Distributed photovoltaic access method and system for low-voltage power distribution network

Abstract

The invention relates to the field of distributed control, in particular to a distributed photovoltaic access method and system for a low-voltage power distribution network, and the method comprises the steps: collecting related data in the process of accessing distributed photovoltaic to the low-voltage power distribution network; carrying out self-correlation analysis and prediction on the power grid load data, and obtaining a power grid load unbalance degree in combination with the numerical change characteristics of the neutral line zero-sequence current data; analyzing the change distribution of the amplitude data of the positive sequence and negative sequence components of the three-phase voltage, and combining the change trend of the amplitude data of the negative sequence components to obtain the abnormality of the sequence components; decomposing the three-phase voltage zero-sequence component, analyzing distance distribution between all decomposition signals combined pairwise, and combining the phase data of the zero-sequence component to obtain the unbalance of the zero-sequence component; obtaining the three-phase imbalance of each monitoring window; and judging whether distributed photovoltaic access of the low-voltage power distribution network is controlled or not. The invention aims to capture the unbalanced state in the low-voltage power distribution network and accurately realize intelligent control of photovoltaic access.

Description

Technical Field

[0001] The present application relates to the field of distributed control, and specifically to a distributed photovoltaic access method and system for low-voltage distribution networks. Background Art

[0002] With the continuous expansion of the installation scale of new energy power generation systems in distribution networks, distributed photovoltaic energy, as a clean energy source, has become an important way to address energy and environmental crises. The access of distributed photovoltaic energy has transformed the distribution network from a "passive network" into an "active network". On the basis of optimizing the energy structure, it has improved the responsiveness of dynamic dispatching of electricity demand, reduced the dependence on long-distance power transmission, reduced transmission losses, effectively ensured the resilience and operational reliability of the power grid system, and promoted the development of the green electricity market.

[0003] However, the output level of distributed photovoltaics in the distribution network is greatly affected by uncertain factors such as weather, and its randomness and volatility are likely to cause the drawback of three-phase imbalance in the distribution system. According to the public content in the literature "Monitoring and Governance Analysis of Three-Phase Imbalance Operation in Distribution Substations" and the patent document "CN112018787A Three-Phase Imbalance Time-Series Simulation Method for Distributed Power Sources", when carrying out three-phase imbalance monitoring and governance, relying solely on electricity consumption cannot accurately measure the three-phase balance state of the distribution system, and the distribution network has the characteristic of distribution imbalance. The output level of distributed photovoltaics has strong volatility, resulting in the inability to accurately capture the imbalance state in the low-voltage distribution network. Summary of the Invention

[0004] In view of the above, it is necessary to provide a distributed photovoltaic access method and system for low-voltage distribution networks to solve the above problems.

[0005] The first aspect of the present application provides a distributed photovoltaic access method for low-voltage distribution networks, and the method includes:

[0006] Collect the grid load data of the main line, the zero-sequence current of the neutral line, the amplitude data of all sequence components of the three-phase voltage, and the phase data of the zero-sequence component at all times during the process of distributed photovoltaics accessing the low-voltage distribution network;

[0007] Preset a monitoring window, perform autocorrelation analysis and prediction on the grid load data in each monitoring window respectively, and combine the numerical change characteristics of the zero-sequence current data of the neutral line to obtain the grid load imbalance degree of each monitoring window;

[0008] Analyze the change distribution of the positive-sequence component amplitude data of the three-phase voltage in each monitoring window, and combine the change trend of the negative-sequence component amplitude data in each monitoring window to obtain the sequence component abnormality of each monitoring window;

[0009] Decompose the zero-sequence components of the three-phase voltages of each monitoring window to obtain each independent signal component after the decomposition of the zero-sequence components; analyze the distance distribution between all pairwise combinations of independent signal components in each monitoring window, and combine the degree of chaos of the zero-sequence component phase data to obtain the zero-sequence component imbalance of the three-phase voltages in each monitoring window.

[0010] Based on the sequence component abnormality and zero-sequence component imbalance of each monitoring window, obtain the power quality impairment situation of each monitoring window and the degree of grid load imbalance, and obtain the three-phase imbalance of each monitoring window.

[0011] Based on the three-phase imbalance, determine whether to control the access of distributed photovoltaics to the low-voltage distribution network.

[0012] Preferably, the degree of grid load imbalance of each monitoring window is specifically calculated by the formula: In the formula, A i is the degree of grid load imbalance of the low-voltage distribution network in the i-th monitoring window; a i is the prediction difference of the grid load corresponding to the i-th monitoring window; norm[] is the normalization function; r i is the correlation strength of the grid load data sequence corresponding to the i-th monitoring window; J i is the total number of data in the zero-sequence current data sequence of the neutral line corresponding to the i-th monitoring window; b i,j is the absolute value of the difference between the j-th data and its previous data in the zero-sequence current data sequence of the neutral line corresponding to the i-th monitoring window; exp() is the exponential function with the natural constant e as the base.

