Multi-converter harmonic coupling analysis method and system considering randomness of photovoltaic output

Abstract

The present invention relates to the technical field of distribution network, and in particular to a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output, including: establishing a node coupling admittance matrix of the distribution network; calculating the photovoltaic relative output of each photovoltaic power station, and clustering the photovoltaic relative output to obtain the best scene clustering result; according to the best scene clustering result, establishing the photovoltaic output state transfer matrix under each scene to simulate and generate the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period; according to the photovoltaic output prediction sequence, obtaining the converter equivalent harmonic source of each photovoltaic power station, and combining the node coupling admittance matrix to analyze and obtain the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output. The present invention can more accurately judge the converter output current harmonic characteristics by considering the mutual coupling between the converter output harmonic currents and taking into account the random volatility of photovoltaic power generation output, thereby providing a basis for ensuring the safe operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output. Background Art

[0002] In recent years, new energy represented by solar energy has been connected to the grid on a large scale through converters to generate electricity, which has effectively alleviated the energy crisis. However, it has also brought about a series of power quality problems, among which the grid harmonic problem has become increasingly prominent. The main reasons for the grid harmonic problem are: the output current of the converter is affected by its own circuit structure, output unit power, dead zone effect, etc., so that it contains low-order harmonics and high-order harmonics near the switching frequency. At the same time, the harmonic characteristics of the converter are relatively complex and will be affected by background harmonics. The parallel coupling of multiple converters will lead to harmonic amplification or even resonance, making the harmonic problem more complex and serious.

[0003] Photovoltaic output is not only affected by natural factors such as the position of the sun, clouds in the sky and other obstructions, but also by photovoltaic equipment such as the arrangement of photovoltaic arrays and the characteristics of photovoltaic cells. It has a certain regularity and strong random volatility. Photovoltaic output affects the converter output modulation ratio, thereby indirectly affecting the converter output harmonic amplitude. The existing power grid rarely considers the volatility and randomness of photovoltaic output, which makes the multi-converter harmonic coupling analysis more difficult. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output, which can comprehensively consider the mutual coupling between the harmonic currents output by the converters and the random volatility of the photovoltaic power generation output, and comprehensively consider the characteristics of the converter output harmonics when the photovoltaic output changes randomly.

[0005] In order to solve the above technical problems, the present invention provides a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output.

[0006] In a first aspect, the present invention provides a multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output, the method comprising the following steps:

[0007] According to the converter harmonic equivalent model, the node coupling admittance matrix of the distribution network is established;

[0008] Acquire photovoltaic historical data of each photovoltaic power station, and calculate the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model;

[0009] Clustering the photovoltaic relative output using a preset clustering algorithm to obtain an optimal scenario clustering result of the photovoltaic relative output sequence under a preset time scale;

[0010] According to the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period;

[0011] According to the photovoltaic output prediction sequence of each photovoltaic power station, an equivalent harmonic source of a converter of the corresponding photovoltaic power station is obtained;

[0012] According to the converter equivalent harmonic source and the node coupling admittance matrix, the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output are analyzed and obtained.

[0013] In a further embodiment, the equation of the node coupling admittance matrix is:

[0014] I o =I s -Y eq ·V m

[0015] =I s -Y eq ·Z m I s

[0016] =(EY eq ·Z m )·I s

[0017] Where m represents the number of converter network nodes connected to the converter; I o Represents the converter output current vector; I s Represents the equivalent harmonic source of the converter; Y eq represents the equivalent admittance of the converter; V m represents the node voltage vector connected to the converter; Z m Indicates I s With V m The impedance matrix between them; E is the unit matrix.

[0018] In a further implementation scheme, the photovoltaic historical data includes light zone information data of new energy photovoltaic output of each photovoltaic power station at a preset time scale within a preset historical time period, photovoltaic power station geographic information, and photovoltaic array inclination.

[0019] In a further embodiment, the step of calculating the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model includes:

[0020] According to the clearance model of the photovoltaic power station and the photovoltaic historical data, the photovoltaic clearance theoretical output of the photovoltaic power station is calculated;

[0021] According to the actual output of photovoltaic power generation and the photovoltaic clearance theoretical output, the photovoltaic relative output based on the clearance model at each day and time is obtained.

