Multi-direct-current power spring control method based on distributed Kalman filtering consistency

Abstract

The invention belongs to the technical field of DC micro-grid power spring cooperative control, and particularly relates to a multi-DC power spring control method based on distributed Kalman filtering consistency, and the method comprises the following steps: constructing a system state relation model based on a multi-DC power spring main circuit topology; establishing a multi-direct-current power spring system state equation; enabling external random interference and measurement errors to be equivalent to process noise and observation noise of the system respectively, and adding the process noise and the observation noise into an input end and an output end of the system to obtain a new state after filtering; replacing a previous non-interference state with a new state, and further solving a distributed Kalman filter to carry out state prediction and state updating on consistency control; performing fusion state estimation on the dynamic consistency control by using the corrected Kalman filtering; and iteratively executing state prediction and updating. According to the method, the stability of the system is ensured, the voltage reference value of each section of direct-current bus can still be accurately obtained under the interference of white noise, and the robustness is relatively high.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated control of power springs in a DC microgrid, and in particular relates to a multi-DC power spring control method based on distributed Kalman filter consistency. Background Art

[0002] Since distributed power sources such as photovoltaic and wind power are intermittent and random, they will cause voltage fluctuations in the DC microgrid bus, which will have a significant impact on the power quality on the user side. As a new demand-side management technology, DC power springs can achieve stable voltage control of key loads under the condition of renewable energy output fluctuations and improve the power quality on the user side. When the DC microgrid contains a high proportion of renewable energy, a single DC power spring is difficult to effectively support the stability of the entire DC microgrid bus voltage due to its limited storage energy. Therefore, multiple DC power springs are used to jointly adjust the DC bus voltage to achieve DC bus voltage stability.

[0003] In order to overcome the problems of low voltage accuracy of decentralized control, high communication cost of centralized control, and insufficient flexibility of calculation methods, the distributed coordinated control method based on consistency theory has attracted widespread attention. The dynamic consistency control strategy can reach asymptotic consensus only through local information interaction between adjacent control nodes, without the need for a centralized central controller, and with flexible application forms. Therefore, most current studies are based on consistency theory for distributed control of multiple DC power springs.

[0004] However, when establishing mathematical models of multiple DC power springs in DC microgrids, they usually face a series of complex problems such as external random interference and sensor measurement errors. If the measured data information with errors is directly used for consistency control, it will be difficult to accurately give the voltage reference value of each section of the DC bus, the control accuracy will be affected, the coordinated control performance between multiple DC power springs will deteriorate, and it will be difficult to ensure the bus voltage stability of the DC microgrid.

[0005] In view of this, the inventor hopes to design a multi-DC power spring control method based on distributed Kalman filter consistency to solve the problem of error interference affecting consistency communication and realize multi-DC power spring coordinated control to regulate bus voltage stability. Summary of the invention

[0006] The purpose of the present invention is to overcome the above problems existing in the traditional technology and provide a multi-DC electric spring control method based on distributed Kalman filter consistency.

[0007] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0008] The present invention provides a multi-DC power spring control method based on distributed Kalman filter consistency, comprising the following steps:

[0009] S1. Construct a system state relationship model based on the main circuit topology of multiple DC power springs;

[0010] S2. Establish the state equation of the multi-DC power spring system using the state relationship model;

[0011] S3. Design a Kalman filter, equate the external random interference and measurement error to the process noise and observation noise of the system respectively, add them to the input and output of the system, and obtain the new state after filtering;

[0012] S4, replace the previous interference-free state with the new state after filtering, and then find the distributed Kalman filter to perform state prediction and state update for consistency control;

[0013] S5. Use the new state after Kalman filtering to estimate the fusion state of dynamic consistency control;

[0014] S6. Iteratively perform state prediction and update, and send the new state estimate and covariance matrix to the adjacent DC power springs after each update.

[0015] Furthermore, in step S1, the system state relationship model is specifically:

[0016]

[0017] Among them, L i and C i are the inductance and capacitance of the LC low-pass filter of the i-th power spring, V es,i is the output voltage of the ith power spring, I L,i is the i-th power spring filter inductor current, V dc is the voltage of the energy storage battery, d i is the spring switch duty cycle, I 0.i is the CL current at the DC bus i.

