Power quality evaluation method, device and system for distributed photovoltaic grid connection

Abstract

The invention discloses a distributed photovoltaic grid-connected electric energy quality evaluation method, device and system, and the method comprises the following steps: processing distributed photovoltaic grid-connected point data based on time probability distribution, and obtaining a probability time distribution matrix of an electric energy quality index; processing the probability time distribution matrix by using an analytic hierarchy process to obtain a subjective index weight; processing the probability time distribution matrix by using an entropy weight method to obtain an objective index weight; obtaining a comprehensive weight based on the subjective index weight and the objective index weight; and calculating the comprehensive weight by adopting an approximate ideal solution sorting method to obtain an evaluation result. According to the method, a plurality of electric energy quality indexes can be comprehensively evaluated, and a comprehensive weighting method considering distributed photovoltaic power generation volatility is established according to the electric energy quality indexes, so that the electric energy quality of distributed photovoltaic grid connection is evaluated, and the electric energy quality evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power quality assessment, and in particular relates to a method, device and system for assessing power quality of distributed photovoltaic grid-connected power. Background Art

[0002] With the rapid economic development in recent years, the scale of the power grid has continued to expand, and total electricity consumption has rapidly increased. Traditional centralized power plants, due to their large footprint and high investment, are mostly located in remote areas. This not only results in high costs and long transmission distances, but is also prone to curtailment. Compared to traditional centralized power generation, distributed photovoltaic grid-connected systems offer advantages such as decentralized layout, clean and renewable energy, and reduced grid transmission losses. However, the operation of photovoltaic power generation systems is susceptible to natural factors such as weather, resulting in uncertainty, leading to unstable output power in distributed systems and severely impacting the power quality of the distribution network. Furthermore, the presence of numerous nonlinear power electronic devices in photovoltaic power generation systems injects high-order harmonic components into the distribution network, causing harmonic pollution in the grid. Therefore, research on power quality monitoring devices for distributed photovoltaic power plants is essential. Furthermore, the assessment of distributed photovoltaic power quality is essential for managing power quality. A sound assessment system can identify power quality issues and guide power quality management planning and prevention. Preventing power quality issues before they occur is more economical than addressing them after they occur. Therefore, a comprehensive assessment of the power quality of distributed photovoltaic power plants connected to the distribution network is necessary. Summary of the Invention

[0003] In order to solve the problem that existing methods are not accurate and comprehensive enough in power quality assessment, the present invention provides a power quality assessment method, device and system for distributed photovoltaic grid-connected power, which can comprehensively evaluate multiple power quality indicators and establish a comprehensive weighting method that takes into account the volatility of distributed photovoltaic power generation based on the power quality indicators to evaluate the power quality of distributed photovoltaic grid-connected power and improve the accuracy of power quality assessment.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A first aspect of the present invention provides a method for evaluating power quality of a distributed photovoltaic grid-connected system, comprising the following steps:

[0006] Processing distributed photovoltaic grid-connected point data based on time probability distribution to obtain a probability time distribution matrix of power quality indicators, including voltage deviation, frequency deviation, power harmonic content, voltage fluctuation, and three-phase imbalance;

[0007] The probability time distribution matrix is processed using the hierarchical analysis method to obtain the subjective indicator weight;

[0008] The probability time distribution matrix is processed using the entropy weight method to obtain the objective indicator weight;

[0009] Obtaining a comprehensive weight based on the subjective indicator weight and the objective indicator weight;

[0010] The evaluation result is calculated by using the approximate ideal solution sorting method for the comprehensive weight.

[0011] The second aspect of the present invention provides a detection platform, comprising a memory and a control unit that are communicatively connected in sequence, wherein a computer program is stored on the memory, and the control unit is used to read the computer program and execute a distributed photovoltaic grid-connected power quality assessment method described in the first aspect and any possible embodiment thereof.

[0012] The third aspect of the present invention provides a distributed photovoltaic grid-connected power quality assessment system, including a signal conditioning circuit, a filtering circuit, a data acquisition module, an edge computing module and a detection platform that are connected in sequence. The detection platform is a distributed photovoltaic grid-connected power quality assessment method described in the second aspect.

