Electric energy quality comprehensive treatment control method and device based on data analysis

Abstract

The invention relates to the technical field of power system power quality control, and discloses a power quality comprehensive treatment control method and device based on data analysis, and the device comprises a multi-source data collection module, an edge calculation unit, a cloud analysis platform, a comprehensive treatment execution module, and a human-computer interaction interface. By integrating a multi-source data acquisition module, an edge computing unit, a cloud analysis platform, a comprehensive treatment execution module and a human-computer interaction interface, comprehensive monitoring, accurate prediction and intelligent control of electric energy quality are realized, and by acquiring power grid data in real time and establishing an analysis model, an optimal control strategy is dynamically generated, so that the treatment efficiency and stability are improved, and the power quality is improved. Through multi-source data acquisition, machine learning prediction and a multi-target optimization algorithm, harmonic suppression, reactive compensation and a three-phase balance strategy are dynamically generated, intelligent cooperative treatment of electric energy quality is realized, the stability of a power grid is remarkably improved, and the method is suitable for complex scenes such as new energy access and high-precision manufacturing industry.

Description

Technical Field

[0001] The present invention relates to the technical field of power system power quality control, and particularly to a comprehensive power quality control method and device based on data analysis. Background Art

[0002] The power system is an electric energy production and consumption system composed of links such as power generation, transformation, transmission, distribution, and power consumption. Its function is to convert the primary energy in nature into electric energy through power generation power devices (mainly including boilers, steam turbines, generators, and auxiliary production systems of power plants), and then supply the electric energy to each load center through the transmission and transformation systems and the distribution system. Since most power source points and load centers are in different regions and cannot be stored in large quantities, power production must always maintain a balance with consumption. Therefore, the centralized development and decentralized use of electric energy, as well as the continuous supply of electric energy and the random changes of loads, restrict the structure and operation of the power system. With the wide application of new energy power generation, nonlinear loads, and power electronic devices, power quality problems in the power grid have become increasingly prominent, resulting in equipment losses, reduced energy efficiency, and even system failures.

[0003] Traditional power quality control methods (such as static reactive power compensation, active filtering, etc.) mostly rely on fixed thresholds or single-parameter control, lacking adaptability to complex working conditions and global optimization capabilities. In addition, existing devices usually operate independently and are difficult to achieve multi-parameter collaborative control and dynamic response.

[0004] Therefore, we propose a comprehensive power quality control method and device based on data analysis. Summary of the Invention

[0005] The present invention mainly solves the technical problems existing in the above-mentioned prior art, and provides a comprehensive power quality control method and device based on data analysis.

[0006] To achieve the above object, the present invention adopts the following technical solutions. A comprehensive power quality control device based on data analysis includes a multi-source data acquisition module, an edge computing unit, a cloud analysis platform, a comprehensive control execution module, and a human-machine interaction interface. The multi-source data acquisition module includes high-precision sensors and smart meters, and supports high-speed synchronous acquisition of electrical parameters of each node in the power grid.

[0007] Preferably, the edge computing unit includes an FPGA and an embedded processor, and is used for data preprocessing, feature extraction, and local real-time analysis.

[0008] Preferably, the cloud analysis platform machine includes a learning model and a big data storage system, and realizes historical data mining and global optimization strategy generation.

[0009] Preferably, the comprehensive treatment execution module includes an active power filter (APF), a static var generator (SVG), an energy storage device, and an intelligent switch, which receive control instructions and perform compensation operations.

[0010] Preferably, the human-machine interaction interface includes a touch screen, an audible and visual alarm, and a control interface, which are used to provide visual monitoring of power quality, alarm functions, and manual intervention of strategy parameters.

[0011] The power quality comprehensive treatment control method based on data analysis includes the power quality comprehensive treatment control device based on data analysis as described above, and specifically includes the following steps:

[0012] The first step: Data collection and preprocessing: Real-time collect data such as voltage, current, frequency, harmonic components, three-phase unbalance degree, and load characteristics in the power grid, and generate a standardized data set through filtering, denoising, and normalization processing;

[0013] The second step: Power quality feature extraction: Use fast Fourier transform (FFT), wavelet analysis, and principal component analysis (PCA) to extract key features and construct a multi-dimensional power quality evaluation index;

[0014] The third step: Data analysis and prediction model: Establish a power quality dynamic prediction model based on long short-term memory network (LSTM) and random forest algorithm to predict the harmonic distribution, voltage fluctuation trend, and load change in the future period;

[0015] The fourth step: Generation of dynamic control strategy: According to the prediction results and real-time data, use a multi-objective optimization algorithm to calculate the optimal treatment parameters, including the compensation current of the active power filter (APF), the reactive power output of the static var generator (SVG), and the charge and discharge strategy of the energy storage system, to achieve coordinated control of multiple devices;

[0016] The fifth step: Closed-loop feedback and adaptive adjustment: By real-time monitoring the treatment effect, update the model parameters in combination with the reinforcement learning algorithm, and dynamically adjust the control strategy to cope with the changes in the power grid operating conditions.

