherein D' represents the distance between the current individual whale and the whale at the optimal position;
X(t+1)＝D′·e bl ·cos(2πl)+X best (t)
where b is a constant coefficient and l is a random number between [ -1,1 ].
4. The method for optimizing the droop coefficient of a direct current micro-grid based on an improved whale algorithm according to claim 1, wherein the selection of three stages of searching foraging, contracting surrounding and spiral updating position is jointly determined by the values of a probability factor p and a coefficient |A|, and when p is more than or equal to 0.5, the stage of spiral updating position is entered; p <0.5, entering a search foraging stage and a contraction bounding stage, the mathematical model of contraction bounding and foraging can be expressed as:
5. the method for optimizing the droop coefficient of the direct current micro-grid based on the improved whale algorithm according to claim 4, wherein the position update formula when the whale searches for food is:
X(t+1)＝X rand (t)-w·A·D
wherein w is a nonlinear time-varying adaptive weight factor defined as follows:
where k is the adjustment coefficient.
6. The method for optimizing droop coefficients of a direct current micro-grid based on improved whale algorithm according to claim 4, wherein the position update formula when whale shrink is enclosed is:
X(t+1)＝X best (t)-A·D+λ
wherein λ is a differential variation perturbation factor, and is defined as follows:
λ＝F·(X best (t)-X(t))
wherein F is a variation scale factor.
7. The method for optimizing droop coefficients of a direct current micro-grid based on improved whale algorithm according to claim 4, wherein the position update formula when the whale screw is updated is:
X(t+1)＝w·D′·(b·l)·cos(2πl)+X best (t)。
8. the utility model provides a direct current micro grid droop coefficient optimizing system based on improvement whale algorithm which is characterized by comprising a plurality of micro grid isolated islands connected in parallel on the direct current bus, a droop controller and a WOA module, wherein the droop controller obtains the current power of the direct current bus, calculates the current droop coefficient, and sends the droop system to the WOA module, the WOA module executes the method of any one of claims 1 to 7, the obtained optimized droop coefficient is sent to the droop controller, and the droop controller controls the power output of the micro grid isolated islands according to the optimized droop coefficient.
9. The system for optimizing a direct current micro grid droop system according to claim 8, wherein the WOA module comprises an active deviation calculation unit, an fitness function calculation unit and a clock update unit; the output end of the sagging controller is in communication connection with the active deviation calculating unit, the active deviation calculating unit is in communication connection with the fitness function calculating unit, the fitness function calculating unit is in communication connection with the clock updating unit, and the clock updating unit is in communication connection with the sagging controller.
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