[0013] Preferably, the prediction difference is specifically:

[0014] Based on the grid load data of a preset duration in each monitoring window, perform prediction to obtain the grid load prediction sequence;

[0015] According to the distance difference between the sequence composed of all grid load data in each monitoring window and the grid load prediction sequence, obtain the prediction difference of each monitoring window.

[0016] Preferably, the correlation strength is specifically the mean value of the autocorrelation coefficients of all grid composite data in each monitoring window.

[0017] Preferably, obtaining the sequence component abnormality of each monitoring window is specifically:

[0018] Based on the trend distribution of all elements in the first-order difference sequence of the negative-sequence components of the three-phase voltages in each monitoring window, obtain the rising strength of the negative-sequence components of the three-phase voltages in each monitoring window.

[0019] Obtain the cumulative sum of the differences between adjacent data in the first-order difference sequence of the positive-sequence components of the three-phase voltage in each monitoring window of the low-voltage distribution network, and perform forward fusion with the negative-sequence components of the three-phase voltage in each monitoring window of the low-voltage distribution network to obtain the sequence-component abnormality of the three-phase voltage in each monitoring window of the low-voltage distribution network.

[0020] Preferably, the specific method for obtaining the rising intensity of the negative-sequence components of the three-phase voltage in each monitoring window is as follows:

[0021] Perform trend analysis on the first-order difference sequence of the negative-sequence components of the three-phase voltage in each monitoring window of the low-voltage distribution network to obtain the trend-term intensity of all elements, and use the Hurst exponent of all trend-term intensities as the rising intensity of the negative-sequence components of the three-phase voltage.

[0022] Preferably, the specific method for obtaining the zero-sequence component imbalance of the three-phase voltage in each monitoring window is as follows:

[0023] Through the negative-correlation mapping result of the cumulative sum of the DTW distances between all independent signal components after the decomposition of the zero-sequence components corresponding to the three-phase voltage, combined with the information entropy of the zero-sequence component phase data sequence of the three-phase voltage in each monitoring window of the low-voltage distribution network, obtain the zero-sequence component imbalance of the three-phase voltage in each monitoring window of the low-voltage distribution network.

[0024] Preferably, the specific process for obtaining the three-phase imbalance of each monitoring window is as follows:

[0025] Use the normalized value after forward fusion of the sequence-component abnormality corresponding to each monitoring window and the zero-sequence component imbalance as the power quality impairment of each monitoring window of the low-voltage distribution network after connecting distributed photovoltaics;

[0026] Use the normalized value obtained by multiplying the power quality impairment corresponding to each monitoring window by the grid compliance imbalance degree as the three-phase imbalance of each monitoring window of the low-voltage distribution network after connecting distributed photovoltaics.

[0027] Preferably, the specific process for determining whether to control the access of distributed photovoltaics to the low-voltage distribution network is as follows:

[0028] When the three-phase imbalance of a monitoring window is less than the preset three-phase imbalance threshold, do not control the photovoltaic inverter during the access of distributed photovoltaics; otherwise, control the photovoltaic inverter during the access of distributed photovoltaics.

[0029] In a second aspect, the embodiments of the present application further provide a distributed photovoltaic access system for a low-voltage distribution network, which implements the distributed photovoltaic access method for a low-voltage distribution network described in any one of the above, and the system includes:

[0030] The power grid data acquisition module is used to acquire the power grid load data of the main line, the zero-sequence current of the neutral line, the amplitude data of all sequence components of the three-phase voltage, and the phase data of the zero-sequence component at all times during the process of distributed photovoltaic access to the low-voltage distribution network;

[0031] The power grid data processing and analysis module includes a data preprocessing unit and a data analysis unit, where:

[0032] The data preprocessing unit is used to preprocess all the acquired data;

[0033] The data analysis unit is used to preset a monitoring window, perform autocorrelation analysis and prediction on the power grid load data in each monitoring window respectively, and combine the numerical change characteristics of the zero-sequence current data of the neutral line to obtain the power grid load imbalance degree of each monitoring window;

[0034] Analyze the change distribution of the positive-sequence component amplitude data of the three-phase voltage in each monitoring window, and combine the change trend of the negative-sequence component amplitude data in each monitoring window to obtain the sequence component abnormality of each monitoring window;

[0035] Decompose the zero-sequence component of the three-phase voltage in each monitoring window to obtain each independent signal component after decomposition of the zero-sequence component; analyze the distance distribution between all pairwise combinations of independent signal components in each monitoring window, and combine the chaos degree of the zero-sequence component phase data to obtain the zero-sequence component imbalance of the three-phase voltage in each monitoring window;

[0036] Based on the sequence component abnormality, zero-sequence component imbalance, and power grid load imbalance degree of each monitoring window, obtain the three-phase imbalance of each monitoring window;

[0037] The power grid auxiliary decision-making module is used to judge whether to control the distributed photovoltaic access to the low-voltage distribution network based on the three-phase imbalance;

[0038] The power grid data transmission module is used for data transmission between modules and data transmission between units.