[0022] In a further embodiment, the step of clustering the photovoltaic relative output using a preset clustering algorithm to obtain the best scenario clustering result of the photovoltaic relative output sequence under a preset time scale includes:

[0023] Dividing the photovoltaic relative output at a preset time scale to obtain a photovoltaic relative output sequence at the preset time scale;

[0024] The volatility characteristics of the photovoltaic relative output sequence are selected as the characteristic vector of the photovoltaic relative output sequence;

[0025] The improved FCM clustering algorithm is used to perform cluster analysis on the characteristic vectors of the photovoltaic relative output sequence under the preset time scale, and the optimal scenario clustering result of the photovoltaic relative output sequence under the preset time scale is obtained.

[0026] In a further implementation scheme, the step of establishing a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result to simulate and generate a photovoltaic output prediction sequence of a photovoltaic power station within a simulation period includes:

[0027] According to the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established;

[0028] Based on the photovoltaic output state transfer matrix, randomly extract the power reference value and the fluctuation coefficient value of the corresponding day from the power value reference probability distribution and the fluctuation coefficient value probability distribution of the scene type to which the photovoltaic power station belongs;

[0029] The power reference value and the fluctuation coefficient value are superimposed to obtain the simulated relative output at each time of each day in the simulation period;

[0030] According to the simulated relative output and the pre-acquired photovoltaic clearance theoretical output, a photovoltaic output prediction sequence of the photovoltaic power station within the simulation period is simulated and obtained.

[0031] In a further implementation, the step of obtaining the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station includes:

[0032] Based on the photovoltaic output prediction sequence, the modulation ratio of each converter is calculated;

[0033] According to the modulation ratio of each converter, the output line voltage effective value of each converter is obtained;

[0034] According to the effective value of the output line voltage of each converter, the corresponding converter equivalent harmonic source is calculated;

[0035] Among them, the modulation ratio calculation formula of each converter is:

[0036]

[0037] Where M represents the modulation ratio of each converter; U dc represents the DC side voltage; P′(d,t) represents the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period; U g Indicates the effective value of the phase voltage on the AC side of the grid-connected inverter; ω1 indicates the fundamental frequency of the grid; L1 and L2 indicate the reactance value of the filter; L g Indicates the grid-side impedance.

[0038] In a second aspect, the present invention provides a multi-converter harmonic coupling analysis system taking into account the randomness of photovoltaic output, the system comprising:

[0039] A coupling matrix building module is used to build a node coupling admittance matrix of the distribution network according to the converter harmonic equivalent model;

[0040] The relative output clustering module is used to obtain the photovoltaic historical data of each photovoltaic power station, and calculate the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model; cluster the photovoltaic relative output using a preset clustering algorithm to obtain the optimal scenario clustering result of the photovoltaic relative output sequence under a preset time scale;

[0041] A photovoltaic output prediction module is used to establish a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result, so as to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period;

[0042] The coupling characteristic analysis module is used to obtain the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station; and is also used to analyze and obtain the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output according to the converter equivalent harmonic source and the node coupling admittance matrix.

[0043] At the same time, in a third aspect, the present invention also provides a computer device, including a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device performs the steps of implementing the above method.

[0044] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0045] The present invention provides a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output. The method uses a preset clustering algorithm to cluster the acquired photovoltaic relative output to obtain the optimal scene clustering result of the photovoltaic relative output sequence under a preset time scale; according to the optimal scene clustering result, a photovoltaic output state transfer matrix under each scene is established to simulate and generate a photovoltaic output prediction sequence of a photovoltaic power station within a simulation period, so as to obtain the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station, and at the same time, combined with the pre-established node coupling admittance matrix, the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output are analyzed. Compared with the prior art, the method aims at the mutual coupling between the random volatility characteristics of photovoltaic output and the converter output harmonic current in the distribution network, realizes a method for more accurate and comprehensive analysis of the converter output harmonic characteristics, and has the advantages of scientific rationality and high prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of a multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output provided by an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of a harmonic equivalent model of a converter provided by an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of a multi-converter harmonic coupling analysis process provided by an embodiment of the present invention;

[0049] Figure 4 is a block diagram of a multi-converter harmonic coupling analysis system taking into account the randomness of photovoltaic output provided by an embodiment of the present invention;

[0050] Figure 5 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0052] refer to Figure 1 , the embodiment of the present invention provides a multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output, such as Figure 1 As shown, the method comprises the following steps:

[0053] S1. According to the converter harmonic equivalent model, the node coupling admittance matrix of the distribution network is established.

