Real-time monitoring system for power quality of photovoltaic power stations

Abstract

The present invention relates to the field of electric power engineering technology, and specifically to a real-time monitoring system for power quality of a photovoltaic power station, comprising a data acquisition module, a data processing module, a power quality assessment module, a fault warning module, an adaptive control module, and a visualization module. The data acquisition module monitors power quality parameters in real time, and the data processing module uses a multivariate analysis method to identify anomalies and generate problem reports. The power quality assessment module evaluates the health status of the photovoltaic power station based on standard power quality indicators and combined with data processing results. The fault warning module automatically triggers an alarm mechanism to notify administrators or maintenance personnel to handle the issue in a timely manner. The adaptive control module intelligently adjusts the power station operation strategy to optimize power quality based on the score and warning information. The visualization module presents all relevant information to the user through an interface. The present invention effectively improves the accuracy, response speed, and optimization efficiency of power quality monitoring, and contributes to the stable operation and efficient power generation of the photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a real-time monitoring system for power quality of a photovoltaic power station. Background Art

[0002] With the increasing global demand for renewable energy, photovoltaic power plants are gaining widespread adoption as an important form of green energy. By converting solar energy into electricity, photovoltaic power plants not only meet various electricity needs but also reduce reliance on traditional energy sources, offering significant environmental advantages. However, during operation, photovoltaic power plants are subject to various factors, which can lead to power quality issues. Power quality includes parameters such as voltage, frequency, harmonics, and power factor, which directly impact the operational stability and power generation efficiency of photovoltaic power plants.

[0003] As photovoltaic power plants continue to expand in scale, relying solely on traditional power quality monitoring equipment can no longer meet the needs of PV plants for comprehensive and accurate power quality monitoring. Traditional systems typically rely on monitoring a single power quality parameter and lack the ability to comprehensively analyze and intelligently diagnose multiple power quality issues. Existing technologies often employ simple threshold alarm mechanisms, failing to effectively identify complex power quality issues, such as correlation analysis and anomaly detection across multiple power quality parameters. Furthermore, existing systems often fail to achieve automatic adjustment and intelligent optimization when power quality issues arise, requiring manual intervention to adjust the PV plant's operational strategy. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a real-time monitoring system for power quality of a photovoltaic power station.

[0005] The real-time monitoring system for power quality of photovoltaic power station includes data acquisition module, data processing module, power quality assessment module, fault warning module, adaptive control module and visualization module, among which;

[0006] The data acquisition module is used to collect power quality parameters of the photovoltaic power station in real time, and the power quality parameters include voltage, current, frequency and power factor;

[0007] The data processing module receives the power quality parameters collected by the data acquisition module, and processes and analyzes the power quality parameters using a multivariate analysis method, identifies anomalies in the power quality, and generates a power quality problem report;

[0008] The power quality assessment module combines the analysis results of the data processing module and evaluates the power quality of the photovoltaic power station based on standard power quality indicators, generates a power quality score, and derives the health status of the photovoltaic power station based on the score;

[0009] The fault warning module is connected to the power quality assessment module. When the power quality assessment module detects abnormal power quality, it automatically triggers an alarm mechanism and sends a warning message to the administrator or maintenance personnel;

[0010] The adaptive control module is connected to the power quality assessment module and the fault warning module, and intelligently adjusts the operation strategy of the photovoltaic power station according to the power quality score and warning information to optimize the power quality;

[0011] The visualization module is used to present power quality assessment results, warning information and operation strategies to users through a visualization interface, so that users can monitor and manage the status of the power station in real time.

[0012] Optionally, the data acquisition module includes:

[0013] Voltage data acquisition: The data acquisition module collects voltage data in real time through voltage sensors installed in the photovoltaic power station.

[0014] Current data acquisition: The data acquisition module collects current data in real time through the current sensor.

[0015] Frequency calculation: The data acquisition module calculates the frequency of the power system by analyzing the collected voltage signals;

[0016] Power factor calculation: Power factor is an important indicator for measuring power quality, which represents the ratio of active power to apparent power. The data acquisition module calculates the power factor by collecting current and voltage data in real time.

[0017] Optionally, the data processing module includes:

[0018] Data reception and preprocessing: The data processing module receives the power quality parameters collected by the data acquisition module and performs preprocessing. The preprocessing includes filling missing values, removing noise and standardizing data to ensure the accuracy of subsequent analysis.