[0013] Compared with the prior art, the present invention has at least the following advantages and beneficial effects:

[0014] The present invention comprehensively evaluates multiple power quality indicators and establishes a comprehensive weighting method that takes into account the volatility of distributed photovoltaic power generation based on the power quality indicators to evaluate the power quality of distributed photovoltaic grid-connected power and improve the accuracy of power quality assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flow chart of the power quality assessment method for distributed photovoltaic grid-connected power systems of the present invention;

[0017] Figure 2 A schematic diagram of the power quality hierarchy system for classifying power quality indicators;

[0018] Figure 3 This is a schematic diagram of the distributed photovoltaic grid-connected power quality assessment system of the present invention;

[0019] Figure 4 This is the circuit schematic diagram of the voltage transformer sampling circuit;

[0020] Figure 5This is the circuit schematic diagram of the current transformer sampling circuit;

[0021] Figure 6 Circuit diagram of the filter circuit

[0022] Figure 7 This is the schematic diagram of the edge computing module. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are also within the scope of protection of the present invention.

[0025] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0026] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0027] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is typically placed when in use, or are the orientations or positional relationships commonly understood by those skilled in the art. These terms are intended only to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0029] like Figure 1 As shown, the first aspect of the present invention discloses a method for evaluating the power quality of distributed photovoltaic grid-connected power, which includes steps S01 to S05.

[0030] Step S01: Processing distributed photovoltaic grid-connected point data based on time probability distribution to obtain a probability time distribution matrix of power quality indicators.

[0031] According to the operating characteristics of distributed photovoltaics, voltage deviation, frequency deviation, harmonic distortion, voltage fluctuation and three-phase imbalance are selected as power quality evaluation indicators.

[0032] According to the national GT / B series power quality standards, there are five levels of indicator evaluation, from 1 to 5, from small to large, including unqualified, qualified, medium, good and excellent. The levels of each indicator are shown in Table 3.

[0033] Table 3 Distributed photovoltaic grid-connected power quality index levels

[0034]

[0035] After obtaining the distributed photovoltaic grid-connected point data, the operating data is analyzed to construct a time distribution matrix of power quality indicators. Assuming the total measurement time is T, and y1 to y5 are the five power quality indicators in Table 3, the power quality indicator measurement data matrix at the grid-connected point can be expressed as:

[0036]

[0037] Where i is the different test points of the indicator, j is the power quality indicator, and k is the number of time periods into which the total detection time T is divided.

[0038] Assume the indicator detection value y ij The statistical duration at each level is t ij , after integration, the probability time distribution matrix of the indicator is:

[0039]

[0040] Where, t 1j ~t rjThey represent the statistical duration of the j-th indicator in levels 1 to r.

[0041] In order to improve the accuracy of the evaluation, it is also necessary to correct the elements in the probability time distribution matrix.

[0042] Specifically, the elements in the probability time distribution matrix corresponding to levels 1 and 2 are corrected, that is, the elements are corrected when they are unqualified or qualified.

[0043] The correction method is:

[0044]

[0045] That is, when the element T in the power quality index time distribution matrix hj When it is greater than or equal to 0.4 and less than or equal to 0.6, the element is corrected to αT hj +β; when the element T in the power quality index time distribution matrix hj When it is greater than 0.6, the element is corrected to a, b and c are all indicator penalty factors.

[0046] Elements T between 0 and 0.4 hj No corrections are made.

[0047] When the degree of exceeding the standard gradually increases, the impact on the source load does not increase linearly. For indicators that exceed the standard slightly or are close to exceeding the standard, a linear function is used to amplify the indicator. For indicators that exceed the standard seriously, a power function is used to amplify the indicator.

[0048] We use 0.4 as the warning threshold, 0.6 as the penalty threshold, and c as 0.25. Considering the continuity of the corrected curve, a is 2.4 and b is -0.56. Therefore, when data approaches the limit, the impact on the assessment results will be amplified, and when data exceeds the limit, the impact will be further amplified.

[0049] Step S02: Use the hierarchical analysis method to process the probability time distribution matrix to obtain subjective indicator weights.

[0050] The AHP method quantifies the degree of importance using a scaling method. First, the target is divided into different levels, and each indicator is compared in turn. A weight judgment matrix is derived using the 5-scaling method. The judgment matrix eigenvalues and the eigenvalue vectors are then normalized to obtain the indicator weights. Specifically, this step includes steps S021 to S024.

[0051] Step S021: Divide the power quality indicators into two categories according to voltage and frequency, and form a three-layer power quality hierarchy system based on the affiliation: indicator layer, criterion layer, and target layer. The division results are as follows: Figure 2 shown.