[0054] After obtaining the operating parameters of each converter in the distribution network, such as output voltage, output current, impedance characteristics and harmonic characteristics, this embodiment establishes a node admittance matrix of the converter network in the distribution network under the excitation source frequency f according to the converter harmonic equivalent model, distribution network circuit parameters and topology structure:

[0055] I=Y*V

[0056] Where I represents the node injection current vector of the converter network; Y represents the node admittance matrix of the converter network; V represents the node voltage vector of the converter network.

[0057] In this embodiment, the converter harmonic equivalent model is as follows: Figure 2 As shown, the converter output current expression is:

[0058] i gh =Y i u ih -Y eq u gh

[0059] =I s -Y eq u gh

[0060] in,

[0061]

[0062]

[0063] In the formula, u ih Indicates the switching harmonics generated by the inverter itself caused by dead zone, SPWM modulation, etc.; u gh Represents the background harmonic of the power grid; Z L1 , Z L2 , Z C Represents the impedance of the inductor L1, L2 and capacitor C of the LCL filter in the converter; G ci represents the current loop proportional resonant (PR) controller transfer function; K PWM Indicates the equivalent gain of the PWM modulation link.

[0064] In this embodiment, the number of nodes in the converter network is set to n, where m nodes are connected to the converter (m≤n), and the converter output current vector is I o , the equivalent harmonic source of the converter is I s , the equivalent admittance of the converter is Y eq , the node voltage vector connected to the converter is V m According to the circuit network theory, there is the following relationship between the injected node current and the equivalent harmonic source of the converter:

[0065] I o = I s - Y eq · V m

[0066] = I s - Y eq · Z m · I s

[0067] = (E - Y eq · Z m )· I s

[0068] = XI s

[0069] In the formula, in this embodiment, matrix X = E - Y eq Z m , the node injection current I o = [I o1 , I o2 ,..., I om T , I s = [I s1 , I s2 ,..., I sm T , Z m represents the impedance matrix between I s and V m ; E is the identity matrix.

[0070] The above formula can be expanded as:

[0071]

[0072] It can be seen from the above formula that when each node in the converter network is connected to a converter, at this time, m = n, and the Z m matrix is an n×n matrix; when some nodes in the converter network are not connected to a converter,, m < n. To simplify the calculation and avoid singular values in matrix operations, the elements corresponding to I s are extracted from the converter network node impedance matrix Z to form a new m×m Z m matrix.

[0073] Matrix X reflects the amplification effect of the harmonic current injected by the converter on the injection current of different nodes. The diagonal element X ii of matrix X represents the influence of the current - type converter connected at the i - th node on the injection current of the i - th node; the non - diagonal element X ij ​​It reflects the influence of the current-type converter connected to the j-th node on the injected current of the ith node. This embodiment considers the equivalent output admittance of the converter to establish a coupling admittance matrix that can better reflect the output harmonic characteristics of the converter. It analyzes the amplification effect of the harmonic current injected by the converter on the injected current of different nodes from a mechanism perspective, thereby effectively analyzing the harmonic coupling amplification effect between converters.

[0074] From the above analysis, we can see that the two main factors affecting the node injection current are: one is the matrix X, that is, the coupling interaction between the converters; the other is the converter equivalent harmonic source I s size; According to existing engineering experience and modeling analysis, the converter equivalent harmonic source is affected by the modulation ratio M. Without changing other circuit parameters, the modulation ratio M is mainly affected by the active output of the photovoltaic power station. Therefore, the active output of the photovoltaic power station will be analyzed below.