[0018] Furthermore, in step S2, the state relationship model establishes the multi-DC power spring state equation specifically as follows:

[0019] Based on the state space averaging of multiple DC power spring H-bridge converters, the linearization equation is established as shown in the formula:

[0020]

[0021] Furthermore, in step S3, both the process noise and the observation noise are white noise, and the covariance is specifically:

[0022]

[0023] When t = n, otherwise

[0024] Furthermore, in step S3, the new state after filtering is obtained as follows:

[0025] The state variables are selected as the inductor current and the power spring output voltage, and the control variables are the spring switch duty cycle and the CL current of the DC bus i:

[0026]

[0027] Take the output spring voltage as the output variable, that is,

[0028] y i (t) =

[01] x i (t)

[0029] The discrete state space equation is obtained by linear discretization and is expressed as:

[0030]

[0031] y i (t) =

[01] x i (t)

[0032] The state equation of the actual disturbed system is expressed as:

[0033] x i (t+1)=Ax i (t)+Bu i (t)+w i (t)

[0034] In the formula, A and B are the state transfer matrix and input matrix of the system respectively, and w(t) represents the system process noise matrix;

[0035] The measurement equation is expressed as:

[0036] y i (t+1)=Cx i (t+1)+v i (t+1)

[0037] Where v(t) represents the measurement noise matrix. For the Kalman filter, in general, the average and expected values ​​of the noise are zero and are uncorrelated with each other.

[0038] Furthermore, in step S4, the Kalman filter in the non-interference state is specifically:

[0039] First, the influence of input signal, process noise and measurement noise on the system is not considered, and the estimated values ​​of state variables and output signals are obtained. Then, the estimated value of state variables is corrected by weighting the estimated error of output signal:

[0040]

[0041] Compute the estimated error of the output signal:

[0042]

[0043] Then the estimated value of the system state variable after error compensation is:

[0044]

[0045] Among them, K i is the Kalman gain, specifically the weighting matrix. The key to the Kalman filter is to adjust K i The size of makes the mean square value of the corrected state estimation error minimum.

[0046] Further, in step S4, the state prediction and state update of consistency control based on Kalman filtering are specifically as follows:

[0047] In the multi-DC electric spring control method based on distributed Kalman filter consistency control, the analysis of the system composition shows that the algorithm implementation steps are as follows: first, the node measurement value z i and the observed noise covariance value R i As the input of the high-pass consistency filter, the node's estimated value of the target is obtained through a series of fusion iterative processing;

[0048] 1) Initialization: x i =x0,P i =P0;

[0049] 2) The sensor measures the target and receives relevant information from neighboring nodes;

[0050] 3) Preprocess the observation values ​​and observation noise covariance matrix of each node, that is:

[0051] in

[0052]

[0053] 4) Calculate the estimated value at the current time:

[0054] The actual measured value f(t) is compared with the output obtained based on the measurement equation, and the system state is corrected based on the difference, thereby obtaining the posterior estimate at this moment. With the covariance matrix M i(t):

[0055]

[0056] 5) Update the state value of the Kalman consistency filter:

[0057] Based on the consistency algorithm, the posterior estimate of the state variable at time t can be used To estimate the initial estimate of the next moment And the covariance matrix P of the next moment i (t+1);

[0058]

[0059] Furthermore, in step S5, the fusion state estimation of the dynamic consistency control is performed using the filtered new state as follows:

[0060] Considering that each DC microgrid has line impedance and different DC bus voltage reference values ​​are different, it is impossible to set the same reference bus voltage to achieve the coordinated operation of multiple DC power springs. In order to obtain the voltage reference values ​​at different positions, a dynamic consistency control method is designed to achieve real-time dynamic update of the voltage reference values ​​of each power spring. The expression is modified to:

[0061]

[0062] Among them, a ij Indicates the degree of communication between the DC power spring located at DC bus i and the power spring located at bus j.

[0063] The beneficial effects of the present invention are:

[0064] 1. The present invention can maintain the stability of the DC bus voltage by coordinating multiple DC power springs, and can still accurately realize the real-time dynamic update of the voltage reference value under the influence of white noise interference, and has strong robustness.

[0065] 2. The distributed Kalman filter consistency control in the present invention is composed of the superposition of the Kalman filter algorithm and the consistency algorithm. Because the Kalman filter can obtain the local optimal estimate of the target voltage for a single electric spring, and the consistency algorithm can generalize the local optimal estimate to the entire system so that all nodes reach a consistent state, the combination of these two algorithms can not only improve the estimation accuracy, but also reduce the difference between the node estimation values.