[0052] Step S022: Construct a judgment matrix A based on the importance of each element in each layer to the upper layer index in the power quality hierarchy system. ij ) n×n .

[0053]

[0054] Each element of the matrix represents the importance comparison value between elements in the same layer. ij It represents the importance comparison scale value obtained by comparing the i-th element with the j-th element.

[0055] The quantitative analysis of relative importance is usually determined using a 9-level scale method, as shown in Table 4:

[0056] Table 4 9-level quantitative scaling method

[0057]

[0058] Step S023: Calculate the weight vector of the corresponding indicator according to the judgment matrix.

[0059] Specifically, first determine the matrix A=(a ij ) n×n Perform normalization to obtain the matrix The element b ij for:

[0060]

[0061] Then the matrix Summing, we get vector c=(c1,c2,2,c n ) T , where element c i for:

[0062]

[0063] Then for vector c=(c1,c2,2,c n ) T Perform normalization to obtain the index weight coefficient, that is, the eigenvector ω of the maximum eigenvalue j :

[0064]

[0065] Finally, the maximum eigenvalue λ of the matrix is calculated according to the indicator weight coefficient max .

[0066]

[0067] Step S024: Determine the consistency of the probability time distribution matrix based on the weight vector.

[0068] The establishment of a judgment matrix is subjective and may deviate from objective reality, so it is necessary to test the consistency of the judgment matrix. The degree of consistency deviation is measured by the consistency index CI:

[0069]

[0070] n is the dimension of the judgment matrix. A smaller CI indicates better matrix consistency. A CI value less than 0.1 indicates good and acceptable consistency. Because CI increases with increasing matrix dimension n, the random consistency index (RI) of the judgment matrix is introduced. This value, CR, is used instead of CI to measure matrix consistency.

[0071]

[0072] When CR>0.1, the consistency of the judgment matrix is too poor and must be corrected. The relationship between RI and the dimension N of the judgment matrix is shown in Table 5:

[0073] Table 5 RI-N relationship table

[0074]

[0075] Step S03: Use the entropy weight method to process the probability time distribution matrix to obtain objective indicator weights.

[0076] Regarding power quality issues caused by distributed photovoltaics, it is believed that the greater the volatility, the greater the weight of the indicator. Combining it with the hierarchical analysis method can reduce subjective influences. The specific steps of the comprehensive evaluation of power quality entropy method include steps S031 to S034.

[0077] Step S031: Perform n-level quality evaluation on m power quality indicators, and the evaluation value of the i-th indicator at the j-th level is recorded as y ij , establish the matrix model Y.

[0078]

[0079] Step S032: homogenize each evaluation value in the matrix model, convert it into a unified positive term or inverse term, and perform standardization to obtain a standardized evaluation value z ij .

[0080]

[0081] Step S033: Calculate each evaluation value z ij The entropy value E i .

[0082]

[0083] Step S034: Add adjustment items based on the entropy value and calculate the weight w of each power quality indicator in the comprehensive evaluation. i .

[0084] Add an adjustment item based on the entropy value to reduce the problem that when the entropy value approaches 1, a small change in the indicator entropy value will cause the entropy value to change exponentially.

[0085] in,

[0086] Step S04: Obtaining a comprehensive weight based on the subjective indicator weight and the objective indicator weight.

[0087] Comprehensively weighting distributed photovoltaic power quality indicators is a key step in comprehensive evaluation. The subjective weighting method reflects the decision-making experience of experts to a certain extent, but the results are subjective and arbitrary. The objective weighting method obtains weights based on mathematical basis, but ignores the decision-making experience of experts. Both subjective and objective weighting methods have limitations, so the subjective weighting method and the objective weighting method are combined to comprehensively consider the decision-making experience of experts and mathematical basis. Currently, the subjective and objective comprehensive weighting methods include geometric mean method, minimum deviation combination method, multiplication synthesis method, mathematical programming method, etc. This patent adopts the multiplication synthesis method, and its method is as follows:

[0088] The subjective weight is multiplied by the objective weight, and the product is normalized. The comprehensive weight assignment method is:

[0089]

[0090] Where, α j The weight obtained by the subjective weighting method is ω j , β j The weight obtained by the objective weighting method is w i , n is the number of indicators.

[0091] Step S05: Calculate the evaluation result based on the comprehensive weight using the approximate ideal solution sorting method.