[0017] Preferably, the multi-dimensional power quality evaluation index in the second step includes the total harmonic distortion THD, the voltage sag depth, and the flicker severity.

[0018] Preferably, the multi-objective optimization algorithm in the fourth step is specifically the particle swarm optimization PSO.

[0019] Beneficial effects

[0020] The present invention provides a power quality comprehensive treatment control method and device based on data analysis. It has the following beneficial effects:

[0021] 1. The power quality comprehensive management and control method and device based on data analysis integrate a multi-source data acquisition module, an edge computing unit, a cloud analysis platform, a comprehensive management execution module, and a human-machine interface, achieving comprehensive monitoring, accurate prediction, and intelligent control of power quality. By collecting grid data in real time and establishing an analysis model, it dynamically generates optimal control strategies, improves governance efficiency and stability. Through multi-source data acquisition, machine learning prediction, and multi-objective optimization algorithms, it dynamically generates harmonic suppression, reactive power compensation, and three-phase balance strategies, realizing intelligent collaborative governance of power quality, significantly enhancing grid stability, and being applicable to complex scenarios such as new energy access and high-precision manufacturing.

[0022] 2. The power quality comprehensive management and control method and device based on data analysis set up a multi-source data acquisition module, which can obtain key electrical parameters in the power grid in real time, providing a data basis for subsequent analysis.

[0023] 3. The power quality comprehensive management and control method and device based on data analysis set up an edge computing unit, achieving rapid preprocessing and feature extraction of data, effectively reducing the burden on the cloud and improving the response speed.

[0024] 4. The power quality comprehensive management and control method and device based on data analysis set up a cloud analysis platform. Relying on a powerful learning model and a large data storage system, the cloud analysis platform deeply explores the value of historical data, providing strong support for the formulation of global optimization strategies.

[0025] 5. The power quality comprehensive management and control method and device based on data analysis set up a comprehensive management execution module and a human-machine interface. The comprehensive management execution module integrates various power quality management devices, can receive and accurately execute control instructions from the cloud or the edge computing unit, and realizes precise compensation and adjustment. The human-machine interface provides an intuitive and convenient monitoring and operation platform for users, making the power quality management process more transparent and controllable. Description of the Drawings

[0026] Figure 1 It is the system architecture diagram of the device of the present invention;

[0027] Figure 2 It is the method flow chart of the present invention. Detailed Embodiments

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained based on the provided drawings.

[0029] The structures, ratios, sizes, etc. illustrated in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0030] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0031] In the description of the embodiments of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "inner", "outer", "side", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of this invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0032] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific situations.

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0034] Embodiment 1: A comprehensive power quality control device based on data analysis, such as Figure 1 shown, includes a multi-source data acquisition module, an edge computing unit, a cloud analysis platform, a comprehensive governance execution module, and a human-machine interface. The multi-source data acquisition module includes high-precision sensors and smart meters, supporting high-speed synchronous acquisition of electrical parameters at each node of the power grid. The edge computing unit includes an FPGA and an embedded processor, used for data preprocessing, feature extraction, and local real-time analysis. The cloud analysis platform machine includes a learning model and a big data storage system, realizing historical data mining and global optimization strategy generation. The comprehensive governance execution module includes an active power filter (APF), a static var generator (SVG), an energy storage device, and intelligent switches, receiving control instructions and performing compensation operations. The human-machine interface includes a touch screen, an audible and visual alarm, and a control interface, used for providing visual monitoring of power quality, alarm functions, and manual intervention of strategy parameters. By integrating the multi-source data acquisition module, the edge computing unit, the cloud analysis platform, the comprehensive governance execution module, and the human-machine interface, comprehensive monitoring, accurate prediction, and intelligent control of power quality are achieved. By collecting power grid data in real time and establishing an analysis model, optimal control strategies are dynamically generated, improving governance efficiency and stability. Through multi-source data acquisition, machine learning prediction, and multi-objective optimization algorithms, harmonic suppression, reactive power compensation, and three-phase balance strategies are dynamically generated, realizing intelligent collaborative governance of power quality, significantly improving power grid stability, and being applicable to complex scenarios such as new energy access and high-precision manufacturing.