[0075] S2. Obtain the photovoltaic historical data of each photovoltaic power station, and calculate the relative photovoltaic output of each photovoltaic power station based on the photovoltaic historical data and the clearance model; wherein the photovoltaic historical data includes the light zone information data of the new energy photovoltaic output of each photovoltaic power station at a preset time scale within a preset historical time period, the geographic information of the photovoltaic power station, and the inclination angle of the photovoltaic array.

[0076] S3. Clustering the photovoltaic relative output using a preset clustering algorithm to obtain the best scenario clustering result of the photovoltaic relative output sequence under a preset time scale.

[0077] Since photovoltaic output is not only affected by natural factors such as the position of the sun, clouds in the sky and other obstructions, but also by photovoltaic equipment such as the arrangement of photovoltaic arrays and the characteristics of photovoltaic cells, photovoltaic output has both certain regularity and strong randomness. Its regularity can be simulated by the clearance model. Therefore, as long as the longitude and latitude, altitude, time information, air quality and other information of a certain place are given, the relative photovoltaic output of the photovoltaic power station based on the clearance theoretical output can be calculated through the clearance model. Specifically:

[0078] In this embodiment, the light zone information data of the new energy photovoltaic output of each photovoltaic power station in each month of the year, the geographic information of the photovoltaic power station and the array inclination angle are collected as photovoltaic historical data, and the solar radiation intensity of the photovoltaic power station in a certain period of time is calculated according to the clearance model of the photovoltaic power station and the photovoltaic historical data, and then the photovoltaic clearance theoretical output of the photovoltaic power station is calculated according to the solar radiation intensity, wherein the clearance model is defined as:

[0079] The instantaneous solar radiation intensity of the circumscribed plane of the atmosphere is only related to the solar radiation intensity and solar radiation direction of the upper boundary of the atmosphere, which can be accurately calculated through relevant astronomical formulas. Assuming that it is a sunny day and there is no cloud cover, the relationship model between the instantaneous solar radiation intensity near the ground and the instantaneous solar radiation intensity of the circumscribed plane of the atmosphere is calculated and established. Based on this model, the theoretical maximum instantaneous solar radiation near the ground at a specific moment can be calculated in real time. This model is the clearance model, and the clearance model of the photovoltaic power station is obtained by using the global solar radiation model without considering any obstruction and the photoelectric conversion model without considering the influence of temperature.

[0080] In this embodiment, the photovoltaic power station's photovoltaic clearance theoretical output P DCI The relationship between (d, t) and solar radiation intensity A(d, t) can be approximately expressed as:

[0081]

[0082] Where P DCI (d, t) represents the theoretical output of the photovoltaic power station at the t-th sampling time on the d-th day; A(d, t) represents the solar radiation intensity received by the photovoltaic power station at the t-th sampling time on the d-th day; A std Represents the light intensity per unit area under standard conditions, R c Indicates the light intensity of a specific intensity, P sn Indicates the rated output of the photovoltaic power station under standard conditions, T t Indicates the temperature of the photovoltaic panel cell at time t, T stc Indicates the standard battery temperature, generally 25°C. Represents the temperature coefficient of the photovoltaic panel.

[0083] In this embodiment, after calculating the photovoltaic clearance theoretical output of the photovoltaic power station at the t-th sampling time on the d-th day according to the solar radiation intensity, the photovoltaic relative output based on the clearance model at each time on each day is calculated according to the photovoltaic clearance theoretical output and the actual photovoltaic power generation output. The calculation formula of the photovoltaic relative output based on the clearance model on each day is:

[0084]

[0085] Where P N (d, t) is the relative output of photovoltaic power generation, which is affected by factors such as weather that affects photovoltaic power generation at the tth moment on the dth day. P(d, t) is the actual output of photovoltaic power generation at the tth moment on the dth day. P DCI (d,t) is the net theoretical output at the tth moment on the dth day.

