A harmonic optimization control method for off-grid operation of photovoltaic storage based on substation interconnection device

Abstract

The present invention provides a harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device. First, a mathematical model is established according to the topological structure of the inverter using a model predictive control method. A discrete mathematical model is obtained by discretizing the mathematical model, and a multi-objective cost function is constructed. The target weight factor of the multi-objective cost function is calculated according to the NSGA algorithm, and then the target control pulse of the inverter is obtained, and the target control pulse is applied to the inverter. The present invention belongs to the field of power engineering technology. The target control pulse obtained by this method is applied to the inverter. When the inverter is in off-grid operation, it can not only realize adaptive tracking of the AC voltage, but also realize optimal suppression of harmonics, thereby improving the quality of the output voltage.

Description

Technical Field

[0001] The present invention belongs to the field of electric power engineering technology, and in particular relates to a harmonic optimization control method for off-grid photovoltaic and energy storage operation based on a substation interconnection device. Background Art

[0002] Large-scale distributed photovoltaic systems will be integrated into distribution networks, leading to increasingly severe harmonic problems. Interconnecting substations based on low-voltage flexible direct current (HVDC) is a promising technology for addressing renewable energy integration. By connecting photovoltaic power generation, energy storage, and charging stations via a DC bus, a small DC microgrid system can be formed, capable of independent operation from the main grid. During off-grid operation, without grid support, renewable energy and energy storage systems supplying nonlinear AC loads will generate significant voltage harmonics, impacting system operation. Currently used L-type or LCL filters effectively suppress high-frequency harmonics but struggle to address low-order harmonics. Harmonic suppression through active power filters (APFs) requires significant investment in power quality compensation equipment, making them uneconomical. Low-order harmonics can be suppressed using multi-proportional resonance and feedforward proportional control in AC / DC inverters, but this is limited by the influence of multiple PI control loop parameters, making parameter adjustment difficult in practical applications. Summary of the Invention

[0003] In view of the above problems, an embodiment of the present invention provides a harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device, so as to overcome the above problems or at least partially solve the above problems.

[0004] An embodiment of the present invention provides a harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device, the method comprising:

[0005] Step 1: Establish a mathematical model of the inverter, including the inverter AC side output voltage v i , inverter AC side output current i k , load side output voltage v c , load side output current i j ;

[0006] Step 2: Based on the mathematical model of the inverter, a discrete-time model of the mathematical model is obtained; wherein the discrete-time model is used to predict the output voltage of the load side at the next moment;

[0007] Step 3: Based on the discrete time model, construct the predicted voltage control cost function g v , switch cost function g f , harmonic cost function g h ;

[0008] Step 4: Based on the voltage prediction control cost function g v , the switch cost function gf , the harmonic cost function g h , construct the multi-objective predictive control cost function g mi n;

[0009] Step 5: Use the third generation non-dominated sorting genetic algorithm NSGA-III to set constraints and calculate the multi-objective predictive control cost function g mi n is solved to obtain the target value of the multi-objective predictive control cost function;

[0010] Step 6: Based on the target value of the multi-objective predictive control cost function, obtain the target control pulse of the inverter, act on the inverter, and realize the harmonic optimization control of the inverter.

[0011] Furthermore, the mathematical model expression of the inverter is:

[0012]

[0013] Among them, v i Indicates the output voltage of the inverter AC side, v c Indicates the output voltage on the load side, i k Indicates the output current of the inverter AC side, i j Indicates the output current on the load side; L f Indicates the filter inductance, C f Indicates filter capacitor.

[0014] Furthermore, the expression of the discrete-time model is:

[0015] x(k+1)=A q x(k)+B q v i (k)+B dq i j (k)

[0016] in, T s represents the sampling time interval, τ represents the time constant, x(k) represents the load side output voltage at sampling time k, x(k+1) represents the predicted load side output voltage at time k+1, v i (k) represents the AC side output voltage of the inverter at sampling time k, i j (k) represents the load-side output current at sampling time k.

