Photovoltaic system rapid power control method considering environmental parameter estimation

Abstract

The invention relates to a photovoltaic system rapid power control method considering environmental parameter estimation. The method comprises the following steps: acquiring P-V curve segmentation and local maximum power point information under a local shadow condition by using improved global maximum power point tracking based on search-jump-judgment logic; in the global maximum power point tracking process, whether a trigger signal for executing environmental parameter estimation exists or not is monitored in real time, if yes, environmental parameter estimation is executed until the trigger signal disappears, and environmental parameters of the photovoltaic system are obtained through estimation; performing multi-peak power characteristic curve fitting based on a photovoltaic equivalent circuit model based on the P-V curve segmentation and local maximum power point information and the environmental parameters of the photovoltaic system; and determining a photovoltaic working point based on a given power reference value and the multi-peak power characteristic curve to realize rapid power control of the photovoltaic system. Compared with the prior art, the method has the advantages that no additional sensor is needed, and rapid and flexible tracking of photovoltaic power can be realized.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic system power control, and in particular to a photovoltaic system fast power control method considering environmental parameter estimation. Background Art

[0002] With the global emphasis on renewable energy, photovoltaic (PV) systems are becoming more and more widely used as a clean and sustainable energy solution. At the same time, in the power grid, with the continuous increase in the penetration rate of renewable energy, the rotational inertia of traditional power sources is gradually reduced, and the system is evolving towards low inertia. This transformation has led to a decrease in the grid's ability to respond to sudden load changes, increased the risk of unbalanced power shocks in the system, and further weakened the grid's frequency control ability. In this case, the frequency security of the power grid is threatened, especially when high loads or emergencies occur, which may lead to instability in the power system. In order to ensure the safe and stable operation of the power grid, the photovoltaic system needs to have the ability to flexibly adjust its own output power, such as the ability to participate in the frequency and voltage support of the power grid. This usually involves introducing a flexible power adjustment mechanism in the power control loop of the photovoltaic system so that when the grid frequency fluctuates, the output power can be quickly adjusted to provide the necessary active and reactive support.

[0003] However, photovoltaic systems face many challenges in actual operation, especially in flexible power control. The existing MPPT and flexible power point tracking (FPPT) methods perform particularly poorly under partial shading conditions (PSC). Under PSC, due to the unevenness of light, the power-voltage (PV) characteristic curve of the PV panel has multiple non-monotonic intervals, making it difficult to correctly select the operating point of the photovoltaic system. Therefore, a global flexible power point tracking method (Global FPPT, GFPPT) for PSC has emerged, and the existing GFPPT methods are mainly divided into iterative search methods and direct calculation methods. The iterative search method is used to achieve global flexible power control of the photovoltaic system by adjusting the working point of the photovoltaic system in a directional and fixed step size, comparing the changes in the power of the photovoltaic system, and judging the specific position of the current operating point on the power-voltage characteristic curve of the photovoltaic system, and continuing until the actual power of the photovoltaic system oscillates repeatedly near the reference power value. In this way, the flexible power point of the photovoltaic system can be found, but the adjustment speed of the system power is slow, and the continuous oscillation near the reference power will inject disturbances into the power grid. Based on the direct calculation method, the global flexible power control of the photovoltaic system is realized by fitting the power-voltage characteristic curve of the photovoltaic system in sections, constructing a simplified power characteristic relationship, and then directly substituting the reference power into the fitting curve to solve the working voltage reference value. Although it has advantages in response speed, its power control accuracy is often limited by curve fitting, which easily introduces large errors. This contradiction between accuracy and response speed makes it difficult for existing technologies to achieve optimal performance when dealing with auxiliary services such as grid inertia response and fast frequency response. There is also an artificial intelligence method to realize global flexible power control of the photovoltaic system. Based on the optimization algorithm, the appropriate working point is searched so that the power of the photovoltaic system jumps directly to the reference value. However, the search process will cause large power oscillations, and the voltage step changes frequently during the search process, which accelerates the aging of photovoltaic components and affects the overall service life of the photovoltaic system controller.