[0092] When calculating the ideal solution, the original data should be weighted in the same manner as the traditional TOPSIS method for ranking near-ideal solutions. The Euclidean distance should also be weighted, and the solutions should be ranked based on the weighted Euclidean distance. After weighting the Euclidean distance, if multiple solutions have the same Euclidean distance to the positive ideal solution, the solution with a greater distance to the negative ideal solution is preferred. The power quality assessment steps based on the improved TOPSIS include steps S051 to S054.

[0093] Step S051: construct a weighted norm matrix C based on the comprehensive weights.hj ) r×n .

[0094] Construct the weighted normalized matrix C = (C hj ) r×n , the indicator weight vector is r=[r1,r2,2r n ] T , the probability time distribution matrix is defined as:

[0095]

[0096] Step S052: confirm the weight normalization matrix C=(C hj ) r×n The positive ideal solution S + , negative ideal solution S - .

[0097] Assume that the properties of the positive and negative ideal solutions of the jth index value are

[0098] but:

[0099]

[0100]

[0101] J + It is a benefit-type set, that is, the larger the index value, the better; J - It is a cost-type set, that is, the smaller the index value, the better.

[0102] Step S053, respectively calculate the positive ideal solution S + , negative ideal solution S - The weighted Euclidean distance

[0103]

[0104] Step S054: According to the weighted Euclidean distance Calculate comprehensive evaluation indicators.

[0105]

[0106] According to the evaluation index h h Sort the evaluation schemes by quality, and use the evaluation index h h The larger the value, the better the evaluation object.

[0107] The method of this scheme is used to comprehensively evaluate multiple power quality indicators. Based on the power quality indicators, a comprehensive weighting method considering the volatility of distributed photovoltaic power generation is established to evaluate the power quality of distributed photovoltaic grid-connected power and improve the accuracy of power quality assessment.

[0108] Based on the above method, the second aspect of the present invention discloses a detection platform, which includes a memory and a control unit connected in sequence, wherein a computer program is stored on the memory, and the control unit is used to read the computer program and execute the first aspect and any possible method for evaluating the power quality of a distributed photovoltaic grid-connected device. For example, the memory may include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory (Flash Memory), a first-input first-output (FIFO) and a first-input last-output (FILO) memory, etc.; the control unit may be limited to a microcontroller unit of the STM32F105 series. In addition, the computer device may also include, but is not limited to, a power supply unit, a display screen and other necessary components.

[0109] The distributed photovoltaic grid-connected point data in the above method is collected by the monitoring module.

[0110] Specifically, such as Figure 3 As shown, the monitoring module includes a signal conditioning circuit, a filtering circuit, a data acquisition module, and an edge computing module, which are sequentially connected. The monitoring module sends the monitored data to the detection platform via the edge computing module.

[0111] The monitoring module provides real-time monitoring of parameters including voltage / current effective value, frequency, active power, reactive power, apparent power, power factor, voltage deviation, frequency deviation, harmonic voltage including harmonic voltage distortion rate and the content rate of each voltage harmonic, harmonic current including harmonic current distortion rate and the content rate of each current harmonic, voltage fluctuation and flicker.

[0112] This monitoring module is designed to monitor grid-connected photovoltaic power plants with an installed capacity of approximately 20MW and a voltage level of 10-35kV. By collecting voltage and current signals at the common connection point between the public grid and the grid-connected photovoltaic power plant, it provides real-time monitoring of the power quality of the grid-connected photovoltaic power plant. High-voltage AC bus voltage and current signals typically require conversion to 100V (5A) using a PT / CT. The NI9220 module used in this invention has a measurement range of ±10V, so the 100V (5A) voltage and current signals must be converted to values within the measurement range using a sensor. This module sets the maximum harmonic detection order to 7. This is because harmonics up to the 7th order have a high content, while harmonics above the 7th order have a low content and therefore have a minimal impact on the power grid. Furthermore, it reduces computational effort and improves system speed. Given the need to measure harmonics up to the 7th order, according to the sampling theorem, the system sampling frequency should be no less than 1kHz. To ensure computational accuracy, this device collects at least 256 points per cycle, requiring a sampling frequency of at least 12.8kHz.

[0113] Specifically, the signal conditioning circuit is responsible for sampling and conditioning the input voltage or current signal, converting high-voltage and high-current signals into small signals within the measurement range to ensure they meet the 0.5V limit required by the A / D sampling channel. This design primarily consists of two parts: the voltage transformer sampling circuit and the current transformer sampling circuit.