[0017] Furthermore, the voltage prediction control cost function is expressed as:

[0018]

[0019] in, The output AC reference voltage is transformed into the components under α and β, v cα 、v cβ It indicates the predicted load side output voltage at the next moment is obtained by Clarke transformation to obtain the components under α and β.

[0020] Furthermore, the expression of the switch cost function is:

[0021]

[0022] Among them, f igbt(i) Indicates that the output voltage of the inverter AC side is v i When , the switching frequency of the i-th switching device, N represents the number of switching devices, Indicates the average switching frequency of N switching devices, max(f igbt(i) ) represents the maximum switching frequency of the i-th switching device.

[0023] Furthermore, the expression of the harmonic cost function is:

[0024]

[0025] Among them, u h (k) represents the amplitude of the kth harmonic, which is calculated by comparing the load side output voltage v c DFT calculation shows that M is the harmonic order.

[0026] Furthermore, the multi-objective predictive control cost function expression is:

[0027] g min =λ1g f +λ2g v +λ3g h

[0028] Among them, λ1, λ2, and λ3 represent weight factors, and λ1+λ2+λ3=1, and the weight factors take values ​​of λ1, λ2, and λ3∈(0,1).

[0029] Furthermore, the expression for setting the constraint condition is:

[0030]

[0031] Among them, f max Indicates the maximum frequency of a single switching device, f min Indicates the minimum frequency of a single switching device, g hmax Indicates the maximum total harmonic content value, g vmax Indicates the maximum deviation between the voltage prediction control value and the reference value.

[0032] Furthermore, the multi-objective prediction control cost function g min Solving to obtain the target value of the multi-objective predictive control cost function specifically includes:

[0033] 1) Initialize the parent population Pt of size N in the NSGA-III algorithm, where N is the population size;

[0034] 2) Crossover and mutation generate the offspring population Qt, and the parent population and the offspring population are merged to generate a 2N population Rt = Qt + Pt;

[0035] 3) Perform fast non-dominated sorting on the new population Rt to obtain a non-dominated set, and then select the target individuals as the parent population of the next generation based on the association between the reference point and the non-dominated set;

[0036] 4) Continue to repeat steps 1) and 2) until the algorithm reaches the maximum genetic generation number, end the iteration process, and generate g min The value is the target value.

[0037] Furthermore, the target value of the multi-objective predictive control cost function is obtained, and the target control pulse of the inverter is applied to the inverter to achieve harmonic optimization control of the inverter, including:

[0038] obtaining a target value of the load-side output voltage at a next moment based on the target value of the multi-objective predictive control cost function;

[0039] Based on the target value of the load-side output voltage at the next moment, a target control pulse of the inverter is obtained and applied to the inverter to achieve harmonic optimization control of the inverter.

[0040] The present invention provides a harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device. By predicting the output voltage of the load side at the next moment, as well as the relationship between the output voltage of the load side at the next moment and the AC reference voltage, the switching state of the inverter switching device and the harmonics, a multi-objective prediction control cost function is constructed, and the target value of the multi-objective prediction control cost function is calculated using the NSGA-III algorithm. The output voltage of the load side at the next moment is obtained by the target value, and then the inverter target pulse corresponding to the output voltage of the load side is obtained. By applying the target pulse to the switching device of the inverter, it is possible to achieve adaptive tracking of the AC voltage under off-grid operation through high-performance nonlinear control and multi-objective optimal control algorithm, and control to achieve optimal suppression of harmonics, thereby improving the quality of the output voltage. The method not only has the characteristics of low cost, low modification difficulty and low harmonic content, but also can reduce the loss of electronic components in the inverter and increase the service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 This is a schematic diagram of off-grid operation of photovoltaic storage in a transformer area interconnection scenario provided by an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of an AC / DC grid-connected inverter topology provided by an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of a prediction model optimization control algorithm based on NSGA-III provided in an embodiment of the present invention;

[0045] Figure 4 Schematic diagram of output voltage harmonics based on traditional PI control provided by an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of voltage harmonics output by the method provided by the present invention, provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will describe in more detail exemplary embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0048] In view of the problems of high cost and complex engineering implementation in the current off-grid operation harmonic suppression methods under area interconnection, the present invention proposes a high-performance nonlinear control and multi-objective optimal control algorithm to achieve adaptive tracking of AC voltage and optimal harmonic suppression under off-grid operation.