[0086] Then, this embodiment uses the improved FCM clustering algorithm to cluster the photovoltaic relative output, and calculates the optimal scene clustering result of the photovoltaic relative output sequence under the preset time scale, which is specifically:

[0087] In this embodiment, the photovoltaic relative output is divided at a preset time scale to obtain a photovoltaic relative output sequence at a preset time scale. The preset time scale in this embodiment is preferably set to monthly, that is, the photovoltaic relative output is divided according to the month. Then, a feature vector that can reflect the volatility of the photovoltaic relative output sequence of each month is selected as the photovoltaic relative output sequence feature vector. The improved FCM (Fuzzy C Means Clustering) clustering algorithm is used to perform cluster analysis on the photovoltaic relative output sequence feature vectors of each month to obtain the optimal scene number of the photovoltaic relative output sequence of each month. The objective function of the algorithm is as follows:

[0088]

[0089]

[0090] In the formula, c represents the number of clusters; m′ represents the convergence factor; u ij Represents the original vector y j The membership function corresponding to the i-th clustering scenario; D ij Represents the original vector y j With cluster center vi The Euclidean distance of .

[0091] The clustering effectiveness index DVI is calculated based on the results of internal optimization. The calculation formula is as follows:

[0092]

[0093] Where D(i,j) represents the distance between different clusters; D'(k) represents the intra-cluster distance between the same cluster; DVI represents the shortest distance between any two clusters (between clusters) divided by the maximum distance in the cluster (within clusters). The larger the DVI, the larger the inter-cluster distance. At the same time, the smaller the intra-cluster distance, the better the clustering effect. The k corresponding to the maximum DVI index is the optimal scene number, and the optimal scene clustering result is output.

[0094] This embodiment adopts the fuzzy C-means clustering algorithm optimized by the DVI clustering validity index, fully explores the similarities and differences of photovoltaic relative output in different spaces and times, realizes the precise division of photovoltaic output scenes, reduces the error of artificial division, improves the accuracy of generating photovoltaic output sequences, and facilitates the accurate calculation of converter harmonic sources.

[0095] S4. Based on the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established, and a photovoltaic output prediction sequence of the photovoltaic power station within the simulation period is simulated and generated.

[0096] In one embodiment, the step of establishing a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result to simulate and generate a photovoltaic output prediction sequence of a photovoltaic power station within a simulation period includes:

[0097] According to the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established;

[0098] Based on the photovoltaic output state transfer matrix, randomly extract the power reference value and the fluctuation coefficient value of the corresponding day from the power value reference probability distribution and the fluctuation coefficient value probability distribution of the scene type to which the photovoltaic power station belongs;

[0099] The power reference value and the fluctuation coefficient value are superimposed to obtain the simulated relative output at each time of each day in the simulation period;

[0100] According to the simulated relative output and the pre-acquired photovoltaic clearance theoretical output, a photovoltaic output prediction sequence of the photovoltaic power station within the simulation period is simulated and obtained.

[0101] Specifically, this embodiment regards the transition between various scenes as a Markov random process, and uses a Markov chain to simulate the transition between various weather scenes. According to the scene types throughout the year, the historical scene process state transition probability matrix can be statistically obtained to form a scene type sequence table, wherein the state transition probability is calculated as follows:

[0102]

[0103] Where, subscripts i and j represent the scene type; P (i-j) N represents the probability of the i-th type of scene transferring to the j-th type of weather scene; (i-j) N represents the number of times the i-th type of scene is transferred to the j-th type of scene; (i) Indicates the number of times the i-th type of scene occurs.

[0104] This embodiment roughly estimates the fluctuation coefficient value ΔP under various scene types N (i, t) approximately obeys the normal distribution. Therefore, the power reference value P under each scenario can be calculated according to the number of weather scenarios divided each month. S The one-dimensional probability distribution of (i) is to randomly extract the power benchmark value P of the day from the power value benchmark probability distribution and the fluctuation coefficient value probability distribution of the scene type of the photovoltaic power station on the dth day. S (d) and the fluctuation coefficient ΔP N (d, t), the power reference value P S (d) and the fluctuation coefficient ΔP N (d, t) superposition to obtain the simulated relative output P at each time of a day in the simulation cycleN ′(d,t), and then through P′(d,t)=P DCI (d,t)×P N The calculation formula of ′(d,t) is used to simulate the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period.