[0039] This application has at least the following beneficial effects:

[0040] 1. Compared with the traditional power grid load analysis technology, the power grid load imbalance degree constructed in this application further considers the abnormal increase phenomenon of the zero-sequence current of the neutral line caused by the aggravation of the three-phase imbalance of the low-voltage distribution network, and more accurately reflects the power grid load imbalance condition caused by the uneven distributed photovoltaic access;

[0041] 2. Based on the abnormal characteristics of the sequence components of the three-phase voltage and the component imbalance characteristics, this application establishes the abnormality of the sequence components and the imbalance of the zero-sequence components, effectively reducing the risk of the decrease in the accuracy of evaluating the three-phase imbalance degree of the low-voltage distribution network only by relying on the grid load imbalance degree, and more accurately evaluating the grid stability and the damage condition of the power quality after the distributed photovoltaic is connected to the low-voltage distribution network;

[0042] 3. By evaluating the three-phase imbalance condition of the low-voltage distribution network through the three-phase imbalance, and controlling the distributed photovoltaic connected to the low-voltage distribution network through the single-phase photovoltaic inverter, the power quality of the distribution network is improved, avoiding the drawback that the three-phase balance state of the distribution system cannot be accurately evaluated only by relying on the power quantity, and being able to accurately reflect the three-phase imbalance state and the degree of power quality damage of the low-voltage distribution network according to the changes in the amplitude and phase of the sequence components of the three-phase voltage and the grid load condition. Brief Description of the Drawings

[0043] Figure 1 It is a flowchart of the steps of a method for distributed photovoltaic access to a low-voltage distribution network provided by an embodiment of this application;

[0044] Figure 2 It is a block diagram of a distributed photovoltaic access system for a low-voltage distribution network provided by an embodiment of this application. Detailed Embodiment

[0045] In the description of the embodiments of this application, words such as "exemplary", "or", "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary", "or", "for example" aims to present relevant concepts in a specific way.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0047] In addition, it should be noted that the terms "first" and "second" in this application and the drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the method disclosed in the embodiments of this application or the method shown in the flowchart, including one or more steps for implementing the method, without departing from the protection scope of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0049] The following specifically describes the specific solutions of the distributed photovoltaic access method and system provided by this application for low-voltage distribution networks in conjunction with the accompanying drawings.

[0050] Please refer to Figure 1 , which shows the flowchart of the steps of the distributed photovoltaic access method for low-voltage distribution networks provided by an embodiment of this application. The method includes:

[0051] The first step: Collect the grid load data of the main line, the zero-sequence current of the neutral line, the amplitude data of all sequence components of the three-phase voltage, and the phase data of the zero-sequence component at all times during the process of distributed photovoltaic access to the low-voltage distribution network.

[0052] This application first obtains the relevant data of the main line of the distributed photovoltaic access to the low-voltage distribution network, and transmits the obtained data to the data processing and analysis module through the grid data transmission module for analysis.

[0053] Specifically, referring to the public content in the reference document "Real-time Detection Method of Grid Sequence Components Based on Orthogonal Signals and Delayed Sampling Periods", by arranging an AGC (Automatic Generation Control) automatic power generation control system and current sensors in the multi-functional fusion measurement and control box in the grid data acquisition module, continuously collect the grid load data based on time series, the zero-sequence current of the neutral line, and the amplitude of each sequence component and the phase data of the zero-sequence component of the three-phase voltage after the distributed photovoltaic access to the low-voltage distribution network. The specific sequence components of the three-phase voltage are positive sequence components, negative sequence components, and zero-sequence components.

[0054] It should be noted that compared with the traditional symmetrical component method for obtaining the sequence components of the three-phase voltage, the method of real-time detecting the sequence components of the grid based on orthogonal signals and delayed sampling periods has the advantages of simple structure and easy implementation. On the basis of a short dynamic response time, it can better handle complex grid conditions such as amplitude and phase mutations, voltage asymmetry, and noise, providing a more accurate data basis for subsequent analysis of the three-phase imbalance and asymmetry caused by distributed photovoltaic access to the low-voltage distribution network.

[0055] Transmit the relevant data of the main line of the distributed photovoltaic access to the low-voltage distribution network obtained in the grid data acquisition module to the grid data processing and analysis module. In the data preprocessing unit, use the mean filling method to fill in the missing values of the obtained grid load data, the zero-sequence current of the neutral line, and the amplitude of each sequence component and the phase of the zero-sequence component of the three-phase voltage, and use data cleaning and Z-Score standardization to remove redundant data and unify the dimension. Since the mean filling method, data cleaning, and Z-Score standardization are all well-known technologies, the specific acquisition process will not be elaborated too much.

[0056] At this point, the low-voltage distribution network load data sequence, the neutral line zero-sequence current data sequence, and the amplitude data sequence of each sequence component of the three-phase voltage and the zero-sequence component phase data sequence can be obtained by the above method.

[0057] The second step is to preset monitoring windows, perform autocorrelation analysis and prediction on the grid load data in each monitoring window, and obtain the grid load imbalance degree in each monitoring window by combining the numerical variation characteristics of the neutral line zero-sequence current data.