[0035] Embodiment 2: On the basis of Embodiment 1, such as Figure 1 shown, the multi-source data acquisition module includes high-precision sensors and smart meters, supporting high-speed synchronous acquisition of electrical parameters at each node of the power grid. The edge computing unit includes an FPGA and an embedded processor, used for data preprocessing, feature extraction, and local real-time analysis. The cloud analysis platform machine includes a learning model and a big data storage system, realizing historical data mining and global optimization strategy generation. The comprehensive governance execution module includes an active power filter (APF), a static var generator (SVG), an energy storage device, and intelligent switches, receiving control instructions and performing compensation operations. The human-machine interface includes a touch screen, an audible and visual alarm, and a control interface, used for providing visual monitoring of power quality, alarm functions, and manual intervention of strategy parameters. By setting up the multi-source data acquisition module, the multi-source data acquisition module can obtain key electrical parameters in the power grid in real time, providing a data basis for subsequent analysis.

[0036] Embodiment 3: On the basis of Embodiment 1 and Embodiment 2, such as Figure 1As shown, the edge computing unit includes an FPGA and an embedded processor, which are used for data preprocessing, feature extraction, and local real-time analysis. The cloud analysis platform machine includes a learning model and a big data storage system to achieve historical data mining and global optimization strategy generation. The comprehensive governance execution module includes an active power filter (APF), a static var generator (SVG), an energy storage device, and intelligent switches, which receive control instructions and perform compensation operations. The human-machine interface includes a touch screen, an audible and visual alarm, and a control interface, which are used to provide visual monitoring of power quality, alarm functions, and manual intervention for strategy parameters. By setting up the edge computing unit, rapid data preprocessing and feature extraction are achieved, effectively reducing the burden on the cloud and improving the response speed.

[0037] Embodiment 4: Based on Embodiments 1, 2, and 3, as Figure 1 shown, the cloud analysis platform machine includes a learning model and a big data storage system to achieve historical data mining and global optimization strategy generation. The comprehensive governance execution module includes an active power filter (APF), a static var generator (SVG), an energy storage device, and intelligent switches, which receive control instructions and perform compensation operations. The human-machine interface includes a touch screen, an audible and visual alarm, and a control interface, which are used to provide visual monitoring of power quality, alarm functions, and manual intervention for strategy parameters. By setting up the cloud analysis platform, relying on the powerful learning model and big data storage system, the cloud analysis platform deeply explores the value of historical data and provides strong support for the formulation of global optimization strategies.

[0038] Embodiment 5: A power quality comprehensive governance control method based on data analysis, including the above-mentioned power quality comprehensive governance control device based on data analysis, as Figure 2As shown in the figure, it specifically includes the following steps: The first step: Data collection and preprocessing: Real-time collect data on voltage, current, frequency, harmonic components, three-phase unbalance degree, and load characteristics in the power grid, and generate a standardized data set through filtering, denoising, and normalization processing; The second step: Power quality feature extraction: Use the Fast Fourier Transform (FFT), wavelet analysis, and Principal Component Analysis (PCA) to extract key features and construct a multi-dimensional power quality evaluation index; The third step: Data analysis and prediction model: Based on the Long Short-Term Memory Network (LSTM) and the Random Forest algorithm, establish a dynamic power quality prediction model to predict the harmonic distribution, voltage fluctuation trend, and load changes in the future time period; The fourth step: Generation of dynamic control strategies: According to the prediction results and real-time data, use a multi-objective optimization algorithm to calculate the optimal governance parameters, including the compensation current of the Active Power Filter (APF), the reactive power output of the Static Var Generator (SVG), and the charge and discharge strategies of the energy storage system, to achieve coordinated control of multiple devices; The fifth step: Closed-loop feedback and adaptive adjustment: Through real-time monitoring of the governance effect, combine the reinforcement learning algorithm to update the model parameters and dynamically adjust the control strategy to cope with the changes in the power grid operating conditions. The multi-dimensional power quality evaluation index in the second step includes the Total Harmonic Distortion (THD), the depth of voltage sag, and the severity of flicker. The multi-objective optimization algorithm in the fourth step is specifically the Particle Swarm Optimization (PSO). By setting up a comprehensive governance execution module and a human-machine interface, the comprehensive governance execution module integrates a variety of power quality governance devices, can receive and accurately execute control instructions from the cloud or edge computing unit, and realizes precise compensation and adjustment. The human-machine interface provides users with an intuitive and convenient monitoring and operation platform, making the power quality governance process more transparent and controllable.