[0049] Therefore, the present invention provides a harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device to optimize the problems existing in off-grid operation under current substation interconnection.

[0050] Reference Figure 1 , Figure 1This is a schematic diagram of off-grid operation of photovoltaic storage in a substation interconnection scenario provided by an embodiment of the present invention. As shown in the figure, the application is mainly used for off-grid operation, where distributed photovoltaic and energy storage power the DC load in the substation and output DC voltage. Among them, photovoltaic is controlled by MPPT mode, and AC / DC grid-connected converter adopts v / f control mode. It is converted into AC voltage by AC / DC inverter to power the AC load in the substation. AC / DC inverter adopts v / f control mode. AC / DC inverter v / f control establishes a mathematical model based on model predictive control method. Figure 2 , Figure 2 This is a schematic diagram of an AC / DC grid-connected inverter topology provided by an embodiment of the present invention. As shown in the figure, V dc is the input DC voltage of the inverter, v i is the AC side output voltage of the inverter, v c is the output voltage on the load side, i k is the output current of the inverter AC side, i j is the load side output current, a is the voltage reference point between the switching devices sw1 and sw4, b is the voltage reference point between the switching devices sw2 and sw5, and c is the voltage reference point between the switching devices sw3 and sw6.

[0051] The on and off states of the switching device correspond to different inverter output voltages, and the state function expression of the switching device is:

[0052]

[0053]

[0054]

[0055]

[0056] Where S represents the state function of the switching device, S a 、S b 、S c Respectively represent the states of a corresponding set of switches, a=e j2π / 3 Indicates the phase difference.

[0057] Step 1: Establish a mathematical model of the inverter, including the inverter AC side output voltage v i , inverter AC side output current i k , load side output voltage v c , load side output current i j .

[0058] According to the inverter topology and reference direction, combined with Kirchhoff's current and voltage laws, we can know that:

[0059] Inverter AC side output voltage v i :

[0060] Among them, v aN 、v bN 、v cN To represent the phase vector of the inverter AC output voltage in the three-phase stationary coordinate system, according to the voltage on the DC side of the inverter and the switching state of the inverter, the inverter AC output voltage can be obtained. Therefore, combined with formula (1), it can be known that the output voltage of the inverter is, v i =V dc S, where V dc Indicates the DC side voltage of the inverter.

[0061] Inverter AC side output current i k :

[0062] Among them, i ka 、i kb 、i kc They represent the phase vectors of the inverter AC output current in the three-phase stationary coordinate system, a=e j2π / 3 is the phase difference.

[0063] Load side output voltage v c :

[0064] Among them, v ca 、v cb 、v cc They respectively represent the phase vectors of the load side output voltage in the three-phase stationary coordinate system.

[0065] Load side output current i j :

[0066] Among them, i ja 、i jb 、i jc They respectively represent the phase vectors of the load side output current in the three-phase stationary coordinate system.

[0067] Establish the mathematical model of the inverter:

[0068]

[0069] in, L f Indicates the filter inductance, C f Indicates filter capacitor.

[0070] Step 2: Based on the mathematical model of the inverter, a discrete-time model of the mathematical model is obtained; wherein the discrete-time model is used to predict the output voltage of the load side at the next moment;

[0071] (1) Using Clarke transform, the inverter mathematical model of formula (6) is rewritten into its corresponding state space equation:

[0072]

[0073] in, x is the state variable, and A, B, and C are the corresponding coefficient matrices.