[0058] The output of distributed photovoltaic power generation is affected by many factors, such as the intensity of solar radiation and weather conditions, and therefore has a high degree of randomness and volatility. This volatility may cause grid load fluctuations, especially in low-voltage distribution networks, where the load changes of a certain phase may be uneven, further exacerbating the three-phase imbalance problem. This imbalance will not only cause additional power loss in the line, but may also reduce the quality of power. In addition, after distributed photovoltaic power generation is connected to the low-voltage distribution network, due to the three-phase imbalance, zero-sequence current may be generated on the neutral line, thereby increasing the loss of the transformer and even damaging the electrical equipment.

[0059] Specifically, when distributed photovoltaics are connected to the low-voltage distribution network, the photovoltaic power resources with strong daily periodicity will cause the grid load to show obvious periodic fluctuations, and the external weather influence will directly affect the output of photovoltaic power generation, resulting in uneven access to distributed photovoltaics, causing rapid and drastic fluctuations in the load of the low-voltage distribution network in a short period of time; at the same time, when the low-voltage distribution network cannot effectively handle the unevenly accessed distributed photovoltaics, the neutral line zero-sequence current will show a strong fluctuating upward trend.

[0060] Based on the above analysis, the present application constructs a load imbalance mathematical model of the low-voltage distribution network in the data analysis unit, which is used to characterize the grid load and zero-sequence current abnormal conditions caused by the three-phase imbalance after the distributed photovoltaic is connected to the low-voltage distribution network. The preset time period is now used as a monitoring window for subsequent analysis, and the grid load data sequence of any monitoring window is used as input. The autocorrelation function is used to output a series of autocorrelation coefficients, and the mean of all autocorrelation coefficients is used as the correlation strength of the grid load data sequence corresponding to each monitoring window; in this embodiment, the preset time period is 24h.

[0061] Take the sequence composed of the data within the preset duration in the power grid load data sequence corresponding to each monitoring window in chronological order as the power grid load subsequence corresponding to each monitoring window. Use the power grid load subsequence as the input, and adopt a data sequence prediction algorithm to obtain a sequence of power grid load prediction values that is based on the power grid load subsequence and has the same length as the corresponding power grid load data sequence, which is denoted as the power grid load prediction sequence. In this embodiment, the preset duration is set to 8h, and the implementer can adjust it according to the actual situation. In one implementation case of this application, the data sequence prediction algorithm can select the ARIMA (Autoregressive Integrated Moving Average Model). Since both the autocorrelation function and the data sequence prediction algorithm are well-known technologies, the specific acquisition process will not be elaborated here. The specific construction method of the load imbalance mathematical model is as follows: In the formula, A i is the power grid load imbalance degree of the low-voltage distribution network at the i-th monitoring window; a i is the prediction difference of the power grid load corresponding to the i-th monitoring window; norm[] is a normalization function that makes the value range of A i fall within [0, 1]; r i is the correlation strength of the power grid load data sequence corresponding to the i-th monitoring window; J i is the total number of data in the neutral line zero-sequence current data sequence corresponding to the i-th monitoring window; b i,j is the absolute value of the difference between the j-th data and its previous data in the neutral line zero-sequence current data sequence corresponding to the i-th monitoring window; exp() is an exponential function with the natural constant e as the base.

[0062] Among them, the processing method of the prediction difference in this embodiment is: take the cumulative result of the absolute values of the differences between all data in the power grid load prediction sequence corresponding to the monitoring window and its corresponding power grid load data sequence as the prediction difference. In other embodiments, it can be obtained by calculating the Dynamic Time Warping (DTW) distance between the power grid load prediction sequence corresponding to the monitoring window and its corresponding power grid load data sequence. In another embodiment, the DTW distance can be replaced by the Jensen-Shannon Divergence (JS divergence). It should be noted that both the DTW distance and the JS divergence are existing well-known technologies.

[0063] It should be understood that the degree of grid load imbalance reflects the grid load imbalance condition in the low-voltage distribution network caused by the uneven distributed photovoltaic access; the prediction difference reflects the difficulty of predicting the grid load in the low-voltage distribution network due to the randomness of photovoltaic output within the corresponding time range of the monitoring window; when the unevenness of distributed photovoltaic access to the low-voltage distribution network is more significant, the grid load fluctuation is more intense, the periodic change of the grid load is more complex, the correlation strength of each monitoring window becomes larger, and the upward trend of the zero-sequence current in the neutral line due to the aggravation of the three-phase imbalance condition in the distribution network is more obvious. The value is also larger.

[0064] The third step: Analyze the change distribution of the positive-sequence component amplitude data of the three-phase voltage in each monitoring window, and combine the change trend of the negative-sequence component amplitude data in each monitoring window to obtain the sequence component abnormality of each monitoring window.

[0065] When only relying on the grid load imbalance to evaluate the three-phase imbalance phenomenon generated when distributed photovoltaic is connected to the low-voltage distribution network, the change characteristics between the sequence components of the three-phase voltage in the low-voltage distribution network cannot be captured. That is, when the overall grid load in the distribution network is relatively balanced, the local photovoltaic access imbalance and the instantaneous voltage imbalance caused by distributed photovoltaic may still cause the low-voltage distribution network to enter a three-phase imbalance state, while the amplitude and phase changes of the sequence components of each phase voltage in the low-voltage distribution network can more accurately evaluate the grid stability and power quality after the distributed photovoltaic is connected.