[0004] Flexible power regulation becomes particularly important in power grids with high penetration of renewable energy. Modern grid specifications require photovoltaic systems to have features such as power ramp rate limitation, voltage frequency response, and primary frequency regulation to ensure the safety and reliability of the power system. Current research focuses on improving power control accuracy, while relatively less attention is paid to response speed, which may result in photovoltaic systems failing to meet the assessment standards of system operators when providing ancillary services. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a photovoltaic system fast power control method taking into account environmental parameter estimation (EPE), which can ensure that the photovoltaic system can accurately execute scheduling instructions under complex environmental conditions and meet the power grid's requirements for power accuracy and regulation speed.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A photovoltaic system fast power control method considering environmental parameter estimation comprises the following steps:

[0008] The improved global maximum power point tracking based on search-jump-judgment logic is used to obtain the PV curve segmentation and local maximum power point information under local shadow conditions;

[0009] In the global maximum power point tracking process, it is monitored in real time whether there is a trigger signal for performing environmental parameter estimation, and if so, the environmental parameter estimation is performed until the trigger signal disappears, and the environmental parameters of the photovoltaic system are estimated;

[0010] Based on the PV curve segmentation and local maximum power point information and the environmental parameters of the photovoltaic system, a multi-peak power characteristic curve fitting based on a photovoltaic equivalent circuit model is performed;

[0011] The photovoltaic operating point is determined based on a given power reference value and the multi-peak power characteristic curve, thereby realizing rapid power control of the photovoltaic system.

[0012] Furthermore, the process of obtaining the PV curve segmentation and local maximum power point information under local shadow conditions includes:

[0013] S101, locate the local maximum power point and update the global maximum power P m ;

[0014] S102, continuously disturbing the photovoltaic system to find segmentation points, and recording the photovoltaic current and voltage at the segmentation points;

[0015] S103, through V ref =P m / I sd Calculate the reference voltage of the jump point of equal power jump and record the current I at the jump point equal , if I equal ≥0.9I sd , then the jump point is located in the area between the current segment point and the next local maximum power point, otherwise, execute step S104;

[0016] S104, determine whether there is a power to voltage change rate less than 0, if so, the jump point is on the right side of the next peak value, return to step S102, if not, cancel the jump, return to step S101;

[0017] S105. When the photovoltaic system voltage is greater than 0.9 times the open circuit voltage, it is determined that the PV curve search is completed and the process ends.

[0018] Furthermore, the local maximum power point is located using MPPT based on a perturbation and observation method.

[0019] Furthermore, when searching for the segmentation point, the segmentation point is identified when the rate of change of power to voltage changes from negative to positive.

[0020] Further, when one of the local maximum power points is found, a trigger signal for estimating the execution environment parameters is generated, and when one of the segmentation points is found, the trigger signal for estimating the execution environment parameters disappears.

[0021] Furthermore, in the environmental parameter estimation, the existing estimation results are used to perform sequential step-by-step voltage correction, and based on the corrected sampling data, the PV curve is fitted by the least squares method to obtain the environmental parameter estimation value, and the environmental parameters include the ratio of the photovoltaic module temperature to the standard temperature and the irradiance.

[0022] Furthermore, in the step-by-step voltage correction, the port voltage V n The correction formula is:

[0023]

[0024] Among them, V pv and I pv is the sampled system port voltage and current, N i is the number of photovoltaic panels in the i-th group, V Diode is the forward voltage drop of the diode, I phi ,I si , R si and R shi are the photovoltaic short-circuit current, reverse saturation current, series resistance and parallel resistance of the photovoltaic panel of the i-th group under illumination, N Cut is the number of panels protected by the bypass diode, a i is the diode ideal factor of the equivalent model of the ith photovoltaic panel, W Vi Simplified expression of Lambert W function for the photovoltaic equivalent model of the i-th group of panels.

[0025] Furthermore, the photovoltaic equivalent circuit model is a single diode model, and the multi-peak power characteristic curve fitting is expressed as:

[0026]

[0027] Among them, I phn ,I sn , R sn , R shn and a n are the values ​​of the single diode model parameters under the nth group of shadow conditions, which are photovoltaic short-circuit current, reverse saturation current, series resistance, parallel resistance and diode ideal factor, respectively. i is the number of photovoltaic panels in the i-th group, i=1~n, I sdn is the current value corresponding to each segment point, I pv is the sampled system port current, W Vi Simplified expression of Lambert W function for the photovoltaic equivalent model of the i-th group of panels.

[0028] Furthermore, when determining the photovoltaic operating point, a plurality of candidate operating points are first determined based on a given power reference value and the multi-peak power characteristic curve, and then the candidate operating point closest to the current operating point is selected as the photovoltaic operating point.

[0029] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the photovoltaic system fast power control method considering environmental parameter estimation as described above.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The method of the present invention takes into account the rapid response of the direct calculation method and the high precision of the iterative search method, ensuring that the photovoltaic system can accurately execute the dispatching instructions under complex environmental conditions and meet the power grid's requirements for power accuracy and regulation speed.

[0032] 2. The present invention uses an improved global maximum power point tracking (GMPPT) based on search-skip-judge (SSJ) logic to obtain PV curve segmentation and local maximum power point information under local shadow conditions. The modified SSJ-based GMPPT (SSJ-GMPPT) can quickly locate the global maximum power point by recording the operating data of the photovoltaic system at each peak on the PV curve.

