CN117691689A - Photovoltaic inverter parameter identification method based on multi-strategy improved bald eagle search algorithm - Google Patents
Photovoltaic inverter parameter identification method based on multi-strategy improved bald eagle search algorithm Download PDFInfo
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Abstract
A photovoltaic inverter parameter identification method based on a multi-strategy improved bald eagle search algorithm comprises the following steps: step 1, building a photovoltaic inverter control model and determining control parameters to be identified; step 2: based on a hardware-in-loop simulation test platform, collecting a photovoltaic inverter output response data set under multiple working conditions, and taking the photovoltaic inverter output response data set as identification data; step 3: and (3) based on the identification data obtained in the step (2), identifying the control parameters of the photovoltaic inverter by utilizing a multi-strategy improved bald eagle search algorithm. The method improves the position updating formulas of the traditional BES algorithm in three stages by utilizing the Levy flight strategy, the Cauchy variation strategy and the self-adaptive weighting factor respectively, effectively balances the global searching performance and the local development capability of the BES algorithm, improves the convergence speed and the optimizing precision of the BES algorithm, and improves the parameter identification precision of the photovoltaic inverter.
Description
Technical Field
The invention relates to the field of photovoltaic power generation control, in particular to a photovoltaic inverter parameter identification method based on a multi-strategy improved bald eagle search algorithm.
Background
Accurate modeling of photovoltaic power generation systems is critical to analysis of grid stable operation. The inverter is used as a core component of the photovoltaic power generation system, the accuracy of a model of the inverter is closely related to the accuracy of the acquired control parameters, and the reliability of grid-connected operation analysis is directly affected. In recent years, researchers have more fully explored the circuit structure and controller model structure of grid-connected inverters. Wherein there are some unknown control parameters of the PI regulator in the controller. Proper setting of these parameters will provide the controller with desirable dynamic response characteristics, thereby enhancing the stability of the photovoltaic power generation system. However, in practice, because the accurate model and parameters of the controller cannot be obtained due to the intellectual property protection of the manufacturer or lack of testing means, the method has important significance in the research of identifying the parameters of the grid-connected inverter. The parameter identification research is helpful for improving the accuracy of the grid-connected inverter model and provides a more reliable analysis basis for the stable operation of the photovoltaic power generation system.
At present, the intelligent algorithm is one of research hot spots in the optimization field, the intelligent optimization algorithm is used for identifying the parameters of the nonlinear model, the intelligent optimization algorithm is used for identifying the parameters, or the intelligent algorithm and the traditional method are combined to have higher precision, so that the intelligent algorithm is widely applied. The bald hawk search algorithm (bald eagle search, BES) is a novel heuristic algorithm which is provided by a malaysian scholars through simulating intelligent behaviors in the bald hawk predation process, and has high optimizing precision and high convergence speed compared with other algorithms. BES algorithm has good performance in the aspect of identification optimization, and is concerned, but the BES algorithm still has the problems of easy local optimization, uncoordinated global search and local development and the like.
Disclosure of Invention
In order to solve the problems that partial traditional algorithms are prone to being in a locally optimal solution, the convergence speed is low and the capacity of processing nonlinear problems is extremely poor, so that optimizing precision in the aspect of photovoltaic inverter parameter identification is poor, identification results are not accurate enough, and the problem that the actual output characteristics of a photovoltaic inverter are difficult to describe is solved. The invention provides a photovoltaic inverter parameter identification method based on a multi-strategy improved balying search algorithm, which introduces a Levy flight strategy, a Cauchy variation strategy and a self-adaptive inertia weight to improve the traditional balying search algorithm BES, and effectively improves the identification efficiency and accuracy of the balying search algorithm BES in terms of photovoltaic inverter parameter identification.