[0066] Specifically, when the distributed photovoltaic connected to the low-voltage distribution network is more uneven, the symmetry of the three-phase voltage is further damaged, and the difference between the positive-sequence component amplitudes representing the symmetric components of the three-phase voltage gradually increases; when the randomness and volatility of the output degree of distributed photovoltaic within a certain time interval are stronger, it will aggravate the three-phase voltage imbalance condition in the low-voltage distribution network, making the increase of the negative-sequence component more significant.

[0067] Based on the above analysis, the present application constructs a voltage sequence component abnormality mathematical model for the low-voltage distribution network in the data analysis unit, calculates the first-order difference sequences of the amplitude data sequences of the positive-sequence components and the negative-sequence components of the three-phase voltage in the low-voltage distribution network respectively, takes the first-order difference sequence of the amplitude data sequence of the negative-sequence components of the three-phase voltage in the low-voltage distribution network as the input, uses the STL (Seasonal and Trend decomposition using Loess) sequence decomposition algorithm to obtain the trend item strength of each first-order difference data, and calculates the Hurst index of the trend item strength corresponding to all first-order difference data as the rising strength of the negative-sequence component of the three-phase voltage. Since the acquisition of the STL sequence decomposition algorithm and the Hurst index are well-known technologies, the specific acquisition process will not be elaborated too much.

[0068] The specific construction method of the voltage sequence component anomaly mathematical model is as follows: Obtain the cumulative sum of the differences between adjacent data in the first-order difference sequence of the positive sequence components of the three-phase voltages in the low-voltage distribution network for each monitoring window, and perform positive fusion with the rising intensity of the negative sequence components of the three-phase voltages in the low-voltage distribution network for each monitoring window to obtain the sequence component anomaly of the three-phase voltages in the low-voltage distribution network for each monitoring window. In this embodiment, the difference between data is calculated using the absolute value of the difference; multiple variables are fused by multiplication.

[0069] It should be understood that the sequence component anomaly reflects the abnormal change conditions of the positive sequence components and negative sequence components of the three-phase voltages in the low-voltage distribution network caused by the access of distributed photovoltaic power generation. The rising intensity of the negative sequence components of the three-phase voltages reflects the abnormal increase degree of the negative sequence components caused by the three-phase imbalance in the low-voltage distribution network within a certain time range; when the three-phase imbalance condition caused by the access of distributed photovoltaic power generation to the low-voltage distribution network is more obvious, the numerical difference between the amplitude data of the positive sequence components of the three-phase voltages within a certain time range is more significant, the larger the cumulative sum, the greater the increasing trend of the negative sequence components of the three-phase voltages, and the larger the sequence component anomaly.

[0070] The fourth step: Decompose the zero-sequence components of the three-phase voltages for each monitoring window to obtain each independent signal component after the zero-sequence component decomposition; analyze the distance distribution between all pairwise combinations of independent signal components in each monitoring window, and combine the chaos degree of the zero-sequence component phase data to obtain the zero-sequence component imbalance of the three-phase voltages for each monitoring window.

[0071] The access of unevenly distributed photovoltaic power generation to the low-voltage distribution network will not only cause abnormal changes in the positive sequence components and negative sequence components, but also may cause phase chaos of the zero-sequence components of the three-phase voltages due to the randomness and volatility of photovoltaic power output. In addition, based on the idea of symmetrical components, the three-phase imbalance in the low-voltage distribution network reduces the similarity between various influencing factors, thereby affecting the stability of the zero-sequence components.

[0072] Based on the above analysis, in the data analysis unit, decompose the zero-sequence components corresponding to the three-phase voltages of the low-voltage distribution network to obtain each independent signal component after the zero-sequence component decomposition. Specifically, in one processing case of this embodiment, use the data sequence of the zero-sequence components corresponding to the three-phase voltages of the low-voltage distribution network as the input, and use the Independent Component Analysis (ICA) algorithm to obtain all independent components after the zero-sequence component decomposition; in other embodiments, use the empirical mode decomposition algorithm to decompose the zero-sequence components; in other embodiments, the variational mode decomposition algorithm can be used; it should be noted that the independent component analysis algorithm, the empirical mode decomposition algorithm, and the variational mode decomposition algorithm are all existing well-known technologies, and this application will not elaborate on them.

[0073] Next, construct a zero-sequence component anomaly model: Through the negative correlation mapping result of the cumulative sum of the DTW distances between all independent signal components after the decomposition of the zero-sequence components corresponding to the three-phase voltages, combined with the degree of chaos of the zero-sequence component phase data sequence of the three-phase voltages in the low-voltage distribution network in each monitoring window, obtain the zero-sequence component imbalance of the three-phase voltages in the low-voltage distribution network in each monitoring window.