[0033] 3. The environmental parameter estimation strategy proposed in the present invention, which is performed synchronously with the maximum power point tracking, utilizes the PV system voltage and current data collected during the SSJ-GMPPT process to accurately update the irradiance, temperature, and shadow condition of each PV panel without the need for additional sensors.

[0034] 4. The segmented information obtained by the improved SSJ-GMPPT is used to perform segmented fitting of the PV curve, and a multi-peak PV curve fitting algorithm based on the photovoltaic equivalent model is used to quickly establish an accurate PV relationship.

[0035] 5. Compared with the global flexible power control of the photovoltaic system based on the iterative search method, the present invention greatly improves the response speed of power regulation and eliminates steady-state power oscillations; compared with the global flexible power control of the photovoltaic system based on the direct calculation method, the present invention achieves a significant improvement in power control accuracy and can operate in a more complex irradiation environment; compared with the global flexible power control of the photovoltaic system based on the artificial intelligence method, the present invention has no large-scale random power disturbances and supports a wide range of real-time and precise power regulation.

[0036] 6. The rapid power control method for photovoltaic systems under partial shading conditions that takes into account environmental parameter estimation can monitor the operating status and environmental changes of the photovoltaic system in real time, quickly respond to light and load fluctuations, and improve the speed of power adjustment. By introducing a curve fitting method based on a PV equivalent model, the present invention ensures high-precision power control while maintaining a high direct and algorithmic adjustment speed, eliminating the steady-state oscillation of the iterative method. In addition, the method has good adaptability and can quickly adjust power under complex conditions such as complex partial shading conditions and changes in light, while maintaining the same high precision. The method does not need to rely on additional irradiance and temperature sensors, reduces system complexity and construction costs, and simplifies the maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the working logic of the improved SSJ-GMPPT of the present invention;

[0038] Figure 2 Schematic diagram of triggering logic of environmental parameter estimation strategy of the present invention, wherein (a) is the SSJ-GMPPT process, and (b) is the triggering signal of environmental parameter estimation;

[0039] Figure 3 is an optional operating point of the photovoltaic system on the multi-peak PV curve under local shadow conditions of the present invention;

[0040] Figure 4 A flowchart of the fast power control method proposed by the present invention;

[0041] Figure 5A schematic diagram of the structure of a research system in an embodiment of the present invention;

[0042] Figure 6 A schematic diagram showing a comparison between a photovoltaic multi-peak power characteristic fitting curve and an actual photovoltaic power characteristic curve in an embodiment of the present invention;

[0043] Figure 7 : It is the simulation result of environmental parameter estimation in the embodiment of the present invention, where (a) is the GMPPT process and trigger signal, and (b) is the irradiance and temperature estimation value;

[0044] Figure 8 The simulation results for comparing and verifying the fast power control method in the embodiment of the present invention are shown in FIG. 1 , where (a) is the photovoltaic system power and (b) is the power control error;

[0045] Fig. 9 The virtual inertia comparison and verification simulation results are provided for the fast power control method in the embodiment of the present invention, wherein (a) is the grid load disturbance, (b) is the photovoltaic system power of the embodiment, (c) is the photovoltaic system power comparison of the comparative example, (d) is the system frequency comparison between the embodiment and the comparative example, (e) is the system frequency change rate comparison between the embodiment and the comparative example, and (f) and (g) correspond to the partial enlarged views of (d) and (e), respectively. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0047] The present embodiment provides a method for rapid power control of a photovoltaic system taking environmental parameter estimation into consideration, comprising the following steps: using an improved global maximum power point tracking based on search-jump-judgment logic to obtain PV curve segmentation and local maximum power point information under local shadow conditions; during the global maximum power point tracking process, real-time monitoring is performed to determine whether there is a trigger signal for performing environmental parameter estimation, and if so, performing environmental parameter estimation until the trigger signal disappears, thereby estimating the environmental parameters of the photovoltaic system; performing multi-peak power characteristic curve fitting based on a photovoltaic equivalent circuit model based on the PV curve segmentation and local maximum power point information and the environmental parameters of the photovoltaic system; determining the photovoltaic operating point based on a given power reference value and the multi-peak power characteristic curve, thereby realizing rapid power control of the photovoltaic system.