[0019] Multivariate analysis: The data processing module uses multivariate analysis methods to analyze the processed power quality parameters, including correlation analysis and anomaly detection;

[0020] Power quality problem identification: The data processing module identifies anomalies in power quality based on the results of multivariate analysis;

[0021] Problem report generation: The data processing module generates a power quality problem report based on the identified anomalies.

[0022] Optionally, the correlation analysis in the multivariate analysis includes:

[0023] Correlation analysis: Based on the data characteristics of power quality parameters, the Pearson correlation coefficient is selected as the correlation analysis method to measure the linear relationship between power quality parameters;

[0024] Calculate the correlation matrix: Calculate the correlation matrix between different power quality parameters using the Pearson correlation coefficient.

[0025] Identify significant correlations: After obtaining the correlation matrix through calculation, the data processing module identifies significantly correlated power quality parameter pairs based on the preset correlation threshold.

[0026] Analyze the impact of correlation on power quality: Based on the results of correlation analysis, analyze the impact of highly correlated power quality parameters. For example, if the correlation coefficient between voltage and current is high, it can be inferred that current fluctuations may be caused by voltage changes, or vice versa. This analysis helps with subsequent fault warnings and optimization of adaptive control strategies.

[0027] Optionally, the anomaly detection in the multivariate analysis includes:

[0028] Select anomaly detection method: The data processing module uses a combination of the Z-score method and the isolation forest algorithm to perform anomaly detection.

[0029] Z-score anomaly detection: The data processing module applies the Z-score method to each power quality parameter and calculates the parameter's Z-score value. For each parameter, if the absolute value of its Z-score value exceeds a preset threshold (for example, 3), the power quality parameter is considered to have a significant deviation at that time point and is marked as potential abnormal data;

[0030] Isolation forest anomaly detection: After the initial screening by the Z-score method, the data processing module further applies the isolation forest algorithm to perform multi-dimensional anomaly detection on the power quality parameters;

[0031] Fusion detection results: The data processing module fuses the detection results of the Z-score method and the isolation forest algorithm, giving priority to abnormal data detected by the isolation forest.

[0032] Abnormal data marking: The data processing module marks the data points that have undergone abnormality detection.

[0033] Optionally, the power quality assessment module includes:

[0034] Preliminary assessment based on standard power quality indicators: The power quality assessment module conducts a preliminary assessment of the PV plant's voltage, current, frequency, and power factor based on standard power quality indicators (such as IEC 61000 and IEEE 519), generating a preliminary power quality score.

[0035] Score correction: The power quality assessment module combines the abnormality identification results provided by the data processing module, analyzes the impact of abnormal areas on power quality, adjusts the scoring formula, and generates an adjusted power quality score;

[0036] Determine the health status: The power quality assessment module assesses the power quality health status of the PV power station based on the adjusted score and generates a health status report.

[0037] Optionally, the fault warning module includes:

[0038] Analyze power quality anomalies: The fault warning module analyzes the power quality score based on the output of the power quality assessment module to see if it is lower than the preset threshold;

[0039] Triggering the alarm mechanism: If the score is higher than the threshold, the PV power station is in normal working condition and no alarm needs to be triggered. If the power quality score is lower than the threshold, the fault warning module automatically triggers the alarm mechanism;

[0040] Generate early warning report: After the fault early warning module triggers the alarm mechanism, it generates a detailed early warning report.

[0041] Send warning information: The fault warning module will send the generated warning report to the corresponding management personnel or maintenance team through the management system.

[0042] Optionally, the adaptive control module includes:

[0043] Receiving power quality scores and warning information: The adaptive control module receives the power quality scores from the power quality assessment module and the warning information from the fault warning module;

[0044] Analyzing power quality scores and warning information: The adaptive control module analyzes the current operating status of the PV power station based on the received power quality scores and warning information.

[0045] Determine adjustment targets: Based on the analysis results, the adaptive control module determines the optimization targets.

[0046] Adjusting the operation strategy of the photovoltaic power station: Based on the analysis results and optimization objectives, the adaptive control module adjusts the operation strategy of the photovoltaic power station.

[0047] Operation strategy optimization: The adaptive control module regularly evaluates the effectiveness of the adjusted operation strategy and optimizes it.

[0048] Optionally, the visualization module includes:

[0049] Visual display: The visualization module displays power quality assessment results, warning information and operation strategies to users through a visual interface;

[0050] Interactive operation interface: The visualization module provides user interaction functions, allowing users to view more detailed power quality parameters by clicking, dragging and zooming.