[0074] (2) According to the state space equation of formula (7), the state space equation is approximated by the Euler method to obtain the corresponding discrete time model. The state space equation obtains the discrete time model, which is:

[0075] x(k+1)=A q x(k)+B q v i (k)+B dq i j (k) (8)

[0076] in, T s represents the sampling time interval, τ represents the time constant, x(k) represents the load side output voltage at sampling time k, x(k+1) represents the predicted load side output voltage at time k+1, v i (k) represents the AC side output voltage of the inverter at sampling time k, i j (k) represents the load side output current at sampling time k. In this embodiment, T s represents the sampling time interval, which is generally set to 100 μs. This embodiment does not limit this and the value is determined according to actual conditions. τ is the time constant of the RLC filter.

[0077] The following is an analysis of the load side output current at sampling time k-1:

[0078] The capacitance differential equation in formula (6) is discretized to obtain a discrete mathematical model, which is expressed as follows:

[0079]

[0080] Among them, i k (k-1) represents the AC output current of the inverter at sampling time k-1, v c (k) represents the load side output voltage at sampling time k, v c(k-1) represents the load side output voltage at sampling time k-1, i j (k-1) represents the capacitor discretization mathematical model obtained by sampling the inverter AC output current at time k-1, T s Indicates the sampling time interval, generally T s The sampling time interval is relatively small, specifically 100 μs, which is not limited in this embodiment. The sampling time interval is set according to the actual sampling requirements. Therefore, it can be determined that the current output value at the current moment is approximately equal to the value at the previous moment, that is, i j (k)≈i j (k-1), so i j (k) represents the load side output current at sampling time k-1. The load side output current at time k can be obtained by sampling the load side output current at time k-1.

[0081] Step 3: Based on the discrete mathematical model, construct the predicted voltage control cost function g v , switch cost function g f , harmonic cost function g h ;

[0082] (1) One of the control objectives of the inverter in this application is to stabilize the output voltage on the load side near the reference value. In order to effectively track the output voltage on the load side, the reference value of the AC voltage and the square sum of the predicted voltage error are selected as the tracking term, and the predicted voltage control cost function g is constructed. v :

[0083]

[0084] in, The output AC reference voltage is transformed into the components under α and β, v cα 、v cβ It represents the predicted output voltage on the load side at the next moment, and the components under α and β are obtained through Clarke transformation.

[0085] (2) The second control goal of the inverter in this application is to reduce the switching loss of the inverter during operation and ensure the reliability of the inverter device operation. Therefore, the switching cost function g is constructed. f :

[0086]

[0087] Among them, f igbt(i) Indicates that the output voltage of the inverter AC side is v i When , the switching frequency of the i-th switching device, N represents the number of switching devices, Indicates the average switching frequency of N switching devices, max(f igbt(i)) represents the maximum switching frequency of the i-th switching device.

[0088] In this embodiment, the number of switching devices is 6, specifically 6 IGBTs (insulated gate bipolar transistors). The average switching frequency of the average switching device is introduced into the switching cost function to keep the total loss of the switching device within a controllable range. By controlling the average switching frequency of the 6 control switching devices, the loss of the switching device of the inverter can be controlled during the operation of the inverter, thereby ensuring the operating efficiency of the inverter. igbt(i) Indicates that the output voltage of the inverter AC side is v i When , corresponding to the switching frequency of the i-th switching device, the output voltage of the inverter AC side is in a corresponding relationship with the switching frequency. As long as the voltage on the AC side of the inverter is obtained, the switching states corresponding to the six switching devices can be obtained. Similarly, as long as the switching states of the six switching devices are obtained, the output voltage of the inverter AC side can also be calculated.

[0089] The maximum switching frequency of the i-th switching device is introduced into the switching cost function to improve the reliability of the device and prevent the frequency of a single switching device from being too high, which may cause device over-temperature protection and make the inverter device unable to operate.

[0090] (3) The third control goal of the inverter in this application is to reduce the low-order harmonic content of the output voltage on the load side and quantitatively control the harmonic content. Therefore, the harmonic cost function g is constructed. h :

[0091]

[0092] Among them, u h (k) represents the amplitude of the kth harmonic, which is calculated by comparing the load side output voltage v c DFT calculation shows that M is the harmonic order.