[0048] The above method can realize fast power control of photovoltaic systems under local shadow conditions by considering environmental parameter estimation. In the MPPT stage of photovoltaic systems, an environmental parameter estimation algorithm is introduced that is attached to the power tracking process and does not require additional sensors. This can effectively estimate the uneven irradiation and temperature conditions of the photovoltaic system. Subsequently, the PV characteristic curve is constructed by combining the environmental parameters and the equivalent model of the photovoltaic system. Finally, according to the fitted PV curve and the power command of the photovoltaic system, the appropriate photovoltaic system voltage reference value is calculated and selected, and output to the photovoltaic controller to realize flexible tracking of photovoltaic power.

[0049] The above method is described in detail as follows:

[0050] 1.SSJ-GMPPT method

[0051] The first step in implementing GFPPT in a photovoltaic system is to obtain the global maximum power point. This embodiment uses the SSJ-GMPPT (SSJ-based GMPPT) method to achieve this goal. The main principle of SSJ-GMPPT is to jump horizontally to the isopower point of the local maximum power point (LMPP) and ignore the partial power area below the local maximum power point, thereby narrowing the search range and improving the tracking efficiency.

[0052] The implementation steps of SSJ-GMPPT are as follows:

[0053] Phase 1: LMPP search. Use MPPT based on perturbation and observation (P&O) to locate LMPP and update the global maximum power P by comparison. m At this stage, the P&O-based MPPT can be replaced by other GMPPT algorithms, such as the conductance increment method. The i-th LMPP found is recorded as M i .

[0054] Phase 2: Section-dividing point (SDP) search. After locating the LMPP, the improved SSJ-GMPPT continuously disturbs the PV system to find the SDP and records the PV current and voltage at the SDP as I sd and V sd At this stage, when the rate of change of power to voltage (dP / dV) changes from negative to positive, the SDP is identified, and the SDP corresponding to the i-th LMPP is recorded as SDP i .

[0055] Phase 3: Isopower jump. Through V ref =P m / I sdCalculate the reference voltage of the jump point A, then the photovoltaic operating point jumps to A, and record the current I at the jump point equal , the jump point corresponding to the i-th LMPP is recorded as A i .

[0056] Phase 4: Jump point determination. First, the improved SSJ-GMPPT needs to determine the jump point location. If Iequal≥0.9I sd , then the jump point is located in the area between SDP and the next LMPP, and the algorithm returns to Stage 1 to find the next peak. sd , then it is necessary to judge dP / dV. If dP / dV<0, the jump point is on the right side of the next peak, and the improved SSJ-GMPPT directly enters stage 2. If dP / dV>0, the jump point ignores the entire peak, and it is necessary to cancel this jump, return to SDP, and enter stage 1.

[0057] Phase 5: Termination judgment. If the PV system voltage is greater than 0.9 times the open circuit voltage (Open circuit voltage, V oc ), it is considered that the PV curve has been completely searched, GMPPT is terminated, and 0.9 times the open circuit is recorded as the termination voltage V END .

[0058] The working logic diagram of the improved SSJ-GMPPT is as follows: Figure 1 As shown in the figure, the change process of the photovoltaic working point during the tracking process is demonstrated. The initial point of SSJ-GMPPT is defined as V Start .

[0059] In the above stage 1, the purpose of using the MPPT based on the perturbation observation method to locate the LMPP is to obtain some data of the multi-peak PV curve. In other embodiments, this process can also be replaced by INC, hybrid GMPPT method and other similar methods.

[0060] 2. Environmental parameter estimation strategy based on voltage identification

[0061] The second step of the photovoltaic fast power control method proposed in this embodiment is to estimate the environmental parameters added to the SSJ-GMPPT process, which provides the required data for the subsequent power characteristic curve fitting based on the photovoltaic model. However, under partial shadow conditions, the working points of each group of panels with different irradiances in the series photovoltaic system are different, making it difficult to directly collect the port voltage data of the panel.

[0062] To this end, this embodiment proposes a sampling data correction method based on a photovoltaic model, which is attached to the SSJ-GMPPT search process, and uses the obtained parameter estimation results to correct the data of the sampled voltage to identify the port voltage of each photovoltaic panel. In this embodiment, the photovoltaic model adopts a single diode model. In other implementations, the photovoltaic panel port voltage can also be extracted based on other similar photovoltaic equivalent models through iterative calculation.

[0063] The single diode model has a simple structure and fewer parameters, and can accurately characterize the current-voltage characteristics of PV. Based on this model, the IV characteristics of the PV module are defined as:

[0064]

[0065] Where, I is the output current of the photovoltaic module; I ph is the photovoltaic short-circuit current under illumination; V is the terminal voltage; I s is the reverse saturation current of the diode; R s , R sh are series and parallel resistances respectively; a=n s nV T , n s is the number of PV cells in series on the panel, n is the ideal factor, V T =T cell k / q is the thermal voltage of the photovoltaic cell, where k = 1.38 × 10 -23 J / K is the Boltzmann constant; q is the elementary charge; T cell is the PN junction temperature. These parameters are determined by the physical properties of the PV panel itself and environmental parameters. Usually, the parameters can be measured using standard test conditions (25°C, 1000W / m 2 ) is solved by combining the standard values ​​determined under the environmental parameters.