[0051] Beneficial effects of the present invention:

[0052] This invention provides a real-time monitoring system based on multivariate analysis and standard power quality indicators. This system comprehensively assesses the power quality of photovoltaic power plants and accurately captures key power quality parameters (such as voltage, current, frequency, and power factor) through a data acquisition module. The data processing module utilizes multivariate analysis methods, including correlation analysis and anomaly detection, to promptly identify potential power quality issues. This process not only improves power quality monitoring accuracy but also provides strong data support for subsequent health status assessment and optimized control.

[0053] This invention, through the close collaboration of the fault warning module and the adaptive control module, automatically triggers an alarm mechanism when power quality anomalies occur and sends real-time warning information to administrators or maintenance personnel. This intelligent warning mechanism can quickly identify power quality anomalies and take appropriate measures to avoid power plant shutdowns or equipment damage. Furthermore, the adaptive control module automatically adjusts the power plant's operating strategy based on real-time power quality scores and warning information, optimizing power quality and ensuring stable operation of the photovoltaic power plant under different environmental conditions, thereby improving the plant's adaptability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or 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 for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A schematic diagram of a system flow diagram of an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of the data processing module flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0058] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1-Figure 2 As shown, the real-time monitoring system for power quality of photovoltaic power station includes data acquisition module, data processing module, power quality assessment module, fault warning module, adaptive control module and visualization module, among which;

[0061] The data acquisition module is used to collect the power quality parameters of the photovoltaic power station in real time. The power quality parameters include voltage, current, frequency and power factor;

[0062] The data processing module receives the power quality parameters collected by the data acquisition module, processes and analyzes the power quality parameters using a multivariate analysis method, identifies anomalies in the power quality, and generates a power quality problem report;

[0063] The power quality assessment module combines the analysis results of the data processing module and evaluates the power quality of the photovoltaic power station based on standard power quality indicators, generates a power quality score, and derives the health status of the photovoltaic power station based on the score;

[0064] The fault warning module is connected to the power quality assessment module. When the power quality assessment module detects abnormal power quality, it automatically triggers the alarm mechanism and sends a warning message to the administrator or maintenance personnel;

[0065] The adaptive control module is connected to the power quality assessment module and the fault warning module. Based on the power quality score and warning information, it intelligently adjusts the operation strategy of the photovoltaic power station to optimize the power quality.

[0066] The visualization module is used to present power quality assessment results, early warning information and operation strategies to users through a visual interface, making it easier for users to monitor and manage the status of the power station in real time.

[0067] The data acquisition module includes:

[0068] Voltage Data Acquisition: The data acquisition module collects voltage data in real time using voltage sensors installed throughout the PV power plant. These high-precision digital sensors accurately measure both AC and DC voltages. The data collected includes the voltage at the PV power plant's output terminal, as well as the voltage values ​​at each grid connection point and before and after the transformer. The data is collected at least once per second to ensure real-time monitoring of voltage fluctuations.

[0069] Current Data Acquisition: The data acquisition module collects current data in real time using current sensors, which use Hall-effect sensors or current transformers. These sensors accurately measure the current at the output of the PV power plant and the currents of each subsystem. The collected current data includes both DC and AC currents, and the data acquisition frequency is also at least once per second to ensure real-time monitoring of current fluctuations.

[0070] Frequency calculation: The data acquisition module calculates the frequency of the power system by analyzing the collected voltage signal. The frequency reflects the periodic changes of the voltage waveform and is calculated using the following formula:

[0071] ;

[0072] in, is the frequency, The period of the voltage waveform is obtained by analyzing the collected voltage signal data. The real-time and accuracy of frequency calculation are crucial to the stability of the monitoring system. The acquisition frequency is at least once per second.

[0073] Power factor calculation: Power factor is an important indicator for measuring power quality, indicating the ratio of active power to apparent power. The data acquisition module calculates the power factor by collecting current and voltage data in real time. The specific formula is:

[0074] ;

[0075] in, is the active power, is the apparent power, active power Calculated by the phase difference between voltage and current, the formula is:

[0076] ;

[0077] in, is the voltage, is the current, is the phase angle between voltage and current, apparent power Calculated as:

[0078] ;

[0079] The power factor is an indicator that reflects the effective utilization of electric energy and can help determine the load status and power loss of the power system. The acquisition frequency is also at least once per second to ensure real-time calculation and monitoring of the power factor.