[0105] S5. According to the photovoltaic output prediction sequence of each photovoltaic power station, the converter equivalent harmonic source of the corresponding photovoltaic power station is obtained.

[0106] In one embodiment, the step of obtaining the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station includes:

[0107] According to the photovoltaic output prediction sequence, the modulation ratio of each converter is calculated;

[0108] According to the modulation ratio of each converter, the output line voltage effective value of each converter is obtained;

[0109] According to the effective value of the output line voltage of each converter, the corresponding converter equivalent harmonic source is calculated.

[0110] Specifically, after simulating and obtaining the photovoltaic output prediction sequence of each photovoltaic power station in the distribution network within the simulation period, this embodiment calculates the converter equivalent harmonic source according to the photovoltaic output prediction sequence of each photovoltaic power station to analyze the multi-converter harmonic coupling characteristics taking into account the randomness of the photovoltaic output. It should be noted that the photovoltaic output does not need to consider reactive power. When other circuit parameters do not change, the converter modulation ratio M is mainly affected by the active output P′(d, t) of the photovoltaic power station. The modulation ratio of each converter can be calculated by the following formula, and then the output line voltage effective value u′ of each converter is calculated: ih The size of the converter is finally calculated, and the equivalent harmonic source of the converter is calculated. The modulation ratio calculation formula of each converter is:

[0111]

[0112] Where M represents the modulation ratio of each converter; U dc represents the DC side voltage; P′(d,t) represents the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period; U g Indicates the effective value of the phase voltage on the AC side of the grid-connected inverter; ω1 indicates the fundamental frequency of the grid; L1 and L2 indicate the reactance value of the filter; L g Indicates the grid-side impedance.

[0113] For ease of understanding, this embodiment takes SPWM modulation as an example for brief description. The effective value of the output line voltage u′ of the grid-connected inverter is ih The component amplitudes are:

[0114] The fundamental component of the output line voltage is:

[0115]

[0116] The frequency is nω c ±kω r The harmonic amplitude of is:

[0117]

[0118] In the formula, ω c Indicates the carrier frequency; ω r represents the modulation wave frequency; when n=1, 3, 5, ..., k is an even number other than an integer multiple of 3; when n=2, 4, 6, ..., k is an odd number other than an integer multiple of 3.

[0119] Therefore, through I s =Y i ×u ih Calculate the harmonic source I′ of each converter separately s1 , I′ s2 …I′ sm , forming the converter equivalent harmonic source I′ s =[I′ s1 , I′ s2 , ..., I′ sm ] T .

[0120] S6. According to the converter equivalent harmonic source and the node coupling admittance matrix, the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output are analyzed and obtained.

[0121] After calculating and obtaining the harmonic sources of each converter, this embodiment analyzes the harmonic coupling characteristics of multiple converters under random fluctuations of photovoltaic output in combination with the matrix X derived in step S1. Since the matrix X reflects the amplification effect of the harmonic current injected by the converter on the injected current of different nodes, the harmonic sources of each converter fully exploit the similarities and differences of the relative photovoltaic output in different spaces and times. Therefore, this embodiment combines the harmonic sources of each converter with the matrix X reflecting the coupling interaction between each converter to analyze the harmonic coupling characteristics of multiple converters, which can comprehensively consider the mutual coupling between the harmonic currents output by the converters and the random fluctuation of the photovoltaic power generation output, thereby comprehensively describing the characteristics of the converter output harmonics when the photovoltaic output changes randomly.

[0122] The embodiment of the present invention provides a multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output. The method uses a fuzzy C-means clustering algorithm and a photovoltaic output state transfer matrix to generate a photovoltaic output prediction sequence of a photovoltaic power station within a simulation period; determines the converter equivalent harmonic source, and combines the node coupling admittance matrix to obtain the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output. Compared with the prior art, the embodiment of the present invention uses a fuzzy C-means clustering algorithm optimized by a DVI clustering effectiveness index to fully explore the similarities and differences of photovoltaic relative output in different spaces and times, and more accurately realizes the division of photovoltaic output scenes. At the same time, combined with a coupling admittance matrix based on the converter equivalent output admittance that can better reflect the converter output harmonic characteristics, the characteristics of the converter output harmonics when the photovoltaic output changes randomly are analyzed more comprehensively and accurately.

