Intelligent power saver

Abstract

The invention discloses an intelligent power saver, and relates to the technical field of power saving equipment. The intelligent power saver comprises a multi-phase winding transformer, a protection system, a deep learning optimization control system, a modular APF system, a multi-dimensional data display system, a transmission system, an energy management system and an energy optimization scheduling system. The intelligent power saver adopts an advanced three-phase electromagnetic balance technology and a deep learning optimization control system, can effectively improve the quality of electric energy, realizes the functions of harmonic suppression, three-phase balance, voltage stabilization and the like, uses electric equipment to operate in a better electric energy environment, and reduces the energy consumption of the equipment, thereby achieving a remarkable power saving effect, and being suitable for popularization and application. The modular APF system can effectively restrain harmonic current in a power grid, the total harmonic distortion rate of the power grid is reduced, and the influence of zero-sequence current on the power grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power-saving equipment, in particular to an intelligent power saver. Background Art

[0002] Traditional energy-saving technologies are mostly limited to small-load lighting and power energy saving. Better-developed technologies include high-voltage frequency conversion technology for high-power air conditioners, fans, and water pumps, and large temperature difference cold storage technology. However, they also have limitations and local energy saving, and cannot meet the comprehensive energy-saving needs in complex power grid systems and diversified electricity usage scenarios.

[0003] In complex power grid systems, especially industrial power grids with a large number of nonlinear loads, a large number of harmonics are generated. Most traditional energy-saving technologies do not have a complete harmonic control function. For example, some simple inductive energy-saving devices, although they can limit the current through the reactor, are powerless against harmonic currents. The presence of harmonics will cause distortion of the grid voltage and current waveforms, increase equipment losses, reduce power quality, and may even cause equipment failure. Moreover, in complex power grid systems, three-phase imbalance is relatively common. Traditional energy-saving devices rarely have effective three-phase balance adjustment functions. For example, in some rural power grids or small factory power grids, the large number of single-phase loads connected will cause three-phase imbalance. Traditional energy-saving devices cannot effectively adjust this imbalance. The unbalanced three-phase voltage and current will increase transformer and line losses and reduce power transmission efficiency. In response to the shortcomings of the existing technology, the present invention provides an intelligent energy saver to solve the above problems. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an intelligent power saver that uses advanced three-phase electromagnetic balancing technology and deep learning optimization control system to effectively improve the quality of electric energy, realize functions such as harmonic control, three-phase balance, and voltage stability, and use electrical equipment to operate in a better power environment, reducing equipment energy consumption, thereby achieving significant power saving effects and helping users save electricity costs.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent power saver, which adopts a parallel and distributed hybrid architecture to connect to the power grid system, and the power saver includes:

[0006] Multi-phase winding transformer, consisting of multiple mutually insulated windings, which can be flexibly configured according to actual load requirements;

[0007] Protection system with multi-level fault detection and protection functions, including power quality monitoring unit;

[0008] Deep learning optimization control system, including data acquisition and data analysis modules, is used to collect grid operation data in real time and establish a grid operation model. It uses deep learning algorithms to learn and optimize the model and predict grid load changes and power quality fluctuations.

[0009] The modular APF system is connected in series to the main circuit of the energy saver and in parallel with the load to reduce the zero-sequence current generated between the neutral and phase lines on the grid side;

[0010] Multi-dimensional data display system, which displays various power quality parameters of the power grid in real time through high-precision instruments and sensors;

[0011] Transmission system, supporting multiple data transmission modes including wired and wireless;

[0012] Energy management system, real-time monitoring and analysis of users' electricity usage information;

[0013] The energy optimization and scheduling system supports the access and management of distributed energy, realizes the coordinated operation of distributed energy and traditional power grids, and predicts and optimizes the power generation of distributed energy.

[0014] Preferably, the modular APF system includes:

[0015] Core control module: Adopting a DSP+FPGA dual-core architecture, the DSP executes an adaptive harmonic detection algorithm, analyzes the grid current spectrum in real time based on fast Fourier transform, and dynamically generates compensation instructions; the FPGA is responsible for high-speed PWM signal generation and logic control, reducing the total harmonic distortion rate of the grid;

[0016] Power module: Using an H-bridge cascade topology, each H-bridge module is independently configured with an IGBT switch and DC-side capacitor, and multi-level output is achieved through phase-shift carrier modulation technology;

[0017] Monitoring and protection module: integrated voltage, current, and temperature sensors to collect grid parameters and module status in real time; with DC side voltage protection, AC side current protection, IGBT over-temperature protection, and support for fault recording function;

[0018] Communication interface module: provides a communication interface and reserves a cloud platform access interface.