[0114] Among them, the voltage transformer sampling circuit can be used as follows Figure 4 The circuit structure shown in Figure 1 uses a set of high-precision, low-temperature-coefficient first resistors for voltage division, followed by isolation and conversion using a voltage transformer. This achieves the isolation and conversion of the high-voltage AC voltage signal into a low voltage signal suitable for the A / D input range, i.e., less than 0.5V. Here, the first resistors include resistors R63 through R67. This monitoring device uses a TV19 voltage transformer, which has an operating frequency range of 20Hz to 20kHz and a linearity better than 0.1%. Its specific performance indicators are shown in Table 1.

[0115] Table 1 Performance indicators of voltage transformer

[0116]

[0117] When implementing a circuit design using a TV19 current-type voltage transformer, a resistor is connected in series on its secondary side to convert the current signal into a voltage signal, enabling further signal amplitude conversion, filtering, and other processing. Due to the TV19's small phase offset, the voltage transformer sampling circuit does not incorporate phase compensation. Specifically, when the monitoring device inputs an AC voltage signal LA, based on the performance specifications of the TA19 voltage transformer in Table 1, the output current at pin 9 of the TV19 secondary side is calculated to be I1 = -LA / (5×R63). Taking into account the A / D sampling input voltage range, the output current I1 is converted to a voltage by the second resistor R60 and then amplified by the signal amplifier circuit. The signal amplifier circuit uses a TLV2461 for amplitude conversion. The relationship between the voltage transformer sampling circuit output voltage and the input voltage LA is: VA = LA / (10×R63)×R60+A1.65V, where A1.65V represents the bias voltage. Varistor RV7 primarily provides voltage-limiting protection.

[0118] Among them, the current transformer sampling circuit can be used as follows Figure 5 The circuit structure shown in FIG1 uses a TA17-05 current transformer. The operating frequency range of the TA17-05 current transformer is between 20 Hz and 20 kHz, and the linearity is better than 0.2%. The specific performance indicators of the current transformer are shown in Table 2.

[0119] Table 2 Performance indicators of current transformer

[0120]

[0121] When the monitoring device uses the TA17-05 current transformer to implement the current sampling circuit design, the sensor sampling output is considered to be a current signal. Therefore, a third resistor is required on its secondary side to convert the current signal into a voltage signal so that signal filtering, A / D sampling and other processing can be achieved in the next step. When the monitoring device inputs the measured AC current signal, the measured signal is converted by the CT1 current transformer, and after the IV conversion is achieved by the sampling resistors R41 and R42, the voltage differential circuit transmits it in the form of a voltage differential signal with a difference of no more than 1V. The output voltage amplitude is IA + -IA-=I in / 3000×(R41+R42), where I in Indicates the measured AC current signal.

[0122] A Butterworth filter can be used as the filter circuit. To address the design requirements of the monitoring device and analyze harmonics up to the 63rd order on the user side, the filter design bandwidth is 3.3kHz, and the oversampling sampling rate is 1.024MHz. This invention utilizes the Filterwizard™ analog filter design wizard from Analog Devices (ADI) to design the filter. By inputting various parameters, a recommended Butterworth filter circuit is obtained. Figure 6 This is a circuit schematic diagram actually constructed using the circuit simulation software Pspice based on the filter circuit given by FilterwizardTM. Figure 6 In the figure, the phase A voltage channel is used as an example. The sampled output voltage of the input analog voltage transformer sampling channel is VA. After the third-order Butterworth filter, the output end is connected to the 490K resistor R6 (the DC input impedance of the analog end of the phase A voltage channel), and the output voltage is VA_OUT.

[0123] Among them, the data acquisition module includes an A / D conversion module and a controller.

[0124] The A / D conversion module is an important component of this detection device, and its A / D conversion accuracy and reliability are related to the performance of the monitoring device. According to the design requirements of this device, the sampling frequency must be greater than 12.8kHz. This system needs to collect voltage and current at the same time, and more than 6 channels are required for a three-phase power supply system. In order to ensure the accuracy of the system, an A / D conversion module with a resolution of at least 14 bits must be selected. The present invention uses the C series analog-to-digital conversion module NI9220 developed by NI as the data acquisition module of this system. Its circuit schematic is shown in the figure below. Figure 5 As shown in the figure, this module features 16 input channels with 16-bit measurement accuracy, and each channel provides a ±10V voltage measurement range. It supports synchronous differential analog input, with each channel equipped with an independent signal path and analog-to-digital converter. After signal conditioning, the ADC performs analog-to-digital conversion, with a maximum sampling rate of 100 kS / s, meeting the data acquisition requirements of this system.