[0080] The data acquisition module uses voltage and current sensors and corresponding calculation methods to collect and calculate power quality parameters such as voltage, current, frequency, and power factor in real time. This module ensures the real-time and accuracy of power quality, providing accurate data support for subsequent data processing, quality assessment, and monitoring. It also possesses high calculation accuracy, ensuring comprehensive monitoring of power quality in photovoltaic power plants.

[0081] The data processing module includes:

[0082] Data reception and preprocessing: The data processing module receives the power quality parameters collected by the data acquisition module and performs preprocessing. The preprocessing includes filling missing values, removing noise and standardizing data to ensure the accuracy of subsequent analysis.

[0083] Multivariate analysis: The data processing module uses multivariate analysis methods to analyze the processed power quality parameters, including correlation analysis and anomaly detection;

[0084] Power quality problem identification: The data processing module identifies anomalies in power quality based on the results of multivariate analysis;

[0085] Problem report generation: The data processing module generates a power quality problem report based on the identified anomalies. The report includes specific information about the anomaly data (such as the time when the anomaly occurred, the anomaly type, the anomaly value, etc.).

[0086] Correlation analysis in multivariate analysis includes:

[0087] Correlation analysis: Based on the data characteristics of power quality parameters, the Pearson correlation coefficient is selected as the correlation analysis method to measure the linear relationship between power quality parameters. The calculation formula of the Pearson correlation coefficient is:

[0088] ;

[0089] in, is the data point, is the mean of the data, is the total number of samples;

[0090] Calculate the correlation matrix: Use the Pearson correlation coefficient to calculate the correlation matrix between different power quality parameters. The correlation matrix contains the correlation coefficients between all two power quality parameters. Each element in the matrix represents the degree of correlation between two power quality parameters. The result is a symmetric matrix in which the diagonal elements are 1 (indicating perfect self-correlation).

[0091] Identifying significant correlations: After calculating the correlation matrix, the data processing module identifies significantly correlated power quality parameter pairs based on a preset correlation threshold. The steps include:

[0092] Sort the correlation coefficient values ​​in the matrix and find out the power quality parameter pairs with strong correlation.

[0093] A threshold (for example, a correlation coefficient greater than 0.8 or less than -0.8) is set to screen out highly correlated power quality parameter pairs. These highly correlated parameter pairs may have mutual influence or causal relationship.

[0094] Analyze the impact of correlation on power quality: Based on the results of correlation analysis, analyze the impact of highly correlated power quality parameters. For example, if the correlation coefficient between voltage and current is high, it can be inferred that current fluctuations may be caused by voltage changes, or vice versa. This analysis helps with subsequent fault warnings and optimization of adaptive control strategies.

[0095] Anomaly detection in multivariate analysis includes:

[0096] Select anomaly detection method: The data processing module uses a combination of the Z-score method and the isolation forest algorithm for anomaly detection. The Z-score method is used to quickly identify data points that deviate significantly from normal values, while the isolation forest algorithm is used to detect potential abnormal patterns in the data.

[0097] Z-score anomaly detection: The data processing module applies the Z-score method to each power quality parameter and calculates the parameter's Z-score value. For each parameter, if the absolute value of its Z-score exceeds a preset threshold (for example, 3), the power quality parameter is considered to have a significant deviation at that time point and is marked as potentially abnormal data.

[0098] Z-score method: By calculating the Z-score value of each power quality parameter, it is determined whether the parameter is abnormal. The Z-score calculation formula is:

[0099] ;

[0100] in, is the current sample data, is the mean value of the parameter, is the standard deviation of the parameter. If the absolute value of the Z-score exceeds a preset threshold (e.g., 3), the data point is considered an anomaly.

[0101] Isolation Forest Anomaly Detection: After initial screening using the Z-score method, the data processing module further applies the Isolation Forest algorithm to perform multidimensional anomaly detection on power quality parameters. This algorithm gradually isolates data samples by constructing multiple decision trees and assesses whether a data point is anomaly based on the degree of isolation. If a power quality parameter has a high degree of isolation in multidimensional space, the point is marked as an anomaly.