[0019] Preferably, in the core control module of the modular APF system, the adaptive harmonic detection algorithm adopts a "fundamental wave extraction + harmonic separation" dual-loop control strategy, extracts the fundamental component through a low-pass filter, and subtracts it from the command current to obtain the harmonic compensation amount. At the same time, repetitive control and predictive control algorithms are introduced to suppress periodic interference and improve dynamic response performance.

[0020] Preferably, the power modules of the modular APF system realize inter-module synchronous control through an optical fiber ring network, and adopt carrier phase shift modulation technology to eliminate inter-module circulation; support parallel expansion, and realize multi-module power sharing through master-slave control mode.

[0021] Preferably, the power module of the modular APF system adopts a forced air cooling + heat sink composite heat dissipation solution, a built-in NTC thermistor monitors the IGBT junction temperature in real time, and optimizes the air duct so that the module temperature rise is ≤45K under rated load conditions.

[0022] Preferably, the protection system monitors the basic parameters of the power grid system, such as voltage, current and frequency, in real time, and has the function of quickly cutting off faulty equipment and switching to backup power supply.

[0023] Preferably, the deep learning optimization control system establishes a power grid operation model by real-time collection and analysis of power grid operation data, uses deep learning algorithms to learn and optimize the model, predicts power grid load changes and power quality fluctuations, and adjusts the power saver operation parameters in advance; the deep learning optimization control system supports adaptive control, and automatically selects the optimal control strategy according to different power-consuming equipment and power grid conditions; the deep learning optimization control system has remote monitoring and control functions, and users can view the power saver operation status and power quality data in real time through mobile terminal devices, and perform remote parameter setting and control operations.

[0024] Preferably, the transmission system adopts 5G, LoRa, Wi-Fi, Bluetooth, and Ethernet communication technologies.

[0025] Preferably, the multi-dimensional data display system displays various power quality parameters of the power grid, including voltage, current, frequency, power factor, harmonic content, and provides a graphical interface.

[0026] Preferably, the energy management system monitors and analyzes the user's electricity load, electricity usage time, and electricity cost information in real time.

[0027] The present invention discloses an intelligent energy saver, which has the following beneficial effects:

[0028] 1. This intelligent energy saver uses advanced three-phase electromagnetic balancing technology and deep learning optimization control system to effectively improve power quality, achieve harmonic control, three-phase balance, stable voltage and other functions, and use electrical equipment to operate in a better power environment, reducing equipment energy consumption, thereby achieving significant power saving effects and helping users save electricity costs.

[0029] 2. This intelligent power saver and modular APF system can effectively suppress harmonic currents in the power grid, reduce the total harmonic distortion rate of the power grid, and minimize the impact of zero-sequence current on the power grid. This can prevent problems such as neutral line current overload and transformer heating, purify the power quality of the power grid, and provide cleaner and more stable power to the entire power grid system, which is conducive to improving the operational efficiency and reliability of the power grid. Through the synergistic effect of the deep learning optimization control system and the power saver, it can achieve balanced regulation of three-phase voltage and current, solve the problem of three-phase imbalance, make the three-phase load of the power grid more balanced, reduce energy loss and equipment vibration caused by three-phase imbalance, and further improve power utilization efficiency and equipment operational stability.

[0030] 3. This intelligent power saver and deep learning optimization control system have remote monitoring and control functions. Users can view the power saver's operating status and power quality data anytime and anywhere through mobile phones, computers, and other terminal devices, and perform remote parameter settings and control operations. The energy management system and energy optimization and scheduling system can monitor and analyze users' electricity usage information in real time, formulate optimal power consumption plans and energy-saving strategies, support the access and management of distributed energy resources, achieve optimal energy allocation and efficient utilization, and provide users with intelligent and convenient energy management solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] 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.