[0066] 3. The present invention studies the consistency control problem of a multi-DC electric spring system under white noise interference, and proposes a consistency control method based on a Kalman filter. The state model of the multi-DC electric spring system is corrected by utilizing the error between the predicted value and the actual value to solve the problem of poor control effect of the consistency control method when there is white noise interference, thereby achieving effective consistency control of the system under white noise interference.

[0067] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0069] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0070] Figure 2 It is the topological structure diagram of the main circuit of multiple DC power springs;

[0071] Figure 3 The control principle diagram of multiple DC power springs based on Kalman filter consistency;

[0072] Figure 4 It is a comparison diagram of dynamic target tracking curves of multiple DC electric springs based on Kalman consistency filter control;

[0073] Figure 5 Comparison diagram of the consistency state estimation error before and after Kalman filtering. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] This embodiment provides a multi-DC electric spring control method based on distributed Kalman filter consistency, comprising the following steps:

[0076] S1. Construct a system state relationship model based on the main circuit topology of multiple DC power springs;

[0077] S2. Establish the state equation of the multi-DC power spring system using the state relationship model;

[0078] S3. Design a Kalman filter, equate the external random interference and measurement error to the process noise and observation noise of the system respectively, add them to the input and output of the system, and obtain the new state after filtering;

[0079] S4, replace the previous interference-free state with the new state after filtering, and then find the distributed Kalman filter to perform state prediction and state update for consistency control;

[0080] S5. Using the modified Kalman filter to estimate the fusion state of dynamic consistency control;

[0081] S6. Iteratively perform state prediction and update, and send the new state estimate and covariance matrix to the adjacent DC power springs after each update.

[0082] The S1 system state relationship model is specifically:

[0083]

[0084] Among them, L i and C i are the inductance and capacitance of the LC low-pass filter of the i-th power spring, V es,i is the output voltage of the ith power spring, I L,i is the i-th power spring filter inductor current, V dc is the voltage of the energy storage battery, d i is the spring switch duty cycle, I 0.i is the CL current at the DC bus i.

[0085] The state relationship model establishes the state equation of multiple DC power springs as follows:

[0086] Based on the state space averaging of multiple DC power spring H-bridge converters, the linearization equation is established as shown in the formula:

[0087]

[0088] In step S3, both the process noise and the observation noise are white noise, and the covariance is specifically:

[0089]

[0090] Here when t = n otherwise

[0091] A multi-DC power spring state model is constructed, and a Kalman filter is designed. External random interference and measurement error are respectively equivalent to the process noise and observation noise of the system, and added to the input and output of the system to obtain the new state after filtering:

[0092] The state variables are selected as the inductor current and the power spring output voltage, and the control variables are the spring switch duty cycle and the CL current of the DC bus i:

[0093]

[0094] Take the output spring voltage as the output variable, that is,

[0095] y i (t) = [0 1] x i (t)

[0096] The discrete state space equation is obtained by linear discretization and is expressed as:

[0097]

[0098] y i (t) =

[01] x i (t)

[0099] The state equation of the actual disturbed system can be expressed as:

[0100] x i (t+1)=Ax i (t)+Bu i (t)+w i (t)

[0101] Where A and B are the state transfer matrix and input matrix of the system respectively, and w(t) represents the system process noise matrix.

[0102] Its measurement equation is expressed as:

[0103] y i (t+1)=Cx i (t+1)+v i (t+1)

[0104] in, A, B, and C are all state space equation coefficient matrices. i (t) represents the measurement noise matrix. For the Kalman filter, in general, the average value and expectation of the noise are zero, and the two are uncorrelated.

[0105] The Kalman filter in the non-interference state is specifically:

[0106] First, the influence of input signal, process noise and measurement noise on the system is not considered, and the estimated values ​​of state variables and output signals are obtained. Then, the estimated value of state variables is corrected by weighting the estimated error of output signal.

[0107]

[0108] Compute the estimated error of the output signal:

[0109]

[0110] Then the estimated value of the system state variable after error compensation is:

[0111]

[0112] Where K i is the Kalman gain, specifically the weighting matrix. The key to the Kalman filter is to adjust K i The size of makes the mean square value of the corrected state estimation error minimum.