[0102] Fusion detection results: The data processing module fuses the detection results of the Z-score method and the isolation forest algorithm, giving priority to abnormal data detected by the isolation forest. The specific steps include:

[0103] If both the Z-score and isolation forest algorithms mark a certain power quality parameter as abnormal, then the abnormality is confirmed;

[0104] If only the Z-score method marks an anomaly, but the isolation forest method does not find an anomaly, the authenticity of the anomaly will be reconfirmed by the isolation forest algorithm to reduce false positives;

[0105] If neither method marks the data point as abnormal, the data point is considered normal.

[0106] Abnormal data marking: The data processing module marks the data points that have undergone abnormality detection.

[0107] The power quality assessment module includes:

[0108] Preliminary assessment based on standard power quality indicators: The power quality assessment module performs a preliminary assessment of the voltage, current, frequency, and power factor of the PV power plant based on standard power quality indicators (such as IEC 61000 and IEEE 519), generating a preliminary power quality score. The scoring formula is as follows:

[0109] ;

[0110] in, For the preliminary power quality score, Score voltage compliance, Score the current compliance, Score frequency compliance, Score power factor compliance, is the weight coefficient of each indicator (according to the weight specified in the power quality standard);

[0111] Score correction: The power quality assessment module combines the abnormality identification results provided by the data processing module, analyzes the impact of abnormal areas on power quality, adjusts the scoring formula, and generates an adjusted power quality score. If power quality abnormalities are detected (such as voltage, current, or frequency deviations from the standard), the score formula is corrected to:

[0112] ;

[0113] in, is the adjusted power quality score, The penalty value for each detected anomaly, is the number of abnormal detection results;

[0114] Determining the health status: The power quality assessment module evaluates the power quality health status of the PV power plant based on the adjusted score and generates a health status report. The report includes the power quality compliance assessment and its possible impact. The score range of the health status report is as follows:

[0115] when When it is >80, it means the power quality is good and the photovoltaic power station is operating normally;

[0116] When 60≤ When ≤80, it indicates that there are certain problems with the power quality and further inspection may be required;

[0117] when When it is less than 60, it indicates that the power quality is poor and the power station may face the risk of failure or efficiency loss.

[0118] The fault warning module includes:

[0119] Analyze power quality anomalies: The fault warning module analyzes whether the power quality score is lower than the preset threshold based on the output of the power quality assessment module ( <60);

[0120] Triggering the alarm mechanism: If the score is higher than the threshold, the PV power station is in normal working condition and no alarm needs to be triggered. If the power quality score is lower than the threshold, the fault warning module automatically triggers the alarm mechanism;

[0121] Generate early warning report: After the fault early warning module triggers the alarm mechanism, it generates a detailed early warning report. The report content includes:

[0122] Abnormal type (such as voltage, current, frequency deviation, etc.);

[0123] The time and area where the anomaly occurred;

[0124] Analysis of the impact on power quality and possible fault types (such as equipment damage caused by excessively high or low voltage, efficiency loss caused by frequency fluctuations, etc.);

[0125] Power quality ratings and specific factors that influence the ratings.

[0126] Sending warning information: The fault warning module sends the generated warning report to the relevant management personnel or maintenance team through the management system, ensuring that relevant personnel can obtain power quality abnormality information in a timely manner and take corresponding measures. The report can be sent through email, SMS, phone notification, system interface pop-up window, etc.

[0127] The adaptive control module includes:

[0128] Receiving power quality scores and warning information: The adaptive control module receives the power quality scores from the power quality assessment module and warning information from the fault warning module. The power quality scores reflect the current power quality status of the PV power station, while the warning information indicates whether there are any abnormalities in the power quality.

[0129] Analyzing power quality scores and warning information: The adaptive control module analyzes the current operating status of the PV power station based on the received power quality scores and warning information. If the power quality score is lower than the preset normal threshold, or the fault warning module issues an abnormal alarm, the adaptive control module identifies it as a power quality problem and requires adjustment.

[0130] If the power quality score is normal (above the threshold), the existing operation strategy is maintained;

[0131] If the power quality score is abnormal (below the threshold), the next adjustment stage will be entered.

[0132] Determine adjustment targets: Based on the analysis results, the adaptive control module determines the optimization targets. For example, when the power quality score is below the threshold, the following parameters may need to be adjusted:

[0133] The operating mode of the power plant inverter (e.g. switching to a more efficient operating mode);

[0134] Power plant load allocation strategies (e.g. adjusting power generation capacity in different areas);

[0135] Power plant grid connection strategy (e.g., adjusting grid voltage and frequency to match grid requirements).