[0032] Figure 1 This is a connection diagram between the intelligent energy saver of the present invention and the power grid system;

[0033] Figure 2 This is an outline diagram of the intelligent power saver of the present invention;

[0034] Figure 3 This is a wiring diagram of a multi-phase winding transformer according to the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0036] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0037] Example 1:

[0038] The embodiment of the present invention discloses an intelligent power saver. Figure 1-3 As shown, the energy saver is connected to the power grid system using a hybrid architecture of parallel and distributed. The energy saver includes:

[0039] Multi-phase winding transformers are composed of multiple mutually insulated windings and can be flexibly configured according to actual load requirements. Multi-phase winding transformers reduce the transformer's own hysteresis loss and eddy current loss, reduce heat generation, and improve voltage regulation and waveform distortion, providing a higher-quality power foundation for subsequent power-saving processing.

[0040] like Figure 3 As shown in the figure, the multi-phase winding transformer consists of multiple mutually insulated windings. The figure shows the winding layout of the three phases A, B, and C and the neutral line N. This structure can be flexibly configured according to actual load requirements to adapt to different power usage scenarios and load types.

[0041] Phases A, B, and C each have corresponding windings A, B, and C, which are connected together using a specific connection method. This connection method helps optimize electromagnetic balance, reduce the transformer's own hysteresis and eddy current losses, reduce heat generation, and improve power conversion efficiency, while also improving voltage regulation and waveform distortion.

[0042] The neutral line (N) and the three-phase windings (A, B, and C) together form the transformer's wiring structure. The neutral line plays an important role in achieving three-phase balance, stabilizing voltage, and handling zero-sequence current, helping to improve power quality and system stability.

[0043] The protection system features multi-level fault detection and protection capabilities, including a power quality monitoring unit. This system monitors basic grid parameters such as voltage, current, and frequency, as well as complex power quality issues such as harmonics, zero-sequence voltage, and negative-sequence current. Once a fault signal is detected, it uses intelligent algorithms to rapidly analyze and determine the fault type and severity, automatically selecting a protective action, such as rapidly disconnecting the faulty equipment or switching to a backup power source, to ensure the safe operation of the entire energy-saving system and its electrical equipment.

[0044] The deep learning optimization control system includes data acquisition and analysis modules for real-time grid operation data collection and building a grid operation model. This model is then trained and optimized using deep learning algorithms to predict grid load changes and power quality fluctuations. The deep learning optimization control system uses high-precision instruments and sensors to collect real-time grid operation data, including power quality parameters and equipment operating status, and builds a grid operation model based on this data. The model is then trained and optimized using deep learning algorithms, enabling accurate predictions of grid load changes and power quality fluctuations. Based on these predictions, the control system proactively adjusts the energy saver's operating parameters, such as dynamic voltage regulation, reactive power compensation, and harmonic suppression, to ensure the energy saver maintains optimal operation and achieves optimal energy savings. Furthermore, it supports adaptive control, automatically selecting the optimal control strategy based on different electrical devices and grid conditions. It also features remote monitoring and control capabilities, allowing users to view the energy saver's operating status and power quality data anytime, anywhere, and perform remote parameter settings and control operations via mobile phones, computers, and other devices.

[0045] The modular APF system is connected in series with the energy saver's main circuit and in parallel with the load to reduce zero-sequence current generated between the neutral and phase lines on the grid side. The system utilizes a core control module with a DSP+FPGA dual-core architecture. The DSP executes an adaptive harmonic detection algorithm, analyzes the grid current spectrum in real time based on Fast Fourier Transform, and dynamically generates compensation instructions. The FPGA is responsible for high-speed PWM signal generation and logic control, ensuring extremely fast harmonic compensation response time and rapidly reducing the total harmonic distortion (THD) of the grid, thereby effectively suppressing harmonic currents in the grid and improving power quality. The power module utilizes an H-bridge cascade topology, with each H-bridge module independently configured with IGBT switches and DC-side capacitors. Phase-shifted carrier modulation technology achieves multi-level output, supporting various capacity expansions to meet the harmonic compensation needs of grids of varying sizes. The monitoring and protection module integrates multiple sensors to collect real-time grid parameters and module status. It also features various protection functions, fault recording, and black box functionality to ensure stable and reliable system operation. The communication interface module provides multiple communication interfaces and a reserved cloud platform access interface, which can be seamlessly connected with SCADA systems, energy management systems, etc. to achieve remote parameter configuration, firmware upgrades and real-time data upload.