[0113] The state prediction and state update of consistency control based on distributed Kalman filtering are as follows:

[0114] In the distributed Kalman filter consensus algorithm, analysis Figure 3 Given the system configuration, we know that the algorithm implementation steps are as follows: First, the node measurement values ​​y i (t) and the observed noise covariance value R i As the input of the high-pass consistency filter, the optimal estimate of the target state of each DC power spring is obtained through a series of fusion iterative processing.

[0115] 1) Initialization: x i =x0,P i =P0;

[0116] 2) The sensor measures the target and receives relevant information from neighboring nodes;

[0117] 3) Preprocess the observation values ​​and observation noise covariance matrix of each node, that is:

[0118] in

[0119]

[0120] 4) Calculate the estimated value at the current time:

[0121] The actual measured value f(t) is compared with the output obtained based on the measurement equation, and the system state is corrected based on the difference, thereby obtaining the posterior estimate at this moment. With the covariance matrix Mi (t):

[0122]

[0123] 5) Update the state value of the Kalman consistency filter:

[0124] Based on the consistency algorithm, the posterior estimate of the state variable at time t can be used To estimate the initial estimate of the next moment And the covariance matrix P of the next moment i (t+1).

[0125]

[0126] P i (t+1)=AM i (t)A T +BQ i (k)B T

[0127] From the above steps, we can see that the information transmitted by the DC power spring to its neighbor spring at each moment in the distributed Kalman filter consistency control algorithm is: In the whole algorithm, the observation value f(t) and the observation noise covariance G(t) of the neighboring nodes are first preprocessed to prepare for the application of the distributed Kalman filter algorithm below. Then, in step 4), the correction term is obtained from the measurement value of the sensor at the current moment, and the predicted value is corrected and optimized. The consistency algorithm is then combined to obtain the state estimate of the DC power spring point at the current moment. Finally, the relevant information is updated in step 5).

[0128] The modified Kalman filter is used to estimate the fusion state of dynamic consistency control, specifically:

[0129] Considering that each DC microgrid has line impedance and different DC bus voltage reference values ​​are different, it is impossible to set the same reference bus voltage to achieve coordinated operation of multiple DC power springs. In order to obtain voltage reference values ​​at different locations, a dynamic consistency control method is designed. Considering the influence of external random interference and measurement error, error compensation is added to achieve real-time dynamic update of each power spring voltage reference value. The expression is modified to:

[0130]

[0131] Among them, a ij Indicates the degree of communication between the DC power spring located at DC bus i and the DC power spring located at DC bus j.

[0132] Simulation Results Analysis

[0133] To verify the effectiveness of the present invention, the simulation results of the control method on the MATLAB simulation platform are given. The parameters are designed as follows: N = 120 (number of sampling points) is equivalent to 120 seconds of measurement, CON = 48 (reference voltage expected value), x (1) = 48.3V (set initial voltage), P (1) = 0.1 (initial state covariance), y (1) = 47.8V (initial voltage measurement value), Q = 0.01 (process noise covariance), G = 0.25 (observation noise covariance).

[0134] By analyzing the simulation results, Figure 4 , 5 As shown in Figure 2. It can be seen that although the noise amplitude is small, the interference caused by the noise has a great impact on the stability of the system. The target voltage state reference value curve in the multi-DC electric spring system is shown in Figure 2. Figure 4 It can be seen that when there are external random disturbances and measurement errors in the system, the observed values ​​and true values ​​without corrected Kalman filter compensation cannot output a stable reference voltage value, but it is obvious that the Kalman filter consistency control compensation prediction method can more effectively suppress the influence of disturbances than the conventional Kalman predictive control method, and the control is smoother, the response speed is faster, the robustness is stronger, and it is closer to the expected value of the reference voltage given by the target. Figure 5 It can be seen that the state estimation error obtained by using Kalman filter consistency control is much smaller than the actual measurement error, which ensures the stability of the voltage reference value of each DC power spring. The feasibility and effectiveness of the algorithm proposed in the present invention are verified through simulation experiments.