[0136] Adjust the operation strategy of the photovoltaic power station: Based on the analysis results and optimization goals, the adaptive control module adjusts the operation strategy of the photovoltaic power station. The adjustment process includes:

[0137] Adjust inverter output: Based on the power quality score, adjust the inverter's output power, frequency, and voltage to optimize power quality and reduce fluctuations;

[0138] Adjusting grid-connected voltage and frequency: According to the operation requirements of the power grid, adjust the voltage and frequency of the power output of the power station to make it more consistent with the grid stability requirements and reduce power quality issues;

[0139] Optimize load distribution: Optimize load configuration for each component and area within the photovoltaic power station to avoid power quality problems caused by unbalanced loads.

[0140] Adjusting the energy storage system: If the power station is equipped with an energy storage system, the charging and discharging strategies of the energy storage equipment can be adjusted based on the power quality analysis results to balance power supply and load demand and stabilize power quality.

[0141] Operation strategy optimization: The adaptive control module regularly evaluates the effectiveness of the adjusted operation strategy and optimizes it. The evaluation process includes:

[0142] Compare the power quality scores before and after adjustment to verify the optimization effect;

[0143] Adjust strategy parameters such as inverter power, grid voltage, frequency, etc. based on the evaluation results to achieve optimal power quality.

[0144] The visualization module includes:

[0145] Visualization display: The visualization module displays power quality assessment results, warning information and operation strategies to users through a visual interface, including:

[0146] Power quality score chart: displays the real-time trend of power quality scores, which can be represented by color coding (e.g. green for normal, yellow for minor anomalies, and red for major anomalies);

[0147] Power quality indicator chart: Displays the real-time values ​​of key power quality parameters such as voltage, current, frequency, and power factor, and uses line charts or bar charts to show their fluctuations;

[0148] Fault warning information: Displays abnormal information in real time through warning icons or warning boxes, including abnormal changes in voltage, frequency or power factor. Warning information can distinguish different levels of abnormalities through color identification;

[0149] Adaptive control adjustment diagram: displays the adjustment strategy of the adaptive control module and shows the changes in control parameters such as inverter adjustment, load distribution, and grid voltage, so that users can understand the specific operations of power quality optimization;

[0150] Interactive Operation Interface: The visualization module provides user interaction functions, allowing users to view more detailed power quality parameters by clicking, dragging, and zooming, including:

[0151] Time selection function: users can choose to view the power quality assessment results or warning information within a certain time period and compare the data;

[0152] Dynamic chart adjustment function: Users can customize chart settings, choose to display different power quality parameters or adjust the display method (such as switching to a bar chart, line chart or pie chart).

[0153] Alarm confirmation function: When a fault warning message is triggered, the user can click the warning icon to confirm and view detailed information to determine the cause of the fault and the corresponding countermeasures.

[0154] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0155] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Photovoltaic power station power quality real-time monitoring system, characterized by: It includes data acquisition module, data processing module, power quality assessment module, fault warning module, adaptive control module and visualization module, among which; The data acquisition module is used to collect power quality parameters of the photovoltaic power station in real time, and the power quality parameters include voltage, current, frequency and power factor; The data processing module receives the power quality parameters collected by the data acquisition module, and processes and analyzes the power quality parameters using a multivariate analysis method, identifies anomalies in the power quality, and generates a power quality problem report; The data processing module specifically includes: Data reception and preprocessing: The data processing module receives the power quality parameters collected by the data acquisition module and performs preprocessing, which includes filling missing values, removing noise and standardizing data. Multivariate analysis: The data processing module uses multivariate analysis methods to analyze the processed power quality parameters, including correlation analysis and anomaly detection; Power quality problem identification: The data processing module identifies anomalies in power quality based on the results of multivariate analysis; Problem report generation: the data processing module generates a power quality problem report based on the identified anomalies; The anomaly detection in the multivariate analysis includes: Select anomaly detection method: The data processing module uses a combination of the Z-score method and the isolation forest algorithm to perform anomaly detection; Z-score anomaly detection: The data processing module applies the Z-score method to process each power quality parameter and calculates the parameter's Z-score value. For each parameter, if the absolute value of its Z-score value exceeds a preset threshold, the power quality parameter is considered to have deviated at that time point and is marked as potentially abnormal data. Isolation forest anomaly detection: After the initial screening by the Z-score method, the data processing module further applies the isolation forest algorithm to perform multi-dimensional anomaly detection on the power quality parameters; Fusion detection results: The data processing module fuses the detection results of the Z-score method and the isolation forest algorithm, giving priority to abnormal data detected by the isolation forest; Abnormal data marking: the data processing module marks the data points that have undergone abnormality detection; The power quality assessment module combines the analysis results of the data processing module and evaluates the power quality of the photovoltaic power station based on standard power quality indicators, generates a power quality score, and derives the health status of the photovoltaic power station based on the score; The fault warning module is connected to the power quality assessment module. When the power quality assessment module detects abnormal power quality, it automatically triggers an alarm mechanism and sends a warning message to the administrator or maintenance personnel; The adaptive control module is connected to the power quality assessment module and the fault warning module, and intelligently adjusts the operation strategy of the photovoltaic power station according to the power quality score and warning information to optimize the power quality; The visualization module is used to present power quality assessment results, warning information and operation strategies to users through a visualization interface, so that users can monitor and manage the status of the power station in real time.