The technical scheme adopted by the invention is as follows:
a photovoltaic inverter parameter identification method based on a multi-strategy improved bald eagle search algorithm comprises the following steps:
step 1: establishing a photovoltaic inverter control model, and determining control parameters to be identified;
step 2: based on a hardware-in-loop simulation test platform, collecting a photovoltaic inverter output response data set under multiple working conditions, and taking the photovoltaic inverter output response data set as identification data;
step 3: and (3) based on the identification data obtained in the step (2), identifying the control parameters of the photovoltaic inverter by utilizing a multi-strategy improved bald eagle search algorithm.
In the step 1, a photovoltaic inverter controller model is established, wherein the voltage outer loop controller model is as follows:
wherein K is pU 、K iU The proportional coefficient and the integral coefficient of the direct current voltage controller are respectively; k (K) pQ 、K iQ The proportional coefficient and the integral coefficient of the reactive power controller are respectively; u (U) dc 、U dc_ref Respectively the actual value and the reference value of the direct current voltage; q (Q) s 、Q s_ref Respectively an actual value and a reference value of reactive power; i.e d_ref 、i q_ref Respectively the d-axis reference value and the q-axis reference value of the inner loop current; s is a Lawster transform operator.
The current inner loop controller model is:
wherein K is pI1 、K iI1 D-axis is the proportional coefficient and integral coefficient of the inner loop current controller respectively; k (K) pI2 、K iI2 The proportional coefficient and the integral coefficient of the q-axis inner loop current controller are respectively; i.e d 、i q The actual values of the d axis and the q axis of the inner loop current are respectively; u (u) d 、u q D and q axis voltage control signals respectively; u (U) sd 、U sq The components are grid-connected point voltage d and q axes respectively; l is a filter inductance; ω is the synchronous angular velocity.
Based on the photovoltaic inverter control model, the control parameters to be identified in the model comprise K pU 、K iU 、K pQ 、K iQ 、K pI1 、K iI1 、K pI2 And K iI2 。
In the step (3) of the above-mentioned process,
(1) The Levy flight strategy is utilized to improve a position update formula of a search space selection stage of a bald eagle search algorithm, the Levy flight can search a solution space more comprehensively, a local optimal solution is easy to jump out, and a step length update formula of the Levy flight is as follows:
wherein α=1.5, u and v obey normal distribution;
where Γ is a standard gamma function; u is normal distribution; v is normal distribution; sigma (sigma) u A normal distribution scale parameter of u; sigma (sigma) v Is the normal distribution scale parameter of v.
(2) The method has the advantages that the cauchy mutation strategy is integrated to improve the diving and prey capturing stage of the balying search algorithm, the global search capability of the algorithm is improved, and the search space is enlarged. The standard cauchy distribution probability density function expression is as follows:
where x is the density function argument.
The main idea of cauchy variation is to generate an optimal solution P for each iteration best Generating a new optimal solution P by Kexil variation disturbance best,new In order to reduce the time spent in searching the global optimal solution, the global optimal value of the dive capturing prey stage is mutated by the following formula:
P best,new ＝P best (1+C(λ))
wherein C (lambda) is a random number of (0, 1) in the Kexil distribution probability density function.
(3) The method comprises the steps of improving a position updating formula of a search space prey stage of a bald eagle searching algorithm by adopting an adaptive inertia weight, wherein the adaptive inertia weight calculating formula is as follows:
wherein omega max Is the maximum inertial weight; omega min Is the minimum inertial weight; g is the maximum iteration number; g is the current iteration number.
Further comprising step 4: and verifying the validity of the identification result of the improved bald eagle searching algorithm based on multiple strategies by using a simulation example.
And calculating the error of the identification curve and the real curve by taking the average absolute error as an evaluation index, and verifying the validity of the identification result of the control parameter of the photovoltaic inverter, wherein the error calculation formula is as follows:
wherein MAE is id Mean absolute error of d-axis current; MAE (MAE) iq Mean absolute error of q-axis current; i.e d_real (i)、i d_iden (i) The measured data and the identification data of the d-axis current in the photovoltaic inverter model are respectively; i.e q_real (i)、i q_iden (i) Respectively measuring data and identification data of q-axis current in a photovoltaic inverter model; n is the number of sampled data.