[0123] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] In one embodiment, Figure 4 As shown, an embodiment of the present invention provides a multi-converter harmonic coupling analysis system taking into account the randomness of photovoltaic output, the system comprising:

[0125] A coupling matrix establishing module 101 is used to establish a node coupling admittance matrix of the distribution network according to a converter harmonic equivalent model;

[0126] The relative output clustering module 102 is used to obtain the photovoltaic historical data of each photovoltaic power station, and calculate the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model; cluster the photovoltaic relative output using a preset clustering algorithm to obtain the optimal scenario clustering result of the photovoltaic relative output sequence under a preset time scale;

[0127] The photovoltaic output prediction module 103 is used to establish a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result, so as to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period;

[0128] The coupling characteristic analysis module 104 is used to obtain the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station; and is also used to analyze and obtain the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output according to the converter equivalent harmonic source and the node coupling admittance matrix.

[0129] For the specific definition of a multi-converter harmonic coupling analysis system taking into account the randomness of photovoltaic output, please refer to the above-mentioned definition of a multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] The embodiment of the present invention provides a multi-converter harmonic coupling analysis system that takes into account the randomness of photovoltaic output. The system coupling matrix establishment module establishes a coupling admittance matrix that can better reflect the harmonic characteristics of the converter output; the photovoltaic relative output is clustered by the relative output clustering module to obtain the optimal scenario clustering result of the photovoltaic relative output sequence under the preset time scale; the photovoltaic output prediction module simulates and generates the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period; the coupling characteristic analysis module comprehensively considers the mutual coupling between the converter output harmonic currents and the random volatility of the photovoltaic power generation output, and comprehensively analyzes the characteristics of the converter output harmonics when the photovoltaic output changes randomly. Compared with the prior art, the present application takes into account the mutual coupling between the converter output harmonic currents and the random volatility of the photovoltaic power generation output, and realizes accurate photovoltaic power generation output prediction and effective analysis of the converter output harmonic characteristics.

[0131] Figure 5 A computer device provided by an embodiment of the present invention includes a memory, a processor and a transceiver, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor, and the processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0132] The memory may include a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example but not limitation, the programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0133] Additionally, the memory may be a physically separate unit or may be integrated with the processor.

[0134] It can be understood by those skilled in the art that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0135] In one embodiment, the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0136] An embodiment of the present invention provides a multi-converter harmonic coupling analysis method and system taking into account the randomness of photovoltaic output. The multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output takes into account the randomness and volatility of photovoltaic output and the mutual coupling between converter output harmonic currents, and realizes reliable and comprehensive analysis of the harmonic coupling characteristics of multi-converters under random fluctuations of photovoltaic output, further ensuring the safe, stable operation and development of the power system.

[0137] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., an SSD), etc.

[0138] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0139] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output, characterized in that: The following steps are involved: According to the converter harmonic equivalent model, the node coupling admittance matrix of the distribution network is established; Acquire photovoltaic historical data of each photovoltaic power station, and calculate the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model; Clustering the photovoltaic relative output using a preset clustering algorithm to obtain an optimal scenario clustering result of the photovoltaic relative output sequence under a preset time scale; According to the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period; According to the photovoltaic output prediction sequence of each photovoltaic power station, an equivalent harmonic source of a converter of the corresponding photovoltaic power station is obtained; According to the converter equivalent harmonic source and the node coupling admittance matrix, the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output are analyzed and obtained.

2. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 1, characterized in that: The equation of the node coupling admittance matrix is: I o =I s -Y eq ·V m =I s -Y eq ·Z m ·I s =(E-Y eq ·Z m )·I s Where m represents the number of converter network nodes connected to the converter; I o Represents the converter output current vector; I s Represents the equivalent harmonic source of the converter; Y eq represents the equivalent admittance of the converter; V m represents the node voltage vector connected to the converter; Z m I s With V m The impedance matrix between them; E is the unit matrix.

3. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 1, characterized in that: The photovoltaic historical data includes light zone information data of new energy photovoltaic output of each photovoltaic power station at a preset time scale within a preset historical time period, photovoltaic power station geographical information, and photovoltaic array inclination angle.

4. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 3, characterized in that: The step of calculating the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model comprises: According to the clearance model of the photovoltaic power station and the photovoltaic historical data, the photovoltaic clearance theoretical output of the photovoltaic power station is calculated; According to the actual output of photovoltaic power generation and the photovoltaic clearance theoretical output, the photovoltaic relative output based on the clearance model at each day and time is obtained.

5. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 1, characterized in that: The step of clustering the photovoltaic relative output using a preset clustering algorithm to obtain the optimal scene clustering result of the photovoltaic relative output sequence under a preset time scale includes: Dividing the photovoltaic relative output at a preset time scale to obtain a photovoltaic relative output sequence at the preset time scale; The volatility characteristics of the photovoltaic relative output sequence are selected as the characteristic vector of the photovoltaic relative output sequence; The improved FCM clustering algorithm is used to perform cluster analysis on the characteristic vectors of the photovoltaic relative output sequence under the preset time scale, and the optimal scenario clustering result of the photovoltaic relative output sequence under the preset time scale is obtained.

6. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 1, characterized in that: The step of establishing a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period includes: According to the optimal scenario clustering results, a photovoltaic output state transfer matrix under each scenario is established; Based on the photovoltaic output state transfer matrix, randomly extract the power reference value and the fluctuation coefficient value of the corresponding day from the power value reference probability distribution and the fluctuation coefficient value probability distribution of the scene type to which the photovoltaic power station belongs; The power reference value and the fluctuation coefficient value are superimposed to obtain the simulated relative output at each time of each day in the simulation period; According to the simulated relative output and the pre-acquired photovoltaic clearance theoretical output, a photovoltaic output prediction sequence of the photovoltaic power station within the simulation period is simulated and obtained.

7. A multi-converter harmonic coupling analysis method taking into account the randomness of photovoltaic output as claimed in claim 1, characterized in that: The step of obtaining the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station comprises: Based on the photovoltaic output prediction sequence, the modulation ratio of each converter is calculated; According to the modulation ratio of each converter, the output line voltage effective value of each converter is obtained; According to the effective value of the output line voltage of each converter, the corresponding converter equivalent harmonic source is calculated; Among them, the modulation ratio calculation formula of each converter is: Where M represents the modulation ratio of each converter; U dc represents the DC side voltage; P′(d,t) represents the photovoltaic output prediction sequence of the photovoltaic power station within the simulation period; U g Indicates the effective value of the phase voltage on the AC side of the grid-connected inverter; ω1 indicates the fundamental frequency of the grid; L1 and L2 indicate the reactance value of the filter; L g Indicates the grid-side impedance.

8. A multi-converter harmonic coupling analysis system taking into account the randomness of photovoltaic output, characterized in that: The system comprises: A coupling matrix building module is used to build a node coupling admittance matrix of the distribution network according to a converter harmonic equivalent model; The relative output clustering module is used to obtain the photovoltaic historical data of each photovoltaic power station, and calculate the photovoltaic relative output of each photovoltaic power station according to the photovoltaic historical data and the clearance model; cluster the photovoltaic relative output using a preset clustering algorithm to obtain the optimal scenario clustering result of the photovoltaic relative output sequence under a preset time scale; A photovoltaic output prediction module is used to establish a photovoltaic output state transfer matrix under each scenario according to the optimal scenario clustering result, so as to simulate and generate a photovoltaic output prediction sequence of the photovoltaic power station within a simulation period; The coupling characteristic analysis module is used to obtain the converter equivalent harmonic source of the corresponding photovoltaic power station according to the photovoltaic output prediction sequence of each photovoltaic power station; and is also used to analyze and obtain the multi-converter harmonic coupling characteristics taking into account the randomness of photovoltaic output according to the converter equivalent harmonic source and the node coupling admittance matrix.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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