[0135] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-DC power spring control method based on distributed Kalman filter consistency, characterized in that: The following steps are involved: S1. Construct a system state relationship model based on the main circuit topology of multiple DC power springs; S2. Establish the state equation of the multi-DC power spring system using the state relationship model; S3. Design a Kalman filter, equate the external random interference and measurement error to the process noise and observation noise of the system respectively, add them to the input and output of the system, and obtain the new state after filtering; S4, replace the previous interference-free state with the new state after filtering, and then find the distributed Kalman filter to perform state prediction and state update for consistency control; S5. Use the new state after Kalman filtering to estimate the fusion state of dynamic consistency control; S6. Iteratively perform state prediction and update, and send the new state estimate and covariance matrix to the adjacent DC power springs after each update.

2. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 1 is characterized in that: In step S1, the system state relationship model is specifically: Among them, L i and C i are the inductance and capacitance of the LC low-pass filter of the i-th power spring, V es,i is the output voltage of the ith power spring, I L,i is the i-th power spring filter inductor current, V dc is the voltage of the energy storage battery, d i is the spring switch duty cycle, I 0.i is the CL current at the DC bus i.

3. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 2 is characterized in that: In step S2, the state relationship model establishes the state equation of multiple DC power springs as follows: Based on the state space averaging of multiple DC power spring H-bridge converters, the linearization equation is established as shown in the formula:

4. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 3 is characterized in that: In step S3, both the process noise and the observation noise are white noise, and the covariance is specifically: When t = n, otherwise 5. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 4 is characterized in that: In step S3, the new state after filtering is obtained as follows: The state variables are selected as the inductor current and the power spring output voltage, and the control variables are the spring switch duty cycle and the CL current of the DC bus i: Take the output spring voltage as the output variable, that is, y i (t)=[01]x i (t) The discrete state space equation is obtained by linear discretization and is expressed as: y i (t)=[01]x i (t) The state equation of the actual disturbed system is expressed as: x i (t+1)=Ax i (t)+Bu i (t)+w i (t) In the formula, A and B are the state transfer matrix and input matrix of the system respectively, and w(t) represents the system process noise matrix; The measurement equation is expressed as: y i (t+1)=Cx i (t+1)+v i (t+1) Where v(t) represents the measurement noise matrix. For the Kalman filter, in general, the average and expected values ​​of the noise are zero and are uncorrelated with each other.

6. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 5 is characterized in that: In step S4, the Kalman filter in the non-interference state is specifically: First, the influence of input signal, process noise and measurement noise on the system is not considered, and the estimated values ​​of state variables and output signals are obtained. Then, the estimated value of state variables is corrected by weighting the estimated error of output signal: Compute the estimated error of the output signal: Then the estimated value of the system state variable after error compensation is: Among them, K i is the Kalman gain, specifically the weighting matrix. The key to the Kalman filter is to adjust K i The size of makes the mean square value of the corrected state estimation error minimum.

7. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 6 is characterized in that: In step S4, the state prediction and state update of consistency control based on Kalman filtering are specifically as follows: In the multi-DC electric spring control method based on distributed Kalman filter consistency control, the analysis of the system composition shows that the algorithm implementation steps are as follows: first, the node measurement value z i and the observed noise covariance value R i As the input of the high-pass consistency filter, the node's estimated value of the target is obtained through a series of fusion iterative processing; 1) Initialization: x i =x0,P i =P0; 2) The sensor measures the target and receives relevant information from neighboring nodes; 3) Preprocess the observation values ​​and observation noise covariance matrix of each node, that is: in 4) Calculate the estimated value at the current time: The actual measured value f(t) is compared with the output obtained based on the measurement equation, and the system state is corrected based on the difference, thereby obtaining the posterior estimate at this moment. With the covariance matrix M i (t): 5) Update the state value of the Kalman consistency filter: Based on the consistency algorithm, the posterior estimate of the state variable at time t can be used To estimate the initial estimate of the next moment And the covariance matrix P of the next moment i (t+1); P i (t+1)=AM i (t)A T +BQ i (k)B T 。 8. The multi-DC power spring control method based on distributed Kalman filter consistency according to claim 7 is characterized in that: In step S5, the fusion state estimation of dynamic consistency control is performed using the filtered new state as follows: Considering that each DC microgrid has line impedance and different DC bus voltage reference values ​​are different, it is impossible to set the same reference bus voltage to achieve the coordinated operation of multiple DC power springs. In order to obtain the voltage reference values ​​at different positions, a dynamic consistency control method is designed to achieve real-time dynamic update of the voltage reference values ​​of each power spring. The expression is modified to: Among them, a ij Indicates the degree of communication between the DC power spring located at DC bus i and the power spring located at bus j.

Images