The invention discloses a photovoltaic inverter parameter identification method based on a multi-strategy improved bald eagle search algorithm, which has the following technical effects:
1) The method improves the position updating formulas of the traditional BES algorithm in three stages by utilizing the Levy flight strategy, the Cauchy variation strategy and the self-adaptive weighting factor respectively, effectively balances the global searching performance and the local development capability of the BES algorithm, improves the convergence speed and the optimizing precision of the BES algorithm, and improves the parameter identification precision of the photovoltaic inverter.
2) The method is based on the actual measurement data of the hardware in the loop, identifies the parameters of the photovoltaic inverter black box controller, can effectively embody the actual operation characteristics of the photovoltaic inverter, and accurately analyzes the influence of the actual operation characteristics on the stable operation of an actual system.
Drawings
Fig. 1 is a flowchart of a photovoltaic inverter parameter identification method based on a multi-strategy improved bald eagle search algorithm of the present invention.
Fig. 2 is a control block diagram of the photovoltaic inverter model of the present invention.
FIG. 3 is a graph showing the comparison of the identification curve and the measured curve of the d-axis current under the steady-state condition.
FIG. 4 is a graph showing the comparison of the identification curve and the measured curve of the q-axis current under the steady-state condition.
FIG. 5 is a graph showing the comparison of the identification curve and the measured curve of the d-axis current under the condition of 50% voltage drop.
FIG. 6 is a graph showing the comparison of the identification curve and the measured curve of the q-axis current under the condition of 50% voltage drop.
FIG. 7 is a graph showing the comparison of the d-axis current identification curve and the measured curve under the condition of 80% voltage drop.
FIG. 8 is a graph comparing the identification curve of the q-axis current with the measured curve under the condition of 80% voltage drop.
Detailed Description
As shown in fig. 1, the photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm comprises the following steps:
step 1: establishing a photovoltaic inverter control model, and determining control parameters to be identified;
step 2: based on a hardware-in-loop simulation test platform, collecting a photovoltaic inverter output response data set under multiple working conditions, and taking the photovoltaic inverter output response data set as identification data;
step 3: based on the obtained identification data, utilizing a multi-strategy improved bald eagle search algorithm to identify control parameters of the photovoltaic inverter;
step 4: and verifying the validity of the identification result of the improved bald eagle searching algorithm based on multiple strategies by using a simulation example.
As shown in fig. 2, step 1 includes the steps of:
establishing a photovoltaic inverter controller model, wherein the voltage outer loop controller model is as follows:
wherein K is pU 、K iU The proportional coefficient and the integral coefficient of the direct current voltage controller are respectively; k (K) pQ 、K iQ The proportional coefficient and the integral coefficient of the reactive power controller are respectively; u (U) dc 、U dc_ref Respectively the actual value and the reference value of the direct current voltage; q (Q) s 、Q s_ref Respectively an actual value and a reference value of reactive power; i.e d_ref 、i q_ref The inner loop current d-axis and q-axis reference values, respectively.
The current inner loop controller model is:
wherein K is pI1 、K iI1 D-axis is the proportional coefficient and integral coefficient of the inner loop current controller respectively; k (K) pI2 、K iI2 The proportional coefficient and the integral coefficient of the q-axis inner loop current controller are respectively; i.e d 、i q The actual values of the d axis and the q axis of the inner loop current are respectively; u (u) d 、u q D and q axis voltage control signals respectively; u (U) sd 、U sq The components are grid-connected point voltage d and q axes respectively; l is a filter inductance; ω is the synchronous angular velocity.