[0066] When the SSJ-GMPPT operates at the first peak, only the photovoltaic panels with the highest irradiance are in operation, and the environmental parameters of these photovoltaic systems can be directly fitted by equation (6). When the photovoltaic system operates on the right side of the second local peak, the data sampled can be directly subtracted from the voltage of the first group of panels and the conduction voltage drop V of the conduction diode. Diode At this time, the operating voltage of the second group of panels is:

[0067] V2=V pv -N Cut V Diode -V1 (2)

[0068] Among them, V2 is the sum of the voltages of the panels with an irradiance of Ir2, and V1 is the sum of the voltages of the panels with an irradiance of Ir1 (Ir1>Ir2). Its value can be solved based on the first estimated environmental parameters and photovoltaic model. Similarly, when the parameters of the nth peak are estimated, the n-1 groups of existing estimation results can be used to perform sequential step-by-step voltage correction. In the environmental parameter estimation, the method of sampling data correction is defined as:

[0069]

[0070] Among them, V pv and I pv is the system port voltage and current sampled during the phase 2 process of SSJ-GMPPT, a i is the diode ideal factor of the equivalent model of the ith photovoltaic panel, W Vi is the simplified expression of Lambert W function of the photovoltaic equivalent model of the i-th group of panels, and N i is the number of panels in the i-th group:

[0071] N i =floor(V n / 0.8V OC ) (4)

[0072] The corrected terminal voltage V of the nth photovoltaic panel n It can be directly regarded as N under uniform illumination. i For a PV system with 100 panels connected in series, the photovoltaic equivalent model can be used to accurately fit the environmental parameters. The model parameters considering the influence of environmental factors are defined as:

[0073]

[0074] Among them, I ph0 ,I s0 , R s0 and R sh0 are the values ​​of short-circuit current, reverse saturation current, series resistance and parallel resistance measured under standard test conditions (STC); α Iph is the short-circuit current I ph Normalized temperature coefficient, T is the ratio of the PV module temperature to the standard temperature (25°C); G is the irradiance; constant E k is the temperature correction factor for the silicon bandgap, defined as E k =(1 / T0-0.000267)E0 / k, used to simplify the numerical calculation of silicon energy gap of photovoltaic modules at actual temperature and standard test temperature, where E0 = 1.7958e -19J, T0 = 298K. Substituting (5) into (1) yields a new IV characteristic expression containing only four independent variables: port voltage (V), current (I), T and Ir, namely:

[0075]

[0076] Using this new IV characteristic expression, the estimated values ​​of environmental parameters T and Ir can be obtained by fitting the PV curve through the least squares method (LSM). In this process, the LSM method minimizes the sum of squares of the ordered deviations between the PV voltage and current measurements and their estimated values ​​by making the partial derivatives of T and Ir zero.

[0077] It should be noted that in the improved SSJ-GMPPT process, the EPE strategy needs to sample the PV system data in the order of the PV curve peaks. In conjunction with the modified SSJ-GMPPT described above, a special EPE tracking auxiliary trigger logic that does not disturb the PV power is designed and proposed. Specifically, considering that the EPE strategy requires the operating data of the PV system on the right side of the LMPP, which corresponds to the operating area of ​​stage 2 in the improved SSJ-GMPPT, when the LMPP is found, the EPE is activated, and the parameter estimation continues until the SDP is found. In a similar Figure 1 In the GMPPT process, the trigger signal of environmental parameter estimation is as follows Figure 2 shown.

[0078] In this embodiment, an environmental parameter estimation algorithm based on the least squares method is used to process the identified port voltage to achieve environmental parameter estimation. In other implementations, an environmental parameter estimation algorithm based on other methods may also be used to process the port voltage to obtain environmental parameters.