[0093] In this embodiment, in actual off-grid operation, although the purpose of reducing harmonics can be achieved by controlling the voltage prediction control cost function and the switch cost function to a very small value, the harmonic content value of the final load-side output voltage is not controllable. Therefore, a harmonic cost function is constructed to quantitatively control the harmonic content and control the target harmonic content value, u h (k) is the output voltage v at the load side at the next moment c The corresponding kth harmonic amplitude, M is the harmonic number, by v c Perform DFT calculation to obtain the amplitude of each harmonic and the total harmonic content at the next moment, and return them to the total cost function. Since k=1 represents 50 Hz, where 50 Hz is the fundamental wave, the harmonic cost function in this embodiment starts calculating harmonics from k=2.

[0094] Step 4: Based on the voltage prediction control cost function g v , the switch cost function g f , the harmonic cost function g h , construct the multi-objective predictive control cost function g min .

[0095] According to formulas (10), (11), and (12), the weight factor is introduced and weighted to construct the multi-objective predictive control cost function g min The purpose is to make the voltage prediction control cost function g v , switch cost function g f , harmonic cost function g h In preparation for the minimum sum of the values, the foundation is laid for realizing adaptive tracking of AC voltage and optimal control of harmonics by using the target optimal algorithm under off-grid operation. This can not only satisfy the controllable harmonics of the output voltage on the load side, but also satisfy the controllable loss of the switch during the operation of the inverter, and also ensure the reliability of the operation and the quality of the output voltage on the load side. Therefore, a multi-objective predictive control cost function g is constructed. min The expression is:

[0096] g min =λ1g f +λ2g v +λ3g h (13)

[0097] Among them, λ1, λ2, and λ3 represent weight factors, and λ1+λ2+λ3=1, and the weight factors take values ​​of λ1, λ2, and λ3∈(0,1).

[0098] In this embodiment, the weight factor scale is 0.1. For example, the value range of λ1 is 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9. The value ranges of λ2 and λ3 are the same as λ1 and are not further described here. This embodiment does not limit the value scale and can be selected according to actual needs.

[0099] Step 5: Use the third generation non-dominated sorting genetic algorithm NSGA-III to set constraints and calculate the multi-objective predictive control cost function g min Solve and obtain the target value of the multi-objective predictive control cost function.

[0100] Set the constraint condition. The constraint condition expression is:

[0101]

[0102] Among them, f max Indicates the maximum frequency of a single switching device, f minIndicates the minimum frequency of a single switching device, g hmax Indicates the maximum total harmonic content value, g vmax Indicates the maximum deviation between the voltage prediction control value and the reference value. The upper and lower limits of the switching device frequency are set to make the loss of the switching device within the control range. hmax Corresponding to the maximum total harmonic content value, the maximum total harmonic content value is set to make the harmonics of the load side output voltage within the control range, that is, to achieve the purpose of optimizing harmonics, g vmax The maximum voltage prediction control value is intended to keep the difference between the load-side output voltage and the reference voltage within a small range, ensuring that the quality of the load-side output voltage is controllable. This application does not limit the specific values ​​of the constraint conditions; they are subject to the actual off-grid operation requirements.

[0103] The calculation process of the NSGA-III algorithm is as follows:

[0104] Set the constraint conditions, use the NSGA-III third generation non-dominated sorting genetic algorithm, introduce the constraint conditions of formula (14), and calculate the multi-objective predictive control cost function g of formula (13) min Solve it.