[0046] The multi-dimensional data display system uses high-precision instruments and sensors to display various power quality parameters of the power grid in real time. With the help of high-precision instruments and sensors, the multi-dimensional data display system measures and displays various key power quality parameters of the power grid in real time, such as voltage, current, frequency, power factor, harmonic content, etc., and presents them to users in an intuitive graphical interface, allowing users to clearly and conveniently understand the operating status of the power grid and the power quality.

[0047] The transmission system supports multiple data transmission methods, including wired and wireless. The transmission system uses multiple advanced communication technologies such as 5G, LoRa, Wi-Fi, Bluetooth, Ethernet, etc. to achieve fast and stable data transmission. It can adapt well to both wired and wireless transmission methods, ensuring that power quality data, energy saver operation status information, etc. can be transmitted to cloud servers, user terminal devices, and energy management systems in a timely and accurate manner, realizing efficient data sharing and remote monitoring.

[0048] The energy management system monitors and analyzes users' electricity usage in real time. This system monitors and analyzes users' electricity load, usage time, and cost in real time, providing a comprehensive understanding of their electricity habits and patterns. By combining different electricity pricing policies with users' electricity needs, it uses intelligent algorithms to develop optimal electricity plans and energy-saving strategies, guiding users to use electricity rationally and reduce electricity costs.

[0049] The energy optimization and dispatching system supports the access and management of distributed energy resources, enabling coordinated operation between distributed energy resources and traditional power grids. It also predicts and optimizes the power generation of distributed energy resources. The energy optimization and dispatching system supports the access and management of distributed energy resources, including solar and wind energy, enabling coordinated operation between distributed energy resources and traditional power grids. It accurately predicts and optimizes the power generation of distributed energy resources, enabling them to better cooperate with power grid operations, improve energy efficiency, and reduce reliance on traditional energy sources. It also features energy trading capabilities, allowing users to trade excess electricity in the power market, generating significant economic benefits.

[0050] The modular APF system includes:

[0051] Core control module: Adopting a DSP+FPGA dual-core architecture, the DSP executes an adaptive harmonic detection algorithm, analyzes the grid current spectrum in real time based on fast Fourier transform, and dynamically generates compensation instructions; the FPGA is responsible for high-speed PWM signal generation and logic control, reducing the total harmonic distortion rate of the grid;

[0052] Power module: Using an H-bridge cascade topology, each H-bridge module is independently configured with an IGBT switch and DC-side capacitor, and multi-level output is achieved through phase-shift carrier modulation technology;

[0053] Monitoring and protection module: integrated voltage, current, and temperature sensors to collect grid parameters and module status in real time; with DC side voltage protection, AC side current protection, IGBT over-temperature protection, and support for fault recording function;

[0054] Communication interface module: provides a communication interface and reserves a cloud platform access interface.

[0055] In the core control module of the modular APF system, the adaptive harmonic detection algorithm adopts a dual-loop control strategy of "fundamental wave extraction + harmonic separation". The fundamental component is extracted through a low-pass filter and the harmonic compensation is obtained by subtracting it from the command current. At the same time, repetitive control and predictive control algorithms are introduced to suppress periodic interference and improve dynamic response performance.

[0056] The power modules of the modular APF system achieve inter-module synchronous control through an optical fiber ring network, and adopt carrier phase shift modulation technology to eliminate inter-module circulating current; support parallel expansion, and achieve multi-module power sharing through master-slave control mode.

[0057] The power module of the modular APF system adopts a forced air cooling + heat sink composite heat dissipation solution, with a built-in NTC thermistor to monitor the IGBT junction temperature in real time and optimize the air duct to ensure that the module temperature rise is ≤45K under rated load conditions.

[0058] The protection system monitors the basic parameters of the power grid system, such as voltage, current and frequency, in real time. The protection system has the functions of quickly cutting off faulty equipment and switching to backup power supply.

[0059] The steps for using this intelligent energy saver are as follows:

[0060] Step S1, Installation Preparation: Select a smart energy saver of appropriate specifications and models based on actual power usage and grid conditions. Ensure the installation environment meets the device requirements, such as adequate space, good ventilation, and the absence of corrosive gases. Prepare the necessary tools and materials for installation, such as screwdrivers, wrenches, wires, and cables.