[0079] 3. Multi-peak power characteristic curve fitting method based on photovoltaic equivalent circuit model

[0080] The power of a single photovoltaic panel P panel The relationship between photovoltaic current and voltage given by the single diode model can be solved, and formula (1) can be obtained:

[0081]

[0082] Obviously, equation (7) is an implicit formula, which usually needs to be solved iteratively using numerical methods. Under PSC, the irradiance of photovoltaic panels is different, while the working current of the series photovoltaic system is the same, so the working voltage of each group of panels is different. However, the port voltage and current of each photovoltaic panel meet their own characteristic curves. Therefore, when the bypass diode conduction voltage (about 0.7V) is ignored, solving the working voltage using the power reference value requires connecting several photovoltaic equivalent models with different parameters in series:

[0083]

[0084] Among them I pv is the series working current, P pvn , V pvn and N n are the power, voltage and number of panels of the nth group of photovoltaic panels, N n The calculation of will be given in the next section. Lambert W function is used to simplify the implicit function expression obtained from the photovoltaic model. The simplified explicit expression of the relationship between the photovoltaic panel voltage and current is:

[0085]

[0086] Where W(x) is the function f(x)=xe in the interval [-1,+∞] x The inverse function of I and W V is a simplified form of the Lambert W function based on the photovoltaic current and voltage, which are defined as:

[0087]

[0088] Substituting into (3), we can get the fitting formula of the power characteristic curve of the series PV system:

[0089]

[0090] Among them I phn ,I sn , R sn , R shn and a n are the values ​​of the single diode model parameters under the shadow condition corresponding to the nth group of photovoltaic panels, which are photovoltaic short-circuit current, reverse saturation current, series resistance, parallel resistance and diode ideal factor, respectively. Their values ​​can be calculated according to formula (5). It should be noted that when fitting the power or current characteristic curve of the series photovoltaic system under PSC based on formula (11), the photovoltaic PV characteristic curve should be segmented according to the local peak values. The segmented curve fitting method is as follows:

[0091]

[0092] Among them I sdn The SDP information of the power characteristic curve of the PV system corresponding to the current value of each SDP has been obtained in the SSJ-GMPPT and environmental parameter estimation process.

[0093] In other embodiments, other photovoltaic equivalent models similar to the photovoltaic single diode model may also be used to fit the photovoltaic multi-peak characteristic curve in series.

[0094] 4. Rapid power control of photovoltaic systems considering environmental parameter estimation

[0095] In summary, the proposed fast power control method starts from the improved SSJ-GMPPT, collects the key point information of the photovoltaic power-voltage curve during the tracking process, and uses the environmental parameter strategy attached to the SSJ-GMPPT process to estimate the environmental parameters of the photovoltaic system. Then, the environmental parameters and the key points of the power-voltage curve are used for model-based multi-peak power characteristic curve fitting.

[0096] When the FPP is given and input into the fitting curve, in each control cycle, according to the power reference value P ref =P Total Solving for the unique variable I pv , and then the voltage reference value of the series system can be obtained:

[0097] V ref =N Cut V Diode +P ref / I pv (13)

[0098] Where N Cut is the number of panels protected by the bypass diode, V Diode is the conduction voltage drop of the diode. The calculated reference voltage is the working voltage when the photovoltaic system outputs the corresponding reference power.

[0099] However, for a given power reference value, there may be multiple candidate operating points for the PV system under partial shadow conditions, such as Figure 3 shown.

[0100] In this embodiment, the working point closest to the current working point is usually selected to alleviate the power fluctuations that may be caused by the large changes in the photovoltaic working point, improve the system output stability, reduce the voltage adjustment range, increase the power adjustment response speed, and extend the service life of the converter. The calculation rule of the output reference voltage is:

[0101]

[0102] Where V ref1 To V refn Corresponding to the multiple candidate operating voltages obtained by equation (13), V i ref Indicates the voltage reference value of the previous control cycle.

[0103] In summary, the control flow chart of the fast power control of the photovoltaic system considering environmental parameter estimation under partial shadow conditions proposed by the present invention is summarized as follows: Figure 4 .

[0104] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0105] In other embodiments, an electronic device may be provided, including one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method described above.

[0106] In this embodiment, the fast power control method proposed above and the curve fitting and environmental parameter estimation algorithms included therein are verified in MATLAB / Simulink software. Figure 5 As shown, it includes a set of photovoltaic arrays, DC boost circuits, inverters, filter circuits and a synchronous generator to represent the grid equivalently. The photovoltaic array consists of 10 photovoltaic panels connected in series, with a rated power of 3.05kW. The model of the photovoltaic module used is SunPowerSPR-305E-WHT-D, and its parameters are shown in Table 1. In the embodiment, the photovoltaic system is affected by local shadows, and the irradiance of each panel is uneven. The irradiance of 3 panels is 1000W / m 2 The irradiance of the four panels is 600W / m 2 The irradiance of the three panels is 300W / m 2 , at this time the maximum power of the photovoltaic system is 1.347kW.