[0074] In this embodiment, the negative correlation mapping result of the variable is specifically the reciprocal value of the variable. It should be noted that to avoid the situation where the denominator is 0, a preset parameter with a value of 0.01 needs to be added to the denominator of the negative correlation mapping, and the implementer can adjust it according to the actual situation; use information entropy to calculate the degree of chaos of the corresponding data in each monitoring window; combine one variable with another variable and calculate by multiplication.

[0075] It should be understood that the zero-sequence component imbalance reflects the degree of imbalance of the zero-sequence components after the distributed photovoltaic is connected to the low-voltage distribution network; the signal complexity reflects the difference between the independent signal components of the zero-sequence components caused by the three-phase imbalance after the distributed photovoltaic is connected to the low-voltage distribution network, and further reflects the complex situation of the zero-sequence component signal components caused by the randomness and volatility of the distributed photovoltaic. When the three-phase imbalance situation after the distributed photovoltaic is connected to the low-voltage distribution network is more obvious, the phase change of the zero-sequence components of the three-phase voltages is more chaotic, and the zero-sequence component imbalance is greater.

[0076] The fifth step: Based on the sequence component anomaly, zero-sequence component imbalance, and grid load imbalance degree of each monitoring window, obtain the three-phase imbalance of each monitoring window.

[0077] When the distributed photovoltaic is connected to the low-voltage distribution network, the more obvious the abnormal changes of the positive-sequence and negative-sequence components caused by the uneven distributed photovoltaic, and the stronger the asymmetry of the zero-sequence components of the three-phase voltages, it indicates that the grid stability is worse and the power quality is lower after the distributed photovoltaic is connected to the low-voltage distribution network.

[0078] Therefore, in this application, a power quality impairment is constructed in the data analysis unit to characterize the power quality degradation situation of the low-voltage distribution network caused by the uneven distributed photovoltaic access. Specifically: Use the normalized value after the positive fusion of the sequence component anomaly corresponding to each monitoring window and the zero-sequence component imbalance as the power quality impairment of each monitoring window after the distributed photovoltaic is connected to the low-voltage distribution network. In this embodiment, the positive fusion of multiple variables uses a multiplication calculation method; the normalization uses the maximum-minimum normalization method.

[0079] It should be understood that the power quality impairment reflects the abnormal changes in the positive-sequence component and negative-sequence component and the imbalance of the zero-sequence component caused by the access to distributed photovoltaics in the low-voltage distribution network. The more significant the difference in the amplitude of the positive-sequence component of the three-phase voltage, the more obvious the abnormal increase in the negative-sequence component, and the greater the impact of the zero-sequence component on the fluctuation of the distributed photovoltaic output, the more serious the three-phase imbalance in the low-voltage distribution network, the higher the risk of line damage, and the greater the power quality impairment.

[0080] Furthermore, when distributed photovoltaics are connected to the low-voltage distribution network, the more severe the grid load fluctuation caused by the uneven access of distributed photovoltaics, the more obvious the rising trend of the neutral line zero-sequence current, the more obvious the abnormal changes and imbalance characteristics of the three-phase voltage components of the low-voltage distribution network, the greater the possibility of uneven distributed photovoltaics connected to the low-voltage distribution network, and the more serious the three-phase imbalance caused by the randomness and volatility of distributed photovoltaics.

[0081] Therefore, this application constructs the three-phase imbalance of the low-voltage distribution network, which is used to characterize the decline in grid stability caused by three-phase imbalance after the distributed photovoltaic is connected to the low-voltage distribution network. Specifically, the normalized value obtained by multiplying the power quality impairment corresponding to each monitoring window by the degree of grid imbalance is used as the three-phase imbalance of each monitoring window after the low-voltage distribution network is connected to the distributed photovoltaic. In this embodiment, the normalization method adopts the maximum and minimum value normalization method.

[0082] It should be understood that the more obvious the three-phase imbalance caused by the low-voltage distribution network being connected to distributed photovoltaics, the higher the degree of grid load imbalance; the more serious the impact on each sequence component of the three-phase voltage, the more obvious the damage to the power quality.

[0083] The sixth step: based on the three-phase imbalance, determine whether to control the access of distributed photovoltaic to the low-voltage distribution network.

[0084] The three-phase imbalance obtained by the data analysis and processing module is transmitted to the grid auxiliary decision module by the grid data transmission module, and the three-phase imbalance threshold is set. In this embodiment, the three-phase imbalance threshold is 0.7, and the implementer can adjust it according to the actual situation. When the three-phase imbalance of the low-voltage distribution network connected to the distributed photovoltaic in any monitoring window is less than the three-phase imbalance threshold, the distributed photovoltaic access is uniform, and the three-phase imbalance generated in the low-voltage distribution network is relatively mild, and there is no need to control the photovoltaic inverter when the distributed photovoltaic is connected.

[0085] When the three-phase unbalance of a low-voltage distribution network with distributed photovoltaic (PV) integration in any monitoring window is greater than or equal to the three-phase unbalance threshold, at this time, the distributed PV integration is uneven, the low-voltage distribution network is severely affected by the randomness and volatility of distributed PV, the three-phase unbalance risk is relatively high, the power quality in the low-voltage distribution network is poor, and the line is severely damaged.