[0061] Step S2, grid connection: Connect the power saver to the grid using a hybrid parallel and distributed architecture, based on the power saver's access requirements and the grid system's wiring method. To do this, first disconnect the grid power supply, then connect the power saver's input to the grid and its output to the user's load. During the connection process, pay attention to the cable specifications, color coding, and connection accuracy to ensure a secure and reliable connection and avoid problems like loose connections and short circuits.

[0062] Step S3, Installation and Commissioning: After completing wiring, reconnect the power supply and enable each system module of the power saver. Use the power saver's human-machine interface or control software to perform initial device setup and parameter configuration. This includes setting basic parameters such as grid voltage, current, and frequency, and configuring the power saver's operating mode, control strategy, and communication parameters. During commissioning, observe the power saver's operating status indicators and data display to verify that each system is functioning properly, such as whether the protection system is detecting properly, whether the APF system is compensating properly, and whether the data display is accurate.

[0063] Step S4, Trial Operation and Monitoring: After initial commissioning, the trial operation phase begins. During trial operation, the power saver's operating parameters and the grid's power quality indicators are closely monitored to determine whether the power saver effectively improves power quality, such as reducing harmonic content, balancing three-phase voltage and current, and stabilizing voltage. Furthermore, the power saver is checked for its ability to automatically adjust operating parameters based on grid conditions, achieving adaptive control. If any abnormalities are detected, the power saver's parameter settings are adjusted promptly, or the device is inspected and repaired.

[0064] Step S5, Daily Operation and Maintenance: After the trial run is complete, the daily operation phase begins. Regular maintenance is performed on the power saver, including cleaning the device surface, checking module connections, tightening screws, and replacing aging components to ensure long-term stable operation. The power saver's multi-dimensional data display system and intelligent transmission system monitor the device's operating status and power quality data in real time. Power quality reports and energy-saving effect evaluation reports are regularly generated, providing users with a more intuitive reference. Based on the report analysis results, the power saver's operating parameters are optimized and adjusted as necessary to maintain optimal energy-saving results.

[0065] This intelligent energy saver has a significant power-saving effect. It adopts advanced three-phase electromagnetic balance technology and deep learning optimization control system, which can effectively improve the power quality, realize harmonic control, three-phase balance, stable voltage and other functions, and use electrical equipment to operate in a better power environment, reducing equipment energy consumption, thereby achieving significant power-saving effects and helping users save electricity costs.

[0066] The modular APF system can effectively suppress harmonic currents in the power grid, reduce the total harmonic distortion (THD) of the grid, and minimize the impact of zero-sequence current on the grid. This can prevent problems such as neutral line current overload and transformer overheating, purify the power quality of the grid, and provide cleaner and more stable electricity to the entire grid system, thereby improving the grid's operational efficiency and reliability. Through the synergistic effect of the deep learning optimization control system and the power saver, it can achieve balanced regulation of three-phase voltage and current, resolve three-phase imbalance, and achieve a more balanced three-phase load on the grid. This reduces energy loss and equipment vibration caused by three-phase imbalance, further improving power utilization efficiency and equipment operational stability.

[0067] The deep learning optimization control system features remote monitoring and control capabilities. Users can view the energy saver's operating status and power quality data anytime, anywhere via mobile phones, computers, and other terminal devices, and perform remote parameter settings and control operations. The energy management system and energy optimization and scheduling system monitor and analyze user electricity usage in real time, develop optimal power consumption plans and energy-saving strategies, support the access and management of distributed energy resources, and achieve optimized energy allocation and efficient utilization, providing users with intelligent and convenient energy management solutions.

[0068] This intelligent energy saver utilizes a hybrid parallel and distributed architecture to connect to the power grid, enabling online switching of devices without disrupting the grid's normal operation. Each system module of the energy saver features comprehensive protection functions and reliable operating mechanisms. For example, the modular APF system boasts high fault location accuracy, short protection action times, and a robust heat dissipation solution, ensuring the safe and reliable operation of the entire energy saver system under various operating conditions.