[0105] 1) g h 、g f 、g v The values ​​within the range are coded in binary, that is, the individuals of the population are represented by binary coded symbols of length Y (such as the switching frequency f igbt(i) The value range is [f min , f max ], use 00000000 0000000100000010 00000011...... to correspond to fmin, fmin+δ, ....fmax, δ=1HZ, g v and g h The encoding process is the same), initialize the parent population Pt of size Y in the NSGA-III algorithm;

[0106] 2) Crossover and mutation generate the offspring population Qt, and the parent population and the offspring population are merged to generate the 2Y population Rt = Qt + Pt;

[0107] 3) Perform fast non-dominated sorting on the new population Rt to obtain the non-dominated set St, and then select the target individuals as the parent population of the next generation based on the association between the reference point and the non-dominated set;

[0108] 4) Continue to repeat steps 1) and 2) until the algorithm reaches the maximum genetic generation number, end the iteration process, and generate g min The target value is g minMultiple g obtained during the end of the iteration min The minimum value in .

[0109] Step 6: Based on the target value of the multi-objective predictive control cost function, obtain the target control pulse of the inverter, act on the inverter, and realize the harmonic optimization control of the inverter.

[0110] According to the target value g in step 5 min The NSGA-III algorithm can be used to determine the values ​​of the weight factors λ1, λ2, and λ3 corresponding to the target value, as well as the voltage prediction control cost function g v , the switching cost function g f , the harmonic cost function g h The value of voltage prediction control cost function g v , the switching cost function g f , the harmonic cost function g h The values ​​of are all within the range of constraints, which can achieve the purpose of reducing harmonics.

[0111] Finally, g v Substituting into formula (10), we can determine the load side output voltage at the next moment, which is the component v under α and β obtained by Clarke transformation. cα 、v cβ The value, and then the Clarke transform can be used to obtain the inverter AC side output voltage v at the next moment i , through the formula v i =V dc S, we can get the S state variable, which is substituted into formula (1) to obtain the switching state of the switching devices sw1~sw6 corresponding to the state variable. The switching state can also be understood as the optimal control pulse. The optimal control pulse is applied to the control end of each corresponding switching device in the inverter to achieve harmonic optimization control of the inverter.

[0112] Reference Figure 3 , Figure 3 It is a schematic diagram of a prediction model optimization control algorithm based on NSGA-III provided by an embodiment of the present invention. Through a photovoltaic energy storage off-grid operation harmonic optimization control method based on a substation interconnection device of the present invention, the load side output voltage at the previous moment and the output current of the inverter at the previous moment can be collected. Combined with the AC voltage reference value, the target control pulse at the next moment, that is, the switching state of the inverter switching devices sw1, sw2, sw3, sw4, sw5, and sw6, can be obtained, and acted on the AC / DC inverter to achieve photovoltaic energy storage off-grid operation harmonic optimization control.

[0113] refer to Figure 4 and Figure 5 , Figure 4Schematic diagram of output voltage harmonics based on traditional PI control provided by an embodiment of the present invention. Figure 5 1 is a schematic diagram of voltage harmonics output by a method provided by the present invention, wherein the horizontal axis represents time and the vertical axis represents voltage;

[0114] from Figure 5 It can be seen that after the nonlinear load is switched on at 0.1s, the harmonic THD in the output voltage of the nonlinear load is 6.09%.

[0115] In this embodiment, setting f min =800HZ, f max =2000HZ, set the reference voltage to 380V, the voltage deviation range to 5%, and the maximum total harmonic content value to 0.04. Figure 5 It can be seen that after the nonlinear load is switched on at 0.1s, the harmonic THD in the output voltage of the nonlinear load is 0.73%. Using the method of the present application, the voltage harmonics output on the load side are much smaller than the output voltage harmonics using traditional PI control, and the output voltage almost coincides with the reference voltage, and the output voltage quality is also very high.

[0116] The present invention provides a method for optimizing the control of harmonics in off-grid operation of photovoltaic storage based on a substation interconnection device. High-performance nonlinear control and a multi-objective optimal control algorithm are used to achieve adaptive tracking of the AC voltage during off-grid operation and optimal suppression of harmonics, thereby improving the quality of the output voltage. The method not only has the characteristics of low cost, low modification difficulty, and low harmonic content, but can also reduce the loss of electronic components in the inverter and increase its service life.