[0086] Multiple distributed delta compensation groups and distributed star compensation groups can be formed by the single-phase PV inverters distributed in the low-voltage distribution network, and compensation is performed according to the three-phase unbalance in the distribution network system. The star compensation group determines whether to start compensation based on the voltage deviation, calculates the reactive power reference value through a consensus algorithm, independently adjusts the voltage of each phase, and utilizes the reactive power capacity of the PV inverter to achieve the control of the three-phase unbalance of the distribution network and improve the voltage quality of the distribution network. Specifically, in a processing case of this application, reference can be made to the content disclosed in "CN110829469B A Method for Improving the Voltage Quality of a Distribution Network Based on a Single-Phase PV Inverter" for control.

[0087] Based on the same inventive concept as the above method, an embodiment of this application also provides a distributed PV integration system for a low-voltage distribution network to implement any one of the above methods for distributed PV integration in a low-voltage distribution network. The system includes:

[0088] A grid data acquisition module, which is used to acquire the grid load data of the main line, the zero-sequence current of the neutral line, the amplitude data of all sequence components of the three-phase voltage, and the phase data of the zero-sequence component at all times during the process of distributed PV integration into the low-voltage distribution network;

[0089] The grid data processing and analysis module includes a data preprocessing unit and a data analysis unit, where:

[0090] The data preprocessing unit is used to preprocess all the acquired data;

[0091] The data analysis unit is used to preset a monitoring window, perform autocorrelation analysis and prediction on the grid load data in each monitoring window respectively, and combine the numerical change characteristics of the zero-sequence current data of the neutral line to obtain the grid load unbalance degree in each monitoring window;

[0092] Analyze the change distribution of the positive-sequence component amplitude data of the three-phase voltage in each monitoring window, and combine the change trend of the negative-sequence component amplitude data in each monitoring window to obtain the sequence component abnormality in each monitoring window;

[0093] Decompose the zero-sequence component of the three-phase voltage in each monitoring window to obtain each independent signal component after decomposition of the zero-sequence component; analyze the distance distribution between all pairwise combinations of independent signal components in each monitoring window, and combine the chaos degree of the zero-sequence component phase data to obtain the zero-sequence component unbalance of the three-phase voltage in each monitoring window;

[0094] Based on the sequence component abnormality, zero-sequence component imbalance, and grid load imbalance degree of each monitoring window, the three-phase imbalance of each monitoring window is obtained;

[0095] The grid auxiliary decision-making module is used to judge whether to control the distributed photovoltaic access of the low-voltage distribution network based on the three-phase imbalance;

[0096] The grid data transmission module is used for data transmission between modules and data transmission between units.

[0097] Among them, the block diagram of the distributed photovoltaic access system for the low-voltage distribution network is as Figure 2 shown.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0099] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed photovoltaic access method for a low voltage distribution network, characterized in that: The method comprises the following steps: Collect grid load data, neutral line zero-sequence current, amplitude data of all sequence components of three-phase voltage, and phase data of zero-sequence components of the main line at all times during the process of distributed photovoltaic access to the low-voltage distribution network; Preset monitoring windows, perform autocorrelation analysis and prediction on the grid load data in each monitoring window, and obtain the grid load imbalance degree in each monitoring window by combining the numerical variation characteristics of the neutral line zero-sequence current data; Analyze the change distribution of the amplitude data of the positive sequence component of the three-phase voltage in each monitoring window, and combine the change trend of the amplitude data of the negative sequence component in each monitoring window to obtain the abnormality of the sequence component in each monitoring window; Decompose the zero-sequence component of the three-phase voltage in each monitoring window to obtain the independent signal components after the zero-sequence component decomposition; analyze the distance distribution between all the independent signal components in each monitoring window, and combine the degree of confusion of the zero-sequence component phase data to obtain the zero-sequence component imbalance of the three-phase voltage in each monitoring window; Based on the abnormality of the sequence component and the imbalance of the zero-sequence component in each monitoring window, the power quality damage of each monitoring window and the degree of imbalance of the power grid load are obtained, and the three-phase imbalance of each monitoring window is obtained; Based on the three-phase imbalance, it is determined whether to control the access of distributed photovoltaic power generation to the low-voltage distribution network.

2. The distributed photovoltaic access method for a low-voltage distribution network according to claim 1, characterized in that: The specific formula for the imbalance degree of power grid load in each monitoring window is: In the formula, A i is the load imbalance degree of the low-voltage distribution network in the i-th monitoring window; a i is the predicted difference of the grid load corresponding to the ith monitoring window; norm[] is the normalization function; r i is the correlation strength of the grid load data sequence corresponding to the i-th monitoring window; J i is the total number of data in the neutral line zero-sequence current data sequence corresponding to the i-th monitoring window; b i,j is the absolute value of the difference between the jth data and its previous data in the neutral line zero-sequence current data sequence corresponding to the i-th monitoring window; exp() is an exponential function with the natural constant e as the base.