[0039] Working principle of the present invention: First, the high-precision sensors and smart meters in the multi-source data acquisition module will synchronously collect the electrical parameters of each node in the power grid in real time. These parameters include but are not limited to key data such as voltage, current, and frequency. Subsequently, this data is transmitted to the edge computing unit, which uses FPGA and embedded processors to quickly preprocess, extract features, and perform local real-time analysis on the data. The preprocessing process may include steps such as filtering and denoising to ensure the accuracy and reliability of the data. Feature extraction uses technologies such as fast Fourier transform (FFT), wavelet analysis, and principal component analysis (PCA) to extract valuable feature information for power quality assessment from the original data. After being processed by the edge computing unit, the data is uploaded to the cloud analysis platform. Relying on its powerful learning model and big data storage system, the cloud analysis platform deeply mines the historical data and constructs a dynamic power quality prediction model. This model is based on long short-term memory network (LSTM) and random forest algorithm, and can predict the harmonic distribution, voltage fluctuation trend, and load change in the future period, providing a scientific basis for formulating subsequent governance strategies. After obtaining the prediction results, the system enters the dynamic control strategy generation stage. According to the prediction results and real-time data, multi-objective optimization algorithms such as particle swarm optimization (PSO) are used to calculate the optimal governance parameters, including the compensation current of the active power filter (APF), the reactive power output of the static var generator (SVG), and the charge and discharge strategy of the energy storage system. These parameters are sent to the comprehensive governance execution module, which integrates a variety of power quality governance devices and can receive and accurately execute the control instructions from the cloud or the edge computing unit to achieve precise compensation and regulation. Finally, the system continuously updates the model parameters by combining the reinforcement learning algorithm through real-time monitoring of the governance effect, realizing closed-loop feedback and adaptive adjustment. This process ensures that the governance strategy can be dynamically adjusted with the change of the power grid working conditions, thereby maintaining the stability and optimization of the power quality. By integrating modules such as multi-source data acquisition, edge computing, cloud analysis, comprehensive governance execution, and human-computer interaction, the comprehensive monitoring, precise prediction, and intelligent control of power quality are realized. This process not only improves the governance efficiency and stability, but also significantly enhances the adaptability and robustness of the power grid, providing strong support for power quality management in complex scenarios such as new energy access and high-precision manufacturing.

[0040] The above has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive power quality control device based on data analysis, characterized in that: It includes a multi-source data acquisition module, an edge computing unit, a cloud analysis platform, a comprehensive governance execution module and a human-computer interaction interface. The multi-source data acquisition module includes high-precision sensors and smart meters.

2. The power quality comprehensive management and control device based on data analysis according to claim 1 is characterized in that: The edge computing unit includes an FPGA and an embedded processor.

3. The power quality comprehensive management and control device based on data analysis according to claim 1 is characterized in that: The cloud analysis platform machine includes a learning model and a big data storage system.

4. The power quality comprehensive management and control device based on data analysis according to claim 1 is characterized in that: The comprehensive management execution module includes an active power filter (APF), a static VAR generator (SVG), an energy storage device and an intelligent switch.

5. The power quality comprehensive management and control device based on data analysis according to claim 1 is characterized in that: The human-computer interaction interface includes a touch screen, an audible and visual alarm, and a control interface.

6. A comprehensive power quality control method based on data analysis, characterized in that: The device for comprehensive power quality management and control based on data analysis according to any one of claims 1 to 5 specifically comprises the following steps: Step 1: Data collection and preprocessing: Real-time data collection of voltage, current, frequency, harmonic components, three-phase imbalance and load characteristics in the power grid, and generation of standardized data sets through filtering, denoising and normalization. Step 2: Power quality feature extraction: Use fast Fourier transform (FFT), wavelet analysis and principal component analysis (PCA) to extract key features and construct multidimensional power quality evaluation indicators; Step 3: Data analysis and prediction model: Based on the long short-term memory network (LSTM) and random forest algorithm, a dynamic prediction model for power quality is established to predict the harmonic distribution, voltage fluctuation trend and load change in the future period; Step 4: Dynamic control strategy generation: Based on the prediction results and real-time data, a multi-objective optimization algorithm is used to calculate the optimal management parameters, including the compensation current of the active power filter (APF), the reactive output of the static VAR generator (SVG), and the charging and discharging strategy of the energy storage system, to achieve coordinated control of multiple devices; Step 5: Closed-loop feedback and adaptive adjustment: By real-time monitoring of governance effects and combining reinforcement learning algorithms to update model parameters, control strategies are dynamically adjusted to cope with changes in grid conditions.

7. The method for comprehensive power quality management and control based on data analysis according to claim 6 is characterized in that: The multi-dimensional power quality evaluation indicators in the second step include harmonic distortion rate THD, voltage sag depth and flicker severity.

8. The method for comprehensive power quality management and control based on data analysis according to claim 6 is characterized in that: The multi-objective optimization algorithm in the fourth step is specifically particle swarm optimization PSO.

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