2. The photovoltaic power station power quality real-time monitoring system according to claim 1, characterized in that: The data acquisition module includes: Voltage data acquisition: The data acquisition module collects voltage data in real time through voltage sensors installed in the photovoltaic power station; Current data acquisition: The data acquisition module collects current data in real time through the current sensor; Frequency calculation: The data acquisition module calculates the frequency of the power system by analyzing the collected voltage signals; Power factor calculation: The data acquisition module calculates the power factor by collecting current and voltage data in real time.

3. The photovoltaic power station power quality real-time monitoring system according to claim 1, characterized in that: The correlation analysis in the multivariate analysis includes: Correlation analysis: Based on the data characteristics of power quality parameters, the Pearson correlation coefficient is selected as the correlation analysis method to measure the linear relationship between power quality parameters; Calculate the correlation matrix: Calculate the correlation matrix between different power quality parameters through the Pearson correlation coefficient; Identifying significant correlations: After calculating the correlation matrix, the data processing module identifies significantly correlated power quality parameter pairs based on a preset correlation threshold. Analyze the impact of correlation on power quality: Based on the results of correlation analysis, analyze the impact relationship between power quality parameters with strong correlation.

4. The photovoltaic power station power quality real-time monitoring system according to claim 1, characterized in that: The power quality assessment module includes: Preliminary assessment based on standard power quality indicators: The power quality assessment module conducts a preliminary assessment of the voltage, current, frequency, and power factor of the PV power station based on standard power quality indicators, generating a preliminary power quality score. Score correction: The power quality assessment module combines the abnormality identification results provided by the data processing module, analyzes the impact of abnormal areas on power quality, adjusts the scoring formula, and generates an adjusted power quality score; Determine the health status: The power quality assessment module assesses the power quality health status of the PV power station based on the adjusted score and generates a health status report.

5. The photovoltaic power station power quality real-time monitoring system according to claim 4, characterized in that: The fault warning module includes: Analyze power quality anomalies: The fault warning module analyzes the power quality score based on the output of the power quality assessment module to see if it is lower than the preset threshold; Triggering the alarm mechanism: If the power quality score is lower than the threshold, the fault warning module automatically triggers the alarm mechanism; Generate early warning report: After the fault early warning module triggers the alarm mechanism, it generates an early warning report; Send warning information: The fault warning module will send the generated warning report to the corresponding management personnel or maintenance team through the management system.

6. The photovoltaic power station power quality real-time monitoring system according to claim 5, characterized in that: The adaptive control module includes: Receiving power quality scores and warning information: The adaptive control module receives the power quality scores from the power quality assessment module and the warning information from the fault warning module; Analyzing power quality scores and warning information: The adaptive control module analyzes the current operating status of the PV power station based on the received power quality scores and warning information; Determine adjustment targets: Based on the analysis results, the adaptive control module determines the optimization target; Adjusting the operation strategy of the photovoltaic power station: Based on the analysis results and optimization goals, the adaptive control module adjusts the operation strategy of the photovoltaic power station; Operation strategy optimization: The adaptive control module regularly evaluates the effectiveness of the adjusted operation strategy and optimizes it.

7. The photovoltaic power station power quality real-time monitoring system according to claim 1, characterized in that: The visualization module includes: Visual display: The visualization module displays power quality assessment results, warning information and operation strategies to users through a visual interface; Interactive operation interface: The visualization module provides user interaction functions, allowing users to view power quality parameters by clicking, dragging and zooming.

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