[0107] Table 1 SunPower SPR-305E-WHT-D photovoltaic panel parameters

[0108]

[0109] In this case study, three sets of examples are set up:

[0110] 1. Comparison and verification of the proposed photovoltaic multi-peak power characteristic fitting curve and the actual photovoltaic power characteristic curve;

[0111] 2. Verification of the proposed environmental parameter estimation strategy based on voltage identification;

[0112] 3. Comparative verification of the proposed fast power control method:

[0113] A. Comparative example: PV system uses iterative search method for flexible power control;

[0114] B. Example: A photovoltaic system is controlled using the proposed fast power tracking method.

[0115] 1. Comparison and verification of the proposed photovoltaic multi-peak power characteristic fitting curve and the actual photovoltaic power characteristic curve

[0116] The curve fitting method proposed in the present invention is used to fit the multi-peak power characteristic curve of the photovoltaic system under partial shadow conditions. The fitting result is compared with the actual power characteristic curve of the photovoltaic system. Figure 6 As shown in the figure, the left axis represents the photovoltaic system power and the right axis represents the absolute error of the multi-peak curve fitting. It is obvious that the fitting accuracy on the left side of the local maximum power point is very high with the proposed model-based photovoltaic curve fitting method, which meets the power regulation requirements of almost all scenarios. Moreover, although the fitting error on the right side is a bit higher, the maximum error is kept below 0.2% (~2.2W) in the entire range, and the information of the peak power point is effectively captured.

[0117] It can be seen that the proposed photovoltaic system power characteristic curve fitting algorithm has excellent fitting accuracy and advancement.

[0118] 2. Verification of the proposed environmental parameter estimation strategy based on voltage identification

[0119] The environmental parameter estimation strategy based on voltage identification proposed in this invention is simulated and verified, and the simulation results are as follows: Figure 7 As shown in Figure 2, when the modified SSJ-GMPPT is started, it starts to search for the global maximum power point, such as Figure 7 As shown in (a), when GMPPT searches for the first local power peak and continues to move forward in the downhill area to the right of the global maximum power point, GMPPT runs into the inflection point search mode (stage 2). At the same time, the rising edge of stage 2 triggers the trigger signal for environmental parameter estimation ( Figure 7 The gray curve in (a) indicates that the environmental parameter estimation algorithm starts running.

[0120] The estimated results corresponding to each peak can be found in Figure 7 As shown in (b), as described in the present invention, as the GMPPT operates at different peak values, the environmental parameter estimation algorithm can gradually estimate the environmental parameters of each photovoltaic panel with short-circuit current from high to low. Figure 7As shown in the solid line in (b), the irradiance of the photovoltaic module corresponding to each peak is accurately estimated, while the single observation Figure 7 The local enlarged image in (b) shows the process of gradual iterative convergence of environmental parameter estimation during the sampling period. The results obtained after each sampling and estimation are relatively close, which shows that the proposed environmental parameter estimation has high stability and accuracy.

[0121] 3. Comparative verification of the proposed fast power control method

[0122] The photovoltaic system is operated under the control methods of the embodiment and the comparative example respectively, and the reference power of the photovoltaic system is changed to compare the flexible power control capabilities of the two control methods. The simulation results of the comparison verification are as follows: Figure 8 As shown, Figure 8 (a) shows the reference power of GFPPT and the power curve of PV system under PSC, while Figure 8 (b) shows the absolute error of power control for the two control methods.

[0123] The proposed fast power control method has higher power control accuracy and tracking speed, and there is no iterative search power search, convergence process and steady-state oscillation. Figure 8 As can be seen from (a), the embodiment has a high response speed and only requires one calculation iteration to track the accurate reference power. Figure 8 As can be seen from the partial enlarged diagram in (b), the comparative example is in a steady-state oscillation state, and the absolute error is relatively high. At the same stage, the power control error of the embodiment is less than 5W (<0.5%), so the accuracy and speed advantages of the embodiment are very obvious.

[0124] The application scenario is extended to the photovoltaic system to provide virtual inertia to the power grid. At this time, the comparison and verification simulation results of the embodiment and the comparative example are as follows: Fig. 9 As shown. Fig. 9 As shown in (a)-(e) of FIG. 1 , under the same load disturbance, the photovoltaic system will adjust the photovoltaic reference power according to the rate of change of frequency (RoCoF). The embodiment is significantly better than the comparative example in terms of power adjustment speed, and can effectively track the rapid reference power changes and provide full inertial support. However, the comparative example fails to track the power command in real time, resulting in insufficient support in the initial inertial support stage, lower system inertia during disturbance, and higher RoCoF.