3. The distributed photovoltaic access method for a low-voltage distribution network according to claim 2, characterized in that: The prediction differences are specifically: Based on the grid load data of preset time length in each monitoring window, a grid load prediction sequence is obtained; According to the distance difference between the sequence composed of all power grid load data in each monitoring window and the power grid load prediction sequence, the prediction difference of each monitoring window is obtained.

4. The distributed photovoltaic access method for a low-voltage distribution network according to claim 2, characterized in that: The correlation strength is specifically the mean value of the autocorrelation coefficients of all power grid composite data in each monitoring window.

5. The distributed photovoltaic access method for a low-voltage distribution network according to claim 1, characterized in that: The sequence component abnormality of each monitoring window is obtained as follows: Based on the trend distribution of all elements in the first-order difference sequence of the three-phase voltage negative sequence component in each monitoring window, the rising intensity of the three-phase voltage negative sequence component in each monitoring window is obtained; The cumulative sum of the differences between adjacent data in the first-order difference sequence of the positive sequence component of the three-phase voltage of the low-voltage distribution network in each monitoring window is obtained, and forward fusion is performed with the negative sequence component of the three-phase voltage of the low-voltage distribution network in each monitoring window to obtain the abnormality of the sequence component of the three-phase voltage of the low-voltage distribution network in each monitoring window.

6. The distributed photovoltaic access method for a low-voltage distribution network according to claim 5, characterized in that: The obtained rising intensity of the negative sequence component of the three-phase voltage in each monitoring window is specifically: The trend analysis is performed on the first-order difference sequence of the negative-sequence component of the three-phase voltage in each monitoring window of the low-voltage distribution network to obtain the trend item strength of all elements, and the Hurst exponent of the strength of all trend items is used as the rising intensity of the negative-sequence component of the three-phase voltage.

7. The distributed photovoltaic access method for a low-voltage distribution network according to claim 1, characterized in that: The zero-sequence component imbalance of the three-phase voltage in each monitoring window is obtained as follows: Through the negative correlation mapping results of the DTW distance accumulation results between all independent signal components after the decomposition of the three-phase voltage corresponding to the zero-sequence component, combined with the information entropy of the phase data sequence of the zero-sequence component of the three-phase voltage of the low-voltage distribution network in each monitoring window, the zero-sequence component imbalance of the three-phase voltage of the low-voltage distribution network in each monitoring window is obtained.

8. The distributed photovoltaic access method for a low-voltage distribution network according to claim 1, characterized in that: The specific process of obtaining the three-phase imbalance of each monitoring window is as follows: The normalized value after forward fusion of the sequence component abnormality and the zero-sequence component imbalance corresponding to each monitoring window is used as the power quality impairment of each monitoring window after the low-voltage distribution network is connected to the distributed photovoltaic system; The normalized value obtained by multiplying the power quality impairment corresponding to each monitoring window by the degree of grid imbalance is used as the three-phase imbalance of each monitoring window after the low-voltage distribution network is connected to distributed photovoltaics.

9. The distributed photovoltaic access method for a low-voltage distribution network according to claim 1, characterized in that: The process of determining whether to control the access of distributed photovoltaic power generation to the low-voltage distribution network is specifically as follows: When the three-phase imbalance of a monitoring window is less than a preset three-phase imbalance threshold, the photovoltaic inverter when the distributed photovoltaic is connected is not controlled; otherwise, the photovoltaic inverter when the distributed photovoltaic is connected is controlled.

10. A distributed photovoltaic access system for a low-voltage distribution network, implementing a distributed photovoltaic access method for a low-voltage distribution network as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The power grid data acquisition module is used to collect the power grid load data of the trunk line at all times during the process of distributed photovoltaic access to the low-voltage distribution network, the neutral line zero-sequence current, the amplitude data of all sequence components of the three-phase voltage, and the zero-sequence component phase data; The power grid data processing and analysis module includes a data preprocessing unit and a data analysis unit, wherein: The data preprocessing unit is used to preprocess all collected data; The data analysis unit is used to preset monitoring windows, perform autocorrelation analysis and prediction on the grid load data in each monitoring window, and obtain the grid load imbalance degree in each monitoring window in combination with the numerical variation characteristics of the neutral line zero-sequence current data; Analyze the change distribution of the amplitude data of the positive sequence component of the three-phase voltage in each monitoring window, and combine the change trend of the amplitude data of the negative sequence component in each monitoring window to obtain the abnormality of the sequence component in each monitoring window; Decompose the zero-sequence component of the three-phase voltage in each monitoring window to obtain the independent signal components after the zero-sequence component decomposition; analyze the distance distribution between all the independent signal components in each monitoring window, and combine the degree of confusion of the zero-sequence component phase data to obtain the zero-sequence component imbalance of the three-phase voltage in each monitoring window; Based on the abnormality of the sequence component, the imbalance of the zero-sequence component and the imbalance degree of the power grid load in each monitoring window, the three-phase imbalance of each monitoring window is obtained; The grid auxiliary decision module is used to determine whether to control the distributed photovoltaic access to the low-voltage distribution network based on the three-phase imbalance; The power grid data transmission module is used for data transmission between modules and between units.

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