[0069] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0070] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent power saver, characterized in that: The energy saver is connected to the power grid system using a hybrid architecture of parallel and distributed. The energy saver includes: Multi-phase winding transformer, consisting of multiple mutually insulated windings, which can be flexibly configured according to actual load requirements; Protection system with multi-level fault detection and protection functions, including power quality monitoring unit; Deep learning optimization control system, including data acquisition and data analysis modules, is used to collect grid operation data in real time and establish a grid operation model. It uses deep learning algorithms to learn and optimize the model and predict grid load changes and power quality fluctuations. The modular APF system is connected in series to the main circuit of the energy saver and in parallel with the load to reduce the zero-sequence current generated between the neutral and phase lines on the grid side; Multi-dimensional data display system, which displays various power quality parameters of the power grid in real time through high-precision instruments and sensors; Transmission system, supporting multiple data transmission modes including wired and wireless; Energy management system, real-time monitoring and analysis of users' electricity usage information; The energy optimization and scheduling system supports the access and management of distributed energy, realizes the coordinated operation of distributed energy and traditional power grids, and predicts and optimizes the power generation of distributed energy.

2. The intelligent energy saver according to claim 1, characterized in that: The modular APF system includes: Core control module: Adopting a DSP+FPGA dual-core architecture, the DSP executes an adaptive harmonic detection algorithm, analyzes the grid current spectrum in real time based on fast Fourier transform, and dynamically generates compensation instructions; the FPGA is responsible for high-speed PWM signal generation and logic control, reducing the total harmonic distortion rate of the grid; Power module: Using an H-bridge cascade topology, each H-bridge module is independently configured with an IGBT switch and DC-side capacitor, and multi-level output is achieved through phase-shift carrier modulation technology; Monitoring and protection module: integrated voltage, current, and temperature sensors to collect grid parameters and module status in real time; with DC side voltage protection, AC side current protection, IGBT over-temperature protection, and support for fault recording function; Communication interface module: provides a communication interface and reserves a cloud platform access interface.

3. The intelligent energy saver according to claim 2, characterized in that: In the core control module of the modular APF system, the adaptive harmonic detection algorithm adopts a dual-loop control strategy of "fundamental wave extraction + harmonic separation". The fundamental component is extracted through a low-pass filter and the difference is made with the command current to obtain the harmonic compensation amount. At the same time, repetitive control and predictive control algorithms are introduced to suppress periodic interference and improve dynamic response performance.

4. The intelligent energy saver according to claim 2, characterized in that: The power modules of the modular APF system achieve inter-module synchronous control through an optical fiber ring network, and adopt carrier phase shift modulation technology to eliminate inter-module circulating current; support parallel expansion, and achieve multi-module power sharing through master-slave control mode.

5. The intelligent power saver according to claim 2, characterized in that: The power module of the modular APF system adopts a forced air cooling + heat sink composite heat dissipation solution, with a built-in NTC thermistor to monitor the IGBT junction temperature in real time and optimize the air duct to ensure that the module temperature rise is ≤45K under rated load conditions.

6. The intelligent power saver according to claim 1, characterized in that: The protection system monitors the basic parameters of the power grid system, such as voltage, current and frequency, in real time. The protection system has the functions of quickly cutting off faulty equipment and switching to backup power supply.

7. The intelligent power saver according to claim 1, characterized in that: The deep learning optimization control system establishes a power grid operation model by real-time collection and analysis of power grid operation data, uses deep learning algorithms to learn and optimize the model, predicts power grid load changes and power quality fluctuations, and adjusts the power saver operation parameters in advance; the deep learning optimization control system supports adaptive control and automatically selects the optimal control strategy according to different power-consuming equipment and power grid conditions; the deep learning optimization control system has remote monitoring and control functions, and users can view the power saver operation status and power quality data in real time through mobile terminal devices, and perform remote parameter setting and control operations.

8. The intelligent power saver according to claim 1, characterized in that: The transmission system adopts 5G, LoRa, Wi-Fi, Bluetooth, and Ethernet communication technologies.

9. The intelligent power saver according to claim 1, characterized in that: The multi-dimensional data display system displays various power quality parameters of the power grid, including voltage, current, frequency, power factor, harmonic content, and provides a graphical interface.

10. The intelligent power saver according to claim 1, characterized in that: The energy management system monitors and analyzes the user's electricity load, electricity usage time, and electricity cost information in real time.

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