[0125] Depend on Fig. 9As shown in the partial enlarged diagrams of (f)-(g), at 5.5s to 6.5s, the power control of the embodiment under the same virtual inertia strategy is faster and more stable, without periodic fluctuations, and has better performance. The minimum RoCoF is -0.014Hz / s, while the maximum RoCoF of the comparative example is -0.016Hz / s. It can be seen that the embodiment has fast, flexible and accurate power control capabilities, meets the diverse auxiliary service needs, and improves the active protection capability of the photovoltaic system.

[0126] Thus, the functionality and advancement of the proposed fast power control method are verified.

[0127] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A photovoltaic system fast power control method considering environmental parameter estimation, characterized in that: The following steps are involved: The improved global maximum power point tracking based on search-jump-judgment logic is used to obtain the PV curve segmentation and local maximum power point information under local shadow conditions; In the global maximum power point tracking process, it is monitored in real time whether there is a trigger signal for performing environmental parameter estimation, and if so, the environmental parameter estimation is performed until the trigger signal disappears, and the environmental parameters of the photovoltaic system are estimated; Based on the PV curve segmentation and local maximum power point information and the environmental parameters of the photovoltaic system, a multi-peak power characteristic curve fitting based on a photovoltaic equivalent circuit model is performed; The photovoltaic operating point is determined based on a given power reference value and the multi-peak power characteristic curve, thereby realizing rapid power control of the photovoltaic system.

2. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 1, characterized in that: The process of obtaining the PV curve segmentation and local maximum power point information under local shadow conditions includes: S101, locate the local maximum power point and update the global maximum power P m ; S102, continuously disturbing the photovoltaic system to find segmentation points, and recording the photovoltaic current and voltage at the segmentation points; S103, through V ref =P m / I sd Calculate the reference voltage of the jump point of equal power jump and record the current I at the jump point equal , if I equal ≥0.9I sd , then the jump point is located in the area between the current segment point and the next local maximum power point, otherwise, execute step S104; S104, determine whether there is a power to voltage change rate less than 0, if so, the jump point is on the right side of the next peak value, return to step S102, if not, cancel the jump, return to step S101; S105. When the photovoltaic system voltage is greater than 0.9 times the open circuit voltage, it is determined that the PV curve search is completed and the process ends.

3. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 2, characterized in that: The local maximum power point is located using MPPT based on the perturbation and observation method.

4. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 2, characterized in that: When searching for the segmentation point, the segmentation point is identified when the rate of change of power to voltage changes from negative to positive.

5. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 2, characterized in that: When one of the local maximum power points is found, a trigger signal for performing the environmental parameter estimation is generated, and when one of the segmentation points is found, the trigger signal for performing the environmental parameter estimation disappears.

6. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 1, characterized in that: In the environmental parameter estimation, the existing estimation results are used to perform sequential step-by-step voltage correction. Based on the corrected sampling data, the PV curve is fitted by the least squares method to obtain the environmental parameter estimation value. The environmental parameters include the ratio of the photovoltaic module temperature to the standard temperature and the irradiance.

7. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 6, characterized in that: In the step-by-step voltage correction, the port voltage V n The correction formula is: Among them, V pv and I pv is the sampled system port voltage and current, N i is the number of photovoltaic panels in the i-th group, V Diode is the forward voltage drop of the diode, I phi ,I si , R si and R shi are the photovoltaic short-circuit current, reverse saturation current, series resistance and parallel resistance of the photovoltaic panel of the i-th group under illumination, N Cut is the number of panels protected by the bypass diode, a i is the diode ideal factor of the equivalent model of the ith photovoltaic panel, W Vi Simplified expression of Lambert W function for the photovoltaic equivalent model of the i-th group of panels.

8. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 1, characterized in that: The photovoltaic equivalent circuit model is a single diode model, and the multi-peak power characteristic curve fitting is expressed as: Among them, I phn ,I sn , R sn , R shn and a n are the values ​​of the single diode model parameters under the nth group of shadow conditions, which are photovoltaic short-circuit current, reverse saturation current, series resistance, parallel resistance and diode ideal factor, respectively. i is the number of photovoltaic panels in the i-th group, i=1~n, I sdn is the current value corresponding to each segment point, I pv is the sampled system port current, W Vi Simplified expression of Lambert W function for the photovoltaic equivalent model of the i-th group of panels.

9. The photovoltaic system rapid power control method considering environmental parameter estimation according to claim 1, characterized in that: When determining the photovoltaic operating point, a plurality of candidate operating points are first determined based on a given power reference value and the multi-peak power characteristic curve, and then the candidate operating point closest to the current operating point is selected as the photovoltaic operating point.

10. A computer-readable storage medium, characterized in that: The method comprises one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs comprise instructions for executing a photovoltaic system fast power control method considering environmental parameter estimation as claimed in any one of claims 1 to 9.

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