Photovoltaic inverter based on the aboveThe control model comprises control parameters to be identified, wherein the control parameters to be identified in the model comprise K pU 、K iU 、K pQ 、K iQ 、K pI1 、K iI1 、K Pi2 And K iI2 。
In the step 2, the test platform comprises an RT-LAB simulator and a photovoltaic controller, wherein the RT-LAB simulator is used for running the simulation model in real time, and the photovoltaic controller outputs an inverter control signal which is connected through a communication line. Collecting a photovoltaic inverter output response data set under multiple working conditions, wherein the output response data set comprises i d 、i q 、i d_ref 、i q_ref 。
In the step 3, the multi-strategy improved bald eagle searching algorithm is specifically as follows:
the BES algorithm includes 3 stages of selecting a search space, searching for space prey, and dive capturing prey.
1) Search space selection:
and randomly selecting a search space stage, and updating the optimal search position according to the number of the hunting objects, wherein a position updating formula is as follows:
P i,new ＝P best +α·r·(P mean -P i )
wherein P is i,new Updating the post-position for the ith bald eagle of the search space stage; p (P) best The current best searching position is obtained; p (P) i To update the position of the ith bald eagle; p (P) mean Is an average distribution position; alpha is a position change control parameter, and the value range is (1.5, 2); r is a random number within (0, 1).
2) Search space prey:
within the selected optimal search space, the bald eagle moves in a spiral to search for the optimal hunting position. The spiral flight position update formula is:
θ(i)＝δ·π·r
γ(i)＝θ(i)+R·r
wherein θ (i) and γ (i) are the polar angle and the polar diameter of the spiral flight equation respectively; delta and R are spiral track control parameters, and the delta value range is (5, 10); r is in the value range of (0.5, 2); and x (i) and y (i) are polar coordinate positions of bald hawk, and the range of values is (-1, 1). The bald eagle position update formula at the search space prey stage is:
P i,new ＝P i +x(i)·(P i -P mean )+y(i)·(P i -P i+1 )
wherein P is i,new Updating the position for searching the ith bald eagle in the hunting stage; p (P) i+1 The position was updated once for the ith bald eagle.
3) Diving to capture prey:
after searching for the hunting site, the bald eagle is quickly bumped from the optimal search space and flies down to the hunting, and other individuals in the population move to the hunting and initiate an attack. The dive position update at this stage is:
θ 1 (i)＝δ·π·r
γ 1 (i)＝θ 1 (i)
in θ 1 (i)、γ 1 (i) Respectively diving the polar angle and the polar diameter of the capturing stage; x is x 1 (i)、y 1 (i) The polar coordinate position of the bald hawk is captured for diving. The position updating formula of the falcon in the diving and prey capturing stage is as follows:
P i,new ＝r·P best +x 1 (i)·(P i -c 1 P mean )+y 1 (i)·(P i -c 2 P best )
wherein P is i,new Updating the position of the ith bald eagle in the diving predation stage; c 1 And c 2 The intensity of the bald hawk moving to the optimal point and the center point is respectively in the range of (1, 2).
The photovoltaic inverter parameter identification method based on the multi-strategy improved balk search algorithm utilizes the Levy flight strategy to improve a position update formula of a search space selection stage of the balk search algorithm, and the step length update formula of the Levy flight is as follows:
where α=1.5, u and v obey normal distribution.
Where Γ is the standard gamma function.
Introducing a Levy flight strategy during the search space selection phase improves the location update formula to:
P i,new ＝P best +α·r·Levy·(P mean -P i )
the photovoltaic inverter parameter identification method based on the multi-strategy improved balk search algorithm integrates the Cauchy mutation strategy to improve the diving and prey hunting stage of the balk search algorithm, improves the global search capability of the algorithm and expands the search space. The standard cauchy distribution probability density function expression is as follows:
the main idea of cauchy variation is to generate an optimal solution P for each iteration best Generating a new optimal solution P by Kexil variation disturbance best,new In order to reduce the time spent in searching the global optimal solution, the global optimal value of the dive capturing prey stage is mutated by the following formula:
P best,new ＝P best (1+C(λ))
wherein C (lambda) is a random number of (0, 1) in the Kexil distribution probability density function.