[0117] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0118] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods and apparatuses according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0122] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements that are inherent to such process, method, article, or terminal device. In the absence of further restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0123] The above is a detailed introduction to the harmonic optimization control method for off-grid operation of photovoltaic storage based on a substation interconnection device provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for harmonic optimization control of off-grid operation of photovoltaic storage based on a substation interconnection device, characterized in that: The method comprises: Step 1: Establish a mathematical model of the inverter, including the inverter AC side output voltage , inverter AC side output current , load side output voltage , load side output current ; Step 2: Based on the mathematical model of the inverter, a discrete-time model of the mathematical model is obtained; wherein the discrete-time model is used to predict the output voltage of the load side at the next moment; Step 3: Based on the discrete time model, construct a predicted voltage control cost function , switch cost function , harmonic cost function ; Wherein, the expression of the voltage prediction control cost function is: in, 、 Indicates that the output AC reference voltage is obtained by Clark transformation 、 The amount of 、 It indicates that the load side output voltage at the next moment is predicted by Clark transformation. 、 The amount of the following; The expression of the switch cost function is: in, Indicates that the inverter AC side output voltage is When , the switching frequency of the i-th switching device, N represents the number of switching devices, represents the average switching frequency of N switching devices, represents the maximum switching frequency of the i-th switching device; The expression of the harmonic cost function is: in, Indicates the amplitude of the kth harmonic, by the output voltage on the load side Perform DFT calculations and find that M is the harmonic number; Step 4: Based on the voltage prediction control cost function , the switching cost function , the harmonic cost function , construct multi-objective predictive control cost function ; Step 5: Use the third generation non-dominated sorting genetic algorithm NSGA-III to set constraints and calculate the multi-objective predictive control cost function Solving to obtain the target value of the multi-objective predictive control cost function; Step 6: Based on the target value of the multi-objective predictive control cost function, obtain the target control pulse of the inverter, act on the inverter, and realize the harmonic optimization control of the inverter.

2. The method according to claim 1, characterized in that The mathematical model expression of the inverter is: in, Indicates the output voltage of the inverter AC side, Indicates the output voltage on the load side, Indicates the output current of the inverter AC side, Indicates the load side output current, represents the filter inductance, Indicates filter capacitor.

3. The method according to claim 1, characterized in that The expression of the discrete-time model is: in, , , , represents the sampling time interval, represents the time constant, represents the load side output voltage at sampling time k, It indicates the predicted load side output voltage at time k+1, represents the AC side output voltage of the inverter at sampling time k, Represents the load side output current at sampling time k.

4. The method according to claim 1, wherein The multi-objective predictive control cost function expression is: in, represents the weight factor, and , weight factor value .

5. The method according to claim 1, characterized in that The expression for setting the constraint condition is: in, Indicates the maximum value of the frequency of a single switching device, Indicates the minimum frequency of a single switching device, Indicates the maximum total harmonic content value, Indicates the maximum deviation between the voltage prediction control value and the reference value.

6. The method according to claim 1, characterized in that The multi-objective prediction control cost function Solving to obtain the target value of the multi-objective predictive control cost function specifically includes: 1) Initialize the parent population Pt of size N in the NSGA-III algorithm, where N is the population size; 2) Crossover and mutation generate the offspring population Qt, and the parent population and the offspring population are merged to generate the 2N population Rt=Qt+Pt; 3) Perform fast non-dominated sorting on the new population Rt to obtain a non-dominated set, and then select the target individuals as the parent population of the next generation based on the association between the reference point and the non-dominated set; 4) Continue to repeat steps 1) and 2) until the algorithm reaches the maximum genetic generation number, end the iteration process, and generate The value is the target value.

7. The method according to claim 6, characterized in that The target value of the multi-objective predictive control cost function is obtained, and the target control pulse of the inverter is applied to the inverter to achieve harmonic optimization control of the inverter, including: obtaining a target value of the load-side output voltage at a next moment based on the target value of the multi-objective predictive control cost function; Based on the target value of the load-side output voltage at the next moment, a target control pulse of the inverter is obtained and applied to the inverter to achieve harmonic optimization control of the inverter.

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