[0125] The need for real-time online analysis and monitoring of power quality in grid-connected photovoltaic power generation systems requires a controller with excellent environmental adaptability and long-term stable operation. Furthermore, the monitoring system requires high-speed data processing and accurate data analysis capabilities, so the controller's performance is directly related to the performance of the monitoring system. Based on the system's data processing requirements, the environmental demands of the monitored objects, and cost considerations, the NI cRIO-9066 was selected as the monitoring system controller. The NI cRIO-9066 is a cost-effective, industrial-grade embedded controller designed by National Instruments for embedded control and distributed condition monitoring. It offers reliable and stable performance, ensuring 24 / 7 stable operation and strong environmental adaptability. It features a 667MHz ARM Cortex-A9 dual-core processor and up to 85,000 logic cells, providing powerful data processing capabilities and significantly improving FPGA compilation performance. Furthermore, it features 16 DMA FIFO channels for multi-channel data processing.

[0126] The power supply circuit, clock circuit, etc. that ensure the operation of the data acquisition module can be implemented using existing technologies and will not be described in detail here.

[0127] Among them, the edge computing module can be based on Figure 7 The schematic implementation shown here consists of an ESP32 control board, a 4G communication module, an Ethernet communication module, and an OLED display. The ESP32 control board is responsible for collecting and processing on-site power quality data. It features a built-in dual-core 32-bit processor with a processing frequency of 240MHz and integrated Wi-Fi, providing strong computing power. The 4G communication module transmits processed data to the cloud-based power quality monitoring platform. It uses a CAT.1 rating and supports LTE and GSM networks, offering wide coverage and meeting power quality data transmission requirements. The Ethernet communication module uses an Ethernet module that supports simultaneous communication via eight independent hardware sockets, enabling simultaneous collection of three-phase power quality waveform data. The OLED display provides real-time display of data processing progress and status information.

[0128] In this system and method, voltage and current signals are first converted to their range-allowed values by a signal conditioning circuit. After conversion, the signals are filtered by a filtering circuit. Given that the harmonic analysis component of the present invention detects up to 7 harmonics, filtering of higher-order harmonics and high-frequency noise is necessary to prevent spectral aliasing and ensure measurement accuracy. The processed signals are converted to digital signals using the NI9220 C Series module. The signals are then transmitted to the edge computing module via Ethernet using the TCP / IP protocol by the cRIO-9066 controller. The data is then transmitted to a host computer for analysis. Standard Ethernet transmission speeds reach 10 Mbit / s, ensuring the system's real-time performance.

[0129] Power quality assessment indicators include busbar voltage deviation, frequency deviation, harmonic distortion, voltage fluctuation, and three-phase imbalance. The workstation communicates with the power quality monitoring terminal and the monitoring center's main station, transmitting data collected by the terminal to the monitoring center's main station. Based on the power quality indicators collected by the monitoring center's main station, the subjective weights are derived using the Analytic Hierarchy Process (AHP). The objective weights are calculated using the improved entropy method, and a combined weight is obtained by combining the subjective and objective factors. The power quality assessment grading module categorizes the power quality standard data and generates assessment results using the improved TOPSIS method.

[0130] The present invention designs a power quality monitoring and assessment system to address the power quality issues caused by the connection of distributed photovoltaic power stations to the distribution network. The system comprehensively evaluates multiple power quality indicators and establishes a comprehensive weighting method based on the power quality indicators that takes into account the volatility of distributed photovoltaic power generation to evaluate the power quality of distributed photovoltaic grid-connected power stations.

[0131] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the power quality of distributed photovoltaic grid-connected power, characterized in that: The following steps are involved: Processing distributed photovoltaic grid-connected point data based on time probability distribution to obtain a probability time distribution matrix of power quality indicators, including voltage deviation, frequency deviation, power harmonic content, voltage fluctuation, and three-phase imbalance; The probability time distribution matrix is processed using the hierarchical analysis method to obtain the subjective indicator weight; The probability time distribution matrix is processed using the entropy weight method to obtain the objective indicator weight; Obtaining a comprehensive weight based on the subjective indicator weight and the objective indicator weight; The evaluation result is calculated by using the approximate ideal solution sorting method for the comprehensive weight.