The photovoltaic inverter parameter identification method based on the multi-strategy improved balying search algorithm adopts the self-adaptive inertia weight to improve the position update formula of the search space hunting stage of the balying search algorithm, and the self-adaptive inertia weight calculation formula is as follows:
wherein omega max Is the maximum inertial weight; omega min Is the minimum inertial weight; g is the maximum iteration number; g is the current iteration number.
Introducing an adaptive inertial weight strategy during the nose down capture prey phase improves the position update formula to:
P i,new ＝ω·P best +x 1 (i)·(ω·P i -c 1 P mean )+y 1 (i)·(ω·P i -c 2 P best )
the photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm uses the average absolute error as an evaluation index, calculates the error of an identification curve and a real curve, verifies the validity of the identification result of the control parameter of the photovoltaic inverter, and adopts the following error calculation formula:
wherein MAE is id Mean absolute error of d-axis current; MAE (MAE) iq Mean absolute error of q-axis current; i.e d_real (i)、i d_iden (i) The measured data and the identification data of the d-axis current in the photovoltaic inverter model are respectively; i.e q_real (i)、i q_iden (i) Respectively measuring data and identification data of q-axis current in a photovoltaic inverter model; n is the number of sampled data.
FIG. 3 is a graph showing the comparison of the identification curve and the measured curve of the d-axis current under the steady-state condition. As can be seen from fig. 3, the d-axis output current fits well during steady state, and the dynamic process effect of the current inner loop parameter error on the transient is within an acceptable range. FIG. 4 is a graph showing the comparison of the identification curve and the measured curve of the q-axis current under the steady-state condition. As can be seen from fig. 4, the q-axis output current fits well during steady state, and the dynamic process effect of the current inner loop parameter error on the transient is within an acceptable range. FIG. 5 is a graph showing the comparison of the identification curve and the measured curve of the d-axis current under the condition of 50% voltage drop. As can be seen from fig. 5, the simulation result of the d-axis current substantially coincides with the hardware-in-loop measurement under the condition that the voltage drops to 50%. And after the fault is cut off, the stable operation state is quickly restored. The d-axis current loop parameter identification error has little influence on the overall dynamic characteristics of the system.
FIG. 6 is a graph showing the comparison of the identification curve and the measured curve of the q-axis current under the condition of 50% voltage drop. As can be seen from fig. 6, the simulation result of q-axis current substantially matches the hardware-in-loop measurement under the condition that the voltage drops to 50%. And after the fault is cut off, the stable operation state is quickly restored. The q-axis current loop parameter identification error has little influence on the overall dynamic characteristics of the system.
FIG. 7 is a graph showing the comparison of the d-axis current identification curve and the measured curve under the condition of 80% voltage drop. As can be seen from fig. 7, the simulation result of the d-axis current substantially coincides with the hardware-in-loop measurement under the condition that the voltage drops to 80%. And after the fault is cut off, the stable operation state is quickly restored. The d-axis current loop parameter identification error has little influence on the overall dynamic characteristics of the system.
FIG. 8 is a graph comparing the identification curve of the q-axis current with the measured curve under the condition of 80% voltage drop. As can be seen from fig. 8, the simulation result of q-axis current substantially matches the hardware-in-loop measurement under the condition that the voltage drops to 80%. And after the fault is cut off, the stable operation state is quickly restored. The q-axis current loop parameter identification error has little influence on the overall dynamic characteristics of the system.
In fig. 3 to 8, the average absolute error between the identification curve and the measured curve is shown in table 1.
TABLE 1 identification errors for photovoltaic inverters under different conditions
| Error name | Steady state | The voltage drops to 50% | The voltage drops to 80% |
| MAE id | 0.104 | 0.35 | 0.87 |
| MAE iq | 0.183 | 0.78 | 1.53 |
From table 1, it can be seen that the application of the multi-strategy improved balying search algorithm provided by the invention can obtain a better parameter identification result, the identification error is minimum in a steady state, the error increases with the increase of the voltage drop degree in a transient state, but the response curve error is within 2% overall, and the identification result has good stability.
Claims (4)
1. The photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm is characterized by comprising the following steps of:
step 1: establishing a photovoltaic inverter control model, and determining control parameters to be identified;
step 2: collecting a photovoltaic inverter output response data set under multiple working conditions, and taking the output response data set as identification data;
step 3: and (3) based on the identification data obtained in the step (2), identifying the control parameters of the photovoltaic inverter by utilizing a multi-strategy improved bald eagle search algorithm.
2. The photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the step 1, a photovoltaic inverter controller model is established, wherein the voltage outer loop controller model is as follows:
wherein K is pU 、K iU The proportional coefficient and the integral coefficient of the direct current voltage controller are respectively; k (K) pQ 、K iQ The proportional coefficient and the integral coefficient of the reactive power controller are respectively; u (U) dc 、U dc_ref Respectively the actual value and the reference value of the direct current voltage; q (Q) s 、Q s_ref Respectively an actual value and a reference value of reactive power; i.e d_ref 、i q_ref Respectively the d-axis reference value and the q-axis reference value of the inner loop current; s is a Lawster transform operator;
the current inner loop controller model is:
wherein K is pI1 、K iI1 D-axis is the proportional coefficient and integral coefficient of the inner loop current controller respectively; k (K) pI2 、K iI2 The proportional coefficient and the integral coefficient of the q-axis inner loop current controller are respectively; i.e d 、i q The actual values of the d axis and the q axis of the inner loop current are respectively; u (u) d 、u q D and q axis voltage control signals respectively; u (U) sd 、U sq The components are grid-connected point voltage d and q axes respectively; l is a filter inductance; omega is the synchronous angular velocity;
based on the photovoltaic inverter control model, the control parameters to be identified in the model comprise K pU 、K iU 、K pQ 、K iQ 、K pI1 、K iI1 、K pI2 And K iI2 。
3. The photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm as claimed in claim 1, wherein the method is characterized by comprising the following steps: in the step (3) of the above-mentioned process,
(1) The method comprises the steps of improving a position update formula of a search space selection stage of a bald eagle search algorithm by utilizing a Levy flight strategy, wherein the step length update formula of the Levy flight is as follows:
wherein α=1.5, u and v obey normal distribution;
where Γ is a standard gamma function; u is normal distribution; v is normal distribution; sigma (sigma) u A normal distribution scale parameter of u; sigma (sigma) v The normal distribution scale parameter is v;
(2) The modification of the falcite search algorithm in the diving and prey stage is realized by integrating the Cauchy mutation strategy, and the standard Cauchy distribution probability density function expression is as follows:
wherein x is a density function argument;
the cauchy variation includes: the optimal solution P generated by each iteration best Generating a new optimal solution P by Kexil variation disturbance best,new The global optimum for the dive capture prey stage is mutated using the following formula:
P best,new ＝P best (1+C(λ))
wherein C (lambda) is a random number of (0, 1) in the Kexil distribution probability density function;
(3) The method comprises the steps of improving a position updating formula of a search space prey stage of a bald eagle searching algorithm by adopting an adaptive inertia weight, wherein the adaptive inertia weight calculating formula is as follows:
wherein omega max Is the maximum inertial weight; omega min Is the minimum inertial weight; g is the maximum iteration number; g is the current iteration number.
4. The photovoltaic inverter parameter identification method based on the multi-strategy improved bald eagle search algorithm as claimed in claim 1, wherein the method is characterized by comprising the following steps: further comprising step 4: and calculating the error of the identification curve and the real curve by taking the average absolute error as an evaluation index, and verifying the validity of the identification result of the control parameter of the photovoltaic inverter, wherein the error calculation formula is as follows:
wherein MAE is id Mean absolute error of d-axis current; MAE (MAE) iq Mean absolute error of q-axis current;
i d_real (i)、i d_iden (i) The measured data and the identification data of the d-axis current in the photovoltaic inverter model are respectively; i.e q_real (i)、i q_iden (i) Respectively measuring data and identification data of q-axis current in a photovoltaic inverter model; n is the number of sampled data.
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