2. The method for evaluating power quality of distributed photovoltaic grid-connected power plants according to claim 1, wherein: After obtaining the probability time distribution matrix of the power quality index, the method further includes: Determining the level corresponding to each element in the probability time distribution matrix; When the level is unqualified or qualified, the elements are modified. When the element T in the power quality index time distribution matrix is hj When it is greater than or equal to 0.4 and less than or equal to 0.6, the element is corrected to αT hj +β; when the element T in the power quality index time distribution matrix hj When it is greater than 0.6, the element is corrected to a, b and c are all indicator penalty factors.

3. The method for evaluating power quality of distributed photovoltaic grid-connected power plants according to claim 1, wherein: The method of using the hierarchical analysis method to process the probability time distribution matrix to obtain the subjective indicator weight includes: The power quality indicators are divided into two categories according to voltage and frequency, and a three-layer power quality hierarchy system is formed according to the affiliation: indicator layer, criterion layer, and target layer; Based on the importance of each element in each layer to the indicators of the upper layer in the power quality hierarchy system, a judgment matrix A=(a ij ) n×n ; According to the judgment matrix A=(a ij ) n×n , calculate the weight vector of the corresponding indicator; The consistency of the probability time distribution matrix is determined based on the weight vector.

4. A method for evaluating power quality of distributed photovoltaic grid-connected power according to claim 3, characterized in that: According to the judgment matrix A=(a ij ) n×n , calculate the weight vector of the corresponding indicators, including: For the judgment matrix A=(a ij ) n×n Perform normalization processing, and get the matrix For the matrix Sum the rows and get the vector c=(c1,c2,2,c n ) T , For the vector c=(c1,c2,2,c n ) T Perform normalization processing to obtain the indicator weight coefficient; The maximum eigenvalue of the matrix is calculated according to the indicator weight coefficient.

5. The method for evaluating power quality of distributed photovoltaic grid-connected power plants according to claim 1, wherein: The method of using entropy weight to process the probability time distribution matrix to obtain objective indicator weights includes: performing n-level quality evaluation on m power quality indicators, and recording the evaluation value of the i-th indicator at the j-th pole as y ij , establish a matrix model; Homogenize each evaluation value in the matrix model, convert it into a unified positive term or inverse term, and perform standardization to obtain a standardized evaluation value z ij ; Calculate each evaluation value z ij The entropy value of Add adjustment items based on the entropy value and calculate the weight w of each power quality indicator in the comprehensive evaluation i .

6. The method for evaluating power quality of distributed photovoltaic grid-connected power plants according to claim 1, wherein: The method of calculating the evaluation result by using the approximate ideal solution sorting method for the comprehensive weight includes: Based on the comprehensive weights, a weighted normative matrix C is constructed. hj ) r×n , Confirm that the weight normalization matrix C = (C hj ) r×n The positive ideal solution S + , negative ideal solution S - , Calculate the positive ideal solution S respectively + , negative ideal solution S - The weighted Euclidean distance According to the weighted Euclidean distance Calculate comprehensive evaluation indicators.

7. A detection platform comprising a memory and a control unit that are communicatively connected in sequence, wherein a computer program is stored in the memory, characterized in that: The control unit is used to read the computer program and execute the power quality assessment method for distributed photovoltaic grid-connected power according to any one of claims 1 to 6.

8. A distributed photovoltaic grid-connected power quality assessment system, characterized by: It includes a signal conditioning circuit, a filtering circuit, a data acquisition module, an edge computing module and a detection platform that are connected in sequence. The detection platform is a distributed photovoltaic grid-connected power quality assessment method as described in claim 7.

9. A distributed photovoltaic grid-connected power quality assessment system according to claim 8, characterized in that: The signal conditioning circuit includes a voltage transformer sampling circuit and a current transformer sampling circuit for dividing and isolating the high-voltage AC voltage signal to a voltage less than 0.5V; The voltage transformer sampling circuit includes a set of first resistors for voltage division, a voltage transformer for isolation conversion, a second resistor connected in series on the secondary side of the voltage transformer for converting a current signal into a voltage signal, and a signal amplification circuit, which are electrically connected in sequence; The current transformer sampling circuit includes a current transformer electrically connected in sequence, two sampling resistors for converting current signals into voltage signals, and a voltage differential circuit for outputting a voltage signal with a difference of less than 1V at both ends of the sampling resistor.

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